disable img preprocess for extra submit

base64 img

CHANGELOG.md

@@ -1,3 +1,39 @@
+## 1.9.3
+
+### Bug Fixes:
+*  fix get_crop_region_v2 ([#15594](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15594)) 
+
+## 1.9.2
+
+### Extensions and API:
+* restore 1.8.0-style naming of scripts
+
+## 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:
@@ -85,7 +121,6 @@
 * 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))
 * Update to ROCm5.7 and PyTorch ([#14820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14820))

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/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

@@ -2,7 +2,7 @@ import os
 
 from PIL import Image
 
-from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, infotext_utils
+from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, infotext_utils, util
 from modules.shared import opts
 
 
@@ -31,6 +31,8 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
             for filename in image_list:
                 yield filename, filename
         else:
+            if isinstance(image, str):
+                image = util.decode_base64_to_image(image)
             assert image, 'image not selected'
             yield image, None
 
@@ -62,11 +64,13 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
         else:
             image_data = image_placeholder
 
+        image_data = image_data if image_data.mode in ("RGBA", "RGB") else image_data.convert("RGB")
+
         parameters, existing_pnginfo = images.read_info_from_image(image_data)
         if parameters:
             existing_pnginfo["parameters"] = parameters
 
-        initial_pp = scripts_postprocessing.PostprocessedImage(image_data if image_data.mode in ("RGBA", "RGB") else image_data.convert("RGB"))
+        initial_pp = scripts_postprocessing.PostprocessedImage(image_data)
 
         scripts.scripts_postproc.run(initial_pp, args)
 

modules/processing.py

@@ -1611,16 +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)
@@ -1648,6 +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

@@ -2,7 +2,6 @@ import os
 import importlib.util
 
 from modules import errors
-import sys
 
 
 loaded_scripts = {}
@@ -14,10 +13,6 @@ def load_module(path):
     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/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_postprocessing.py

@@ -54,6 +54,7 @@ def create_ui():
             output_panel.html_log,
         ],
         show_progress=False,
+        preprocess=False,
     )
 
     parameters_copypaste.add_paste_fields("extras", extras_image, None)

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:

modules/util.py

@@ -211,3 +211,13 @@ Requested path was: {path}
         subprocess.Popen(["wsl-open", path])
     else:
         subprocess.Popen(["xdg-open", path])
+
+
+def decode_base64_to_image(base64_str: str):
+    from modules import images
+    from io import BytesIO
+    import base64
+    if base64_str.startswith("data:image/"):
+        base64_str = base64_str.partition(';')[2].partition(',')[2]
+    image = images.read(BytesIO(base64.b64decode(base64_str)))
+    return image

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}"