.dockerignore

@@ -1 +0,0 @@
-.idea

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,18 @@
+---
+name: Bug report
+about: Describe a problem
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Read Troubleshoot**
+
+[x] I admit that I have read the [Troubleshoot](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md) before making this issue.
+
+**Describe the problem**
+A clear and concise description of what the bug is.
+
+**Full Console Log**
+Paste **full** console log here. You will make our job easier if you give a **full** log.

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,107 +0,0 @@
-name: Bug Report
-description: You think something is broken in Fooocus
-title: "[Bug]: "
-labels: ["bug", "triage"]
-
-body:
-  - type: markdown
-    attributes:
-      value: |
-        > The title of the bug report should be short and descriptive.
-        > Use relevant keywords for searchability.
-        > Do not leave it blank, but also do not put an entire error log in it.
-  - type: checkboxes
-    attributes:
-      label: Checklist
-      description: |
-        Please perform basic debugging to see if your configuration is the cause of the issue.
-        Basic debug procedure
-        　2. Update Fooocus - sometimes things just need to be updated
-        　3. Backup and remove your config.txt - check if the issue is caused by bad configuration
-        　5. Try a fresh installation of Fooocus in a different directory - see if a clean installation solves the issue
-        Before making a issue report please, check that the issue hasn't been reported recently.
-      options:
-        - label: The issue has not been resolved by following the [troubleshooting guide](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md)
-        - label: The issue exists on a clean installation of Fooocus
-        - label: The issue exists in the current version of Fooocus
-        - label: The issue has not been reported before recently
-        - label: The issue has been reported before but has not been fixed yet
-  - type: markdown
-    attributes:
-      value: |
-        > Please fill this form with as much information as possible. Don't forget to add information about "What browsers" and provide screenshots if possible
-  - type: textarea
-    id: what-did
-    attributes:
-      label: What happened?
-      description: Tell us what happened in a very clear and simple way
-      placeholder: |
-        image generation is not working as intended.
-    validations:
-      required: true
-  - type: textarea
-    id: steps
-    attributes:
-      label: Steps to reproduce the problem
-      description: Please provide us with precise step by step instructions on how to reproduce the bug
-      placeholder: |
-        1. Go to ...
-        2. Press ...
-        3. ...
-    validations:
-      required: true
-  - type: textarea
-    id: what-should
-    attributes:
-      label: What should have happened?
-      description: Tell us what you think the normal behavior should be
-      placeholder: |
-        Fooocus should ...
-    validations:
-      required: true
-  - type: dropdown
-    id: browsers
-    attributes:
-      label: What browsers do you use to access Fooocus?
-      multiple: true
-      options:
-        - Mozilla Firefox
-        - Google Chrome
-        - Brave
-        - Apple Safari
-        - Microsoft Edge
-        - Android
-        - iOS
-        - Other
-  - type: dropdown
-    id: hosting
-    attributes:
-      label: Where are you running Fooocus?
-      multiple: false
-      options:
-        - Locally
-        - Locally with virtualization (e.g. Docker)
-        - Cloud (Google Colab)
-        - Cloud (other)
-  - type: input
-    id: operating-system
-    attributes:
-      label: What operating system are you using?
-      placeholder: |
-        Windows 10
-  - type: textarea
-    id: logs
-    attributes:
-      label: Console logs
-      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.
-      render: Shell
-    validations:
-      required: true
-  - type: textarea
-    id: misc
-    attributes:
-      label: Additional information
-      description: | 
-        Please provide us with any relevant additional info or context.
-        Examples:
-        　I have updated my GPU driver recently.

.github/ISSUE_TEMPLATE/config.yml

@@ -1,5 +0,0 @@
-blank_issues_enabled: false
-contact_links:
-  - name: Ask a question
-    url: https://github.com/lllyasviel/Fooocus/discussions/new?category=q-a
-    about: Ask the community for help

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,14 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the idea you'd like**
+A clear and concise description of what you want to happen.

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,40 +0,0 @@
-name: Feature request
-description: Suggest an idea for this project
-title: "[Feature Request]: "
-labels: ["enhancement", "triage"]
-
-body:
-  - type: checkboxes
-    attributes:
-      label: Is there an existing issue for this?
-      description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.
-      options:
-        - label: I have searched the existing issues and checked the recent builds/commits
-          required: true
-  - type: markdown
-    attributes:
-      value: |
-        *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
-  - type: textarea
-    id: feature
-    attributes:
-      label: What would your feature do?
-      description: Tell us about your feature in a very clear and simple way, and what problem it would solve
-    validations:
-      required: true
-  - type: textarea
-    id: workflow
-    attributes:
-      label: Proposed workflow
-      description: Please provide us with step by step information on how you'd like the feature to be accessed and used
-      value: |
-        1. Go to .... 
-        2. Press ....
-        3. ...
-    validations:
-      required: true
-  - type: textarea
-    id: misc
-    attributes:
-      label: Additional information
-      description: Add any other context or screenshots about the feature request here.

.gitignore

@@ -20,7 +20,6 @@ user_path_config.txt
 user_path_config-deprecated.txt
 /modules/*.png
 /repositories
-/fooocus_env
 /venv
 /tmp
 /ui-config.json
@@ -51,4 +50,3 @@ user_path_config-deprecated.txt
 /package-lock.json
 /.coverage*
 /auth.json
-.DS_Store

Dockerfile

@@ -1,29 +0,0 @@
-FROM nvidia/cuda:12.3.1-base-ubuntu22.04
-ENV DEBIAN_FRONTEND noninteractive
-ENV CMDARGS --listen
-
-RUN apt-get update -y && \
-	apt-get install -y curl libgl1 libglib2.0-0 python3-pip python-is-python3 git && \
-	apt-get clean && \
-	rm -rf /var/lib/apt/lists/*
-
-COPY requirements_docker.txt requirements_versions.txt /tmp/
-RUN pip install --no-cache-dir -r /tmp/requirements_docker.txt -r /tmp/requirements_versions.txt && \
-	rm -f /tmp/requirements_docker.txt /tmp/requirements_versions.txt
-RUN pip install --no-cache-dir xformers==0.0.23 --no-dependencies
-RUN curl -fsL -o /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64 && \
-	chmod +x /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2
-
-RUN adduser --disabled-password --gecos '' user && \
-	mkdir -p /content/app /content/data
-
-COPY entrypoint.sh /content/
-RUN chown -R user:user /content
-
-WORKDIR /content
-USER user
-
-RUN git clone https://github.com/lllyasviel/Fooocus /content/app
-RUN mv /content/app/models /content/app/models.org
-
-CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ]

args_manager.py

@@ -1,13 +1,8 @@
 import ldm_patched.modules.args_parser as args_parser
-import os
 
-from tempfile import gettempdir
 
 args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
-
 args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.")
-args_parser.parser.add_argument("--disable-preset-selection", action='store_true',
-                                help="Disables preset selection in Gradio.")
 
 args_parser.parser.add_argument("--language", type=str, default='default',
                                 help="Translate UI using json files in [language] folder. "
@@ -23,16 +18,7 @@ args_parser.parser.add_argument("--disable-image-log", action='store_true',
                                 help="Prevent writing images and logs to hard drive.")
 
 args_parser.parser.add_argument("--disable-analytics", action='store_true',
-                                help="Disables analytics for Gradio.")
-
-args_parser.parser.add_argument("--disable-metadata", action='store_true',
-                                help="Disables saving metadata to images.")
-
-args_parser.parser.add_argument("--disable-preset-download", action='store_true',
-                                help="Disables downloading models for presets", default=False)
-
-args_parser.parser.add_argument("--always-download-new-model", action='store_true',
-                                help="Always download newer models ", default=False)
+                                help="Disables analytics for Gradio", default=False)
 
 args_parser.parser.set_defaults(
     disable_cuda_malloc=True,
@@ -49,7 +35,4 @@ if args_parser.args.disable_analytics:
     import os
     os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 
-if args_parser.args.disable_in_browser:
-    args_parser.args.in_browser = False
-
 args = args_parser.args

css/style.css

@@ -1,136 +1,5 @@
 /* based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/style.css */
 
-.loader-container {
-  display: flex; /* Use flex to align items horizontally */
-  align-items: center; /* Center items vertically within the container */
-  white-space: nowrap; /* Prevent line breaks within the container */
-}
-
-.loader {
-  border: 8px solid #f3f3f3; /* Light grey */
-  border-top: 8px solid #3498db; /* Blue */
-  border-radius: 50%;
-  width: 30px;
-  height: 30px;
-  animation: spin 2s linear infinite;
-}
-
-@keyframes spin {
-  0% { transform: rotate(0deg); }
-  100% { transform: rotate(360deg); }
-}
-
-/* Style the progress bar */
-progress {
-  appearance: none; /* Remove default styling */
-  height: 20px; /* Set the height of the progress bar */
-  border-radius: 5px; /* Round the corners of the progress bar */
-  background-color: #f3f3f3; /* Light grey background */
-  width: 100%;
-}
-
-/* Style the progress bar container */
-.progress-container {
-  margin-left: 20px;
-  margin-right: 20px;
-  flex-grow: 1; /* Allow the progress container to take up remaining space */
-}
-
-/* Set the color of the progress bar fill */
-progress::-webkit-progress-value {
-  background-color: #3498db; /* Blue color for the fill */
-}
-
-progress::-moz-progress-bar {
-  background-color: #3498db; /* Blue color for the fill in Firefox */
-}
-
-/* Style the text on the progress bar */
-progress::after {
-  content: attr(value '%'); /* Display the progress value followed by '%' */
-  position: absolute;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%);
-  color: white; /* Set text color */
-  font-size: 14px; /* Set font size */
-}
-
-/* Style other texts */
-.loader-container > span {
-  margin-left: 5px; /* Add spacing between the progress bar and the text */
-}
-
-.progress-bar > .generating {
-  display: none !important;
-}
-
-.progress-bar{
-  height: 30px !important;
-}
-
-.type_row{
-  height: 80px !important;
-}
-
-.type_row_half{
-  height: 32px !important;
-}
-
-.scroll-hide{
-  resize: none !important;
-}
-
-.refresh_button{
-  border: none !important;
-  background: none !important;
-  font-size: none !important;
-  box-shadow: none !important;
-}
-
-.advanced_check_row{
-  width: 250px !important;
-}
-
-.min_check{
-  min-width: min(1px, 100%) !important;
-}
-
-.resizable_area {
-  resize: vertical;
-  overflow: auto !important;
-}
-
-.aspect_ratios label {
-    width: 140px !important;
-}
-
-.aspect_ratios label span {
-    white-space: nowrap !important;
-}
-
-.aspect_ratios label input {
-    margin-left: -5px !important;
-}
-
-.lora_enable label {
-  height: 100%;
-}
-
-.lora_enable label input {
-  margin: auto;
-}
-
-.lora_enable label span {
-  display: none;
-}
-
-@-moz-document url-prefix() {
-  .lora_weight input[type=number] {
-    width: 80px;
-  }
-}
-
 #context-menu{
     z-index:9999;
     position:absolute;
@@ -327,70 +196,3 @@ progress::after {
     pointer-events: none;
     display: none;
 }
-
-#stylePreviewOverlay {
-    opacity: 0;
-    pointer-events: none;
-    width: 128px;
-    height: 128px;
-    position: fixed;
-    top: 0px;
-    left: 0px;
-    border: solid 1px lightgrey;
-    transform: translate(-140px, 20px);
-    background-size: cover;
-    background-position: center;
-    background-color: rgba(0, 0, 0, 0.3);
-    border-radius: 5px;
-    z-index: 100;
-    transition: transform 0.1s ease, opacity 0.3s ease;
-}
-
-#stylePreviewOverlay.lower-half {
-    transform: translate(-140px, -140px);
-}
-
-/* scrollable box for style selections */
-.contain .tabs {
-  height: 100%;
-}
-
-.contain .tabs .tabitem.style_selections_tab {
-  height: 100%;
-}
-
-.contain .tabs .tabitem.style_selections_tab > div:first-child {
-  height: 100%;
-}
-
-.contain .tabs .tabitem.style_selections_tab .style_selections {
-  min-height: 200px;
-  height: 100%;
-}
-
-.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] {
-  position: absolute; /* remove this to disable scrolling within the checkbox-group */
-  overflow: auto;
-  padding-right: 2px;
-  max-height: 100%;
-}
-
-.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] label {
-  /* max-width: calc(35% - 15px) !important; */ /* add this to enable 3 columns layout */
-  flex: calc(50% - 5px) !important;
-}
-
-.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] label span {
-  /* white-space:nowrap; */ /* add this to disable text wrapping (better choice for 3 columns layout) */
-  overflow: hidden;
-  text-overflow: ellipsis;
-}
-
-/* styles preview tooltip */
-.preview-tooltip {
-  background-color: #fff8;
-  font-family: monospace;
-  text-align: center;
-  border-radius-top: 5px;
-  display: none; /* remove this to enable tooltip in preview image */
-}

docker-compose.yml

@@ -1,38 +0,0 @@
-version: '3.9'
-
-volumes:
-  fooocus-data:
-
-services:
-  app:
-    build: .
-    image: fooocus
-    ports:
-     - "7865:7865"
-    environment:
-      - CMDARGS=--listen    # Arguments for launch.py.
-      - DATADIR=/content/data   # Directory which stores models, outputs dir
-      - config_path=/content/data/config.txt
-      - config_example_path=/content/data/config_modification_tutorial.txt
-      - path_checkpoints=/content/data/models/checkpoints/
-      - path_loras=/content/data/models/loras/
-      - path_embeddings=/content/data/models/embeddings/
-      - path_vae_approx=/content/data/models/vae_approx/
-      - path_upscale_models=/content/data/models/upscale_models/
-      - path_inpaint=/content/data/models/inpaint/
-      - path_controlnet=/content/data/models/controlnet/
-      - path_clip_vision=/content/data/models/clip_vision/
-      - path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/
-      - path_outputs=/content/app/outputs/    # Warning: If it is not located under '/content/app', you can't see history log!
-    volumes:
-      - fooocus-data:/content/data
-      #- ./models:/import/models   # Once you import files, you don't need to mount again.
-      #- ./outputs:/import/outputs  # Once you import files, you don't need to mount again.
-    tty: true
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              device_ids: ['0']
-              capabilities: [compute, utility]

docker.md

@@ -1,66 +0,0 @@
-# Fooocus on Docker
-
-The docker image is based on NVIDIA CUDA 12.3 and PyTorch 2.0, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details.
-
-## Quick start
-
-**This is just an easy way for testing. Please find more information in the [notes](#notes).**
-
-1. Clone this repository
-2. Build the image with `docker compose build`
-3. Run the docker container with `docker compose up`. Building the image takes some time.
-
-When you see the message  `Use the app with http://0.0.0.0:7865/` in the console, you can access the URL in your browser.
-
-Your models and outputs are stored in the `fooocus-data` volume, which, depending on OS, is stored in `/var/lib/docker/volumes`.
-
-## Details
-
-### Update the container manually
-
-When you are using `docker compose up` continuously, the container is not updated to the latest version of Fooocus automatically.
-Run `git pull` before executing `docker compose build --no-cache` to build an image with the latest Fooocus version.
-You can then start it with `docker compose up`
-
-### Import models, outputs
-If you want to import files from models or the outputs folder, you can uncomment the following settings in the [docker-compose.yml](docker-compose.yml):
-```
-#- ./models:/import/models   # Once you import files, you don't need to mount again.
-#- ./outputs:/import/outputs  # Once you import files, you don't need to mount again.
-```
-After running `docker compose up`, your files will be copied into `/content/data/models` and `/content/data/outputs`
-Since `/content/data` is a persistent volume folder, your files will be persisted even when you re-run `docker compose up --build` without above volume settings.
-
-
-### Paths inside the container
-
-|Path|Details|
-|-|-|
-|/content/app|The application stored folder|
-|/content/app/models.org|Original 'models' folder.<br> Files are copied to the '/content/app/models' which is symlinked to '/content/data/models' every time the container boots. (Existing files will not be overwritten.) |
-|/content/data|Persistent volume mount point|
-|/content/data/models|The folder is symlinked to '/content/app/models'|
-|/content/data/outputs|The folder is symlinked to '/content/app/outputs'|
-
-### Environments
-
-You can change `config.txt` parameters by using environment variables.
-**The priority of using the environments is higher than the values defined in `config.txt`, and they will be saved to the `config_modification_tutorial.txt`**
-
-Docker specified environments are there. They are used by 'entrypoint.sh'
-|Environment|Details|
-|-|-|
-|DATADIR|'/content/data' location.|
-|CMDARGS|Arguments for [entry_with_update.py](entry_with_update.py) which is called by [entrypoint.sh](entrypoint.sh)|
-|config_path|'config.txt' location|
-|config_example_path|'config_modification_tutorial.txt' location|
-
-You can also use the same json key names and values explained in the 'config_modification_tutorial.txt' as the environments.
-See examples in the [docker-compose.yml](docker-compose.yml)
-
-## Notes
-
-- Please keep 'path_outputs' under '/content/app'. Otherwise, you may get an error when you open the history log.
-- Docker on Mac/Windows still has issues in the form of slow volume access when you use "bind mount" volumes. Please refer to [this article](https://docs.docker.com/storage/volumes/#use-a-volume-with-docker-compose) for not using "bind mount".
-- The MPS backend (Metal Performance Shaders, Apple Silicon M1/M2/etc.) is not yet supported in Docker, see https://github.com/pytorch/pytorch/issues/81224
-- You can also use `docker compose up -d` to start the container detached and connect to the logs with `docker compose logs -f`. This way you can also close the terminal and keep the container running.

entrypoint.sh

@@ -1,33 +0,0 @@
-#!/bin/bash
-
-ORIGINALDIR=/content/app
-# Use predefined DATADIR if it is defined
-[[ x"${DATADIR}" == "x" ]] && DATADIR=/content/data
-
-# Make persistent dir from original dir
-function mklink () {
-	mkdir -p $DATADIR/$1
-	ln -s $DATADIR/$1 $ORIGINALDIR
-}
-
-# Copy old files from import dir
-function import () {
-	(test -d /import/$1 && cd /import/$1 && cp -Rpn . $DATADIR/$1/)
-}
-
-cd $ORIGINALDIR
-
-# models
-mklink models
-# Copy original files
-(cd $ORIGINALDIR/models.org && cp -Rpn . $ORIGINALDIR/models/)
-# Import old files
-import models
-
-# outputs
-mklink outputs
-# Import old files
-import outputs
-
-# Start application
-python launch.py $*

extras/expansion.py

@@ -112,9 +112,6 @@ class FooocusExpansion:
         max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0))
         max_new_tokens = max_token_length - current_token_length
 
-        if max_new_tokens == 0:
-            return prompt[:-1]
-
         # https://huggingface.co/blog/introducing-csearch
         # https://huggingface.co/docs/transformers/generation_strategies
         features = self.model.generate(**tokenized_kwargs,

extras/ip_adapter.py

@@ -2,13 +2,12 @@ import torch
 import ldm_patched.modules.clip_vision
 import safetensors.torch as sf
 import ldm_patched.modules.model_management as model_management
+import contextlib
 import ldm_patched.ldm.modules.attention as attention
 
 from extras.resampler import Resampler
 from ldm_patched.modules.model_patcher import ModelPatcher
 from modules.core import numpy_to_pytorch
-from modules.ops import use_patched_ops
-from ldm_patched.modules.ops import manual_cast
 
 
 SD_V12_CHANNELS = [320] * 4 + [640] * 4 + [1280] * 4 + [1280] * 6 + [640] * 6 + [320] * 6 + [1280] * 2
@@ -117,16 +116,14 @@ def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path):
         clip_extra_context_tokens = ip_state_dict["image_proj"]["proj.weight"].shape[0] // cross_attention_dim
         clip_embeddings_dim = None
 
-    with use_patched_ops(manual_cast):
-        ip_adapter = IPAdapterModel(
-            ip_state_dict,
-            plus=plus,
-            cross_attention_dim=cross_attention_dim,
-            clip_embeddings_dim=clip_embeddings_dim,
-            clip_extra_context_tokens=clip_extra_context_tokens,
-            sdxl_plus=sdxl_plus
-        )
-
+    ip_adapter = IPAdapterModel(
+        ip_state_dict,
+        plus=plus,
+        cross_attention_dim=cross_attention_dim,
+        clip_embeddings_dim=clip_embeddings_dim,
+        clip_extra_context_tokens=clip_extra_context_tokens,
+        sdxl_plus=sdxl_plus
+    )
     ip_adapter.sdxl = sdxl
     ip_adapter.load_device = load_device
     ip_adapter.offload_device = offload_device

extras/preprocessors.py

@@ -1,26 +1,27 @@
 import cv2
 import numpy as np
+import modules.advanced_parameters as advanced_parameters
 
 
-def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
+def centered_canny(x: np.ndarray):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 2 and x.dtype == np.uint8
 
-    y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
+    y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold))
     y = y.astype(np.float32) / 255.0
     return y
 
 
-def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
+def centered_canny_color(x: np.ndarray):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
-    result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
+    result = [centered_canny(x[..., i]) for i in range(3)]
     result = np.stack(result, axis=2)
     return result
 
 
-def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
+def pyramid_canny_color(x: np.ndarray):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
@@ -30,7 +31,7 @@ def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold
     for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
         Hs, Ws = int(H * k), int(W * k)
         small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
-        edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
+        edge = centered_canny_color(small)
         if acc_edge is None:
             acc_edge = edge
         else:
@@ -53,11 +54,11 @@ def norm255(x, low=4, high=96):
     return x * 255.0
 
 
-def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
+def canny_pyramid(x):
     # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
     # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
 
-    color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
+    color_canny = pyramid_canny_color(x)
     result = np.sum(color_canny, axis=2)
 
     return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)

extras/resampler.py

@@ -108,7 +108,8 @@ class Resampler(nn.Module):
             )
 
     def forward(self, x):
-        latents = self.latents.repeat(x.size(0), 1, 1).to(x)
+        
+        latents = self.latents.repeat(x.size(0), 1, 1)
         
         x = self.proj_in(x)
         
@@ -117,4 +118,4 @@ class Resampler(nn.Module):
             latents = ff(latents) + latents
             
         latents = self.proj_out(latents)
-        return self.norm_out(latents)
+        return self.norm_out(latents)

fooocus_colab.ipynb

@@ -12,7 +12,7 @@
     "%cd /content\n",
     "!git clone https://github.com/lllyasviel/Fooocus.git\n",
     "%cd /content/Fooocus\n",
-    "!python entry_with_update.py --share --always-high-vram\n"
+    "!python entry_with_update.py --share\n"
    ]
   }
  ],

fooocus_version.py

@@ -1 +1 @@
-version = '2.3.1'
+version = '2.1.851'

javascript/contextMenus.js

@@ -154,8 +154,12 @@ let cancelGenerateForever = function() {
     let generateOnRepeatForButtons = function() {
         generateOnRepeat('#generate_button', '#stop_button');
     };
-    appendContextMenuOption('#generate_button', 'Generate forever', generateOnRepeatForButtons);
 
+    appendContextMenuOption('#generate_button', 'Generate forever', generateOnRepeatForButtons);
+//    appendContextMenuOption('#stop_button', 'Generate forever', generateOnRepeatForButtons);
+
+//    appendContextMenuOption('#stop_button', 'Cancel generate forever', cancelGenerateForever);
+//    appendContextMenuOption('#generate_button', 'Cancel generate forever', cancelGenerateForever);
 })();
 //End example Context Menu Items
 

javascript/localization.js

@@ -45,9 +45,6 @@ function processTextNode(node) {
     var tl = getTranslation(text);
     if (tl !== undefined) {
         node.textContent = tl;
-        if (text && node.parentElement) {
-          node.parentElement.setAttribute("data-original-text", text);
-        }
     }
 }
 

javascript/script.js

@@ -119,7 +119,6 @@ document.addEventListener("DOMContentLoaded", function() {
         }
     });
     mutationObserver.observe(gradioApp(), {childList: true, subtree: true});
-    initStylePreviewOverlay();
 });
 
 /**
@@ -146,46 +145,6 @@ document.addEventListener('keydown', function(e) {
     }
 });
 
-function initStylePreviewOverlay() {
-    let overlayVisible = false;
-    const samplesPath = document.querySelector("meta[name='samples-path']").getAttribute("content")
-    const overlay = document.createElement('div');
-    const tooltip = document.createElement('div');
-    tooltip.className = 'preview-tooltip';
-    overlay.appendChild(tooltip);
-    overlay.id = 'stylePreviewOverlay';
-    document.body.appendChild(overlay);
-    document.addEventListener('mouseover', function (e) {
-        const label = e.target.closest('.style_selections label');
-        if (!label) return;
-        label.removeEventListener("mouseout", onMouseLeave);
-        label.addEventListener("mouseout", onMouseLeave);
-        overlayVisible = true;
-        overlay.style.opacity = "1";
-        const originalText = label.querySelector("span").getAttribute("data-original-text");
-        const name = originalText || label.querySelector("span").textContent;
-        overlay.style.backgroundImage = `url("${samplesPath.replace(
-            "fooocus_v2",
-            name.toLowerCase().replaceAll(" ", "_")
-        ).replaceAll("\\", "\\\\")}")`;
-
-        tooltip.textContent = name;
-
-        function onMouseLeave() {
-            overlayVisible = false;
-            overlay.style.opacity = "0";
-            overlay.style.backgroundImage = "";
-            label.removeEventListener("mouseout", onMouseLeave);
-        }
-    });
-    document.addEventListener('mousemove', function (e) {
-        if (!overlayVisible) return;
-        overlay.style.left = `${e.clientX}px`;
-        overlay.style.top = `${e.clientY}px`;
-        overlay.className = e.clientY > window.innerHeight / 2 ? "lower-half" : "upper-half";
-    });
-}
-
 /**
  * checks that a UI element is not in another hidden element or tab content
  */

language/en.json

@@ -38,12 +38,9 @@
     "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)": "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)",
     "Setting": "Setting",
     "Style": "Style",
-    "Preset": "Preset",
     "Performance": "Performance",
     "Speed": "Speed",
     "Quality": "Quality",
-    "Extreme Speed": "Extreme Speed",
-    "Lightning": "Lightning",
     "Aspect Ratios": "Aspect Ratios",
     "width \u00d7 height": "width \u00d7 height",
     "Image Number": "Image Number",
@@ -51,9 +48,6 @@
     "Describing what you do not want to see.": "Describing what you do not want to see.",
     "Random": "Random",
     "Seed": "Seed",
-    "Disable seed increment": "Disable seed increment",
-    "Disable automatic seed increment when image number is > 1.": "Disable automatic seed increment when image number is > 1.",
-    "Read wildcards in order": "Read wildcards in order",
     "\ud83d\udcda History Log": "\uD83D\uDCDA History Log",
     "Image Style": "Image Style",
     "Fooocus V2": "Fooocus V2",
@@ -348,10 +342,6 @@
     "Forced Overwrite of Denoising Strength of \"Vary\"": "Forced Overwrite of Denoising Strength of \"Vary\"",
     "Set as negative number to disable. For developer debugging.": "Set as negative number to disable. For developer debugging.",
     "Forced Overwrite of Denoising Strength of \"Upscale\"": "Forced Overwrite of Denoising Strength of \"Upscale\"",
-    "Disable Preview": "Disable Preview",
-    "Disable preview during generation.": "Disable preview during generation.",
-    "Disable Intermediate Results": "Disable Intermediate Results",
-    "Disable intermediate results during generation, only show final gallery.": "Disable intermediate results during generation, only show final gallery.",
     "Inpaint Engine": "Inpaint Engine",
     "v1": "v1",
     "Version of Fooocus inpaint model": "Version of Fooocus inpaint model",
@@ -371,19 +361,12 @@
     "B2": "B2",
     "S1": "S1",
     "S2": "S2",
+    "Extreme Speed": "Extreme Speed",
     "\uD83D\uDD0E Type here to search styles ...": "\uD83D\uDD0E Type here to search styles ...",
     "Type prompt here.": "Type prompt here.",
     "Outpaint Expansion Direction:": "Outpaint Expansion Direction:",
     "* Powered by Fooocus Inpaint Engine (beta)": "* Powered by Fooocus Inpaint Engine (beta)",
     "Fooocus Enhance": "Fooocus Enhance",
     "Fooocus Cinematic": "Fooocus Cinematic",
-    "Fooocus Sharp": "Fooocus Sharp",
-    "Drag any image generated by Fooocus here": "Drag any image generated by Fooocus here",
-    "Metadata": "Metadata",
-    "Apply Metadata": "Apply Metadata",
-    "Metadata Scheme": "Metadata Scheme",
-    "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.",
-    "fooocus (json)": "fooocus (json)",
-    "a1111 (plain text)": "a1111 (plain text)",
-    "Unsupported image type in input": "Unsupported image type in input"
+    "Fooocus Sharp": "Fooocus Sharp"
 }

launch.py

@@ -1,6 +1,6 @@
 import os
-import ssl
 import sys
+import ssl
 
 print('[System ARGV] ' + str(sys.argv))
 
@@ -10,17 +10,20 @@ os.chdir(root)
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
-if "GRADIO_SERVER_PORT" not in os.environ:
-    os.environ["GRADIO_SERVER_PORT"] = "7865"
+os.environ["GRADIO_SERVER_PORT"] = "7865"
 
 ssl._create_default_https_context = ssl._create_unverified_context
 
+
 import platform
 import fooocus_version
 
 from build_launcher import build_launcher
-from modules.launch_util import is_installed, run, python, run_pip, requirements_met, delete_folder_content
+from modules.launch_util import is_installed, run, python, run_pip, requirements_met
 from modules.model_loader import load_file_from_url
+from modules.config import path_checkpoints, path_loras, path_vae_approx, path_fooocus_expansion, \
+    checkpoint_downloads, path_embeddings, embeddings_downloads, lora_downloads
+
 
 REINSTALL_ALL = False
 TRY_INSTALL_XFORMERS = False
@@ -40,7 +43,7 @@ def prepare_environment():
 
     if TRY_INSTALL_XFORMERS:
         if REINSTALL_ALL or not is_installed("xformers"):
-            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23')
+            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
             if platform.system() == "Windows":
                 if platform.python_version().startswith("3.10"):
                     run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
@@ -67,6 +70,25 @@ vae_approx_filenames = [
 ]
 
 
+def download_models():
+    for file_name, url in checkpoint_downloads.items():
+        load_file_from_url(url=url, model_dir=path_checkpoints, file_name=file_name)
+    for file_name, url in embeddings_downloads.items():
+        load_file_from_url(url=url, model_dir=path_embeddings, file_name=file_name)
+    for file_name, url in lora_downloads.items():
+        load_file_from_url(url=url, model_dir=path_loras, file_name=file_name)
+    for file_name, url in vae_approx_filenames:
+        load_file_from_url(url=url, model_dir=path_vae_approx, file_name=file_name)
+
+    load_file_from_url(
+        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',
+        model_dir=path_fooocus_expansion,
+        file_name='pytorch_model.bin'
+    )
+
+    return
+
+
 def ini_args():
     from args_manager import args
     return args
@@ -76,61 +98,12 @@ prepare_environment()
 build_launcher()
 args = ini_args()
 
+
 if args.gpu_device_id is not None:
     os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)
     print("Set device to:", args.gpu_device_id)
 
-from modules import config
 
-os.environ['GRADIO_TEMP_DIR'] = config.temp_path
-
-if config.temp_path_cleanup_on_launch:
-    print(f'[Cleanup] Attempting to delete content of temp dir {config.temp_path}')
-    result = delete_folder_content(config.temp_path, '[Cleanup] ')
-    if result:
-        print("[Cleanup] Cleanup successful")
-    else:
-        print(f"[Cleanup] Failed to delete content of temp dir.")
-
-
-def download_models(default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads):
-    for file_name, url in vae_approx_filenames:
-        load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)
-
-    load_file_from_url(
-        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',
-        model_dir=config.path_fooocus_expansion,
-        file_name='pytorch_model.bin'
-    )
-
-    if args.disable_preset_download:
-        print('Skipped model download.')
-        return default_model, checkpoint_downloads
-
-    if not args.always_download_new_model:
-        if not os.path.exists(os.path.join(config.paths_checkpoints[0], default_model)):
-            for alternative_model_name in previous_default_models:
-                if os.path.exists(os.path.join(config.paths_checkpoints[0], alternative_model_name)):
-                    print(f'You do not have [{default_model}] but you have [{alternative_model_name}].')
-                    print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
-                          f'but you are not using the latest models.')
-                    print('Use --always-download-new-model to avoid fallback and always get new models.')
-                    checkpoint_downloads = {}
-                    default_model = alternative_model_name
-                    break
-
-    for file_name, url in checkpoint_downloads.items():
-        load_file_from_url(url=url, model_dir=config.paths_checkpoints[0], file_name=file_name)
-    for file_name, url in embeddings_downloads.items():
-        load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
-    for file_name, url in lora_downloads.items():
-        load_file_from_url(url=url, model_dir=config.paths_loras[0], file_name=file_name)
-
-    return default_model, checkpoint_downloads
-
-
-config.default_base_model_name, config.checkpoint_downloads = download_models(
-    config.default_base_model_name, config.previous_default_models, config.checkpoint_downloads,
-    config.embeddings_downloads, config.lora_downloads)
+download_models()
 
 from webui import *

ldm_patched/contrib/external.py

@@ -11,7 +11,7 @@ import math
 import time
 import random
 
-from PIL import Image, ImageOps, ImageSequence
+from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
 import safetensors.torch
@@ -361,62 +361,6 @@ class VAEEncodeForInpaint:
 
         return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
 
-
-class InpaintModelConditioning:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "vae": ("VAE", ),
-                             "pixels": ("IMAGE", ),
-                             "mask": ("MASK", ),
-                             }}
-
-    RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/inpaint"
-
-    def encode(self, positive, negative, pixels, vae, mask):
-        x = (pixels.shape[1] // 8) * 8
-        y = (pixels.shape[2] // 8) * 8
-        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
-
-        orig_pixels = pixels
-        pixels = orig_pixels.clone()
-        if pixels.shape[1] != x or pixels.shape[2] != y:
-            x_offset = (pixels.shape[1] % 8) // 2
-            y_offset = (pixels.shape[2] % 8) // 2
-            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
-            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
-
-        m = (1.0 - mask.round()).squeeze(1)
-        for i in range(3):
-            pixels[:,:,:,i] -= 0.5
-            pixels[:,:,:,i] *= m
-            pixels[:,:,:,i] += 0.5
-        concat_latent = vae.encode(pixels)
-        orig_latent = vae.encode(orig_pixels)
-
-        out_latent = {}
-
-        out_latent["samples"] = orig_latent
-        out_latent["noise_mask"] = mask
-
-        out = []
-        for conditioning in [positive, negative]:
-            c = []
-            for t in conditioning:
-                d = t[1].copy()
-                d["concat_latent_image"] = concat_latent
-                d["concat_mask"] = mask
-                n = [t[0], d]
-                c.append(n)
-            out.append(c)
-        return (out[0], out[1], out_latent)
-
-
 class SaveLatent:
     def __init__(self):
         self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
@@ -1468,32 +1412,17 @@ class LoadImage:
     FUNCTION = "load_image"
     def load_image(self, image):
         image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)
-        img = Image.open(image_path)
-        output_images = []
-        output_masks = []
-        for i in ImageSequence.Iterator(img):
-            i = ImageOps.exif_transpose(i)
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
-            image = i.convert("RGB")
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            if 'A' in i.getbands():
-                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
-            else:
-                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
-
-        if len(output_images) > 1:
-            output_image = torch.cat(output_images, dim=0)
-            output_mask = torch.cat(output_masks, dim=0)
+        i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
+        image = i.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
         else:
-            output_image = output_images[0]
-            output_mask = output_masks[0]
-
-        return (output_image, output_mask)
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        return (image, mask.unsqueeze(0))
 
     @classmethod
     def IS_CHANGED(s, image):
@@ -1530,8 +1459,6 @@ class LoadImageMask:
         i = Image.open(image_path)
         i = ImageOps.exif_transpose(i)
         if i.getbands() != ("R", "G", "B", "A"):
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
             i = i.convert("RGBA")
         mask = None
         c = channel[0].upper()
@@ -1553,10 +1480,13 @@ class LoadImageMask:
         return m.digest().hex()
 
     @classmethod
-    def VALIDATE_INPUTS(s, image):
+    def VALIDATE_INPUTS(s, image, channel):
         if not ldm_patched.utils.path_utils.exists_annotated_filepath(image):
             return "Invalid image file: {}".format(image)
 
+        if channel not in s._color_channels:
+            return "Invalid color channel: {}".format(channel)
+
         return True
 
 class ImageScale:
@@ -1686,11 +1616,10 @@ class ImagePadForOutpaint:
     def expand_image(self, image, left, top, right, bottom, feathering):
         d1, d2, d3, d4 = image.size()
 
-        new_image = torch.ones(
+        new_image = torch.zeros(
             (d1, d2 + top + bottom, d3 + left + right, d4),
             dtype=torch.float32,
-        ) * 0.5
-
+        )
         new_image[:, top:top + d2, left:left + d3, :] = image
 
         mask = torch.ones(
@@ -1782,7 +1711,6 @@ NODE_CLASS_MAPPINGS = {
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
     "GLIGENLoader": GLIGENLoader,
     "GLIGENTextBoxApply": GLIGENTextBoxApply,
-    "InpaintModelConditioning": InpaintModelConditioning,
 
     "CheckpointLoader": CheckpointLoader,
     "DiffusersLoader": DiffusersLoader,
@@ -1943,9 +1871,6 @@ def init_custom_nodes():
         "nodes_video_model.py",
         "nodes_sag.py",
         "nodes_perpneg.py",
-        "nodes_stable3d.py",
-        "nodes_sdupscale.py",
-        "nodes_photomaker.py",
     ]
 
     for node_file in extras_files:

ldm_patched/contrib/external_canny.py

@@ -78,7 +78,7 @@ def spatial_gradient(input, normalized: bool = True):
     Return:
         the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`.
     .. note::
-       See a working example `here <https://kornia.readthedocs.io/en/latest/
+       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
        filtering_edges.html>`__.
     Examples:
         >>> input = torch.rand(1, 3, 4, 4)
@@ -120,7 +120,7 @@ def rgb_to_grayscale(image, rgb_weights = None):
         grayscale version of the image with shape :math:`(*,1,H,W)`.
 
     .. note::
-       See a working example `here <https://kornia.readthedocs.io/en/latest/
+       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
        color_conversions.html>`__.
 
     Example:
@@ -176,7 +176,7 @@ def canny(
         - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`.
         - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`.
     .. note::
-       See a working example `here <https://kornia.readthedocs.io/en/latest/
+       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
        canny.html>`__.
     Example:
         >>> input = torch.rand(5, 3, 4, 4)

ldm_patched/contrib/external_custom_sampler.py

@@ -15,7 +15,6 @@ class BasicScheduler:
                     {"model": ("MODEL",),
                      "scheduler": (ldm_patched.modules.samplers.SCHEDULER_NAMES, ),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                       }
                }
     RETURN_TYPES = ("SIGMAS",)
@@ -23,14 +22,8 @@ class BasicScheduler:
 
     FUNCTION = "get_sigmas"
 
-    def get_sigmas(self, model, scheduler, steps, denoise):
-        total_steps = steps
-        if denoise < 1.0:
-            total_steps = int(steps/denoise)
-
-        ldm_patched.modules.model_management.load_models_gpu([model])
-        sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu()
-        sigmas = sigmas[-(steps + 1):]
+    def get_sigmas(self, model, scheduler, steps):
+        sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, steps).cpu()
         return (sigmas, )
 
 
@@ -96,7 +89,6 @@ class SDTurboScheduler:
         return {"required":
                     {"model": ("MODEL",),
                      "steps": ("INT", {"default": 1, "min": 1, "max": 10}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
                       }
                }
     RETURN_TYPES = ("SIGMAS",)
@@ -104,10 +96,8 @@ class SDTurboScheduler:
 
     FUNCTION = "get_sigmas"
 
-    def get_sigmas(self, model, steps, denoise):
-        start_step = 10 - int(10 * denoise)
-        timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
-        ldm_patched.modules.model_management.load_models_gpu([model])
+    def get_sigmas(self, model, steps):
+        timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[:steps]
         sigmas = model.model.model_sampling.sigma(timesteps)
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )

ldm_patched/contrib/external_freelunch.py

@@ -36,7 +36,7 @@ class FreeU:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "model_patches"
+    CATEGORY = "_for_testing"
 
     def patch(self, model, b1, b2, s1, s2):
         model_channels = model.model.model_config.unet_config["model_channels"]
@@ -75,7 +75,7 @@ class FreeU_V2:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "model_patches"
+    CATEGORY = "_for_testing"
 
     def patch(self, model, b1, b2, s1, s2):
         model_channels = model.model.model_config.unet_config["model_channels"]

ldm_patched/contrib/external_hypertile.py

@@ -34,29 +34,29 @@ class HyperTile:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "model_patches"
+    CATEGORY = "_for_testing"
 
     def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
         model_channels = model.model.model_config.unet_config["model_channels"]
 
+        apply_to = set()
+        temp = model_channels
+        for x in range(max_depth + 1):
+            apply_to.add(temp)
+            temp *= 2
+
         latent_tile_size = max(32, tile_size) // 8
         self.temp = None
 
         def hypertile_in(q, k, v, extra_options):
-            model_chans = q.shape[-2]
-            orig_shape = extra_options['original_shape']
-            apply_to = []
-            for i in range(max_depth + 1):
-                apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i)))
-
-            if model_chans in apply_to:
+            if q.shape[-1] in apply_to:
                 shape = extra_options["original_shape"]
                 aspect_ratio = shape[-1] / shape[-2]
 
                 hw = q.size(1)
                 h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
 
-                factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1
+                factor = 2**((q.shape[-1] // model_channels) - 1) if scale_depth else 1
                 nh = random_divisor(h, latent_tile_size * factor, swap_size)
                 nw = random_divisor(w, latent_tile_size * factor, swap_size)
 

ldm_patched/contrib/external_latent.py

@@ -124,34 +124,10 @@ class LatentBatch:
         samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
         return (samples_out,)
 
-class LatentBatchSeedBehavior:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT",),
-                              "seed_behavior": (["random", "fixed"],),}}
-
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples, seed_behavior):
-        samples_out = samples.copy()
-        latent = samples["samples"]
-        if seed_behavior == "random":
-            if 'batch_index' in samples_out:
-                samples_out.pop('batch_index')
-        elif seed_behavior == "fixed":
-            batch_number = samples_out.get("batch_index", [0])[0]
-            samples_out["batch_index"] = [batch_number] * latent.shape[0]
-
-        return (samples_out,)
-
 NODE_CLASS_MAPPINGS = {
     "LatentAdd": LatentAdd,
     "LatentSubtract": LatentSubtract,
     "LatentMultiply": LatentMultiply,
     "LatentInterpolate": LatentInterpolate,
     "LatentBatch": LatentBatch,
-    "LatentBatchSeedBehavior": LatentBatchSeedBehavior,
 }

ldm_patched/contrib/external_mask.py

@@ -8,7 +8,6 @@ import ldm_patched.modules.utils
 from ldm_patched.contrib.external import MAX_RESOLUTION
 
 def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
-    source = source.to(destination.device)
     if resize_source:
         source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
 
@@ -23,7 +22,7 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
     if mask is None:
         mask = torch.ones_like(source)
     else:
-        mask = mask.to(destination.device, copy=True)
+        mask = mask.clone()
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
         mask = ldm_patched.modules.utils.repeat_to_batch_size(mask, source.shape[0])
 

ldm_patched/contrib/external_model_merging.py

@@ -121,48 +121,6 @@ class ModelMergeBlocks:
             m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
         return (m, )
 
-def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None):
-    full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, output_dir)
-    prompt_info = ""
-    if prompt is not None:
-        prompt_info = json.dumps(prompt)
-
-    metadata = {}
-
-    enable_modelspec = True
-    if isinstance(model.model, ldm_patched.modules.model_base.SDXL):
-        metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
-    elif isinstance(model.model, ldm_patched.modules.model_base.SDXLRefiner):
-        metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
-    else:
-        enable_modelspec = False
-
-    if enable_modelspec:
-        metadata["modelspec.sai_model_spec"] = "1.0.0"
-        metadata["modelspec.implementation"] = "sgm"
-        metadata["modelspec.title"] = "{} {}".format(filename, counter)
-
-    #TODO:
-    # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512",
-    # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
-    # "v2-inpainting"
-
-    if model.model.model_type == ldm_patched.modules.model_base.ModelType.EPS:
-        metadata["modelspec.predict_key"] = "epsilon"
-    elif model.model.model_type == ldm_patched.modules.model_base.ModelType.V_PREDICTION:
-        metadata["modelspec.predict_key"] = "v"
-
-    if not args.disable_server_info:
-        metadata["prompt"] = prompt_info
-        if extra_pnginfo is not None:
-            for x in extra_pnginfo:
-                metadata[x] = json.dumps(extra_pnginfo[x])
-
-    output_checkpoint = f"{filename}_{counter:05}_.safetensors"
-    output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
-
-    ldm_patched.modules.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata)
-
 class CheckpointSave:
     def __init__(self):
         self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
@@ -181,7 +139,46 @@ class CheckpointSave:
     CATEGORY = "advanced/model_merging"
 
     def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
-        save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
+        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir)
+        prompt_info = ""
+        if prompt is not None:
+            prompt_info = json.dumps(prompt)
+
+        metadata = {}
+
+        enable_modelspec = True
+        if isinstance(model.model, ldm_patched.modules.model_base.SDXL):
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
+        elif isinstance(model.model, ldm_patched.modules.model_base.SDXLRefiner):
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
+        else:
+            enable_modelspec = False
+
+        if enable_modelspec:
+            metadata["modelspec.sai_model_spec"] = "1.0.0"
+            metadata["modelspec.implementation"] = "sgm"
+            metadata["modelspec.title"] = "{} {}".format(filename, counter)
+
+        #TODO:
+        # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512",
+        # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
+        # "v2-inpainting"
+
+        if model.model.model_type == ldm_patched.modules.model_base.ModelType.EPS:
+            metadata["modelspec.predict_key"] = "epsilon"
+        elif model.model.model_type == ldm_patched.modules.model_base.ModelType.V_PREDICTION:
+            metadata["modelspec.predict_key"] = "v"
+
+        if not args.disable_server_info:
+            metadata["prompt"] = prompt_info
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata[x] = json.dumps(extra_pnginfo[x])
+
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        ldm_patched.modules.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata)
         return {}
 
 class CLIPSave:

ldm_patched/contrib/external_photomaker.py

@@ -1,189 +0,0 @@
-# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
-
-import torch
-import torch.nn as nn
-import ldm_patched.utils.path_utils
-import ldm_patched.modules.clip_model
-import ldm_patched.modules.clip_vision
-import ldm_patched.modules.ops
-
-# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0
-VISION_CONFIG_DICT = {
-    "hidden_size": 1024,
-    "image_size": 224,
-    "intermediate_size": 4096,
-    "num_attention_heads": 16,
-    "num_channels": 3,
-    "num_hidden_layers": 24,
-    "patch_size": 14,
-    "projection_dim": 768,
-    "hidden_act": "quick_gelu",
-}
-
-class MLP(nn.Module):
-    def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=ldm_patched.modules.ops):
-        super().__init__()
-        if use_residual:
-            assert in_dim == out_dim
-        self.layernorm = operations.LayerNorm(in_dim)
-        self.fc1 = operations.Linear(in_dim, hidden_dim)
-        self.fc2 = operations.Linear(hidden_dim, out_dim)
-        self.use_residual = use_residual
-        self.act_fn = nn.GELU()
-
-    def forward(self, x):
-        residual = x
-        x = self.layernorm(x)
-        x = self.fc1(x)
-        x = self.act_fn(x)
-        x = self.fc2(x)
-        if self.use_residual:
-            x = x + residual
-        return x
-
-
-class FuseModule(nn.Module):
-    def __init__(self, embed_dim, operations):
-        super().__init__()
-        self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations)
-        self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations)
-        self.layer_norm = operations.LayerNorm(embed_dim)
-
-    def fuse_fn(self, prompt_embeds, id_embeds):
-        stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)
-        stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds
-        stacked_id_embeds = self.mlp2(stacked_id_embeds)
-        stacked_id_embeds = self.layer_norm(stacked_id_embeds)
-        return stacked_id_embeds
-
-    def forward(
-        self,
-        prompt_embeds,
-        id_embeds,
-        class_tokens_mask,
-    ) -> torch.Tensor:
-        # id_embeds shape: [b, max_num_inputs, 1, 2048]
-        id_embeds = id_embeds.to(prompt_embeds.dtype)
-        num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
-        batch_size, max_num_inputs = id_embeds.shape[:2]
-        # seq_length: 77
-        seq_length = prompt_embeds.shape[1]
-        # flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
-        flat_id_embeds = id_embeds.view(
-            -1, id_embeds.shape[-2], id_embeds.shape[-1]
-        )
-        # valid_id_mask [b*max_num_inputs]
-        valid_id_mask = (
-            torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
-            < num_inputs[:, None]
-        )
-        valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]
-
-        prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
-        class_tokens_mask = class_tokens_mask.view(-1)
-        valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
-        # slice out the image token embeddings
-        image_token_embeds = prompt_embeds[class_tokens_mask]
-        stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
-        assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
-        prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
-        updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
-        return updated_prompt_embeds
-
-class PhotoMakerIDEncoder(ldm_patched.modules.clip_model.CLIPVisionModelProjection):
-    def __init__(self):
-        self.load_device = ldm_patched.modules.model_management.text_encoder_device()
-        offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()
-        dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device)
-
-        super().__init__(VISION_CONFIG_DICT, dtype, offload_device, ldm_patched.modules.ops.manual_cast)
-        self.visual_projection_2 = ldm_patched.modules.ops.manual_cast.Linear(1024, 1280, bias=False)
-        self.fuse_module = FuseModule(2048, ldm_patched.modules.ops.manual_cast)
-
-    def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask):
-        b, num_inputs, c, h, w = id_pixel_values.shape
-        id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)
-
-        shared_id_embeds = self.vision_model(id_pixel_values)[2]
-        id_embeds = self.visual_projection(shared_id_embeds)
-        id_embeds_2 = self.visual_projection_2(shared_id_embeds)
-
-        id_embeds = id_embeds.view(b, num_inputs, 1, -1)
-        id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)
-
-        id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
-        updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)
-
-        return updated_prompt_embeds
-
-
-class PhotoMakerLoader:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "photomaker_model_name": (ldm_patched.utils.path_utils.get_filename_list("photomaker"), )}}
-
-    RETURN_TYPES = ("PHOTOMAKER",)
-    FUNCTION = "load_photomaker_model"
-
-    CATEGORY = "_for_testing/photomaker"
-
-    def load_photomaker_model(self, photomaker_model_name):
-        photomaker_model_path = ldm_patched.utils.path_utils.get_full_path("photomaker", photomaker_model_name)
-        photomaker_model = PhotoMakerIDEncoder()
-        data = ldm_patched.modules.utils.load_torch_file(photomaker_model_path, safe_load=True)
-        if "id_encoder" in data:
-            data = data["id_encoder"]
-        photomaker_model.load_state_dict(data)
-        return (photomaker_model,)
-
-
-class PhotoMakerEncode:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "photomaker": ("PHOTOMAKER",),
-                              "image": ("IMAGE",),
-                              "clip": ("CLIP", ),
-                              "text": ("STRING", {"multiline": True, "default": "photograph of photomaker"}),
-                             }}
-
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "apply_photomaker"
-
-    CATEGORY = "_for_testing/photomaker"
-
-    def apply_photomaker(self, photomaker, image, clip, text):
-        special_token = "photomaker"
-        pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()
-        try:
-            index = text.split(" ").index(special_token) + 1
-        except ValueError:
-            index = -1
-        tokens = clip.tokenize(text, return_word_ids=True)
-        out_tokens = {}
-        for k in tokens:
-            out_tokens[k] = []
-            for t in tokens[k]:
-                f = list(filter(lambda x: x[2] != index, t))
-                while len(f) < len(t):
-                    f.append(t[-1])
-                out_tokens[k].append(f)
-
-        cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True)
-
-        if index > 0:
-            token_index = index - 1
-            num_id_images = 1
-            class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)]
-            out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device),
-                            class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0))
-        else:
-            out = cond
-
-        return ([[out, {"pooled_output": pooled}]], )
-
-
-NODE_CLASS_MAPPINGS = {
-    "PhotoMakerLoader": PhotoMakerLoader,
-    "PhotoMakerEncode": PhotoMakerEncode,
-}
-

ldm_patched/contrib/external_post_processing.py

@@ -35,7 +35,6 @@ class Blend:
     CATEGORY = "image/postprocessing"
 
     def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
-        image2 = image2.to(image1.device)
         if image1.shape != image2.shape:
             image2 = image2.permute(0, 3, 1, 2)
             image2 = ldm_patched.modules.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')

ldm_patched/contrib/external_rebatch.py

@@ -101,40 +101,10 @@ class LatentRebatch:
 
         return (output_list,)
 
-class ImageRebatch:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "images": ("IMAGE",),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                              }}
-    RETURN_TYPES = ("IMAGE",)
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True, )
-
-    FUNCTION = "rebatch"
-
-    CATEGORY = "image/batch"
-
-    def rebatch(self, images, batch_size):
-        batch_size = batch_size[0]
-
-        output_list = []
-        all_images = []
-        for img in images:
-            for i in range(img.shape[0]):
-                all_images.append(img[i:i+1])
-
-        for i in range(0, len(all_images), batch_size):
-            output_list.append(torch.cat(all_images[i:i+batch_size], dim=0))
-
-        return (output_list,)
-
 NODE_CLASS_MAPPINGS = {
     "RebatchLatents": LatentRebatch,
-    "RebatchImages": ImageRebatch,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "RebatchLatents": "Rebatch Latents",
-    "RebatchImages": "Rebatch Images",
-}
+}

ldm_patched/contrib/external_sag.py

@@ -60,7 +60,7 @@ def create_blur_map(x0, attn, sigma=3.0, threshold=1.0):
     attn = attn.reshape(b, -1, hw1, hw2)
     # Global Average Pool
     mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
-    ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length()
+    ratio = math.ceil(math.sqrt(lh * lw / hw1))
     mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
 
     # Reshape
@@ -145,8 +145,6 @@ class SelfAttentionGuidance:
             sigma = args["sigma"]
             model_options = args["model_options"]
             x = args["input"]
-            if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding
-                return cfg_result
 
             # create the adversarially blurred image
             degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold)
@@ -155,7 +153,7 @@ class SelfAttentionGuidance:
             (sag, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options)
             return cfg_result + (degraded - sag) * sag_scale
 
-        m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True)
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
 
         # from diffusers:
         # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch

ldm_patched/contrib/external_sdupscale.py

@@ -1,49 +0,0 @@
-# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
-
-import torch
-import ldm_patched.contrib.external
-import ldm_patched.modules.utils
-
-class SD_4XUpscale_Conditioning:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "images": ("IMAGE",),
-                              "positive": ("CONDITIONING",),
-                              "negative": ("CONDITIONING",),
-                              "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                              "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                             }}
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/upscale_diffusion"
-
-    def encode(self, images, positive, negative, scale_ratio, noise_augmentation):
-        width = max(1, round(images.shape[-2] * scale_ratio))
-        height = max(1, round(images.shape[-3] * scale_ratio))
-
-        pixels = ldm_patched.modules.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center")
-
-        out_cp = []
-        out_cn = []
-
-        for t in positive:
-            n = [t[0], t[1].copy()]
-            n[1]['concat_image'] = pixels
-            n[1]['noise_augmentation'] = noise_augmentation
-            out_cp.append(n)
-
-        for t in negative:
-            n = [t[0], t[1].copy()]
-            n[1]['concat_image'] = pixels
-            n[1]['noise_augmentation'] = noise_augmentation
-            out_cn.append(n)
-
-        latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
-        return (out_cp, out_cn, {"samples":latent})
-
-NODE_CLASS_MAPPINGS = {
-    "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning,
-}

ldm_patched/contrib/external_stable3d.py

@@ -1,104 +0,0 @@
-# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
-
-import torch
-import ldm_patched.contrib.external
-import ldm_patched.modules.utils
-
-def camera_embeddings(elevation, azimuth):
-    elevation = torch.as_tensor([elevation])
-    azimuth = torch.as_tensor([azimuth])
-    embeddings = torch.stack(
-        [
-                torch.deg2rad(
-                    (90 - elevation) - (90)
-                ),  # Zero123 polar is 90-elevation
-                torch.sin(torch.deg2rad(azimuth)),
-                torch.cos(torch.deg2rad(azimuth)),
-                torch.deg2rad(
-                    90 - torch.full_like(elevation, 0)
-                ),
-        ], dim=-1).unsqueeze(1)
-
-    return embeddings
-
-
-class StableZero123_Conditioning:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "clip_vision": ("CLIP_VISION",),
-                              "init_image": ("IMAGE",),
-                              "vae": ("VAE",),
-                              "width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
-                              "height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                              "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                              "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                             }}
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/3d_models"
-
-    def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
-        output = clip_vision.encode_image(init_image)
-        pooled = output.image_embeds.unsqueeze(0)
-        pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
-        encode_pixels = pixels[:,:,:,:3]
-        t = vae.encode(encode_pixels)
-        cam_embeds = camera_embeddings(elevation, azimuth)
-        cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1)
-
-        positive = [[cond, {"concat_latent_image": t}]]
-        negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
-        return (positive, negative, {"samples":latent})
-
-class StableZero123_Conditioning_Batched:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "clip_vision": ("CLIP_VISION",),
-                              "init_image": ("IMAGE",),
-                              "vae": ("VAE",),
-                              "width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
-                              "height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                              "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                              "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                              "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                              "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                             }}
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/3d_models"
-
-    def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
-        output = clip_vision.encode_image(init_image)
-        pooled = output.image_embeds.unsqueeze(0)
-        pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
-        encode_pixels = pixels[:,:,:,:3]
-        t = vae.encode(encode_pixels)
-
-        cam_embeds = []
-        for i in range(batch_size):
-            cam_embeds.append(camera_embeddings(elevation, azimuth))
-            elevation += elevation_batch_increment
-            azimuth += azimuth_batch_increment
-
-        cam_embeds = torch.cat(cam_embeds, dim=0)
-        cond = torch.cat([ldm_patched.modules.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1)
-
-        positive = [[cond, {"concat_latent_image": t}]]
-        negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
-        return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
-
-
-NODE_CLASS_MAPPINGS = {
-    "StableZero123_Conditioning": StableZero123_Conditioning,
-    "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
-}

ldm_patched/contrib/external_video_model.py

@@ -5,7 +5,6 @@ import torch
 import ldm_patched.modules.utils
 import ldm_patched.modules.sd
 import ldm_patched.utils.path_utils
-import ldm_patched.contrib.external_model_merging
 
 
 class ImageOnlyCheckpointLoader:
@@ -81,26 +80,10 @@ class VideoLinearCFGGuidance:
         m.set_model_sampler_cfg_function(linear_cfg)
         return (m, )
 
-class ImageOnlyCheckpointSave(ldm_patched.contrib.external_model_merging.CheckpointSave):
-    CATEGORY = "_for_testing"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "clip_vision": ("CLIP_VISION",),
-                              "vae": ("VAE",),
-                              "filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),},
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
-
-    def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None):
-        ldm_patched.contrib.external_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
-        return {}
-
 NODE_CLASS_MAPPINGS = {
     "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader,
     "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning,
     "VideoLinearCFGGuidance": VideoLinearCFGGuidance,
-    "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {

ldm_patched/ldm/models/autoencoder.py

@@ -8,7 +8,6 @@ from ldm_patched.ldm.modules.distributions.distributions import DiagonalGaussian
 
 from ldm_patched.ldm.util import instantiate_from_config
 from ldm_patched.ldm.modules.ema import LitEma
-import ldm_patched.modules.ops
 
 class DiagonalGaussianRegularizer(torch.nn.Module):
     def __init__(self, sample: bool = True):
@@ -162,12 +161,12 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
             },
             **kwargs,
         )
-        self.quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(
+        self.quant_conv = torch.nn.Conv2d(
             (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
             (1 + ddconfig["double_z"]) * embed_dim,
             1,
         )
-        self.post_quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
         self.embed_dim = embed_dim
 
     def get_autoencoder_params(self) -> list:

ldm_patched/ldm/modules/attention.py

@@ -1,9 +1,12 @@
+from inspect import isfunction
 import math
 import torch
 import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange, repeat
 from typing import Optional, Any
+from functools import partial
+
 
 from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -174,7 +177,6 @@ def attention_sub_quad(query, key, value, heads, mask=None):
         kv_chunk_size_min=kv_chunk_size_min,
         use_checkpoint=False,
         upcast_attention=upcast_attention,
-        mask=mask,
     )
 
     hidden_states = hidden_states.to(dtype)
@@ -237,12 +239,6 @@ def attention_split(q, k, v, heads, mask=None):
                 else:
                     s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
 
-                if mask is not None:
-                    if len(mask.shape) == 2:
-                        s1 += mask[i:end]
-                    else:
-                        s1 += mask[:, i:end]
-
                 s2 = s1.softmax(dim=-1).to(v.dtype)
                 del s1
                 first_op_done = True
@@ -298,14 +294,11 @@ def attention_xformers(q, k, v, heads, mask=None):
         (q, k, v),
     )
 
-    if mask is not None:
-        pad = 8 - q.shape[1] % 8
-        mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device)
-        mask_out[:, :, :mask.shape[-1]] = mask
-        mask = mask_out[:, :, :mask.shape[-1]]
-
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
+    # actually compute the attention, what we cannot get enough of
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
 
+    if exists(mask):
+        raise NotImplementedError
     out = (
         out.unsqueeze(0)
         .reshape(b, heads, -1, dim_head)
@@ -330,6 +323,7 @@ def attention_pytorch(q, k, v, heads, mask=None):
 
 
 optimized_attention = attention_basic
+optimized_attention_masked = attention_basic
 
 if model_management.xformers_enabled():
     print("Using xformers cross attention")
@@ -345,18 +339,15 @@ else:
         print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --attention-split")
         optimized_attention = attention_sub_quad
 
-optimized_attention_masked = optimized_attention
+if model_management.pytorch_attention_enabled():
+    optimized_attention_masked = attention_pytorch
 
-def optimized_attention_for_device(device, mask=False, small_input=False):
-    if small_input:
+def optimized_attention_for_device(device, mask=False):
+    if device == torch.device("cpu"): #TODO
         if model_management.pytorch_attention_enabled():
-            return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases
+            return attention_pytorch
         else:
             return attention_basic
-
-    if device == torch.device("cpu"):
-        return attention_sub_quad
-
     if mask:
         return optimized_attention_masked
 

ldm_patched/ldm/modules/diffusionmodules/model.py

@@ -41,7 +41,7 @@ def nonlinearity(x):
 
 
 def Normalize(in_channels, num_groups=32):
-    return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
 
 
 class Upsample(nn.Module):

ldm_patched/ldm/modules/diffusionmodules/openaimodel.py

@@ -1,9 +1,12 @@
 from abc import abstractmethod
+import math
 
+import numpy as np
 import torch as th
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
+from functools import partial
 
 from .util import (
     checkpoint,
@@ -434,6 +437,9 @@ class UNetModel(nn.Module):
         operations=ops,
     ):
         super().__init__()
+        assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
 
         if context_dim is not None:
             assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
@@ -450,6 +456,7 @@ class UNetModel(nn.Module):
         if num_head_channels == -1:
             assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
 
+        self.image_size = image_size
         self.in_channels = in_channels
         self.model_channels = model_channels
         self.out_channels = out_channels
@@ -495,7 +502,7 @@ class UNetModel(nn.Module):
 
         if self.num_classes is not None:
             if isinstance(self.num_classes, int):
-                self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
             elif self.num_classes == "continuous":
                 print("setting up linear c_adm embedding layer")
                 self.label_emb = nn.Linear(1, time_embed_dim)

ldm_patched/ldm/modules/diffusionmodules/upscaling.py

@@ -41,14 +41,10 @@ class AbstractLowScaleModel(nn.Module):
         self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
         self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
 
-    def q_sample(self, x_start, t, noise=None, seed=None):
-        if noise is None:
-            if seed is None:
-                noise = torch.randn_like(x_start)
-            else:
-                noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device)
-        return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise)
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
 
     def forward(self, x):
         return x, None
@@ -73,12 +69,12 @@ class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
         super().__init__(noise_schedule_config=noise_schedule_config)
         self.max_noise_level = max_noise_level
 
-    def forward(self, x, noise_level=None, seed=None):
+    def forward(self, x, noise_level=None):
         if noise_level is None:
             noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
         else:
             assert isinstance(noise_level, torch.Tensor)
-        z = self.q_sample(x, noise_level, seed=seed)
+        z = self.q_sample(x, noise_level)
         return z, noise_level
 
 

ldm_patched/ldm/modules/diffusionmodules/util.py

@@ -51,9 +51,9 @@ class AlphaBlender(nn.Module):
         if self.merge_strategy == "fixed":
             # make shape compatible
             # alpha = repeat(self.mix_factor, '1 -> b () t  () ()', t=t, b=bs)
-            alpha = self.mix_factor.to(image_only_indicator.device)
+            alpha = self.mix_factor
         elif self.merge_strategy == "learned":
-            alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device))
+            alpha = torch.sigmoid(self.mix_factor)
             # make shape compatible
             # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
         elif self.merge_strategy == "learned_with_images":
@@ -61,7 +61,7 @@ class AlphaBlender(nn.Module):
             alpha = torch.where(
                 image_only_indicator.bool(),
                 torch.ones(1, 1, device=image_only_indicator.device),
-                rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
+                rearrange(torch.sigmoid(self.mix_factor), "... -> ... 1"),
             )
             alpha = rearrange(alpha, self.rearrange_pattern)
             # make shape compatible

ldm_patched/ldm/modules/encoders/noise_aug_modules.py

@@ -15,21 +15,21 @@ class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
 
     def scale(self, x):
         # re-normalize to centered mean and unit variance
-        x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device)
+        x = (x - self.data_mean) * 1. / self.data_std
         return x
 
     def unscale(self, x):
         # back to original data stats
-        x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device)
+        x = (x * self.data_std) + self.data_mean
         return x
 
-    def forward(self, x, noise_level=None, seed=None):
+    def forward(self, x, noise_level=None):
         if noise_level is None:
             noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
         else:
             assert isinstance(noise_level, torch.Tensor)
         x = self.scale(x)
-        z = self.q_sample(x, noise_level, seed=seed)
+        z = self.q_sample(x, noise_level)
         z = self.unscale(z)
         noise_level = self.time_embed(noise_level)
         return z, noise_level

ldm_patched/ldm/modules/sub_quadratic_attention.py

@@ -61,7 +61,6 @@ def _summarize_chunk(
     value: Tensor,
     scale: float,
     upcast_attention: bool,
-    mask,
 ) -> AttnChunk:
     if upcast_attention:
         with torch.autocast(enabled=False, device_type = 'cuda'):
@@ -85,8 +84,6 @@ def _summarize_chunk(
     max_score, _ = torch.max(attn_weights, -1, keepdim=True)
     max_score = max_score.detach()
     attn_weights -= max_score
-    if mask is not None:
-        attn_weights += mask
     torch.exp(attn_weights, out=attn_weights)
     exp_weights = attn_weights.to(value.dtype)
     exp_values = torch.bmm(exp_weights, value)
@@ -99,12 +96,11 @@ def _query_chunk_attention(
     value: Tensor,
     summarize_chunk: SummarizeChunk,
     kv_chunk_size: int,
-    mask,
 ) -> Tensor:
     batch_x_heads, k_channels_per_head, k_tokens = key_t.shape
     _, _, v_channels_per_head = value.shape
 
-    def chunk_scanner(chunk_idx: int, mask) -> AttnChunk:
+    def chunk_scanner(chunk_idx: int) -> AttnChunk:
         key_chunk = dynamic_slice(
             key_t,
             (0, 0, chunk_idx),
@@ -115,13 +111,10 @@ def _query_chunk_attention(
             (0, chunk_idx, 0),
             (batch_x_heads, kv_chunk_size, v_channels_per_head)
         )
-        if mask is not None:
-            mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size]
-
-        return summarize_chunk(query, key_chunk, value_chunk, mask=mask)
+        return summarize_chunk(query, key_chunk, value_chunk)
 
     chunks: List[AttnChunk] = [
-        chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
+        chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
     ]
     acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
     chunk_values, chunk_weights, chunk_max = acc_chunk
@@ -142,7 +135,6 @@ def _get_attention_scores_no_kv_chunking(
     value: Tensor,
     scale: float,
     upcast_attention: bool,
-    mask,
 ) -> Tensor:
     if upcast_attention:
         with torch.autocast(enabled=False, device_type = 'cuda'):
@@ -164,8 +156,6 @@ def _get_attention_scores_no_kv_chunking(
             beta=0,
         )
 
-    if mask is not None:
-        attn_scores += mask
     try:
         attn_probs = attn_scores.softmax(dim=-1)
         del attn_scores
@@ -193,7 +183,6 @@ def efficient_dot_product_attention(
     kv_chunk_size_min: Optional[int] = None,
     use_checkpoint=True,
     upcast_attention=False,
-    mask = None,
 ):
     """Computes efficient dot-product attention given query, transposed key, and value.
       This is efficient version of attention presented in
@@ -220,22 +209,13 @@ def efficient_dot_product_attention(
     if kv_chunk_size_min is not None:
         kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
 
-    if mask is not None and len(mask.shape) == 2:
-        mask = mask.unsqueeze(0)
-
     def get_query_chunk(chunk_idx: int) -> Tensor:
         return dynamic_slice(
             query,
             (0, chunk_idx, 0),
             (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
         )
-
-    def get_mask_chunk(chunk_idx: int) -> Tensor:
-        if mask is None:
-            return None
-        chunk = min(query_chunk_size, q_tokens)
-        return mask[:,chunk_idx:chunk_idx + chunk]
-
+    
     summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention)
     summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
     compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
@@ -257,7 +237,6 @@ def efficient_dot_product_attention(
             query=query,
             key_t=key_t,
             value=value,
-            mask=mask,
         )
     
     # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
@@ -267,7 +246,6 @@ def efficient_dot_product_attention(
             query=get_query_chunk(i * query_chunk_size),
             key_t=key_t,
             value=value,
-            mask=get_mask_chunk(i * query_chunk_size)
         ) for i in range(math.ceil(q_tokens / query_chunk_size))
     ], dim=1)
     return res

ldm_patched/ldm/modules/temporal_ae.py

@@ -82,14 +82,14 @@ class VideoResBlock(ResnetBlock):
 
             x = self.time_stack(x, temb)
 
-            alpha = self.get_alpha(bs=b // timesteps).to(x.device)
+            alpha = self.get_alpha(bs=b // timesteps)
             x = alpha * x + (1.0 - alpha) * x_mix
 
             x = rearrange(x, "b c t h w -> (b t) c h w")
         return x
 
 
-class AE3DConv(ops.Conv2d):
+class AE3DConv(torch.nn.Conv2d):
     def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs):
         super().__init__(in_channels, out_channels, *args, **kwargs)
         if isinstance(video_kernel_size, Iterable):
@@ -97,7 +97,7 @@ class AE3DConv(ops.Conv2d):
         else:
             padding = int(video_kernel_size // 2)
 
-        self.time_mix_conv = ops.Conv3d(
+        self.time_mix_conv = torch.nn.Conv3d(
             in_channels=out_channels,
             out_channels=out_channels,
             kernel_size=video_kernel_size,
@@ -167,7 +167,7 @@ class AttnVideoBlock(AttnBlock):
         emb = emb[:, None, :]
         x_mix = x_mix + emb
 
-        alpha = self.get_alpha().to(x.device)
+        alpha = self.get_alpha()
         x_mix = self.time_mix_block(x_mix, timesteps=timesteps)
         x = alpha * x + (1.0 - alpha) * x_mix  # alpha merge
 

ldm_patched/licenses-3rd/chainer

@@ -1,20 +0,0 @@
-Copyright (c) 2015 Preferred Infrastructure, Inc.
-Copyright (c) 2015 Preferred Networks, Inc.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.

ldm_patched/licenses-3rd/comfyui

@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
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-  13. Use with the GNU Affero General Public License.
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-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
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-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
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-                     END OF TERMS AND CONDITIONS
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-            How to Apply These Terms to Your New Programs
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-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
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-to attach them to the start of each source file to most effectively
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-    Copyright (C) <year>  <name of author>
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-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
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-    (at your option) any later version.
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-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
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-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

ldm_patched/licenses-3rd/diffusers

@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
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-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
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-      subsequently incorporated within the Work.
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-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
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-      Work and such Derivative Works in Source or Object form.
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-   3. Grant of Patent License. Subject to the terms and conditions of
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-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
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-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
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-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
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-      the Work or Derivative Works thereof, You may choose to offer,
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-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
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-   END OF TERMS AND CONDITIONS
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-   APPENDIX: How to apply the Apache License to your work.
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-      To apply the Apache License to your work, attach the following
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-   See the License for the specific language governing permissions and
-   limitations under the License.

ldm_patched/licenses-3rd/kdiffusion

@@ -1,19 +0,0 @@
-Copyright (c) 2022 Katherine Crowson
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.

ldm_patched/licenses-3rd/ldm

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

ldm_patched/licenses-3rd/taesd

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023 Ollin Boer Bohan
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

ldm_patched/licenses-3rd/transformers

@@ -1,203 +0,0 @@
-Copyright 2018- The Hugging Face team. All rights reserved.
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
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-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
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-   limitations under the License.

ldm_patched/modules/args_parser.py

@@ -66,8 +66,6 @@ fpvae_group.add_argument("--vae-in-fp16", action="store_true")
 fpvae_group.add_argument("--vae-in-fp32", action="store_true")
 fpvae_group.add_argument("--vae-in-bf16", action="store_true")
 
-parser.add_argument("--vae-in-cpu", action="store_true")
-
 fpte_group = parser.add_mutually_exclusive_group()
 fpte_group.add_argument("--clip-in-fp8-e4m3fn", action="store_true")
 fpte_group.add_argument("--clip-in-fp8-e5m2", action="store_true")
@@ -100,7 +98,8 @@ vram_group.add_argument("--always-high-vram", action="store_true")
 vram_group.add_argument("--always-normal-vram", action="store_true")
 vram_group.add_argument("--always-low-vram", action="store_true")
 vram_group.add_argument("--always-no-vram", action="store_true")
-vram_group.add_argument("--always-cpu", type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1)
+vram_group.add_argument("--always-cpu", action="store_true")
+
 
 parser.add_argument("--always-offload-from-vram", action="store_true")
 parser.add_argument("--pytorch-deterministic", action="store_true")
@@ -111,8 +110,6 @@ parser.add_argument("--is-windows-embedded-python", action="store_true")
 
 parser.add_argument("--disable-server-info", action="store_true")
 
-parser.add_argument("--multi-user", action="store_true")
-
 if ldm_patched.modules.options.args_parsing:
     args = parser.parse_args([])
 else:

ldm_patched/modules/clip_model.py

@@ -57,7 +57,7 @@ class CLIPEncoder(torch.nn.Module):
         self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)])
 
     def forward(self, x, mask=None, intermediate_output=None):
-        optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
+        optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None)
 
         if intermediate_output is not None:
             if intermediate_output < 0:
@@ -151,7 +151,7 @@ class CLIPVisionEmbeddings(torch.nn.Module):
 
     def forward(self, pixel_values):
         embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device)
+        return torch.cat([self.class_embedding.expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight
 
 
 class CLIPVision(torch.nn.Module):

ldm_patched/modules/clip_vision.py

@@ -1,6 +1,7 @@
-from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
+from .utils import load_torch_file, transformers_convert, common_upscale
 import os
 import torch
+import contextlib
 import json
 
 import ldm_patched.modules.ops
@@ -40,13 +41,9 @@ class ClipVisionModel():
         self.model.eval()
 
         self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
-
     def load_sd(self, sd):
         return self.model.load_state_dict(sd, strict=False)
 
-    def get_sd(self):
-        return self.model.state_dict()
-
     def encode_image(self, image):
         ldm_patched.modules.model_management.load_model_gpu(self.patcher)
         pixel_values = clip_preprocess(image.to(self.load_device)).float()
@@ -79,9 +76,6 @@ def convert_to_transformers(sd, prefix):
             sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1)
 
         sd = transformers_convert(sd, prefix, "vision_model.", 48)
-    else:
-        replace_prefix = {prefix: ""}
-        sd = state_dict_prefix_replace(sd, replace_prefix)
     return sd
 
 def load_clipvision_from_sd(sd, prefix="", convert_keys=False):

ldm_patched/modules/conds.py

@@ -1,8 +1,11 @@
+import enum
 import torch
 import math
 import ldm_patched.modules.utils
 
 
+def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
+    return abs(a*b) // math.gcd(a, b)
 
 class CONDRegular:
     def __init__(self, cond):
@@ -39,7 +42,7 @@ class CONDCrossAttn(CONDRegular):
             if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
                 return False
 
-            mult_min = math.lcm(s1[1], s2[1])
+            mult_min = lcm(s1[1], s2[1])
             diff = mult_min // min(s1[1], s2[1])
             if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
                 return False
@@ -50,7 +53,7 @@ class CONDCrossAttn(CONDRegular):
         crossattn_max_len = self.cond.shape[1]
         for x in others:
             c = x.cond
-            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
+            crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
             conds.append(c)
 
         out = []

ldm_patched/modules/controlnet.py

@@ -1,6 +1,7 @@
 import torch
 import math
 import os
+import contextlib
 import ldm_patched.modules.utils
 import ldm_patched.modules.model_management
 import ldm_patched.modules.model_detection
@@ -125,10 +126,7 @@ class ControlBase:
                         if o[i] is None:
                             o[i] = prev_val
                         else:
-                            if o[i].shape[0] < prev_val.shape[0]:
-                                o[i] = prev_val + o[i]
-                            else:
-                                o[i] += prev_val
+                            o[i] += prev_val
         return out
 
 class ControlNet(ControlBase):
@@ -285,7 +283,7 @@ class ControlLora(ControlNet):
         cm = self.control_model.state_dict()
 
         for k in sd:
-            weight = sd[k]
+            weight = ldm_patched.modules.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k)
             try:
                 ldm_patched.modules.utils.set_attr(self.control_model, k, weight)
             except:

ldm_patched/modules/diffusers_load.py

@@ -1,3 +1,4 @@
+import json
 import os
 
 import ldm_patched.modules.sd

ldm_patched/modules/gligen.py

@@ -1,5 +1,5 @@
 import torch
-from torch import nn
+from torch import nn, einsum
 from ldm_patched.ldm.modules.attention import CrossAttention
 from inspect import isfunction
 

ldm_patched/modules/latent_formats.py

@@ -33,7 +33,3 @@ class SDXL(LatentFormat):
                     [-0.3112, -0.2359, -0.2076]
                 ]
         self.taesd_decoder_name = "taesdxl_decoder"
-
-class SD_X4(LatentFormat):
-    def __init__(self):
-        self.scale_factor = 0.08333

ldm_patched/modules/model_base.py

@@ -1,11 +1,12 @@
 import torch
-from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
+from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel
 from ldm_patched.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
-from ldm_patched.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
+from ldm_patched.ldm.modules.diffusionmodules.openaimodel import Timestep
 import ldm_patched.modules.model_management
 import ldm_patched.modules.conds
 import ldm_patched.modules.ops
 from enum import Enum
+import contextlib
 from . import utils
 
 class ModelType(Enum):
@@ -77,9 +78,8 @@ class BaseModel(torch.nn.Module):
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
-            if hasattr(extra, "dtype"):
-                if extra.dtype != torch.int and extra.dtype != torch.long:
-                    extra = extra.to(dtype)
+            if hasattr(extra, "to"):
+                extra = extra.to(dtype)
             extra_conds[o] = extra
 
         model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
@@ -99,29 +99,11 @@ class BaseModel(torch.nn.Module):
         if self.inpaint_model:
             concat_keys = ("mask", "masked_image")
             cond_concat = []
-            denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
-            concat_latent_image = kwargs.get("concat_latent_image", None)
-            if concat_latent_image is None:
-                concat_latent_image = kwargs.get("latent_image", None)
-            else:
-                concat_latent_image = self.process_latent_in(concat_latent_image)
-
+            denoise_mask = kwargs.get("denoise_mask", None)
+            latent_image = kwargs.get("latent_image", None)
             noise = kwargs.get("noise", None)
             device = kwargs["device"]
 
-            if concat_latent_image.shape[1:] != noise.shape[1:]:
-                concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-            concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])
-
-            if len(denoise_mask.shape) == len(noise.shape):
-                denoise_mask = denoise_mask[:,:1]
-
-            denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
-            if denoise_mask.shape[-2:] != noise.shape[-2:]:
-                denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-            denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
-
             def blank_inpaint_image_like(latent_image):
                 blank_image = torch.ones_like(latent_image)
                 # these are the values for "zero" in pixel space translated to latent space
@@ -134,9 +116,9 @@ class BaseModel(torch.nn.Module):
             for ck in concat_keys:
                 if denoise_mask is not None:
                     if ck == "mask":
-                        cond_concat.append(denoise_mask.to(device))
+                        cond_concat.append(denoise_mask[:,:1].to(device))
                     elif ck == "masked_image":
-                        cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
+                        cond_concat.append(latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
                 else:
                     if ck == "mask":
                         cond_concat.append(torch.ones_like(noise)[:,:1])
@@ -144,15 +126,9 @@ class BaseModel(torch.nn.Module):
                         cond_concat.append(blank_inpaint_image_like(noise))
             data = torch.cat(cond_concat, dim=1)
             out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(data)
-
         adm = self.encode_adm(**kwargs)
         if adm is not None:
             out['y'] = ldm_patched.modules.conds.CONDRegular(adm)
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)
-
         return out
 
     def load_model_weights(self, sd, unet_prefix=""):
@@ -178,28 +154,23 @@ class BaseModel(torch.nn.Module):
     def process_latent_out(self, latent):
         return self.latent_format.process_out(latent)
 
-    def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
-        extra_sds = []
-        if clip_state_dict is not None:
-            extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict))
-        if vae_state_dict is not None:
-            extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict))
-        if clip_vision_state_dict is not None:
-            extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))
+    def state_dict_for_saving(self, clip_state_dict, vae_state_dict):
+        clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict)
+        unet_sd = self.diffusion_model.state_dict()
+        unet_state_dict = {}
+        for k in unet_sd:
+            unet_state_dict[k] = ldm_patched.modules.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k)
 
-        unet_state_dict = self.diffusion_model.state_dict()
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
-
+        vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict)
         if self.get_dtype() == torch.float16:
-            extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds)
+            clip_state_dict = utils.convert_sd_to(clip_state_dict, torch.float16)
+            vae_state_dict = utils.convert_sd_to(vae_state_dict, torch.float16)
 
         if self.model_type == ModelType.V_PREDICTION:
             unet_state_dict["v_pred"] = torch.tensor([])
 
-        for sd in extra_sds:
-            unet_state_dict.update(sd)
-
-        return unet_state_dict
+        return {**unet_state_dict, **vae_state_dict, **clip_state_dict}
 
     def set_inpaint(self):
         self.inpaint_model = True
@@ -218,7 +189,7 @@ class BaseModel(torch.nn.Module):
             return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
 
 
-def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
+def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0):
     adm_inputs = []
     weights = []
     noise_aug = []
@@ -227,7 +198,7 @@ def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge
             weight = unclip_cond["strength"]
             noise_augment = unclip_cond["noise_augmentation"]
             noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
-            c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed)
+            c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device))
             adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
             weights.append(weight)
             noise_aug.append(noise_augment)
@@ -253,11 +224,11 @@ class SD21UNCLIP(BaseModel):
         if unclip_conditioning is None:
             return torch.zeros((1, self.adm_channels))
         else:
-            return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10)
+            return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05))
 
 def sdxl_pooled(args, noise_augmentor):
     if "unclip_conditioning" in args:
-        return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280]
+        return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor)[:,:1280]
     else:
         return args["pooled_output"]
 
@@ -351,75 +322,9 @@ class SVD_img2vid(BaseModel):
 
         out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image)
 
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)
-
         if "time_conditioning" in kwargs:
             out["time_context"] = ldm_patched.modules.conds.CONDCrossAttn(kwargs["time_conditioning"])
 
         out['image_only_indicator'] = ldm_patched.modules.conds.CONDConstant(torch.zeros((1,), device=device))
         out['num_video_frames'] = ldm_patched.modules.conds.CONDConstant(noise.shape[0])
         return out
-
-class Stable_Zero123(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
-        super().__init__(model_config, model_type, device=device)
-        self.cc_projection = ldm_patched.modules.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
-        self.cc_projection.weight.copy_(cc_projection_weight)
-        self.cc_projection.bias.copy_(cc_projection_bias)
-
-    def extra_conds(self, **kwargs):
-        out = {}
-
-        latent_image = kwargs.get("concat_latent_image", None)
-        noise = kwargs.get("noise", None)
-
-        if latent_image is None:
-            latent_image = torch.zeros_like(noise)
-
-        if latent_image.shape[1:] != noise.shape[1:]:
-            latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-        latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
-
-        out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image)
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            if cross_attn.shape[-1] != 768:
-                cross_attn = self.cc_projection(cross_attn)
-            out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)
-        return out
-
-class SD_X4Upscaler(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
-        super().__init__(model_config, model_type, device=device)
-        self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350)
-
-    def extra_conds(self, **kwargs):
-        out = {}
-
-        image = kwargs.get("concat_image", None)
-        noise = kwargs.get("noise", None)
-        noise_augment = kwargs.get("noise_augmentation", 0.0)
-        device = kwargs["device"]
-        seed = kwargs["seed"] - 10
-
-        noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment)
-
-        if image is None:
-            image = torch.zeros_like(noise)[:,:3]
-
-        if image.shape[1:] != noise.shape[1:]:
-            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-        noise_level = torch.tensor([noise_level], device=device)
-        if noise_augment > 0:
-            image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed)
-
-        image = utils.resize_to_batch_size(image, noise.shape[0])
-
-        out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(image)
-        out['y'] = ldm_patched.modules.conds.CONDRegular(noise_level)
-        return out

ldm_patched/modules/model_detection.py

@@ -34,6 +34,7 @@ def detect_unet_config(state_dict, key_prefix, dtype):
     unet_config = {
         "use_checkpoint": False,
         "image_size": 32,
+        "out_channels": 4,
         "use_spatial_transformer": True,
         "legacy": False
     }
@@ -49,12 +50,6 @@ def detect_unet_config(state_dict, key_prefix, dtype):
     model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
     in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
 
-    out_key = '{}out.2.weight'.format(key_prefix)
-    if out_key in state_dict:
-        out_channels = state_dict[out_key].shape[0]
-    else:
-        out_channels = 4
-
     num_res_blocks = []
     channel_mult = []
     attention_resolutions = []
@@ -127,7 +122,6 @@ def detect_unet_config(state_dict, key_prefix, dtype):
         transformer_depth_middle = -1
 
     unet_config["in_channels"] = in_channels
-    unet_config["out_channels"] = out_channels
     unet_config["model_channels"] = model_channels
     unet_config["num_res_blocks"] = num_res_blocks
     unet_config["transformer_depth"] = transformer_depth

ldm_patched/modules/model_management.py

@@ -60,9 +60,6 @@ except:
     pass
 
 if args.always_cpu:
-    if args.always_cpu > 0:
-        torch.set_num_threads(args.always_cpu)
-    print(f"Running on {torch.get_num_threads()} CPU threads")
     cpu_state = CPUState.CPU
 
 def is_intel_xpu():
@@ -178,7 +175,7 @@ try:
         if int(torch_version[0]) >= 2:
             if ENABLE_PYTORCH_ATTENTION == False and args.attention_split == False and args.attention_quad == False:
                 ENABLE_PYTORCH_ATTENTION = True
-            if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
+            if torch.cuda.is_bf16_supported():
                 VAE_DTYPE = torch.bfloat16
     if is_intel_xpu():
         if args.attention_split == False and args.attention_quad == False:
@@ -189,9 +186,6 @@ except:
 if is_intel_xpu():
     VAE_DTYPE = torch.bfloat16
 
-if args.vae_in_cpu:
-    VAE_DTYPE = torch.float32
-
 if args.vae_in_fp16:
     VAE_DTYPE = torch.float16
 elif args.vae_in_bf16:
@@ -224,8 +218,15 @@ if args.all_in_fp16:
     FORCE_FP16 = True
 
 if lowvram_available:
-    if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
-        vram_state = set_vram_to
+    try:
+        import accelerate
+        if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
+            vram_state = set_vram_to
+    except Exception as e:
+        import traceback
+        print(traceback.format_exc())
+        print("ERROR: LOW VRAM MODE NEEDS accelerate.")
+        lowvram_available = False
 
 
 if cpu_state != CPUState.GPU:
@@ -265,14 +266,6 @@ print("VAE dtype:", VAE_DTYPE)
 
 current_loaded_models = []
 
-def module_size(module):
-    module_mem = 0
-    sd = module.state_dict()
-    for k in sd:
-        t = sd[k]
-        module_mem += t.nelement() * t.element_size()
-    return module_mem
-
 class LoadedModel:
     def __init__(self, model):
         self.model = model
@@ -305,20 +298,8 @@ class LoadedModel:
 
         if lowvram_model_memory > 0:
             print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024))
-            mem_counter = 0
-            for m in self.real_model.modules():
-                if hasattr(m, "ldm_patched_cast_weights"):
-                    m.prev_ldm_patched_cast_weights = m.ldm_patched_cast_weights
-                    m.ldm_patched_cast_weights = True
-                    module_mem = module_size(m)
-                    if mem_counter + module_mem < lowvram_model_memory:
-                        m.to(self.device)
-                        mem_counter += module_mem
-                elif hasattr(m, "weight"): #only modules with ldm_patched_cast_weights can be set to lowvram mode
-                    m.to(self.device)
-                    mem_counter += module_size(m)
-                    print("lowvram: loaded module regularly", m)
-
+            device_map = accelerate.infer_auto_device_map(self.real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"})
+            accelerate.dispatch_model(self.real_model, device_map=device_map, main_device=self.device)
             self.model_accelerated = True
 
         if is_intel_xpu() and not args.disable_ipex_hijack:
@@ -328,11 +309,7 @@ class LoadedModel:
 
     def model_unload(self):
         if self.model_accelerated:
-            for m in self.real_model.modules():
-                if hasattr(m, "prev_ldm_patched_cast_weights"):
-                    m.ldm_patched_cast_weights = m.prev_ldm_patched_cast_weights
-                    del m.prev_ldm_patched_cast_weights
-
+            accelerate.hooks.remove_hook_from_submodules(self.real_model)
             self.model_accelerated = False
 
         self.model.unpatch_model(self.model.offload_device)
@@ -425,14 +402,14 @@ def load_models_gpu(models, memory_required=0):
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
+            lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
             if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary
                 vram_set_state = VRAMState.LOW_VRAM
             else:
                 lowvram_model_memory = 0
 
         if vram_set_state == VRAMState.NO_VRAM:
-            lowvram_model_memory = 64 * 1024 * 1024
+            lowvram_model_memory = 256 * 1024 * 1024
 
         cur_loaded_model = loaded_model.model_load(lowvram_model_memory)
         current_loaded_models.insert(0, loaded_model)
@@ -561,8 +538,6 @@ def intermediate_device():
         return torch.device("cpu")
 
 def vae_device():
-    if args.vae_in_cpu:
-        return torch.device("cpu")
     return get_torch_device()
 
 def vae_offload_device():
@@ -591,11 +566,6 @@ def supports_dtype(device, dtype): #TODO
         return True
     return False
 
-def device_supports_non_blocking(device):
-    if is_device_mps(device):
-        return False #pytorch bug? mps doesn't support non blocking
-    return True
-
 def cast_to_device(tensor, device, dtype, copy=False):
     device_supports_cast = False
     if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
@@ -606,7 +576,9 @@ def cast_to_device(tensor, device, dtype, copy=False):
         elif is_intel_xpu():
             device_supports_cast = True
 
-    non_blocking = device_supports_non_blocking(device)
+    non_blocking = True
+    if is_device_mps(device):
+        non_blocking = False #pytorch bug? mps doesn't support non blocking
 
     if device_supports_cast:
         if copy:
@@ -770,11 +742,11 @@ def soft_empty_cache(force=False):
             torch.cuda.empty_cache()
             torch.cuda.ipc_collect()
 
-def unload_all_models():
-    free_memory(1e30, get_torch_device())
-
-
-def resolve_lowvram_weight(weight, model, key): #TODO: remove
+def resolve_lowvram_weight(weight, model, key):
+    if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break.
+        key_split = key.split('.')              # I have no idea why they don't just leave the weight there instead of using the meta device.
+        op = ldm_patched.modules.utils.get_attr(model, '.'.join(key_split[:-1]))
+        weight = op._hf_hook.weights_map[key_split[-1]]
     return weight
 
 #TODO: might be cleaner to put this somewhere else

ldm_patched/modules/model_patcher.py

@@ -28,9 +28,13 @@ class ModelPatcher:
         if self.size > 0:
             return self.size
         model_sd = self.model.state_dict()
-        self.size = ldm_patched.modules.model_management.module_size(self.model)
+        size = 0
+        for k in model_sd:
+            t = model_sd[k]
+            size += t.nelement() * t.element_size()
+        self.size = size
         self.model_keys = set(model_sd.keys())
-        return self.size
+        return size
 
     def clone(self):
         n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
@@ -51,18 +55,14 @@ class ModelPatcher:
     def memory_required(self, input_shape):
         return self.model.memory_required(input_shape=input_shape)
 
-    def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False):
+    def set_model_sampler_cfg_function(self, sampler_cfg_function):
         if len(inspect.signature(sampler_cfg_function).parameters) == 3:
             self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
         else:
             self.model_options["sampler_cfg_function"] = sampler_cfg_function
-        if disable_cfg1_optimization:
-            self.model_options["disable_cfg1_optimization"] = True
 
-    def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
+    def set_model_sampler_post_cfg_function(self, post_cfg_function):
         self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
-        if disable_cfg1_optimization:
-            self.model_options["disable_cfg1_optimization"] = True
 
     def set_model_unet_function_wrapper(self, unet_wrapper_function):
         self.model_options["model_function_wrapper"] = unet_wrapper_function
@@ -174,41 +174,40 @@ class ModelPatcher:
                     sd.pop(k)
         return sd
 
-    def patch_model(self, device_to=None, patch_weights=True):
+    def patch_model(self, device_to=None):
         for k in self.object_patches:
             old = getattr(self.model, k)
             if k not in self.object_patches_backup:
                 self.object_patches_backup[k] = old
             setattr(self.model, k, self.object_patches[k])
 
-        if patch_weights:
-            model_sd = self.model_state_dict()
-            for key in self.patches:
-                if key not in model_sd:
-                    print("could not patch. key doesn't exist in model:", key)
-                    continue
+        model_sd = self.model_state_dict()
+        for key in self.patches:
+            if key not in model_sd:
+                print("could not patch. key doesn't exist in model:", key)
+                continue
 
-                weight = model_sd[key]
+            weight = model_sd[key]
 
-                inplace_update = self.weight_inplace_update
+            inplace_update = self.weight_inplace_update
 
-                if key not in self.backup:
-                    self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
-
-                if device_to is not None:
-                    temp_weight = ldm_patched.modules.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
-                else:
-                    temp_weight = weight.to(torch.float32, copy=True)
-                out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
-                if inplace_update:
-                    ldm_patched.modules.utils.copy_to_param(self.model, key, out_weight)
-                else:
-                    ldm_patched.modules.utils.set_attr(self.model, key, out_weight)
-                del temp_weight
+            if key not in self.backup:
+                self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
 
             if device_to is not None:
-                self.model.to(device_to)
-                self.current_device = device_to
+                temp_weight = ldm_patched.modules.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+            else:
+                temp_weight = weight.to(torch.float32, copy=True)
+            out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
+            if inplace_update:
+                ldm_patched.modules.utils.copy_to_param(self.model, key, out_weight)
+            else:
+                ldm_patched.modules.utils.set_attr(self.model, key, out_weight)
+            del temp_weight
+
+        if device_to is not None:
+            self.model.to(device_to)
+            self.current_device = device_to
 
         return self.model
 

ldm_patched/modules/ops.py

@@ -1,92 +1,27 @@
 import torch
-import ldm_patched.modules.model_management
-
-def cast_bias_weight(s, input):
-    bias = None
-    non_blocking = ldm_patched.modules.model_management.device_supports_non_blocking(input.device)
-    if s.bias is not None:
-        bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
-    weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
-    return weight, bias
-
+from contextlib import contextmanager
 
 class disable_weight_init:
     class Linear(torch.nn.Linear):
-        ldm_patched_cast_weights = False
         def reset_parameters(self):
             return None
 
-        def forward_ldm_patched_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.linear(input, weight, bias)
-
-        def forward(self, *args, **kwargs):
-            if self.ldm_patched_cast_weights:
-                return self.forward_ldm_patched_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
     class Conv2d(torch.nn.Conv2d):
-        ldm_patched_cast_weights = False
         def reset_parameters(self):
             return None
 
-        def forward_ldm_patched_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
-
-        def forward(self, *args, **kwargs):
-            if self.ldm_patched_cast_weights:
-                return self.forward_ldm_patched_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
     class Conv3d(torch.nn.Conv3d):
-        ldm_patched_cast_weights = False
         def reset_parameters(self):
             return None
 
-        def forward_ldm_patched_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
-
-        def forward(self, *args, **kwargs):
-            if self.ldm_patched_cast_weights:
-                return self.forward_ldm_patched_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
     class GroupNorm(torch.nn.GroupNorm):
-        ldm_patched_cast_weights = False
         def reset_parameters(self):
             return None
 
-        def forward_ldm_patched_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
-
-        def forward(self, *args, **kwargs):
-            if self.ldm_patched_cast_weights:
-                return self.forward_ldm_patched_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-
     class LayerNorm(torch.nn.LayerNorm):
-        ldm_patched_cast_weights = False
         def reset_parameters(self):
             return None
 
-        def forward_ldm_patched_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
-
-        def forward(self, *args, **kwargs):
-            if self.ldm_patched_cast_weights:
-                return self.forward_ldm_patched_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
     @classmethod
     def conv_nd(s, dims, *args, **kwargs):
         if dims == 2:
@@ -96,19 +31,35 @@ class disable_weight_init:
         else:
             raise ValueError(f"unsupported dimensions: {dims}")
 
+def cast_bias_weight(s, input):
+    bias = None
+    if s.bias is not None:
+        bias = s.bias.to(device=input.device, dtype=input.dtype)
+    weight = s.weight.to(device=input.device, dtype=input.dtype)
+    return weight, bias
 
 class manual_cast(disable_weight_init):
     class Linear(disable_weight_init.Linear):
-        ldm_patched_cast_weights = True
+        def forward(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.linear(input, weight, bias)
 
     class Conv2d(disable_weight_init.Conv2d):
-        ldm_patched_cast_weights = True
+        def forward(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return self._conv_forward(input, weight, bias)
 
     class Conv3d(disable_weight_init.Conv3d):
-        ldm_patched_cast_weights = True
+        def forward(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return self._conv_forward(input, weight, bias)
 
     class GroupNorm(disable_weight_init.GroupNorm):
-        ldm_patched_cast_weights = True
+        def forward(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
     class LayerNorm(disable_weight_init.LayerNorm):
-        ldm_patched_cast_weights = True
+        def forward(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)

ldm_patched/modules/sample.py

@@ -28,6 +28,7 @@ def prepare_noise(latent_image, seed, noise_inds=None):
 def prepare_mask(noise_mask, shape, device):
     """ensures noise mask is of proper dimensions"""
     noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
+    noise_mask = noise_mask.round()
     noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
     noise_mask = ldm_patched.modules.utils.repeat_to_batch_size(noise_mask, shape[0])
     noise_mask = noise_mask.to(device)
@@ -46,8 +47,7 @@ def convert_cond(cond):
         temp = c[1].copy()
         model_conds = temp.get("model_conds", {})
         if c[0] is not None:
-            model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) #TODO: remove
-            temp["cross_attn"] = c[0]
+            model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0])
         temp["model_conds"] = model_conds
         out.append(temp)
     return out

ldm_patched/modules/samplers.py

@@ -1,9 +1,13 @@
 from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
 from ldm_patched.unipc import uni_pc
 import torch
+import enum
 import collections
 from ldm_patched.modules import model_management
 import math
+from ldm_patched.modules import model_base
+import ldm_patched.modules.utils
+import ldm_patched.modules.conds
 
 def get_area_and_mult(conds, x_in, timestep_in):
     area = (x_in.shape[2], x_in.shape[3], 0, 0)
@@ -240,7 +244,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
 #The main sampling function shared by all the samplers
 #Returns denoised
 def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
-        if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
+        if math.isclose(cond_scale, 1.0):
             uncond_ = None
         else:
             uncond_ = uncond
@@ -595,13 +599,6 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
     calculate_start_end_timesteps(model, negative)
     calculate_start_end_timesteps(model, positive)
 
-    if latent_image is not None:
-        latent_image = model.process_latent_in(latent_image)
-
-    if hasattr(model, 'extra_conds'):
-        positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
-        negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
-
     #make sure each cond area has an opposite one with the same area
     for c in positive:
         create_cond_with_same_area_if_none(negative, c)
@@ -613,6 +610,13 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
     apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
     apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
 
+    if latent_image is not None:
+        latent_image = model.process_latent_in(latent_image)
+
+    if hasattr(model, 'extra_conds'):
+        positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask)
+        negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask)
+
     extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}
 
     samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
@@ -635,7 +639,7 @@ def calculate_sigmas_scheduler(model, scheduler_name, steps):
     elif scheduler_name == "sgm_uniform":
         sigmas = normal_scheduler(model, steps, sgm=True)
     else:
-        print("error invalid scheduler", scheduler_name)
+        print("error invalid scheduler", self.scheduler)
     return sigmas
 
 def sampler_object(name):

ldm_patched/modules/sd.py

@@ -1,6 +1,9 @@
 import torch
+import contextlib
+import math
 
 from ldm_patched.modules import model_management
+from ldm_patched.ldm.util import instantiate_from_config
 from ldm_patched.ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
 import yaml
 
@@ -154,8 +157,6 @@ class VAE:
 
         self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
         self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
-        self.downscale_ratio = 8
-        self.latent_channels = 4
 
         if config is None:
             if "decoder.mid.block_1.mix_factor" in sd:
@@ -171,11 +172,6 @@ class VAE:
             else:
                 #default SD1.x/SD2.x VAE parameters
                 ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
-
-                if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
-                    ddconfig['ch_mult'] = [1, 2, 4]
-                    self.downscale_ratio = 4
-
                 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
         else:
             self.first_stage_model = AutoencoderKL(**(config['params']))
@@ -208,9 +204,9 @@ class VAE:
 
         decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
         output = torch.clamp((
-            (ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) +
-            ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) +
-             ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar))
+            (ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar) +
+            ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar) +
+             ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar))
             / 3.0) / 2.0, min=0.0, max=1.0)
         return output
 
@@ -221,9 +217,9 @@ class VAE:
         pbar = ldm_patched.modules.utils.ProgressBar(steps)
 
         encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float()
-        samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
-        samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
-        samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
+        samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar)
+        samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar)
+        samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar)
         samples /= 3.0
         return samples
 
@@ -235,7 +231,7 @@ class VAE:
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
-            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device)
+            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device=self.output_device)
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
                 pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0)
@@ -259,7 +255,7 @@ class VAE:
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device)
+            samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device=self.output_device)
             for x in range(0, pixel_samples.shape[0], batch_number):
                 pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)
                 samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
@@ -531,14 +527,7 @@ def load_unet(unet_path):
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
     return model
 
-def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None):
-    clip_sd = None
-    load_models = [model]
-    if clip is not None:
-        load_models.append(clip.load_model())
-        clip_sd = clip.get_sd()
-
-    model_management.load_models_gpu(load_models)
-    clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
-    sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd)
+def save_checkpoint(output_path, model, clip, vae, metadata=None):
+    model_management.load_models_gpu([model, clip.load_model()])
+    sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd())
     ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata)

ldm_patched/modules/sd1_clip.py

@@ -6,6 +6,7 @@ import torch
 import traceback
 import zipfile
 from . import model_management
+import contextlib
 import ldm_patched.modules.clip_model
 import json
 

ldm_patched/modules/supported_models.py

@@ -252,59 +252,5 @@ class SVD_img2vid(supported_models_base.BASE):
     def clip_target(self):
         return None
 
-class Stable_Zero123(supported_models_base.BASE):
-    unet_config = {
-        "context_dim": 768,
-        "model_channels": 320,
-        "use_linear_in_transformer": False,
-        "adm_in_channels": None,
-        "use_temporal_attention": False,
-        "in_channels": 8,
-    }
-
-    unet_extra_config = {
-        "num_heads": 8,
-        "num_head_channels": -1,
-    }
-
-    clip_vision_prefix = "cond_stage_model.model.visual."
-
-    latent_format = latent_formats.SD15
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"])
-        return out
-
-    def clip_target(self):
-        return None
-
-class SD_X4Upscaler(SD20):
-    unet_config = {
-        "context_dim": 1024,
-        "model_channels": 256,
-        'in_channels': 7,
-        "use_linear_in_transformer": True,
-        "adm_in_channels": None,
-        "use_temporal_attention": False,
-    }
-
-    unet_extra_config = {
-        "disable_self_attentions": [True, True, True, False],
-        "num_classes": 1000,
-        "num_heads": 8,
-        "num_head_channels": -1,
-    }
-
-    latent_format = latent_formats.SD_X4
-
-    sampling_settings = {
-        "linear_start": 0.0001,
-        "linear_end": 0.02,
-    }
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.SD_X4Upscaler(self, device=device)
-        return out
-
-models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler]
+models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega]
 models += [SVD_img2vid]

ldm_patched/modules/supported_models_base.py

@@ -65,12 +65,6 @@ class BASE:
         replace_prefix = {"": "cond_stage_model."}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
-    def process_clip_vision_state_dict_for_saving(self, state_dict):
-        replace_prefix = {}
-        if self.clip_vision_prefix is not None:
-            replace_prefix[""] = self.clip_vision_prefix
-        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
-
     def process_unet_state_dict_for_saving(self, state_dict):
         replace_prefix = {"": "model.diffusion_model."}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)

ldm_patched/pfn/architecture/HAT.py

@@ -14,7 +14,7 @@ from .timm.weight_init import trunc_normal_
 
 def drop_path(x, drop_prob: float = 0.0, training: bool = False):
     """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-    From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py
+    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
     """
     if drop_prob == 0.0 or not training:
         return x
@@ -30,7 +30,7 @@ def drop_path(x, drop_prob: float = 0.0, training: bool = False):
 
 class DropPath(nn.Module):
     """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py
+    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
     """
 
     def __init__(self, drop_prob=None):

ldm_patched/pfn/architecture/RRDB.py

@@ -13,7 +13,7 @@ import torch.nn.functional as F
 from . import block as B
 
 
-# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/esrgan.py
+# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/ESRGAN.py
 # Which enhanced stuff that was already here
 class RRDBNet(nn.Module):
     def __init__(

ldm_patched/pfn/architecture/face/codeformer.py

@@ -2,7 +2,7 @@
 Modified from https://github.com/sczhou/CodeFormer
 VQGAN code, adapted from the original created by the Unleashing Transformers authors:
 https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
-This version of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.
+This verison of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.
 """
 import math
 from typing import Optional

ldm_patched/taesd/taesd.py

@@ -7,10 +7,9 @@ import torch
 import torch.nn as nn
 
 import ldm_patched.modules.utils
-import ldm_patched.modules.ops
 
 def conv(n_in, n_out, **kwargs):
-    return ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+    return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
 
 class Clamp(nn.Module):
     def forward(self, x):
@@ -20,7 +19,7 @@ class Block(nn.Module):
     def __init__(self, n_in, n_out):
         super().__init__()
         self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
-        self.skip = ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+        self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
         self.fuse = nn.ReLU()
     def forward(self, x):
         return self.fuse(self.conv(x) + self.skip(x))

ldm_patched/utils/path_utils.py

@@ -29,14 +29,11 @@ folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes
 
 folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
 
-folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions)
-
 folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
 
 output_directory = os.path.join(os.getcwd(), "output")
 temp_directory = os.path.join(os.getcwd(), "temp")
 input_directory = os.path.join(os.getcwd(), "input")
-user_directory = os.path.join(os.getcwd(), "user")
 
 filename_list_cache = {}
 
@@ -140,27 +137,15 @@ def recursive_search(directory, excluded_dir_names=None):
         excluded_dir_names = []
 
     result = []
-    dirs = {}
-
-    # Attempt to add the initial directory to dirs with error handling
-    try:
-        dirs[directory] = os.path.getmtime(directory)
-    except FileNotFoundError:
-        print(f"Warning: Unable to access {directory}. Skipping this path.")
-        
+    dirs = {directory: os.path.getmtime(directory)}
     for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
         subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
         for file_name in filenames:
             relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
             result.append(relative_path)
-        
         for d in subdirs:
             path = os.path.join(dirpath, d)
-            try:
-                dirs[path] = os.path.getmtime(path)
-            except FileNotFoundError:
-                print(f"Warning: Unable to access {path}. Skipping this path.")
-                continue
+            dirs[path] = os.path.getmtime(path)
     return result, dirs
 
 def filter_files_extensions(files, extensions):
@@ -199,7 +184,8 @@ def cached_filename_list_(folder_name):
     if folder_name not in filename_list_cache:
         return None
     out = filename_list_cache[folder_name]
-
+    if time.perf_counter() < (out[2] + 0.5):
+        return out
     for x in out[1]:
         time_modified = out[1][x]
         folder = x

modules/advanced_parameters.py

@@ -0,0 +1,30 @@
+disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,  \
+    scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
+    overwrite_vary_strength, overwrite_upscale_strength, \
+    mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
+    debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
+    refiner_swap_method, \
+    freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
+    debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field = [None] * 32
+
+
+def set_all_advanced_parameters(*args):
+    global disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
+        scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
+        overwrite_vary_strength, overwrite_upscale_strength, \
+        mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
+        debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
+        refiner_swap_method, \
+        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
+        debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field
+
+    disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
+        scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
+        overwrite_vary_strength, overwrite_upscale_strength, \
+        mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
+        debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
+        refiner_swap_method, \
+        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
+        debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field = args
+
+    return

modules/async_worker.py

@@ -1,16 +1,11 @@
 import threading
-import re
-from modules.patch import PatchSettings, patch_settings, patch_all
 
-patch_all()
 
 class AsyncTask:
     def __init__(self, args):
         self.args = args
         self.yields = []
         self.results = []
-        self.last_stop = False
-        self.processing = False
 
 
 async_tasks = []
@@ -19,11 +14,9 @@ async_tasks = []
 def worker():
     global async_tasks
 
-    import os
     import traceback
     import math
     import numpy as np
-    import cv2
     import torch
     import time
     import shared
@@ -38,22 +31,16 @@ def worker():
     import extras.preprocessors as preprocessors
     import modules.inpaint_worker as inpaint_worker
     import modules.constants as constants
+    import modules.advanced_parameters as advanced_parameters
     import extras.ip_adapter as ip_adapter
     import extras.face_crop
-    import fooocus_version
-    import args_manager
 
-    from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
+    from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion
     from modules.private_logger import log
     from extras.expansion import safe_str
-    from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
-        get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras
+    from modules.util import remove_empty_str, HWC3, resize_image, \
+        get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image
     from modules.upscaler import perform_upscale
-    from modules.flags import Performance
-    from modules.meta_parser import get_metadata_parser, MetadataScheme
-
-    pid = os.getpid()
-    print(f'Started worker with PID {pid}')
 
     try:
         async_gradio_app = shared.gradio_root
@@ -81,20 +68,19 @@ def worker():
         return
 
     def build_image_wall(async_task):
-        results = []
-
-        if len(async_task.results) < 2:
+        if not advanced_parameters.generate_image_grid:
             return
 
-        for img in async_task.results:
-            if isinstance(img, str) and os.path.exists(img):
-                img = cv2.imread(img)
-                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        results = async_task.results
+
+        if len(results) < 2:
+            return
+
+        for img in results:
             if not isinstance(img, np.ndarray):
                 return
             if img.ndim != 3:
                 return
-            results.append(img)
 
         H, W, C = results[0].shape
 
@@ -128,7 +114,6 @@ def worker():
     @torch.inference_mode()
     def handler(async_task):
         execution_start_time = time.perf_counter()
-        async_task.processing = True
 
         args = async_task.args
         args.reverse()
@@ -136,18 +121,16 @@ def worker():
         prompt = args.pop()
         negative_prompt = args.pop()
         style_selections = args.pop()
-        performance_selection = Performance(args.pop())
+        performance_selection = args.pop()
         aspect_ratios_selection = args.pop()
         image_number = args.pop()
-        output_format = args.pop()
         image_seed = args.pop()
-        read_wildcards_in_order = args.pop()
         sharpness = args.pop()
         guidance_scale = args.pop()
         base_model_name = args.pop()
         refiner_model_name = args.pop()
         refiner_switch = args.pop()
-        loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
+        loras = [[str(args.pop()), float(args.pop())] for _ in range(5)]
         input_image_checkbox = args.pop()
         current_tab = args.pop()
         uov_method = args.pop()
@@ -155,50 +138,9 @@ def worker():
         outpaint_selections = args.pop()
         inpaint_input_image = args.pop()
         inpaint_additional_prompt = args.pop()
-        inpaint_mask_image_upload = args.pop()
-
-        disable_preview = args.pop()
-        disable_intermediate_results = args.pop()
-        disable_seed_increment = args.pop()
-        adm_scaler_positive = args.pop()
-        adm_scaler_negative = args.pop()
-        adm_scaler_end = args.pop()
-        adaptive_cfg = args.pop()
-        sampler_name = args.pop()
-        scheduler_name = args.pop()
-        overwrite_step = args.pop()
-        overwrite_switch = args.pop()
-        overwrite_width = args.pop()
-        overwrite_height = args.pop()
-        overwrite_vary_strength = args.pop()
-        overwrite_upscale_strength = args.pop()
-        mixing_image_prompt_and_vary_upscale = args.pop()
-        mixing_image_prompt_and_inpaint = args.pop()
-        debugging_cn_preprocessor = args.pop()
-        skipping_cn_preprocessor = args.pop()
-        canny_low_threshold = args.pop()
-        canny_high_threshold = args.pop()
-        refiner_swap_method = args.pop()
-        controlnet_softness = args.pop()
-        freeu_enabled = args.pop()
-        freeu_b1 = args.pop()
-        freeu_b2 = args.pop()
-        freeu_s1 = args.pop()
-        freeu_s2 = args.pop()
-        debugging_inpaint_preprocessor = args.pop()
-        inpaint_disable_initial_latent = args.pop()
-        inpaint_engine = args.pop()
-        inpaint_strength = args.pop()
-        inpaint_respective_field = args.pop()
-        inpaint_mask_upload_checkbox = args.pop()
-        invert_mask_checkbox = args.pop()
-        inpaint_erode_or_dilate = args.pop()
-
-        save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
-        metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
 
         cn_tasks = {x: [] for x in flags.ip_list}
-        for _ in range(flags.controlnet_image_count):
+        for _ in range(4):
             cn_img = args.pop()
             cn_stop = args.pop()
             cn_weight = args.pop()
@@ -223,9 +165,17 @@ def worker():
             print(f'Refiner disabled because base model and refiner are same.')
             refiner_model_name = 'None'
 
-        steps = performance_selection.steps()
+        assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
 
-        if performance_selection == Performance.EXTREME_SPEED:
+        steps = 30
+
+        if performance_selection == 'Speed':
+            steps = 30
+
+        if performance_selection == 'Quality':
+            steps = 60
+
+        if performance_selection == 'Extreme Speed':
             print('Enter LCM mode.')
             progressbar(async_task, 1, 'Downloading LCM components ...')
             loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
@@ -234,51 +184,30 @@ def worker():
                 print(f'Refiner disabled in LCM mode.')
 
             refiner_model_name = 'None'
-            sampler_name = 'lcm'
-            scheduler_name = 'lcm'
-            sharpness = 0.0
-            guidance_scale = 1.0
-            adaptive_cfg = 1.0
+            sampler_name = advanced_parameters.sampler_name = 'lcm'
+            scheduler_name = advanced_parameters.scheduler_name = 'lcm'
+            modules.patch.sharpness = sharpness = 0.0
+            cfg_scale = guidance_scale = 1.0
+            modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0
             refiner_switch = 1.0
-            adm_scaler_positive = 1.0
-            adm_scaler_negative = 1.0
-            adm_scaler_end = 0.0
+            modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0
+            modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0
+            modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
+            steps = 8
 
-        elif performance_selection == Performance.LIGHTNING:
-            print('Enter Lightning mode.')
-            progressbar(async_task, 1, 'Downloading Lightning components ...')
-            loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
+        modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg
+        print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
 
-            if refiner_model_name != 'None':
-                print(f'Refiner disabled in Lightning mode.')
+        modules.patch.sharpness = sharpness
+        print(f'[Parameters] Sharpness = {modules.patch.sharpness}')
 
-            refiner_model_name = 'None'
-            sampler_name = 'euler'
-            scheduler_name = 'sgm_uniform'
-            sharpness = 0.0
-            guidance_scale = 1.0
-            adaptive_cfg = 1.0
-            refiner_switch = 1.0
-            adm_scaler_positive = 1.0
-            adm_scaler_negative = 1.0
-            adm_scaler_end = 0.0
-
-        print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
-        print(f'[Parameters] Sharpness = {sharpness}')
-        print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
+        modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
+        modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
+        modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end
         print(f'[Parameters] ADM Scale = '
-              f'{adm_scaler_positive} : '
-              f'{adm_scaler_negative} : '
-              f'{adm_scaler_end}')
-
-        patch_settings[pid] = PatchSettings(
-            sharpness,
-            adm_scaler_end,
-            adm_scaler_positive,
-            adm_scaler_negative,
-            controlnet_softness,
-            adaptive_cfg
-        )
+              f'{modules.patch.positive_adm_scale} : '
+              f'{modules.patch.negative_adm_scale} : '
+              f'{modules.patch.adm_scaler_end}')
 
         cfg_scale = float(guidance_scale)
         print(f'[Parameters] CFG = {cfg_scale}')
@@ -291,9 +220,10 @@ def worker():
         width, height = int(width), int(height)
 
         skip_prompt_processing = False
+        refiner_swap_method = advanced_parameters.refiner_swap_method
 
         inpaint_worker.current_task = None
-        inpaint_parameterized = inpaint_engine != 'None'
+        inpaint_parameterized = advanced_parameters.inpaint_engine != 'None'
         inpaint_image = None
         inpaint_mask = None
         inpaint_head_model_path = None
@@ -307,12 +237,15 @@ def worker():
         seed = int(image_seed)
         print(f'[Parameters] Seed = {seed}')
 
+        sampler_name = advanced_parameters.sampler_name
+        scheduler_name = advanced_parameters.scheduler_name
+
         goals = []
         tasks = []
 
         if input_image_checkbox:
             if (current_tab == 'uov' or (
-                    current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \
+                    current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
                     and uov_method != flags.disabled and uov_input_image is not None:
                 uov_input_image = HWC3(uov_input_image)
                 if 'vary' in uov_method:
@@ -322,45 +255,37 @@ def worker():
                     if 'fast' in uov_method:
                         skip_prompt_processing = True
                     else:
-                        steps = performance_selection.steps_uov()
+                        steps = 18
+
+                        if performance_selection == 'Speed':
+                            steps = 18
+
+                        if performance_selection == 'Quality':
+                            steps = 36
+
+                        if performance_selection == 'Extreme Speed':
+                            steps = 8
 
                     progressbar(async_task, 1, 'Downloading upscale models ...')
                     modules.config.downloading_upscale_model()
             if (current_tab == 'inpaint' or (
-                    current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \
+                    current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
                     and isinstance(inpaint_input_image, dict):
                 inpaint_image = inpaint_input_image['image']
                 inpaint_mask = inpaint_input_image['mask'][:, :, 0]
-
-                if inpaint_mask_upload_checkbox:
-                    if isinstance(inpaint_mask_image_upload, np.ndarray):
-                        if inpaint_mask_image_upload.ndim == 3:
-                            H, W, C = inpaint_image.shape
-                            inpaint_mask_image_upload = resample_image(inpaint_mask_image_upload, width=W, height=H)
-                            inpaint_mask_image_upload = np.mean(inpaint_mask_image_upload, axis=2)
-                            inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
-                            inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload)
-
-                if int(inpaint_erode_or_dilate) != 0:
-                    inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate)
-
-                if invert_mask_checkbox:
-                    inpaint_mask = 255 - inpaint_mask
-
                 inpaint_image = HWC3(inpaint_image)
                 if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
                         and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
-                    progressbar(async_task, 1, 'Downloading upscale models ...')
-                    modules.config.downloading_upscale_model()
                     if inpaint_parameterized:
                         progressbar(async_task, 1, 'Downloading inpainter ...')
+                        modules.config.downloading_upscale_model()
                         inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
-                            inpaint_engine)
+                            advanced_parameters.inpaint_engine)
                         base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
                         print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
                         if refiner_model_name == 'None':
                             use_synthetic_refiner = True
-                            refiner_switch = 0.8
+                            refiner_switch = 0.5
                     else:
                         inpaint_head_model_path, inpaint_patch_model_path = None, None
                         print(f'[Inpaint] Parameterized inpaint is disabled.')
@@ -371,8 +296,8 @@ def worker():
                             prompt = inpaint_additional_prompt + '\n' + prompt
                     goals.append('inpaint')
             if current_tab == 'ip' or \
-                    mixing_image_prompt_and_vary_upscale or \
-                    mixing_image_prompt_and_inpaint:
+                    advanced_parameters.mixing_image_prompt_and_inpaint or \
+                    advanced_parameters.mixing_image_prompt_and_vary_upscale:
                 goals.append('cn')
                 progressbar(async_task, 1, 'Downloading control models ...')
                 if len(cn_tasks[flags.cn_canny]) > 0:
@@ -391,19 +316,19 @@ def worker():
         ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
         ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)
 
-        if overwrite_step > 0:
-            steps = overwrite_step
-
         switch = int(round(steps * refiner_switch))
 
-        if overwrite_switch > 0:
-            switch = overwrite_switch
+        if advanced_parameters.overwrite_step > 0:
+            steps = advanced_parameters.overwrite_step
 
-        if overwrite_width > 0:
-            width = overwrite_width
+        if advanced_parameters.overwrite_switch > 0:
+            switch = advanced_parameters.overwrite_switch
 
-        if overwrite_height > 0:
-            height = overwrite_height
+        if advanced_parameters.overwrite_width > 0:
+            width = advanced_parameters.overwrite_width
+
+        if advanced_parameters.overwrite_height > 0:
+            height = advanced_parameters.overwrite_height
 
         print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
         print(f'[Parameters] Steps = {steps} - {switch}')
@@ -432,19 +357,14 @@ def worker():
 
             progressbar(async_task, 3, 'Processing prompts ...')
             tasks = []
-            
             for i in range(image_number):
-                if disable_seed_increment:
-                    task_seed = seed % (constants.MAX_SEED + 1)
-                else:
-                    task_seed = (seed + i) % (constants.MAX_SEED + 1)  # randint is inclusive, % is not
-
+                task_seed = (seed + i) % (constants.MAX_SEED + 1)  # randint is inclusive, % is not
                 task_rng = random.Random(task_seed)  # may bind to inpaint noise in the future
-                task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
-                task_prompt = apply_arrays(task_prompt, i)
-                task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
-                task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
-                task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
+
+                task_prompt = apply_wildcards(prompt, task_rng)
+                task_negative_prompt = apply_wildcards(negative_prompt, task_rng)
+                task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts]
+                task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts]
 
                 positive_basic_workloads = []
                 negative_basic_workloads = []
@@ -476,8 +396,8 @@ def worker():
                     uc=None,
                     positive_top_k=len(positive_basic_workloads),
                     negative_top_k=len(negative_basic_workloads),
-                    log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts),
-                    log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts),
+                    log_positive_prompt='; '.join([task_prompt] + task_extra_positive_prompts),
+                    log_negative_prompt='; '.join([task_negative_prompt] + task_extra_negative_prompts),
                 ))
 
             if use_expansion:
@@ -507,8 +427,8 @@ def worker():
                 denoising_strength = 0.5
             if 'strong' in uov_method:
                 denoising_strength = 0.85
-            if overwrite_vary_strength > 0:
-                denoising_strength = overwrite_vary_strength
+            if advanced_parameters.overwrite_vary_strength > 0:
+                denoising_strength = advanced_parameters.overwrite_vary_strength
 
             shape_ceil = get_image_shape_ceil(uov_input_image)
             if shape_ceil < 1024:
@@ -571,16 +491,16 @@ def worker():
                 direct_return = False
 
             if direct_return:
-                d = [('Upscale (Fast)', 'upscale_fast', '2x')]
-                uov_input_image_path = log(uov_input_image, d, output_format=output_format)
-                yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True)
+                d = [('Upscale (Fast)', '2x')]
+                log(uov_input_image, d, single_line_number=1)
+                yield_result(async_task, uov_input_image, do_not_show_finished_images=True)
                 return
 
             tiled = True
             denoising_strength = 0.382
 
-            if overwrite_upscale_strength > 0:
-                denoising_strength = overwrite_upscale_strength
+            if advanced_parameters.overwrite_upscale_strength > 0:
+                denoising_strength = advanced_parameters.overwrite_upscale_strength
 
             initial_pixels = core.numpy_to_pytorch(uov_input_image)
             progressbar(async_task, 13, 'VAE encoding ...')
@@ -614,29 +534,29 @@ def worker():
 
                 H, W, C = inpaint_image.shape
                 if 'left' in outpaint_selections:
-                    inpaint_image = np.pad(inpaint_image, [[0, 0], [int(W * 0.3), 0], [0, 0]], mode='edge')
-                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(W * 0.3), 0]], mode='constant',
+                    inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge')
+                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant',
                                           constant_values=255)
                 if 'right' in outpaint_selections:
-                    inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(W * 0.3)], [0, 0]], mode='edge')
-                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(W * 0.3)]], mode='constant',
+                    inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge')
+                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant',
                                           constant_values=255)
 
                 inpaint_image = np.ascontiguousarray(inpaint_image.copy())
                 inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
-                inpaint_strength = 1.0
-                inpaint_respective_field = 1.0
+                advanced_parameters.inpaint_strength = 1.0
+                advanced_parameters.inpaint_respective_field = 1.0
 
-            denoising_strength = inpaint_strength
+            denoising_strength = advanced_parameters.inpaint_strength
 
             inpaint_worker.current_task = inpaint_worker.InpaintWorker(
                 image=inpaint_image,
                 mask=inpaint_mask,
                 use_fill=denoising_strength > 0.99,
-                k=inpaint_respective_field
+                k=advanced_parameters.inpaint_respective_field
             )
 
-            if debugging_inpaint_preprocessor:
+            if advanced_parameters.debugging_inpaint_preprocessor:
                 yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
                              do_not_show_finished_images=True)
                 return
@@ -682,7 +602,7 @@ def worker():
                     model=pipeline.final_unet
                 )
 
-            if not inpaint_disable_initial_latent:
+            if not advanced_parameters.inpaint_disable_initial_latent:
                 initial_latent = {'samples': latent_fill}
 
             B, C, H, W = latent_fill.shape
@@ -695,24 +615,24 @@ def worker():
                 cn_img, cn_stop, cn_weight = task
                 cn_img = resize_image(HWC3(cn_img), width=width, height=height)
 
-                if not skipping_cn_preprocessor:
-                    cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold)
+                if not advanced_parameters.skipping_cn_preprocessor:
+                    cn_img = preprocessors.canny_pyramid(cn_img)
 
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)
-                if debugging_cn_preprocessor:
+                if advanced_parameters.debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_cpds]:
                 cn_img, cn_stop, cn_weight = task
                 cn_img = resize_image(HWC3(cn_img), width=width, height=height)
 
-                if not skipping_cn_preprocessor:
+                if not advanced_parameters.skipping_cn_preprocessor:
                     cn_img = preprocessors.cpds(cn_img)
 
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)
-                if debugging_cn_preprocessor:
+                if advanced_parameters.debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_ip]:
@@ -723,21 +643,21 @@ def worker():
                 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
 
                 task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
-                if debugging_cn_preprocessor:
+                if advanced_parameters.debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_ip_face]:
                 cn_img, cn_stop, cn_weight = task
                 cn_img = HWC3(cn_img)
 
-                if not skipping_cn_preprocessor:
+                if not advanced_parameters.skipping_cn_preprocessor:
                     cn_img = extras.face_crop.crop_image(cn_img)
 
                 # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
                 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
 
                 task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
-                if debugging_cn_preprocessor:
+                if advanced_parameters.debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
 
@@ -746,14 +666,14 @@ def worker():
             if len(all_ip_tasks) > 0:
                 pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
 
-        if freeu_enabled:
+        if advanced_parameters.freeu_enabled:
             print(f'FreeU is enabled!')
             pipeline.final_unet = core.apply_freeu(
                 pipeline.final_unet,
-                freeu_b1,
-                freeu_b2,
-                freeu_s1,
-                freeu_s2
+                advanced_parameters.freeu_b1,
+                advanced_parameters.freeu_b2,
+                advanced_parameters.freeu_s1,
+                advanced_parameters.freeu_s2
             )
 
         all_steps = steps * image_number
@@ -793,14 +713,13 @@ def worker():
             done_steps = current_task_id * steps + step
             async_task.yields.append(['preview', (
                 int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
-                f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)])
+                f'Step {step}/{total_steps} in the {current_task_id + 1}-th Sampling',
+                y)])
 
         for current_task_id, task in enumerate(tasks):
             execution_start_time = time.perf_counter()
 
             try:
-                if async_task.last_stop is not False:
-                    ldm_patched.modules.model_management.interrupt_current_processing()
                 positive_cond, negative_cond = task['c'], task['uc']
 
                 if 'cn' in goals:
@@ -828,8 +747,7 @@ def worker():
                     denoise=denoising_strength,
                     tiled=tiled,
                     cfg_scale=cfg_scale,
-                    refiner_swap_method=refiner_swap_method,
-                    disable_preview=disable_preview
+                    refiner_swap_method=refiner_swap_method
                 )
 
                 del task['c'], task['uc'], positive_cond, negative_cond  # Save memory
@@ -837,62 +755,36 @@ def worker():
                 if inpaint_worker.current_task is not None:
                     imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
 
-                img_paths = []
                 for x in imgs:
-                    d = [('Prompt', 'prompt', task['log_positive_prompt']),
-                         ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
-                         ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
-                         ('Styles', 'styles', str(raw_style_selections)),
-                         ('Performance', 'performance', performance_selection.value)]
-
-                    if performance_selection.steps() != steps:
-                        d.append(('Steps', 'steps', steps))
-
-                    d += [('Resolution', 'resolution', str((width, height))),
-                          ('Guidance Scale', 'guidance_scale', guidance_scale),
-                          ('Sharpness', 'sharpness', sharpness),
-                          ('ADM Guidance', 'adm_guidance', str((
-                              modules.patch.patch_settings[pid].positive_adm_scale,
-                              modules.patch.patch_settings[pid].negative_adm_scale,
-                              modules.patch.patch_settings[pid].adm_scaler_end))),
-                          ('Base Model', 'base_model', base_model_name),
-                          ('Refiner Model', 'refiner_model', refiner_model_name),
-                          ('Refiner Switch', 'refiner_switch', refiner_switch)]
-
-                    if refiner_model_name != 'None':
-                        if overwrite_switch > 0:
-                            d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch))
-                        if refiner_swap_method != flags.refiner_swap_method:
-                            d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
-                    if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
-                        d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
-
-                    d.append(('Sampler', 'sampler', sampler_name))
-                    d.append(('Scheduler', 'scheduler', scheduler_name))
-                    d.append(('Seed', 'seed', str(task['task_seed'])))
-
-                    if freeu_enabled:
-                        d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
-
-                    for li, (n, w) in enumerate(loras):
+                    d = [
+                        ('Prompt', task['log_positive_prompt']),
+                        ('Negative Prompt', task['log_negative_prompt']),
+                        ('Fooocus V2 Expansion', task['expansion']),
+                        ('Styles', str(raw_style_selections)),
+                        ('Performance', performance_selection),
+                        ('Resolution', str((width, height))),
+                        ('Sharpness', sharpness),
+                        ('Guidance Scale', guidance_scale),
+                        ('ADM Guidance', str((
+                            modules.patch.positive_adm_scale,
+                            modules.patch.negative_adm_scale,
+                            modules.patch.adm_scaler_end))),
+                        ('Base Model', base_model_name),
+                        ('Refiner Model', refiner_model_name),
+                        ('Refiner Switch', refiner_switch),
+                        ('Sampler', sampler_name),
+                        ('Scheduler', scheduler_name),
+                        ('Seed', task['task_seed'])
+                    ]
+                    for n, w in loras:
                         if n != 'None':
-                            d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
+                            d.append((f'LoRA [{n}] weight', w))
+                    log(x, d, single_line_number=3)
 
-                    metadata_parser = None
-                    if save_metadata_to_images:
-                        metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
-                        metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
-                                                 task['log_negative_prompt'], task['negative'],
-                                                 steps, base_model_name, refiner_model_name, loras)
-                    d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
-                    d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
-                    img_paths.append(log(x, d, metadata_parser, output_format))
-
-                yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
+                yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1)
             except ldm_patched.modules.model_management.InterruptProcessingException as e:
-                if async_task.last_stop == 'skip':
+                if shared.last_stop == 'skip':
                     print('User skipped')
-                    async_task.last_stop = False
                     continue
                 else:
                     print('User stopped')
@@ -900,27 +792,21 @@ def worker():
 
             execution_time = time.perf_counter() - execution_start_time
             print(f'Generating and saving time: {execution_time:.2f} seconds')
-        async_task.processing = False
+
         return
 
     while True:
         time.sleep(0.01)
         if len(async_tasks) > 0:
             task = async_tasks.pop(0)
-            generate_image_grid = task.args.pop(0)
-
             try:
                 handler(task)
-                if generate_image_grid:
-                    build_image_wall(task)
+                build_image_wall(task)
                 task.yields.append(['finish', task.results])
                 pipeline.prepare_text_encoder(async_call=True)
             except:
                 traceback.print_exc()
                 task.yields.append(['finish', task.results])
-            finally:
-                if pid in modules.patch.patch_settings:
-                    del modules.patch.patch_settings[pid]
     pass
 
 

modules/config.py

@@ -3,41 +3,23 @@ import json
 import math
 import numbers
 import args_manager
-import tempfile
 import modules.flags
 import modules.sdxl_styles
 
 from modules.model_loader import load_file_from_url
-from modules.util import get_files_from_folder, makedirs_with_log
-from modules.flags import OutputFormat, Performance, MetadataScheme
+from modules.util import get_files_from_folder
 
 
-def get_config_path(key, default_value):
-    env = os.getenv(key)
-    if env is not None and isinstance(env, str):
-        print(f"Environment: {key} = {env}")
-        return env
-    else:
-        return os.path.abspath(default_value)
-
-
-config_path = get_config_path('config_path', "./config.txt")
-config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt")
+config_path = os.path.abspath("./config.txt")
+config_example_path = os.path.abspath("config_modification_tutorial.txt")
 config_dict = {}
 always_save_keys = []
 visited_keys = []
 
-try:
-    with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file:
-        config_dict.update(json.load(json_file))
-except Exception as e:
-    print(f'Load default preset failed.')
-    print(e)
-
 try:
     if os.path.exists(config_path):
         with open(config_path, "r", encoding="utf-8") as json_file:
-            config_dict.update(json.load(json_file))
+            config_dict = json.load(json_file)
             always_save_keys = list(config_dict.keys())
 except Exception as e:
     print(f'Failed to load config file "{config_path}" . The reason is: {str(e)}')
@@ -97,50 +79,23 @@ def try_load_deprecated_user_path_config():
 
 try_load_deprecated_user_path_config()
 
-
-def get_presets():
-    preset_folder = 'presets'
-    presets = ['initial']
-    if not os.path.exists(preset_folder):
-        print('No presets found.')
-        return presets
-
-    return presets + [f[:f.index('.json')] for f in os.listdir(preset_folder) if f.endswith('.json')]
-
-
-def try_get_preset_content(preset):
-    if isinstance(preset, str):
-        preset_path = os.path.abspath(f'./presets/{preset}.json')
-        try:
-            if os.path.exists(preset_path):
-                with open(preset_path, "r", encoding="utf-8") as json_file:
-                    json_content = json.load(json_file)
-                    print(f'Loaded preset: {preset_path}')
-                    return json_content
-            else:
-                raise FileNotFoundError
-        except Exception as e:
-            print(f'Load preset [{preset_path}] failed')
-            print(e)
-    return {}
-
-available_presets = get_presets()
 preset = args_manager.args.preset
-config_dict.update(try_get_preset_content(preset))
 
-def get_path_output() -> str:
-    """
-    Checking output path argument and overriding default path.
-    """
-    global config_dict
-    path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True)
-    if args_manager.args.output_path:
-        print(f'Overriding config value path_outputs with {args_manager.args.output_path}')
-        config_dict['path_outputs'] = path_output = args_manager.args.output_path
-    return path_output
+if isinstance(preset, str):
+    preset_path = os.path.abspath(f'./presets/{preset}.json')
+    try:
+        if os.path.exists(preset_path):
+            with open(preset_path, "r", encoding="utf-8") as json_file:
+                config_dict.update(json.load(json_file))
+                print(f'Loaded preset: {preset_path}')
+        else:
+            raise FileNotFoundError
+    except Exception as e:
+        print(f'Load preset [{preset_path}] failed')
+        print(e)
 
 
-def get_dir_or_set_default(key, default_value, as_array=False, make_directory=False):
+def get_dir_or_set_default(key, default_value):
     global config_dict, visited_keys, always_save_keys
 
     if key not in visited_keys:
@@ -149,44 +104,20 @@ def get_dir_or_set_default(key, default_value, as_array=False, make_directory=Fa
     if key not in always_save_keys:
         always_save_keys.append(key)
 
-    v = os.getenv(key)
-    if v is not None:
-        print(f"Environment: {key} = {v}")
-        config_dict[key] = v
-    else:
-        v = config_dict.get(key, None)
-
-    if isinstance(v, str):
-        if make_directory:
-            makedirs_with_log(v)
-        if os.path.exists(v) and os.path.isdir(v):
-            return v if not as_array else [v]
-    elif isinstance(v, list):
-        if make_directory:
-            for d in v:
-                makedirs_with_log(d)
-        if all([os.path.exists(d) and os.path.isdir(d) for d in v]):
-            return v
-
-    if v is not None:
-        print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')
-    if isinstance(default_value, list):
-        dp = []
-        for path in default_value:
-            abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path))
-            dp.append(abs_path)
-            os.makedirs(abs_path, exist_ok=True)
+    v = config_dict.get(key, None)
+    if isinstance(v, str) and os.path.exists(v) and os.path.isdir(v):
+        return v
     else:
+        if v is not None:
+            print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')
         dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value))
         os.makedirs(dp, exist_ok=True)
-        if as_array:
-            dp = [dp]
-    config_dict[key] = dp
-    return dp
+        config_dict[key] = dp
+        return dp
 
 
-paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True)
-paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)
+path_checkpoints = get_dir_or_set_default('path_checkpoints', '../models/checkpoints/')
+path_loras = get_dir_or_set_default('path_loras', '../models/loras/')
 path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/')
 path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/')
 path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')
@@ -194,8 +125,7 @@ path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/')
 path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/')
 path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/')
 path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion')
-path_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/')
-path_outputs = get_path_output()
+path_outputs = get_dir_or_set_default('path_outputs', '../outputs/')
 
 
 def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False):
@@ -204,11 +134,6 @@ def get_config_item_or_set_default(key, default_value, validator, disable_empty_
     if key not in visited_keys:
         visited_keys.append(key)
     
-    v = os.getenv(key)
-    if v is not None:
-        print(f"Environment: {key} = {v}")
-        config_dict[key] = v
-
     if key not in config_dict:
         config_dict[key] = default_value
         return default_value
@@ -226,109 +151,50 @@ def get_config_item_or_set_default(key, default_value, validator, disable_empty_
         return default_value
 
 
-def init_temp_path(path: str | None, default_path: str) -> str:
-    if args_manager.args.temp_path:
-        path = args_manager.args.temp_path
-
-    if path != '' and path != default_path:
-        try:
-            if not os.path.isabs(path):
-                path = os.path.abspath(path)
-            os.makedirs(path, exist_ok=True)
-            print(f'Using temp path {path}')
-            return path
-        except Exception as e:
-            print(f'Could not create temp path {path}. Reason: {e}')
-            print(f'Using default temp path {default_path} instead.')
-
-    os.makedirs(default_path, exist_ok=True)
-    return default_path
-
-
-default_temp_path = os.path.join(tempfile.gettempdir(), 'fooocus')
-temp_path = init_temp_path(get_config_item_or_set_default(
-    key='temp_path',
-    default_value=default_temp_path,
-    validator=lambda x: isinstance(x, str),
-), default_temp_path)
-temp_path_cleanup_on_launch = get_config_item_or_set_default(
-    key='temp_path_cleanup_on_launch',
-    default_value=True,
-    validator=lambda x: isinstance(x, bool)
-)
-default_base_model_name = default_model = get_config_item_or_set_default(
+default_base_model_name = get_config_item_or_set_default(
     key='default_model',
-    default_value='model.safetensors',
+    default_value='juggernautXL_version6Rundiffusion.safetensors',
     validator=lambda x: isinstance(x, str)
 )
-previous_default_models = get_config_item_or_set_default(
-    key='previous_default_models',
-    default_value=[],
-    validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x)
-)
-default_refiner_model_name = default_refiner = get_config_item_or_set_default(
+default_refiner_model_name = get_config_item_or_set_default(
     key='default_refiner',
     default_value='None',
     validator=lambda x: isinstance(x, str)
 )
 default_refiner_switch = get_config_item_or_set_default(
     key='default_refiner_switch',
-    default_value=0.8,
+    default_value=0.5,
     validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1
 )
-default_loras_min_weight = get_config_item_or_set_default(
-    key='default_loras_min_weight',
-    default_value=-2,
-    validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10
-)
-default_loras_max_weight = get_config_item_or_set_default(
-    key='default_loras_max_weight',
-    default_value=2,
-    validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10
-)
 default_loras = get_config_item_or_set_default(
     key='default_loras',
     default_value=[
         [
-            True,
+            "sd_xl_offset_example-lora_1.0.safetensors",
+            0.1
+        ],
+        [
             "None",
             1.0
         ],
         [
-            True,
             "None",
             1.0
         ],
         [
-            True,
             "None",
             1.0
         ],
         [
-            True,
-            "None",
-            1.0
-        ],
-        [
-            True,
             "None",
             1.0
         ]
     ],
-    validator=lambda x: isinstance(x, list) and all(
-        len(y) == 3 and isinstance(y[0], bool) and isinstance(y[1], str) and isinstance(y[2], numbers.Number)
-        or len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number)
-        for y in x)
-)
-default_loras = [(y[0], y[1], y[2]) if len(y) == 3 else (True, y[0], y[1]) for y in default_loras]
-default_max_lora_number = get_config_item_or_set_default(
-    key='default_max_lora_number',
-    default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5,
-    validator=lambda x: isinstance(x, int) and x >= 1
+    validator=lambda x: isinstance(x, list) and all(len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x)
 )
 default_cfg_scale = get_config_item_or_set_default(
     key='default_cfg_scale',
-    default_value=7.0,
+    default_value=4.0,
     validator=lambda x: isinstance(x, numbers.Number)
 )
 default_sample_sharpness = get_config_item_or_set_default(
@@ -369,37 +235,31 @@ default_prompt = get_config_item_or_set_default(
 )
 default_performance = get_config_item_or_set_default(
     key='default_performance',
-    default_value=Performance.SPEED.value,
-    validator=lambda x: x in Performance.list()
+    default_value='Speed',
+    validator=lambda x: x in modules.flags.performance_selections
 )
 default_advanced_checkbox = get_config_item_or_set_default(
     key='default_advanced_checkbox',
     default_value=False,
     validator=lambda x: isinstance(x, bool)
 )
-default_max_image_number = get_config_item_or_set_default(
-    key='default_max_image_number',
-    default_value=32,
-    validator=lambda x: isinstance(x, int) and x >= 1
-)
-default_output_format = get_config_item_or_set_default(
-    key='default_output_format',
-    default_value='png',
-    validator=lambda x: x in OutputFormat.list()
-)
 default_image_number = get_config_item_or_set_default(
     key='default_image_number',
     default_value=2,
-    validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number
+    validator=lambda x: isinstance(x, int) and 1 <= x <= 32
 )
 checkpoint_downloads = get_config_item_or_set_default(
     key='checkpoint_downloads',
-    default_value={},
+    default_value={
+        "juggernautXL_version6Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_version6Rundiffusion.safetensors"
+    },
     validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
 )
 lora_downloads = get_config_item_or_set_default(
     key='lora_downloads',
-    default_value={},
+    default_value={
+        "sd_xl_offset_example-lora_1.0.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors"
+    },
     validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
 )
 embeddings_downloads = get_config_item_or_set_default(
@@ -450,51 +310,30 @@ example_inpaint_prompts = get_config_item_or_set_default(
     ],
     validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x)
 )
-default_save_metadata_to_images = get_config_item_or_set_default(
-    key='default_save_metadata_to_images',
-    default_value=False,
-    validator=lambda x: isinstance(x, bool)
-)
-default_metadata_scheme = get_config_item_or_set_default(
-    key='default_metadata_scheme',
-    default_value=MetadataScheme.FOOOCUS.value,
-    validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x]
-)
-metadata_created_by = get_config_item_or_set_default(
-    key='metadata_created_by',
-    default_value='',
-    validator=lambda x: isinstance(x, str)
-)
 
 example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
 
-config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
+config_dict["default_loras"] = default_loras = default_loras[:5] + [['None', 1.0] for _ in range(5 - len(default_loras))]
+
+possible_preset_keys = [
+    "default_model",
+    "default_refiner",
+    "default_refiner_switch",
+    "default_loras",
+    "default_cfg_scale",
+    "default_sample_sharpness",
+    "default_sampler",
+    "default_scheduler",
+    "default_performance",
+    "default_prompt",
+    "default_prompt_negative",
+    "default_styles",
+    "default_aspect_ratio",
+    "checkpoint_downloads",
+    "embeddings_downloads",
+    "lora_downloads",
+]
 
-# mapping config to meta parameter 
-possible_preset_keys = {
-    "default_model": "base_model",
-    "default_refiner": "refiner_model",
-    "default_refiner_switch": "refiner_switch",
-    "previous_default_models": "previous_default_models",
-    "default_loras_min_weight": "default_loras_min_weight",
-    "default_loras_max_weight": "default_loras_max_weight",
-    "default_loras": "<processed>",
-    "default_cfg_scale": "guidance_scale",
-    "default_sample_sharpness": "sharpness",
-    "default_sampler": "sampler",
-    "default_scheduler": "scheduler",
-    "default_overwrite_step": "steps",
-    "default_performance": "performance",
-    "default_image_number": "image_number",
-    "default_prompt": "prompt",
-    "default_prompt_negative": "negative_prompt",
-    "default_styles": "styles",
-    "default_aspect_ratio": "resolution",
-    "default_save_metadata_to_images": "default_save_metadata_to_images",
-    "checkpoint_downloads": "checkpoint_downloads",
-    "embeddings_downloads": "embeddings_downloads",
-    "lora_downloads": "lora_downloads"
-}
 
 REWRITE_PRESET = False
 
@@ -533,30 +372,21 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
                       'and there is no "," before the last "}". \n\n\n')
     json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4)
 
+
+os.makedirs(path_outputs, exist_ok=True)
+
 model_filenames = []
 lora_filenames = []
-wildcard_filenames = []
-
-sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
-sdxl_lightning_lora = 'sdxl_lightning_4step_lora.safetensors'
-loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora]
 
 
-def get_model_filenames(folder_paths, extensions=None, name_filter=None):
-    if extensions is None:
-        extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
-    files = []
-    for folder in folder_paths:
-        files += get_files_from_folder(folder, extensions, name_filter)
-    return files
+def get_model_filenames(folder_path, name_filter=None):
+    return get_files_from_folder(folder_path, ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch'], name_filter)
 
 
-def update_files():
-    global model_filenames, lora_filenames, wildcard_filenames, available_presets
-    model_filenames = get_model_filenames(paths_checkpoints)
-    lora_filenames = get_model_filenames(paths_loras)
-    wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
-    available_presets = get_presets()
+def update_all_model_names():
+    global model_filenames, lora_filenames
+    model_filenames = get_model_filenames(path_checkpoints)
+    lora_filenames = get_model_filenames(path_loras)
     return
 
 
@@ -601,18 +431,10 @@ def downloading_inpaint_models(v):
 def downloading_sdxl_lcm_lora():
     load_file_from_url(
         url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors',
-        model_dir=paths_loras[0],
-        file_name=sdxl_lcm_lora
+        model_dir=path_loras,
+        file_name='sdxl_lcm_lora.safetensors'
     )
-    return sdxl_lcm_lora
-
-def downloading_sdxl_lightning_lora():
-    load_file_from_url(
-        url='https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_4step_lora.safetensors',
-        model_dir=paths_loras[0],
-        file_name=sdxl_lightning_lora
-    )
-    return sdxl_lightning_lora
+    return 'sdxl_lcm_lora.safetensors'
 
 
 def downloading_controlnet_canny():
@@ -680,4 +502,4 @@ def downloading_upscale_model():
     return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin')
 
 
-update_files()
+update_all_model_names()

modules/core.py

@@ -1,3 +1,8 @@
+from modules.patch import patch_all
+
+patch_all()
+
+
 import os
 import einops
 import torch
@@ -11,6 +16,7 @@ import ldm_patched.modules.controlnet
 import modules.sample_hijack
 import ldm_patched.modules.samplers
 import ldm_patched.modules.latent_formats
+import modules.advanced_parameters
 
 from ldm_patched.modules.sd import load_checkpoint_guess_config
 from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \
@@ -18,7 +24,6 @@ from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode,
 from ldm_patched.contrib.external_freelunch import FreeU_V2
 from ldm_patched.modules.sample import prepare_mask
 from modules.lora import match_lora
-from modules.util import get_file_from_folder_list
 from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip
 from modules.config import path_embeddings
 from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete
@@ -73,14 +78,14 @@ class StableDiffusionModel:
 
         loras_to_load = []
 
-        for filename, weight in loras:
-            if filename == 'None':
+        for name, weight in loras:
+            if name == 'None':
                 continue
 
-            if os.path.exists(filename):
-                lora_filename = filename
+            if os.path.exists(name):
+                lora_filename = name
             else:
-                lora_filename = get_file_from_folder_list(filename, modules.config.paths_loras)
+                lora_filename = os.path.join(modules.config.path_loras, name)
 
             if not os.path.exists(lora_filename):
                 print(f'Lora file not found: {lora_filename}')
@@ -263,7 +268,7 @@ def get_previewer(model):
 def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
              scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
              force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
-             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False):
+             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None):
 
     if sigmas is not None:
         sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device())
@@ -294,7 +299,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
     def callback(step, x0, x, total_steps):
         ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()
         y = None
-        if previewer is not None and not disable_preview:
+        if previewer is not None and not modules.advanced_parameters.disable_preview:
             y = previewer(x0, previewer_start + step, previewer_end)
         if callback_function is not None:
             callback_function(previewer_start + step, x0, x, previewer_end, y)

modules/default_pipeline.py

@@ -11,7 +11,6 @@ from extras.expansion import FooocusExpansion
 
 from ldm_patched.modules.model_base import SDXL, SDXLRefiner
 from modules.sample_hijack import clip_separate
-from modules.util import get_file_from_folder_list, get_enabled_loras
 
 
 model_base = core.StableDiffusionModel()
@@ -61,7 +60,7 @@ def assert_model_integrity():
 def refresh_base_model(name):
     global model_base
 
-    filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
+    filename = os.path.abspath(os.path.realpath(os.path.join(modules.config.path_checkpoints, name)))
 
     if model_base.filename == filename:
         return
@@ -77,7 +76,7 @@ def refresh_base_model(name):
 def refresh_refiner_model(name):
     global model_refiner
 
-    filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
+    filename = os.path.abspath(os.path.realpath(os.path.join(modules.config.path_checkpoints, name)))
 
     if model_refiner.filename == filename:
         return
@@ -254,7 +253,7 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 refresh_everything(
     refiner_model_name=modules.config.default_refiner_model_name,
     base_model_name=modules.config.default_base_model_name,
-    loras=get_enabled_loras(modules.config.default_loras)
+    loras=modules.config.default_loras
 )
 
 
@@ -316,7 +315,7 @@ def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'):
 
 @torch.no_grad()
 @torch.inference_mode()
-def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False):
+def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'):
     target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \
         = final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip
 
@@ -375,7 +374,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             refiner_switch=switch,
             previewer_start=0,
             previewer_end=steps,
-            disable_preview=disable_preview
         )
         decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled)
 
@@ -394,7 +392,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             scheduler=scheduler_name,
             previewer_start=0,
             previewer_end=steps,
-            disable_preview=disable_preview
         )
         print('Refiner swapped by changing ksampler. Noise preserved.')
 
@@ -417,7 +414,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             scheduler=scheduler_name,
             previewer_start=switch,
             previewer_end=steps,
-            disable_preview=disable_preview
         )
 
         target_model = target_refiner_vae
@@ -426,7 +422,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
         decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
 
     if refiner_swap_method == 'vae':
-        modules.patch.patch_settings[os.getpid()].eps_record = 'vae'
+        modules.patch.eps_record = 'vae'
 
         if modules.inpaint_worker.current_task is not None:
             modules.inpaint_worker.current_task.unswap()
@@ -444,8 +440,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             sampler_name=sampler_name,
             scheduler=scheduler_name,
             previewer_start=0,
-            previewer_end=steps,
-            disable_preview=disable_preview
+            previewer_end=steps
         )
         print('Fooocus VAE-based swap.')
 
@@ -464,7 +459,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
                                   denoise=denoise)[switch:] * k_sigmas
         len_sigmas = len(sigmas) - 1
 
-        noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)
+        noise_mean = torch.mean(modules.patch.eps_record, dim=1, keepdim=True)
 
         if modules.inpaint_worker.current_task is not None:
             modules.inpaint_worker.current_task.swap()
@@ -484,8 +479,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             previewer_start=switch,
             previewer_end=steps,
             sigmas=sigmas,
-            noise_mean=noise_mean,
-            disable_preview=disable_preview
+            noise_mean=noise_mean
         )
 
         target_model = target_refiner_vae
@@ -494,5 +488,5 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
         decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
 
     images = core.pytorch_to_numpy(decoded_latent)
-    modules.patch.patch_settings[os.getpid()].eps_record = None
+    modules.patch.eps_record = None
     return images

modules/flags.py

@@ -1,5 +1,3 @@
-from enum import IntEnum, Enum
-
 disabled = 'Disabled'
 enabled = 'Enabled'
 subtle_variation = 'Vary (Subtle)'
@@ -12,49 +10,16 @@ uov_list = [
     disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast
 ]
 
-CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"]
-
-# fooocus: a1111 (Civitai)
-KSAMPLER = {
-    "euler": "Euler",
-    "euler_ancestral": "Euler a",
-    "heun": "Heun",
-    "heunpp2": "",
-    "dpm_2": "DPM2",
-    "dpm_2_ancestral": "DPM2 a",
-    "lms": "LMS",
-    "dpm_fast": "DPM fast",
-    "dpm_adaptive": "DPM adaptive",
-    "dpmpp_2s_ancestral": "DPM++ 2S a",
-    "dpmpp_sde": "DPM++ SDE",
-    "dpmpp_sde_gpu": "DPM++ SDE",
-    "dpmpp_2m": "DPM++ 2M",
-    "dpmpp_2m_sde": "DPM++ 2M SDE",
-    "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
-    "dpmpp_3m_sde": "",
-    "dpmpp_3m_sde_gpu": "",
-    "ddpm": "",
-    "lcm": "LCM"
-}
-
-SAMPLER_EXTRA = {
-    "ddim": "DDIM",
-    "uni_pc": "UniPC",
-    "uni_pc_bh2": ""
-}
-
-SAMPLERS = KSAMPLER | SAMPLER_EXTRA
-
-KSAMPLER_NAMES = list(KSAMPLER.keys())
+KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+                  "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
+                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
 
 SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"]
-SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
+SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
 
 sampler_list = SAMPLER_NAMES
 scheduler_list = SCHEDULER_NAMES
 
-refiner_swap_method = 'joint'
-
 cn_ip = "ImagePrompt"
 cn_ip_face = "FaceSwap"
 cn_canny = "PyraCanny"
@@ -67,9 +32,9 @@ default_parameters = {
     cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0)
 }  # stop, weight
 
-output_formats = ['png', 'jpeg', 'webp']
-
 inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
+performance_selections = ['Speed', 'Quality', 'Extreme Speed']
+
 inpaint_option_default = 'Inpaint or Outpaint (default)'
 inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)'
 inpaint_option_modify = 'Modify Content (add objects, change background, etc.)'
@@ -77,63 +42,3 @@ inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option
 
 desc_type_photo = 'Photograph'
 desc_type_anime = 'Art/Anime'
-
-
-class MetadataScheme(Enum):
-    FOOOCUS = 'fooocus'
-    A1111 = 'a1111'
-
-
-metadata_scheme = [
-    (f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value),
-    (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value),
-]
-
-controlnet_image_count = 4
-
-
-class OutputFormat(Enum):
-    PNG = 'png'
-    JPEG = 'jpeg'
-    WEBP = 'webp'
-
-    @classmethod
-    def list(cls) -> list:
-        return list(map(lambda c: c.value, cls))
-
-
-class Steps(IntEnum):
-    QUALITY = 60
-    SPEED = 30
-    EXTREME_SPEED = 8
-    LIGHTNING = 4
-
-
-class StepsUOV(IntEnum):
-    QUALITY = 36
-    SPEED = 18
-    EXTREME_SPEED = 8
-    LIGHTNING = 4
-
-
-class Performance(Enum):
-    QUALITY = 'Quality'
-    SPEED = 'Speed'
-    EXTREME_SPEED = 'Extreme Speed'
-    LIGHTNING = 'Lightning'
-
-    @classmethod
-    def list(cls) -> list:
-        return list(map(lambda c: c.value, cls))
-
-    @classmethod
-    def has_restricted_features(cls, x) -> bool:
-        if isinstance(x, Performance):
-            x = x.value
-        return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value]
-
-    def steps(self) -> int | None:
-        return Steps[self.name].value if Steps[self.name] else None
-
-    def steps_uov(self) -> int | None:
-        return StepsUOV[self.name].value if Steps[self.name] else None

modules/gradio_hijack.py

@@ -17,7 +17,7 @@ from gradio_client.documentation import document, set_documentation_group
 from gradio_client.serializing import ImgSerializable
 from PIL import Image as _Image  # using _ to minimize namespace pollution
 
-from gradio import processing_utils, utils, Error
+from gradio import processing_utils, utils
 from gradio.components.base import IOComponent, _Keywords, Block
 from gradio.deprecation import warn_style_method_deprecation
 from gradio.events import (
@@ -275,10 +275,7 @@ class Image(
                 x, mask = x["image"], x["mask"]
 
         assert isinstance(x, str)
-        try:
-            im = processing_utils.decode_base64_to_image(x)
-        except PIL.UnidentifiedImageError:
-            raise Error("Unsupported image type in input")
+        im = processing_utils.decode_base64_to_image(x)
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             im = im.convert(self.image_mode)

modules/html.py

@@ -1,3 +1,118 @@
+css = '''
+.loader-container {
+  display: flex; /* Use flex to align items horizontally */
+  align-items: center; /* Center items vertically within the container */
+  white-space: nowrap; /* Prevent line breaks within the container */
+}
+
+.loader {
+  border: 8px solid #f3f3f3; /* Light grey */
+  border-top: 8px solid #3498db; /* Blue */
+  border-radius: 50%;
+  width: 30px;
+  height: 30px;
+  animation: spin 2s linear infinite;
+}
+
+@keyframes spin {
+  0% { transform: rotate(0deg); }
+  100% { transform: rotate(360deg); }
+}
+
+/* Style the progress bar */
+progress {
+  appearance: none; /* Remove default styling */
+  height: 20px; /* Set the height of the progress bar */
+  border-radius: 5px; /* Round the corners of the progress bar */
+  background-color: #f3f3f3; /* Light grey background */
+  width: 100%;
+}
+
+/* Style the progress bar container */
+.progress-container {
+  margin-left: 20px;
+  margin-right: 20px;
+  flex-grow: 1; /* Allow the progress container to take up remaining space */
+}
+
+/* Set the color of the progress bar fill */
+progress::-webkit-progress-value {
+  background-color: #3498db; /* Blue color for the fill */
+}
+
+progress::-moz-progress-bar {
+  background-color: #3498db; /* Blue color for the fill in Firefox */
+}
+
+/* Style the text on the progress bar */
+progress::after {
+  content: attr(value '%'); /* Display the progress value followed by '%' */
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%);
+  color: white; /* Set text color */
+  font-size: 14px; /* Set font size */
+}
+
+/* Style other texts */
+.loader-container > span {
+  margin-left: 5px; /* Add spacing between the progress bar and the text */
+}
+
+.progress-bar > .generating {
+  display: none !important;
+}
+
+.progress-bar{
+  height: 30px !important;
+}
+
+.type_row{
+  height: 80px !important;
+}
+
+.type_row_half{
+  height: 32px !important;
+}
+
+.scroll-hide{
+  resize: none !important;
+}
+
+.refresh_button{
+  border: none !important;
+  background: none !important;
+  font-size: none !important;
+  box-shadow: none !important;
+}
+
+.advanced_check_row{
+  width: 250px !important;
+}
+
+.min_check{
+  min-width: min(1px, 100%) !important;
+}
+
+.resizable_area {
+  resize: vertical;
+  overflow: auto !important;
+}
+
+.aspect_ratios label {
+    width: 140px !important;
+}
+
+.aspect_ratios label span {
+    white-space: nowrap !important;
+}
+
+.aspect_ratios label input {
+    margin-left: -5px !important;
+}
+
+'''
 progress_html = '''
 <div class="loader-container">
   <div class="loader"></div>

modules/inpaint_worker.py

@@ -4,7 +4,6 @@ import numpy as np
 from PIL import Image, ImageFilter
 from modules.util import resample_image, set_image_shape_ceil, get_image_shape_ceil
 from modules.upscaler import perform_upscale
-import cv2
 
 
 inpaint_head_model = None
@@ -29,25 +28,19 @@ def box_blur(x, k):
     return np.array(x)
 
 
-def max_filter_opencv(x, ksize=3):
-    # Use OpenCV maximum filter
-    # Make sure the input type is int16
-    return cv2.dilate(x, np.ones((ksize, ksize), dtype=np.int16))
+def max33(x):
+    x = Image.fromarray(x)
+    x = x.filter(ImageFilter.MaxFilter(3))
+    return np.array(x)
 
 
 def morphological_open(x):
-    # Convert array to int16 type via threshold operation
-    x_int16 = np.zeros_like(x, dtype=np.int16)
-    x_int16[x > 127] = 256
-
-    for i in range(32):
-        # Use int16 type to avoid overflow
-        maxed = max_filter_opencv(x_int16, ksize=3) - 8
-        x_int16 = np.maximum(maxed, x_int16)
-
-    # Clip negative values to 0 and convert back to uint8 type
-    x_uint8 = np.clip(x_int16, 0, 255).astype(np.uint8)
-    return x_uint8
+    x_int32 = np.zeros_like(x).astype(np.int32)
+    x_int32[x > 127] = 256
+    for _ in range(32):
+        maxed = max33(x_int32) - 8
+        x_int32 = np.maximum(maxed, x_int32)
+    return x_int32.clip(0, 255).astype(np.uint8)
 
 
 def up255(x, t=0):

modules/launch_util.py

@@ -1,19 +1,16 @@
 import os
 import importlib
 import importlib.util
-import shutil
 import subprocess
 import sys
 import re
 import logging
-import importlib.metadata
-import packaging.version
-from packaging.requirements import Requirement
+
 
 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())
 
-re_requirement = re.compile(r"\s*([-\w]+)\s*(?:==\s*([-+.\w]+))?\s*")
+re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
 
 python = sys.executable
 default_command_live = (os.environ.get('LAUNCH_LIVE_OUTPUT') == "1")
@@ -76,42 +73,35 @@ def run_pip(command, desc=None, live=default_command_live):
 
 
 def requirements_met(requirements_file):
+    """
+    Does a simple parse of a requirements.txt file to determine if all rerqirements in it
+    are already installed. Returns True if so, False if not installed or parsing fails.
+    """
+
+    import importlib.metadata
+    import packaging.version
+
     with open(requirements_file, "r", encoding="utf8") as file:
         for line in file:
-            line = line.strip()
-            if line == "" or line.startswith('#'):
+            if line.strip() == "":
                 continue
 
-            requirement = Requirement(line)
-            package = requirement.name
+            m = re.match(re_requirement, line)
+            if m is None:
+                return False
+
+            package = m.group(1).strip()
+            version_required = (m.group(2) or "").strip()
+
+            if version_required == "":
+                continue
 
             try:
                 version_installed = importlib.metadata.version(package)
-                installed_version = packaging.version.parse(version_installed)
+            except Exception:
+                return False
 
-                # Check if the installed version satisfies the requirement
-                if installed_version not in requirement.specifier:
-                    print(f"Version mismatch for {package}: Installed version {version_installed} does not meet requirement {requirement}")
-                    return False
-            except Exception as e:
-                print(f"Error checking version for {package}: {e}")
+            if packaging.version.parse(version_required) != packaging.version.parse(version_installed):
                 return False
 
     return True
-
-
-def delete_folder_content(folder, prefix=None):
-    result = True
-
-    for filename in os.listdir(folder):
-        file_path = os.path.join(folder, filename)
-        try:
-            if os.path.isfile(file_path) or os.path.islink(file_path):
-                os.unlink(file_path)
-            elif os.path.isdir(file_path):
-                shutil.rmtree(file_path)
-        except Exception as e:
-            print(f'{prefix}Failed to delete {file_path}. Reason: {e}')
-            result = False
-
-    return result

modules/meta_parser.py

@@ -1,609 +0,0 @@
-import json
-import re
-from abc import ABC, abstractmethod
-from pathlib import Path
-
-import gradio as gr
-from PIL import Image
-
-import fooocus_version
-import modules.config
-import modules.sdxl_styles
-from modules.flags import MetadataScheme, Performance, Steps
-from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS
-from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, sha256
-
-re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
-re_param = re.compile(re_param_code)
-re_imagesize = re.compile(r"^(\d+)x(\d+)$")
-
-hash_cache = {}
-
-
-def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
-    loaded_parameter_dict = raw_metadata
-    if isinstance(raw_metadata, str):
-        loaded_parameter_dict = json.loads(raw_metadata)
-    assert isinstance(loaded_parameter_dict, dict)
-
-    results = [len(loaded_parameter_dict) > 0]
-
-    get_image_number('image_number', 'Image Number', loaded_parameter_dict, results)
-    get_str('prompt', 'Prompt', loaded_parameter_dict, results)
-    get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
-    get_list('styles', 'Styles', loaded_parameter_dict, results)
-    get_str('performance', 'Performance', loaded_parameter_dict, results)
-    get_steps('steps', 'Steps', loaded_parameter_dict, results)
-    get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
-    get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
-    get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
-    get_float('sharpness', 'Sharpness', loaded_parameter_dict, results)
-    get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results)
-    get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results)
-    get_float('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)
-    get_str('base_model', 'Base Model', loaded_parameter_dict, results)
-    get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results)
-    get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
-    get_str('sampler', 'Sampler', loaded_parameter_dict, results)
-    get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
-    get_seed('seed', 'Seed', loaded_parameter_dict, results)
-
-    if is_generating:
-        results.append(gr.update())
-    else:
-        results.append(gr.update(visible=True))
-
-    results.append(gr.update(visible=False))
-
-    get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)
-
-    for i in range(modules.config.default_max_lora_number):
-        get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results)
-
-    return results
-
-
-def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        assert isinstance(h, str)
-        results.append(h)
-    except:
-        results.append(gr.update())
-
-
-def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        h = eval(h)
-        assert isinstance(h, list)
-        results.append(h)
-    except:
-        results.append(gr.update())
-
-
-def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        assert h is not None
-        h = float(h)
-        results.append(h)
-    except:
-        results.append(gr.update())
-
-
-def get_image_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        assert h is not None
-        h = int(h)
-        h = min(h, modules.config.default_max_image_number)
-        results.append(h)
-    except:
-        results.append(1)
-
-
-def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        assert h is not None
-        h = int(h)
-        # if not in steps or in steps and performance is not the same
-        if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ',
-                                                                                                          '_').casefold():
-            results.append(h)
-            return
-        results.append(-1)
-    except:
-        results.append(-1)
-
-
-def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        width, height = eval(h)
-        formatted = modules.config.add_ratio(f'{width}*{height}')
-        if formatted in modules.config.available_aspect_ratios:
-            results.append(formatted)
-            results.append(-1)
-            results.append(-1)
-        else:
-            results.append(gr.update())
-            results.append(int(width))
-            results.append(int(height))
-    except:
-        results.append(gr.update())
-        results.append(gr.update())
-        results.append(gr.update())
-
-
-def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        assert h is not None
-        h = int(h)
-        results.append(False)
-        results.append(h)
-    except:
-        results.append(gr.update())
-        results.append(gr.update())
-
-
-def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        p, n, e = eval(h)
-        results.append(float(p))
-        results.append(float(n))
-        results.append(float(e))
-    except:
-        results.append(gr.update())
-        results.append(gr.update())
-        results.append(gr.update())
-
-
-def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
-    try:
-        h = source_dict.get(key, source_dict.get(fallback, default))
-        b1, b2, s1, s2 = eval(h)
-        results.append(True)
-        results.append(float(b1))
-        results.append(float(b2))
-        results.append(float(s1))
-        results.append(float(s2))
-    except:
-        results.append(False)
-        results.append(gr.update())
-        results.append(gr.update())
-        results.append(gr.update())
-        results.append(gr.update())
-
-
-def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
-    try:
-        split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ')
-        enabled = True
-        name = split_data[0]
-        weight = split_data[1]
-
-        if len(split_data) == 3:
-            enabled = split_data[0] == 'True'
-            name = split_data[1]
-            weight = split_data[2]
-
-        weight = float(weight)
-        results.append(enabled)
-        results.append(name)
-        results.append(weight)
-    except:
-        results.append(True)
-        results.append('None')
-        results.append(1)
-
-
-def get_sha256(filepath):
-    global hash_cache
-    if filepath not in hash_cache:
-        # is_safetensors = os.path.splitext(filepath)[1].lower() == '.safetensors'
-        hash_cache[filepath] = sha256(filepath)
-
-    return hash_cache[filepath]
-
-
-def parse_meta_from_preset(preset_content):
-    assert isinstance(preset_content, dict)
-    preset_prepared = {}
-    items = preset_content
-
-    for settings_key, meta_key in modules.config.possible_preset_keys.items():
-        if settings_key == "default_loras":
-            loras = getattr(modules.config, settings_key)
-            if settings_key in items:
-                loras = items[settings_key]
-            for index, lora in enumerate(loras[:5]):
-                preset_prepared[f'lora_combined_{index + 1}'] = ' : '.join(map(str, lora))
-        elif settings_key == "default_aspect_ratio":
-            if settings_key in items and items[settings_key] is not None:
-                default_aspect_ratio = items[settings_key]
-                width, height = default_aspect_ratio.split('*')
-            else:
-                default_aspect_ratio = getattr(modules.config, settings_key)
-                width, height = default_aspect_ratio.split('×')
-                height = height[:height.index(" ")]
-            preset_prepared[meta_key] = (width, height)
-        else:
-            preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[
-                settings_key] is not None else getattr(modules.config, settings_key)
-
-        if settings_key == "default_styles" or settings_key == "default_aspect_ratio":
-            preset_prepared[meta_key] = str(preset_prepared[meta_key])
-
-    return preset_prepared
-
-
-class MetadataParser(ABC):
-    def __init__(self):
-        self.raw_prompt: str = ''
-        self.full_prompt: str = ''
-        self.raw_negative_prompt: str = ''
-        self.full_negative_prompt: str = ''
-        self.steps: int = 30
-        self.base_model_name: str = ''
-        self.base_model_hash: str = ''
-        self.refiner_model_name: str = ''
-        self.refiner_model_hash: str = ''
-        self.loras: list = []
-
-    @abstractmethod
-    def get_scheme(self) -> MetadataScheme:
-        raise NotImplementedError
-
-    @abstractmethod
-    def parse_json(self, metadata: dict | str) -> dict:
-        raise NotImplementedError
-
-    @abstractmethod
-    def parse_string(self, metadata: dict) -> str:
-        raise NotImplementedError
-
-    def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
-                 refiner_model_name, loras):
-        self.raw_prompt = raw_prompt
-        self.full_prompt = full_prompt
-        self.raw_negative_prompt = raw_negative_prompt
-        self.full_negative_prompt = full_negative_prompt
-        self.steps = steps
-        self.base_model_name = Path(base_model_name).stem
-
-        base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints)
-        self.base_model_hash = get_sha256(base_model_path)
-
-        if refiner_model_name not in ['', 'None']:
-            self.refiner_model_name = Path(refiner_model_name).stem
-            refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints)
-            self.refiner_model_hash = get_sha256(refiner_model_path)
-
-        self.loras = []
-        for (lora_name, lora_weight) in loras:
-            if lora_name != 'None':
-                lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
-                lora_hash = get_sha256(lora_path)
-                self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
-
-    @staticmethod
-    def remove_special_loras(lora_filenames):
-        for lora_to_remove in modules.config.loras_metadata_remove:
-            if lora_to_remove in lora_filenames:
-                lora_filenames.remove(lora_to_remove)
-
-
-class A1111MetadataParser(MetadataParser):
-    def get_scheme(self) -> MetadataScheme:
-        return MetadataScheme.A1111
-
-    fooocus_to_a1111 = {
-        'raw_prompt': 'Raw prompt',
-        'raw_negative_prompt': 'Raw negative prompt',
-        'negative_prompt': 'Negative prompt',
-        'styles': 'Styles',
-        'performance': 'Performance',
-        'steps': 'Steps',
-        'sampler': 'Sampler',
-        'scheduler': 'Scheduler',
-        'guidance_scale': 'CFG scale',
-        'seed': 'Seed',
-        'resolution': 'Size',
-        'sharpness': 'Sharpness',
-        'adm_guidance': 'ADM Guidance',
-        'refiner_swap_method': 'Refiner Swap Method',
-        'adaptive_cfg': 'Adaptive CFG',
-        'overwrite_switch': 'Overwrite Switch',
-        'freeu': 'FreeU',
-        'base_model': 'Model',
-        'base_model_hash': 'Model hash',
-        'refiner_model': 'Refiner',
-        'refiner_model_hash': 'Refiner hash',
-        'lora_hashes': 'Lora hashes',
-        'lora_weights': 'Lora weights',
-        'created_by': 'User',
-        'version': 'Version'
-    }
-
-    def parse_json(self, metadata: str) -> dict:
-        metadata_prompt = ''
-        metadata_negative_prompt = ''
-
-        done_with_prompt = False
-
-        *lines, lastline = metadata.strip().split("\n")
-        if len(re_param.findall(lastline)) < 3:
-            lines.append(lastline)
-            lastline = ''
-
-        for line in lines:
-            line = line.strip()
-            if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"):
-                done_with_prompt = True
-                line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip()
-            if done_with_prompt:
-                metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line
-            else:
-                metadata_prompt += ('' if metadata_prompt == '' else "\n") + line
-
-        found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt)
-
-        data = {
-            'prompt': prompt,
-            'negative_prompt': negative_prompt
-        }
-
-        for k, v in re_param.findall(lastline):
-            try:
-                if v != '' and v[0] == '"' and v[-1] == '"':
-                    v = unquote(v)
-
-                m = re_imagesize.match(v)
-                if m is not None:
-                    data['resolution'] = str((m.group(1), m.group(2)))
-                else:
-                    data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v
-            except Exception:
-                print(f"Error parsing \"{k}: {v}\"")
-
-        # workaround for multiline prompts
-        if 'raw_prompt' in data:
-            data['prompt'] = data['raw_prompt']
-            raw_prompt = data['raw_prompt'].replace("\n", ', ')
-            if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles:
-                found_styles.append(modules.sdxl_styles.fooocus_expansion)
-
-        if 'raw_negative_prompt' in data:
-            data['negative_prompt'] = data['raw_negative_prompt']
-
-        data['styles'] = str(found_styles)
-
-        # try to load performance based on steps, fallback for direct A1111 imports
-        if 'steps' in data and 'performance' not in data:
-            try:
-                data['performance'] = Performance[Steps(int(data['steps'])).name].value
-            except ValueError | KeyError:
-                pass
-
-        if 'sampler' in data:
-            data['sampler'] = data['sampler'].replace(' Karras', '')
-            # get key
-            for k, v in SAMPLERS.items():
-                if v == data['sampler']:
-                    data['sampler'] = k
-                    break
-
-        for key in ['base_model', 'refiner_model']:
-            if key in data:
-                for filename in modules.config.model_filenames:
-                    path = Path(filename)
-                    if data[key] == path.stem:
-                        data[key] = filename
-                        break
-
-        lora_data = ''
-        if 'lora_weights' in data and data['lora_weights'] != '':
-            lora_data = data['lora_weights']
-        elif 'lora_hashes' in data and data['lora_hashes'] != '' and data['lora_hashes'].split(', ')[0].count(':') == 2:
-            lora_data = data['lora_hashes']
-
-        if lora_data != '':
-            lora_filenames = modules.config.lora_filenames.copy()
-            self.remove_special_loras(lora_filenames)
-            for li, lora in enumerate(lora_data.split(', ')):
-                lora_split = lora.split(': ')
-                lora_name = lora_split[0]
-                lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1]
-                for filename in lora_filenames:
-                    path = Path(filename)
-                    if lora_name == path.stem:
-                        data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
-                        break
-
-        return data
-
-    def parse_string(self, metadata: dict) -> str:
-        data = {k: v for _, k, v in metadata}
-
-        width, height = eval(data['resolution'])
-
-        sampler = data['sampler']
-        scheduler = data['scheduler']
-        if sampler in SAMPLERS and SAMPLERS[sampler] != '':
-            sampler = SAMPLERS[sampler]
-            if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
-                sampler += f' Karras'
-
-        generation_params = {
-            self.fooocus_to_a1111['steps']: self.steps,
-            self.fooocus_to_a1111['sampler']: sampler,
-            self.fooocus_to_a1111['seed']: data['seed'],
-            self.fooocus_to_a1111['resolution']: f'{width}x{height}',
-            self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'],
-            self.fooocus_to_a1111['sharpness']: data['sharpness'],
-            self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'],
-            self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem,
-            self.fooocus_to_a1111['base_model_hash']: self.base_model_hash,
-
-            self.fooocus_to_a1111['performance']: data['performance'],
-            self.fooocus_to_a1111['scheduler']: scheduler,
-            # workaround for multiline prompts
-            self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
-            self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
-        }
-
-        if self.refiner_model_name not in ['', 'None']:
-            generation_params |= {
-                self.fooocus_to_a1111['refiner_model']: self.refiner_model_name,
-                self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
-            }
-
-        for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
-            if key in data:
-                generation_params[self.fooocus_to_a1111[key]] = data[key]
-
-        if len(self.loras) > 0:
-            lora_hashes = []
-            lora_weights = []
-            for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
-                # workaround for Fooocus not knowing LoRA name in LoRA metadata
-                lora_hashes.append(f'{lora_name}: {lora_hash}')
-                lora_weights.append(f'{lora_name}: {lora_weight}')
-            lora_hashes_string = ', '.join(lora_hashes)
-            lora_weights_string = ', '.join(lora_weights)
-            generation_params[self.fooocus_to_a1111['lora_hashes']] = lora_hashes_string
-            generation_params[self.fooocus_to_a1111['lora_weights']] = lora_weights_string
-
-        generation_params[self.fooocus_to_a1111['version']] = data['version']
-
-        if modules.config.metadata_created_by != '':
-            generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by
-
-        generation_params_text = ", ".join(
-            [k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if
-             v is not None])
-        positive_prompt_resolved = ', '.join(self.full_prompt)
-        negative_prompt_resolved = ', '.join(self.full_negative_prompt)
-        negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
-        return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
-
-
-class FooocusMetadataParser(MetadataParser):
-    def get_scheme(self) -> MetadataScheme:
-        return MetadataScheme.FOOOCUS
-
-    def parse_json(self, metadata: dict) -> dict:
-        model_filenames = modules.config.model_filenames.copy()
-        lora_filenames = modules.config.lora_filenames.copy()
-        self.remove_special_loras(lora_filenames)
-        for key, value in metadata.items():
-            if value in ['', 'None']:
-                continue
-            if key in ['base_model', 'refiner_model']:
-                metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
-            elif key.startswith('lora_combined_'):
-                metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
-            else:
-                continue
-
-        return metadata
-
-    def parse_string(self, metadata: list) -> str:
-        for li, (label, key, value) in enumerate(metadata):
-            # remove model folder paths from metadata
-            if key.startswith('lora_combined_'):
-                name, weight = value.split(' : ')
-                name = Path(name).stem
-                value = f'{name} : {weight}'
-                metadata[li] = (label, key, value)
-
-        res = {k: v for _, k, v in metadata}
-
-        res['full_prompt'] = self.full_prompt
-        res['full_negative_prompt'] = self.full_negative_prompt
-        res['steps'] = self.steps
-        res['base_model'] = self.base_model_name
-        res['base_model_hash'] = self.base_model_hash
-
-        if self.refiner_model_name not in ['', 'None']:
-            res['refiner_model'] = self.refiner_model_name
-            res['refiner_model_hash'] = self.refiner_model_hash
-
-        res['loras'] = self.loras
-
-        if modules.config.metadata_created_by != '':
-            res['created_by'] = modules.config.metadata_created_by
-
-        return json.dumps(dict(sorted(res.items())))
-
-    @staticmethod
-    def replace_value_with_filename(key, value, filenames):
-        for filename in filenames:
-            path = Path(filename)
-            if key.startswith('lora_combined_'):
-                name, weight = value.split(' : ')
-                if name == path.stem:
-                    return f'{filename} : {weight}'
-            elif value == path.stem:
-                return filename
-
-
-def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
-    match metadata_scheme:
-        case MetadataScheme.FOOOCUS:
-            return FooocusMetadataParser()
-        case MetadataScheme.A1111:
-            return A1111MetadataParser()
-        case _:
-            raise NotImplementedError
-
-
-def read_info_from_image(filepath) -> tuple[str | None, MetadataScheme | None]:
-    with Image.open(filepath) as image:
-        items = (image.info or {}).copy()
-
-    parameters = items.pop('parameters', None)
-    metadata_scheme = items.pop('fooocus_scheme', None)
-    exif = items.pop('exif', None)
-
-    if parameters is not None and is_json(parameters):
-        parameters = json.loads(parameters)
-    elif exif is not None:
-        exif = image.getexif()
-        # 0x9286 = UserComment
-        parameters = exif.get(0x9286, None)
-        # 0x927C = MakerNote
-        metadata_scheme = exif.get(0x927C, None)
-
-        if is_json(parameters):
-            parameters = json.loads(parameters)
-
-    try:
-        metadata_scheme = MetadataScheme(metadata_scheme)
-    except ValueError:
-        metadata_scheme = None
-
-        # broad fallback
-        if isinstance(parameters, dict):
-            metadata_scheme = MetadataScheme.FOOOCUS
-
-        if isinstance(parameters, str):
-            metadata_scheme = MetadataScheme.A1111
-
-    return parameters, metadata_scheme
-
-
-def get_exif(metadata: str | None, metadata_scheme: str):
-    exif = Image.Exif()
-    # tags see see https://github.com/python-pillow/Pillow/blob/9.2.x/src/PIL/ExifTags.py
-    # 0x9286 = UserComment
-    exif[0x9286] = metadata
-    # 0x0131 = Software
-    exif[0x0131] = 'Fooocus v' + fooocus_version.version
-    # 0x927C = MakerNote
-    exif[0x927C] = metadata_scheme
-    return exif

modules/ops.py

@@ -1,19 +0,0 @@
-import torch
-import contextlib
-
-
-@contextlib.contextmanager
-def use_patched_ops(operations):
-    op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm']
-    backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names}
-
-    try:
-        for op_name in op_names:
-            setattr(torch.nn, op_name, getattr(operations, op_name))
-
-        yield
-
-    finally:
-        for op_name in op_names:
-            setattr(torch.nn, op_name, backups[op_name])
-    return

modules/patch.py

@@ -17,6 +17,7 @@ import ldm_patched.controlnet.cldm
 import ldm_patched.modules.model_patcher
 import ldm_patched.modules.samplers
 import ldm_patched.modules.args_parser
+import modules.advanced_parameters as advanced_parameters
 import warnings
 import safetensors.torch
 import modules.constants as constants
@@ -28,25 +29,15 @@ from modules.patch_precision import patch_all_precision
 from modules.patch_clip import patch_all_clip
 
 
-class PatchSettings:
-    def __init__(self,
-                 sharpness=2.0,
-                 adm_scaler_end=0.3,
-                 positive_adm_scale=1.5,
-                 negative_adm_scale=0.8,
-                 controlnet_softness=0.25,
-                 adaptive_cfg=7.0):
-        self.sharpness = sharpness
-        self.adm_scaler_end = adm_scaler_end
-        self.positive_adm_scale = positive_adm_scale
-        self.negative_adm_scale = negative_adm_scale
-        self.controlnet_softness = controlnet_softness
-        self.adaptive_cfg = adaptive_cfg
-        self.global_diffusion_progress = 0
-        self.eps_record = None
+sharpness = 2.0
 
+adm_scaler_end = 0.3
+positive_adm_scale = 1.5
+negative_adm_scale = 0.8
 
-patch_settings = {}
+adaptive_cfg = 7.0
+global_diffusion_progress = 0
+eps_record = None
 
 
 def calculate_weight_patched(self, patches, weight, key):
@@ -210,13 +201,14 @@ class BrownianTreeNoiseSamplerPatched:
 
 
 def compute_cfg(uncond, cond, cfg_scale, t):
-    pid = os.getpid()
-    mimic_cfg = float(patch_settings[pid].adaptive_cfg)
+    global adaptive_cfg
+
+    mimic_cfg = float(adaptive_cfg)
     real_cfg = float(cfg_scale)
 
     real_eps = uncond + real_cfg * (cond - uncond)
 
-    if cfg_scale > patch_settings[pid].adaptive_cfg:
+    if cfg_scale > adaptive_cfg:
         mimicked_eps = uncond + mimic_cfg * (cond - uncond)
         return real_eps * t + mimicked_eps * (1 - t)
     else:
@@ -224,13 +216,13 @@ def compute_cfg(uncond, cond, cfg_scale, t):
 
 
 def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
-    pid = os.getpid()
+    global eps_record
 
-    if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False):
+    if math.isclose(cond_scale, 1.0):
         final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
 
-        if patch_settings[pid].eps_record is not None:
-            patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu()
+        if eps_record is not None:
+            eps_record = ((x - final_x0) / timestep).cpu()
 
         return final_x0
 
@@ -239,16 +231,16 @@ def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
     positive_eps = x - positive_x0
     negative_eps = x - negative_x0
 
-    alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress
+    alpha = 0.001 * sharpness * global_diffusion_progress
 
     positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
     positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
 
     final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,
-                            cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress)
+                            cfg_scale=cond_scale, t=global_diffusion_progress)
 
-    if patch_settings[pid].eps_record is not None:
-        patch_settings[pid].eps_record = (final_eps / timestep).cpu()
+    if eps_record is not None:
+        eps_record = (final_eps / timestep).cpu()
 
     return x - final_eps
 
@@ -263,19 +255,20 @@ def round_to_64(x):
 
 
 def sdxl_encode_adm_patched(self, **kwargs):
+    global positive_adm_scale, negative_adm_scale
+
     clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
     width = kwargs.get("width", 1024)
     height = kwargs.get("height", 1024)
     target_width = width
     target_height = height
-    pid = os.getpid()
 
     if kwargs.get("prompt_type", "") == "negative":
-        width = float(width) * patch_settings[pid].negative_adm_scale
-        height = float(height) * patch_settings[pid].negative_adm_scale
+        width = float(width) * negative_adm_scale
+        height = float(height) * negative_adm_scale
     elif kwargs.get("prompt_type", "") == "positive":
-        width = float(width) * patch_settings[pid].positive_adm_scale
-        height = float(height) * patch_settings[pid].positive_adm_scale
+        width = float(width) * positive_adm_scale
+        height = float(height) * positive_adm_scale
 
     def embedder(number_list):
         h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
@@ -329,7 +322,7 @@ def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale,
 
 def timed_adm(y, timesteps):
     if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
-        y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]
+        y_mask = (timesteps > 999.0 * (1.0 - float(adm_scaler_end))).to(y)[..., None]
         y_with_adm = y[..., :2816].clone()
         y_without_adm = y[..., 2816:].clone()
         return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
@@ -339,7 +332,6 @@ def timed_adm(y, timesteps):
 def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
     t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
     emb = self.time_embed(t_emb)
-    pid = os.getpid()
 
     guided_hint = self.input_hint_block(hint, emb, context)
 
@@ -365,17 +357,19 @@ def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
     h = self.middle_block(h, emb, context)
     outs.append(self.middle_block_out(h, emb, context))
 
-    if patch_settings[pid].controlnet_softness > 0:
+    if advanced_parameters.controlnet_softness > 0:
         for i in range(10):
             k = 1.0 - float(i) / 9.0
-            outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k)
+            outs[i] = outs[i] * (1.0 - advanced_parameters.controlnet_softness * k)
 
     return outs
 
 
 def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
+    global global_diffusion_progress
+
     self.current_step = 1.0 - timesteps.to(x) / 999.0
-    patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
+    global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
 
     y = timed_adm(y, timesteps)
 
@@ -486,10 +480,6 @@ def build_loaded(module, loader_name):
 
 
 def patch_all():
-    if ldm_patched.modules.model_management.directml_enabled:
-        ldm_patched.modules.model_management.lowvram_available = True
-        ldm_patched.modules.model_management.OOM_EXCEPTION = Exception
-
     patch_all_precision()
     patch_all_clip()
 

modules/patch_clip.py

@@ -16,12 +16,30 @@ import ldm_patched.modules.samplers
 import ldm_patched.modules.sd
 import ldm_patched.modules.sd1_clip
 import ldm_patched.modules.clip_vision
+import ldm_patched.modules.model_management as model_management
 import ldm_patched.modules.ops as ops
+import contextlib
 
-from modules.ops import use_patched_ops
 from transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection
 
 
+@contextlib.contextmanager
+def use_patched_ops(operations):
+    op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm']
+    backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names}
+
+    try:
+        for op_name in op_names:
+            setattr(torch.nn, op_name, getattr(operations, op_name))
+
+        yield
+
+    finally:
+        for op_name in op_names:
+            setattr(torch.nn, op_name, backups[op_name])
+    return
+
+
 def patched_encode_token_weights(self, token_weight_pairs):
     to_encode = list()
     max_token_len = 0

modules/private_logger.py

@@ -1,131 +1,60 @@
 import os
 import args_manager
 import modules.config
-import json
-import urllib.parse
 
 from PIL import Image
-from PIL.PngImagePlugin import PngInfo
-from modules.flags import OutputFormat
-from modules.meta_parser import MetadataParser, get_exif
 from modules.util import generate_temp_filename
 
+
 log_cache = {}
 
 
-def get_current_html_path(output_format=None):
-    output_format = output_format if output_format else modules.config.default_output_format
+def get_current_html_path():
     date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs,
-                                                                         extension=output_format)
+                                                                         extension='png')
     html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
     return html_name
 
 
-def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
-    path_outputs = modules.config.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
-    output_format = output_format if output_format else modules.config.default_output_format
-    date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
-    os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
-
-    parsed_parameters = metadata_parser.parse_string(metadata.copy()) if metadata_parser is not None else ''
-    image = Image.fromarray(img)
-
-    if output_format == OutputFormat.PNG.value:
-        if parsed_parameters != '':
-            pnginfo = PngInfo()
-            pnginfo.add_text('parameters', parsed_parameters)
-            pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value)
-        else:
-            pnginfo = None
-        image.save(local_temp_filename, pnginfo=pnginfo)
-    elif output_format == OutputFormat.JPEG.value:
-        image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
-    elif output_format == OutputFormat.WEBP.value:
-        image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
-    else:
-        image.save(local_temp_filename)
-
+def log(img, dic, single_line_number=3):
     if args_manager.args.disable_image_log:
-        return local_temp_filename
+        return
 
+    date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs, extension='png')
+    os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
+    Image.fromarray(img).save(local_temp_filename)
     html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
 
-    css_styles = (
-        "<style>"
-        "body { background-color: #121212; color: #E0E0E0; } "
-        "a { color: #BB86FC; } "
-        ".metadata { border-collapse: collapse; width: 100%; } "
-        ".metadata .label { width: 15%; } "
-        ".metadata .value { width: 85%; font-weight: bold; } "
-        ".metadata th, .metadata td { border: 1px solid #4d4d4d; padding: 4px; } "
-        ".image-container img { height: auto; max-width: 512px; display: block; padding-right:10px; } "
-        ".image-container div { text-align: center; padding: 4px; } "
-        "hr { border-color: gray; } "
-        "button { background-color: black; color: white; border: 1px solid grey; border-radius: 5px; padding: 5px 10px; text-align: center; display: inline-block; font-size: 16px; cursor: pointer; }"
-        "button:hover {background-color: grey; color: black;}"
-        "</style>"
-    )
+    existing_log = log_cache.get(html_name, None)
 
-    js = (
-        """<script>
-        function to_clipboard(txt) { 
-        txt = decodeURIComponent(txt);
-        if (navigator.clipboard && navigator.permissions) {
-            navigator.clipboard.writeText(txt)
-        } else {
-            const textArea = document.createElement('textArea')
-            textArea.value = txt
-            textArea.style.width = 0
-            textArea.style.position = 'fixed'
-            textArea.style.left = '-999px'
-            textArea.style.top = '10px'
-            textArea.setAttribute('readonly', 'readonly')
-            document.body.appendChild(textArea)
-
-            textArea.select()
-            document.execCommand('copy')
-            document.body.removeChild(textArea)
-        }
-        alert('Copied to Clipboard!\\nPaste to prompt area to load parameters.\\nCurrent clipboard content is:\\n\\n' + txt);
-        }
-        </script>"""
-    )
-
-    begin_part = f"<!DOCTYPE html><html><head><title>Fooocus Log {date_string}</title>{css_styles}</head><body>{js}<p>Fooocus Log {date_string} (private)</p>\n<p>Metadata is embedded if enabled in the config or developer debug mode. You can find the information for each image in line Metadata Scheme.</p><!--fooocus-log-split-->\n\n"
-    end_part = f'\n<!--fooocus-log-split--></body></html>'
-
-    middle_part = log_cache.get(html_name, "")
-
-    if middle_part == "":
+    if existing_log is None:
         if os.path.exists(html_name):
-            existing_split = open(html_name, 'r', encoding='utf-8').read().split('<!--fooocus-log-split-->')
-            if len(existing_split) == 3:
-                middle_part = existing_split[1]
-            else:
-                middle_part = existing_split[0]
+            existing_log = open(html_name, encoding='utf-8').read()
+        else:
+            existing_log = f'<p>Fooocus Log {date_string} (private)</p>\n<p>All images do not contain any hidden data.</p>'
 
     div_name = only_name.replace('.', '_')
-    item = f"<div id=\"{div_name}\" class=\"image-container\"><hr><table><tr>\n"
-    item += f"<td><a href=\"{only_name}\" target=\"_blank\"><img src='{only_name}' onerror=\"this.closest('.image-container').style.display='none';\" loading='lazy'/></a><div>{only_name}</div></td>"
-    item += "<td><table class='metadata'>"
-    for label, key, value in metadata:
-        value_txt = str(value).replace('\n', ' </br> ')
-        item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
-    item += "</table>"
-
-    js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v in metadata}, indent=0), safe='')
-    item += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
-
+    item = f'<div id="{div_name}">\n'
+    item += "<table><tr>"
+    item += f"<td><img src=\"{only_name}\" width=auto height=100% loading=lazy style=\"height:auto;max-width:512px\" onerror=\"document.getElementById('{div_name}').style.display = 'none';\"></img></p></td>"
+    item += f"<td style=\"padding-left:10px;\"><p>{only_name}</p>\n"
+    for i, (k, v) in enumerate(dic):
+        if i < single_line_number:
+            item += f"<p>{k}: <b>{v}</b></p>\n"
+        else:
+            if (i - single_line_number) % 2 == 0:
+                item += f"<p>{k}: <b>{v}</b>, "
+            else:
+                item += f"{k}: <b>{v}</b></p>\n"
     item += "</td>"
-    item += "</tr></table></div>\n\n"
-
-    middle_part = item + middle_part
+    item += "</tr></table><hr></div>\n"
+    existing_log = item + existing_log
 
     with open(html_name, 'w', encoding='utf-8') as f:
-        f.write(begin_part + middle_part + end_part)
+        f.write(existing_log)
 
     print(f'Image generated with private log at: {html_name}')
 
-    log_cache[html_name] = middle_part
+    log_cache[html_name] = existing_log
 
-    return local_temp_filename
+    return

modules/sample_hijack.py

@@ -99,13 +99,6 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas
     calculate_start_end_timesteps(model, negative)
     calculate_start_end_timesteps(model, positive)
 
-    if latent_image is not None:
-        latent_image = model.process_latent_in(latent_image)
-
-    if hasattr(model, 'extra_conds'):
-        positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask)
-        negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask)
-
     #make sure each cond area has an opposite one with the same area
     for c in positive:
         create_cond_with_same_area_if_none(negative, c)
@@ -118,6 +111,13 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas
     apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
     apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
 
+    if latent_image is not None:
+        latent_image = model.process_latent_in(latent_image)
+
+    if hasattr(model, 'extra_conds'):
+        positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask)
+        negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask)
+
     extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}
 
     if current_refiner is not None and hasattr(current_refiner.model, 'extra_conds'):
@@ -174,7 +174,7 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
     elif scheduler_name == "sgm_uniform":
         sigmas = normal_scheduler(model, steps, sgm=True)
     elif scheduler_name == "turbo":
-        sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0]
+        sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps)[0]
     else:
         raise TypeError("error invalid scheduler")
     return sigmas

modules/sdxl_styles.py

@@ -1,13 +1,13 @@
 import os
 import re
 import json
-import math
-import modules.config
 
 from modules.util import get_files_from_folder
 
+
 # cannot use modules.config - validators causing circular imports
 styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/'))
+wildcards_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../wildcards/'))
 wildcards_max_bfs_depth = 64
 
 
@@ -31,8 +31,7 @@ for x in ['sdxl_styles_fooocus.json',
           'sdxl_styles_sai.json',
           'sdxl_styles_mre.json',
           'sdxl_styles_twri.json',
-          'sdxl_styles_diva.json',
-          'sdxl_styles_marc_k3nt3l.json']:
+          'sdxl_styles_diva.json']:
     if x in styles_files:
         styles_files.remove(x)
         styles_files.append(x)
@@ -59,7 +58,7 @@ def apply_style(style, positive):
     return p.replace('{prompt}', positive).splitlines(), n.splitlines()
 
 
-def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order):
+def apply_wildcards(wildcard_text, rng, directory=wildcards_path):
     for _ in range(wildcards_max_bfs_depth):
         placeholders = re.findall(r'__([\w-]+)__', wildcard_text)
         if len(placeholders) == 0:
@@ -68,14 +67,10 @@ def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order):
         print(f'[Wildcards] processing: {wildcard_text}')
         for placeholder in placeholders:
             try:
-                matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder]
-                words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines()
+                words = open(os.path.join(directory, f'{placeholder}.txt'), encoding='utf-8').read().splitlines()
                 words = [x for x in words if x != '']
                 assert len(words) > 0
-                if read_wildcards_in_order:
-                    wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1)
-                else:
-                    wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
+                wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
             except:
                 print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '
                       f'Using "{placeholder}" as a normal word.')
@@ -84,38 +79,3 @@ def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order):
 
     print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
     return wildcard_text
-
-
-def get_words(arrays, totalMult, index):
-    if len(arrays) == 1:
-        return [arrays[0].split(',')[index]]
-    else:
-        words = arrays[0].split(',')
-        word = words[index % len(words)]
-        index -= index % len(words)
-        index /= len(words)
-        index = math.floor(index)
-        return [word] + get_words(arrays[1:], math.floor(totalMult/len(words)), index)
-
-
-def apply_arrays(text, index):
-    arrays = re.findall(r'\[\[(.*?)\]\]', text)
-    if len(arrays) == 0:
-        return text
-
-    print(f'[Arrays] processing: {text}')
-    mult = 1
-    for arr in arrays:
-        words = arr.split(',')
-        mult *= len(words)
-    
-    index %= mult
-    chosen_words = get_words(arrays, mult, index)
-    
-    i = 0
-    for arr in arrays:
-        text = text.replace(f'[[{arr}]]', chosen_words[i], 1)   
-        i = i+1
-    
-    return text
-

modules/style_sorter.py

@@ -15,14 +15,11 @@ def try_load_sorted_styles(style_names, default_selected):
     try:
         if os.path.exists('sorted_styles.json'):
             with open('sorted_styles.json', 'rt', encoding='utf-8') as fp:
-                sorted_styles = []
-                for x in json.load(fp):
-                    if x in all_styles:
-                        sorted_styles.append(x)
-                for x in all_styles:
-                    if x not in sorted_styles:
-                        sorted_styles.append(x)
-                all_styles = sorted_styles
+                sorted_styles = json.load(fp)
+                if len(sorted_styles) == len(all_styles):
+                    if all(x in all_styles for x in sorted_styles):
+                        if all(x in sorted_styles for x in all_styles):
+                            all_styles = sorted_styles
     except Exception as e:
         print('Load style sorting failed.')
         print(e)

modules/ui_gradio_extensions.py

@@ -30,7 +30,6 @@ def javascript_html():
     edit_attention_js_path = webpath('javascript/edit-attention.js')
     viewer_js_path = webpath('javascript/viewer.js')
     image_viewer_js_path = webpath('javascript/imageviewer.js')
-    samples_path = webpath(os.path.abspath('./sdxl_styles/samples/fooocus_v2.jpg'))
     head = f'<script type="text/javascript">{localization_js(args_manager.args.language)}</script>\n'
     head += f'<script type="text/javascript" src="{script_js_path}"></script>\n'
     head += f'<script type="text/javascript" src="{context_menus_js_path}"></script>\n'
@@ -39,7 +38,6 @@ def javascript_html():
     head += f'<script type="text/javascript" src="{edit_attention_js_path}"></script>\n'
     head += f'<script type="text/javascript" src="{viewer_js_path}"></script>\n'
     head += f'<script type="text/javascript" src="{image_viewer_js_path}"></script>\n'
-    head += f'<meta name="samples-path" content="{samples_path}"></meta>\n'
 
     if args_manager.args.theme:
         head += f'<script type="text/javascript">set_theme(\"{args_manager.args.theme}\");</script>\n'

modules/util.py

@@ -1,28 +1,13 @@
-import typing
-
 import numpy as np
 import datetime
 import random
 import math
 import os
-import cv2
-import json
-import hashlib
 
 from PIL import Image
 
-import modules.sdxl_styles
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
-HASH_SHA256_LENGTH = 10
-
-def erode_or_dilate(x, k):
-    k = int(k)
-    if k > 0:
-        return cv2.dilate(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=k)
-    if k < 0:
-        return cv2.erode(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=-k)
-    return x
 
 
 def resample_image(im, width, height):
@@ -160,235 +145,23 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
     random_number = random.randint(1000, 9999)
     filename = f"{time_string}_{random_number}.{extension}"
     result = os.path.join(folder, date_string, filename)
-    return date_string, os.path.abspath(result), filename
+    return date_string, os.path.abspath(os.path.realpath(result)), filename
 
 
-def get_files_from_folder(folder_path, extensions=None, name_filter=None):
+def get_files_from_folder(folder_path, exensions=None, name_filter=None):
     if not os.path.isdir(folder_path):
         raise ValueError("Folder path is not a valid directory.")
 
     filenames = []
 
-    for root, dirs, files in os.walk(folder_path, topdown=False):
+    for root, dirs, files in os.walk(folder_path):
         relative_path = os.path.relpath(root, folder_path)
         if relative_path == ".":
             relative_path = ""
-        for filename in sorted(files, key=lambda s: s.casefold()):
+        for filename in files:
             _, file_extension = os.path.splitext(filename)
-            if (extensions is None or file_extension.lower() in extensions) and (name_filter is None or name_filter in _):
+            if (exensions == None or file_extension.lower() in exensions) and (name_filter == None or name_filter in _):
                 path = os.path.join(relative_path, filename)
                 filenames.append(path)
 
-    return filenames
-
-
-def sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):
-    print(f"Calculating sha256 for {filename}: ", end='')
-    if use_addnet_hash:
-        with open(filename, "rb") as file:
-            sha256_value = addnet_hash_safetensors(file)
-    else:
-        sha256_value = calculate_sha256(filename)
-    print(f"{sha256_value}")
-
-    return sha256_value[:length] if length is not None else sha256_value
-
-
-def addnet_hash_safetensors(b):
-    """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"""
-    hash_sha256 = hashlib.sha256()
-    blksize = 1024 * 1024
-
-    b.seek(0)
-    header = b.read(8)
-    n = int.from_bytes(header, "little")
-
-    offset = n + 8
-    b.seek(offset)
-    for chunk in iter(lambda: b.read(blksize), b""):
-        hash_sha256.update(chunk)
-
-    return hash_sha256.hexdigest()
-
-
-def calculate_sha256(filename) -> str:
-    hash_sha256 = hashlib.sha256()
-    blksize = 1024 * 1024
-
-    with open(filename, "rb") as f:
-        for chunk in iter(lambda: f.read(blksize), b""):
-            hash_sha256.update(chunk)
-
-    return hash_sha256.hexdigest()
-
-
-def quote(text):
-    if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
-        return text
-
-    return json.dumps(text, ensure_ascii=False)
-
-
-def unquote(text):
-    if len(text) == 0 or text[0] != '"' or text[-1] != '"':
-        return text
-
-    try:
-        return json.loads(text)
-    except Exception:
-        return text
-
-
-def unwrap_style_text_from_prompt(style_text, prompt):
-    """
-    Checks the prompt to see if the style text is wrapped around it. If so,
-    returns True plus the prompt text without the style text. Otherwise, returns
-    False with the original prompt.
-
-    Note that the "cleaned" version of the style text is only used for matching
-    purposes here. It isn't returned; the original style text is not modified.
-    """
-    stripped_prompt = prompt
-    stripped_style_text = style_text
-    if "{prompt}" in stripped_style_text:
-        # Work out whether the prompt is wrapped in the style text. If so, we
-        # return True and the "inner" prompt text that isn't part of the style.
-        try:
-            left, right = stripped_style_text.split("{prompt}", 2)
-        except ValueError as e:
-            # If the style text has multple "{prompt}"s, we can't split it into
-            # two parts. This is an error, but we can't do anything about it.
-            print(f"Unable to compare style text to prompt:\n{style_text}")
-            print(f"Error: {e}")
-            return False, prompt, ''
-
-        left_pos = stripped_prompt.find(left)
-        right_pos = stripped_prompt.find(right)
-        if 0 <= left_pos < right_pos:
-            real_prompt = stripped_prompt[left_pos + len(left):right_pos]
-            prompt = stripped_prompt.replace(left + real_prompt + right, '', 1)
-            if prompt.startswith(", "):
-                prompt = prompt[2:]
-            if prompt.endswith(", "):
-                prompt = prompt[:-2]
-            return True, prompt, real_prompt
-    else:
-        # Work out whether the given prompt starts with the style text. If so, we
-        # return True and the prompt text up to where the style text starts.
-        if stripped_prompt.endswith(stripped_style_text):
-            prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]
-            if prompt.endswith(", "):
-                prompt = prompt[:-2]
-            return True, prompt, prompt
-
-    return False, prompt, ''
-
-
-def extract_original_prompts(style, prompt, negative_prompt):
-    """
-    Takes a style and compares it to the prompt and negative prompt. If the style
-    matches, returns True plus the prompt and negative prompt with the style text
-    removed. Otherwise, returns False with the original prompt and negative prompt.
-    """
-    if not style.prompt and not style.negative_prompt:
-        return False, prompt, negative_prompt
-
-    match_positive, extracted_positive, real_prompt = unwrap_style_text_from_prompt(
-        style.prompt, prompt
-    )
-    if not match_positive:
-        return False, prompt, negative_prompt, ''
-
-    match_negative, extracted_negative, _ = unwrap_style_text_from_prompt(
-        style.negative_prompt, negative_prompt
-    )
-    if not match_negative:
-        return False, prompt, negative_prompt, ''
-
-    return True, extracted_positive, extracted_negative, real_prompt
-
-
-def extract_styles_from_prompt(prompt, negative_prompt):
-    extracted = []
-    applicable_styles = []
-
-    for style_name, (style_prompt, style_negative_prompt) in modules.sdxl_styles.styles.items():
-        applicable_styles.append(PromptStyle(name=style_name, prompt=style_prompt, negative_prompt=style_negative_prompt))
-
-    real_prompt = ''
-
-    while True:
-        found_style = None
-
-        for style in applicable_styles:
-            is_match, new_prompt, new_neg_prompt, new_real_prompt = extract_original_prompts(
-                style, prompt, negative_prompt
-            )
-            if is_match:
-                found_style = style
-                prompt = new_prompt
-                negative_prompt = new_neg_prompt
-                if real_prompt == '' and new_real_prompt != '' and new_real_prompt != prompt:
-                    real_prompt = new_real_prompt
-                break
-
-        if not found_style:
-            break
-
-        applicable_styles.remove(found_style)
-        extracted.append(found_style.name)
-
-    # add prompt expansion if not all styles could be resolved
-    if prompt != '':
-        if real_prompt != '':
-            extracted.append(modules.sdxl_styles.fooocus_expansion)
-        else:
-            # find real_prompt when only prompt expansion is selected
-            first_word = prompt.split(', ')[0]
-            first_word_positions = [i for i in range(len(prompt)) if prompt.startswith(first_word, i)]
-            if len(first_word_positions) > 1:
-                real_prompt = prompt[:first_word_positions[-1]]
-                extracted.append(modules.sdxl_styles.fooocus_expansion)
-                if real_prompt.endswith(', '):
-                    real_prompt = real_prompt[:-2]
-
-    return list(reversed(extracted)), real_prompt, negative_prompt
-
-
-class PromptStyle(typing.NamedTuple):
-    name: str
-    prompt: str
-    negative_prompt: str
-
-
-def is_json(data: str) -> bool:
-    try:
-        loaded_json = json.loads(data)
-        assert isinstance(loaded_json, dict)
-    except (ValueError, AssertionError):
-        return False
-    return True
-
-
-def get_file_from_folder_list(name, folders):
-    for folder in folders:
-        filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))
-        if os.path.isfile(filename):
-            return filename
-
-    return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))
-
-
-def ordinal_suffix(number: int) -> str:
-    return 'th' if 10 <= number % 100 <= 20 else {1: 'st', 2: 'nd', 3: 'rd'}.get(number % 10, 'th')
-
-
-def makedirs_with_log(path):
-    try:
-        os.makedirs(path, exist_ok=True)
-    except OSError as error:
-        print(f'Directory {path} could not be created, reason: {error}')
-
-
-def get_enabled_loras(loras: list) -> list:
-    return [[lora[1], lora[2]] for lora in loras if lora[0]]
+    return sorted(filenames, key=lambda x: -1 if os.sep in x else 1)

presets/.gitignore

@@ -1,6 +0,0 @@
-*.json
-!anime.json
-!default.json
-!lcm.json
-!realistic.json
-!sai.json