update changelog

fix mistake in #15583

CHANGELOG.md

@@ -1,3 +1,29 @@
+## 1.9.1
+
+### Minor:
+* Add avif support ([#15582](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15582))
+* Add filename patterns: `[sampler_scheduler]` and `[scheduler]` ([#15581](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15581))
+
+### Extensions and API:
+* undo adding scripts to sys.modules
+* Add schedulers API endpoint ([#15577](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15577))
+* Remove API upscaling factor limits ([#15560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15560))
+
+### Bug Fixes:
+* Fix images do not match / Coordinate 'right' is less than 'left' ([#15534](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15534))
+* fix: remove_callbacks_for_function should also remove from the ordered map ([#15533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15533))
+* fix x1 upscalers ([#15555](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15555))
+* Fix cls.__module__ value in extension script ([#15532](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15532))
+* fix typo in function call (eror -> error) ([#15531](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15531))
+
+### Other:
+* Hide 'No Image data blocks found.' message ([#15567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15567))
+* Allow webui.sh to be runnable from arbitrary directories containing a .git file ([#15561](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15561))
+* Compatibility with Debian 11, Fedora 34+ and openSUSE 15.4+ ([#15544](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15544))
+* numpy DeprecationWarning product -> prod ([#15547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15547))
+* get_crop_region_v2 ([#15583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15583), [#15587](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15587))
+
+
 ## 1.9.0
 
 ### Features:
@@ -81,6 +107,9 @@
 * minor bug fix of sd model memory management ([#15350](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15350))
 * Fix CodeFormer weight ([#15414](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15414))
 * Fix: Remove script callbacks in ordered_callbacks_map ([#15428](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15428))
+* fix limited file write (thanks, Sylwia)
+* Fix extra-single-image API not doing upscale failed ([#15465](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15465))
+* error handling paste_field callables ([#15470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15470))
 
 ### Hardware:
 * Add training support and change lspci for Ascend NPU ([#14981](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14981))
@@ -112,6 +141,8 @@
 * Add Size as an XYZ Grid option ([#15354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15354))
 * Use HF_ENDPOINT variable for HuggingFace domain with default ([#15443](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15443))
 * re-add update_file_entry ([#15446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15446))
+* create_infotext allow index and callable, re-work Hires prompt infotext ([#15460](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15460))
+* update restricted_opts to include more options for --hide-ui-dir-config ([#15492](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15492))
 
 
 ## 1.8.0

extensions-builtin/hypertile/scripts/hypertile_xyz.py

@@ -1,7 +1,7 @@
 from modules import scripts
 from modules.shared import opts
 
-xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
+xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "scripts.xyz_grid"][0].module
 
 def int_applier(value_name:str, min_range:int = -1, max_range:int = -1):
     """

javascript/extraNetworks.js

@@ -568,7 +568,7 @@ function extraNetworksShowMetadata(text) {
             return;
         }
     } catch (error) {
-        console.eror(error);
+        console.error(error);
     }
 
     var elem = document.createElement('pre');

modules/api/api.py

@@ -17,7 +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, shared_items, script_callbacks, infotext_utils, sd_models
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models, sd_schedulers
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -221,6 +221,7 @@ class Api:
         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/schedulers", self.get_schedulers, methods=["GET"], response_model=list[models.SchedulerItem])
         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])
@@ -683,6 +684,17 @@ class Api:
     def get_samplers(self):
         return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
 
+    def get_schedulers(self):
+        return [
+            {
+                "name": scheduler.name,
+                "label": scheduler.label,
+                "aliases": scheduler.aliases,
+                "default_rho": scheduler.default_rho,
+                "need_inner_model": scheduler.need_inner_model,
+            }
+            for scheduler in sd_schedulers.schedulers]
+
     def get_upscalers(self):
         return [
             {

modules/api/models.py

@@ -147,7 +147,7 @@ class ExtrasBaseRequest(BaseModel):
     gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
     codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
     codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
-    upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.")
+    upscaling_resize: float = Field(default=2, title="Upscaling Factor", gt=0, description="By how much to upscale the image, only used when resize_mode=0.")
     upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
     upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
     upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
@@ -235,6 +235,13 @@ class SamplerItem(BaseModel):
     aliases: list[str] = Field(title="Aliases")
     options: dict[str, str] = Field(title="Options")
 
+class SchedulerItem(BaseModel):
+    name: str = Field(title="Name")
+    label: str = Field(title="Label")
+    aliases: Optional[list[str]] = Field(title="Aliases")
+    default_rho: Optional[float] = Field(title="Default Rho")
+    need_inner_model: Optional[bool] = Field(title="Needs Inner Model")
+
 class UpscalerItem(BaseModel):
     name: str = Field(title="Name")
     model_name: Optional[str] = Field(title="Model Name")

modules/hypernetworks/hypernetwork.py

@@ -11,7 +11,7 @@ import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
 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 import textual_inversion, saving_settings
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
@@ -533,7 +533,7 @@ def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch
             model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds),
             **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}
         )
-        logging.save_settings_to_file(log_directory, {**saved_params, **locals()})
+        saving_settings.save_settings_to_file(log_directory, {**saved_params, **locals()})
 
     latent_sampling_method = ds.latent_sampling_method
 

modules/images.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import datetime
-
+import functools
 import pytz
 import io
 import math
@@ -13,6 +13,8 @@ import numpy as np
 import piexif
 import piexif.helper
 from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin, ImageOps
+# pillow_avif needs to be imported somewhere in code for it to work
+import pillow_avif # noqa: F401
 import string
 import json
 import hashlib
@@ -347,6 +349,32 @@ def sanitize_filename_part(text, replace_spaces=True):
     return text
 
 
+@functools.cache
+def get_scheduler_str(sampler_name, scheduler_name):
+    """Returns {Scheduler} if the scheduler is applicable to the sampler"""
+    if scheduler_name == 'Automatic':
+        config = sd_samplers.find_sampler_config(sampler_name)
+        scheduler_name = config.options.get('scheduler', 'Automatic')
+    return scheduler_name.capitalize()
+
+
+@functools.cache
+def get_sampler_scheduler_str(sampler_name, scheduler_name):
+    """Returns the '{Sampler} {Scheduler}' if the scheduler is applicable to the sampler"""
+    return f'{sampler_name} {get_scheduler_str(sampler_name, scheduler_name)}'
+
+
+def get_sampler_scheduler(p, sampler):
+    """Returns '{Sampler} {Scheduler}' / '{Scheduler}' / 'NOTHING_AND_SKIP_PREVIOUS_TEXT'"""
+    if hasattr(p, 'scheduler') and hasattr(p, 'sampler_name'):
+        if sampler:
+            sampler_scheduler = get_sampler_scheduler_str(p.sampler_name, p.scheduler)
+        else:
+            sampler_scheduler = get_scheduler_str(p.sampler_name, p.scheduler)
+        return sanitize_filename_part(sampler_scheduler, replace_spaces=False)
+    return NOTHING_AND_SKIP_PREVIOUS_TEXT
+
+
 class FilenameGenerator:
     replacements = {
         'seed': lambda self: self.seed if self.seed is not None else '',
@@ -358,6 +386,8 @@ class FilenameGenerator:
         'height': lambda self: self.image.height,
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
+        'sampler_scheduler': lambda self: self.p and get_sampler_scheduler(self.p, True),
+        'scheduler': lambda self: self.p and get_sampler_scheduler(self.p, False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
         'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
@@ -569,6 +599,16 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
             })
 
             piexif.insert(exif_bytes, filename)
+    elif extension.lower() == '.avif':
+        if opts.enable_pnginfo and geninfo is not None:
+            exif_bytes = piexif.dump({
+                "Exif": {
+                    piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
+                },
+            })
+
+
+        image.save(filename,format=image_format, exif=exif_bytes)
     elif extension.lower() == ".gif":
         image.save(filename, format=image_format, comment=geninfo)
     else:
@@ -747,7 +787,6 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
             exif_comment = exif_comment.decode('utf8', errors="ignore")
 
         if exif_comment:
-            items['exif comment'] = exif_comment
             geninfo = exif_comment
     elif "comment" in items: # for gif
         geninfo = items["comment"].decode('utf8', errors="ignore")

modules/infotext_utils.py

@@ -8,7 +8,7 @@ import sys
 
 import gradio as gr
 from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions, images, prompt_parser
+from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions, images, prompt_parser, errors
 from PIL import Image
 
 sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__]  # alias for old name
@@ -488,7 +488,11 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
 
         for output, key in paste_fields:
             if callable(key):
-                v = key(params)
+                try:
+                    v = key(params)
+                except Exception:
+                    errors.report(f"Error executing {key}", exc_info=True)
+                    v = None
             else:
                 v = params.get(key, None)
 

modules/masking.py

@@ -1,17 +1,39 @@
 from PIL import Image, ImageFilter, ImageOps
 
 
-def get_crop_region(mask, pad=0):
-    """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
-    For example, if a user has painted the top-right part of a 512x512 image, the result may be (256, 0, 512, 256)"""
-    mask_img = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
-    box = mask_img.getbbox()
-    if box:
+def get_crop_region_v2(mask, pad=0):
+    """
+    Finds a rectangular region that contains all masked ares in a mask.
+    Returns None if mask is completely black mask (all 0)
+
+    Parameters:
+    mask: PIL.Image.Image L mode or numpy 1d array
+    pad: int number of pixels that the region will be extended on all sides
+    Returns: (x1, y1, x2, y2) | None
+
+    Introduced post 1.9.0
+    """
+    mask = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
+    if box := mask.getbbox():
         x1, y1, x2, y2 = box
-    else:  # when no box is found
-        x1, y1 = mask_img.size
-        x2 = y2 = 0
-    return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask_img.size[0]), min(y2 + pad, mask_img.size[1])
+        return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask.size[0]), min(y2 + pad, mask.size[1]) if pad else box
+
+
+def get_crop_region(mask, pad=0):
+    """
+    Same function as get_crop_region_v2 but handles completely black mask (all 0) differently
+    when mask all black still return coordinates but the coordinates may be invalid ie x2>x1 or y2>y1
+    Notes: it is possible for the coordinates to be "valid" again if pad size is sufficiently large
+    (mask_size.x-pad, mask_size.y-pad, pad, pad)
+
+    Extension developer should use get_crop_region_v2 instead unless for compatibility considerations.
+    """
+    mask = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
+    if box := get_crop_region_v2(mask, pad):
+        return box
+    x1, y1 = mask.size
+    x2 = y2 = 0
+    return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask.size[0]), min(y2 + pad, mask.size[1])
 
 
 def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):

modules/postprocessing.py

@@ -136,6 +136,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     args = scripts.scripts_postproc.create_args_for_run({
         "Upscale": {
+            "upscale_enabled": True,
             "upscale_mode": resize_mode,
             "upscale_by": upscaling_resize,
             "max_side_length": max_side_length,

modules/processing.py

@@ -608,7 +608,7 @@ class Processed:
             "version": self.version,
         }
 
-        return json.dumps(obj)
+        return json.dumps(obj, default=lambda o: None)
 
     def infotext(self, p: StableDiffusionProcessing, index):
         return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
@@ -703,8 +703,54 @@ def program_version():
     return res
 
 
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None, all_hr_prompts=None, all_hr_negative_prompts=None):
-    if index is None:
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+    """
+    this function is used to generate the infotext that is stored in the generated images, it's contains the parameters that are required to generate the imagee
+    Args:
+        p: StableDiffusionProcessing
+        all_prompts: list[str]
+        all_seeds: list[int]
+        all_subseeds: list[int]
+        comments: list[str]
+        iteration: int
+        position_in_batch: int
+        use_main_prompt: bool
+        index: int
+        all_negative_prompts: list[str]
+
+    Returns: str
+
+    Extra generation params
+    p.extra_generation_params dictionary allows for additional parameters to be added to the infotext
+    this can be use by the base webui or extensions.
+    To add a new entry, add a new key value pair, the dictionary key will be used as the key of the parameter in the infotext
+    the value generation_params can be defined as:
+        - str | None
+        - List[str|None]
+        - callable func(**kwargs) -> str | None
+
+    When defined as a string, it will be used as without extra processing; this is this most common use case.
+
+    Defining as a list allows for parameter that changes across images in the job, for example, the 'Seed' parameter.
+    The list should have the same length as the total number of images in the entire job.
+
+    Defining as a callable function allows parameter cannot be generated earlier or when extra logic is required.
+    For example 'Hires prompt', due to reasons the hr_prompt might be changed by process in the pipeline or extensions
+    and may vary across different images, defining as a static string or list would not work.
+
+    The function takes locals() as **kwargs, as such will have access to variables like 'p' and 'index'.
+    the base signature of the function should be:
+        func(**kwargs) -> str | None
+    optionally it can have additional arguments that will be used in the function:
+        func(p, index, **kwargs) -> str | None
+    note: for better future compatibility even though this function will have access to all variables in the locals(),
+        it is recommended to only use the arguments present in the function signature of create_infotext.
+    For actual implementation examples, see StableDiffusionProcessingTxt2Img.init > get_hr_prompt.
+    """
+
+    if use_main_prompt:
+        index = 0
+    elif index is None:
         index = position_in_batch + iteration * p.batch_size
 
     if all_negative_prompts is None:
@@ -715,6 +761,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     token_merging_ratio = p.get_token_merging_ratio()
     token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
 
+    prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
+    negative_prompt = p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]
+
     uses_ensd = opts.eta_noise_seed_delta != 0
     if uses_ensd:
         uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
@@ -747,22 +796,24 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
         "Tiling": "True" if p.tiling else None,
-        "Hires prompt": None,  # This is set later, insert here to keep order
-        "Hires negative prompt": None,  # This is set later, insert here to keep order
         **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,
         "User": p.user if opts.add_user_name_to_info else None,
     }
 
-    if all_hr_prompts := all_hr_prompts or getattr(p, 'all_hr_prompts', None):
-        generation_params['Hires prompt'] = all_hr_prompts[index] if all_hr_prompts[index] != all_prompts[index] else None
-    if all_hr_negative_prompts := all_hr_negative_prompts or getattr(p, 'all_hr_negative_prompts', None):
-        generation_params['Hires negative prompt'] = all_hr_negative_prompts[index] if all_hr_negative_prompts[index] != all_negative_prompts[index] else None
+    for key, value in generation_params.items():
+        try:
+            if isinstance(value, list):
+                generation_params[key] = value[index]
+            elif callable(value):
+                generation_params[key] = value(**locals())
+        except Exception:
+            errors.report(f'Error creating infotext for key "{key}"', exc_info=True)
+            generation_params[key] = None
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
-    negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {negative_prompt}" if negative_prompt else ""
 
     return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -1204,6 +1255,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
                 self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
 
+            def get_hr_prompt(p, index, prompt_text, **kwargs):
+                hr_prompt = p.all_hr_prompts[index]
+                return hr_prompt if hr_prompt != prompt_text else None
+
+            def get_hr_negative_prompt(p, index, negative_prompt, **kwargs):
+                hr_negative_prompt = p.all_hr_negative_prompts[index]
+                return hr_negative_prompt if hr_negative_prompt != negative_prompt else None
+
+            self.extra_generation_params["Hires prompt"] = get_hr_prompt
+            self.extra_generation_params["Hires negative prompt"] = get_hr_negative_prompt
+
             self.extra_generation_params["Hires schedule type"] = None  # to be set in sd_samplers_kdiffusion.py
 
             if self.hr_scheduler is None:
@@ -1549,16 +1611,23 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if self.inpaint_full_res:
                 self.mask_for_overlay = image_mask
                 mask = image_mask.convert('L')
-                crop_region = masking.get_crop_region(mask, self.inpaint_full_res_padding)
-                crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
-                x1, y1, x2, y2 = crop_region
-
-                mask = mask.crop(crop_region)
-                image_mask = images.resize_image(2, mask, self.width, self.height)
-                self.paste_to = (x1, y1, x2-x1, y2-y1)
-
-                self.extra_generation_params["Inpaint area"] = "Only masked"
-                self.extra_generation_params["Masked area padding"] = self.inpaint_full_res_padding
+                crop_region = masking.get_crop_region_v2(mask, self.inpaint_full_res_padding)
+                if crop_region:
+                    crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
+                    x1, y1, x2, y2 = crop_region
+                    mask = mask.crop(crop_region)
+                    image_mask = images.resize_image(2, mask, self.width, self.height)
+                    self.paste_to = (x1, y1, x2-x1, y2-y1)
+                    self.extra_generation_params["Inpaint area"] = "Only masked"
+                    self.extra_generation_params["Masked area padding"] = self.inpaint_full_res_padding
+                else:
+                    crop_region = None
+                    image_mask = None
+                    self.mask_for_overlay = None
+                    self.inpaint_full_res = False
+                    massage = 'Unable to perform "Inpaint Only mask" because mask is blank, switch to img2img mode.'
+                    model_hijack.comments.append(massage)
+                    logging.info(massage)
             else:
                 image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
                 np_mask = np.array(image_mask)
@@ -1586,6 +1655,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image = images.resize_image(self.resize_mode, image, self.width, self.height)
 
             if image_mask is not None:
+                if self.mask_for_overlay.size != (image.width, image.height):
+                    self.mask_for_overlay = images.resize_image(self.resize_mode, self.mask_for_overlay, image.width, image.height)
                 image_masked = Image.new('RGBa', (image.width, image.height))
                 image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
 

modules/script_callbacks.py

@@ -448,6 +448,9 @@ def remove_callbacks_for_function(callback_func):
     for callback_list in callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
             callback_list.remove(callback_to_remove)
+    for ordered_callback_list in ordered_callbacks_map.values():
+        for callback_to_remove in [cb for cb in ordered_callback_list if cb.callback == callback_func]:
+            ordered_callback_list.remove(callback_to_remove)
 
 
 def on_app_started(callback, *, name=None):

modules/script_loading.py

@@ -2,22 +2,19 @@ import os
 import importlib.util
 
 from modules import errors
-import sys
 
 
 loaded_scripts = {}
 
 
 def load_module(path):
-    module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
+    module_name, _ = os.path.splitext(os.path.basename(path))
+    full_module_name = "scripts." + module_name
+    module_spec = importlib.util.spec_from_file_location(full_module_name, path)
     module = importlib.util.module_from_spec(module_spec)
     module_spec.loader.exec_module(module)
 
     loaded_scripts[path] = module
-
-    module_name, _ = os.path.splitext(os.path.basename(path))
-    sys.modules["scripts." + module_name] = module
-
     return module
 
 

modules/scripts.py

@@ -739,12 +739,17 @@ class ScriptRunner:
         def onload_script_visibility(params):
             title = params.get('Script', None)
             if title:
-                title_index = self.titles.index(title)
-                visibility = title_index == self.script_load_ctr
-                self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
-                return gr.update(visible=visibility)
-            else:
-                return gr.update(visible=False)
+                try:
+                    title_index = self.titles.index(title)
+                    visibility = title_index == self.script_load_ctr
+                    self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
+                    return gr.update(visible=visibility)
+                except ValueError:
+                    params['Script'] = None
+                    massage = f'Cannot find Script: "{title}"'
+                    print(massage)
+                    gr.Warning(massage)
+            return gr.update(visible=False)
 
         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])

modules/shared_options.py

@@ -19,7 +19,9 @@ restricted_opts = {
     "outdir_grids",
     "outdir_txt2img_grids",
     "outdir_save",
-    "outdir_init_images"
+    "outdir_init_images",
+    "temp_dir",
+    "clean_temp_dir_at_start",
 }
 
 categories.register_category("saving", "Saving images")

modules/textual_inversion/image_embedding.py

@@ -1,12 +1,16 @@
 import base64
 import json
+import os.path
 import warnings
+import logging
 
 import numpy as np
 import zlib
 from PIL import Image, ImageDraw
 import torch
 
+logger = logging.getLogger(__name__)
+
 
 class EmbeddingEncoder(json.JSONEncoder):
     def default(self, obj):
@@ -43,7 +47,7 @@ def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
 
 def xor_block(block):
     g = lcg()
-    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+    randblock = np.array([next(g) for _ in range(np.prod(block.shape))]).astype(np.uint8).reshape(block.shape)
     return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
 
 
@@ -114,7 +118,7 @@ def extract_image_data_embed(image):
     outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
     black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
     if black_cols[0].shape[0] < 2:
-        print('No Image data blocks found.')
+        logger.debug(f'{os.path.basename(getattr(image, "filename", "unknown image file"))}: no embedded information found.')
         return None
 
     data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)

modules/textual_inversion/textual_inversion.py

@@ -17,7 +17,7 @@ import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
-from modules.textual_inversion.logging import save_settings_to_file
+from modules.textual_inversion.saving_settings import save_settings_to_file
 
 
 TextualInversionTemplate = namedtuple("TextualInversionTemplate", ["name", "path"])

modules/ui_extensions.py

@@ -58,8 +58,9 @@ def apply_and_restart(disable_list, update_list, disable_all):
 
 def save_config_state(name):
     current_config_state = config_states.get_config()
-    if not name:
-        name = "Config"
+
+    name = os.path.basename(name or "Config")
+
     current_config_state["name"] = name
     timestamp = datetime.now().strftime('%Y_%m_%d-%H_%M_%S')
     filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")

modules/upscaler.py

@@ -57,7 +57,7 @@ class Upscaler:
         dest_h = int((img.height * scale) // 8 * 8)
 
         for _ in range(3):
-            if img.width >= dest_w and img.height >= dest_h:
+            if img.width >= dest_w and img.height >= dest_h and scale != 1:
                 break
 
             if shared.state.interrupted:

requirements.txt

@@ -30,3 +30,4 @@ torch
 torchdiffeq
 torchsde
 transformers==4.30.2
+pillow-avif-plugin==1.4.3

requirements_versions.txt

@@ -29,3 +29,4 @@ torchdiffeq==0.2.3
 torchsde==0.2.6
 transformers==4.30.2
 httpx==0.24.1
+pillow-avif-plugin==1.4.3

webui.sh

@@ -113,13 +113,13 @@ then
     exit 1
 fi
 
-if [[ -d .git ]]
+if [[ -d "$SCRIPT_DIR/.git" ]]
 then
     printf "\n%s\n" "${delimiter}"
     printf "Repo already cloned, using it as install directory"
     printf "\n%s\n" "${delimiter}"
-    install_dir="${PWD}/../"
-    clone_dir="${PWD##*/}"
+    install_dir="${SCRIPT_DIR}/../"
+    clone_dir="${SCRIPT_DIR##*/}"
 fi
 
 # Check prerequisites
@@ -243,7 +243,7 @@ prepare_tcmalloc() {
         for lib in "${TCMALLOC_LIBS[@]}"
         do
             # Determine which type of tcmalloc library the library supports
-            TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -P $lib | head -n 1)"
+            TCMALLOC="$(PATH=/sbin:/usr/sbin:$PATH ldconfig -p | grep -P $lib | head -n 1)"
             TC_INFO=(${TCMALLOC//=>/})
             if [[ ! -z "${TC_INFO}" ]]; then
                 echo "Check TCMalloc: ${TC_INFO}"