Fix unload of bundled emb

Fix lint
Add warning when meet emb name conflicting
2023-10-22 17:57:59 +08:00 · 2023-10-10 15:07:25 +08:00 · 2023-10-10 14:44:20 +08:00 · 2023-10-10 12:11:00 +08:00 · 2023-10-10 12:09:33 +08:00 · 2023-10-09 22:52:09 +08:00
142 changed files with 7615 additions and 3916 deletions
@@ -74,6 +74,7 @@ module.exports = {
        create_submit_args: "readonly",
        restart_reload: "readonly",
        updateInput: "readonly",
+        onEdit: "readonly",
        //extraNetworks.js
        requestGet: "readonly",
        popup: "readonly",
@@ -87,5 +88,11 @@ module.exports = {
        modalNextImage: "readonly",
        // token-counters.js
        setupTokenCounters: "readonly",
+        // localStorage.js
+        localSet: "readonly",
+        localGet: "readonly",
+        localRemove: "readonly",
+        // resizeHandle.js
+        setupResizeHandle: "writable"
    }
 };
@@ -26,7 +26,7 @@ body:
    id: steps
    attributes:
      label: Steps to reproduce the problem
-      description: Please provide us with precise step by step information on how to reproduce the bug
+      description: Please provide us with precise step by step instructions on how to reproduce the bug
      value: |
        1. Go to .... 
        2. Press ....
@@ -37,64 +37,14 @@ body:
    id: what-should
    attributes:
      label: What should have happened?
-      description: Tell what you think the normal behavior should be
+      description: Tell us what you think the normal behavior should be
    validations:
      required: true
-  - type: input
-    id: commit
+  - type: textarea
+    id: sysinfo
    attributes:
-      label: Version or Commit where the problem happens
-      description: "Which webui version or commit are you running ? (Do not write *Latest Version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Version: v1.2.3** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)"
-    validations:
-      required: true
-  - type: dropdown
-    id: py-version
-    attributes:
-      label: What Python version are you running on ?
-      multiple: false
-      options:
-        - Python 3.10.x
-        - Python 3.11.x (above, no supported yet)
-        - Python 3.9.x (below, no recommended)
-  - type: dropdown
-    id: platforms
-    attributes:
-      label: What platforms do you use to access the UI ?
-      multiple: true
-      options:
-        - Windows
-        - Linux
-        - MacOS
-        - iOS
-        - Android
-        - Other/Cloud
-  - type: dropdown
-    id: device
-    attributes:
-        label: What device are you running WebUI on?
-        multiple: true
-        options:
-        - Nvidia GPUs (RTX 20 above)
-        - Nvidia GPUs (GTX 16 below)
-        - AMD GPUs (RX 6000 above)
-        - AMD GPUs (RX 5000 below)
-        - CPU
-        - Other GPUs
-  - type: dropdown
-    id: cross_attention_opt
-    attributes:
-      label: Cross attention optimization
-      description: What cross attention optimization are you using, Settings -> Optimizations -> Cross attention optimization
-      multiple: false
-      options:
-        - Automatic
-        - xformers
-        - sdp-no-mem
-        - sdp
-        - Doggettx
-        - V1 
-        - InvokeAI
-        - "None "
+      label: Sysinfo
+      description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
    validations:
      required: true
  - type: dropdown
@@ -108,21 +58,7 @@ body:
        - Brave
        - Apple Safari
        - Microsoft Edge
-  - type: textarea
-    id: cmdargs
-    attributes:
-      label: Command Line Arguments
-      description: Are you using any launching parameters/command line arguments (modified webui-user .bat/.sh) ? If yes, please write them below. Write "No" otherwise.
-      render: Shell
-    validations:
-      required: true
-  - type: textarea
-    id: extensions
-    attributes:
-      label: List of extensions
-      description: Are you using any extensions other than built-ins? If yes, provide a list, you can copy it at "Extensions" tab. Write "No" otherwise.
-    validations:
-      required: true
+        - Other
  - type: textarea
    id: logs
    attributes:
@@ -1,3 +1,158 @@
+## 1.6.0
+
+### Features:
+ * refiner support [#12371](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371)
+ * add NV option for Random number generator source setting, which allows to generate same pictures on CPU/AMD/Mac as on NVidia videocards
+ * add style editor dialog
+ * hires fix: add an option to use a different checkpoint for second pass ([#12181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12181))
+ * option to keep multiple loaded models in memory ([#12227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12227))
+ * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542))
+ * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers:
+   * makes all of them work with img2img
+   * makes prompt composition posssible (AND)
+   * makes them available for SDXL
+ * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808))
+ * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599))
+ * textual inversion inference support for SDXL
+ * extra networks UI: show metadata for SD checkpoints
+ * checkpoint merger: add metadata support 
+ * prompt editing and attention: add support for whitespace after the number ([ red : green : 0.5 ]) (seed breaking change) ([#12177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12177))
+ * VAE: allow selecting own VAE for each checkpoint (in user metadata editor)
+ * VAE: add selected VAE to infotext
+ * options in main UI: add own separate setting for txt2img and img2img, correctly read values from pasted infotext, add setting for column count ([#12551](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12551))
+ * add resize handle to txt2img and img2img tabs, allowing to change the amount of horizontable space given to generation parameters and resulting image gallery ([#12687](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12687), [#12723](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12723))
+ * change default behavior for batching cond/uncond -- now it's on by default, and is disabled by an UI setting (Optimizatios -> Batch cond/uncond) - if you are on lowvram/medvram and are getting OOM exceptions, you will need to enable it
+ * show current position in queue and make it so that requests are processed in the order of arrival ([#12707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12707))
+ * add `--medvram-sdxl` flag that only enables `--medvram` for SDXL models
+ * prompt editing timeline has separate range for first pass and hires-fix pass (seed breaking change) ([#12457](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12457))
+
+### Minor:
+ * img2img batch: RAM savings, VRAM savings, .tif, .tiff in img2img batch ([#12120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12120), [#12514](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12514), [#12515](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12515))
+ * postprocessing/extras: RAM savings ([#12479](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12479))
+ * XYZ: in the axis labels, remove pathnames from model filenames
+ * XYZ: support hires sampler ([#12298](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12298))
+ * XYZ: new option: use text inputs instead of dropdowns ([#12491](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12491))
+ * add gradio version warning
+ * sort list of VAE checkpoints ([#12297](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12297))
+ * use transparent white for mask in inpainting, along with an option to select the color ([#12326](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12326))
+ * move some settings to their own section: img2img, VAE
+ * add checkbox to show/hide dirs for extra networks
+ * Add TAESD(or more) options for all the VAE encode/decode operation ([#12311](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12311))
+ * gradio theme cache, new gradio themes, along with explanation that the user can input his own values ([#12346](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12346), [#12355](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12355))
+ * sampler fixes/tweaks: s_tmax, s_churn, s_noise, s_tmax ([#12354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12354), [#12356](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12356), [#12357](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12357), [#12358](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12358), [#12375](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12375), [#12521](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12521))
+ * update README.md with correct instructions for Linux installation ([#12352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12352))
+ * option to not save incomplete images, on by default ([#12338](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12338))
+ * enable cond cache by default
+ * git autofix for repos that are corrupted ([#12230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12230))
+ * allow to open images in new browser tab by middle mouse button ([#12379](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12379))
+ * automatically open webui in browser when running "locally" ([#12254](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12254))
+ * put commonly used samplers on top, make DPM++ 2M Karras the default choice
+ * zoom and pan: option to auto-expand a wide image, improved integration ([#12413](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12413), [#12727](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12727))
+ * option to cache Lora networks in memory
+ * rework hires fix UI to use accordion
+ * face restoration and tiling moved to settings - use "Options in main UI" setting if you want them back
+ * change quicksettings items to have variable width
+ * Lora: add Norm module, add support for bias ([#12503](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12503))
+ * Lora: output warnings in UI rather than fail for unfitting loras; switch to logging for error output in console
+ * support search and display of hashes for all extra network items ([#12510](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12510))
+ * add extra noise param for img2img operations ([#12564](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12564))
+ * support for Lora with bias ([#12584](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12584))
+ * make interrupt quicker ([#12634](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12634))
+ * configurable gallery height ([#12648](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12648))
+ * make results column sticky ([#12645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12645))
+ * more hash filename patterns ([#12639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12639))
+ * make image viewer actually fit the whole page ([#12635](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12635))
+ * make progress bar work independently from live preview display which results in it being updated a lot more often
+ * forbid Full live preview method for medvram and add a setting to undo the forbidding
+ * make it possible to localize tooltips and placeholders
+ * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818))
+ * Restore faces and Tiling generation parameters have been moved to settings out of main UI
+   * if you want to put them back into main UI, use `Options in main UI` setting on the UI page.
+
+### Extensions and API:
+ * gradio 3.41.2
+ * also bump versions for packages: transformers, GitPython, accelerate, scikit-image, timm, tomesd
+ * support tooltip kwarg for gradio elements: gr.Textbox(label='hello', tooltip='world')
+ * properly clear the total console progressbar when using txt2img and img2img from API
+ * add cmd_arg --disable-extra-extensions and --disable-all-extensions ([#12294](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12294))
+ * shared.py and webui.py split into many files
+ * add --loglevel commandline argument for logging
+ * add a custom UI element that combines accordion and checkbox
+ * avoid importing gradio in tests because it spams warnings
+ * put infotext label for setting into OptionInfo definition rather than in a separate list
+ * make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur` ([#12470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12470))
+ * option to make scripts UI without gr.Group
+ * add a way for scripts to register a callback for before/after just a single component's creation
+ * use dataclass for StableDiffusionProcessing
+ * store patches for Lora in a specialized module instead of inside torch
+ * support http/https URLs in API ([#12663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12663), [#12698](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12698))
+ * add extra noise callback ([#12616](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12616))
+ * dump current stack traces when exiting with SIGINT
+ * add type annotations for extra fields of shared.sd_model
+
+### Bug Fixes:
+ * Don't crash if out of local storage quota for javascriot localStorage
+ * XYZ plot do not fail if an exception occurs
+ * fix missing TI hash in infotext if generation uses both negative and positive TI ([#12269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12269))
+ * localization fixes ([#12307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12307))
+ * fix sdxl model invalid configuration after the hijack
+ * correctly toggle extras checkbox for infotext paste ([#12304](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12304))
+ * open raw sysinfo link in new page ([#12318](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12318))
+ * prompt parser: Account for empty field in alternating words syntax ([#12319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12319))
+ * add tab and carriage return to invalid filename chars ([#12327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12327))
+ * fix api only Lora not working ([#12387](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12387))
+ * fix options in main UI misbehaving when there's just one element
+ * make it possible to use a sampler from infotext even if it's hidden in the dropdown
+ * fix styles missing from the prompt in infotext when making a grid of batch of multiplie images
+ * prevent bogus progress output in console when calculating hires fix dimensions
+ * fix --use-textbox-seed
+ * fix broken `Lora/Networks: use old method` option ([#12466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12466))
+ * properly return `None` for VAE hash when using `--no-hashing` ([#12463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12463))
+ * MPS/macOS fixes and optimizations ([#12526](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12526))
+ * add second_order to samplers that mistakenly didn't have it
+ * when refreshing cards in extra networks UI, do not discard user's custom resolution
+ * fix processing error that happens if batch_size is not a multiple of how many prompts/negative prompts there are ([#12509](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12509))
+ * fix inpaint upload for alpha masks ([#12588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12588))
+ * fix exception when image sizes are not integers ([#12586](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12586))
+ * fix incorrect TAESD Latent scale ([#12596](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12596))
+ * auto add data-dir to gradio-allowed-path ([#12603](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12603))
+ * fix exception if extensuions dir is missing ([#12607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12607))
+ * fix issues with api model-refresh and vae-refresh ([#12638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12638))
+ * fix img2img background color for transparent images option not being used ([#12633](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12633))
+ * attempt to resolve NaN issue with unstable VAEs in fp32 mk2 ([#12630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12630))
+ * implement missing undo hijack for SDXL
+ * fix xyz swap axes ([#12684](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12684))
+ * fix errors in backup/restore tab if any of config files are broken ([#12689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12689))
+ * fix SD VAE switch error after model reuse ([#12685](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12685))
+ * fix trying to create images too large for the chosen format ([#12667](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12667))
+ * create Gradio temp directory if necessary ([#12717](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12717))
+ * prevent possible cache loss if exiting as it's being written by using an atomic operation to replace the cache with the new version
+ * set devices.dtype_unet correctly
+ * run RealESRGAN on GPU for non-CUDA devices ([#12737](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
+ * prevent extra network buttons being obscured by description for very small card sizes ([#12745](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12745))
+ * fix error that causes some extra networks to be disabled if both <lora:> and <lyco:> are present in the prompt
+ * fix defaults settings page breaking when any of main UI tabs are hidden
+ * fix incorrect save/display of new values in Defaults page in settings
+ * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working
+ * fix an error that prevents VAE being reloaded after an option change if a VAE near the checkpoint exists ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
+ * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
+ * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
+ * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs
+ * fix a bug allowing users to bypass gradio and API authentication (reported by vysecurity) 
+ * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834))
+ * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832))
+ * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855))
+ * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854))
+ * get progressbar to display correctly in extensions tab
+
+
+## 1.5.2
+
+### Bug Fixes:
+ * fix memory leak when generation fails
+ * update doggettx cross attention optimization to not use an unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk
+
+
 ## 1.5.1

 ### Minor:
@@ -0,0 +1,7 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - given-names: AUTOMATIC1111
+title: "Stable Diffusion Web UI"
+date-released: 2022-08-22
+url: "https://github.com/AUTOMATIC1111/stable-diffusion-webui"
@@ -78,7 +78,7 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Clip skip
 - Hypernetworks
 - Loras (same as Hypernetworks but more pretty)
- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt 
+- A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt 
 - Can select to load a different VAE from settings screen
 - Estimated completion time in progress bar
 - API
@@ -88,19 +88,22 @@ A browser interface based on Gradio library for Stable Diffusion.
 - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
 - Now without any bad letters!
 - Load checkpoints in safetensors format
- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
+- Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64
 - Now with a license!
 - Reorder elements in the UI from settings screen

 ## Installation and Running
-Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
+Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for:
+- [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended)
+- [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
+- [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page)

 Alternatively, use online services (like Google Colab):

 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)

 ### Installation on Windows 10/11 with NVidia-GPUs using release package
-1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
+1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract its contents.
 2. Run `update.bat`.
 3. Run `run.bat`.
 > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
@@ -115,7 +118,7 @@ Alternatively, use online services (like Google Colab):
 1. Install the dependencies:
 ```bash
 # Debian-based:
-sudo apt install wget git python3 python3-venv
+sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
 # Red Hat-based:
 sudo dnf install wget git python3
 # Arch-based:
@@ -123,7 +126,7 @@ sudo pacman -S wget git python3
 ```
 2. Navigate to the directory you would like the webui to be installed and execute the following command:
 ```bash
-bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
+wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
 ```
 3. Run `webui.sh`.
 4. Check `webui-user.sh` for options.
@@ -169,5 +172,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
 - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
 - LyCORIS - KohakuBlueleaf
+- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)
@@ -6,9 +6,14 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
    def __init__(self):
        super().__init__('lora')

+        self.errors = {}
+        """mapping of network names to the number of errors the network had during operation"""
+
    def activate(self, p, params_list):
        additional = shared.opts.sd_lora

+        self.errors.clear()
+
        if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
            p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
@@ -56,4 +61,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
                p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)

    def deactivate(self, p):
-        pass
+        if self.errors:
+            p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
+
+            self.errors.clear()
@@ -0,0 +1,33 @@
+import sys
+import copy
+import logging
+
+
+class ColoredFormatter(logging.Formatter):
+    COLORS = {
+        "DEBUG": "\033[0;36m",  # CYAN
+        "INFO": "\033[0;32m",  # GREEN
+        "WARNING": "\033[0;33m",  # YELLOW
+        "ERROR": "\033[0;31m",  # RED
+        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
+        "RESET": "\033[0m",  # RESET COLOR
+    }
+
+    def format(self, record):
+        colored_record = copy.copy(record)
+        levelname = colored_record.levelname
+        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
+        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
+        return super().format(colored_record)
+
+
+logger = logging.getLogger("lora")
+logger.propagate = False
+
+
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(
+        ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s")
+    )
+    logger.addHandler(handler)
@@ -0,0 +1,31 @@
+import torch
+
+import networks
+from modules import patches
+
+
+class LoraPatches:
+    def __init__(self):
+        self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
+        self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict)
+        self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
+        self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict)
+        self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward)
+        self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict)
+        self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward)
+        self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict)
+        self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward)
+        self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict)
+
+    def undo(self):
+        self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward')
+        self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict')
+        self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward')
+        self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict')
+        self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward')
+        self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict')
+        self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward')
+        self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict')
+        self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward')
+        self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict')
+
@@ -93,6 +93,7 @@ class Network:  # LoraModule
        self.unet_multiplier = 1.0
        self.dyn_dim = None
        self.modules = {}
+        self.bundle_embeddings = {}
        self.mtime = None

        self.mentioned_name = None
@@ -133,7 +134,7 @@ class NetworkModule:

        return 1.0

-    def finalize_updown(self, updown, orig_weight, output_shape):
+    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
        if self.bias is not None:
            updown = updown.reshape(self.bias.shape)
            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
@@ -145,7 +146,10 @@ class NetworkModule:
        if orig_weight.size().numel() == updown.size().numel():
            updown = updown.reshape(orig_weight.shape)

-        return updown * self.calc_scale() * self.multiplier()
+        if ex_bias is not None:
+            ex_bias = ex_bias * self.multiplier()
+
+        return updown * self.calc_scale() * self.multiplier(), ex_bias

    def calc_updown(self, target):
        raise NotImplementedError()
@@ -14,9 +14,14 @@ class NetworkModuleFull(network.NetworkModule):
        super().__init__(net, weights)

        self.weight = weights.w.get("diff")
+        self.ex_bias = weights.w.get("diff_b")

    def calc_updown(self, orig_weight):
        output_shape = self.weight.shape
        updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        if self.ex_bias is not None:
+            ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
+        else:
+            ex_bias = None

-        return self.finalize_updown(updown, orig_weight, output_shape)
+        return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
@@ -0,0 +1,28 @@
+import network
+
+
+class ModuleTypeNorm(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["w_norm", "b_norm"]):
+            return NetworkModuleNorm(net, weights)
+
+        return None
+
+
+class NetworkModuleNorm(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.w_norm = weights.w.get("w_norm")
+        self.b_norm = weights.w.get("b_norm")
+
+    def calc_updown(self, orig_weight):
+        output_shape = self.w_norm.shape
+        updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+
+        if self.b_norm is not None:
+            ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+        else:
+            ex_bias = None
+
+        return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
@@ -1,17 +1,23 @@
+import logging
 import os
 import re

+import lora_patches
 import network
 import network_lora
 import network_hada
 import network_ia3
 import network_lokr
 import network_full
+import network_norm

 import torch
 from typing import Union

 from modules import shared, devices, sd_models, errors, scripts, sd_hijack
+from modules.textual_inversion.textual_inversion import Embedding
+
+from lora_logger import logger

 module_types = [
    network_lora.ModuleTypeLora(),
@@ -19,6 +25,7 @@ module_types = [
    network_ia3.ModuleTypeIa3(),
    network_lokr.ModuleTypeLokr(),
    network_full.ModuleTypeFull(),
+    network_norm.ModuleTypeNorm(),
 ]


@@ -31,6 +38,8 @@ suffix_conversion = {
    "resnets": {
        "conv1": "in_layers_2",
        "conv2": "out_layers_3",
+        "norm1": "in_layers_0",
+        "norm2": "out_layers_0",
        "time_emb_proj": "emb_layers_1",
        "conv_shortcut": "skip_connection",
    }
@@ -143,9 +152,19 @@ def load_network(name, network_on_disk):
    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping

    matched_networks = {}
+    bundle_embeddings = {}

    for key_network, weight in sd.items():
        key_network_without_network_parts, network_part = key_network.split(".", 1)
+        if key_network_without_network_parts == "bundle_emb":
+            emb_name, vec_name = network_part.split(".", 1)
+            emb_dict = bundle_embeddings.get(emb_name, {})
+            if vec_name.split('.')[0] == 'string_to_param':
+                _, k2 = vec_name.split('.', 1)
+                emb_dict['string_to_param'] = {k2: weight}
+            else:
+                emb_dict[vec_name] = weight
+            bundle_embeddings[emb_name] = emb_dict

        key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
        sd_module = shared.sd_model.network_layer_mapping.get(key, None)
@@ -189,18 +208,66 @@ def load_network(name, network_on_disk):

        net.modules[key] = net_module

+    embeddings = {}
+    for emb_name, data in bundle_embeddings.items():
+        # textual inversion embeddings
+        if 'string_to_param' in data:
+            param_dict = data['string_to_param']
+            param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11
+            assert len(param_dict) == 1, 'embedding file has multiple terms in it'
+            emb = next(iter(param_dict.items()))[1]
+            vec = emb.detach().to(devices.device, dtype=torch.float32)
+            shape = vec.shape[-1]
+            vectors = vec.shape[0]
+        elif type(data) == dict and 'clip_g' in data and 'clip_l' in data:  # SDXL embedding
+            vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
+            shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
+            vectors = data['clip_g'].shape[0]
+        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
+            assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
+
+            emb = next(iter(data.values()))
+            if len(emb.shape) == 1:
+                emb = emb.unsqueeze(0)
+            vec = emb.detach().to(devices.device, dtype=torch.float32)
+            shape = vec.shape[-1]
+            vectors = vec.shape[0]
+        else:
+            raise Exception(f"Couldn't identify {emb_name} in lora: {name} as neither textual inversion embedding nor diffuser concept.")
+
+        embedding = Embedding(vec, emb_name)
+        embedding.vectors = vectors
+        embedding.shape = shape
+        embedding.loaded = None
+        embeddings[emb_name] = embedding
+
+    net.bundle_embeddings = embeddings
+
    if keys_failed_to_match:
-        print(f"Failed to match keys when loading network {network_on_disk.filename}: {keys_failed_to_match}")
+        logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")

    return net


+def purge_networks_from_memory():
+    while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
+        name = next(iter(networks_in_memory))
+        networks_in_memory.pop(name, None)
+
+    devices.torch_gc()
+
+
 def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
+    emb_db = sd_hijack.model_hijack.embedding_db
    already_loaded = {}

    for net in loaded_networks:
        if net.name in names:
            already_loaded[net.name] = net
+        for emb_name, embedding in net.bundle_embeddings.items():
+            if embedding.loaded:
+                embedding.loaded = None
+                emb_db.register_embedding_by_name(None, shared.sd_model, emb_name)

    loaded_networks.clear()

@@ -212,15 +279,19 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No

    failed_to_load_networks = []

-    for i, name in enumerate(names):
+    for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
        net = already_loaded.get(name, None)

-        network_on_disk = networks_on_disk[i]
-
        if network_on_disk is not None:
+            if net is None:
+                net = networks_in_memory.get(name)
+
            if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
                try:
                    net = load_network(name, network_on_disk)
+
+                    networks_in_memory.pop(name, None)
+                    networks_in_memory[name] = net
                except Exception as e:
                    errors.display(e, f"loading network {network_on_disk.filename}")
                    continue
@@ -231,7 +302,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No

        if net is None:
            failed_to_load_networks.append(name)
-            print(f"Couldn't find network with name {name}")
+            logging.info(f"Couldn't find network with name {name}")
            continue

        net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
@@ -239,24 +310,54 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
        net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
        loaded_networks.append(net)

+        for emb_name, embedding in net.bundle_embeddings.items():
+            if embedding.loaded is None and emb_name in emb_db.word_embeddings:
+                logger.warning(
+                    f'Skip bundle embedding: "{emb_name}"'
+                    ' as it was already loaded from embeddings folder'
+                )
+                continue
+
+            embedding.loaded = False
+            if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape:
+                embedding.loaded = True
+                emb_db.register_embedding(embedding, shared.sd_model)
+            else:
+                emb_db.skipped_embeddings[name] = embedding
+
    if failed_to_load_networks:
-        sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks))
+        sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
+
+    purge_networks_from_memory()


-def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
    weights_backup = getattr(self, "network_weights_backup", None)
+    bias_backup = getattr(self, "network_bias_backup", None)

-    if weights_backup is None:
+    if weights_backup is None and bias_backup is None:
        return

-    if isinstance(self, torch.nn.MultiheadAttention):
-        self.in_proj_weight.copy_(weights_backup[0])
-        self.out_proj.weight.copy_(weights_backup[1])
+    if weights_backup is not None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.in_proj_weight.copy_(weights_backup[0])
+            self.out_proj.weight.copy_(weights_backup[1])
+        else:
+            self.weight.copy_(weights_backup)
+
+    if bias_backup is not None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.out_proj.bias.copy_(bias_backup)
+        else:
+            self.bias.copy_(bias_backup)
    else:
-        self.weight.copy_(weights_backup)
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.out_proj.bias = None
+        else:
+            self.bias = None


-def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
    """
    Applies the currently selected set of networks to the weights of torch layer self.
    If weights already have this particular set of networks applied, does nothing.
@@ -271,7 +372,10 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
    wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)

    weights_backup = getattr(self, "network_weights_backup", None)
-    if weights_backup is None:
+    if weights_backup is None and wanted_names != ():
+        if current_names != ():
+            raise RuntimeError("no backup weights found and current weights are not unchanged")
+
        if isinstance(self, torch.nn.MultiheadAttention):
            weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
        else:
@@ -279,21 +383,41 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn

        self.network_weights_backup = weights_backup

+    bias_backup = getattr(self, "network_bias_backup", None)
+    if bias_backup is None:
+        if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
+            bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
+        elif getattr(self, 'bias', None) is not None:
+            bias_backup = self.bias.to(devices.cpu, copy=True)
+        else:
+            bias_backup = None
+        self.network_bias_backup = bias_backup
+
    if current_names != wanted_names:
        network_restore_weights_from_backup(self)

        for net in loaded_networks:
            module = net.modules.get(network_layer_name, None)
            if module is not None and hasattr(self, 'weight'):
-                with torch.no_grad():
-                    updown = module.calc_updown(self.weight)
+                try:
+                    with torch.no_grad():
+                        updown, ex_bias = module.calc_updown(self.weight)

-                    if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
-                        # inpainting model. zero pad updown to make channel[1]  4 to 9
-                        updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
+                        if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+                            # inpainting model. zero pad updown to make channel[1]  4 to 9
+                            updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))

-                    self.weight += updown
-                    continue
+                        self.weight += updown
+                        if ex_bias is not None and hasattr(self, 'bias'):
+                            if self.bias is None:
+                                self.bias = torch.nn.Parameter(ex_bias)
+                            else:
+                                self.bias += ex_bias
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue

            module_q = net.modules.get(network_layer_name + "_q_proj", None)
            module_k = net.modules.get(network_layer_name + "_k_proj", None)
@@ -301,21 +425,33 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
            module_out = net.modules.get(network_layer_name + "_out_proj", None)

            if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
-                with torch.no_grad():
-                    updown_q = module_q.calc_updown(self.in_proj_weight)
-                    updown_k = module_k.calc_updown(self.in_proj_weight)
-                    updown_v = module_v.calc_updown(self.in_proj_weight)
-                    updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
-                    updown_out = module_out.calc_updown(self.out_proj.weight)
+                try:
+                    with torch.no_grad():
+                        updown_q, _ = module_q.calc_updown(self.in_proj_weight)
+                        updown_k, _ = module_k.calc_updown(self.in_proj_weight)
+                        updown_v, _ = module_v.calc_updown(self.in_proj_weight)
+                        updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+                        updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)

-                    self.in_proj_weight += updown_qkv
-                    self.out_proj.weight += updown_out
-                    continue
+                        self.in_proj_weight += updown_qkv
+                        self.out_proj.weight += updown_out
+                    if ex_bias is not None:
+                        if self.out_proj.bias is None:
+                            self.out_proj.bias = torch.nn.Parameter(ex_bias)
+                        else:
+                            self.out_proj.bias += ex_bias
+
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue

            if module is None:
                continue

-            print(f'failed to calculate network weights for layer {network_layer_name}')
+            logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
+            extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1

        self.network_current_names = wanted_names

@@ -342,7 +478,7 @@ def network_forward(module, input, original_forward):
        if module is None:
            continue

-        y = module.forward(y, input)
+        y = module.forward(input, y)

    return y

@@ -350,48 +486,79 @@ def network_forward(module, input, original_forward):
 def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
    self.network_current_names = ()
    self.network_weights_backup = None
+    self.network_bias_backup = None


 def network_Linear_forward(self, input):
    if shared.opts.lora_functional:
-        return network_forward(self, input, torch.nn.Linear_forward_before_network)
+        return network_forward(self, input, originals.Linear_forward)

    network_apply_weights(self)

-    return torch.nn.Linear_forward_before_network(self, input)
+    return originals.Linear_forward(self, input)


 def network_Linear_load_state_dict(self, *args, **kwargs):
    network_reset_cached_weight(self)

-    return torch.nn.Linear_load_state_dict_before_network(self, *args, **kwargs)
+    return originals.Linear_load_state_dict(self, *args, **kwargs)


 def network_Conv2d_forward(self, input):
    if shared.opts.lora_functional:
-        return network_forward(self, input, torch.nn.Conv2d_forward_before_network)
+        return network_forward(self, input, originals.Conv2d_forward)

    network_apply_weights(self)

-    return torch.nn.Conv2d_forward_before_network(self, input)
+    return originals.Conv2d_forward(self, input)


 def network_Conv2d_load_state_dict(self, *args, **kwargs):
    network_reset_cached_weight(self)

-    return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
+    return originals.Conv2d_load_state_dict(self, *args, **kwargs)
+
+
+def network_GroupNorm_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, originals.GroupNorm_forward)
+
+    network_apply_weights(self)
+
+    return originals.GroupNorm_forward(self, input)
+
+
+def network_GroupNorm_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return originals.GroupNorm_load_state_dict(self, *args, **kwargs)
+
+
+def network_LayerNorm_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, originals.LayerNorm_forward)
+
+    network_apply_weights(self)
+
+    return originals.LayerNorm_forward(self, input)
+
+
+def network_LayerNorm_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return originals.LayerNorm_load_state_dict(self, *args, **kwargs)


 def network_MultiheadAttention_forward(self, *args, **kwargs):
    network_apply_weights(self)

-    return torch.nn.MultiheadAttention_forward_before_network(self, *args, **kwargs)
+    return originals.MultiheadAttention_forward(self, *args, **kwargs)


 def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
    network_reset_cached_weight(self)

-    return torch.nn.MultiheadAttention_load_state_dict_before_network(self, *args, **kwargs)
+    return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)


 def list_available_networks():
@@ -459,9 +626,15 @@ def infotext_pasted(infotext, params):
        params["Prompt"] += "\n" + "".join(added)


+originals: lora_patches.LoraPatches = None
+
+extra_network_lora = None
+
 available_networks = {}
 available_network_aliases = {}
 loaded_networks = []
+loaded_bundle_embeddings = {}
+networks_in_memory = {}
 available_network_hash_lookup = {}
 forbidden_network_aliases = {}

@@ -1,57 +1,30 @@
 import re

-import torch
 import gradio as gr
 from fastapi import FastAPI

 import network
 import networks
 import lora  # noqa:F401
+import lora_patches
 import extra_networks_lora
 import ui_extra_networks_lora
 from modules import script_callbacks, ui_extra_networks, extra_networks, shared

+
 def unload():
-    torch.nn.Linear.forward = torch.nn.Linear_forward_before_network
-    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_network
-    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_network
-    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_network
-    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_network
-    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_network
+    networks.originals.undo()


 def before_ui():
    ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())

-    extra_network = extra_networks_lora.ExtraNetworkLora()
-    extra_networks.register_extra_network(extra_network)
-    extra_networks.register_extra_network_alias(extra_network, "lyco")
+    networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
+    extra_networks.register_extra_network(networks.extra_network_lora)
+    extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")


-if not hasattr(torch.nn, 'Linear_forward_before_network'):
-    torch.nn.Linear_forward_before_network = torch.nn.Linear.forward
-
-if not hasattr(torch.nn, 'Linear_load_state_dict_before_network'):
-    torch.nn.Linear_load_state_dict_before_network = torch.nn.Linear._load_from_state_dict
-
-if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
-    torch.nn.Conv2d_forward_before_network = torch.nn.Conv2d.forward
-
-if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'):
-    torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict
-
-if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
-    torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
-
-if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_network'):
-    torch.nn.MultiheadAttention_load_state_dict_before_network = torch.nn.MultiheadAttention._load_from_state_dict
-
-torch.nn.Linear.forward = networks.network_Linear_forward
-torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict
-torch.nn.Conv2d.forward = networks.network_Conv2d_forward
-torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict
-torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
-torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
+networks.originals = lora_patches.LoraPatches()

 script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
@@ -65,6 +38,7 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
    "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
    "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
    "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
+    "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
 }))


@@ -121,3 +95,5 @@ def infotext_pasted(infotext, d):


 script_callbacks.on_infotext_pasted(infotext_pasted)
+
+shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)
@@ -70,6 +70,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
        metadata = item.get("metadata") or {}

        keys = {
+            'ss_output_name': "Output name:",
            'ss_sd_model_name': "Model:",
            'ss_clip_skip': "Clip skip:",
            'ss_network_module': "Kohya module:",
@@ -167,7 +168,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
                random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)

            with gr.Column(scale=1, min_width=120):
-                generate_random_prompt = gr.Button('Generate').style(full_width=True, size="lg")
+                generate_random_prompt = gr.Button('Generate', size="lg", scale=1)

        self.edit_notes = gr.TextArea(label='Notes', lines=4)

@@ -25,9 +25,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
        item = {
            "name": name,
            "filename": lora_on_disk.filename,
+            "shorthash": lora_on_disk.shorthash,
            "preview": self.find_preview(path),
            "description": self.find_description(path),
-            "search_term": self.search_terms_from_path(lora_on_disk.filename),
+            "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
            "local_preview": f"{path}.{shared.opts.samples_format}",
            "metadata": lora_on_disk.metadata,
            "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
@@ -12,8 +12,22 @@ onUiLoaded(async() => {
        "Sketch": elementIDs.sketch
    };

+
    // Helper functions
    // Get active tab
+
+    /**
+     * Waits for an element to be present in the DOM.
+     */
+    const waitForElement = (id) => new Promise(resolve => {
+        const checkForElement = () => {
+            const element = document.querySelector(id);
+            if (element) return resolve(element);
+            setTimeout(checkForElement, 100);
+        };
+        checkForElement();
+    });
+
    function getActiveTab(elements, all = false) {
        const tabs = elements.img2imgTabs.querySelectorAll("button");

@@ -34,7 +48,7 @@ onUiLoaded(async() => {

    // Wait until opts loaded
    async function waitForOpts() {
-        for (;;) {
+        for (; ;) {
            if (window.opts && Object.keys(window.opts).length) {
                return window.opts;
            }
@@ -42,6 +56,11 @@ onUiLoaded(async() => {
        }
    }

+    // Detect whether the element has a horizontal scroll bar
+    function hasHorizontalScrollbar(element) {
+        return element.scrollWidth > element.clientWidth;
+    }
+
    // Function for defining the "Ctrl", "Shift" and "Alt" keys
    function isModifierKey(event, key) {
        switch (key) {
@@ -201,7 +220,8 @@ onUiLoaded(async() => {
        canvas_hotkey_overlap: "KeyO",
        canvas_disabled_functions: [],
        canvas_show_tooltip: true,
-        canvas_blur_prompt: false
+        canvas_auto_expand: true,
+        canvas_blur_prompt: false,
    };

    const functionMap = {
@@ -249,7 +269,7 @@ onUiLoaded(async() => {
        input?.addEventListener("input", () => restoreImgRedMask(elements));
    }

-    function applyZoomAndPan(elemId) {
+    function applyZoomAndPan(elemId, isExtension = true) {
        const targetElement = gradioApp().querySelector(elemId);

        if (!targetElement) {
@@ -361,6 +381,12 @@ onUiLoaded(async() => {
                panY: 0
            };

+            if (isExtension) {
+                targetElement.style.overflow = "hidden";
+            }
+
+            targetElement.isZoomed = false;
+
            fixCanvas();
            targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`;

@@ -371,8 +397,27 @@ onUiLoaded(async() => {
            toggleOverlap("off");
            fullScreenMode = false;

+            const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']");
+            if (closeBtn) {
+                closeBtn.addEventListener("click", resetZoom);
+            }
+
+            if (canvas && isExtension) {
+                const parentElement = targetElement.closest('[id^="component-"]');
+                if (
+                    canvas &&
+                    parseFloat(canvas.style.width) > parentElement.offsetWidth &&
+                    parseFloat(targetElement.style.width) > parentElement.offsetWidth
+                ) {
+                    fitToElement();
+                    return;
+                }
+
+            }
+
            if (
                canvas &&
+                !isExtension &&
                parseFloat(canvas.style.width) > 865 &&
                parseFloat(targetElement.style.width) > 865
            ) {
@@ -381,9 +426,6 @@ onUiLoaded(async() => {
            }

            targetElement.style.width = "";
-            if (canvas) {
-                targetElement.style.height = canvas.style.height;
-            }
        }

        // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements
@@ -439,7 +481,7 @@ onUiLoaded(async() => {

        // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables
        function updateZoom(newZoomLevel, mouseX, mouseY) {
-            newZoomLevel = Math.max(0.5, Math.min(newZoomLevel, 15));
+            newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15));

            elemData[elemId].panX +=
                mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel;
@@ -450,6 +492,10 @@ onUiLoaded(async() => {
            targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;

            toggleOverlap("on");
+            if (isExtension) {
+                targetElement.style.overflow = "visible";
+            }
+
            return newZoomLevel;
        }

@@ -472,10 +518,12 @@ onUiLoaded(async() => {
                fullScreenMode = false;
                elemData[elemId].zoomLevel = updateZoom(
                    elemData[elemId].zoomLevel +
-                        (operation === "+" ? delta : -delta),
+                    (operation === "+" ? delta : -delta),
                    zoomPosX - targetElement.getBoundingClientRect().left,
                    zoomPosY - targetElement.getBoundingClientRect().top
                );
+
+                targetElement.isZoomed = true;
            }
        }

@@ -489,10 +537,19 @@ onUiLoaded(async() => {
            //Reset Zoom
            targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;

+            let parentElement;
+
+            if (isExtension) {
+                parentElement = targetElement.closest('[id^="component-"]');
+            } else {
+                parentElement = targetElement.parentElement;
+            }
+
+
            // Get element and screen dimensions
            const elementWidth = targetElement.offsetWidth;
            const elementHeight = targetElement.offsetHeight;
-            const parentElement = targetElement.parentElement;
+
            const screenWidth = parentElement.clientWidth;
            const screenHeight = parentElement.clientHeight;

@@ -545,8 +602,12 @@ onUiLoaded(async() => {

            if (!canvas) return;

-            if (canvas.offsetWidth > 862) {
-                targetElement.style.width = canvas.offsetWidth + "px";
+            if (canvas.offsetWidth > 862 || isExtension) {
+                targetElement.style.width = (canvas.offsetWidth + 2) + "px";
+            }
+
+            if (isExtension) {
+                targetElement.style.overflow = "visible";
            }

            if (fullScreenMode) {
@@ -648,8 +709,48 @@ onUiLoaded(async() => {
            mouseY = e.offsetY;
        }

+        // Simulation of the function to put a long image into the screen.
+        // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element.
+        // We hide the image and show it to the user when it is ready.
+
+        targetElement.isExpanded = false;
+        function autoExpand() {
+            const canvas = document.querySelector(`${elemId} canvas[key="interface"]`);
+            if (canvas) {
+                if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) {
+                    targetElement.style.visibility = "hidden";
+                    setTimeout(() => {
+                        fitToScreen();
+                        resetZoom();
+                        targetElement.style.visibility = "visible";
+                        targetElement.isExpanded = true;
+                    }, 10);
+                }
+            }
+        }
+
        targetElement.addEventListener("mousemove", getMousePosition);

+        //observers
+        // Creating an observer with a callback function to handle DOM changes
+        const observer = new MutationObserver((mutationsList, observer) => {
+            for (let mutation of mutationsList) {
+                // If the style attribute of the canvas has changed, by observation it happens only when the picture changes
+                if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
+                    mutation.target.tagName.toLowerCase() === 'canvas') {
+                    targetElement.isExpanded = false;
+                    setTimeout(resetZoom, 10);
+                }
+            }
+        });
+
+        // Apply auto expand if enabled
+        if (hotkeysConfig.canvas_auto_expand) {
+            targetElement.addEventListener("mousemove", autoExpand);
+            // Set up an observer to track attribute changes
+            observer.observe(targetElement, {attributes: true, childList: true, subtree: true});
+        }
+
        // Handle events only inside the targetElement
        let isKeyDownHandlerAttached = false;

@@ -754,6 +855,11 @@ onUiLoaded(async() => {
            if (isMoving && elemId === activeElement) {
                updatePanPosition(e.movementX, e.movementY);
                targetElement.style.pointerEvents = "none";
+
+                if (isExtension) {
+                    targetElement.style.overflow = "visible";
+                }
+
            } else {
                targetElement.style.pointerEvents = "auto";
            }
@@ -764,13 +870,93 @@ onUiLoaded(async() => {
            isMoving = false;
        };

+        // Checks for extension
+        function checkForOutBox() {
+            const parentElement = targetElement.closest('[id^="component-"]');
+            if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) {
+                resetZoom();
+                targetElement.isExpanded = true;
+            }
+
+            if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) {
+                resetZoom();
+            }
+
+            if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) {
+                resetZoom();
+            }
+        }
+
+        if (isExtension) {
+            targetElement.addEventListener("mousemove", checkForOutBox);
+        }
+
+
+        window.addEventListener('resize', (e) => {
+            resetZoom();
+
+            if (isExtension) {
+                targetElement.isExpanded = false;
+                targetElement.isZoomed = false;
+            }
+        });
+
        gradioApp().addEventListener("mousemove", handleMoveByKey);
+
+
    }

-    applyZoomAndPan(elementIDs.sketch);
-    applyZoomAndPan(elementIDs.inpaint);
-    applyZoomAndPan(elementIDs.inpaintSketch);
+    applyZoomAndPan(elementIDs.sketch, false);
+    applyZoomAndPan(elementIDs.inpaint, false);
+    applyZoomAndPan(elementIDs.inpaintSketch, false);

    // Make the function global so that other extensions can take advantage of this solution
-    window.applyZoomAndPan = applyZoomAndPan;
+    const applyZoomAndPanIntegration = async(id, elementIDs) => {
+        const mainEl = document.querySelector(id);
+        if (id.toLocaleLowerCase() === "none") {
+            for (const elementID of elementIDs) {
+                const el = await waitForElement(elementID);
+                if (!el) break;
+                applyZoomAndPan(elementID);
+            }
+            return;
+        }
+
+        if (!mainEl) return;
+        mainEl.addEventListener("click", async() => {
+            for (const elementID of elementIDs) {
+                const el = await waitForElement(elementID);
+                if (!el) break;
+                applyZoomAndPan(elementID);
+            }
+        }, {once: true});
+    };
+
+    window.applyZoomAndPan = applyZoomAndPan; // Only 1 elements, argument elementID, for example applyZoomAndPan("#txt2img_controlnet_ControlNet_input_image")
+
+    window.applyZoomAndPanIntegration = applyZoomAndPanIntegration; // for any extension
+
+    /*
+        The function `applyZoomAndPanIntegration` takes two arguments:
+
+        1. `id`: A string identifier for the element to which zoom and pan functionality will be applied on click.
+        If the `id` value is "none", the functionality will be applied to all elements specified in the second argument without a click event.
+
+        2. `elementIDs`: An array of string identifiers for elements. Zoom and pan functionality will be applied to each of these elements on click of the element specified by the first argument.
+        If "none" is specified in the first argument, the functionality will be applied to each of these elements without a click event.
+
+        Example usage:
+        applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]);
+        In this example, zoom and pan functionality will be applied to the element with the identifier "txt2img_controlnet_ControlNet_input_image" upon clicking the element with the identifier "txt2img_controlnet".
+    */
+
+    // More examples
+    // Add integration with ControlNet txt2img One TAB
+    // applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]);
+
+    // Add integration with ControlNet txt2img Tabs
+    // applyZoomAndPanIntegration("#txt2img_controlnet",Array.from({ length: 10 }, (_, i) => `#txt2img_controlnet_ControlNet-${i}_input_image`));
+
+    // Add integration with Inpaint Anything
+    // applyZoomAndPanIntegration("None", ["#ia_sam_image", "#ia_sel_mask"]);
 });
@@ -9,6 +9,7 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas
    "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
    "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
    "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
+    "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"),
    "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
    "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
 }))
@@ -61,3 +61,6 @@
  to {opacity: 1;}
 }

+.styler {
+  overflow:inherit !important;
+}
@@ -1,5 +1,7 @@
+import math
+
 import gradio as gr
-from modules import scripts, shared, ui_components, ui_settings
+from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
 from modules.ui_components import FormColumn


@@ -19,18 +21,38 @@ class ExtraOptionsSection(scripts.Script):
    def ui(self, is_img2img):
        self.comps = []
        self.setting_names = []
+        self.infotext_fields = []
+        extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
+
+        mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}

        with gr.Blocks() as interface:
-            with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and shared.opts.extra_options else gr.Group(), gr.Row():
-                for setting_name in shared.opts.extra_options:
-                    with FormColumn():
-                        comp = ui_settings.create_setting_component(setting_name)
+            with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():

-                    self.comps.append(comp)
-                    self.setting_names.append(setting_name)
+                row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols)
+
+                for row in range(row_count):
+                    with gr.Row():
+                        for col in range(shared.opts.extra_options_cols):
+                            index = row * shared.opts.extra_options_cols + col
+                            if index >= len(extra_options):
+                                break
+
+                            setting_name = extra_options[index]
+
+                            with FormColumn():
+                                comp = ui_settings.create_setting_component(setting_name)
+
+                            self.comps.append(comp)
+                            self.setting_names.append(setting_name)
+
+                            setting_infotext_name = mapping.get(setting_name)
+                            if setting_infotext_name is not None:
+                                self.infotext_fields.append((comp, setting_infotext_name))

        def get_settings_values():
-            return [ui_settings.get_value_for_setting(key) for key in self.setting_names]
+            res = [ui_settings.get_value_for_setting(key) for key in self.setting_names]
+            return res[0] if len(res) == 1 else res

        interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False)

@@ -43,6 +65,10 @@ class ExtraOptionsSection(scripts.Script):


 shared.options_templates.update(shared.options_section(('ui', "User interface"), {
-    "extra_options": shared.OptionInfo([], "Options in main UI", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img/img2img interfaces").needs_restart(),
-    "extra_options_accordion": shared.OptionInfo(False, "Place options in main UI into an accordion")
+    "extra_options_txt2img": shared.OptionInfo([], "Options in main UI - txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
+    "extra_options_img2img": shared.OptionInfo([], "Options in main UI - img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
+    "extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
+    "extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
 }))
+
+
@@ -20,7 +20,13 @@ function reportWindowSize() {
        var button = gradioApp().getElementById(tab + '_generate_box');
        var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column');
        target.insertBefore(button, target.firstElementChild);
+
+        gradioApp().getElementById(tab + '_results').classList.toggle('mobile', currentlyMobile);
    }
 }

 window.addEventListener("resize", reportWindowSize);
+
+onUiLoaded(function() {
+    reportWindowSize();
+});
@@ -119,7 +119,7 @@ window.addEventListener('paste', e => {
    }

    const firstFreeImageField = visibleImageFields
-        .filter(el => el.querySelector('input[type=file]'))?.[0];
+        .filter(el => !el.querySelector('img'))?.[0];

    dropReplaceImage(
        firstFreeImageField ?
@@ -18,22 +18,11 @@ function keyupEditAttention(event) {
        const before = text.substring(0, selectionStart);
        let beforeParen = before.lastIndexOf(OPEN);
        if (beforeParen == -1) return false;
-        let beforeParenClose = before.lastIndexOf(CLOSE);
-        while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
-            beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
-            beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
-        }

        // Find closing parenthesis around current cursor
        const after = text.substring(selectionStart);
        let afterParen = after.indexOf(CLOSE);
        if (afterParen == -1) return false;
-        let afterParenOpen = after.indexOf(OPEN);
-        while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
-            afterParen = after.indexOf(CLOSE, afterParen + 1);
-            afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
-        }
-        if (beforeParen === -1 || afterParen === -1) return false;

        // Set the selection to the text between the parenthesis
        const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
@@ -46,9 +35,14 @@ function keyupEditAttention(event) {

    function selectCurrentWord() {
        if (selectionStart !== selectionEnd) return false;
-        const delimiters = opts.keyedit_delimiters + " \r\n\t";
+        const whitespace_delimiters = {"Tab": "\t", "Carriage Return": "\r", "Line Feed": "\n"};
+        let delimiters = opts.keyedit_delimiters;

-        // seek backward until to find beggining
+        for (let i of opts.keyedit_delimiters_whitespace) {
+            delimiters += whitespace_delimiters[i];
+        }
+
+        // seek backward to find beginning
        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
            selectionStart--;
        }
@@ -92,7 +86,7 @@ function keyupEditAttention(event) {
    }

    var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
-    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
+    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + end));
    if (isNaN(weight)) return;

    weight += isPlus ? delta : -delta;
@@ -33,7 +33,7 @@ function extensions_check() {


    var id = randomId();
-    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() {
+    requestProgress(id, gradioApp().getElementById('extensions_installed_html'), null, function() {

    });

@@ -1,20 +1,38 @@
+function toggleCss(key, css, enable) {
+    var style = document.getElementById(key);
+    if (enable && !style) {
+        style = document.createElement('style');
+        style.id = key;
+        style.type = 'text/css';
+        document.head.appendChild(style);
+    }
+    if (style && !enable) {
+        document.head.removeChild(style);
+    }
+    if (style) {
+        style.innerHTML == '';
+        style.appendChild(document.createTextNode(css));
+    }
+}
+
 function setupExtraNetworksForTab(tabname) {
    gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks');

    var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div');
-    var search = gradioApp().querySelector('#' + tabname + '_extra_search textarea');
+    var searchDiv = gradioApp().getElementById(tabname + '_extra_search');
+    var search = searchDiv.querySelector('textarea');
    var sort = gradioApp().getElementById(tabname + '_extra_sort');
    var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder');
    var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
+    var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs');
+    var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input');

-    search.classList.add('search');
-    sort.classList.add('sort');
-    sortOrder.classList.add('sortorder');
    sort.dataset.sortkey = 'sortDefault';
-    tabs.appendChild(search);
+    tabs.appendChild(searchDiv);
    tabs.appendChild(sort);
    tabs.appendChild(sortOrder);
    tabs.appendChild(refresh);
+    tabs.appendChild(showDirsDiv);

    var applyFilter = function() {
        var searchTerm = search.value.toLowerCase();
@@ -80,6 +98,15 @@ function setupExtraNetworksForTab(tabname) {
    });

    extraNetworksApplyFilter[tabname] = applyFilter;
+
+    var showDirsUpdate = function() {
+        var css = '#' + tabname + '_extra_tabs .extra-network-subdirs { display: none; }';
+        toggleCss(tabname + '_extra_show_dirs_style', css, !showDirs.checked);
+        localSet('extra-networks-show-dirs', showDirs.checked ? 1 : 0);
+    };
+    showDirs.checked = localGet('extra-networks-show-dirs', 1) == 1;
+    showDirs.addEventListener("change", showDirsUpdate);
+    showDirsUpdate();
 }

 function applyExtraNetworkFilter(tabname) {
@@ -113,14 +140,15 @@ function setupExtraNetworks() {

 onUiLoaded(setupExtraNetworks);

-var re_extranet = /<([^:]+:[^:]+):[\d.]+>(.*)/;
-var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
+var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
+var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;

 function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
    var m = text.match(re_extranet);
    var replaced = false;
    var newTextareaText;
    if (m) {
+        var extraTextBeforeNet = opts.extra_networks_add_text_separator;
        var extraTextAfterNet = m[2];
        var partToSearch = m[1];
        var foundAtPosition = -1;
@@ -134,8 +162,13 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
            return found;
        });

-        if (foundAtPosition >= 0 && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
-            newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
+        if (foundAtPosition >= 0) {
+            if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
+                newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
+            }
+            if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) {
+                newTextareaText = newTextareaText.substr(0, foundAtPosition - extraTextBeforeNet.length) + newTextareaText.substr(foundAtPosition);
+            }
        }
    } else {
        newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
@@ -179,7 +212,7 @@ function saveCardPreview(event, tabname, filename) {
 }

 function extraNetworksSearchButton(tabs_id, event) {
-    var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea');
+    var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > label > textarea');
    var button = event.target;
    var text = button.classList.contains("search-all") ? "" : button.textContent.trim();

@@ -189,27 +222,24 @@ function extraNetworksSearchButton(tabs_id, event) {

 var globalPopup = null;
 var globalPopupInner = null;
+
 function closePopup() {
    if (!globalPopup) return;
-
    globalPopup.style.display = "none";
 }
+
 function popup(contents) {
    if (!globalPopup) {
        globalPopup = document.createElement('div');
-        globalPopup.onclick = closePopup;
        globalPopup.classList.add('global-popup');

        var close = document.createElement('div');
        close.classList.add('global-popup-close');
-        close.onclick = closePopup;
+        close.addEventListener("click", closePopup);
        close.title = "Close";
        globalPopup.appendChild(close);

        globalPopupInner = document.createElement('div');
-        globalPopupInner.onclick = function(event) {
-            event.stopPropagation(); return false;
-        };
        globalPopupInner.classList.add('global-popup-inner');
        globalPopup.appendChild(globalPopupInner);

@@ -222,6 +252,15 @@ function popup(contents) {
    globalPopup.style.display = "flex";
 }

+var storedPopupIds = {};
+function popupId(id) {
+    if (!storedPopupIds[id]) {
+        storedPopupIds[id] = gradioApp().getElementById(id);
+    }
+
+    popup(storedPopupIds[id]);
+}
+
 function extraNetworksShowMetadata(text) {
    var elem = document.createElement('pre');
    elem.classList.add('popup-metadata');
@@ -299,13 +338,13 @@ function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) {
 function extraNetworksRefreshSingleCard(page, tabname, name) {
    requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) {
        if (data && data.html) {
-            var card = gradioApp().querySelector('.card[data-name=' + JSON.stringify(name) + ']'); // likely using the wrong stringify function
+            var card = gradioApp().querySelector(`#${tabname}_${page.replace(" ", "_")}_cards > .card[data-name="${name}"]`);

            var newDiv = document.createElement('DIV');
            newDiv.innerHTML = data.html;
            var newCard = newDiv.firstElementChild;

-            newCard.style = '';
+            newCard.style.display = '';
            card.parentElement.insertBefore(newCard, card);
            card.parentElement.removeChild(card);
        }
@@ -190,3 +190,14 @@ onUiUpdate(function(mutationRecords) {
        tooltipCheckTimer = setTimeout(processTooltipCheckNodes, 1000);
    }
 });
+
+onUiLoaded(function() {
+    for (var comp of window.gradio_config.components) {
+        if (comp.props.webui_tooltip && comp.props.elem_id) {
+            var elem = gradioApp().getElementById(comp.props.elem_id);
+            if (elem) {
+                elem.title = comp.props.webui_tooltip;
+            }
+        }
+    }
+});
@@ -136,6 +136,11 @@ function setupImageForLightbox(e) {
    var event = isFirefox ? 'mousedown' : 'click';

    e.addEventListener(event, function(evt) {
+        if (evt.button == 1) {
+            open(evt.target.src);
+            evt.preventDefault();
+            return;
+        }
        if (!opts.js_modal_lightbox || evt.button != 0) return;

        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed);
@@ -0,0 +1,37 @@
+var observerAccordionOpen = new MutationObserver(function(mutations) {
+    mutations.forEach(function(mutationRecord) {
+        var elem = mutationRecord.target;
+        var open = elem.classList.contains('open');
+
+        var accordion = elem.parentNode;
+        accordion.classList.toggle('input-accordion-open', open);
+
+        var checkbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
+        checkbox.checked = open;
+        updateInput(checkbox);
+
+        var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
+        if (extra) {
+            extra.style.display = open ? "" : "none";
+        }
+    });
+});
+
+function inputAccordionChecked(id, checked) {
+    var label = gradioApp().querySelector('#' + id + " .label-wrap");
+    if (label.classList.contains('open') != checked) {
+        label.click();
+    }
+}
+
+onUiLoaded(function() {
+    for (var accordion of gradioApp().querySelectorAll('.input-accordion')) {
+        var labelWrap = accordion.querySelector('.label-wrap');
+        observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
+
+        var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
+        if (extra) {
+            labelWrap.insertBefore(extra, labelWrap.lastElementChild);
+        }
+    }
+});
@@ -0,0 +1,26 @@
+
+function localSet(k, v) {
+    try {
+        localStorage.setItem(k, v);
+    } catch (e) {
+        console.warn(`Failed to save ${k} to localStorage: ${e}`);
+    }
+}
+
+function localGet(k, def) {
+    try {
+        return localStorage.getItem(k);
+    } catch (e) {
+        console.warn(`Failed to load ${k} from localStorage: ${e}`);
+    }
+
+    return def;
+}
+
+function localRemove(k) {
+    try {
+        return localStorage.removeItem(k);
+    } catch (e) {
+        console.warn(`Failed to remove ${k} from localStorage: ${e}`);
+    }
+}
@@ -11,11 +11,11 @@ var ignore_ids_for_localization = {
    train_hypernetwork: 'OPTION',
    txt2img_styles: 'OPTION',
    img2img_styles: 'OPTION',
-    setting_random_artist_categories: 'SPAN',
-    setting_face_restoration_model: 'SPAN',
-    setting_realesrgan_enabled_models: 'SPAN',
-    extras_upscaler_1: 'SPAN',
-    extras_upscaler_2: 'SPAN',
+    setting_random_artist_categories: 'OPTION',
+    setting_face_restoration_model: 'OPTION',
+    setting_realesrgan_enabled_models: 'OPTION',
+    extras_upscaler_1: 'OPTION',
+    extras_upscaler_2: 'OPTION',
 };

 var re_num = /^[.\d]+$/;
@@ -107,12 +107,41 @@ function processNode(node) {
    });
 }

+function localizeWholePage() {
+    processNode(gradioApp());
+
+    function elem(comp) {
+        var elem_id = comp.props.elem_id ? comp.props.elem_id : "component-" + comp.id;
+        return gradioApp().getElementById(elem_id);
+    }
+
+    for (var comp of window.gradio_config.components) {
+        if (comp.props.webui_tooltip) {
+            let e = elem(comp);
+
+            let tl = e ? getTranslation(e.title) : undefined;
+            if (tl !== undefined) {
+                e.title = tl;
+            }
+        }
+        if (comp.props.placeholder) {
+            let e = elem(comp);
+            let textbox = e ? e.querySelector('[placeholder]') : null;
+
+            let tl = textbox ? getTranslation(textbox.placeholder) : undefined;
+            if (tl !== undefined) {
+                textbox.placeholder = tl;
+            }
+        }
+    }
+}
+
 function dumpTranslations() {
    if (!hasLocalization()) {
        // If we don't have any localization,
        // we will not have traversed the app to find
        // original_lines, so do that now.
-        processNode(gradioApp());
+        localizeWholePage();
    }
    var dumped = {};
    if (localization.rtl) {
@@ -154,7 +183,7 @@ document.addEventListener("DOMContentLoaded", function() {
        });
    });

-    processNode(gradioApp());
+    localizeWholePage();

    if (localization.rtl) { // if the language is from right to left,
        (new MutationObserver((mutations, observer) => { // wait for the style to load
@@ -15,7 +15,7 @@ onAfterUiUpdate(function() {
        }
    }

-    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] .thumbnail-item > img');
+    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"] div[id$="_results"] .thumbnail-item > img');

    if (galleryPreviews == null) return;

@@ -69,7 +69,6 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
    var dateStart = new Date();
    var wasEverActive = false;
    var parentProgressbar = progressbarContainer.parentNode;
-    var parentGallery = gallery ? gallery.parentNode : null;

    var divProgress = document.createElement('div');
    divProgress.className = 'progressDiv';
@@ -80,32 +79,26 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
    divProgress.appendChild(divInner);
    parentProgressbar.insertBefore(divProgress, progressbarContainer);

-    if (parentGallery) {
-        var livePreview = document.createElement('div');
-        livePreview.className = 'livePreview';
-        parentGallery.insertBefore(livePreview, gallery);
-    }
+    var livePreview = null;

    var removeProgressBar = function() {
+        if (!divProgress) return;
+
        setTitle("");
        parentProgressbar.removeChild(divProgress);
-        if (parentGallery) parentGallery.removeChild(livePreview);
+        if (gallery && livePreview) gallery.removeChild(livePreview);
        atEnd();
+
+        divProgress = null;
    };

-    var fun = function(id_task, id_live_preview) {
-        request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
+    var funProgress = function(id_task) {
+        request("./internal/progress", {id_task: id_task, live_preview: false}, function(res) {
            if (res.completed) {
                removeProgressBar();
                return;
            }

-            var rect = progressbarContainer.getBoundingClientRect();
-
-            if (rect.width) {
-                divProgress.style.width = rect.width + "px";
-            }
-
            let progressText = "";

            divInner.style.width = ((res.progress || 0) * 100.0) + '%';
@@ -119,7 +112,6 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                progressText += " ETA: " + formatTime(res.eta);
            }

-
            setTitle(progressText);

            if (res.textinfo && res.textinfo.indexOf("\n") == -1) {
@@ -142,16 +134,33 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                return;
            }

+            if (onProgress) {
+                onProgress(res);
+            }
+
+            setTimeout(() => {
+                funProgress(id_task, res.id_live_preview);
+            }, opts.live_preview_refresh_period || 500);
+        }, function() {
+            removeProgressBar();
+        });
+    };
+
+    var funLivePreview = function(id_task, id_live_preview) {
+        request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
+            if (!divProgress) {
+                return;
+            }

            if (res.live_preview && gallery) {
-                rect = gallery.getBoundingClientRect();
-                if (rect.width) {
-                    livePreview.style.width = rect.width + "px";
-                    livePreview.style.height = rect.height + "px";
-                }
-
                var img = new Image();
                img.onload = function() {
+                    if (!livePreview) {
+                        livePreview = document.createElement('div');
+                        livePreview.className = 'livePreview';
+                        gallery.insertBefore(livePreview, gallery.firstElementChild);
+                    }
+
                    livePreview.appendChild(img);
                    if (livePreview.childElementCount > 2) {
                        livePreview.removeChild(livePreview.firstElementChild);
@@ -160,18 +169,18 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                img.src = res.live_preview;
            }

-
-            if (onProgress) {
-                onProgress(res);
-            }
-
            setTimeout(() => {
-                fun(id_task, res.id_live_preview);
+                funLivePreview(id_task, res.id_live_preview);
            }, opts.live_preview_refresh_period || 500);
        }, function() {
            removeProgressBar();
        });
    };

-    fun(id_task, 0);
+    funProgress(id_task, 0);
+
+    if (gallery) {
+        funLivePreview(id_task, 0);
+    }
+
 }
@@ -0,0 +1,141 @@
+(function() {
+    const GRADIO_MIN_WIDTH = 320;
+    const GRID_TEMPLATE_COLUMNS = '1fr 16px 1fr';
+    const PAD = 16;
+    const DEBOUNCE_TIME = 100;
+
+    const R = {
+        tracking: false,
+        parent: null,
+        parentWidth: null,
+        leftCol: null,
+        leftColStartWidth: null,
+        screenX: null,
+    };
+
+    let resizeTimer;
+    let parents = [];
+
+    function setLeftColGridTemplate(el, width) {
+        el.style.gridTemplateColumns = `${width}px 16px 1fr`;
+    }
+
+    function displayResizeHandle(parent) {
+        if (window.innerWidth < GRADIO_MIN_WIDTH * 2 + PAD * 4) {
+            parent.style.display = 'flex';
+            if (R.handle != null) {
+                R.handle.style.opacity = '0';
+            }
+            return false;
+        } else {
+            parent.style.display = 'grid';
+            if (R.handle != null) {
+                R.handle.style.opacity = '100';
+            }
+            return true;
+        }
+    }
+
+    function afterResize(parent) {
+        if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != GRID_TEMPLATE_COLUMNS) {
+            const oldParentWidth = R.parentWidth;
+            const newParentWidth = parent.offsetWidth;
+            const widthL = parseInt(parent.style.gridTemplateColumns.split(' ')[0]);
+
+            const ratio = newParentWidth / oldParentWidth;
+
+            const newWidthL = Math.max(Math.floor(ratio * widthL), GRADIO_MIN_WIDTH);
+            setLeftColGridTemplate(parent, newWidthL);
+
+            R.parentWidth = newParentWidth;
+        }
+    }
+
+    function setup(parent) {
+        const leftCol = parent.firstElementChild;
+        const rightCol = parent.lastElementChild;
+
+        parents.push(parent);
+
+        parent.style.display = 'grid';
+        parent.style.gap = '0';
+        parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS;
+
+        const resizeHandle = document.createElement('div');
+        resizeHandle.classList.add('resize-handle');
+        parent.insertBefore(resizeHandle, rightCol);
+
+        resizeHandle.addEventListener('mousedown', (evt) => {
+            if (evt.button !== 0) return;
+
+            evt.preventDefault();
+            evt.stopPropagation();
+
+            document.body.classList.add('resizing');
+
+            R.tracking = true;
+            R.parent = parent;
+            R.parentWidth = parent.offsetWidth;
+            R.handle = resizeHandle;
+            R.leftCol = leftCol;
+            R.leftColStartWidth = leftCol.offsetWidth;
+            R.screenX = evt.screenX;
+        });
+
+        resizeHandle.addEventListener('dblclick', (evt) => {
+            evt.preventDefault();
+            evt.stopPropagation();
+
+            parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS;
+        });
+
+        afterResize(parent);
+    }
+
+    window.addEventListener('mousemove', (evt) => {
+        if (evt.button !== 0) return;
+
+        if (R.tracking) {
+            evt.preventDefault();
+            evt.stopPropagation();
+
+            const delta = R.screenX - evt.screenX;
+            const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH);
+            setLeftColGridTemplate(R.parent, leftColWidth);
+        }
+    });
+
+    window.addEventListener('mouseup', (evt) => {
+        if (evt.button !== 0) return;
+
+        if (R.tracking) {
+            evt.preventDefault();
+            evt.stopPropagation();
+
+            R.tracking = false;
+
+            document.body.classList.remove('resizing');
+        }
+    });
+
+
+    window.addEventListener('resize', () => {
+        clearTimeout(resizeTimer);
+
+        resizeTimer = setTimeout(function() {
+            for (const parent of parents) {
+                afterResize(parent);
+            }
+        }, DEBOUNCE_TIME);
+    });
+
+    setupResizeHandle = setup;
+})();
+
+onUiLoaded(function() {
+    for (var elem of gradioApp().querySelectorAll('.resize-handle-row')) {
+        if (!elem.querySelector('.resize-handle')) {
+            setupResizeHandle(elem);
+        }
+    }
+});
@@ -0,0 +1,46 @@
+let settingsExcludeTabsFromShowAll = {
+    settings_tab_defaults: 1,
+    settings_tab_sysinfo: 1,
+    settings_tab_actions: 1,
+    settings_tab_licenses: 1,
+};
+
+function settingsShowAllTabs() {
+    gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
+        if (settingsExcludeTabsFromShowAll[elem.id]) return;
+
+        elem.style.display = "block";
+    });
+}
+
+function settingsShowOneTab() {
+    gradioApp().querySelector('#settings_show_one_page').click();
+}
+
+onUiLoaded(function() {
+    var edit = gradioApp().querySelector('#settings_search');
+    var editTextarea = gradioApp().querySelector('#settings_search > label > input');
+    var buttonShowAllPages = gradioApp().getElementById('settings_show_all_pages');
+    var settings_tabs = gradioApp().querySelector('#settings div');
+
+    onEdit('settingsSearch', editTextarea, 250, function() {
+        var searchText = (editTextarea.value || "").trim().toLowerCase();
+
+        gradioApp().querySelectorAll('#settings > div[id^=settings_] div[id^=column_settings_] > *').forEach(function(elem) {
+            var visible = elem.textContent.trim().toLowerCase().indexOf(searchText) != -1;
+            elem.style.display = visible ? "" : "none";
+        });
+
+        if (searchText != "") {
+            settingsShowAllTabs();
+        } else {
+            settingsShowOneTab();
+        }
+    });
+
+    settings_tabs.insertBefore(edit, settings_tabs.firstChild);
+    settings_tabs.appendChild(buttonShowAllPages);
+
+
+    buttonShowAllPages.addEventListener("click", settingsShowAllTabs);
+});
@@ -1,10 +1,9 @@
-let promptTokenCountDebounceTime = 800;
-let promptTokenCountTimeouts = {};
-var promptTokenCountUpdateFunctions = {};
+let promptTokenCountUpdateFunctions = {};

 function update_txt2img_tokens(...args) {
    // Called from Gradio
    update_token_counter("txt2img_token_button");
+    update_token_counter("txt2img_negative_token_button");
    if (args.length == 2) {
        return args[0];
    }
@@ -14,6 +13,7 @@ function update_txt2img_tokens(...args) {
 function update_img2img_tokens(...args) {
    // Called from Gradio
    update_token_counter("img2img_token_button");
+    update_token_counter("img2img_negative_token_button");
    if (args.length == 2) {
        return args[0];
    }
@@ -21,16 +21,7 @@ function update_img2img_tokens(...args) {
 }

 function update_token_counter(button_id) {
-    if (opts.disable_token_counters) {
-        return;
-    }
-    if (promptTokenCountTimeouts[button_id]) {
-        clearTimeout(promptTokenCountTimeouts[button_id]);
-    }
-    promptTokenCountTimeouts[button_id] = setTimeout(
-        () => gradioApp().getElementById(button_id)?.click(),
-        promptTokenCountDebounceTime,
-    );
+    promptTokenCountUpdateFunctions[button_id]?.();
 }


@@ -69,10 +60,11 @@ function setupTokenCounting(id, id_counter, id_button) {
    prompt.parentElement.insertBefore(counter, prompt);
    prompt.parentElement.style.position = "relative";

-    promptTokenCountUpdateFunctions[id] = function() {
-        update_token_counter(id_button);
-    };
-    textarea.addEventListener("input", promptTokenCountUpdateFunctions[id]);
+    var func = onEdit(id, textarea, 800, function() {
+        gradioApp().getElementById(id_button)?.click();
+    });
+    promptTokenCountUpdateFunctions[id] = func;
+    promptTokenCountUpdateFunctions[id_button] = func;
 }

 function setupTokenCounters() {
@@ -19,28 +19,11 @@ function all_gallery_buttons() {
 }

 function selected_gallery_button() {
-    var allCurrentButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnail-item.thumbnail-small.selected');
-    var visibleCurrentButton = null;
-    allCurrentButtons.forEach(function(elem) {
-        if (elem.parentElement.offsetParent) {
-            visibleCurrentButton = elem;
-        }
-    });
-    return visibleCurrentButton;
+    return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null;
 }

 function selected_gallery_index() {
-    var buttons = all_gallery_buttons();
-    var button = selected_gallery_button();
-
-    var result = -1;
-    buttons.forEach(function(v, i) {
-        if (v == button) {
-            result = i;
-        }
-    });
-
-    return result;
+    return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected'));
 }

 function extract_image_from_gallery(gallery) {
@@ -152,11 +135,11 @@ function submit() {
    showSubmitButtons('txt2img', false);

    var id = randomId();
-    localStorage.setItem("txt2img_task_id", id);
+    localSet("txt2img_task_id", id);

    requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
        showSubmitButtons('txt2img', true);
-        localStorage.removeItem("txt2img_task_id");
+        localRemove("txt2img_task_id");
        showRestoreProgressButton('txt2img', false);
    });

@@ -171,11 +154,11 @@ function submit_img2img() {
    showSubmitButtons('img2img', false);

    var id = randomId();
-    localStorage.setItem("img2img_task_id", id);
+    localSet("img2img_task_id", id);

    requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
        showSubmitButtons('img2img', true);
-        localStorage.removeItem("img2img_task_id");
+        localRemove("img2img_task_id");
        showRestoreProgressButton('img2img', false);
    });

@@ -189,9 +172,7 @@ function submit_img2img() {

 function restoreProgressTxt2img() {
    showRestoreProgressButton("txt2img", false);
-    var id = localStorage.getItem("txt2img_task_id");
-
-    id = localStorage.getItem("txt2img_task_id");
+    var id = localGet("txt2img_task_id");

    if (id) {
        requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
@@ -205,7 +186,7 @@ function restoreProgressTxt2img() {
 function restoreProgressImg2img() {
    showRestoreProgressButton("img2img", false);

-    var id = localStorage.getItem("img2img_task_id");
+    var id = localGet("img2img_task_id");

    if (id) {
        requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
@@ -218,8 +199,8 @@ function restoreProgressImg2img() {


 onUiLoaded(function() {
-    showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"));
-    showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"));
+    showRestoreProgressButton('txt2img', localGet("txt2img_task_id"));
+    showRestoreProgressButton('img2img', localGet("img2img_task_id"));
 });


@@ -282,21 +263,6 @@ onAfterUiUpdate(function() {
    json_elem.parentElement.style.display = "none";

    setupTokenCounters();
-
-    var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
-    var settings_tabs = gradioApp().querySelector('#settings div');
-    if (show_all_pages && settings_tabs) {
-        settings_tabs.appendChild(show_all_pages);
-        show_all_pages.onclick = function() {
-            gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
-                if (elem.id == "settings_tab_licenses") {
-                    return;
-                }
-
-                elem.style.display = "block";
-            });
-        };
-    }
 });

 onOptionsChanged(function() {
@@ -385,3 +351,20 @@ function switchWidthHeight(tabname) {
    updateInput(height);
    return [];
 }
+
+
+var onEditTimers = {};
+
+// calls func after afterMs milliseconds has passed since the input elem has beed enited by user
+function onEdit(editId, elem, afterMs, func) {
+    var edited = function() {
+        var existingTimer = onEditTimers[editId];
+        if (existingTimer) clearTimeout(existingTimer);
+
+        onEditTimers[editId] = setTimeout(func, afterMs);
+    };
+
+    elem.addEventListener("input", edited);
+
+    return edited;
+}
@@ -1,6 +1,5 @@
 from modules import launch_utils

-
 args = launch_utils.args
 python = launch_utils.python
 git = launch_utils.git
@@ -26,8 +25,18 @@ start = launch_utils.start


 def main():
-    if not args.skip_prepare_environment:
-        prepare_environment()
+    if args.dump_sysinfo:
+        filename = launch_utils.dump_sysinfo()
+
+        print(f"Sysinfo saved as {filename}. Exiting...")
+
+        exit(0)
+
+    launch_utils.startup_timer.record("initial startup")
+
+    with launch_utils.startup_timer.subcategory("prepare environment"):
+        if not args.skip_prepare_environment:
+            prepare_environment()

    if args.test_server:
        configure_for_tests()
@@ -4,6 +4,8 @@ import os
 import time
 import datetime
 import uvicorn
+import ipaddress
+import requests
 import gradio as gr
 from threading import Lock
 from io import BytesIO
@@ -15,7 +17,7 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest

 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -23,12 +25,11 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_aliases
-from modules.sd_vae import vae_dict
+from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
-from typing import Dict, List, Any
+from typing import Any
 import piexif
 import piexif.helper
 from contextlib import closing
@@ -56,7 +57,41 @@ def setUpscalers(req: dict):
    return reqDict


+def verify_url(url):
+    """Returns True if the url refers to a global resource."""
+
+    import socket
+    from urllib.parse import urlparse
+    try:
+        parsed_url = urlparse(url)
+        domain_name = parsed_url.netloc
+        host = socket.gethostbyname_ex(domain_name)
+        for ip in host[2]:
+            ip_addr = ipaddress.ip_address(ip)
+            if not ip_addr.is_global:
+                return False
+    except Exception:
+        return False
+
+    return True
+
+
 def decode_base64_to_image(encoding):
+    if encoding.startswith("http://") or encoding.startswith("https://"):
+        if not opts.api_enable_requests:
+            raise HTTPException(status_code=500, detail="Requests not allowed")
+
+        if opts.api_forbid_local_requests and not verify_url(encoding):
+            raise HTTPException(status_code=500, detail="Request to local resource not allowed")
+
+        headers = {'user-agent': opts.api_useragent} if opts.api_useragent else {}
+        response = requests.get(encoding, timeout=30, headers=headers)
+        try:
+            image = Image.open(BytesIO(response.content))
+            return image
+        except Exception as e:
+            raise HTTPException(status_code=500, detail="Invalid image url") from e
+
    if encoding.startswith("data:image/"):
        encoding = encoding.split(";")[1].split(",")[1]
    try:
@@ -186,17 +221,18 @@ class Api:
        self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
        self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
        self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
-        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
-        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
-        self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
-        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
-        self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
-        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
-        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
-        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
-        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
+        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
+        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
+        self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
+        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
+        self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
+        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
+        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
+        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
        self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
+        self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
        self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
        self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
        self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
@@ -206,7 +242,8 @@ class Api:
        self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
        self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
        self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
-        self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
+        self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
+        self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])

        if shared.cmd_opts.api_server_stop:
            self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@@ -329,6 +366,7 @@ class Api:

        with self.queue_lock:
            with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
+                p.is_api = True
                p.scripts = script_runner
                p.outpath_grids = opts.outdir_txt2img_grids
                p.outpath_samples = opts.outdir_txt2img_samples
@@ -343,6 +381,7 @@ class Api:
                        processed = process_images(p)
                finally:
                    shared.state.end()
+                    shared.total_tqdm.clear()

        b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []

@@ -388,6 +427,7 @@ class Api:
        with self.queue_lock:
            with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
                p.init_images = [decode_base64_to_image(x) for x in init_images]
+                p.is_api = True
                p.scripts = script_runner
                p.outpath_grids = opts.outdir_img2img_grids
                p.outpath_samples = opts.outdir_img2img_samples
@@ -402,6 +442,7 @@ class Api:
                        processed = process_images(p)
                finally:
                    shared.state.end()
+                    shared.total_tqdm.clear()

        b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []

@@ -433,9 +474,6 @@ class Api:
        return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])

    def pnginfoapi(self, req: models.PNGInfoRequest):
-        if(not req.image.strip()):
-            return models.PNGInfoResponse(info="")
-
        image = decode_base64_to_image(req.image.strip())
        if image is None:
            return models.PNGInfoResponse(info="")
@@ -444,9 +482,10 @@ class Api:
        if geninfo is None:
            geninfo = ""

-        items = {**{'parameters': geninfo}, **items}
+        params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
+        script_callbacks.infotext_pasted_callback(geninfo, params)

-        return models.PNGInfoResponse(info=geninfo, items=items)
+        return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)

    def progressapi(self, req: models.ProgressRequest = Depends()):
        # copy from check_progress_call of ui.py
@@ -524,13 +563,13 @@ class Api:

        return options

-    def set_config(self, req: Dict[str, Any]):
+    def set_config(self, req: dict[str, Any]):
        checkpoint_name = req.get("sd_model_checkpoint", None)
        if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
            raise RuntimeError(f"model {checkpoint_name!r} not found")

        for k, v in req.items():
-            shared.opts.set(k, v)
+            shared.opts.set(k, v, is_api=True)

        shared.opts.save(shared.config_filename)
        return
@@ -562,10 +601,12 @@ class Api:
        ]

    def get_sd_models(self):
-        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
+        import modules.sd_models as sd_models
+        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in sd_models.checkpoints_list.values()]

    def get_sd_vaes(self):
-        return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()]
+        import modules.sd_vae as sd_vae
+        return [{"model_name": x, "filename": sd_vae.vae_dict[x]} for x in sd_vae.vae_dict.keys()]

    def get_hypernetworks(self):
        return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
@@ -608,6 +649,10 @@ class Api:
        with self.queue_lock:
            shared.refresh_checkpoints()

+    def refresh_vae(self):
+        with self.queue_lock:
+            shared_items.refresh_vae_list()
+
    def create_embedding(self, args: dict):
        try:
            shared.state.begin(job="create_embedding")
@@ -724,6 +769,25 @@ class Api:
            cuda = {'error': f'{err}'}
        return models.MemoryResponse(ram=ram, cuda=cuda)

+    def get_extensions_list(self):
+        from modules import extensions
+        extensions.list_extensions()
+        ext_list = []
+        for ext in extensions.extensions:
+            ext: extensions.Extension
+            ext.read_info_from_repo()
+            if ext.remote is not None:
+                ext_list.append({
+                    "name": ext.name,
+                    "remote": ext.remote,
+                    "branch": ext.branch,
+                    "commit_hash":ext.commit_hash,
+                    "commit_date":ext.commit_date,
+                    "version":ext.version,
+                    "enabled":ext.enabled
+                })
+        return ext_list
+
    def launch(self, server_name, port, root_path):
        self.app.include_router(self.router)
        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
@@ -1,12 +1,10 @@
 import inspect

 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional
-from typing_extensions import Literal
+from typing import Any, Optional, Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers, opts, parser
-from typing import Dict, List

 API_NOT_ALLOWED = [
    "self",
@@ -50,10 +48,12 @@ class PydanticModelGenerator:
        additional_fields = None,
    ):
        def field_type_generator(k, v):
-            # field_type = str if not overrides.get(k) else overrides[k]["type"]
-            # print(k, v.annotation, v.default)
            field_type = v.annotation

+            if field_type == 'Image':
+                # images are sent as base64 strings via API
+                field_type = 'str'
+
            return Optional[field_type]

        def merge_class_params(class_):
@@ -63,7 +63,6 @@ class PydanticModelGenerator:
                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
            return parameters

-
        self._model_name = model_name
        self._class_data = merge_class_params(class_instance)

@@ -72,7 +71,7 @@ class PydanticModelGenerator:
                field=underscore(k),
                field_alias=k,
                field_type=field_type_generator(k, v),
-                field_value=v.default
+                field_value=None if isinstance(v.default, property) else v.default
            )
            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
        ]
@@ -129,12 +128,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
 ).generate_model()

 class TextToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
    parameters: dict
    info: str

 class ImageToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
    parameters: dict
    info: str

@@ -167,17 +166,18 @@ class FileData(BaseModel):
    name: str = Field(title="File name")

 class ExtrasBatchImagesRequest(ExtrasBaseRequest):
-    imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")

 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: List[str] = Field(title="Images", description="The generated images in base64 format.")
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")

 class PNGInfoRequest(BaseModel):
    image: str = Field(title="Image", description="The base64 encoded PNG image")

 class PNGInfoResponse(BaseModel):
    info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
-    items: dict = Field(title="Items", description="An object containing all the info the image had")
+    items: dict = Field(title="Items", description="A dictionary containing all the other fields the image had")
+    parameters: dict = Field(title="Parameters", description="A dictionary with parsed generation info fields")

 class ProgressRequest(BaseModel):
    skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
@@ -231,8 +231,8 @@ FlagsModel = create_model("Flags", **flags)

 class SamplerItem(BaseModel):
    name: str = Field(title="Name")
-    aliases: List[str] = Field(title="Aliases")
-    options: Dict[str, str] = Field(title="Options")
+    aliases: list[str] = Field(title="Aliases")
+    options: dict[str, str] = Field(title="Options")

 class UpscalerItem(BaseModel):
    name: str = Field(title="Name")
@@ -283,8 +283,8 @@ class EmbeddingItem(BaseModel):
    vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")

 class EmbeddingsResponse(BaseModel):
-    loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
-    skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
+    loaded: dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    skipped: dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")

 class MemoryResponse(BaseModel):
    ram: dict = Field(title="RAM", description="System memory stats")
@@ -302,11 +302,20 @@ class ScriptArg(BaseModel):
    minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
    maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
    step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
-    choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
+    choices: Optional[list[str]] = Field(default=None, title="Choices", description="Possible values for the argument")


 class ScriptInfo(BaseModel):
    name: str = Field(default=None, title="Name", description="Script name")
    is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
    is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
-    args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
+    args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
+
+class ExtensionItem(BaseModel):
+    name: str = Field(title="Name", description="Extension name")
+    remote: str = Field(title="Remote", description="Extension Repository URL")
+    branch: str = Field(title="Branch", description="Extension Repository Branch")
+    commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash")
+    version: str = Field(title="Version", description="Extension Version")
+    commit_date: str = Field(title="Commit Date", description="Extension Repository Commit Date")
+    enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled")
@@ -1,11 +1,12 @@
 import json
+import os
 import os.path
 import threading
 import time

 from modules.paths import data_path, script_path

-cache_filename = os.path.join(data_path, "cache.json")
+cache_filename = os.environ.get('SD_WEBUI_CACHE_FILE', os.path.join(data_path, "cache.json"))
 cache_data = None
 cache_lock = threading.Lock()

@@ -29,9 +30,12 @@ def dump_cache():
            time.sleep(1)

        with cache_lock:
-            with open(cache_filename, "w", encoding="utf8") as file:
+            cache_filename_tmp = cache_filename + "-"
+            with open(cache_filename_tmp, "w", encoding="utf8") as file:
                json.dump(cache_data, file, indent=4)

+            os.replace(cache_filename_tmp, cache_filename)
+
            dump_cache_after = None
            dump_cache_thread = None

@@ -1,11 +1,10 @@
 from functools import wraps
 import html
-import threading
 import time

-from modules import shared, progress, errors
+from modules import shared, progress, errors, devices, fifo_lock

-queue_lock = threading.Lock()
+queue_lock = fifo_lock.FIFOLock()


 def wrap_queued_call(func):
@@ -75,6 +74,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
            error_message = f'{type(e).__name__}: {e}'
            res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]

+        devices.torch_gc()
+
        shared.state.skipped = False
        shared.state.interrupted = False
        shared.state.job_count = 0
@@ -13,8 +13,11 @@ parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py
 parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
 parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
 parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
+parser.add_argument("--log-startup", action='store_true', help="launch.py argument: print a detailed log of what's happening at startup")
 parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
 parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
+parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argument: dump limited sysinfo file (without information about extensions, options) to disk and quit")
+parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None)
 parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
 parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
 parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
@@ -33,9 +36,10 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_
 parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
+parser.add_argument("--medvram-sdxl", action='store_true', help="enable --medvram optimization just for SDXL models")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
 parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
-parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
+parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
@@ -66,6 +70,7 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
 parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -78,14 +83,14 @@ parser.add_argument("--gradio-auth", type=str, help='set gradio authentication l
 parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
 parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
 parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
-parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it")
+parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it", default=[data_path])
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
 parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
-parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="does not do anything", default=False)  # Legacy compatibility, use as default value shared.opts.enable_console_prompts
 parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
@@ -107,6 +112,9 @@ parser.add_argument("--skip-version-check", action='store_true', help="Do not ch
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
 parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
-parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
+parser.add_argument('--add-stop-route', action='store_true', help='does not do anything')
 parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
 parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
+parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
+parser.add_argument("--disable-extra-extensions", action='store_true', help="prevent all extensions except built-in from running regardless of any other settings", default=False)
+parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui", )
@@ -4,18 +4,15 @@ Supports saving and restoring webui and extensions from a known working set of c

 import os
 import json
-import time
 import tqdm

 from datetime import datetime
-from collections import OrderedDict
 import git

 from modules import shared, extensions, errors
 from modules.paths_internal import script_path, config_states_dir

-
-all_config_states = OrderedDict()
+all_config_states = {}


 def list_config_states():
@@ -28,15 +25,19 @@ def list_config_states():
    for filename in os.listdir(config_states_dir):
        if filename.endswith(".json"):
            path = os.path.join(config_states_dir, filename)
-            with open(path, "r", encoding="utf-8") as f:
-                j = json.load(f)
-                j["filepath"] = path
-                config_states.append(j)
+            try:
+                with open(path, "r", encoding="utf-8") as f:
+                    j = json.load(f)
+                    assert "created_at" in j, '"created_at" does not exist'
+                    j["filepath"] = path
+                    config_states.append(j)
+            except Exception as e:
+                print(f'[ERROR]: Config states {path}, {e}')

    config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)

    for cs in config_states:
-        timestamp = time.asctime(time.gmtime(cs["created_at"]))
+        timestamp = datetime.fromtimestamp(cs["created_at"]).strftime('%Y-%m-%d %H:%M:%S')
        name = cs.get("name", "Config")
        full_name = f"{name}: {timestamp}"
        all_config_states[full_name] = cs
@@ -3,7 +3,7 @@ import contextlib
 from functools import lru_cache

 import torch
-from modules import errors
+from modules import errors, shared

 if sys.platform == "darwin":
    from modules import mac_specific
@@ -17,8 +17,6 @@ def has_mps() -> bool:


 def get_cuda_device_string():
-    from modules import shared
-
    if shared.cmd_opts.device_id is not None:
        return f"cuda:{shared.cmd_opts.device_id}"

@@ -40,8 +38,6 @@ def get_optimal_device():


 def get_device_for(task):
-    from modules import shared
-
    if task in shared.cmd_opts.use_cpu:
        return cpu

@@ -64,21 +60,25 @@ def enable_tf32():

        # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
        # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
-        if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
+        device_id = (int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0) or torch.cuda.current_device()
+        if torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16"):
            torch.backends.cudnn.benchmark = True

        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True


-
 errors.run(enable_tf32, "Enabling TF32")

-cpu = torch.device("cpu")
-device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
-dtype = torch.float16
-dtype_vae = torch.float16
-dtype_unet = torch.float16
+cpu: torch.device = torch.device("cpu")
+device: torch.device = None
+device_interrogate: torch.device = None
+device_gfpgan: torch.device = None
+device_esrgan: torch.device = None
+device_codeformer: torch.device = None
+dtype: torch.dtype = torch.float16
+dtype_vae: torch.dtype = torch.float16
+dtype_unet: torch.dtype = torch.float16
 unet_needs_upcast = False


@@ -90,26 +90,10 @@ def cond_cast_float(input):
    return input.float() if unet_needs_upcast else input


-def randn(seed, shape):
-    from modules.shared import opts
-
-    torch.manual_seed(seed)
-    if opts.randn_source == "CPU" or device.type == 'mps':
-        return torch.randn(shape, device=cpu).to(device)
-    return torch.randn(shape, device=device)
-
-
-def randn_without_seed(shape):
-    from modules.shared import opts
-
-    if opts.randn_source == "CPU" or device.type == 'mps':
-        return torch.randn(shape, device=cpu).to(device)
-    return torch.randn(shape, device=device)
+nv_rng = None


 def autocast(disable=False):
-    from modules import shared
-
    if disable:
        return contextlib.nullcontext()

@@ -128,8 +112,6 @@ class NansException(Exception):


 def test_for_nans(x, where):
-    from modules import shared
-
    if shared.cmd_opts.disable_nan_check:
        return

@@ -169,3 +151,4 @@ def first_time_calculation():
    x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
    conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
    conv2d(x)
+
@@ -14,7 +14,8 @@ def record_exception():
    if exception_records and exception_records[-1] == e:
        return

-    exception_records.append((e, tb))
+    from modules import sysinfo
+    exception_records.append(sysinfo.format_exception(e, tb))

    if len(exception_records) > 5:
        exception_records.pop(0)
@@ -83,3 +84,53 @@ def run(code, task):
        code()
    except Exception as e:
        display(task, e)
+
+
+def check_versions():
+    from packaging import version
+    from modules import shared
+
+    import torch
+    import gradio
+
+    expected_torch_version = "2.0.0"
+    expected_xformers_version = "0.0.20"
+    expected_gradio_version = "3.41.2"
+
+    if version.parse(torch.__version__) < version.parse(expected_torch_version):
+        print_error_explanation(f"""
+You are running torch {torch.__version__}.
+The program is tested to work with torch {expected_torch_version}.
+To reinstall the desired version, run with commandline flag --reinstall-torch.
+Beware that this will cause a lot of large files to be downloaded, as well as
+there are reports of issues with training tab on the latest version.
+
+Use --skip-version-check commandline argument to disable this check.
+        """.strip())
+
+    if shared.xformers_available:
+        import xformers
+
+        if version.parse(xformers.__version__) < version.parse(expected_xformers_version):
+            print_error_explanation(f"""
+You are running xformers {xformers.__version__}.
+The program is tested to work with xformers {expected_xformers_version}.
+To reinstall the desired version, run with commandline flag --reinstall-xformers.
+
+Use --skip-version-check commandline argument to disable this check.
+            """.strip())
+
+    if gradio.__version__ != expected_gradio_version:
+        print_error_explanation(f"""
+You are running gradio {gradio.__version__}.
+The program is designed to work with gradio {expected_gradio_version}.
+Using a different version of gradio is extremely likely to break the program.
+
+Reasons why you have the mismatched gradio version can be:
+  - you use --skip-install flag.
+  - you use webui.py to start the program instead of launch.py.
+  - an extension installs the incompatible gradio version.
+
+Use --skip-version-check commandline argument to disable this check.
+        """.strip())
+
@@ -1,7 +1,7 @@
 import os
 import threading

-from modules import shared, errors, cache
+from modules import shared, errors, cache, scripts
 from modules.gitpython_hack import Repo
 from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401

@@ -11,9 +11,9 @@ os.makedirs(extensions_dir, exist_ok=True)


 def active():
-    if shared.opts.disable_all_extensions == "all":
+    if shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
        return []
-    elif shared.opts.disable_all_extensions == "extra":
+    elif shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions == "extra":
        return [x for x in extensions if x.enabled and x.is_builtin]
    else:
        return [x for x in extensions if x.enabled]
@@ -90,8 +90,6 @@ class Extension:
        self.have_info_from_repo = True

    def list_files(self, subdir, extension):
-        from modules import scripts
-
        dirpath = os.path.join(self.path, subdir)
        if not os.path.isdir(dirpath):
            return []
@@ -141,8 +139,12 @@ def list_extensions():
    if not os.path.isdir(extensions_dir):
        return

-    if shared.opts.disable_all_extensions == "all":
+    if shared.cmd_opts.disable_all_extensions:
+        print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
+    elif shared.opts.disable_all_extensions == "all":
        print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+    elif shared.cmd_opts.disable_extra_extensions:
+        print("*** \"--disable-extra-extensions\" arg was used, will only load built-in extensions ***")
    elif shared.opts.disable_all_extensions == "extra":
        print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")

@@ -1,4 +1,7 @@
+import json
+import os
 import re
+import logging
 from collections import defaultdict

 from modules import errors
@@ -84,27 +87,55 @@ class ExtraNetwork:
        raise NotImplementedError


+def lookup_extra_networks(extra_network_data):
+    """returns a dict mapping ExtraNetwork objects to lists of arguments for those extra networks.
+
+    Example input:
+    {
+        'lora': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58310>],
+        'lyco': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58F70>],
+        'hypernet': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D5A800>]
+    }
+
+    Example output:
+
+    {
+        <extra_networks_lora.ExtraNetworkLora object at 0x0000020581BEECE0>: [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58310>, <modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58F70>],
+        <modules.extra_networks_hypernet.ExtraNetworkHypernet object at 0x0000020581BEEE60>: [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D5A800>]
+    }
+    """
+
+    res = {}
+
+    for extra_network_name, extra_network_args in list(extra_network_data.items()):
+        extra_network = extra_network_registry.get(extra_network_name, None)
+        alias = extra_network_aliases.get(extra_network_name, None)
+
+        if alias is not None and extra_network is None:
+            extra_network = alias
+
+        if extra_network is None:
+            logging.info(f"Skipping unknown extra network: {extra_network_name}")
+            continue
+
+        res.setdefault(extra_network, []).extend(extra_network_args)
+
+    return res
+
+
 def activate(p, extra_network_data):
    """call activate for extra networks in extra_network_data in specified order, then call
    activate for all remaining registered networks with an empty argument list"""

    activated = []

-    for extra_network_name, extra_network_args in extra_network_data.items():
-        extra_network = extra_network_registry.get(extra_network_name, None)
-
-        if extra_network is None:
-            extra_network = extra_network_aliases.get(extra_network_name, None)
-
-        if extra_network is None:
-            print(f"Skipping unknown extra network: {extra_network_name}")
-            continue
+    for extra_network, extra_network_args in lookup_extra_networks(extra_network_data).items():

        try:
            extra_network.activate(p, extra_network_args)
            activated.append(extra_network)
        except Exception as e:
-            errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
+            errors.display(e, f"activating extra network {extra_network.name} with arguments {extra_network_args}")

    for extra_network_name, extra_network in extra_network_registry.items():
        if extra_network in activated:
@@ -123,19 +154,16 @@ def deactivate(p, extra_network_data):
    """call deactivate for extra networks in extra_network_data in specified order, then call
    deactivate for all remaining registered networks"""

-    for extra_network_name in extra_network_data:
-        extra_network = extra_network_registry.get(extra_network_name, None)
-        if extra_network is None:
-            continue
+    data = lookup_extra_networks(extra_network_data)

+    for extra_network in data:
        try:
            extra_network.deactivate(p)
        except Exception as e:
-            errors.display(e, f"deactivating extra network {extra_network_name}")
+            errors.display(e, f"deactivating extra network {extra_network.name}")

    for extra_network_name, extra_network in extra_network_registry.items():
-        args = extra_network_data.get(extra_network_name, None)
-        if args is not None:
+        if extra_network in data:
            continue

        try:
@@ -177,3 +205,20 @@ def parse_prompts(prompts):

    return res, extra_data

+
+def get_user_metadata(filename):
+    if filename is None:
+        return {}
+
+    basename, ext = os.path.splitext(filename)
+    metadata_filename = basename + '.json'
+
+    metadata = {}
+    try:
+        if os.path.isfile(metadata_filename):
+            with open(metadata_filename, "r", encoding="utf8") as file:
+                metadata = json.load(file)
+    except Exception as e:
+        errors.display(e, f"reading extra network user metadata from {metadata_filename}")
+
+    return metadata
@@ -7,7 +7,7 @@ import json
 import torch
 import tqdm

-from modules import shared, images, sd_models, sd_vae, sd_models_config
+from modules import shared, images, sd_models, sd_vae, sd_models_config, errors
 from modules.ui_common import plaintext_to_html
 import gradio as gr
 import safetensors.torch
@@ -72,7 +72,20 @@ def to_half(tensor, enable):
    return tensor


-def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
+def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name):
+    metadata = {}
+
+    for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]:
+        checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None)
+        if checkpoint_info is None:
+            continue
+
+        metadata.update(checkpoint_info.metadata)
+
+    return json.dumps(metadata, indent=4, ensure_ascii=False)
+
+
+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json):
    shared.state.begin(job="model-merge")

    def fail(message):
@@ -241,11 +254,25 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
    shared.state.textinfo = "Saving"
    print(f"Saving to {output_modelname}...")

-    metadata = None
+    metadata = {}
+
+    if save_metadata and copy_metadata_fields:
+        if primary_model_info:
+            metadata.update(primary_model_info.metadata)
+        if secondary_model_info:
+            metadata.update(secondary_model_info.metadata)
+        if tertiary_model_info:
+            metadata.update(tertiary_model_info.metadata)

    if save_metadata:
-        metadata = {"format": "pt"}
+        try:
+            metadata.update(json.loads(metadata_json))
+        except Exception as e:
+            errors.display(e, "readin metadata from json")

+        metadata["format"] = "pt"
+
+    if save_metadata and add_merge_recipe:
        merge_recipe = {
            "type": "webui", # indicate this model was merged with webui's built-in merger
            "primary_model_hash": primary_model_info.sha256,
@@ -261,7 +288,6 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
            "is_inpainting": result_is_inpainting_model,
            "is_instruct_pix2pix": result_is_instruct_pix2pix_model
        }
-        metadata["sd_merge_recipe"] = json.dumps(merge_recipe)

        sd_merge_models = {}

@@ -281,11 +307,12 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
        if tertiary_model_info:
            add_model_metadata(tertiary_model_info)

+        metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
        metadata["sd_merge_models"] = json.dumps(sd_merge_models)

    _, extension = os.path.splitext(output_modelname)
    if extension.lower() == ".safetensors":
-        safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
+        safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None)
    else:
        torch.save(theta_0, output_modelname)

@@ -0,0 +1,37 @@
+import threading
+import collections
+
+
+# reference: https://gist.github.com/vitaliyp/6d54dd76ca2c3cdfc1149d33007dc34a
+class FIFOLock(object):
+    def __init__(self):
+        self._lock = threading.Lock()
+        self._inner_lock = threading.Lock()
+        self._pending_threads = collections.deque()
+
+    def acquire(self, blocking=True):
+        with self._inner_lock:
+            lock_acquired = self._lock.acquire(False)
+            if lock_acquired:
+                return True
+            elif not blocking:
+                return False
+
+            release_event = threading.Event()
+            self._pending_threads.append(release_event)
+
+        release_event.wait()
+        return self._lock.acquire()
+
+    def release(self):
+        with self._inner_lock:
+            if self._pending_threads:
+                release_event = self._pending_threads.popleft()
+                release_event.set()
+
+            self._lock.release()
+
+    __enter__ = acquire
+
+    def __exit__(self, t, v, tb):
+        self.release()
@@ -6,10 +6,10 @@ import re

 import gradio as gr
 from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks
+from modules import shared, ui_tempdir, script_callbacks, processing
 from PIL import Image

-re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
+re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
@@ -32,6 +32,7 @@ class ParamBinding:

 def reset():
    paste_fields.clear()
+    registered_param_bindings.clear()


 def quote(text):
@@ -198,7 +199,6 @@ def restore_old_hires_fix_params(res):
    height = int(res.get("Size-2", 512))

    if firstpass_width == 0 or firstpass_height == 0:
-        from modules import processing
        firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height)

    res['Size-1'] = firstpass_width
@@ -280,6 +280,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
    if "Hires sampler" not in res:
        res["Hires sampler"] = "Use same sampler"

+    if "Hires checkpoint" not in res:
+        res["Hires checkpoint"] = "Use same checkpoint"
+
    if "Hires prompt" not in res:
        res["Hires prompt"] = ""

@@ -304,32 +307,28 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
    if "Schedule rho" not in res:
        res["Schedule rho"] = 0

+    if "VAE Encoder" not in res:
+        res["VAE Encoder"] = "Full"
+
+    if "VAE Decoder" not in res:
+        res["VAE Decoder"] = "Full"
+
    return res


 infotext_to_setting_name_mapping = [
-    ('Clip skip', 'CLIP_stop_at_last_layers', ),
+
+]
+"""Mapping of infotext labels to setting names. Only left for backwards compatibility - use OptionInfo(..., infotext='...') instead.
+Example content:
+
+infotext_to_setting_name_mapping = [
    ('Conditional mask weight', 'inpainting_mask_weight'),
    ('Model hash', 'sd_model_checkpoint'),
    ('ENSD', 'eta_noise_seed_delta'),
    ('Schedule type', 'k_sched_type'),
-    ('Schedule max sigma', 'sigma_max'),
-    ('Schedule min sigma', 'sigma_min'),
-    ('Schedule rho', 'rho'),
-    ('Noise multiplier', 'initial_noise_multiplier'),
-    ('Eta', 'eta_ancestral'),
-    ('Eta DDIM', 'eta_ddim'),
-    ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'),
-    ('UniPC variant', 'uni_pc_variant'),
-    ('UniPC skip type', 'uni_pc_skip_type'),
-    ('UniPC order', 'uni_pc_order'),
-    ('UniPC lower order final', 'uni_pc_lower_order_final'),
-    ('Token merging ratio', 'token_merging_ratio'),
-    ('Token merging ratio hr', 'token_merging_ratio_hr'),
-    ('RNG', 'randn_source'),
-    ('NGMS', 's_min_uncond'),
-    ('Pad conds', 'pad_cond_uncond'),
 ]
+"""


 def create_override_settings_dict(text_pairs):
@@ -350,7 +349,8 @@ def create_override_settings_dict(text_pairs):

        params[k] = v.strip()

-    for param_name, setting_name in infotext_to_setting_name_mapping:
+    mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+    for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
        value = params.get(param_name, None)

        if value is None:
@@ -399,10 +399,16 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
        return res

    if override_settings_component is not None:
+        already_handled_fields = {key: 1 for _, key in paste_fields}
+
        def paste_settings(params):
            vals = {}

-            for param_name, setting_name in infotext_to_setting_name_mapping:
+            mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+            for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
+                if param_name in already_handled_fields:
+                    continue
+
                v = params.get(param_name, None)
                if v is None:
                    continue
@@ -23,7 +23,7 @@ class Git(git.Git):
        )
        return self._parse_object_header(ret)

-    def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
+    def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]:
        # Not really streaming, per se; this buffers the entire object in memory.
        # Shouldn't be a problem for our use case, since we're only using this for
        # object headers (commit objects).
@@ -0,0 +1,73 @@
+import gradio as gr
+
+from modules import scripts, ui_tempdir, patches
+
+
+def add_classes_to_gradio_component(comp):
+    """
+    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+    """
+
+    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
+
+    if getattr(comp, 'multiselect', False):
+        comp.elem_classes.append('multiselect')
+
+
+def IOComponent_init(self, *args, **kwargs):
+    self.webui_tooltip = kwargs.pop('tooltip', None)
+
+    if scripts.scripts_current is not None:
+        scripts.scripts_current.before_component(self, **kwargs)
+
+    scripts.script_callbacks.before_component_callback(self, **kwargs)
+
+    res = original_IOComponent_init(self, *args, **kwargs)
+
+    add_classes_to_gradio_component(self)
+
+    scripts.script_callbacks.after_component_callback(self, **kwargs)
+
+    if scripts.scripts_current is not None:
+        scripts.scripts_current.after_component(self, **kwargs)
+
+    return res
+
+
+def Block_get_config(self):
+    config = original_Block_get_config(self)
+
+    webui_tooltip = getattr(self, 'webui_tooltip', None)
+    if webui_tooltip:
+        config["webui_tooltip"] = webui_tooltip
+
+    config.pop('example_inputs', None)
+
+    return config
+
+
+def BlockContext_init(self, *args, **kwargs):
+    res = original_BlockContext_init(self, *args, **kwargs)
+
+    add_classes_to_gradio_component(self)
+
+    return res
+
+
+def Blocks_get_config_file(self, *args, **kwargs):
+    config = original_Blocks_get_config_file(self, *args, **kwargs)
+
+    for comp_config in config["components"]:
+        if "example_inputs" in comp_config:
+            comp_config["example_inputs"] = {"serialized": []}
+
+    return config
+
+
+original_IOComponent_init = patches.patch(__name__, obj=gr.components.IOComponent, field="__init__", replacement=IOComponent_init)
+original_Block_get_config = patches.patch(__name__, obj=gr.blocks.Block, field="get_config", replacement=Block_get_config)
+original_BlockContext_init = patches.patch(__name__, obj=gr.blocks.BlockContext, field="__init__", replacement=BlockContext_init)
+original_Blocks_get_config_file = patches.patch(__name__, obj=gr.blocks.Blocks, field="get_config_file", replacement=Blocks_get_config_file)
+
+
+ui_tempdir.install_ui_tempdir_override()
@@ -10,7 +10,7 @@ import torch
 import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
-from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
+from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
 from modules.textual_inversion import textual_inversion, logging
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
@@ -468,9 +468,8 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
    shared.reload_hypernetworks()


-def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
-    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
-    from modules import images
+def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch_size: int, gradient_step: int, data_root: str, log_directory: str, training_width: int, training_height: int, varsize: bool, steps: int, clip_grad_mode: str, clip_grad_value: float, shuffle_tags: bool, tag_drop_out: bool, latent_sampling_method: str, use_weight: bool, create_image_every: int, save_hypernetwork_every: int, template_filename: str, preview_from_txt2img: bool, preview_prompt: str, preview_negative_prompt: str, preview_steps: int, preview_sampler_name: str, preview_cfg_scale: float, preview_seed: int, preview_width: int, preview_height: int):
+    from modules import images, processing

    save_hypernetwork_every = save_hypernetwork_every or 0
    create_image_every = create_image_every or 0
@@ -699,7 +698,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                        p.prompt = preview_prompt
                        p.negative_prompt = preview_negative_prompt
                        p.steps = preview_steps
-                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+                        p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
                        p.cfg_scale = preview_cfg_scale
                        p.seed = preview_seed
                        p.width = preview_width
@@ -21,8 +21,6 @@ from modules import sd_samplers, shared, script_callbacks, errors
 from modules.paths_internal import roboto_ttf_file
 from modules.shared import opts

-import modules.sd_vae as sd_vae
-
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)


@@ -318,7 +316,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
    return res


-invalid_filename_chars = '<>:"/\\|?*\n'
+invalid_filename_chars = '<>:"/\\|?*\n\r\t'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
@@ -342,16 +340,6 @@ def sanitize_filename_part(text, replace_spaces=True):


 class FilenameGenerator:
-    def get_vae_filename(self): #get the name of the VAE file.
-        if sd_vae.loaded_vae_file is None:
-            return "NoneType"
-        file_name = os.path.basename(sd_vae.loaded_vae_file)
-        split_file_name = file_name.split('.')
-        if len(split_file_name) > 1 and split_file_name[0] == '':
-            return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
-        else:
-            return split_file_name[0]
-
    replacements = {
        'seed': lambda self: self.seed if self.seed is not None else '',
        'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
@@ -367,7 +355,9 @@ class FilenameGenerator:
        'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
        'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
        'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
-        'prompt_hash': lambda self: hashlib.sha256(self.prompt.encode()).hexdigest()[0:8],
+        'prompt_hash': lambda self, *args: self.string_hash(self.prompt, *args),
+        'negative_prompt_hash': lambda self, *args: self.string_hash(self.p.negative_prompt, *args),
+        'full_prompt_hash': lambda self, *args: self.string_hash(f"{self.p.prompt} {self.p.negative_prompt}", *args),  # a space in between to create a unique string
        'prompt': lambda self: sanitize_filename_part(self.prompt),
        'prompt_no_styles': lambda self: self.prompt_no_style(),
        'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
@@ -380,7 +370,8 @@ class FilenameGenerator:
        'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
        'user': lambda self: self.p.user,
        'vae_filename': lambda self: self.get_vae_filename(),
-        'none': lambda self: '', # Overrides the default so you can get just the sequence number
+        'none': lambda self: '',  # Overrides the default, so you can get just the sequence number
+        'image_hash': lambda self, *args: self.image_hash(*args)  # accepts formats: [image_hash<length>] default full hash
    }
    default_time_format = '%Y%m%d%H%M%S'

@@ -391,6 +382,22 @@ class FilenameGenerator:
        self.image = image
        self.zip = zip

+    def get_vae_filename(self):
+        """Get the name of the VAE file."""
+
+        import modules.sd_vae as sd_vae
+
+        if sd_vae.loaded_vae_file is None:
+            return "NoneType"
+
+        file_name = os.path.basename(sd_vae.loaded_vae_file)
+        split_file_name = file_name.split('.')
+        if len(split_file_name) > 1 and split_file_name[0] == '':
+            return split_file_name[1]  # if the first character of the filename is "." then [1] is obtained.
+        else:
+            return split_file_name[0]
+
+
    def hasprompt(self, *args):
        lower = self.prompt.lower()
        if self.p is None or self.prompt is None:
@@ -444,6 +451,14 @@ class FilenameGenerator:

        return sanitize_filename_part(formatted_time, replace_spaces=False)

+    def image_hash(self, *args):
+        length = int(args[0]) if (args and args[0] != "") else None
+        return hashlib.sha256(self.image.tobytes()).hexdigest()[0:length]
+
+    def string_hash(self, text, *args):
+        length = int(args[0]) if (args and args[0] != "") else 8
+        return hashlib.sha256(text.encode()).hexdigest()[0:length]
+
    def apply(self, x):
        res = ''

@@ -546,6 +561,8 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
            })

            piexif.insert(exif_bytes, filename)
+    elif extension.lower() == ".gif":
+        image.save(filename, format=image_format, comment=geninfo)
    else:
        image.save(filename, format=image_format, quality=opts.jpeg_quality)

@@ -585,6 +602,11 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
    """
    namegen = FilenameGenerator(p, seed, prompt, image)

+    # WebP and JPG formats have maximum dimension limits of 16383 and 65535 respectively. switch to PNG which has a much higher limit
+    if (image.height > 65535 or image.width > 65535) and extension.lower() in ("jpg", "jpeg") or (image.height > 16383 or image.width > 16383) and extension.lower() == "webp":
+        print('Image dimensions too large; saving as PNG')
+        extension = ".png"
+
    if save_to_dirs is None:
        save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)

@@ -641,7 +663,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i

        save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)

-        os.replace(temp_file_path, filename_without_extension + extension)
+        filename = filename_without_extension + extension
+        if shared.opts.save_images_replace_action != "Replace":
+            n = 0
+            while os.path.exists(filename):
+                n += 1
+                filename = f"{filename_without_extension}-{n}{extension}"
+        os.replace(temp_file_path, filename)

    fullfn_without_extension, extension = os.path.splitext(params.filename)
    if hasattr(os, 'statvfs'):
@@ -698,7 +726,12 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
    geninfo = items.pop('parameters', None)

    if "exif" in items:
-        exif = piexif.load(items["exif"])
+        exif_data = items["exif"]
+        try:
+            exif = piexif.load(exif_data)
+        except OSError:
+            # memory / exif was not valid so piexif tried to read from a file
+            exif = None
        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
        try:
            exif_comment = piexif.helper.UserComment.load(exif_comment)
@@ -708,6 +741,8 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
        if exif_comment:
            items['exif comment'] = exif_comment
            geninfo = exif_comment
+    elif "comment" in items: # for gif
+        geninfo = items["comment"].decode('utf8', errors="ignore")

    for field in IGNORED_INFO_KEYS:
        items.pop(field, None)
@@ -3,14 +3,14 @@ from contextlib import closing
 from pathlib import Path

 import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
 import gradio as gr

-from modules import sd_samplers, images as imgutil
+from modules import images as imgutil
 from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
-from modules.images import save_image
+from modules.sd_models import get_closet_checkpoint_match
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
@@ -18,9 +18,10 @@ import modules.scripts


 def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
+    output_dir = output_dir.strip()
    processing.fix_seed(p)

-    images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp")))
+    images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))

    is_inpaint_batch = False
    if inpaint_mask_dir:
@@ -32,11 +33,6 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal

    print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")

-    save_normally = output_dir == ''
-
-    p.do_not_save_grid = True
-    p.do_not_save_samples = not save_normally
-
    state.job_count = len(images) * p.n_iter

    # extract "default" params to use in case getting png info fails
@@ -46,7 +42,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
    cfg_scale = p.cfg_scale
    sampler_name = p.sampler_name
    steps = p.steps
-
+    override_settings = p.override_settings
+    sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
    for i, image in enumerate(images):
        state.job = f"{i+1} out of {len(images)}"
        if state.skipped:
@@ -109,42 +106,44 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
            p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
            p.steps = int(parsed_parameters.get("Steps", steps))

+            model_info = get_closet_checkpoint_match(parsed_parameters.get("Model hash", None))
+            if model_info is not None:
+                p.override_settings['sd_model_checkpoint'] = model_info.name
+            elif sd_model_checkpoint_override:
+                p.override_settings['sd_model_checkpoint'] = sd_model_checkpoint_override
+            else:
+                p.override_settings.pop("sd_model_checkpoint", None)
+
+        if output_dir:
+            p.outpath_samples = output_dir
+            p.override_settings['save_to_dirs'] = False
+            p.override_settings['save_images_replace_action'] = "Add number suffix"
+            if p.n_iter > 1 or p.batch_size > 1:
+                p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
+            else:
+                p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
+
        proc = modules.scripts.scripts_img2img.run(p, *args)
+
        if proc is None:
-            proc = process_images(p)
-
-        for n, processed_image in enumerate(proc.images):
-            filename = image_path.stem
-            infotext = proc.infotext(p, n)
-            relpath = os.path.dirname(os.path.relpath(image, input_dir))
-
-            if n > 0:
-                filename += f"-{n}"
-
-            if not save_normally:
-                os.makedirs(os.path.join(output_dir, relpath), exist_ok=True)
-                if processed_image.mode == 'RGBA':
-                    processed_image = processed_image.convert("RGB")
-                save_image(processed_image, os.path.join(output_dir, relpath), None, extension=opts.samples_format, info=infotext, forced_filename=filename, save_to_dirs=False)
+            p.override_settings.pop('save_images_replace_action', None)
+            process_images(p)


-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
    override_settings = create_override_settings_dict(override_settings_texts)

    is_batch = mode == 5

    if mode == 0:  # img2img
-        image = init_img.convert("RGB")
+        image = init_img
        mask = None
    elif mode == 1:  # img2img sketch
-        image = sketch.convert("RGB")
+        image = sketch
        mask = None
    elif mode == 2:  # inpaint
        image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
-        alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
-        mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
-        mask = ImageChops.lighter(alpha_mask, mask).convert('L')
-        image = image.convert("RGB")
+        mask = processing.create_binary_mask(mask)
    elif mode == 3:  # inpaint sketch
        image = inpaint_color_sketch
        orig = inpaint_color_sketch_orig or inpaint_color_sketch
@@ -153,7 +152,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
        mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
        blur = ImageFilter.GaussianBlur(mask_blur)
        image = Image.composite(image.filter(blur), orig, mask.filter(blur))
-        image = image.convert("RGB")
    elif mode == 4:  # inpaint upload mask
        image = init_img_inpaint
        mask = init_mask_inpaint
@@ -180,21 +178,13 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
        prompt=prompt,
        negative_prompt=negative_prompt,
        styles=prompt_styles,
-        seed=seed,
-        subseed=subseed,
-        subseed_strength=subseed_strength,
-        seed_resize_from_h=seed_resize_from_h,
-        seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=seed_enable_extras,
-        sampler_name=sd_samplers.samplers_for_img2img[sampler_index].name,
+        sampler_name=sampler_name,
        batch_size=batch_size,
        n_iter=n_iter,
        steps=steps,
        cfg_scale=cfg_scale,
        width=width,
        height=height,
-        restore_faces=restore_faces,
-        tiling=tiling,
        init_images=[image],
        mask=mask,
        mask_blur=mask_blur,
@@ -213,7 +203,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s

    p.user = request.username

-    if shared.cmd_opts.enable_console_prompts:
+    if shared.opts.enable_console_prompts:
        print(f"\nimg2img: {prompt}", file=shared.progress_print_out)

    if mask:
@@ -0,0 +1,168 @@
+import importlib
+import logging
+import sys
+import warnings
+from threading import Thread
+
+from modules.timer import startup_timer
+
+
+def imports():
+    logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR)  # sshh...
+    logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
+
+    import torch  # noqa: F401
+    startup_timer.record("import torch")
+    import pytorch_lightning  # noqa: F401
+    startup_timer.record("import torch")
+    warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
+    warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
+
+    import gradio  # noqa: F401
+    startup_timer.record("import gradio")
+
+    from modules import paths, timer, import_hook, errors  # noqa: F401
+    startup_timer.record("setup paths")
+
+    import ldm.modules.encoders.modules  # noqa: F401
+    startup_timer.record("import ldm")
+
+    import sgm.modules.encoders.modules  # noqa: F401
+    startup_timer.record("import sgm")
+
+    from modules import shared_init
+    shared_init.initialize()
+    startup_timer.record("initialize shared")
+
+    from modules import processing, gradio_extensons, ui  # noqa: F401
+    startup_timer.record("other imports")
+
+
+def check_versions():
+    from modules.shared_cmd_options import cmd_opts
+
+    if not cmd_opts.skip_version_check:
+        from modules import errors
+        errors.check_versions()
+
+
+def initialize():
+    from modules import initialize_util
+    initialize_util.fix_torch_version()
+    initialize_util.fix_asyncio_event_loop_policy()
+    initialize_util.validate_tls_options()
+    initialize_util.configure_sigint_handler()
+    initialize_util.configure_opts_onchange()
+
+    from modules import modelloader
+    modelloader.cleanup_models()
+
+    from modules import sd_models
+    sd_models.setup_model()
+    startup_timer.record("setup SD model")
+
+    from modules.shared_cmd_options import cmd_opts
+
+    from modules import codeformer_model
+    warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision.transforms.functional_tensor")
+    codeformer_model.setup_model(cmd_opts.codeformer_models_path)
+    startup_timer.record("setup codeformer")
+
+    from modules import gfpgan_model
+    gfpgan_model.setup_model(cmd_opts.gfpgan_models_path)
+    startup_timer.record("setup gfpgan")
+
+    initialize_rest(reload_script_modules=False)
+
+
+def initialize_rest(*, reload_script_modules=False):
+    """
+    Called both from initialize() and when reloading the webui.
+    """
+    from modules.shared_cmd_options import cmd_opts
+
+    from modules import sd_samplers
+    sd_samplers.set_samplers()
+    startup_timer.record("set samplers")
+
+    from modules import extensions
+    extensions.list_extensions()
+    startup_timer.record("list extensions")
+
+    from modules import initialize_util
+    initialize_util.restore_config_state_file()
+    startup_timer.record("restore config state file")
+
+    from modules import shared, upscaler, scripts
+    if cmd_opts.ui_debug_mode:
+        shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
+        scripts.load_scripts()
+        return
+
+    from modules import sd_models
+    sd_models.list_models()
+    startup_timer.record("list SD models")
+
+    from modules import localization
+    localization.list_localizations(cmd_opts.localizations_dir)
+    startup_timer.record("list localizations")
+
+    with startup_timer.subcategory("load scripts"):
+        scripts.load_scripts()
+
+    if reload_script_modules:
+        for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]:
+            importlib.reload(module)
+        startup_timer.record("reload script modules")
+
+    from modules import modelloader
+    modelloader.load_upscalers()
+    startup_timer.record("load upscalers")
+
+    from modules import sd_vae
+    sd_vae.refresh_vae_list()
+    startup_timer.record("refresh VAE")
+
+    from modules import textual_inversion
+    textual_inversion.textual_inversion.list_textual_inversion_templates()
+    startup_timer.record("refresh textual inversion templates")
+
+    from modules import script_callbacks, sd_hijack_optimizations, sd_hijack
+    script_callbacks.on_list_optimizers(sd_hijack_optimizations.list_optimizers)
+    sd_hijack.list_optimizers()
+    startup_timer.record("scripts list_optimizers")
+
+    from modules import sd_unet
+    sd_unet.list_unets()
+    startup_timer.record("scripts list_unets")
+
+    def load_model():
+        """
+        Accesses shared.sd_model property to load model.
+        After it's available, if it has been loaded before this access by some extension,
+        its optimization may be None because the list of optimizaers has neet been filled
+        by that time, so we apply optimization again.
+        """
+
+        shared.sd_model  # noqa: B018
+
+        if sd_hijack.current_optimizer is None:
+            sd_hijack.apply_optimizations()
+
+        from modules import devices
+        devices.first_time_calculation()
+    if not shared.cmd_opts.skip_load_model_at_start:
+        Thread(target=load_model).start()
+
+    from modules import shared_items
+    shared_items.reload_hypernetworks()
+    startup_timer.record("reload hypernetworks")
+
+    from modules import ui_extra_networks
+    ui_extra_networks.initialize()
+    ui_extra_networks.register_default_pages()
+
+    from modules import extra_networks
+    extra_networks.initialize()
+    extra_networks.register_default_extra_networks()
+    startup_timer.record("initialize extra networks")
@@ -0,0 +1,202 @@
+import json
+import os
+import signal
+import sys
+import re
+
+from modules.timer import startup_timer
+
+
+def gradio_server_name():
+    from modules.shared_cmd_options import cmd_opts
+
+    if cmd_opts.server_name:
+        return cmd_opts.server_name
+    else:
+        return "0.0.0.0" if cmd_opts.listen else None
+
+
+def fix_torch_version():
+    import torch
+
+    # Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors
+    if ".dev" in torch.__version__ or "+git" in torch.__version__:
+        torch.__long_version__ = torch.__version__
+        torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
+
+
+def fix_asyncio_event_loop_policy():
+    """
+        The default `asyncio` event loop policy only automatically creates
+        event loops in the main threads. Other threads must create event
+        loops explicitly or `asyncio.get_event_loop` (and therefore
+        `.IOLoop.current`) will fail. Installing this policy allows event
+        loops to be created automatically on any thread, matching the
+        behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
+    """
+
+    import asyncio
+
+    if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
+        # "Any thread" and "selector" should be orthogonal, but there's not a clean
+        # interface for composing policies so pick the right base.
+        _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy  # type: ignore
+    else:
+        _BasePolicy = asyncio.DefaultEventLoopPolicy
+
+    class AnyThreadEventLoopPolicy(_BasePolicy):  # type: ignore
+        """Event loop policy that allows loop creation on any thread.
+        Usage::
+
+            asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+        """
+
+        def get_event_loop(self) -> asyncio.AbstractEventLoop:
+            try:
+                return super().get_event_loop()
+            except (RuntimeError, AssertionError):
+                # This was an AssertionError in python 3.4.2 (which ships with debian jessie)
+                # and changed to a RuntimeError in 3.4.3.
+                # "There is no current event loop in thread %r"
+                loop = self.new_event_loop()
+                self.set_event_loop(loop)
+                return loop
+
+    asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+
+
+def restore_config_state_file():
+    from modules import shared, config_states
+
+    config_state_file = shared.opts.restore_config_state_file
+    if config_state_file == "":
+        return
+
+    shared.opts.restore_config_state_file = ""
+    shared.opts.save(shared.config_filename)
+
+    if os.path.isfile(config_state_file):
+        print(f"*** About to restore extension state from file: {config_state_file}")
+        with open(config_state_file, "r", encoding="utf-8") as f:
+            config_state = json.load(f)
+            config_states.restore_extension_config(config_state)
+        startup_timer.record("restore extension config")
+    elif config_state_file:
+        print(f"!!! Config state backup not found: {config_state_file}")
+
+
+def validate_tls_options():
+    from modules.shared_cmd_options import cmd_opts
+
+    if not (cmd_opts.tls_keyfile and cmd_opts.tls_certfile):
+        return
+
+    try:
+        if not os.path.exists(cmd_opts.tls_keyfile):
+            print("Invalid path to TLS keyfile given")
+        if not os.path.exists(cmd_opts.tls_certfile):
+            print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
+    except TypeError:
+        cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
+        print("TLS setup invalid, running webui without TLS")
+    else:
+        print("Running with TLS")
+    startup_timer.record("TLS")
+
+
+def get_gradio_auth_creds():
+    """
+    Convert the gradio_auth and gradio_auth_path commandline arguments into
+    an iterable of (username, password) tuples.
+    """
+    from modules.shared_cmd_options import cmd_opts
+
+    def process_credential_line(s):
+        s = s.strip()
+        if not s:
+            return None
+        return tuple(s.split(':', 1))
+
+    if cmd_opts.gradio_auth:
+        for cred in cmd_opts.gradio_auth.split(','):
+            cred = process_credential_line(cred)
+            if cred:
+                yield cred
+
+    if cmd_opts.gradio_auth_path:
+        with open(cmd_opts.gradio_auth_path, 'r', encoding="utf8") as file:
+            for line in file.readlines():
+                for cred in line.strip().split(','):
+                    cred = process_credential_line(cred)
+                    if cred:
+                        yield cred
+
+
+def dumpstacks():
+    import threading
+    import traceback
+
+    id2name = {th.ident: th.name for th in threading.enumerate()}
+    code = []
+    for threadId, stack in sys._current_frames().items():
+        code.append(f"\n# Thread: {id2name.get(threadId, '')}({threadId})")
+        for filename, lineno, name, line in traceback.extract_stack(stack):
+            code.append(f"""File: "{filename}", line {lineno}, in {name}""")
+            if line:
+                code.append("  " + line.strip())
+
+    print("\n".join(code))
+
+
+def configure_sigint_handler():
+    # make the program just exit at ctrl+c without waiting for anything
+    def sigint_handler(sig, frame):
+        print(f'Interrupted with signal {sig} in {frame}')
+
+        dumpstacks()
+
+        os._exit(0)
+
+    if not os.environ.get("COVERAGE_RUN"):
+        # Don't install the immediate-quit handler when running under coverage,
+        # as then the coverage report won't be generated.
+        signal.signal(signal.SIGINT, sigint_handler)
+
+
+def configure_opts_onchange():
+    from modules import shared, sd_models, sd_vae, ui_tempdir, sd_hijack
+    from modules.call_queue import wrap_queued_call
+
+    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
+    shared.opts.onchange("sd_vae", wrap_queued_call(lambda: sd_vae.reload_vae_weights()), call=False)
+    shared.opts.onchange("sd_vae_overrides_per_model_preferences", wrap_queued_call(lambda: sd_vae.reload_vae_weights()), call=False)
+    shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
+    shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
+    shared.opts.onchange("cross_attention_optimization", wrap_queued_call(lambda: sd_hijack.model_hijack.redo_hijack(shared.sd_model)), call=False)
+    startup_timer.record("opts onchange")
+
+
+def setup_middleware(app):
+    from starlette.middleware.gzip import GZipMiddleware
+
+    app.middleware_stack = None  # reset current middleware to allow modifying user provided list
+    app.add_middleware(GZipMiddleware, minimum_size=1000)
+    configure_cors_middleware(app)
+    app.build_middleware_stack()  # rebuild middleware stack on-the-fly
+
+
+def configure_cors_middleware(app):
+    from starlette.middleware.cors import CORSMiddleware
+    from modules.shared_cmd_options import cmd_opts
+
+    cors_options = {
+        "allow_methods": ["*"],
+        "allow_headers": ["*"],
+        "allow_credentials": True,
+    }
+    if cmd_opts.cors_allow_origins:
+        cors_options["allow_origins"] = cmd_opts.cors_allow_origins.split(',')
+    if cmd_opts.cors_allow_origins_regex:
+        cors_options["allow_origin_regex"] = cmd_opts.cors_allow_origins_regex
+    app.add_middleware(CORSMiddleware, **cors_options)
+
@@ -186,9 +186,8 @@ class InterrogateModels:
        res = ""
        shared.state.begin(job="interrogate")
        try:
-            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-                lowvram.send_everything_to_cpu()
-                devices.torch_gc()
+            lowvram.send_everything_to_cpu()
+            devices.torch_gc()

            self.load()

@@ -1,7 +1,9 @@
 # this scripts installs necessary requirements and launches main program in webui.py
+import logging
 import re
 import subprocess
 import os
+import shutil
 import sys
 import importlib.util
 import platform
@@ -10,11 +12,11 @@ from functools import lru_cache

 from modules import cmd_args, errors
 from modules.paths_internal import script_path, extensions_dir
-from modules import timer
-
-timer.startup_timer.record("start")
+from modules.timer import startup_timer
+from modules import logging_config

 args, _ = cmd_args.parser.parse_known_args()
+logging_config.setup_logging(args.loglevel)

 python = sys.executable
 git = os.environ.get('GIT', "git")
@@ -62,7 +64,7 @@ Use --skip-python-version-check to suppress this warning.
@lru_cache()
 def commit_hash():
    try:
-        return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
+        return subprocess.check_output([git, "-C", script_path, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
    except Exception:
        return "<none>"

@@ -70,7 +72,7 @@ def commit_hash():
@lru_cache()
 def git_tag():
    try:
-        return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
+        return subprocess.check_output([git, "-C", script_path, "describe", "--tags"], shell=False, encoding='utf8').strip()
    except Exception:
        try:

@@ -141,6 +143,25 @@ def check_run_python(code: str) -> bool:
    return result.returncode == 0


+def git_fix_workspace(dir, name):
+    run(f'"{git}" -C "{dir}" fetch --refetch --no-auto-gc', f"Fetching all contents for {name}", f"Couldn't fetch {name}", live=True)
+    run(f'"{git}" -C "{dir}" gc --aggressive --prune=now', f"Pruning {name}", f"Couldn't prune {name}", live=True)
+    return
+
+
+def run_git(dir, name, command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live, autofix=True):
+    try:
+        return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
+    except RuntimeError:
+        if not autofix:
+            raise
+
+    print(f"{errdesc}, attempting autofix...")
+    git_fix_workspace(dir, name)
+
+    return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
+
+
 def git_clone(url, dir, name, commithash=None):
    # TODO clone into temporary dir and move if successful

@@ -148,15 +169,24 @@ def git_clone(url, dir, name, commithash=None):
        if commithash is None:
            return

-        current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
+        current_hash = run_git(dir, name, 'rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
        if current_hash == commithash:
            return

-        run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
-        run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
+        if run_git(dir, name, 'config --get remote.origin.url', None, f"Couldn't determine {name}'s origin URL", live=False).strip() != url:
+            run_git(dir, name, f'remote set-url origin "{url}"', None, f"Failed to set {name}'s origin URL", live=False)
+
+        run_git(dir, name, 'fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}", autofix=False)
+
+        run_git(dir, name, f'checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
+
        return

-    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
+    try:
+        run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
+    except RuntimeError:
+        shutil.rmtree(dir, ignore_errors=True)
+        raise

    if commithash is not None:
        run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
@@ -198,7 +228,9 @@ def run_extension_installer(extension_dir):
        env = os.environ.copy()
        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"

-        print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
+        stdout = run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env).strip()
+        if stdout:
+            print(stdout)
    except Exception as e:
        errors.report(str(e))

@@ -216,7 +248,7 @@ def list_extensions(settings_file):
    disabled_extensions = set(settings.get('disabled_extensions', []))
    disable_all_extensions = settings.get('disable_all_extensions', 'none')

-    if disable_all_extensions != 'none':
+    if disable_all_extensions != 'none' or args.disable_extra_extensions or args.disable_all_extensions or not os.path.isdir(extensions_dir):
        return []

    return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
@@ -226,8 +258,15 @@ def run_extensions_installers(settings_file):
    if not os.path.isdir(extensions_dir):
        return

-    for dirname_extension in list_extensions(settings_file):
-        run_extension_installer(os.path.join(extensions_dir, dirname_extension))
+    with startup_timer.subcategory("run extensions installers"):
+        for dirname_extension in list_extensions(settings_file):
+            logging.debug(f"Installing {dirname_extension}")
+
+            path = os.path.join(extensions_dir, dirname_extension)
+
+            if os.path.isdir(path):
+                run_extension_installer(path)
+                startup_timer.record(dirname_extension)


 re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
@@ -274,7 +313,6 @@ def prepare_environment():
    requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")

    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
-    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
    clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
    openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")

@@ -285,13 +323,13 @@ def prepare_environment():
    blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')

    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
-    stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "5c10deee76adad0032b412294130090932317a87")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
+    stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
    codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
    blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")

    try:
-        # the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
+        # the existence of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
        os.remove(os.path.join(script_path, "tmp", "restart"))
        os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
    except OSError:
@@ -300,8 +338,11 @@ def prepare_environment():
    if not args.skip_python_version_check:
        check_python_version()

+    startup_timer.record("checks")
+
    commit = commit_hash()
    tag = git_tag()
+    startup_timer.record("git version info")

    print(f"Python {sys.version}")
    print(f"Version: {tag}")
@@ -309,36 +350,30 @@ def prepare_environment():

    if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
        run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
+        startup_timer.record("install torch")

    if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
        raise RuntimeError(
            'Torch is not able to use GPU; '
            'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
        )
-
-    if not is_installed("gfpgan"):
-        run_pip(f"install {gfpgan_package}", "gfpgan")
+    startup_timer.record("torch GPU test")

    if not is_installed("clip"):
        run_pip(f"install {clip_package}", "clip")
+        startup_timer.record("install clip")

    if not is_installed("open_clip"):
        run_pip(f"install {openclip_package}", "open_clip")
+        startup_timer.record("install open_clip")

    if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
-        if platform.system() == "Windows":
-            if platform.python_version().startswith("3.10"):
-                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
-            else:
-                print("Installation of xformers is not supported in this version of Python.")
-                print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
-                if not is_installed("xformers"):
-                    exit(0)
-        elif platform.system() == "Linux":
-            run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+        run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+        startup_timer.record("install xformers")

    if not is_installed("ngrok") and args.ngrok:
        run_pip("install ngrok", "ngrok")
+        startup_timer.record("install ngrok")

    os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)

@@ -348,22 +383,29 @@ def prepare_environment():
    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)

+    startup_timer.record("clone repositores")
+
    if not is_installed("lpips"):
        run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
+        startup_timer.record("install CodeFormer requirements")

    if not os.path.isfile(requirements_file):
        requirements_file = os.path.join(script_path, requirements_file)

    if not requirements_met(requirements_file):
        run_pip(f"install -r \"{requirements_file}\"", "requirements")
+        startup_timer.record("install requirements")

-    run_extensions_installers(settings_file=args.ui_settings_file)
+    if not args.skip_install:
+        run_extensions_installers(settings_file=args.ui_settings_file)

    if args.update_check:
        version_check(commit)
+        startup_timer.record("check version")

    if args.update_all_extensions:
        git_pull_recursive(extensions_dir)
+        startup_timer.record("update extensions")

    if "--exit" in sys.argv:
        print("Exiting because of --exit argument")
@@ -392,3 +434,16 @@ def start():
        webui.api_only()
    else:
        webui.webui()
+
+
+def dump_sysinfo():
+    from modules import sysinfo
+    import datetime
+
+    text = sysinfo.get()
+    filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+
+    with open(filename, "w", encoding="utf8") as file:
+        file.write(text)
+
+    return filename
@@ -1,7 +1,7 @@
 import json
 import os

-from modules import errors
+from modules import errors, scripts

 localizations = {}

@@ -14,22 +14,24 @@ def list_localizations(dirname):
        if ext.lower() != ".json":
            continue

-        localizations[fn] = os.path.join(dirname, file)
+        localizations[fn] = [os.path.join(dirname, file)]

-    from modules import scripts
    for file in scripts.list_scripts("localizations", ".json"):
        fn, ext = os.path.splitext(file.filename)
-        localizations[fn] = file.path
+        if fn not in localizations:
+            localizations[fn] = []
+        localizations[fn].append(file.path)


 def localization_js(current_localization_name: str) -> str:
-    fn = localizations.get(current_localization_name, None)
+    fns = localizations.get(current_localization_name, None)
    data = {}
-    if fn is not None:
-        try:
-            with open(fn, "r", encoding="utf8") as file:
-                data = json.load(file)
-        except Exception:
-            errors.report(f"Error loading localization from {fn}", exc_info=True)
+    if fns is not None:
+        for fn in fns:
+            try:
+                with open(fn, "r", encoding="utf8") as file:
+                    data.update(json.load(file))
+            except Exception:
+                errors.report(f"Error loading localization from {fn}", exc_info=True)

    return f"window.localization = {json.dumps(data)}"
@@ -0,0 +1,16 @@
+import os
+import logging
+
+
+def setup_logging(loglevel):
+    if loglevel is None:
+        loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
+
+    if loglevel:
+        log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
+        logging.basicConfig(
+            level=log_level,
+            format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+        )
+
@@ -1,5 +1,5 @@
 import torch
-from modules import devices
+from modules import devices, shared

 module_in_gpu = None
 cpu = torch.device("cpu")
@@ -14,7 +14,24 @@ def send_everything_to_cpu():
    module_in_gpu = None


+def is_needed(sd_model):
+    return shared.cmd_opts.lowvram or shared.cmd_opts.medvram or shared.cmd_opts.medvram_sdxl and hasattr(sd_model, 'conditioner')
+
+
+def apply(sd_model):
+    enable = is_needed(sd_model)
+    shared.parallel_processing_allowed = not enable
+
+    if enable:
+        setup_for_low_vram(sd_model, not shared.cmd_opts.lowvram)
+    else:
+        sd_model.lowvram = False
+
+
 def setup_for_low_vram(sd_model, use_medvram):
+    if getattr(sd_model, 'lowvram', False):
+        return
+
    sd_model.lowvram = True

    parents = {}
@@ -127,4 +144,4 @@ def setup_for_low_vram(sd_model, use_medvram):


 def is_enabled(sd_model):
-    return getattr(sd_model, 'lowvram', False)
+    return sd_model.lowvram
@@ -4,6 +4,7 @@ import torch
 import platform
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
+from modules import shared

 log = logging.getLogger(__name__)

@@ -30,8 +31,7 @@ has_mps = check_for_mps()

 def torch_mps_gc() -> None:
    try:
-        from modules.shared import state
-        if state.current_latent is not None:
+        if shared.state.current_latent is not None:
            log.debug("`current_latent` is set, skipping MPS garbage collection")
            return
        from torch.mps import empty_cache
@@ -52,9 +52,6 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):


 if has_mps:
-    # MPS fix for randn in torchsde
-    CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
-
    if platform.mac_ver()[0].startswith("13.2."):
        # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
        CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)
@@ -0,0 +1,247 @@
+import json
+import sys
+
+import gradio as gr
+
+from modules import errors
+from modules.shared_cmd_options import cmd_opts
+
+
+class OptionInfo:
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False):
+        self.default = default
+        self.label = label
+        self.component = component
+        self.component_args = component_args
+        self.onchange = onchange
+        self.section = section
+        self.refresh = refresh
+        self.do_not_save = False
+
+        self.comment_before = comment_before
+        """HTML text that will be added after label in UI"""
+
+        self.comment_after = comment_after
+        """HTML text that will be added before label in UI"""
+
+        self.infotext = infotext
+
+        self.restrict_api = restrict_api
+        """If True, the setting will not be accessible via API"""
+
+    def link(self, label, url):
+        self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
+        return self
+
+    def js(self, label, js_func):
+        self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
+        return self
+
+    def info(self, info):
+        self.comment_after += f"<span class='info'>({info})</span>"
+        return self
+
+    def html(self, html):
+        self.comment_after += html
+        return self
+
+    def needs_restart(self):
+        self.comment_after += " <span class='info'>(requires restart)</span>"
+        return self
+
+    def needs_reload_ui(self):
+        self.comment_after += " <span class='info'>(requires Reload UI)</span>"
+        return self
+
+
+class OptionHTML(OptionInfo):
+    def __init__(self, text):
+        super().__init__(str(text).strip(), label='', component=lambda **kwargs: gr.HTML(elem_classes="settings-info", **kwargs))
+
+        self.do_not_save = True
+
+
+def options_section(section_identifier, options_dict):
+    for v in options_dict.values():
+        v.section = section_identifier
+
+    return options_dict
+
+
+options_builtin_fields = {"data_labels", "data", "restricted_opts", "typemap"}
+
+
+class Options:
+    typemap = {int: float}
+
+    def __init__(self, data_labels: dict[str, OptionInfo], restricted_opts):
+        self.data_labels = data_labels
+        self.data = {k: v.default for k, v in self.data_labels.items()}
+        self.restricted_opts = restricted_opts
+
+    def __setattr__(self, key, value):
+        if key in options_builtin_fields:
+            return super(Options, self).__setattr__(key, value)
+
+        if self.data is not None:
+            if key in self.data or key in self.data_labels:
+                assert not cmd_opts.freeze_settings, "changing settings is disabled"
+
+                info = self.data_labels.get(key, None)
+                if info.do_not_save:
+                    return
+
+                comp_args = info.component_args if info else None
+                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+                if cmd_opts.hide_ui_dir_config and key in self.restricted_opts:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+                self.data[key] = value
+                return
+
+        return super(Options, self).__setattr__(key, value)
+
+    def __getattr__(self, item):
+        if item in options_builtin_fields:
+            return super(Options, self).__getattribute__(item)
+
+        if self.data is not None:
+            if item in self.data:
+                return self.data[item]
+
+        if item in self.data_labels:
+            return self.data_labels[item].default
+
+        return super(Options, self).__getattribute__(item)
+
+    def set(self, key, value, is_api=False, run_callbacks=True):
+        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
+
+        oldval = self.data.get(key, None)
+        if oldval == value:
+            return False
+
+        option = self.data_labels[key]
+        if option.do_not_save:
+            return False
+
+        if is_api and option.restrict_api:
+            return False
+
+        try:
+            setattr(self, key, value)
+        except RuntimeError:
+            return False
+
+        if run_callbacks and option.onchange is not None:
+            try:
+                option.onchange()
+            except Exception as e:
+                errors.display(e, f"changing setting {key} to {value}")
+                setattr(self, key, oldval)
+                return False
+
+        return True
+
+    def get_default(self, key):
+        """returns the default value for the key"""
+
+        data_label = self.data_labels.get(key)
+        if data_label is None:
+            return None
+
+        return data_label.default
+
+    def save(self, filename):
+        assert not cmd_opts.freeze_settings, "saving settings is disabled"
+
+        with open(filename, "w", encoding="utf8") as file:
+            json.dump(self.data, file, indent=4)
+
+    def same_type(self, x, y):
+        if x is None or y is None:
+            return True
+
+        type_x = self.typemap.get(type(x), type(x))
+        type_y = self.typemap.get(type(y), type(y))
+
+        return type_x == type_y
+
+    def load(self, filename):
+        with open(filename, "r", encoding="utf8") as file:
+            self.data = json.load(file)
+
+        # 1.6.0 VAE defaults
+        if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:
+            self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')
+
+        # 1.1.1 quicksettings list migration
+        if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
+            self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
+
+        # 1.4.0 ui_reorder
+        if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
+            self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
+
+        bad_settings = 0
+        for k, v in self.data.items():
+            info = self.data_labels.get(k, None)
+            if info is not None and not self.same_type(info.default, v):
+                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
+                bad_settings += 1
+
+        if bad_settings > 0:
+            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
+
+    def onchange(self, key, func, call=True):
+        item = self.data_labels.get(key)
+        item.onchange = func
+
+        if call:
+            func()
+
+    def dumpjson(self):
+        d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
+        d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
+        d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
+        return json.dumps(d)
+
+    def add_option(self, key, info):
+        self.data_labels[key] = info
+        if key not in self.data:
+            self.data[key] = info.default
+
+    def reorder(self):
+        """reorder settings so that all items related to section always go together"""
+
+        section_ids = {}
+        settings_items = self.data_labels.items()
+        for _, item in settings_items:
+            if item.section not in section_ids:
+                section_ids[item.section] = len(section_ids)
+
+        self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
+
+    def cast_value(self, key, value):
+        """casts an arbitrary to the same type as this setting's value with key
+        Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
+        """
+
+        if value is None:
+            return None
+
+        default_value = self.data_labels[key].default
+        if default_value is None:
+            default_value = getattr(self, key, None)
+        if default_value is None:
+            return None
+
+        expected_type = type(default_value)
+        if expected_type == bool and value == "False":
+            value = False
+        else:
+            value = expected_type(value)
+
+        return value
@@ -0,0 +1,64 @@
+from collections import defaultdict
+
+
+def patch(key, obj, field, replacement):
+    """Replaces a function in a module or a class.
+
+    Also stores the original function in this module, possible to be retrieved via original(key, obj, field).
+    If the function is already replaced by this caller (key), an exception is raised -- use undo() before that.
+
+    Arguments:
+        key: identifying information for who is doing the replacement. You can use __name__.
+        obj: the module or the class
+        field: name of the function as a string
+        replacement: the new function
+
+    Returns:
+        the original function
+    """
+
+    patch_key = (obj, field)
+    if patch_key in originals[key]:
+        raise RuntimeError(f"patch for {field} is already applied")
+
+    original_func = getattr(obj, field)
+    originals[key][patch_key] = original_func
+
+    setattr(obj, field, replacement)
+
+    return original_func
+
+
+def undo(key, obj, field):
+    """Undoes the peplacement by the patch().
+
+    If the function is not replaced, raises an exception.
+
+    Arguments:
+        key: identifying information for who is doing the replacement. You can use __name__.
+        obj: the module or the class
+        field: name of the function as a string
+
+    Returns:
+        Always None
+    """
+
+    patch_key = (obj, field)
+
+    if patch_key not in originals[key]:
+        raise RuntimeError(f"there is no patch for {field} to undo")
+
+    original_func = originals[key].pop(patch_key)
+    setattr(obj, field, original_func)
+
+    return None
+
+
+def original(key, obj, field):
+    """Returns the original function for the patch created by the patch() function"""
+    patch_key = (obj, field)
+
+    return originals[key].get(patch_key, None)
+
+
+originals = defaultdict(dict)
@@ -1,6 +1,6 @@
 import os
 import sys
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir  # noqa: F401
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, cwd  # noqa: F401

 import modules.safe  # noqa: F401

@@ -8,6 +8,7 @@ import shlex
 commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 sys.argv += shlex.split(commandline_args)

+cwd = os.getcwd()
 modules_path = os.path.dirname(os.path.realpath(__file__))
 script_path = os.path.dirname(modules_path)

@@ -11,37 +11,32 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,

    shared.state.begin(job="extras")

-    image_data = []
-    image_names = []
    outputs = []

-    if extras_mode == 1:
-        for img in image_folder:
-            if isinstance(img, Image.Image):
-                image = img
-                fn = ''
-            else:
-                image = Image.open(os.path.abspath(img.name))
-                fn = os.path.splitext(img.orig_name)[0]
-            image_data.append(image)
-            image_names.append(fn)
-    elif extras_mode == 2:
-        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
-        assert input_dir, 'input directory not selected'
+    def get_images(extras_mode, image, image_folder, input_dir):
+        if extras_mode == 1:
+            for img in image_folder:
+                if isinstance(img, Image.Image):
+                    image = img
+                    fn = ''
+                else:
+                    image = Image.open(os.path.abspath(img.name))
+                    fn = os.path.splitext(img.orig_name)[0]
+                yield image, fn
+        elif extras_mode == 2:
+            assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+            assert input_dir, 'input directory not selected'

-        image_list = shared.listfiles(input_dir)
-        for filename in image_list:
-            try:
-                image = Image.open(filename)
-            except Exception:
-                continue
-            image_data.append(image)
-            image_names.append(filename)
-    else:
-        assert image, 'image not selected'
-
-        image_data.append(image)
-        image_names.append(None)
+            image_list = shared.listfiles(input_dir)
+            for filename in image_list:
+                try:
+                    image = Image.open(filename)
+                except Exception:
+                    continue
+                yield image, filename
+        else:
+            assert image, 'image not selected'
+            yield image, None

    if extras_mode == 2 and output_dir != '':
        outpath = output_dir
@@ -50,14 +45,16 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,

    infotext = ''

-    for image, name in zip(image_data, image_names):
+    for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
+        image_data: Image.Image
+
        shared.state.textinfo = name

-        parameters, existing_pnginfo = images.read_info_from_image(image)
+        parameters, existing_pnginfo = images.read_info_from_image(image_data)
        if parameters:
            existing_pnginfo["parameters"] = parameters

-        pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
+        pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))

        scripts.scripts_postproc.run(pp, args)

@@ -78,6 +75,8 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
        if extras_mode != 2 or show_extras_results:
            outputs.append(pp.image)

+        image_data.close()
+
    devices.torch_gc()

    return outputs, ui_common.plaintext_to_html(infotext), ''
@@ -0,0 +1,49 @@
+import gradio as gr
+
+from modules import scripts, sd_models
+from modules.ui_common import create_refresh_button
+from modules.ui_components import InputAccordion
+
+
+class ScriptRefiner(scripts.ScriptBuiltinUI):
+    section = "accordions"
+    create_group = False
+
+    def __init__(self):
+        pass
+
+    def title(self):
+        return "Refiner"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        with InputAccordion(False, label="Refiner", elem_id=self.elem_id("enable")) as enable_refiner:
+            with gr.Row():
+                refiner_checkpoint = gr.Dropdown(label='Checkpoint', elem_id=self.elem_id("checkpoint"), choices=sd_models.checkpoint_tiles(), value='', tooltip="switch to another model in the middle of generation")
+                create_refresh_button(refiner_checkpoint, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, self.elem_id("checkpoint_refresh"))
+
+                refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01, elem_id=self.elem_id("switch_at"), tooltip="fraction of sampling steps when the switch to refiner model should happen; 1=never, 0.5=switch in the middle of generation")
+
+        def lookup_checkpoint(title):
+            info = sd_models.get_closet_checkpoint_match(title)
+            return None if info is None else info.title
+
+        self.infotext_fields = [
+            (enable_refiner, lambda d: 'Refiner' in d),
+            (refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),
+            (refiner_switch_at, 'Refiner switch at'),
+        ]
+
+        return enable_refiner, refiner_checkpoint, refiner_switch_at
+
+    def setup(self, p, enable_refiner, refiner_checkpoint, refiner_switch_at):
+        # the actual implementation is in sd_samplers_common.py, apply_refiner
+
+        if not enable_refiner or refiner_checkpoint in (None, "", "None"):
+            p.refiner_checkpoint = None
+            p.refiner_switch_at = None
+        else:
+            p.refiner_checkpoint = refiner_checkpoint
+            p.refiner_switch_at = refiner_switch_at
@@ -0,0 +1,111 @@
+import json
+
+import gradio as gr
+
+from modules import scripts, ui, errors
+from modules.shared import cmd_opts
+from modules.ui_components import ToolButton
+
+
+class ScriptSeed(scripts.ScriptBuiltinUI):
+    section = "seed"
+    create_group = False
+
+    def __init__(self):
+        self.seed = None
+        self.reuse_seed = None
+        self.reuse_subseed = None
+
+    def title(self):
+        return "Seed"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        with gr.Row(elem_id=self.elem_id("seed_row")):
+            if cmd_opts.use_textbox_seed:
+                self.seed = gr.Textbox(label='Seed', value="", elem_id=self.elem_id("seed"), min_width=100)
+            else:
+                self.seed = gr.Number(label='Seed', value=-1, elem_id=self.elem_id("seed"), min_width=100, precision=0)
+
+            random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), tooltip="Set seed to -1, which will cause a new random number to be used every time")
+            reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), tooltip="Reuse seed from last generation, mostly useful if it was randomized")
+
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id=self.elem_id("subseed_show"), value=False)
+
+        with gr.Group(visible=False, elem_id=self.elem_id("seed_extras")) as seed_extras:
+            with gr.Row(elem_id=self.elem_id("subseed_row")):
+                subseed = gr.Number(label='Variation seed', value=-1, elem_id=self.elem_id("subseed"), precision=0)
+                random_subseed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_subseed"))
+                reuse_subseed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_subseed"))
+                subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=self.elem_id("subseed_strength"))
+
+            with gr.Row(elem_id=self.elem_id("seed_resize_from_row")):
+                seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=self.elem_id("seed_resize_from_w"))
+                seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=self.elem_id("seed_resize_from_h"))
+
+        random_seed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("seed") + "')}", show_progress=False, inputs=[], outputs=[])
+        random_subseed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("subseed") + "')}", show_progress=False, inputs=[], outputs=[])
+
+        seed_checkbox.change(lambda x: gr.update(visible=x), show_progress=False, inputs=[seed_checkbox], outputs=[seed_extras])
+
+        self.infotext_fields = [
+            (self.seed, "Seed"),
+            (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
+            (subseed, "Variation seed"),
+            (subseed_strength, "Variation seed strength"),
+            (seed_resize_from_w, "Seed resize from-1"),
+            (seed_resize_from_h, "Seed resize from-2"),
+        ]
+
+        self.on_after_component(lambda x: connect_reuse_seed(self.seed, reuse_seed, x.component, False), elem_id=f'generation_info_{self.tabname}')
+        self.on_after_component(lambda x: connect_reuse_seed(subseed, reuse_subseed, x.component, True), elem_id=f'generation_info_{self.tabname}')
+
+        return self.seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h
+
+    def setup(self, p, seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h):
+        p.seed = seed
+
+        if seed_checkbox and subseed_strength > 0:
+            p.subseed = subseed
+            p.subseed_strength = subseed_strength
+
+        if seed_checkbox and seed_resize_from_w > 0 and seed_resize_from_h > 0:
+            p.seed_resize_from_w = seed_resize_from_w
+            p.seed_resize_from_h = seed_resize_from_h
+
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError:
+            if gen_info_string:
+                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+
+        return [res, gr.update()]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, seed],
+        outputs=[seed, seed]
+    )
@@ -48,6 +48,7 @@ def add_task_to_queue(id_job):
 class ProgressRequest(BaseModel):
    id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for")
    id_live_preview: int = Field(default=-1, title="Live preview image ID", description="id of last received last preview image")
+    live_preview: bool = Field(default=True, title="Include live preview", description="boolean flag indicating whether to include the live preview image")


 class ProgressResponse(BaseModel):
@@ -71,7 +72,12 @@ def progressapi(req: ProgressRequest):
    completed = req.id_task in finished_tasks

    if not active:
-        return ProgressResponse(active=active, queued=queued, completed=completed, id_live_preview=-1, textinfo="In queue..." if queued else "Waiting...")
+        textinfo = "Waiting..."
+        if queued:
+            sorted_queued = sorted(pending_tasks.keys(), key=lambda x: pending_tasks[x])
+            queue_index = sorted_queued.index(req.id_task)
+            textinfo = "In queue: {}/{}".format(queue_index + 1, len(sorted_queued))
+        return ProgressResponse(active=active, queued=queued, completed=completed, id_live_preview=-1, textinfo=textinfo)

    progress = 0

@@ -89,31 +95,30 @@ def progressapi(req: ProgressRequest):
    predicted_duration = elapsed_since_start / progress if progress > 0 else None
    eta = predicted_duration - elapsed_since_start if predicted_duration is not None else None

+    live_preview = None
    id_live_preview = req.id_live_preview
-    shared.state.set_current_image()
-    if opts.live_previews_enable and shared.state.id_live_preview != req.id_live_preview:
-        image = shared.state.current_image
-        if image is not None:
-            buffered = io.BytesIO()

-            if opts.live_previews_image_format == "png":
-                # using optimize for large images takes an enormous amount of time
-                if max(*image.size) <= 256:
-                    save_kwargs = {"optimize": True}
+    if opts.live_previews_enable and req.live_preview:
+        shared.state.set_current_image()
+        if shared.state.id_live_preview != req.id_live_preview:
+            image = shared.state.current_image
+            if image is not None:
+                buffered = io.BytesIO()
+
+                if opts.live_previews_image_format == "png":
+                    # using optimize for large images takes an enormous amount of time
+                    if max(*image.size) <= 256:
+                        save_kwargs = {"optimize": True}
+                    else:
+                        save_kwargs = {"optimize": False, "compress_level": 1}
+
                else:
-                    save_kwargs = {"optimize": False, "compress_level": 1}
+                    save_kwargs = {}

-            else:
-                save_kwargs = {}
-
-            image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
-            base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
-            live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
-            id_live_preview = shared.state.id_live_preview
-        else:
-            live_preview = None
-    else:
-        live_preview = None
+                image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
+                base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
+                live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
+                id_live_preview = shared.state.id_live_preview

    return ProgressResponse(active=active, queued=queued, completed=completed, progress=progress, eta=eta, live_preview=live_preview, id_live_preview=id_live_preview, textinfo=shared.state.textinfo)

@@ -2,7 +2,6 @@ from __future__ import annotations

 import re
 from collections import namedtuple
-from typing import List
 import lark

 # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
@@ -19,14 +18,14 @@ prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
 !emphasized: "(" prompt ")"
        | "(" prompt ":" prompt ")"
        | "[" prompt "]"
-scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
-alternate: "[" prompt ("|" prompt)+ "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER [WHITESPACE] "]"
+alternate: "[" prompt ("|" [prompt])+ "]"
 WHITESPACE: /\s+/
 plain: /([^\\\[\]():|]|\\.)+/
 %import common.SIGNED_NUMBER -> NUMBER
 """)

-def get_learned_conditioning_prompt_schedules(prompts, steps):
+def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=None, use_old_scheduling=False):
    """
    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
    >>> g("test")
@@ -53,18 +52,43 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
    [[3, '((a][:b:c '], [10, '((a][:b:c d']]
    >>> g("[a|(b:1.1)]")
    [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
+    >>> g("[fe|]male")
+    [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
+    >>> g("[fe|||]male")
+    [[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
+    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0]
+    >>> g("a [b:.5] c")
+    [[10, 'a b c']]
+    >>> g("a [b:1.5] c")
+    [[5, 'a  c'], [10, 'a b c']]
    """

+    if hires_steps is None or use_old_scheduling:
+        int_offset = 0
+        flt_offset = 0
+        steps = base_steps
+    else:
+        int_offset = base_steps
+        flt_offset = 1.0
+        steps = hires_steps
+
    def collect_steps(steps, tree):
        res = [steps]

        class CollectSteps(lark.Visitor):
            def scheduled(self, tree):
-                tree.children[-1] = float(tree.children[-1])
-                if tree.children[-1] < 1:
-                    tree.children[-1] *= steps
-                tree.children[-1] = min(steps, int(tree.children[-1]))
-                res.append(tree.children[-1])
+                s = tree.children[-2]
+                v = float(s)
+                if use_old_scheduling:
+                    v = v*steps if v<1 else v
+                else:
+                    if "." in s:
+                        v = (v - flt_offset) * steps
+                    else:
+                        v = (v - int_offset)
+                tree.children[-2] = min(steps, int(v))
+                if tree.children[-2] >= 1:
+                    res.append(tree.children[-2])

            def alternate(self, tree):
                res.extend(range(1, steps+1))
@@ -75,13 +99,14 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
    def at_step(step, tree):
        class AtStep(lark.Transformer):
            def scheduled(self, args):
-                before, after, _, when = args
+                before, after, _, when, _ = args
                yield before or () if step <= when else after
            def alternate(self, args):
-                yield next(args[(step - 1)%len(args)])
+                args = ["" if not arg else arg for arg in args]
+                yield args[(step - 1) % len(args)]
            def start(self, args):
                def flatten(x):
-                    if type(x) == str:
+                    if isinstance(x, str):
                        yield x
                    else:
                        for gen in x:
@@ -129,7 +154,7 @@ class SdConditioning(list):



-def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
+def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False):
    """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
    and the sampling step at which this condition is to be replaced by the next one.

@@ -149,7 +174,7 @@ def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
    """
    res = []

-    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
+    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling)
    cache = {}

    for prompt, prompt_schedule in zip(prompts, prompt_schedules):
@@ -214,17 +239,17 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]):

 class ComposableScheduledPromptConditioning:
    def __init__(self, schedules, weight=1.0):
-        self.schedules: List[ScheduledPromptConditioning] = schedules
+        self.schedules: list[ScheduledPromptConditioning] = schedules
        self.weight: float = weight


 class MulticondLearnedConditioning:
    def __init__(self, shape, batch):
        self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
+        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch


-def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
+def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
    """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
    For each prompt, the list is obtained by splitting the prompt using the AND separator.

@@ -233,7 +258,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne

    res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)

-    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
+    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling)

    res = []
    for indexes in res_indexes:
@@ -252,7 +277,7 @@ class DictWithShape(dict):
        return self["crossattn"].shape


-def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
    param = c[0][0].cond
    is_dict = isinstance(param, dict)

@@ -333,7 +358,7 @@ re_attention = re.compile(r"""
 \\|
 \(|
 \[|
-:([+-]?[.\d]+)\)|
+:\s*([+-]?[.\d]+)\s*\)|
 \)|
 ]|
 [^\\()\[\]:]+|
@@ -55,6 +55,7 @@ class UpscalerRealESRGAN(Upscaler):
            half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
            tile=opts.ESRGAN_tile,
            tile_pad=opts.ESRGAN_tile_overlap,
+            device=self.device,
        )

        upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
@@ -14,7 +14,9 @@ def is_restartable() -> bool:
 def restart_program() -> None:
    """creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again"""

-    (Path(script_path) / "tmp" / "restart").touch()
+    tmpdir = Path(script_path) / "tmp"
+    tmpdir.mkdir(parents=True, exist_ok=True)
+    (tmpdir / "restart").touch()

    stop_program()

@@ -0,0 +1,170 @@
+import torch
+
+from modules import devices, rng_philox, shared
+
+
+def randn(seed, shape, generator=None):
+    """Generate a tensor with random numbers from a normal distribution using seed.
+
+    Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
+
+    manual_seed(seed)
+
+    if shared.opts.randn_source == "NV":
+        return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
+
+    if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
+        return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
+
+    return torch.randn(shape, device=devices.device, generator=generator)
+
+
+def randn_local(seed, shape):
+    """Generate a tensor with random numbers from a normal distribution using seed.
+
+    Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
+
+    if shared.opts.randn_source == "NV":
+        rng = rng_philox.Generator(seed)
+        return torch.asarray(rng.randn(shape), device=devices.device)
+
+    local_device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
+    local_generator = torch.Generator(local_device).manual_seed(int(seed))
+    return torch.randn(shape, device=local_device, generator=local_generator).to(devices.device)
+
+
+def randn_like(x):
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+    Use either randn() or manual_seed() to initialize the generator."""
+
+    if shared.opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
+
+    if shared.opts.randn_source == "CPU" or x.device.type == 'mps':
+        return torch.randn_like(x, device=devices.cpu).to(x.device)
+
+    return torch.randn_like(x)
+
+
+def randn_without_seed(shape, generator=None):
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+    Use either randn() or manual_seed() to initialize the generator."""
+
+    if shared.opts.randn_source == "NV":
+        return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
+
+    if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
+        return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
+
+    return torch.randn(shape, device=devices.device, generator=generator)
+
+
+def manual_seed(seed):
+    """Set up a global random number generator using the specified seed."""
+
+    if shared.opts.randn_source == "NV":
+        global nv_rng
+        nv_rng = rng_philox.Generator(seed)
+        return
+
+    torch.manual_seed(seed)
+
+
+def create_generator(seed):
+    if shared.opts.randn_source == "NV":
+        return rng_philox.Generator(seed)
+
+    device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
+    generator = torch.Generator(device).manual_seed(int(seed))
+    return generator
+
+
+# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
+def slerp(val, low, high):
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+    dot = (low_norm*high_norm).sum(1)
+
+    if dot.mean() > 0.9995:
+        return low * val + high * (1 - val)
+
+    omega = torch.acos(dot)
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    return res
+
+
+class ImageRNG:
+    def __init__(self, shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
+        self.shape = tuple(map(int, shape))
+        self.seeds = seeds
+        self.subseeds = subseeds
+        self.subseed_strength = subseed_strength
+        self.seed_resize_from_h = seed_resize_from_h
+        self.seed_resize_from_w = seed_resize_from_w
+
+        self.generators = [create_generator(seed) for seed in seeds]
+
+        self.is_first = True
+
+    def first(self):
+        noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], self.seed_resize_from_h // 8, self.seed_resize_from_w // 8)
+
+        xs = []
+
+        for i, (seed, generator) in enumerate(zip(self.seeds, self.generators)):
+            subnoise = None
+            if self.subseeds is not None and self.subseed_strength != 0:
+                subseed = 0 if i >= len(self.subseeds) else self.subseeds[i]
+                subnoise = randn(subseed, noise_shape)
+
+            if noise_shape != self.shape:
+                noise = randn(seed, noise_shape)
+            else:
+                noise = randn(seed, self.shape, generator=generator)
+
+            if subnoise is not None:
+                noise = slerp(self.subseed_strength, noise, subnoise)
+
+            if noise_shape != self.shape:
+                x = randn(seed, self.shape, generator=generator)
+                dx = (self.shape[2] - noise_shape[2]) // 2
+                dy = (self.shape[1] - noise_shape[1]) // 2
+                w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
+                h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
+                tx = 0 if dx < 0 else dx
+                ty = 0 if dy < 0 else dy
+                dx = max(-dx, 0)
+                dy = max(-dy, 0)
+
+                x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
+                noise = x
+
+            xs.append(noise)
+
+        eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
+        if eta_noise_seed_delta:
+            self.generators = [create_generator(seed + eta_noise_seed_delta) for seed in self.seeds]
+
+        return torch.stack(xs).to(shared.device)
+
+    def next(self):
+        if self.is_first:
+            self.is_first = False
+            return self.first()
+
+        xs = []
+        for generator in self.generators:
+            x = randn_without_seed(self.shape, generator=generator)
+            xs.append(x)
+
+        return torch.stack(xs).to(shared.device)
+
+
+devices.randn = randn
+devices.randn_local = randn_local
+devices.randn_like = randn_like
+devices.randn_without_seed = randn_without_seed
+devices.manual_seed = manual_seed
@@ -0,0 +1,102 @@
+"""RNG imitiating torch cuda randn on CPU. You are welcome.
+
+Usage:
+
+```
+g = Generator(seed=0)
+print(g.randn(shape=(3, 4)))
+```
+
+Expected output:
+```
+[[-0.92466259 -0.42534415 -2.6438457   0.14518388]
+ [-0.12086647 -0.57972564 -0.62285122 -0.32838709]
+ [-1.07454231 -0.36314407 -1.67105067  2.26550497]]
+```
+"""
+
+import numpy as np
+
+philox_m = [0xD2511F53, 0xCD9E8D57]
+philox_w = [0x9E3779B9, 0xBB67AE85]
+
+two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
+two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)
+
+
+def uint32(x):
+    """Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
+    return x.view(np.uint32).reshape(-1, 2).transpose(1, 0)
+
+
+def philox4_round(counter, key):
+    """A single round of the Philox 4x32 random number generator."""
+
+    v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
+    v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])
+
+    counter[0] = v2[1] ^ counter[1] ^ key[0]
+    counter[1] = v2[0]
+    counter[2] = v1[1] ^ counter[3] ^ key[1]
+    counter[3] = v1[0]
+
+
+def philox4_32(counter, key, rounds=10):
+    """Generates 32-bit random numbers using the Philox 4x32 random number generator.
+
+    Parameters:
+        counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
+        key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
+        rounds (int): The number of rounds to perform.
+
+    Returns:
+        numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
+    """
+
+    for _ in range(rounds - 1):
+        philox4_round(counter, key)
+
+        key[0] = key[0] + philox_w[0]
+        key[1] = key[1] + philox_w[1]
+
+    philox4_round(counter, key)
+    return counter
+
+
+def box_muller(x, y):
+    """Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
+    u = x * two_pow32_inv + two_pow32_inv / 2
+    v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
+
+    s = np.sqrt(-2.0 * np.log(u))
+
+    r1 = s * np.sin(v)
+    return r1.astype(np.float32)
+
+
+class Generator:
+    """RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""
+
+    def __init__(self, seed):
+        self.seed = seed
+        self.offset = 0
+
+    def randn(self, shape):
+        """Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""
+
+        n = 1
+        for x in shape:
+            n *= x
+
+        counter = np.zeros((4, n), dtype=np.uint32)
+        counter[0] = self.offset
+        counter[2] = np.arange(n, dtype=np.uint32)  # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
+        self.offset += 1
+
+        key = np.empty(n, dtype=np.uint64)
+        key.fill(self.seed)
+        key = uint32(key)
+
+        g = philox4_32(counter, key)
+
+        return box_muller(g[0], g[1]).reshape(shape)  # discard g[2] and g[3]
@@ -1,7 +1,7 @@
 import inspect
 import os
 from collections import namedtuple
-from typing import Optional, Dict, Any
+from typing import Optional, Any

 from fastapi import FastAPI
 from gradio import Blocks
@@ -28,6 +28,18 @@ class ImageSaveParams:
        """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""


+class ExtraNoiseParams:
+    def __init__(self, noise, x, xi):
+        self.noise = noise
+        """Random noise generated by the seed"""
+
+        self.x = x
+        """Latent representation of the image"""
+
+        self.xi = xi
+        """Noisy latent representation of the image"""
+
+
 class CFGDenoiserParams:
    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
        self.x = x
@@ -100,6 +112,7 @@ callback_map = dict(
    callbacks_ui_settings=[],
    callbacks_before_image_saved=[],
    callbacks_image_saved=[],
+    callbacks_extra_noise=[],
    callbacks_cfg_denoiser=[],
    callbacks_cfg_denoised=[],
    callbacks_cfg_after_cfg=[],
@@ -189,6 +202,14 @@ def image_saved_callback(params: ImageSaveParams):
            report_exception(c, 'image_saved_callback')


+def extra_noise_callback(params: ExtraNoiseParams):
+    for c in callback_map['callbacks_extra_noise']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'callbacks_extra_noise')
+
+
 def cfg_denoiser_callback(params: CFGDenoiserParams):
    for c in callback_map['callbacks_cfg_denoiser']:
        try:
@@ -237,7 +258,7 @@ def image_grid_callback(params: ImageGridLoopParams):
            report_exception(c, 'image_grid')


-def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
+def infotext_pasted_callback(infotext: str, params: dict[str, Any]):
    for c in callback_map['callbacks_infotext_pasted']:
        try:
            c.callback(infotext, params)
@@ -367,6 +388,14 @@ def on_image_saved(callback):
    add_callback(callback_map['callbacks_image_saved'], callback)


+def on_extra_noise(callback):
+    """register a function to be called before adding extra noise in img2img or hires fix;
+    The callback is called with one argument:
+        - params: ExtraNoiseParams - contains noise determined by seed and latent representation of image
+    """
+    add_callback(callback_map['callbacks_extra_noise'], callback)
+
+
 def on_cfg_denoiser(callback):
    """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
    The callback is called with one argument:
@@ -420,7 +449,7 @@ def on_infotext_pasted(callback):
    """register a function to be called before applying an infotext.
    The callback is called with two arguments:
       - infotext: str - raw infotext.
-       - result: Dict[str, any] - parsed infotext parameters.
+       - result: dict[str, any] - parsed infotext parameters.
    """
    add_callback(callback_map['callbacks_infotext_pasted'], callback)

@@ -3,6 +3,7 @@ import re
 import sys
 import inspect
 from collections import namedtuple
+from dataclasses import dataclass

 import gradio as gr

@@ -21,6 +22,11 @@ class PostprocessBatchListArgs:
        self.images = images


+@dataclass
+class OnComponent:
+    component: gr.blocks.Block
+
+
 class Script:
    name = None
    """script's internal name derived from title"""
@@ -35,9 +41,13 @@ class Script:

    is_txt2img = False
    is_img2img = False
+    tabname = None

    group = None
-    """A gr.Group component that has all script's UI inside it"""
+    """A gr.Group component that has all script's UI inside it."""
+
+    create_group = True
+    """If False, for alwayson scripts, a group component will not be created."""

    infotext_fields = None
    """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
@@ -52,6 +62,15 @@ class Script:
    api_info = None
    """Generated value of type modules.api.models.ScriptInfo with information about the script for API"""

+    on_before_component_elem_id = None
+    """list of callbacks to be called before a component with an elem_id is created"""
+
+    on_after_component_elem_id = None
+    """list of callbacks to be called after a component with an elem_id is created"""
+
+    setup_for_ui_only = False
+    """If true, the script setup will only be run in Gradio UI, not in API"""
+
    def title(self):
        """this function should return the title of the script. This is what will be displayed in the dropdown menu."""

@@ -90,9 +109,16 @@ class Script:

        pass

+    def setup(self, p, *args):
+        """For AlwaysVisible scripts, this function is called when the processing object is set up, before any processing starts.
+        args contains all values returned by components from ui().
+        """
+        pass
+
+
    def before_process(self, p, *args):
        """
-        This function is called very early before processing begins for AlwaysVisible scripts.
+        This function is called very early during processing begins for AlwaysVisible scripts.
        You can modify the processing object (p) here, inject hooks, etc.
        args contains all values returned by components from ui()
        """
@@ -212,6 +238,29 @@ class Script:

        pass

+    def on_before_component(self, callback, *, elem_id):
+        """
+        Calls callback before a component is created. The callback function is called with a single argument of type OnComponent.
+
+        May be called in show() or ui() - but it may be too late in latter as some components may already be created.
+
+        This function is an alternative to before_component in that it also cllows to run before a component is created, but
+        it doesn't require to be called for every created component - just for the one you need.
+        """
+        if self.on_before_component_elem_id is None:
+            self.on_before_component_elem_id = []
+
+        self.on_before_component_elem_id.append((elem_id, callback))
+
+    def on_after_component(self, callback, *, elem_id):
+        """
+        Calls callback after a component is created. The callback function is called with a single argument of type OnComponent.
+        """
+        if self.on_after_component_elem_id is None:
+            self.on_after_component_elem_id = []
+
+        self.on_after_component_elem_id.append((elem_id, callback))
+
    def describe(self):
        """unused"""
        return ""
@@ -220,7 +269,7 @@ class Script:
        """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""

        need_tabname = self.show(True) == self.show(False)
-        tabkind = 'img2img' if self.is_img2img else 'txt2txt'
+        tabkind = 'img2img' if self.is_img2img else 'txt2img'
        tabname = f"{tabkind}_" if need_tabname else ""
        title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))

@@ -232,6 +281,19 @@ class Script:
        """
        pass

+
+class ScriptBuiltinUI(Script):
+    setup_for_ui_only = True
+
+    def elem_id(self, item_id):
+        """helper function to generate id for a HTML element, constructs final id out of tab and user-supplied item_id"""
+
+        need_tabname = self.show(True) == self.show(False)
+        tabname = ('img2img' if self.is_img2img else 'txt2img') + "_" if need_tabname else ""
+
+        return f'{tabname}{item_id}'
+
+
 current_basedir = paths.script_path


@@ -250,7 +312,7 @@ postprocessing_scripts_data = []
 ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])


-def list_scripts(scriptdirname, extension):
+def list_scripts(scriptdirname, extension, *, include_extensions=True):
    scripts_list = []

    basedir = os.path.join(paths.script_path, scriptdirname)
@@ -258,8 +320,9 @@ def list_scripts(scriptdirname, extension):
        for filename in sorted(os.listdir(basedir)):
            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))

-    for ext in extensions.active():
-        scripts_list += ext.list_files(scriptdirname, extension)
+    if include_extensions:
+        for ext in extensions.active():
+            scripts_list += ext.list_files(scriptdirname, extension)

    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]

@@ -288,7 +351,7 @@ def load_scripts():
    postprocessing_scripts_data.clear()
    script_callbacks.clear_callbacks()

-    scripts_list = list_scripts("scripts", ".py")
+    scripts_list = list_scripts("scripts", ".py") + list_scripts("modules/processing_scripts", ".py", include_extensions=False)

    syspath = sys.path

@@ -349,10 +412,17 @@ class ScriptRunner:
        self.selectable_scripts = []
        self.alwayson_scripts = []
        self.titles = []
+        self.title_map = {}
        self.infotext_fields = []
        self.paste_field_names = []
        self.inputs = [None]

+        self.on_before_component_elem_id = {}
+        """dict of callbacks to be called before an element is created; key=elem_id, value=list of callbacks"""
+
+        self.on_after_component_elem_id = {}
+        """dict of callbacks to be called after an element is created; key=elem_id, value=list of callbacks"""
+
    def initialize_scripts(self, is_img2img):
        from modules import scripts_auto_postprocessing

@@ -367,6 +437,7 @@ class ScriptRunner:
            script.filename = script_data.path
            script.is_txt2img = not is_img2img
            script.is_img2img = is_img2img
+            script.tabname = "img2img" if is_img2img else "txt2img"

            visibility = script.show(script.is_img2img)

@@ -379,6 +450,28 @@ class ScriptRunner:
                self.scripts.append(script)
                self.selectable_scripts.append(script)

+        self.apply_on_before_component_callbacks()
+
+    def apply_on_before_component_callbacks(self):
+        for script in self.scripts:
+            on_before = script.on_before_component_elem_id or []
+            on_after = script.on_after_component_elem_id or []
+
+            for elem_id, callback in on_before:
+                if elem_id not in self.on_before_component_elem_id:
+                    self.on_before_component_elem_id[elem_id] = []
+
+                self.on_before_component_elem_id[elem_id].append((callback, script))
+
+            for elem_id, callback in on_after:
+                if elem_id not in self.on_after_component_elem_id:
+                    self.on_after_component_elem_id[elem_id] = []
+
+                self.on_after_component_elem_id[elem_id].append((callback, script))
+
+            on_before.clear()
+            on_after.clear()
+
    def create_script_ui(self, script):
        import modules.api.models as api_models

@@ -398,11 +491,15 @@ class ScriptRunner:

            arg_info = api_models.ScriptArg(label=control.label or "")

-            for field in ("value", "minimum", "maximum", "step", "choices"):
+            for field in ("value", "minimum", "maximum", "step"):
                v = getattr(control, field, None)
                if v is not None:
                    setattr(arg_info, field, v)

+            choices = getattr(control, 'choices', None)  # as of gradio 3.41, some items in choices are strings, and some are tuples where the first elem is the string
+            if choices is not None:
+                arg_info.choices = [x[0] if isinstance(x, tuple) else x for x in choices]
+
            api_args.append(arg_info)

        script.api_info = api_models.ScriptInfo(
@@ -429,15 +526,20 @@ class ScriptRunner:
            if script.alwayson and script.section != section:
                continue

-            with gr.Group(visible=script.alwayson) as group:
-                self.create_script_ui(script)
+            if script.create_group:
+                with gr.Group(visible=script.alwayson) as group:
+                    self.create_script_ui(script)

-            script.group = group
+                script.group = group
+            else:
+                self.create_script_ui(script)

    def prepare_ui(self):
        self.inputs = [None]

    def setup_ui(self):
+        all_titles = [wrap_call(script.title, script.filename, "title") or script.filename for script in self.scripts]
+        self.title_map = {title.lower(): script for title, script in zip(all_titles, self.scripts)}
        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]

        self.setup_ui_for_section(None)
@@ -484,6 +586,8 @@ class ScriptRunner:
        self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
        self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])

+        self.apply_on_before_component_callbacks()
+
        return self.inputs

    def run(self, p, *args):
@@ -577,6 +681,12 @@ class ScriptRunner:
                errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)

    def before_component(self, component, **kwargs):
+        for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
+            try:
+                callback(OnComponent(component=component))
+            except Exception:
+                errors.report(f"Error running on_before_component: {script.filename}", exc_info=True)
+
        for script in self.scripts:
            try:
                script.before_component(component, **kwargs)
@@ -584,12 +694,21 @@ class ScriptRunner:
                errors.report(f"Error running before_component: {script.filename}", exc_info=True)

    def after_component(self, component, **kwargs):
+        for callback, script in self.on_after_component_elem_id.get(component.elem_id, []):
+            try:
+                callback(OnComponent(component=component))
+            except Exception:
+                errors.report(f"Error running on_after_component: {script.filename}", exc_info=True)
+
        for script in self.scripts:
            try:
                script.after_component(component, **kwargs)
            except Exception:
                errors.report(f"Error running after_component: {script.filename}", exc_info=True)

+    def script(self, title):
+        return self.title_map.get(title.lower())
+
    def reload_sources(self, cache):
        for si, script in list(enumerate(self.scripts)):
            args_from = script.args_from
@@ -608,7 +727,6 @@ class ScriptRunner:
                    self.scripts[si].args_from = args_from
                    self.scripts[si].args_to = args_to

-
    def before_hr(self, p):
        for script in self.alwayson_scripts:
            try:
@@ -617,6 +735,17 @@ class ScriptRunner:
            except Exception:
                errors.report(f"Error running before_hr: {script.filename}", exc_info=True)

+    def setup_scrips(self, p, *, is_ui=True):
+        for script in self.alwayson_scripts:
+            if not is_ui and script.setup_for_ui_only:
+                continue
+
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.setup(p, *script_args)
+            except Exception:
+                errors.report(f"Error running setup: {script.filename}", exc_info=True)
+

 scripts_txt2img: ScriptRunner = None
 scripts_img2img: ScriptRunner = None
@@ -631,49 +760,3 @@ def reload_script_body_only():


 reload_scripts = load_scripts  # compatibility alias
-
-
-def add_classes_to_gradio_component(comp):
-    """
-    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
-    """
-
-    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
-
-    if getattr(comp, 'multiselect', False):
-        comp.elem_classes.append('multiselect')
-
-
-
-def IOComponent_init(self, *args, **kwargs):
-    if scripts_current is not None:
-        scripts_current.before_component(self, **kwargs)
-
-    script_callbacks.before_component_callback(self, **kwargs)
-
-    res = original_IOComponent_init(self, *args, **kwargs)
-
-    add_classes_to_gradio_component(self)
-
-    script_callbacks.after_component_callback(self, **kwargs)
-
-    if scripts_current is not None:
-        scripts_current.after_component(self, **kwargs)
-
-    return res
-
-
-original_IOComponent_init = gr.components.IOComponent.__init__
-gr.components.IOComponent.__init__ = IOComponent_init
-
-
-def BlockContext_init(self, *args, **kwargs):
-    res = original_BlockContext_init(self, *args, **kwargs)
-
-    add_classes_to_gradio_component(self)
-
-    return res
-
-
-original_BlockContext_init = gr.blocks.BlockContext.__init__
-gr.blocks.BlockContext.__init__ = BlockContext_init
@@ -3,8 +3,31 @@ import open_clip
 import torch
 import transformers.utils.hub

+from modules import shared

-class DisableInitialization:
+
+class ReplaceHelper:
+    def __init__(self):
+        self.replaced = []
+
+    def replace(self, obj, field, func):
+        original = getattr(obj, field, None)
+        if original is None:
+            return None
+
+        self.replaced.append((obj, field, original))
+        setattr(obj, field, func)
+
+        return original
+
+    def restore(self):
+        for obj, field, original in self.replaced:
+            setattr(obj, field, original)
+
+        self.replaced.clear()
+
+
+class DisableInitialization(ReplaceHelper):
    """
    When an object of this class enters a `with` block, it starts:
    - preventing torch's layer initialization functions from working
@@ -21,7 +44,7 @@ class DisableInitialization:
    """

    def __init__(self, disable_clip=True):
-        self.replaced = []
+        super().__init__()
        self.disable_clip = disable_clip

    def replace(self, obj, field, func):
@@ -86,8 +109,124 @@ class DisableInitialization:
            self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)

    def __exit__(self, exc_type, exc_val, exc_tb):
-        for obj, field, original in self.replaced:
-            setattr(obj, field, original)
+        self.restore()

-        self.replaced.clear()

+class InitializeOnMeta(ReplaceHelper):
+    """
+    Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device,
+    which results in those parameters having no values and taking no memory. model.to() will be broken and
+    will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict.
+
+    Usage:
+    ```
+    with sd_disable_initialization.InitializeOnMeta():
+        sd_model = instantiate_from_config(sd_config.model)
+    ```
+    """
+
+    def __enter__(self):
+        if shared.cmd_opts.disable_model_loading_ram_optimization:
+            return
+
+        def set_device(x):
+            x["device"] = "meta"
+            return x
+
+        linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs)))
+        conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs)))
+        mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs)))
+        self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.restore()
+
+
+class LoadStateDictOnMeta(ReplaceHelper):
+    """
+    Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device.
+    As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory.
+    Meant to be used together with InitializeOnMeta above.
+
+    Usage:
+    ```
+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict):
+        model.load_state_dict(state_dict, strict=False)
+    ```
+    """
+
+    def __init__(self, state_dict, device, weight_dtype_conversion=None):
+        super().__init__()
+        self.state_dict = state_dict
+        self.device = device
+        self.weight_dtype_conversion = weight_dtype_conversion or {}
+        self.default_dtype = self.weight_dtype_conversion.get('')
+
+    def get_weight_dtype(self, key):
+        key_first_term, _ = key.split('.', 1)
+        return self.weight_dtype_conversion.get(key_first_term, self.default_dtype)
+
+    def __enter__(self):
+        if shared.cmd_opts.disable_model_loading_ram_optimization:
+            return
+
+        sd = self.state_dict
+        device = self.device
+
+        def load_from_state_dict(original, module, state_dict, prefix, *args, **kwargs):
+            used_param_keys = []
+
+            for name, param in module._parameters.items():
+                if param is None:
+                    continue
+
+                key = prefix + name
+                sd_param = sd.pop(key, None)
+                if sd_param is not None:
+                    state_dict[key] = sd_param.to(dtype=self.get_weight_dtype(key))
+                    used_param_keys.append(key)
+
+                if param.is_meta:
+                    dtype = sd_param.dtype if sd_param is not None else param.dtype
+                    module._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad)
+
+            for name in module._buffers:
+                key = prefix + name
+
+                sd_param = sd.pop(key, None)
+                if sd_param is not None:
+                    state_dict[key] = sd_param
+                    used_param_keys.append(key)
+
+            original(module, state_dict, prefix, *args, **kwargs)
+
+            for key in used_param_keys:
+                state_dict.pop(key, None)
+
+        def load_state_dict(original, module, state_dict, strict=True):
+            """torch makes a lot of copies of the dictionary with weights, so just deleting entries from state_dict does not help
+            because the same values are stored in multiple copies of the dict. The trick used here is to give torch a dict with
+            all weights on meta device, i.e. deleted, and then it doesn't matter how many copies torch makes.
+
+            In _load_from_state_dict, the correct weight will be obtained from a single dict with the right weights (sd).
+
+            The dangerous thing about this is if _load_from_state_dict is not called, (if some exotic module overloads
+            the function and does not call the original) the state dict will just fail to load because weights
+            would be on the meta device.
+            """
+
+            if state_dict == sd:
+                state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
+
+            original(module, state_dict, strict=strict)
+
+        module_load_state_dict = self.replace(torch.nn.Module, 'load_state_dict', lambda *args, **kwargs: load_state_dict(module_load_state_dict, *args, **kwargs))
+        module_load_from_state_dict = self.replace(torch.nn.Module, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(module_load_from_state_dict, *args, **kwargs))
+        linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
+        conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
+        mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
+        layer_norm_load_from_state_dict = self.replace(torch.nn.LayerNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(layer_norm_load_from_state_dict, *args, **kwargs))
+        group_norm_load_from_state_dict = self.replace(torch.nn.GroupNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(group_norm_load_from_state_dict, *args, **kwargs))
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.restore()
@@ -2,8 +2,7 @@ import torch
 from torch.nn.functional import silu
 from types import MethodType

-import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -11,6 +10,7 @@ from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hija
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.modules.diffusionmodules.openaimodel
+import ldm.models.diffusion.ddpm
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 import ldm.modules.encoders.modules
@@ -30,12 +30,16 @@ ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.Cros
 ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention

 # silence new console spam from SD2
-ldm.modules.attention.print = lambda *args: None
-ldm.modules.diffusionmodules.model.print = lambda *args: None
+ldm.modules.attention.print = shared.ldm_print
+ldm.modules.diffusionmodules.model.print = shared.ldm_print
+ldm.util.print = shared.ldm_print
+ldm.models.diffusion.ddpm.print = shared.ldm_print

 optimizers = []
 current_optimizer: sd_hijack_optimizations.SdOptimization = None

+ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
+sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)

 def list_optimizers():
    new_optimizers = script_callbacks.list_optimizers_callback()
@@ -164,12 +168,13 @@ class StableDiffusionModelHijack:
    clip = None
    optimization_method = None

-    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
-
    def __init__(self):
+        import modules.textual_inversion.textual_inversion
+
        self.extra_generation_params = {}
        self.comments = []

+        self.embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
        self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)

    def apply_optimizations(self, option=None):
@@ -197,7 +202,7 @@ class StableDiffusionModelHijack:
                    conditioner.embedders[i] = sd_hijack_clip.FrozenCLIPEmbedderForSDXLWithCustomWords(embedder, self)
                    text_cond_models.append(conditioner.embedders[i])
                if typename == 'FrozenOpenCLIPEmbedder2':
-                    embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self)
+                    embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self, textual_inversion_key='clip_g')
                    conditioner.embedders[i] = sd_hijack_open_clip.FrozenOpenCLIPEmbedder2WithCustomWords(embedder, self)
                    text_cond_models.append(conditioner.embedders[i])

@@ -237,13 +242,30 @@ class StableDiffusionModelHijack:

        self.layers = flatten(m)

-        if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
-            ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+        if isinstance(m, ldm.models.diffusion.ddpm.LatentDiffusion):
+            sd_unet.original_forward = ldm_original_forward
+        elif isinstance(m, sgm.models.diffusion.DiffusionEngine):
+            sd_unet.original_forward = sgm_original_forward
+        else:
+            sd_unet.original_forward = None

-        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward

    def undo_hijack(self, m):
-        if type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
+        conditioner = getattr(m, 'conditioner', None)
+        if conditioner:
+            for i in range(len(conditioner.embedders)):
+                embedder = conditioner.embedders[i]
+                if isinstance(embedder, (sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords, sd_hijack_open_clip.FrozenOpenCLIPEmbedder2WithCustomWords)):
+                    embedder.wrapped.model.token_embedding = embedder.wrapped.model.token_embedding.wrapped
+                    conditioner.embedders[i] = embedder.wrapped
+                if isinstance(embedder, sd_hijack_clip.FrozenCLIPEmbedderForSDXLWithCustomWords):
+                    embedder.wrapped.transformer.text_model.embeddings.token_embedding = embedder.wrapped.transformer.text_model.embeddings.token_embedding.wrapped
+                    conditioner.embedders[i] = embedder.wrapped
+
+            if hasattr(m, 'cond_stage_model'):
+                delattr(m, 'cond_stage_model')
+
+        elif type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
            m.cond_stage_model = m.cond_stage_model.wrapped

        elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
@@ -263,7 +285,8 @@ class StableDiffusionModelHijack:
        self.layers = None
        self.clip = None

-        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
+        sd_unet.original_forward = None
+

    def apply_circular(self, enable):
        if self.circular_enabled == enable:
@@ -292,10 +315,11 @@ class StableDiffusionModelHijack:


 class EmbeddingsWithFixes(torch.nn.Module):
-    def __init__(self, wrapped, embeddings):
+    def __init__(self, wrapped, embeddings, textual_inversion_key='clip_l'):
        super().__init__()
        self.wrapped = wrapped
        self.embeddings = embeddings
+        self.textual_inversion_key = textual_inversion_key

    def forward(self, input_ids):
        batch_fixes = self.embeddings.fixes
@@ -309,7 +333,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
        vecs = []
        for fixes, tensor in zip(batch_fixes, inputs_embeds):
            for offset, embedding in fixes:
-                emb = devices.cond_cast_unet(embedding.vec)
+                vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
+                emb = devices.cond_cast_unet(vec)
                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])

@@ -161,7 +161,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                    position += 1
                    continue

-                emb_len = int(embedding.vec.shape[0])
+                emb_len = int(embedding.vectors)
                if len(chunk.tokens) + emb_len > self.chunk_length:
                    next_chunk()

@@ -245,6 +245,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                hashes.append(f"{name}: {shorthash}")

            if hashes:
+                if self.hijack.extra_generation_params.get("TI hashes"):
+                    hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
                self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)

        if getattr(self.wrapped, 'return_pooled', False):
@@ -1,97 +0,0 @@
-import torch
-
-import ldm.models.diffusion.ddpm
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-
-from ldm.models.diffusion.ddim import noise_like
-from ldm.models.diffusion.sampling_util import norm_thresholding
-
-
-@torch.no_grad()
-def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
-    b, *_, device = *x.shape, x.device
-
-    def get_model_output(x, t):
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-
-            if isinstance(c, dict):
-                assert isinstance(unconditional_conditioning, dict)
-                c_in = {}
-                for k in c:
-                    if isinstance(c[k], list):
-                        c_in[k] = [
-                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
-                            for i in range(len(c[k]))
-                        ]
-                    else:
-                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
-            else:
-                c_in = torch.cat([unconditional_conditioning, c])
-
-            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        return e_t
-
-    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-    def get_x_prev_and_pred_x0(e_t, index):
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        if dynamic_threshold is not None:
-            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    e_t = get_model_output(x, t)
-    if len(old_eps) == 0:
-        # Pseudo Improved Euler (2nd order)
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-        e_t_next = get_model_output(x_prev, t_next)
-        e_t_prime = (e_t + e_t_next) / 2
-    elif len(old_eps) == 1:
-        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (3 * e_t - old_eps[-1]) / 2
-    elif len(old_eps) == 2:
-        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-    elif len(old_eps) >= 3:
-        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-    return x_prev, pred_x0, e_t
-
-
-def do_inpainting_hijack():
-    # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-
-    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
@@ -1,6 +1,7 @@
 from __future__ import annotations
 import math
 import psutil
+import platform

 import torch
 from torch import einsum
@@ -94,7 +95,10 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
 class SdOptimizationSubQuad(SdOptimization):
    name = "sub-quadratic"
    cmd_opt = "opt_sub_quad_attention"
-    priority = 10
+
+    @property
+    def priority(self):
+        return 1000 if shared.device.type == 'mps' else 10

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
@@ -120,7 +124,7 @@ class SdOptimizationInvokeAI(SdOptimization):

    @property
    def priority(self):
-        return 1000 if not torch.cuda.is_available() else 10
+        return 1000 if shared.device.type != 'mps' and not torch.cuda.is_available() else 10

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
@@ -256,9 +260,9 @@ def split_cross_attention_forward(self, x, context=None, mask=None, **kwargs):
            raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
                               f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')

-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+        slice_size = q.shape[1] // steps
        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
+            end = min(i + slice_size, q.shape[1])
            s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)

            s2 = s1.softmax(dim=-1, dtype=q.dtype)
@@ -427,7 +431,10 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
    qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens

    if chunk_threshold is None:
-        chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
+        if q.device.type == 'mps':
+            chunk_threshold_bytes = 268435456 * (2 if platform.processor() == 'i386' else bytes_per_token)
+        else:
+            chunk_threshold_bytes = int(get_available_vram() * 0.7)
    elif chunk_threshold == 0:
        chunk_threshold_bytes = None
    else:
@@ -7,17 +7,17 @@ import threading
 import torch
 import re
 import safetensors.torch
-from omegaconf import OmegaConf
+from omegaconf import OmegaConf, ListConfig
 from os import mkdir
 from urllib import request
 import ldm.modules.midas as midas

 from ldm.util import instantiate_from_config

-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl
-from modules.sd_hijack_inpainting import do_inpainting_hijack
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
 from modules.timer import Timer
 import tomesd
+import numpy as np

 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -28,13 +28,34 @@ checkpoint_alisases = checkpoint_aliases  # for compatibility with old name
 checkpoints_loaded = collections.OrderedDict()


+def replace_key(d, key, new_key, value):
+    keys = list(d.keys())
+
+    d[new_key] = value
+
+    if key not in keys:
+        return d
+
+    index = keys.index(key)
+    keys[index] = new_key
+
+    new_d = {k: d[k] for k in keys}
+
+    d.clear()
+    d.update(new_d)
+    return d
+
+
 class CheckpointInfo:
    def __init__(self, filename):
        self.filename = filename
        abspath = os.path.abspath(filename)
+        abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None

-        if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
-            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
+        self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
+
+        if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
+            name = abspath.replace(abs_ckpt_dir, '')
        elif abspath.startswith(model_path):
            name = abspath.replace(model_path, '')
        else:
@@ -43,6 +64,19 @@ class CheckpointInfo:
        if name.startswith("\\") or name.startswith("/"):
            name = name[1:]

+        def read_metadata():
+            metadata = read_metadata_from_safetensors(filename)
+            self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
+
+            return metadata
+
+        self.metadata = {}
+        if self.is_safetensors:
+            try:
+                self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
+            except Exception as e:
+                errors.display(e, f"reading metadata for {filename}")
+
        self.name = name
        self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
        self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
@@ -52,17 +86,11 @@ class CheckpointInfo:
        self.shorthash = self.sha256[0:10] if self.sha256 else None

        self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
+        self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'

-        self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
-
-        self.metadata = {}
-
-        _, ext = os.path.splitext(self.filename)
-        if ext.lower() == ".safetensors":
-            try:
-                self.metadata = read_metadata_from_safetensors(filename)
-            except Exception as e:
-                errors.display(e, f"reading checkpoint metadata: {filename}")
+        self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]']
+        if self.shorthash:
+            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']

    def register(self):
        checkpoints_list[self.title] = self
@@ -74,13 +102,20 @@ class CheckpointInfo:
        if self.sha256 is None:
            return

-        self.shorthash = self.sha256[0:10]
+        shorthash = self.sha256[0:10]
+        if self.shorthash == self.sha256[0:10]:
+            return self.shorthash
+
+        self.shorthash = shorthash

        if self.shorthash not in self.ids:
-            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
+            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']

-        checkpoints_list.pop(self.title)
+        old_title = self.title
        self.title = f'{self.name} [{self.shorthash}]'
+        self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
+
+        replace_key(checkpoints_list, old_title, self.title, self)
        self.register()

        return self.shorthash
@@ -96,19 +131,16 @@ except Exception:


 def setup_model():
+    """called once at startup to do various one-time tasks related to SD models"""
+
    os.makedirs(model_path, exist_ok=True)

    enable_midas_autodownload()
+    patch_given_betas()


-def checkpoint_tiles():
-    def convert(name):
-        return int(name) if name.isdigit() else name.lower()
-
-    def alphanumeric_key(key):
-        return [convert(c) for c in re.split('([0-9]+)', key)]
-
-    return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
+def checkpoint_tiles(use_short=False):
+    return [x.short_title if use_short else x.title for x in checkpoints_list.values()]


 def list_models():
@@ -131,12 +163,18 @@ def list_models():
    elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
        print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)

-    for filename in sorted(model_list, key=str.lower):
+    for filename in model_list:
        checkpoint_info = CheckpointInfo(filename)
        checkpoint_info.register()


+re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
+
+
 def get_closet_checkpoint_match(search_string):
+    if not search_string:
+        return None
+
    checkpoint_info = checkpoint_aliases.get(search_string, None)
    if checkpoint_info is not None:
        return checkpoint_info
@@ -145,6 +183,11 @@ def get_closet_checkpoint_match(search_string):
    if found:
        return found[0]

+    search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
+    found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
+    if found:
+        return found[0]
+
    return None


@@ -271,6 +314,8 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
    if checkpoint_info in checkpoints_loaded:
        # use checkpoint cache
        print(f"Loading weights [{sd_model_hash}] from cache")
+        # move to end as latest
+        checkpoints_loaded.move_to_end(checkpoint_info)
        return checkpoints_loaded[checkpoint_info]

    print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
@@ -280,11 +325,27 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
    return res


+class SkipWritingToConfig:
+    """This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight."""
+
+    skip = False
+    previous = None
+
+    def __enter__(self):
+        self.previous = SkipWritingToConfig.skip
+        SkipWritingToConfig.skip = True
+        return self
+
+    def __exit__(self, exc_type, exc_value, exc_traceback):
+        SkipWritingToConfig.skip = self.previous
+
+
 def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
    sd_model_hash = checkpoint_info.calculate_shorthash()
    timer.record("calculate hash")

-    shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
+    if not SkipWritingToConfig.skip:
+        shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title

    if state_dict is None:
        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
@@ -297,18 +358,23 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
        sd_models_xl.extend_sdxl(model)

    model.load_state_dict(state_dict, strict=False)
-    del state_dict
    timer.record("apply weights to model")

    if shared.opts.sd_checkpoint_cache > 0:
        # cache newly loaded model
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+        checkpoints_loaded[checkpoint_info] = state_dict
+
+    del state_dict

    if shared.cmd_opts.opt_channelslast:
        model.to(memory_format=torch.channels_last)
        timer.record("apply channels_last")

-    if not shared.cmd_opts.no_half:
+    if shared.cmd_opts.no_half:
+        model.float()
+        devices.dtype_unet = torch.float32
+        timer.record("apply float()")
+    else:
        vae = model.first_stage_model
        depth_model = getattr(model, 'depth_model', None)

@@ -324,9 +390,9 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
        if depth_model:
            model.depth_model = depth_model

+        devices.dtype_unet = torch.float16
        timer.record("apply half()")

-    devices.dtype_unet = torch.float16 if model.is_sdxl and not shared.cmd_opts.no_half else model.model.diffusion_model.dtype
    devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16

    model.first_stage_model.to(devices.dtype_vae)
@@ -346,7 +412,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer

    sd_vae.delete_base_vae()
    sd_vae.clear_loaded_vae()
-    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
+    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
    sd_vae.load_vae(model, vae_file, vae_source)
    timer.record("load VAE")

@@ -394,6 +460,20 @@ def enable_midas_autodownload():
    midas.api.load_model = load_model_wrapper


+def patch_given_betas():
+    import ldm.models.diffusion.ddpm
+
+    def patched_register_schedule(*args, **kwargs):
+        """a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
+
+        if isinstance(args[1], ListConfig):
+            args = (args[0], np.array(args[1]), *args[2:])
+
+        original_register_schedule(*args, **kwargs)
+
+    original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
+
+
 def repair_config(sd_config):

    if not hasattr(sd_config.model.params, "use_ema"):
@@ -423,6 +503,7 @@ sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
 class SdModelData:
    def __init__(self):
        self.sd_model = None
+        self.loaded_sd_models = []
        self.was_loaded_at_least_once = False
        self.lock = threading.Lock()

@@ -437,6 +518,7 @@ class SdModelData:

                try:
                    load_model()
+
                except Exception as e:
                    errors.display(e, "loading stable diffusion model", full_traceback=True)
                    print("", file=sys.stderr)
@@ -445,14 +527,30 @@ class SdModelData:

        return self.sd_model

-    def set_sd_model(self, v):
+    def set_sd_model(self, v, already_loaded=False):
        self.sd_model = v
+        if already_loaded:
+            sd_vae.base_vae = getattr(v, "base_vae", None)
+            sd_vae.loaded_vae_file = getattr(v, "loaded_vae_file", None)
+            sd_vae.checkpoint_info = v.sd_checkpoint_info
+
+        try:
+            self.loaded_sd_models.remove(v)
+        except ValueError:
+            pass
+
+        if v is not None:
+            self.loaded_sd_models.insert(0, v)


 model_data = SdModelData()


 def get_empty_cond(sd_model):
+
+    p = processing.StableDiffusionProcessingTxt2Img()
+    extra_networks.activate(p, {})
+
    if hasattr(sd_model, 'conditioner'):
        d = sd_model.get_learned_conditioning([""])
        return d['crossattn']
@@ -460,20 +558,46 @@ def get_empty_cond(sd_model):
        return sd_model.cond_stage_model([""])


+def send_model_to_cpu(m):
+    if m.lowvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        m.to(devices.cpu)
+
+    devices.torch_gc()
+
+
+def model_target_device(m):
+    if lowvram.is_needed(m):
+        return devices.cpu
+    else:
+        return devices.device
+
+
+def send_model_to_device(m):
+    lowvram.apply(m)
+
+    if not m.lowvram:
+        m.to(shared.device)
+
+
+def send_model_to_trash(m):
+    m.to(device="meta")
+    devices.torch_gc()
+

 def load_model(checkpoint_info=None, already_loaded_state_dict=None):
-    from modules import lowvram, sd_hijack
+    from modules import sd_hijack
    checkpoint_info = checkpoint_info or select_checkpoint()

+    timer = Timer()
+
    if model_data.sd_model:
-        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+        send_model_to_trash(model_data.sd_model)
        model_data.sd_model = None
-        gc.collect()
        devices.torch_gc()

-    do_inpainting_hijack()
-
-    timer = Timer()
+    timer.record("unload existing model")

    if already_loaded_state_dict is not None:
        state_dict = already_loaded_state_dict
@@ -495,25 +619,35 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
    sd_model = None
    try:
        with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
-            sd_model = instantiate_from_config(sd_config.model)
-    except Exception:
-        pass
+            with sd_disable_initialization.InitializeOnMeta():
+                sd_model = instantiate_from_config(sd_config.model)
+
+    except Exception as e:
+        errors.display(e, "creating model quickly", full_traceback=True)

    if sd_model is None:
        print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
-        sd_model = instantiate_from_config(sd_config.model)
+
+        with sd_disable_initialization.InitializeOnMeta():
+            sd_model = instantiate_from_config(sd_config.model)

    sd_model.used_config = checkpoint_config

    timer.record("create model")

-    load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-
-    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-        lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
+    if shared.cmd_opts.no_half:
+        weight_dtype_conversion = None
    else:
-        sd_model.to(shared.device)
+        weight_dtype_conversion = {
+            'first_stage_model': None,
+            '': torch.float16,
+        }

+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(sd_model), weight_dtype_conversion=weight_dtype_conversion):
+        load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+    timer.record("load weights from state dict")
+
+    send_model_to_device(sd_model)
    timer.record("move model to device")

    sd_hijack.model_hijack.hijack(sd_model)
@@ -521,7 +655,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
    timer.record("hijack")

    sd_model.eval()
-    model_data.sd_model = sd_model
+    model_data.set_sd_model(sd_model)
    model_data.was_loaded_at_least_once = True

    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
@@ -542,10 +676,70 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
    return sd_model


+def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
+    """
+    Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
+    If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
+    If not, returns the model that can be used to load weights from checkpoint_info's file.
+    If no such model exists, returns None.
+    Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
+    """
+
+    already_loaded = None
+    for i in reversed(range(len(model_data.loaded_sd_models))):
+        loaded_model = model_data.loaded_sd_models[i]
+        if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+            already_loaded = loaded_model
+            continue
+
+        if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
+            print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
+            model_data.loaded_sd_models.pop()
+            send_model_to_trash(loaded_model)
+            timer.record("send model to trash")
+
+        if shared.opts.sd_checkpoints_keep_in_cpu:
+            send_model_to_cpu(sd_model)
+            timer.record("send model to cpu")
+
+    if already_loaded is not None:
+        send_model_to_device(already_loaded)
+        timer.record("send model to device")
+
+        model_data.set_sd_model(already_loaded, already_loaded=True)
+
+        if not SkipWritingToConfig.skip:
+            shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title
+            shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256
+
+        print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
+        sd_vae.reload_vae_weights(already_loaded)
+        return model_data.sd_model
+    elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
+        print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
+
+        model_data.sd_model = None
+        load_model(checkpoint_info)
+        return model_data.sd_model
+    elif len(model_data.loaded_sd_models) > 0:
+        sd_model = model_data.loaded_sd_models.pop()
+        model_data.sd_model = sd_model
+
+        sd_vae.base_vae = getattr(sd_model, "base_vae", None)
+        sd_vae.loaded_vae_file = getattr(sd_model, "loaded_vae_file", None)
+        sd_vae.checkpoint_info = sd_model.sd_checkpoint_info
+
+        print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
+        return sd_model
+    else:
+        return None
+
+
 def reload_model_weights(sd_model=None, info=None):
-    from modules import lowvram, devices, sd_hijack
    checkpoint_info = info or select_checkpoint()

+    timer = Timer()
+
    if not sd_model:
        sd_model = model_data.sd_model

@@ -554,19 +748,17 @@ def reload_model_weights(sd_model=None, info=None):
    else:
        current_checkpoint_info = sd_model.sd_checkpoint_info
        if sd_model.sd_model_checkpoint == checkpoint_info.filename:
-            return
+            return sd_model

+    sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
+    if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+        return sd_model
+
+    if sd_model is not None:
        sd_unet.apply_unet("None")
-
-        if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-            lowvram.send_everything_to_cpu()
-        else:
-            sd_model.to(devices.cpu)
-
+        send_model_to_cpu(sd_model)
        sd_hijack.model_hijack.undo_hijack(sd_model)

-    timer = Timer()
-
    state_dict = get_checkpoint_state_dict(checkpoint_info, timer)

    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
@@ -574,7 +766,9 @@ def reload_model_weights(sd_model=None, info=None):
    timer.record("find config")

    if sd_model is None or checkpoint_config != sd_model.used_config:
-        del sd_model
+        if sd_model is not None:
+            send_model_to_trash(sd_model)
+
        load_model(checkpoint_info, already_loaded_state_dict=state_dict)
        return model_data.sd_model

@@ -591,17 +785,19 @@ def reload_model_weights(sd_model=None, info=None):
        script_callbacks.model_loaded_callback(sd_model)
        timer.record("script callbacks")

-        if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+        if not sd_model.lowvram:
            sd_model.to(devices.device)
            timer.record("move model to device")

    print(f"Weights loaded in {timer.summary()}.")

+    model_data.set_sd_model(sd_model)
+    sd_unet.apply_unet()
+
    return sd_model


 def unload_model_weights(sd_model=None, info=None):
-    from modules import devices, sd_hijack
    timer = Timer()

    if model_data.sd_model:
@@ -2,7 +2,7 @@ import os

 import torch

-from modules import shared, paths, sd_disable_initialization
+from modules import shared, paths, sd_disable_initialization, devices

 sd_configs_path = shared.sd_configs_path
 sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
@@ -29,7 +29,6 @@ def is_using_v_parameterization_for_sd2(state_dict):
    """

    import ldm.modules.diffusionmodules.openaimodel
-    from modules import devices

    device = devices.cpu

@@ -0,0 +1,31 @@
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from typing import TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from modules.sd_models import CheckpointInfo
+
+
+class WebuiSdModel(LatentDiffusion):
+    """This class is not actually instantinated, but its fields are created and fieeld by webui"""
+
+    lowvram: bool
+    """True if lowvram/medvram optimizations are enabled -- see modules.lowvram for more info"""
+
+    sd_model_hash: str
+    """short hash, 10 first characters of SHA1 hash of the model file; may be None if --no-hashing flag is used"""
+
+    sd_model_checkpoint: str
+    """path to the file on disk that model weights were obtained from"""
+
+    sd_checkpoint_info: 'CheckpointInfo'
+    """structure with additional information about the file with model's weights"""
+
+    is_sdxl: bool
+    """True if the model's architecture is SDXL"""
+
+    is_sd2: bool
+    """True if the model's architecture is SD 2.x"""
+
+    is_sd1: bool
+    """True if the model's architecture is SD 1.x"""
@@ -56,6 +56,14 @@ def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text,
    return torch.cat(res, dim=1)


+def tokenize(self: sgm.modules.GeneralConditioner, texts):
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:
+        return embedder.tokenize(texts)
+
+    raise AssertionError('no tokenizer available')
+
+
+
 def process_texts(self, texts):
    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
        return embedder.process_texts(texts)
@@ -68,6 +76,7 @@ def get_target_prompt_token_count(self, token_count):

 # those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
 sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
+sgm.modules.GeneralConditioner.tokenize = tokenize
 sgm.modules.GeneralConditioner.process_texts = process_texts
 sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count

@@ -89,10 +98,10 @@ def extend_sdxl(model):
    model.conditioner.wrapped = torch.nn.Module()


-sgm.modules.attention.print = lambda *args: None
-sgm.modules.diffusionmodules.model.print = lambda *args: None
-sgm.modules.diffusionmodules.openaimodel.print = lambda *args: None
-sgm.modules.encoders.modules.print = lambda *args: None
+sgm.modules.attention.print = shared.ldm_print
+sgm.modules.diffusionmodules.model.print = shared.ldm_print
+sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print
+sgm.modules.encoders.modules.print = shared.ldm_print

 # this gets the code to load the vanilla attention that we override
 sgm.modules.attention.SDP_IS_AVAILABLE = True
@@ -1,17 +1,18 @@
-from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
+from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, shared

 # imports for functions that previously were here and are used by other modules
 from modules.sd_samplers_common import samples_to_image_grid, sample_to_image  # noqa: F401

 all_samplers = [
    *sd_samplers_kdiffusion.samplers_data_k_diffusion,
-    *sd_samplers_compvis.samplers_data_compvis,
+    *sd_samplers_timesteps.samplers_data_timesteps,
 ]
 all_samplers_map = {x.name: x for x in all_samplers}

 samplers = []
 samplers_for_img2img = []
 samplers_map = {}
+samplers_hidden = {}


 def find_sampler_config(name):
@@ -38,13 +39,11 @@ def create_sampler(name, model):


 def set_samplers():
-    global samplers, samplers_for_img2img
+    global samplers, samplers_for_img2img, samplers_hidden

-    hidden = set(shared.opts.hide_samplers)
-    hidden_img2img = set(shared.opts.hide_samplers + ['PLMS', 'UniPC'])
-
-    samplers = [x for x in all_samplers if x.name not in hidden]
-    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
+    samplers_hidden = set(shared.opts.hide_samplers)
+    samplers = all_samplers
+    samplers_for_img2img = all_samplers

    samplers_map.clear()
    for sampler in all_samplers:
@@ -53,4 +52,8 @@ def set_samplers():
            samplers_map[alias.lower()] = sampler.name


+def visible_sampler_names():
+    return [x.name for x in samplers if x.name not in samplers_hidden]
+
+
 set_samplers()
@@ -0,0 +1,230 @@
+import torch
+from modules import prompt_parser, devices, sd_samplers_common
+
+from modules.shared import opts, state
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
+
+
+def catenate_conds(conds):
+    if not isinstance(conds[0], dict):
+        return torch.cat(conds)
+
+    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+
+
+def subscript_cond(cond, a, b):
+    if not isinstance(cond, dict):
+        return cond[a:b]
+
+    return {key: vec[a:b] for key, vec in cond.items()}
+
+
+def pad_cond(tensor, repeats, empty):
+    if not isinstance(tensor, dict):
+        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+    return tensor
+
+
+class CFGDenoiser(torch.nn.Module):
+    """
+    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+    negative prompt.
+    """
+
+    def __init__(self, sampler):
+        super().__init__()
+        self.model_wrap = None
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.steps = None
+        """number of steps as specified by user in UI"""
+
+        self.total_steps = None
+        """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
+
+        self.step = 0
+        self.image_cfg_scale = None
+        self.padded_cond_uncond = False
+        self.sampler = sampler
+        self.model_wrap = None
+        self.p = None
+        self.mask_before_denoising = False
+
+    @property
+    def inner_model(self):
+        raise NotImplementedError()
+
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
+    def combine_denoised_for_edit_model(self, x_out, cond_scale):
+        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+
+        return denoised
+
+    def get_pred_x0(self, x_in, x_out, sigma):
+        return x_out
+
+    def update_inner_model(self):
+        self.model_wrap = None
+
+        c, uc = self.p.get_conds()
+        self.sampler.sampler_extra_args['cond'] = c
+        self.sampler.sampler_extra_args['uncond'] = uc
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
+        if state.interrupted or state.skipped:
+            raise sd_samplers_common.InterruptedException
+
+        if sd_samplers_common.apply_refiner(self):
+            cond = self.sampler.sampler_extra_args['cond']
+            uncond = self.sampler.sampler_extra_args['uncond']
+
+        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+        # so is_edit_model is set to False to support AND composition.
+        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+
+        if self.mask_before_denoising and self.mask is not None:
+            x = self.init_latent * self.mask + self.nmask * x
+
+        batch_size = len(conds_list)
+        repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+        if shared.sd_model.model.conditioning_key == "crossattn-adm":
+            image_uncond = torch.zeros_like(image_cond)
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
+        else:
+            image_uncond = image_cond
+            if isinstance(uncond, dict):
+                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
+            else:
+                make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
+
+        if not is_edit_model:
+            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
+        else:
+            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
+
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
+        tensor = denoiser_params.text_cond
+        uncond = denoiser_params.text_uncond
+        skip_uncond = False
+
+        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+            skip_uncond = True
+            x_in = x_in[:-batch_size]
+            sigma_in = sigma_in[:-batch_size]
+
+        self.padded_cond_uncond = False
+        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+            empty = shared.sd_model.cond_stage_model_empty_prompt
+            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+            if num_repeats < 0:
+                tensor = pad_cond(tensor, -num_repeats, empty)
+                self.padded_cond_uncond = True
+            elif num_repeats > 0:
+                uncond = pad_cond(uncond, num_repeats, empty)
+                self.padded_cond_uncond = True
+
+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+            if is_edit_model:
+                cond_in = catenate_conds([tensor, uncond, uncond])
+            elif skip_uncond:
+                cond_in = tensor
+            else:
+                cond_in = catenate_conds([tensor, uncond])
+
+            if shared.opts.batch_cond_uncond:
+                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
+        else:
+            x_out = torch.zeros_like(x_in)
+            batch_size = batch_size*2 if shared.opts.batch_cond_uncond else batch_size
+            for batch_offset in range(0, tensor.shape[0], batch_size):
+                a = batch_offset
+                b = min(a + batch_size, tensor.shape[0])
+
+                if not is_edit_model:
+                    c_crossattn = subscript_cond(tensor, a, b)
+                else:
+                    c_crossattn = torch.cat([tensor[a:b]], uncond)
+
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
+
+            if not skip_uncond:
+                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
+
+        denoised_image_indexes = [x[0][0] for x in conds_list]
+        if skip_uncond:
+            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+
+        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
+        cfg_denoised_callback(denoised_params)
+
+        devices.test_for_nans(x_out, "unet")
+
+        if is_edit_model:
+            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+        elif skip_uncond:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+        else:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+        if not self.mask_before_denoising and self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
+
+        if opts.live_preview_content == "Prompt":
+            preview = self.sampler.last_latent
+        elif opts.live_preview_content == "Negative prompt":
+            preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
+        else:
+            preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
+
+        sd_samplers_common.store_latent(preview)
+
+        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+        cfg_after_cfg_callback(after_cfg_callback_params)
+        denoised = after_cfg_callback_params.x
+
+        self.step += 1
+        return denoised
+
@@ -1,13 +1,22 @@
+import inspect
 from collections import namedtuple
 import numpy as np
 import torch
 from PIL import Image
-from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
-
+from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
 from modules.shared import opts, state
-import modules.shared as shared
+import k_diffusion.sampling

-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+SamplerDataTuple = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+
+class SamplerData(SamplerDataTuple):
+    def total_steps(self, steps):
+        if self.options.get("second_order", False):
+            steps = steps * 2
+
+        return steps


 def setup_img2img_steps(p, steps=None):
@@ -25,19 +34,34 @@ def setup_img2img_steps(p, steps=None):
 approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}


-def single_sample_to_image(sample, approximation=None):
-    if approximation is None:
+def samples_to_images_tensor(sample, approximation=None, model=None):
+    """Transforms 4-channel latent space images into 3-channel RGB image tensors, with values in range [-1, 1]."""
+
+    if approximation is None or (shared.state.interrupted and opts.live_preview_fast_interrupt):
        approximation = approximation_indexes.get(opts.show_progress_type, 0)

+        from modules import lowvram
+        if approximation == 0 and lowvram.is_enabled(shared.sd_model) and not shared.opts.live_preview_allow_lowvram_full:
+            approximation = 1
+
    if approximation == 2:
-        x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
+        x_sample = sd_vae_approx.cheap_approximation(sample)
    elif approximation == 1:
-        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5
+        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype)).detach()
    elif approximation == 3:
-        x_sample = sample * 1.5
-        x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
+        x_sample = sd_vae_taesd.decoder_model()(sample.to(devices.device, devices.dtype)).detach()
+        x_sample = x_sample * 2 - 1
    else:
-        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
+        if model is None:
+            model = shared.sd_model
+        with devices.without_autocast(): # fixes an issue with unstable VAEs that are flaky even in fp32
+            x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype))
+
+    return x_sample
+
+
+def single_sample_to_image(sample, approximation=None):
+    x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5

    x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
@@ -46,6 +70,12 @@ def single_sample_to_image(sample, approximation=None):
    return Image.fromarray(x_sample)


+def decode_first_stage(model, x):
+    x = x.to(devices.dtype_vae)
+    approx_index = approximation_indexes.get(opts.sd_vae_decode_method, 0)
+    return samples_to_images_tensor(x, approx_index, model)
+
+
 def sample_to_image(samples, index=0, approximation=None):
    return single_sample_to_image(samples[index], approximation)

@@ -54,6 +84,34 @@ def samples_to_image_grid(samples, approximation=None):
    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])


+def images_tensor_to_samples(image, approximation=None, model=None):
+    '''image[0, 1] -> latent'''
+    if approximation is None:
+        approximation = approximation_indexes.get(opts.sd_vae_encode_method, 0)
+
+    if approximation == 3:
+        image = image.to(devices.device, devices.dtype)
+        x_latent = sd_vae_taesd.encoder_model()(image)
+    else:
+        if model is None:
+            model = shared.sd_model
+        model.first_stage_model.to(devices.dtype_vae)
+
+        image = image.to(shared.device, dtype=devices.dtype_vae)
+        image = image * 2 - 1
+        if len(image) > 1:
+            x_latent = torch.stack([
+                model.get_first_stage_encoding(
+                    model.encode_first_stage(torch.unsqueeze(img, 0))
+                )[0]
+                for img in image
+            ])
+        else:
+            x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+
+    return x_latent
+
+
 def store_latent(decoded):
    state.current_latent = decoded

@@ -85,11 +143,195 @@ class InterruptedException(BaseException):
    pass


-if opts.randn_source == "CPU":
+def replace_torchsde_browinan():
    import torchsde._brownian.brownian_interval

    def torchsde_randn(size, dtype, device, seed):
-        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
-        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+        return devices.randn_local(seed, size).to(device=device, dtype=dtype)

    torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
+replace_torchsde_browinan()
+
+
+def apply_refiner(cfg_denoiser):
+    completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
+    refiner_switch_at = cfg_denoiser.p.refiner_switch_at
+    refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
+
+    if refiner_switch_at is not None and completed_ratio < refiner_switch_at:
+        return False
+
+    if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
+        return False
+
+    if getattr(cfg_denoiser.p, "enable_hr", False):
+        is_second_pass = cfg_denoiser.p.is_hr_pass
+
+        if opts.hires_fix_refiner_pass == "first pass" and is_second_pass:
+            return False
+
+        if opts.hires_fix_refiner_pass == "second pass" and not is_second_pass:
+            return False
+
+        if opts.hires_fix_refiner_pass != "second pass":
+            cfg_denoiser.p.extra_generation_params['Hires refiner'] = opts.hires_fix_refiner_pass
+
+    cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
+    cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at
+
+    with sd_models.SkipWritingToConfig():
+        sd_models.reload_model_weights(info=refiner_checkpoint_info)
+
+    devices.torch_gc()
+    cfg_denoiser.p.setup_conds()
+    cfg_denoiser.update_inner_model()
+
+    return True
+
+
+class TorchHijack:
+    """This is here to replace torch.randn_like of k-diffusion.
+
+    k-diffusion has random_sampler argument for most samplers, but not for all, so
+    this is needed to properly replace every use of torch.randn_like.
+
+    We need to replace to make images generated in batches to be same as images generated individually."""
+
+    def __init__(self, p):
+        self.rng = p.rng
+
+    def __getattr__(self, item):
+        if item == 'randn_like':
+            return self.randn_like
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+    def randn_like(self, x):
+        return self.rng.next()
+
+
+class Sampler:
+    def __init__(self, funcname):
+        self.funcname = funcname
+        self.func = funcname
+        self.extra_params = []
+        self.sampler_noises = None
+        self.stop_at = None
+        self.eta = None
+        self.config: SamplerData = None  # set by the function calling the constructor
+        self.last_latent = None
+        self.s_min_uncond = None
+        self.s_churn = 0.0
+        self.s_tmin = 0.0
+        self.s_tmax = float('inf')
+        self.s_noise = 1.0
+
+        self.eta_option_field = 'eta_ancestral'
+        self.eta_infotext_field = 'Eta'
+        self.eta_default = 1.0
+
+        self.conditioning_key = shared.sd_model.model.conditioning_key
+
+        self.p = None
+        self.model_wrap_cfg = None
+        self.sampler_extra_args = None
+        self.options = {}
+
+    def callback_state(self, d):
+        step = d['i']
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        self.model_wrap_cfg.steps = steps
+        self.model_wrap_cfg.total_steps = self.config.total_steps(steps)
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except RecursionError:
+            print(
+                'Encountered RecursionError during sampling, returning last latent. '
+                'rho >5 with a polyexponential scheduler may cause this error. '
+                'You should try to use a smaller rho value instead.'
+            )
+            return self.last_latent
+        except InterruptedException:
+            return self.last_latent
+
+    def number_of_needed_noises(self, p):
+        return p.steps
+
+    def initialize(self, p) -> dict:
+        self.p = p
+        self.model_wrap_cfg.p = p
+        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+        self.model_wrap_cfg.step = 0
+        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+        self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0)
+        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
+
+        k_diffusion.sampling.torch = TorchHijack(p)
+
+        extra_params_kwargs = {}
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
+
+        if 'eta' in inspect.signature(self.func).parameters:
+            if self.eta != self.eta_default:
+                p.extra_generation_params[self.eta_infotext_field] = self.eta
+
+            extra_params_kwargs['eta'] = self.eta
+
+        if len(self.extra_params) > 0:
+            s_churn = getattr(opts, 's_churn', p.s_churn)
+            s_tmin = getattr(opts, 's_tmin', p.s_tmin)
+            s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
+            s_noise = getattr(opts, 's_noise', p.s_noise)
+
+            if 's_churn' in extra_params_kwargs and s_churn != self.s_churn:
+                extra_params_kwargs['s_churn'] = s_churn
+                p.s_churn = s_churn
+                p.extra_generation_params['Sigma churn'] = s_churn
+            if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin:
+                extra_params_kwargs['s_tmin'] = s_tmin
+                p.s_tmin = s_tmin
+                p.extra_generation_params['Sigma tmin'] = s_tmin
+            if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax:
+                extra_params_kwargs['s_tmax'] = s_tmax
+                p.s_tmax = s_tmax
+                p.extra_generation_params['Sigma tmax'] = s_tmax
+            if 's_noise' in extra_params_kwargs and s_noise != self.s_noise:
+                extra_params_kwargs['s_noise'] = s_noise
+                p.s_noise = s_noise
+                p.extra_generation_params['Sigma noise'] = s_noise
+
+        return extra_params_kwargs
+
+    def create_noise_sampler(self, x, sigmas, p):
+        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
+        if shared.opts.no_dpmpp_sde_batch_determinism:
+            return None
+
+        from k_diffusion.sampling import BrownianTreeNoiseSampler
+        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
+        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        raise NotImplementedError()
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        raise NotImplementedError()
@@ -1,224 +0,0 @@
-import math
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-
-import numpy as np
-import torch
-
-from modules.shared import state
-from modules import sd_samplers_common, prompt_parser, shared
-import modules.models.diffusion.uni_pc
-
-
-samplers_data_compvis = [
-    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True, "no_sdxl": True}),
-    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {"no_sdxl": True}),
-    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {"no_sdxl": True}),
-]
-
-
-class VanillaStableDiffusionSampler:
-    def __init__(self, constructor, sd_model):
-        self.sampler = constructor(sd_model)
-        self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')
-        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
-        self.is_unipc = isinstance(self.sampler, modules.models.diffusion.uni_pc.UniPCSampler)
-        self.orig_p_sample_ddim = None
-        if self.is_plms:
-            self.orig_p_sample_ddim = self.sampler.p_sample_plms
-        elif self.is_ddim:
-            self.orig_p_sample_ddim = self.sampler.p_sample_ddim
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.sampler_noises = None
-        self.step = 0
-        self.stop_at = None
-        self.eta = None
-        self.config = None
-        self.last_latent = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def number_of_needed_noises(self, p):
-        return 0
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
-        x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
-
-        res = self.orig_p_sample_ddim(x_dec, cond, ts, *args, unconditional_conditioning=unconditional_conditioning, **kwargs)
-
-        x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
-
-        return res
-
-    def before_sample(self, x, ts, cond, unconditional_conditioning):
-        if state.interrupted or state.skipped:
-            raise sd_samplers_common.InterruptedException
-
-        if self.stop_at is not None and self.step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        # Have to unwrap the inpainting conditioning here to perform pre-processing
-        image_conditioning = None
-        uc_image_conditioning = None
-        if isinstance(cond, dict):
-            if self.conditioning_key == "crossattn-adm":
-                image_conditioning = cond["c_adm"]
-                uc_image_conditioning = unconditional_conditioning["c_adm"]
-            else:
-                image_conditioning = cond["c_concat"][0]
-            cond = cond["c_crossattn"][0]
-            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
-
-        assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers'
-        cond = tensor
-
-        # for DDIM, shapes must match, we can't just process cond and uncond independently;
-        # filling unconditional_conditioning with repeats of the last vector to match length is
-        # not 100% correct but should work well enough
-        if unconditional_conditioning.shape[1] < cond.shape[1]:
-            last_vector = unconditional_conditioning[:, -1:]
-            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
-            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
-        elif unconditional_conditioning.shape[1] > cond.shape[1]:
-            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
-
-        if self.mask is not None:
-            img_orig = self.sampler.model.q_sample(self.init_latent, ts)
-            x = img_orig * self.mask + self.nmask * x
-
-        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
-        # Note that they need to be lists because it just concatenates them later.
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
-                unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
-            else:
-                cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
-                unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-        return x, ts, cond, unconditional_conditioning
-
-    def update_step(self, last_latent):
-        if self.mask is not None:
-            self.last_latent = self.init_latent * self.mask + self.nmask * last_latent
-        else:
-            self.last_latent = last_latent
-
-        sd_samplers_common.store_latent(self.last_latent)
-
-        self.step += 1
-        state.sampling_step = self.step
-        shared.total_tqdm.update()
-
-    def after_sample(self, x, ts, cond, uncond, res):
-        if not self.is_unipc:
-            self.update_step(res[1])
-
-        return x, ts, cond, uncond, res
-
-    def unipc_after_update(self, x, model_x):
-        self.update_step(x)
-
-    def initialize(self, p):
-        if self.is_ddim:
-            self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
-        else:
-            self.eta = 0.0
-
-        if self.eta != 0.0:
-            p.extra_generation_params["Eta DDIM"] = self.eta
-
-        if self.is_unipc:
-            keys = [
-                ('UniPC variant', 'uni_pc_variant'),
-                ('UniPC skip type', 'uni_pc_skip_type'),
-                ('UniPC order', 'uni_pc_order'),
-                ('UniPC lower order final', 'uni_pc_lower_order_final'),
-            ]
-
-            for name, key in keys:
-                v = getattr(shared.opts, key)
-                if v != shared.opts.get_default(key):
-                    p.extra_generation_params[name] = v
-
-        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
-            if hasattr(self.sampler, fieldname):
-                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
-        if self.is_unipc:
-            self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))
-
-        self.mask = p.mask if hasattr(p, 'mask') else None
-        self.nmask = p.nmask if hasattr(p, 'nmask') else None
-
-
-    def adjust_steps_if_invalid(self, p, num_steps):
-        if ((self.config.name == 'DDIM') and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS') or (self.config.name == 'UniPC'):
-            if self.config.name == 'UniPC' and num_steps < shared.opts.uni_pc_order:
-                num_steps = shared.opts.uni_pc_order
-            valid_step = 999 / (1000 // num_steps)
-            if valid_step == math.floor(valid_step):
-                return int(valid_step) + 1
-
-        return num_steps
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
-        steps = self.adjust_steps_if_invalid(p, steps)
-        self.initialize(p)
-
-        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
-
-        self.init_latent = x
-        self.last_latent = x
-        self.step = 0
-
-        # Wrap the conditioning models with additional image conditioning for inpainting model
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
-                unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
-            else:
-                conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
-                unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        self.initialize(p)
-
-        self.init_latent = None
-        self.last_latent = x
-        self.step = 0
-
-        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
-
-        # Wrap the conditioning models with additional image conditioning for inpainting model
-        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
-                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
-            else:
-                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
-                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
-
-        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
-
-        return samples_ddim
@@ -0,0 +1,74 @@
+import torch
+import tqdm
+import k_diffusion.sampling
+
+
+@torch.no_grad()
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):
+    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)
+    Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
+    If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
+    """
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    step_id = 0
+    from k_diffusion.sampling import to_d, get_sigmas_karras
+
+    def heun_step(x, old_sigma, new_sigma, second_order=True):
+        nonlocal step_id
+        denoised = model(x, old_sigma * s_in, **extra_args)
+        d = to_d(x, old_sigma, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
+        dt = new_sigma - old_sigma
+        if new_sigma == 0 or not second_order:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+            d_2 = to_d(x_2, new_sigma, denoised_2)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+        step_id += 1
+        return x
+
+    steps = sigmas.shape[0] - 1
+    if restart_list is None:
+        if steps >= 20:
+            restart_steps = 9
+            restart_times = 1
+            if steps >= 36:
+                restart_steps = steps // 4
+                restart_times = 2
+            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
+            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+        else:
+            restart_list = {}
+
+    restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}
+
+    step_list = []
+    for i in range(len(sigmas) - 1):
+        step_list.append((sigmas[i], sigmas[i + 1]))
+        if i + 1 in restart_list:
+            restart_steps, restart_times, restart_max = restart_list[i + 1]
+            min_idx = i + 1
+            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+            if max_idx < min_idx:
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
+                while restart_times > 0:
+                    restart_times -= 1
+                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+
+    last_sigma = None
+    for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
+        if last_sigma is None:
+            last_sigma = old_sigma
+        elif last_sigma < old_sigma:
+            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5
+        x = heun_step(x, old_sigma, new_sigma)
+        last_sigma = new_sigma
+
+    return x
@@ -1,47 +1,60 @@
-from collections import deque
 import torch
 import inspect
 import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
+from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
+from modules.sd_samplers_cfg_denoiser import CFGDenoiser  # noqa: F401
+from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback

-from modules.shared import opts, state
+from modules.shared import opts
 import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
-from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback

 samplers_k_diffusion = [
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
+    ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
+    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
    ('Euler', 'sample_euler', ['k_euler'], {}),
    ('LMS', 'sample_lms', ['k_lms'], {}),
    ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
+    ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
+    ('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
+    ('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
+    ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
+    ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
+    ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
-    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+    ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
 ]

+
 samplers_data_k_diffusion = [
    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
    for label, funcname, aliases, options in samplers_k_diffusion
-    if hasattr(k_diffusion.sampling, funcname)
+    if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
 ]

 sampler_extra_params = {
    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_fast': ['s_noise'],
+    'sample_dpm_2_ancestral': ['s_noise'],
+    'sample_dpmpp_2s_ancestral': ['s_noise'],
+    'sample_dpmpp_sde': ['s_noise'],
+    'sample_dpmpp_2m_sde': ['s_noise'],
+    'sample_dpmpp_3m_sde': ['s_noise'],
 }

 k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
@@ -53,289 +66,27 @@ k_diffusion_scheduler = {
 }


-def catenate_conds(conds):
-    if not isinstance(conds[0], dict):
-        return torch.cat(conds)
+class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
+    @property
+    def inner_model(self):
+        if self.model_wrap is None:
+            denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+            self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)

-    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+        return self.model_wrap


-def subscript_cond(cond, a, b):
-    if not isinstance(cond, dict):
-        return cond[a:b]
+class KDiffusionSampler(sd_samplers_common.Sampler):
+    def __init__(self, funcname, sd_model, options=None):
+        super().__init__(funcname)

-    return {key: vec[a:b] for key, vec in cond.items()}
-
-
-def pad_cond(tensor, repeats, empty):
-    if not isinstance(tensor, dict):
-        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
-
-    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
-    return tensor
-
-
-class CFGDenoiser(torch.nn.Module):
-    """
-    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
-    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
-    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
-    negative prompt.
-    """
-
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.step = 0
-        self.image_cfg_scale = None
-        self.padded_cond_uncond = False
-
-    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
-        return denoised
-
-    def combine_denoised_for_edit_model(self, x_out, cond_scale):
-        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
-        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
-
-        return denoised
-
-    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
-        if state.interrupted or state.skipped:
-            raise sd_samplers_common.InterruptedException
-
-        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
-        # so is_edit_model is set to False to support AND composition.
-        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
-        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
-
-        batch_size = len(conds_list)
-        repeats = [len(conds_list[i]) for i in range(batch_size)]
-
-        if shared.sd_model.model.conditioning_key == "crossattn-adm":
-            image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
-        else:
-            image_uncond = image_cond
-            if isinstance(uncond, dict):
-                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
-            else:
-                make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
-
-        if not is_edit_model:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
-        else:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
-
-        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
-        cfg_denoiser_callback(denoiser_params)
-        x_in = denoiser_params.x
-        image_cond_in = denoiser_params.image_cond
-        sigma_in = denoiser_params.sigma
-        tensor = denoiser_params.text_cond
-        uncond = denoiser_params.text_uncond
-        skip_uncond = False
-
-        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
-        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
-            skip_uncond = True
-            x_in = x_in[:-batch_size]
-            sigma_in = sigma_in[:-batch_size]
-
-        self.padded_cond_uncond = False
-        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
-            empty = shared.sd_model.cond_stage_model_empty_prompt
-            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
-            if num_repeats < 0:
-                tensor = pad_cond(tensor, -num_repeats, empty)
-                self.padded_cond_uncond = True
-            elif num_repeats > 0:
-                uncond = pad_cond(uncond, num_repeats, empty)
-                self.padded_cond_uncond = True
-
-        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
-            if is_edit_model:
-                cond_in = catenate_conds([tensor, uncond, uncond])
-            elif skip_uncond:
-                cond_in = tensor
-            else:
-                cond_in = catenate_conds([tensor, uncond])
-
-            if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
-            else:
-                x_out = torch.zeros_like(x_in)
-                for batch_offset in range(0, x_out.shape[0], batch_size):
-                    a = batch_offset
-                    b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
-        else:
-            x_out = torch.zeros_like(x_in)
-            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
-            for batch_offset in range(0, tensor.shape[0], batch_size):
-                a = batch_offset
-                b = min(a + batch_size, tensor.shape[0])
-
-                if not is_edit_model:
-                    c_crossattn = subscript_cond(tensor, a, b)
-                else:
-                    c_crossattn = torch.cat([tensor[a:b]], uncond)
-
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
-
-            if not skip_uncond:
-                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
-
-        denoised_image_indexes = [x[0][0] for x in conds_list]
-        if skip_uncond:
-            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
-            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
-
-        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
-        cfg_denoised_callback(denoised_params)
-
-        devices.test_for_nans(x_out, "unet")
-
-        if opts.live_preview_content == "Prompt":
-            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
-        elif opts.live_preview_content == "Negative prompt":
-            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
-        if is_edit_model:
-            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
-        elif skip_uncond:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
-        else:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-
-        if self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
-
-        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
-        cfg_after_cfg_callback(after_cfg_callback_params)
-        denoised = after_cfg_callback_params.x
-
-        self.step += 1
-        return denoised
-
-
-class TorchHijack:
-    def __init__(self, sampler_noises):
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
-        # implementation.
-        self.sampler_noises = deque(sampler_noises)
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
-    def randn_like(self, x):
-        if self.sampler_noises:
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        if opts.randn_source == "CPU" or x.device.type == 'mps':
-            return torch.randn_like(x, device=devices.cpu).to(x.device)
-        else:
-            return torch.randn_like(x)
-
-
-class KDiffusionSampler:
-    def __init__(self, funcname, sd_model):
-        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-
-        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
-        self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname)
        self.extra_params = sampler_extra_params.get(funcname, [])
-        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
-        self.sampler_noises = None
-        self.stop_at = None
-        self.eta = None
-        self.config = None  # set by the function calling the constructor
-        self.last_latent = None
-        self.s_min_uncond = None

-        self.conditioning_key = sd_model.model.conditioning_key
+        self.options = options or {}
+        self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)

-    def callback_state(self, d):
-        step = d['i']
-        latent = d["denoised"]
-        if opts.live_preview_content == "Combined":
-            sd_samplers_common.store_latent(latent)
-        self.last_latent = latent
-
-        if self.stop_at is not None and step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        state.sampling_step = step
-        shared.total_tqdm.update()
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except RecursionError:
-            print(
-                'Encountered RecursionError during sampling, returning last latent. '
-                'rho >5 with a polyexponential scheduler may cause this error. '
-                'You should try to use a smaller rho value instead.'
-            )
-            return self.last_latent
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def number_of_needed_noises(self, p):
-        return p.steps
-
-    def initialize(self, p):
-        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
-        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap_cfg.step = 0
-        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
-        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
-        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
-
-        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
-        extra_params_kwargs = {}
-        for param_name in self.extra_params:
-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-                extra_params_kwargs[param_name] = getattr(p, param_name)
-
-        if 'eta' in inspect.signature(self.func).parameters:
-            if self.eta != 1.0:
-                p.extra_generation_params["Eta"] = self.eta
-
-            extra_params_kwargs['eta'] = self.eta
-
-        return extra_params_kwargs
+        self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
+        self.model_wrap = self.model_wrap_cfg.inner_model

    def get_sigmas(self, p, steps):
        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
@@ -376,6 +127,9 @@ class KDiffusionSampler:
            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())

            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
+            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+            sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
        else:
            sigmas = self.model_wrap.get_sigmas(steps)

@@ -384,24 +138,21 @@ class KDiffusionSampler:

        return sigmas

-    def create_noise_sampler(self, x, sigmas, p):
-        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
-        if shared.opts.no_dpmpp_sde_batch_determinism:
-            return None
-
-        from k_diffusion.sampling import BrownianTreeNoiseSampler
-        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
-        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
-
    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)

        sigmas = self.get_sigmas(p, steps)
-
        sigma_sched = sigmas[steps - t_enc - 1:]
+
        xi = x + noise * sigma_sched[0]

+        if opts.img2img_extra_noise > 0:
+            p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
+            extra_noise_params = ExtraNoiseParams(noise, x, xi)
+            extra_noise_callback(extra_noise_params)
+            noise = extra_noise_params.noise
+            xi += noise * opts.img2img_extra_noise
+
        extra_params_kwargs = self.initialize(p)
        parameters = inspect.signature(self.func).parameters

@@ -421,9 +172,12 @@ class KDiffusionSampler:
            noise_sampler = self.create_noise_sampler(x, sigmas, p)
            extra_params_kwargs['noise_sampler'] = noise_sampler

+        if self.config.options.get('solver_type', None) == 'heun':
+            extra_params_kwargs['solver_type'] = 'heun'
+
        self.model_wrap_cfg.init_latent = x
        self.last_latent = x
-        extra_args = {
+        self.sampler_extra_args = {
            'cond': conditioning,
            'image_cond': image_conditioning,
            'uncond': unconditional_conditioning,
@@ -431,7 +185,7 @@ class KDiffusionSampler:
            's_min_uncond': self.s_min_uncond
        }

-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

        if self.model_wrap_cfg.padded_cond_uncond:
            p.extra_generation_params["Pad conds"] = True
@@ -443,34 +197,46 @@ class KDiffusionSampler:

        sigmas = self.get_sigmas(p, steps)

-        x = x * sigmas[0]
+        if opts.sgm_noise_multiplier:
+            p.extra_generation_params["SGM noise multiplier"] = True
+            x = x * torch.sqrt(1.0 + sigmas[0] ** 2.0)
+        else:
+            x = x * sigmas[0]

        extra_params_kwargs = self.initialize(p)
        parameters = inspect.signature(self.func).parameters

+        if 'n' in parameters:
+            extra_params_kwargs['n'] = steps
+
        if 'sigma_min' in parameters:
            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            if 'n' in parameters:
-                extra_params_kwargs['n'] = steps
-        else:
+
+        if 'sigmas' in parameters:
            extra_params_kwargs['sigmas'] = sigmas

        if self.config.options.get('brownian_noise', False):
            noise_sampler = self.create_noise_sampler(x, sigmas, p)
            extra_params_kwargs['noise_sampler'] = noise_sampler

+        if self.config.options.get('solver_type', None) == 'heun':
+            extra_params_kwargs['solver_type'] = 'heun'
+
        self.last_latent = x
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+        self.sampler_extra_args = {
            'cond': conditioning,
            'image_cond': image_conditioning,
            'uncond': unconditional_conditioning,
            'cond_scale': p.cfg_scale,
            's_min_uncond': self.s_min_uncond
-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        }
+
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

        if self.model_wrap_cfg.padded_cond_uncond:
            p.extra_generation_params["Pad conds"] = True

        return samples

+
@@ -0,0 +1,167 @@
+import torch
+import inspect
+import sys
+from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
+from modules.sd_samplers_cfg_denoiser import CFGDenoiser
+from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
+
+from modules.shared import opts
+import modules.shared as shared
+
+samplers_timesteps = [
+    ('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
+    ('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}),
+    ('UniPC', sd_samplers_timesteps_impl.unipc, ['unipc'], {}),
+]
+
+
+samplers_data_timesteps = [
+    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: CompVisSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_timesteps
+]
+
+
+class CompVisTimestepsDenoiser(torch.nn.Module):
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inner_model = model
+
+    def forward(self, input, timesteps, **kwargs):
+        return self.inner_model.apply_model(input, timesteps, **kwargs)
+
+
+class CompVisTimestepsVDenoiser(torch.nn.Module):
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inner_model = model
+
+    def predict_eps_from_z_and_v(self, x_t, t, v):
+        return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
+
+    def forward(self, input, timesteps, **kwargs):
+        model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
+        e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output)
+        return e_t
+
+
+class CFGDenoiserTimesteps(CFGDenoiser):
+
+    def __init__(self, sampler):
+        super().__init__(sampler)
+
+        self.alphas = shared.sd_model.alphas_cumprod
+        self.mask_before_denoising = True
+
+    def get_pred_x0(self, x_in, x_out, sigma):
+        ts = sigma.to(dtype=int)
+
+        a_t = self.alphas[ts][:, None, None, None]
+        sqrt_one_minus_at = (1 - a_t).sqrt()
+
+        pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
+
+        return pred_x0
+
+    @property
+    def inner_model(self):
+        if self.model_wrap is None:
+            denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser
+            self.model_wrap = denoiser(shared.sd_model)
+
+        return self.model_wrap
+
+
+class CompVisSampler(sd_samplers_common.Sampler):
+    def __init__(self, funcname, sd_model):
+        super().__init__(funcname)
+
+        self.eta_option_field = 'eta_ddim'
+        self.eta_infotext_field = 'Eta DDIM'
+        self.eta_default = 0.0
+
+        self.model_wrap_cfg = CFGDenoiserTimesteps(self)
+
+    def get_timesteps(self, p, steps):
+        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+            discard_next_to_last_sigma = True
+            p.extra_generation_params["Discard penultimate sigma"] = True
+
+        steps += 1 if discard_next_to_last_sigma else 0
+
+        timesteps = torch.clip(torch.asarray(list(range(0, 1000, 1000 // steps)), device=devices.device) + 1, 0, 999)
+
+        return timesteps
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+
+        timesteps = self.get_timesteps(p, steps)
+        timesteps_sched = timesteps[:t_enc]
+
+        alphas_cumprod = shared.sd_model.alphas_cumprod
+        sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]])
+        sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]])
+
+        xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
+
+        if opts.img2img_extra_noise > 0:
+            p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
+            extra_noise_params = ExtraNoiseParams(noise, x, xi)
+            extra_noise_callback(extra_noise_params)
+            noise = extra_noise_params.noise
+            xi += noise * opts.img2img_extra_noise * sqrt_alpha_cumprod
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'timesteps' in parameters:
+            extra_params_kwargs['timesteps'] = timesteps_sched
+        if 'is_img2img' in parameters:
+            extra_params_kwargs['is_img2img'] = True
+
+        self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
+        self.sampler_extra_args = {
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps = steps or p.steps
+        timesteps = self.get_timesteps(p, steps)
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'timesteps' in parameters:
+            extra_params_kwargs['timesteps'] = timesteps
+
+        self.last_latent = x
+        self.sampler_extra_args = {
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+
+
+sys.modules['modules.sd_samplers_compvis'] = sys.modules[__name__]
+VanillaStableDiffusionSampler = CompVisSampler  # temp. compatibility with older extensions
@@ -0,0 +1,137 @@
+import torch
+import tqdm
+import k_diffusion.sampling
+import numpy as np
+
+from modules import shared
+from modules.models.diffusion.uni_pc import uni_pc
+
+
+@torch.no_grad()
+def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+    alphas = alphas_cumprod[timesteps]
+    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
+    sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+    sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
+
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones((x.shape[0]))
+    s_x = x.new_ones((x.shape[0], 1, 1, 1))
+    for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+        index = len(timesteps) - 1 - i
+
+        e_t = model(x, timesteps[index].item() * s_in, **extra_args)
+
+        a_t = alphas[index].item() * s_x
+        a_prev = alphas_prev[index].item() * s_x
+        sigma_t = sigmas[index].item() * s_x
+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
+
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
+        noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
+        x = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+    return x
+
+
+@torch.no_grad()
+def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+    alphas = alphas_cumprod[timesteps]
+    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
+    sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    s_x = x.new_ones((x.shape[0], 1, 1, 1))
+    old_eps = []
+
+    def get_x_prev_and_pred_x0(e_t, index):
+        # select parameters corresponding to the currently considered timestep
+        a_t = alphas[index].item() * s_x
+        a_prev = alphas_prev[index].item() * s_x
+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev).sqrt() * e_t
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt
+        return x_prev, pred_x0
+
+    for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+        index = len(timesteps) - 1 - i
+        ts = timesteps[index].item() * s_in
+        t_next = timesteps[max(index - 1, 0)].item() * s_in
+
+        e_t = model(x, ts, **extra_args)
+
+        if len(old_eps) == 0:
+            # Pseudo Improved Euler (2nd order)
+            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+            e_t_next = model(x_prev, t_next, **extra_args)
+            e_t_prime = (e_t + e_t_next) / 2
+        elif len(old_eps) == 1:
+            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (3 * e_t - old_eps[-1]) / 2
+        elif len(old_eps) == 2:
+            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+        else:
+            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+        old_eps.append(e_t)
+        if len(old_eps) >= 4:
+            old_eps.pop(0)
+
+        x = x_prev
+
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+    return x
+
+
+class UniPCCFG(uni_pc.UniPC):
+    def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):
+        super().__init__(None, *args, **kwargs)
+
+        def after_update(x, model_x):
+            callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})
+            self.index += 1
+
+        self.cfg_model = cfg_model
+        self.extra_args = extra_args
+        self.callback = callback
+        self.index = 0
+        self.after_update = after_update
+
+    def get_model_input_time(self, t_continuous):
+        return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.
+
+    def model(self, x, t):
+        t_input = self.get_model_input_time(t)
+
+        res = self.cfg_model(x, t_input, **self.extra_args)
+
+        return res
+
+
+def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False):
+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+
+    ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+    t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None  # this is likely off by a bit - if someone wants to fix it please by all means
+    unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)
+    x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+
+    return x
@@ -1,11 +1,11 @@
 import torch.nn
-import ldm.modules.diffusionmodules.openaimodel

 from modules import script_callbacks, shared, devices

 unet_options = []
 current_unet_option = None
 current_unet = None
+original_forward = None


 def list_unets():
@@ -47,7 +47,7 @@ def apply_unet(option=None):
    if current_unet_option is None:
        current_unet = None

-        if not (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
+        if not shared.sd_model.lowvram:
            shared.sd_model.model.diffusion_model.to(devices.device)

        return
@@ -88,5 +88,5 @@ def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
    if current_unet is not None:
        return current_unet.forward(x, timesteps, context, *args, **kwargs)

-    return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
+    return original_forward(self, x, timesteps, context, *args, **kwargs)

@@ -1,6 +1,9 @@
 import os
 import collections
-from modules import paths, shared, devices, script_callbacks, sd_models
+from dataclasses import dataclass
+
+from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks, lowvram, sd_hijack, hashes
+
 import glob
 from copy import deepcopy

@@ -16,6 +19,23 @@ checkpoint_info = None

 checkpoints_loaded = collections.OrderedDict()

+
+def get_loaded_vae_name():
+    if loaded_vae_file is None:
+        return None
+
+    return os.path.basename(loaded_vae_file)
+
+
+def get_loaded_vae_hash():
+    if loaded_vae_file is None:
+        return None
+
+    sha256 = hashes.sha256(loaded_vae_file, 'vae')
+
+    return sha256[0:10] if sha256 else None
+
+
 def get_base_vae(model):
    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
        return base_vae
@@ -83,6 +103,8 @@ def refresh_vae_list():
        name = get_filename(filepath)
        vae_dict[name] = filepath

+    vae_dict.update(dict(sorted(vae_dict.items(), key=lambda item: shared.natural_sort_key(item[0]))))
+

 def find_vae_near_checkpoint(checkpoint_file):
    checkpoint_path = os.path.basename(checkpoint_file).rsplit('.', 1)[0]
@@ -93,27 +115,74 @@ def find_vae_near_checkpoint(checkpoint_file):
    return None


-def resolve_vae(checkpoint_file):
-    if shared.cmd_opts.vae_path is not None:
-        return shared.cmd_opts.vae_path, 'from commandline argument'
+@dataclass
+class VaeResolution:
+    vae: str = None
+    source: str = None
+    resolved: bool = True

-    is_automatic = shared.opts.sd_vae in {"Automatic", "auto"}  # "auto" for people with old config
+    def tuple(self):
+        return self.vae, self.source

-    vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
-    if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or is_automatic):
-        return vae_near_checkpoint, 'found near the checkpoint'

+def is_automatic():
+    return shared.opts.sd_vae in {"Automatic", "auto"}  # "auto" for people with old config
+
+
+def resolve_vae_from_setting() -> VaeResolution:
    if shared.opts.sd_vae == "None":
-        return None, None
+        return VaeResolution()

    vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
    if vae_from_options is not None:
-        return vae_from_options, 'specified in settings'
+        return VaeResolution(vae_from_options, 'specified in settings')

-    if not is_automatic:
+    if not is_automatic():
        print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")

-    return None, None
+    return VaeResolution(resolved=False)
+
+
+def resolve_vae_from_user_metadata(checkpoint_file) -> VaeResolution:
+    metadata = extra_networks.get_user_metadata(checkpoint_file)
+    vae_metadata = metadata.get("vae", None)
+    if vae_metadata is not None and vae_metadata != "Automatic":
+        if vae_metadata == "None":
+            return VaeResolution()
+
+        vae_from_metadata = vae_dict.get(vae_metadata, None)
+        if vae_from_metadata is not None:
+            return VaeResolution(vae_from_metadata, "from user metadata")
+
+    return VaeResolution(resolved=False)
+
+
+def resolve_vae_near_checkpoint(checkpoint_file) -> VaeResolution:
+    vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
+    if vae_near_checkpoint is not None and (not shared.opts.sd_vae_overrides_per_model_preferences or is_automatic()):
+        return VaeResolution(vae_near_checkpoint, 'found near the checkpoint')
+
+    return VaeResolution(resolved=False)
+
+
+def resolve_vae(checkpoint_file) -> VaeResolution:
+    if shared.cmd_opts.vae_path is not None:
+        return VaeResolution(shared.cmd_opts.vae_path, 'from commandline argument')
+
+    if shared.opts.sd_vae_overrides_per_model_preferences and not is_automatic():
+        return resolve_vae_from_setting()
+
+    res = resolve_vae_from_user_metadata(checkpoint_file)
+    if res.resolved:
+        return res
+
+    res = resolve_vae_near_checkpoint(checkpoint_file)
+    if res.resolved:
+        return res
+
+    res = resolve_vae_from_setting()
+
+    return res


 def load_vae_dict(filename, map_location):
@@ -123,7 +192,7 @@ def load_vae_dict(filename, map_location):


 def load_vae(model, vae_file=None, vae_source="from unknown source"):
-    global vae_dict, loaded_vae_file
+    global vae_dict, base_vae, loaded_vae_file
    # save_settings = False

    cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
@@ -161,6 +230,8 @@ def load_vae(model, vae_file=None, vae_source="from unknown source"):
        restore_base_vae(model)

    loaded_vae_file = vae_file
+    model.base_vae = base_vae
+    model.loaded_vae_file = loaded_vae_file


 # don't call this from outside
@@ -178,8 +249,6 @@ unspecified = object()


 def reload_vae_weights(sd_model=None, vae_file=unspecified):
-    from modules import lowvram, devices, sd_hijack
-
    if not sd_model:
        sd_model = shared.sd_model

@@ -187,14 +256,14 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
    checkpoint_file = checkpoint_info.filename

    if vae_file == unspecified:
-        vae_file, vae_source = resolve_vae(checkpoint_file)
+        vae_file, vae_source = resolve_vae(checkpoint_file).tuple()
    else:
        vae_source = "from function argument"

    if loaded_vae_file == vae_file:
        return

-    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+    if sd_model.lowvram:
        lowvram.send_everything_to_cpu()
    else:
        sd_model.to(devices.cpu)
@@ -206,7 +275,7 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
    sd_hijack.model_hijack.hijack(sd_model)
    script_callbacks.model_loaded_callback(sd_model)

-    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+    if not sd_model.lowvram:
        sd_model.to(devices.device)

    print("VAE weights loaded.")
@@ -81,6 +81,6 @@ def cheap_approximation(sample):

    coefs = torch.tensor(coeffs).to(sample.device)

-    x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+    x_sample = torch.einsum("...lxy,lr -> ...rxy", sample, coefs)

    return x_sample
@@ -44,7 +44,17 @@ def decoder():
    )


-class TAESD(nn.Module):
+def encoder():
+    return nn.Sequential(
+        conv(3, 64), Block(64, 64),
+        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+        conv(64, 4),
+    )
+
+
+class TAESDDecoder(nn.Module):
    latent_magnitude = 3
    latent_shift = 0.5

@@ -55,21 +65,28 @@ class TAESD(nn.Module):
        self.decoder.load_state_dict(
            torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))

-    @staticmethod
-    def unscale_latents(x):
-        """[0, 1] -> raw latents"""
-        return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
+
+class TAESDEncoder(nn.Module):
+    latent_magnitude = 3
+    latent_shift = 0.5
+
+    def __init__(self, encoder_path="taesd_encoder.pth"):
+        """Initialize pretrained TAESD on the given device from the given checkpoints."""
+        super().__init__()
+        self.encoder = encoder()
+        self.encoder.load_state_dict(
+            torch.load(encoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))


 def download_model(model_path, model_url):
    if not os.path.exists(model_path):
        os.makedirs(os.path.dirname(model_path), exist_ok=True)

-        print(f'Downloading TAESD decoder to: {model_path}')
+        print(f'Downloading TAESD model to: {model_path}')
        torch.hub.download_url_to_file(model_url, model_path)


-def model():
+def decoder_model():
    model_name = "taesdxl_decoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_decoder.pth"
    loaded_model = sd_vae_taesd_models.get(model_name)

@@ -78,7 +95,7 @@ def model():
        download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)

        if os.path.exists(model_path):
-            loaded_model = TAESD(model_path)
+            loaded_model = TAESDDecoder(model_path)
            loaded_model.eval()
            loaded_model.to(devices.device, devices.dtype)
            sd_vae_taesd_models[model_name] = loaded_model
@@ -86,3 +103,22 @@ def model():
            raise FileNotFoundError('TAESD model not found')

    return loaded_model.decoder
+
+
+def encoder_model():
+    model_name = "taesdxl_encoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_encoder.pth"
+    loaded_model = sd_vae_taesd_models.get(model_name)
+
+    if loaded_model is None:
+        model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
+        download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
+
+        if os.path.exists(model_path):
+            loaded_model = TAESDEncoder(model_path)
+            loaded_model.eval()
+            loaded_model.to(devices.device, devices.dtype)
+            sd_vae_taesd_models[model_name] = loaded_model
+        else:
+            raise FileNotFoundError('TAESD model not found')
+
+    return loaded_model.encoder
@@ -1,771 +1,51 @@
-import datetime
-import json
-import os
-import re
 import sys
-import threading
-import time
-import logging

 import gradio as gr
-import torch
-import tqdm

-import launch
-import modules.interrogate
-import modules.memmon
-import modules.styles
-import modules.devices as devices
-from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
+from modules import shared_cmd_options, shared_gradio_themes, options, shared_items, sd_models_types
 from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir  # noqa: F401
-from ldm.models.diffusion.ddpm import LatentDiffusion
-from typing import Optional
+from modules import util

-log = logging.getLogger(__name__)
+cmd_opts = shared_cmd_options.cmd_opts
+parser = shared_cmd_options.parser
+
+batch_cond_uncond = True  # old field, unused now in favor of shared.opts.batch_cond_uncond
+parallel_processing_allowed = True
+styles_filename = cmd_opts.styles_file
+config_filename = cmd_opts.ui_settings_file
+hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}

 demo = None

-parser = cmd_args.parser
+device = None

-script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
-script_loading.preload_extensions(extensions_builtin_dir, parser)
+weight_load_location = None

-if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
-    cmd_opts = parser.parse_args()
-else:
-    cmd_opts, _ = parser.parse_known_args()
-
-
-restricted_opts = {
-    "samples_filename_pattern",
-    "directories_filename_pattern",
-    "outdir_samples",
-    "outdir_txt2img_samples",
-    "outdir_img2img_samples",
-    "outdir_extras_samples",
-    "outdir_grids",
-    "outdir_txt2img_grids",
-    "outdir_save",
-    "outdir_init_images"
-}
-
-# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
-gradio_hf_hub_themes = [
-    "gradio/glass",
-    "gradio/monochrome",
-    "gradio/seafoam",
-    "gradio/soft",
-    "freddyaboulton/dracula_revamped",
-    "gradio/dracula_test",
-    "abidlabs/dracula_test",
-    "abidlabs/pakistan",
-    "dawood/microsoft_windows",
-    "ysharma/steampunk"
-]
-
-
-cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
-
-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
-    (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
-
-devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
-devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
-
-device = devices.device
-weight_load_location = None if cmd_opts.lowram else "cpu"
-
-batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
-parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
-config_filename = cmd_opts.ui_settings_file

-os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = {}
+
 loaded_hypernetworks = []

+state = None

-def reload_hypernetworks():
-    from modules.hypernetworks import hypernetwork
-    global hypernetworks
+prompt_styles = None

-    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
-
-
-class State:
-    skipped = False
-    interrupted = False
-    job = ""
-    job_no = 0
-    job_count = 0
-    processing_has_refined_job_count = False
-    job_timestamp = '0'
-    sampling_step = 0
-    sampling_steps = 0
-    current_latent = None
-    current_image = None
-    current_image_sampling_step = 0
-    id_live_preview = 0
-    textinfo = None
-    time_start = None
-    server_start = None
-    _server_command_signal = threading.Event()
-    _server_command: Optional[str] = None
-
-    @property
-    def need_restart(self) -> bool:
-        # Compatibility getter for need_restart.
-        return self.server_command == "restart"
-
-    @need_restart.setter
-    def need_restart(self, value: bool) -> None:
-        # Compatibility setter for need_restart.
-        if value:
-            self.server_command = "restart"
-
-    @property
-    def server_command(self):
-        return self._server_command
-
-    @server_command.setter
-    def server_command(self, value: Optional[str]) -> None:
-        """
-        Set the server command to `value` and signal that it's been set.
-        """
-        self._server_command = value
-        self._server_command_signal.set()
-
-    def wait_for_server_command(self, timeout: Optional[float] = None) -> Optional[str]:
-        """
-        Wait for server command to get set; return and clear the value and signal.
-        """
-        if self._server_command_signal.wait(timeout):
-            self._server_command_signal.clear()
-            req = self._server_command
-            self._server_command = None
-            return req
-        return None
-
-    def request_restart(self) -> None:
-        self.interrupt()
-        self.server_command = "restart"
-        log.info("Received restart request")
-
-    def skip(self):
-        self.skipped = True
-        log.info("Received skip request")
-
-    def interrupt(self):
-        self.interrupted = True
-        log.info("Received interrupt request")
-
-    def nextjob(self):
-        if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
-            self.do_set_current_image()
-
-        self.job_no += 1
-        self.sampling_step = 0
-        self.current_image_sampling_step = 0
-
-    def dict(self):
-        obj = {
-            "skipped": self.skipped,
-            "interrupted": self.interrupted,
-            "job": self.job,
-            "job_count": self.job_count,
-            "job_timestamp": self.job_timestamp,
-            "job_no": self.job_no,
-            "sampling_step": self.sampling_step,
-            "sampling_steps": self.sampling_steps,
-        }
-
-        return obj
-
-    def begin(self, job: str = "(unknown)"):
-        self.sampling_step = 0
-        self.job_count = -1
-        self.processing_has_refined_job_count = False
-        self.job_no = 0
-        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-        self.current_latent = None
-        self.current_image = None
-        self.current_image_sampling_step = 0
-        self.id_live_preview = 0
-        self.skipped = False
-        self.interrupted = False
-        self.textinfo = None
-        self.time_start = time.time()
-        self.job = job
-        devices.torch_gc()
-        log.info("Starting job %s", job)
-
-    def end(self):
-        duration = time.time() - self.time_start
-        log.info("Ending job %s (%.2f seconds)", self.job, duration)
-        self.job = ""
-        self.job_count = 0
-
-        devices.torch_gc()
-
-    def set_current_image(self):
-        """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
-        if not parallel_processing_allowed:
-            return
-
-        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable and opts.show_progress_every_n_steps != -1:
-            self.do_set_current_image()
-
-    def do_set_current_image(self):
-        if self.current_latent is None:
-            return
-
-        import modules.sd_samplers
-        if opts.show_progress_grid:
-            self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
-        else:
-            self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
-
-        self.current_image_sampling_step = self.sampling_step
-
-    def assign_current_image(self, image):
-        self.current_image = image
-        self.id_live_preview += 1
-
-
-state = State()
-state.server_start = time.time()
-
-styles_filename = cmd_opts.styles_file
-prompt_styles = modules.styles.StyleDatabase(styles_filename)
-
-interrogator = modules.interrogate.InterrogateModels("interrogate")
+interrogator = None

 face_restorers = []

+options_templates = None
+opts = None
+restricted_opts = None

-class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
-        self.default = default
-        self.label = label
-        self.component = component
-        self.component_args = component_args
-        self.onchange = onchange
-        self.section = section
-        self.refresh = refresh
-
-        self.comment_before = comment_before
-        """HTML text that will be added after label in UI"""
-
-        self.comment_after = comment_after
-        """HTML text that will be added before label in UI"""
-
-    def link(self, label, url):
-        self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
-        return self
-
-    def js(self, label, js_func):
-        self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
-        return self
-
-    def info(self, info):
-        self.comment_after += f"<span class='info'>({info})</span>"
-        return self
-
-    def html(self, html):
-        self.comment_after += html
-        return self
-
-    def needs_restart(self):
-        self.comment_after += " <span class='info'>(requires restart)</span>"
-        return self
-
-
-
-
-def options_section(section_identifier, options_dict):
-    for v in options_dict.values():
-        v.section = section_identifier
-
-    return options_dict
-
-
-def list_checkpoint_tiles():
-    import modules.sd_models
-    return modules.sd_models.checkpoint_tiles()
-
-
-def refresh_checkpoints():
-    import modules.sd_models
-    return modules.sd_models.list_models()
-
-
-def list_samplers():
-    import modules.sd_samplers
-    return modules.sd_samplers.all_samplers
-
-
-hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
-tab_names = []
-
-options_templates = {}
-
-options_templates.update(options_section(('saving-images', "Saving images/grids"), {
-    "samples_save": OptionInfo(True, "Always save all generated images"),
-    "samples_format": OptionInfo('png', 'File format for images'),
-    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
-    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
-
-    "grid_save": OptionInfo(True, "Always save all generated image grids"),
-    "grid_format": OptionInfo('png', 'File format for grids'),
-    "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
-    "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
-    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
-    "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
-    "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
-    "font": OptionInfo("", "Font for image grids that have text"),
-    "grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
-    "grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
-    "grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
-
-    "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
-    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
-    "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
-    "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
-    "save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
-    "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
-    "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
-    "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
-    "export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
-    "img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
-    "target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
-    "img_max_size_mp": OptionInfo(200, "Maximum image size", gr.Number).info("in megapixels"),
-
-    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
-    "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
-    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
-    "save_init_img": OptionInfo(False, "Save init images when using img2img"),
-
-    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
-    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
-
-}))
-
-options_templates.update(options_section(('saving-paths', "Paths for saving"), {
-    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
-    "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
-    "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
-    "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
-    "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
-    "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
-    "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
-    "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
-    "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
-}))
-
-options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
-    "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
-    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
-}))
-
-options_templates.update(options_section(('upscaling', "Upscaling"), {
-    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
-    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
-    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-}))
-
-options_templates.update(options_section(('face-restoration', "Face restoration"), {
-    "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
-    "code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
-    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
-}))
-
-options_templates.update(options_section(('system', "System"), {
-    "show_warnings": OptionInfo(False, "Show warnings in console."),
-    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
-    "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
-    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
-    "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
-    "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
-    "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
-}))
-
-options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
-    "save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
-    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
-    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
-    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
-    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
-    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
-    "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
-}))
-
-options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
-    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
-    "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
-    "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
-    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
-    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
-    "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
-    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-    "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
-    "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
-    "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
-    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
-    "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
-    "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
-    "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
-}))
-
-options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
-    "sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
-    "sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
-    "sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
-    "sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
-}))
-
-options_templates.update(options_section(('optimizations', "Optimizations"), {
-    "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
-    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
-    "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
-    "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
-    "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
-    "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
-    "experimental_persistent_cond_cache": OptionInfo(False, "persistent cond cache").info("Experimental, keep cond caches across jobs, reduce overhead."),
-}))
-
-options_templates.update(options_section(('compatibility', "Compatibility"), {
-    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
-    "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
-    "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
-    "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
-    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
-    "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
-}))
-
-options_templates.update(options_section(('interrogate', "Interrogate Options"), {
-    "interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
-    "interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
-    "interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
-    "interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
-    "interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
-    "interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
-    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
-    "deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
-    "deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
-    "deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
-}))
-
-options_templates.update(options_section(('extra_networks', "Extra Networks"), {
-    "extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
-    "extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
-    "extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
-    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
-    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
-    "extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
-    "extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
-    "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
-    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
-    "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
-    "textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
-}))
-
-options_templates.update(options_section(('ui', "User interface"), {
-    "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_restart(),
-    "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).needs_restart(),
-    "img2img_editor_height": OptionInfo(720, "img2img: height of image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(),
-    "return_grid": OptionInfo(True, "Show grid in results for web"),
-    "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
-    "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
-    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
-    "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
-    "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
-    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
-    "js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
-    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_restart(),
-    "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_restart(),
-    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
-    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
-    "keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
-    "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
-    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_restart(),
-    "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
-    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
-    "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(),
-    "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
-    "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
-    "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(),
-}))
-
-options_templates.update(options_section(('infotext', "Infotext"), {
-    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
-    "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
-    "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
-    "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
-    "infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
-<li>Ignore: keep prompt and styles dropdown as it is.</li>
-<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
-<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
-<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
-</ul>"""),
-
-}))
-
-options_templates.update(options_section(('ui', "Live previews"), {
-    "show_progressbar": OptionInfo(True, "Show progressbar"),
-    "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
-    "live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
-    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
-    "show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
-    "show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = fast but low quality; Approx cheap = super fast but terrible otherwise"),
-    "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
-    "live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
-}))
-
-options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-    "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}).needs_restart(),
-    "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
-    "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
-    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
-    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    'k_sched_type':  OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
-    'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
-    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
-    'rho':  OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
-    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
-    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
-    'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
-    'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
-    'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
-    'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
-}))
-
-options_templates.update(options_section(('postprocessing', "Postprocessing"), {
-    'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
-    'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
-    'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-}))
-
-options_templates.update(options_section((None, "Hidden options"), {
-    "disabled_extensions": OptionInfo([], "Disable these extensions"),
-    "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
-    "restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
-    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
-}))
-
-
-options_templates.update()
-
-
-class Options:
-    data = None
-    data_labels = options_templates
-    typemap = {int: float}
-
-    def __init__(self):
-        self.data = {k: v.default for k, v in self.data_labels.items()}
-
-    def __setattr__(self, key, value):
-        if self.data is not None:
-            if key in self.data or key in self.data_labels:
-                assert not cmd_opts.freeze_settings, "changing settings is disabled"
-
-                info = opts.data_labels.get(key, None)
-                comp_args = info.component_args if info else None
-                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
-
-                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
-
-                self.data[key] = value
-                return
-
-        return super(Options, self).__setattr__(key, value)
-
-    def __getattr__(self, item):
-        if self.data is not None:
-            if item in self.data:
-                return self.data[item]
-
-        if item in self.data_labels:
-            return self.data_labels[item].default
-
-        return super(Options, self).__getattribute__(item)
-
-    def set(self, key, value):
-        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
-
-        oldval = self.data.get(key, None)
-        if oldval == value:
-            return False
-
-        try:
-            setattr(self, key, value)
-        except RuntimeError:
-            return False
-
-        if self.data_labels[key].onchange is not None:
-            try:
-                self.data_labels[key].onchange()
-            except Exception as e:
-                errors.display(e, f"changing setting {key} to {value}")
-                setattr(self, key, oldval)
-                return False
-
-        return True
-
-    def get_default(self, key):
-        """returns the default value for the key"""
-
-        data_label = self.data_labels.get(key)
-        if data_label is None:
-            return None
-
-        return data_label.default
-
-    def save(self, filename):
-        assert not cmd_opts.freeze_settings, "saving settings is disabled"
-
-        with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file, indent=4)
-
-    def same_type(self, x, y):
-        if x is None or y is None:
-            return True
-
-        type_x = self.typemap.get(type(x), type(x))
-        type_y = self.typemap.get(type(y), type(y))
-
-        return type_x == type_y
-
-    def load(self, filename):
-        with open(filename, "r", encoding="utf8") as file:
-            self.data = json.load(file)
-
-        # 1.1.1 quicksettings list migration
-        if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
-            self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
-
-        # 1.4.0 ui_reorder
-        if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
-            self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
-
-        bad_settings = 0
-        for k, v in self.data.items():
-            info = self.data_labels.get(k, None)
-            if info is not None and not self.same_type(info.default, v):
-                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
-                bad_settings += 1
-
-        if bad_settings > 0:
-            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
-
-    def onchange(self, key, func, call=True):
-        item = self.data_labels.get(key)
-        item.onchange = func
-
-        if call:
-            func()
-
-    def dumpjson(self):
-        d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
-        d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
-        d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
-        return json.dumps(d)
-
-    def add_option(self, key, info):
-        self.data_labels[key] = info
-
-    def reorder(self):
-        """reorder settings so that all items related to section always go together"""
-
-        section_ids = {}
-        settings_items = self.data_labels.items()
-        for _, item in settings_items:
-            if item.section not in section_ids:
-                section_ids[item.section] = len(section_ids)
-
-        self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
-
-    def cast_value(self, key, value):
-        """casts an arbitrary to the same type as this setting's value with key
-        Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
-        """
-
-        if value is None:
-            return None
-
-        default_value = self.data_labels[key].default
-        if default_value is None:
-            default_value = getattr(self, key, None)
-        if default_value is None:
-            return None
-
-        expected_type = type(default_value)
-        if expected_type == bool and value == "False":
-            value = False
-        else:
-            value = expected_type(value)
-
-        return value
-
-
-opts = Options()
-if os.path.exists(config_filename):
-    opts.load(config_filename)
-
-
-class Shared(sys.modules[__name__].__class__):
-    """
-    this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
-    at program startup.
-    """
-
-    sd_model_val = None
-
-    @property
-    def sd_model(self):
-        import modules.sd_models
-
-        return modules.sd_models.model_data.get_sd_model()
-
-    @sd_model.setter
-    def sd_model(self, value):
-        import modules.sd_models
-
-        modules.sd_models.model_data.set_sd_model(value)
-
-
-sd_model: LatentDiffusion = None  # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
-sys.modules[__name__].__class__ = Shared
+sd_model: sd_models_types.WebuiSdModel = None

 settings_components = None
 """assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""

+tab_names = []
+
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
    "Latent": {"mode": "bilinear", "antialias": False},
@@ -784,108 +64,24 @@ progress_print_out = sys.stdout

 gradio_theme = gr.themes.Base()

+total_tqdm = None

-def reload_gradio_theme(theme_name=None):
-    global gradio_theme
-    if not theme_name:
-        theme_name = opts.gradio_theme
+mem_mon = None

-    default_theme_args = dict(
-        font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
-        font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
-    )
+options_section = options.options_section
+OptionInfo = options.OptionInfo
+OptionHTML = options.OptionHTML

-    if theme_name == "Default":
-        gradio_theme = gr.themes.Default(**default_theme_args)
-    else:
-        try:
-            gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
-        except Exception as e:
-            errors.display(e, "changing gradio theme")
-            gradio_theme = gr.themes.Default(**default_theme_args)
+natural_sort_key = util.natural_sort_key
+listfiles = util.listfiles
+html_path = util.html_path
+html = util.html
+walk_files = util.walk_files
+ldm_print = util.ldm_print

+reload_gradio_theme = shared_gradio_themes.reload_gradio_theme

-
-class TotalTQDM:
-    def __init__(self):
-        self._tqdm = None
-
-    def reset(self):
-        self._tqdm = tqdm.tqdm(
-            desc="Total progress",
-            total=state.job_count * state.sampling_steps,
-            position=1,
-            file=progress_print_out
-        )
-
-    def update(self):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.update()
-
-    def updateTotal(self, new_total):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.total = new_total
-
-    def clear(self):
-        if self._tqdm is not None:
-            self._tqdm.refresh()
-            self._tqdm.close()
-            self._tqdm = None
-
-
-total_tqdm = TotalTQDM()
-
-mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
-mem_mon.start()
-
-
-def natural_sort_key(s, regex=re.compile('([0-9]+)')):
-    return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]
-
-
-def listfiles(dirname):
-    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")]
-    return [file for file in filenames if os.path.isfile(file)]
-
-
-def html_path(filename):
-    return os.path.join(script_path, "html", filename)
-
-
-def html(filename):
-    path = html_path(filename)
-
-    if os.path.exists(path):
-        with open(path, encoding="utf8") as file:
-            return file.read()
-
-    return ""
-
-
-def walk_files(path, allowed_extensions=None):
-    if not os.path.exists(path):
-        return
-
-    if allowed_extensions is not None:
-        allowed_extensions = set(allowed_extensions)
-
-    items = list(os.walk(path, followlinks=True))
-    items = sorted(items, key=lambda x: natural_sort_key(x[0]))
-
-    for root, _, files in items:
-        for filename in sorted(files, key=natural_sort_key):
-            if allowed_extensions is not None:
-                _, ext = os.path.splitext(filename)
-                if ext not in allowed_extensions:
-                    continue
-
-            if not opts.list_hidden_files and ("/." in root or "\\." in root):
-                continue
-
-            yield os.path.join(root, filename)
+list_checkpoint_tiles = shared_items.list_checkpoint_tiles
+refresh_checkpoints = shared_items.refresh_checkpoints
+list_samplers = shared_items.list_samplers
+reload_hypernetworks = shared_items.reload_hypernetworks
--- a/Show More
+++ b/Show More