docs: remove link to SimpleSDXL

see https://github.com/lllyasviel/Fooocus/issues/3836

.dockerignore

@@ -0,0 +1,54 @@
+__pycache__
+*.ckpt
+*.safetensors
+*.pth
+*.pt
+*.bin
+*.patch
+*.backup
+*.corrupted
+*.partial
+*.onnx
+sorted_styles.json
+/input
+/cache
+/language/default.json
+/test_imgs
+config.txt
+config_modification_tutorial.txt
+user_path_config.txt
+user_path_config-deprecated.txt
+/modules/*.png
+/repositories
+/fooocus_env
+/venv
+/tmp
+/ui-config.json
+/outputs
+/config.json
+/log
+/webui.settings.bat
+/embeddings
+/styles.csv
+/params.txt
+/styles.csv.bak
+/webui-user.bat
+/webui-user.sh
+/interrogate
+/user.css
+/.idea
+/notification.ogg
+/notification.mp3
+/SwinIR
+/textual_inversion
+.vscode
+/extensions
+/test/stdout.txt
+/test/stderr.txt
+/cache.json*
+/config_states/
+/node_modules
+/package-lock.json
+/.coverage*
+/auth.json
+.DS_Store

.gitattributes

@@ -0,0 +1,3 @@
+# Ensure that shell scripts always use lf line endings, e.g. entrypoint.sh for docker
+* text=auto
+*.sh text eol=lf

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,18 +0,0 @@
----
-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

@@ -0,0 +1,107 @@
+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
+        　1. Update Fooocus - sometimes things just need to be updated
+        　2. Backup and remove your config.txt - check if the issue is caused by bad configuration
+        　3. 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

@@ -0,0 +1,5 @@
+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

@@ -1,14 +0,0 @@
----
-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

@@ -0,0 +1,40 @@
+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.

.github/dependabot.yml

@@ -0,0 +1,6 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "monthly"

.github/workflows/build_container.yml

@@ -0,0 +1,47 @@
+name: Docker image build
+
+on:
+  push:
+    branches:
+      - main
+    tags:
+      - v*
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ghcr.io/${{ github.repository_owner }}/${{ github.event.repository.name }}
+          tags: |
+            type=semver,pattern={{version}}
+            type=semver,pattern={{major}}.{{minor}}
+            type=semver,pattern={{major}}
+            type=edge,branch=main
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          file: ./Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.gitignore

@@ -10,6 +10,7 @@ __pycache__
 *.partial
 *.onnx
 sorted_styles.json
+hash_cache.txt
 /input
 /cache
 /language/default.json
@@ -20,6 +21,7 @@ user_path_config.txt
 user_path_config-deprecated.txt
 /modules/*.png
 /repositories
+/fooocus_env
 /venv
 /tmp
 /ui-config.json
@@ -50,3 +52,4 @@ user_path_config-deprecated.txt
 /package-lock.json
 /.coverage*
 /auth.json
+.DS_Store

Dockerfile

@@ -0,0 +1,29 @@
+FROM nvidia/cuda:12.4.1-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
+
+COPY --chown=user:user . /content/app
+RUN mv /content/app/models /content/app/models.org
+
+CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ]

args_manager.py

@@ -1,8 +1,10 @@
 import ldm_patched.modules.args_parser as args_parser
 
-
 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. "
@@ -15,10 +17,28 @@ args_parser.parser.add_argument("--disable-offload-from-vram", action="store_tru
 
 args_parser.parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
 args_parser.parser.add_argument("--disable-image-log", action='store_true',
-                                help="Prevent writing images and logs to hard drive.")
+                                help="Prevent writing images and logs to the outputs folder.")
 
 args_parser.parser.add_argument("--disable-analytics", action='store_true',
-                                help="Disables analytics for Gradio", default=False)
+                                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("--disable-enhance-output-sorting", action='store_true',
+                                help="Disables enhance output sorting for final image gallery.")
+
+args_parser.parser.add_argument("--enable-auto-describe-image", action='store_true',
+                                help="Enables automatic description of uov and enhance image when prompt is empty", default=False)
+
+args_parser.parser.add_argument("--always-download-new-model", action='store_true',
+                                help="Always download newer models", default=False)
+
+args_parser.parser.add_argument("--rebuild-hash-cache", help="Generates missing model and LoRA hashes.",
+                                type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1)
 
 args_parser.parser.set_defaults(
     disable_cuda_malloc=True,
@@ -35,4 +55,7 @@ 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

@@ -1,5 +1,150 @@
 /* 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%;
+  vertical-align: middle !important;
+}
+
+/* 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;
+}
+
+.progress-bar span {
+    text-align: right;
+    width: 215px;
+}
+div:has(> #positive_prompt) {
+    border: none;
+}
+
+#positive_prompt {
+    padding: 1px;
+    background: var(--background-fill-primary);
+}
+
+.type_row {
+  height: 84px !important;
+}
+
+.type_row_half {
+  height: 34px !important;
+}
+
+.refresh_button {
+  border: none !important;
+  background: none !important;
+  font-size: none !important;
+  box-shadow: none !important;
+}
+
+.advanced_check_row {
+  width: 330px !important;
+}
+
+.min_check {
+  min-width: min(1px, 100%) !important;
+}
+
+.resizable_area {
+  resize: vertical;
+  overflow: auto !important;
+}
+
+.performance_selection label {
+    width: 140px !important;
+}
+
+.aspect_ratios label {
+    flex: calc(50% - 5px) !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;
@@ -196,3 +341,78 @@
     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: 5px 5px 0px 0px;
+  display: none; /* remove this to enable tooltip in preview image */
+}
+
+#inpaint_canvas .canvas-tooltip-info {
+  top: 2px;
+}
+
+#inpaint_brush_color input[type=color]{
+  background: none;
+}

development.md

@@ -0,0 +1,11 @@
+## Running unit tests
+
+Native python:
+```
+python -m unittest tests/
+```
+
+Embedded python (Windows zip file installation method):
+```
+..\python_embeded\python.exe -m unittest
+```

docker-compose.yml

@@ -0,0 +1,36 @@
+volumes:
+  fooocus-data:
+
+services:
+  app:
+    build: .
+    image: ghcr.io/lllyasviel/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

@@ -0,0 +1,131 @@
+# Fooocus on Docker
+
+The docker image is based on NVIDIA CUDA 12.4 and PyTorch 2.1, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details.
+
+## Requirements
+
+- A computer with specs good enough to run Fooocus, and proprietary Nvidia drivers
+- Docker, Docker Compose, or Podman
+
+## Quick start
+
+**More information in the [notes](#notes).**
+
+### Running with Docker Compose
+
+1. Clone this repository
+2. Run the docker container with `docker compose up`.
+
+### Running with Docker
+
+```sh
+docker run -p 7865:7865 -v fooocus-data:/content/data -it \
+--gpus all \
+-e CMDARGS=--listen \
+-e DATADIR=/content/data \
+-e config_path=/content/data/config.txt \
+-e config_example_path=/content/data/config_modification_tutorial.txt \
+-e path_checkpoints=/content/data/models/checkpoints/ \
+-e path_loras=/content/data/models/loras/ \
+-e path_embeddings=/content/data/models/embeddings/ \
+-e path_vae_approx=/content/data/models/vae_approx/ \
+-e path_upscale_models=/content/data/models/upscale_models/ \
+-e path_inpaint=/content/data/models/inpaint/ \
+-e path_controlnet=/content/data/models/controlnet/ \
+-e path_clip_vision=/content/data/models/clip_vision/ \
+-e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \
+-e path_outputs=/content/app/outputs/ \
+ghcr.io/lllyasviel/fooocus
+```
+### Running with Podman
+
+```sh
+podman run -p 7865:7865 -v fooocus-data:/content/data -it \
+--security-opt=no-new-privileges --cap-drop=ALL --security-opt label=type:nvidia_container_t --device=nvidia.com/gpu=all \
+-e CMDARGS=--listen \
+-e DATADIR=/content/data \
+-e config_path=/content/data/config.txt \
+-e config_example_path=/content/data/config_modification_tutorial.txt \
+-e path_checkpoints=/content/data/models/checkpoints/ \
+-e path_loras=/content/data/models/loras/ \
+-e path_embeddings=/content/data/models/embeddings/ \
+-e path_vae_approx=/content/data/models/vae_approx/ \
+-e path_upscale_models=/content/data/models/upscale_models/ \
+-e path_inpaint=/content/data/models/inpaint/ \
+-e path_controlnet=/content/data/models/controlnet/ \
+-e path_clip_vision=/content/data/models/clip_vision/ \
+-e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \
+-e path_outputs=/content/app/outputs/ \
+ghcr.io/lllyasviel/fooocus
+```
+
+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/` (or `~/.local/share/containers/storage/volumes/` when using `podman`).
+
+## Building the container locally
+
+Clone the repository first, and open a terminal in the folder.
+
+Build with `docker`:
+```sh
+docker build . -t fooocus
+```
+
+Build with `podman`:
+```sh
+podman build . -t fooocus
+```
+
+## Details
+
+### Update the container manually (`docker compose`)
+
+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 add the following bind mounts in the [docker-compose.yml](docker-compose.yml) or your preferred method of running the container:
+```
+#- ./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 the container, 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 the container without the above mounts.
+
+
+### 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|
+|HF_MIRROR| huggingface mirror site domain| 
+
+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

@@ -0,0 +1,33 @@
+#!/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 $*

experiments_mask_generation.py

@@ -0,0 +1,24 @@
+# https://github.com/sail-sg/EditAnything/blob/main/sam2groundingdino_edit.py
+
+import numpy as np
+from PIL import Image
+
+from extras.inpaint_mask import SAMOptions, generate_mask_from_image
+
+original_image = Image.open('cat.webp')
+image = np.array(original_image, dtype=np.uint8)
+
+sam_options = SAMOptions(
+    dino_prompt='eye',
+    dino_box_threshold=0.3,
+    dino_text_threshold=0.25,
+    dino_erode_or_dilate=0,
+    dino_debug=False,
+    max_detections=2,
+    model_type='vit_b'
+)
+
+mask_image, _, _, _ = generate_mask_from_image(image, sam_options=sam_options)
+
+merged_masks_img = Image.fromarray(mask_image)
+merged_masks_img.show()

extras/BLIP/models/blip.py

@@ -216,9 +216,9 @@ def is_url(url_or_filename):
 def load_checkpoint(model,url_or_filename):
     if is_url(url_or_filename):
         cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
-        checkpoint = torch.load(cached_file, map_location='cpu') 
+        checkpoint = torch.load(cached_file, map_location='cpu', weights_only=True) 
     elif os.path.isfile(url_or_filename):        
-        checkpoint = torch.load(url_or_filename, map_location='cpu') 
+        checkpoint = torch.load(url_or_filename, map_location='cpu', weights_only=True) 
     else:
         raise RuntimeError('checkpoint url or path is invalid')
         

extras/BLIP/models/blip_nlvr.py

@@ -78,9 +78,9 @@ def blip_nlvr(pretrained='',**kwargs):
 def load_checkpoint(model,url_or_filename):
     if is_url(url_or_filename):
         cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
-        checkpoint = torch.load(cached_file, map_location='cpu') 
+        checkpoint = torch.load(cached_file, map_location='cpu', weights_only=True) 
     elif os.path.isfile(url_or_filename):        
-        checkpoint = torch.load(url_or_filename, map_location='cpu') 
+        checkpoint = torch.load(url_or_filename, map_location='cpu', weights_only=True) 
     else:
         raise RuntimeError('checkpoint url or path is invalid')
     state_dict = checkpoint['model']

extras/GroundingDINO/config/GroundingDINO_SwinT_OGC.py

@@ -0,0 +1,43 @@
+batch_size = 1
+modelname = "groundingdino"
+backbone = "swin_T_224_1k"
+position_embedding = "sine"
+pe_temperatureH = 20
+pe_temperatureW = 20
+return_interm_indices = [1, 2, 3]
+backbone_freeze_keywords = None
+enc_layers = 6
+dec_layers = 6
+pre_norm = False
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.0
+nheads = 8
+num_queries = 900
+query_dim = 4
+num_patterns = 0
+num_feature_levels = 4
+enc_n_points = 4
+dec_n_points = 4
+two_stage_type = "standard"
+two_stage_bbox_embed_share = False
+two_stage_class_embed_share = False
+transformer_activation = "relu"
+dec_pred_bbox_embed_share = True
+dn_box_noise_scale = 1.0
+dn_label_noise_ratio = 0.5
+dn_label_coef = 1.0
+dn_bbox_coef = 1.0
+embed_init_tgt = True
+dn_labelbook_size = 2000
+max_text_len = 256
+text_encoder_type = "bert-base-uncased"
+use_text_enhancer = True
+use_fusion_layer = True
+use_checkpoint = True
+use_transformer_ckpt = True
+use_text_cross_attention = True
+text_dropout = 0.0
+fusion_dropout = 0.0
+fusion_droppath = 0.1
+sub_sentence_present = True

extras/GroundingDINO/util/inference.py

@@ -0,0 +1,100 @@
+from typing import Tuple, List
+
+import ldm_patched.modules.model_management as model_management
+from ldm_patched.modules.model_patcher import ModelPatcher
+from modules.config import path_inpaint
+from modules.model_loader import load_file_from_url
+
+import numpy as np
+import supervision as sv
+import torch
+from groundingdino.util.inference import Model
+from groundingdino.util.inference import load_model, preprocess_caption, get_phrases_from_posmap
+
+
+class GroundingDinoModel(Model):
+    def __init__(self):
+        self.config_file = 'extras/GroundingDINO/config/GroundingDINO_SwinT_OGC.py'
+        self.model = None
+        self.load_device = torch.device('cpu')
+        self.offload_device = torch.device('cpu')
+
+    @torch.no_grad()
+    @torch.inference_mode()
+    def predict_with_caption(
+            self,
+            image: np.ndarray,
+            caption: str,
+            box_threshold: float = 0.35,
+            text_threshold: float = 0.25
+    ) -> Tuple[sv.Detections, torch.Tensor, torch.Tensor, List[str]]:
+        if self.model is None:
+            filename = load_file_from_url(
+                url="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth",
+                file_name='groundingdino_swint_ogc.pth',
+                model_dir=path_inpaint)
+            model = load_model(model_config_path=self.config_file, model_checkpoint_path=filename)
+
+            self.load_device = model_management.text_encoder_device()
+            self.offload_device = model_management.text_encoder_offload_device()
+
+            model.to(self.offload_device)
+
+            self.model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
+
+        model_management.load_model_gpu(self.model)
+
+        processed_image = GroundingDinoModel.preprocess_image(image_bgr=image).to(self.load_device)
+        boxes, logits, phrases = predict(
+            model=self.model,
+            image=processed_image,
+            caption=caption,
+            box_threshold=box_threshold,
+            text_threshold=text_threshold,
+            device=self.load_device)
+        source_h, source_w, _ = image.shape
+        detections = GroundingDinoModel.post_process_result(
+            source_h=source_h,
+            source_w=source_w,
+            boxes=boxes,
+            logits=logits)
+        return detections, boxes, logits, phrases
+
+
+def predict(
+        model,
+        image: torch.Tensor,
+        caption: str,
+        box_threshold: float,
+        text_threshold: float,
+        device: str = "cuda"
+) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
+    caption = preprocess_caption(caption=caption)
+
+    # override to use model wrapped by patcher
+    model = model.model.to(device)
+    image = image.to(device)
+
+    with torch.no_grad():
+        outputs = model(image[None], captions=[caption])
+
+    prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)
+    prediction_boxes = outputs["pred_boxes"].cpu()[0]  # prediction_boxes.shape = (nq, 4)
+
+    mask = prediction_logits.max(dim=1)[0] > box_threshold
+    logits = prediction_logits[mask]  # logits.shape = (n, 256)
+    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)
+
+    tokenizer = model.tokenizer
+    tokenized = tokenizer(caption)
+
+    phrases = [
+        get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
+        for logit
+        in logits
+    ]
+
+    return boxes, logits.max(dim=1)[0], phrases
+
+
+default_groundingdino = GroundingDinoModel().predict_with_caption

extras/censor.py

@@ -0,0 +1,60 @@
+import os
+
+import numpy as np
+import torch
+from transformers import CLIPConfig, CLIPImageProcessor
+
+import ldm_patched.modules.model_management as model_management
+import modules.config
+from extras.safety_checker.models.safety_checker import StableDiffusionSafetyChecker
+from ldm_patched.modules.model_patcher import ModelPatcher
+
+safety_checker_repo_root = os.path.join(os.path.dirname(__file__), 'safety_checker')
+config_path = os.path.join(safety_checker_repo_root, "configs", "config.json")
+preprocessor_config_path = os.path.join(safety_checker_repo_root, "configs", "preprocessor_config.json")
+
+
+class Censor:
+    def __init__(self):
+        self.safety_checker_model: ModelPatcher | None = None
+        self.clip_image_processor: CLIPImageProcessor | None = None
+        self.load_device = torch.device('cpu')
+        self.offload_device = torch.device('cpu')
+
+    def init(self):
+        if self.safety_checker_model is None and self.clip_image_processor is None:
+            safety_checker_model = modules.config.downloading_safety_checker_model()
+            self.clip_image_processor = CLIPImageProcessor.from_json_file(preprocessor_config_path)
+            clip_config = CLIPConfig.from_json_file(config_path)
+            model = StableDiffusionSafetyChecker.from_pretrained(safety_checker_model, config=clip_config)
+            model.eval()
+
+            self.load_device = model_management.text_encoder_device()
+            self.offload_device = model_management.text_encoder_offload_device()
+
+            model.to(self.offload_device)
+
+            self.safety_checker_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
+
+    def censor(self, images: list | np.ndarray) -> list | np.ndarray:
+        self.init()
+        model_management.load_model_gpu(self.safety_checker_model)
+
+        single = False
+        if not isinstance(images, (list, np.ndarray)):
+            images = [images]
+            single = True
+
+        safety_checker_input = self.clip_image_processor(images, return_tensors="pt")
+        safety_checker_input.to(device=self.load_device)
+        checked_images, has_nsfw_concept = self.safety_checker_model.model(images=images,
+                                                                           clip_input=safety_checker_input.pixel_values)
+        checked_images = [image.astype(np.uint8) for image in checked_images]
+
+        if single:
+            checked_images = checked_images[0]
+
+        return checked_images
+
+
+default_censor = Censor().censor

extras/expansion.py

@@ -112,6 +112,9 @@ 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/facexlib/detection/__init__.py

@@ -19,7 +19,7 @@ def init_detection_model(model_name, half=False, device='cuda', model_rootpath=N
         url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
 
     # TODO: clean pretrained model
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+    load_net = torch.load(model_path, map_location=lambda storage, loc: storage, weights_only=True)
     # remove unnecessary 'module.'
     for k, v in deepcopy(load_net).items():
         if k.startswith('module.'):

extras/facexlib/parsing/__init__.py

@@ -17,7 +17,7 @@ def init_parsing_model(model_name='bisenet', half=False, device='cuda', model_ro
 
     model_path = load_file_from_url(
         url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+    load_net = torch.load(model_path, map_location=lambda storage, loc: storage, weights_only=True)
     model.load_state_dict(load_net, strict=True)
     model.eval()
     model = model.to(device)

extras/inpaint_mask.py

@@ -0,0 +1,130 @@
+import sys
+
+import modules.config
+import numpy as np
+import torch
+from extras.GroundingDINO.util.inference import default_groundingdino
+from extras.sam.predictor import SamPredictor
+from rembg import remove, new_session
+from segment_anything import sam_model_registry
+from segment_anything.utils.amg import remove_small_regions
+
+
+class SAMOptions:
+    def __init__(self,
+                 # GroundingDINO
+                 dino_prompt: str = '',
+                 dino_box_threshold=0.3,
+                 dino_text_threshold=0.25,
+                 dino_erode_or_dilate=0,
+                 dino_debug=False,
+
+                 # SAM
+                 max_detections=2,
+                 model_type='vit_b'
+                 ):
+        self.dino_prompt = dino_prompt
+        self.dino_box_threshold = dino_box_threshold
+        self.dino_text_threshold = dino_text_threshold
+        self.dino_erode_or_dilate = dino_erode_or_dilate
+        self.dino_debug = dino_debug
+        self.max_detections = max_detections
+        self.model_type = model_type
+
+
+def optimize_masks(masks: torch.Tensor) -> torch.Tensor:
+    """
+    removes small disconnected regions and holes
+    """
+    fine_masks = []
+    for mask in masks.to('cpu').numpy():  # masks: [num_masks, 1, h, w]
+        fine_masks.append(remove_small_regions(mask[0], 400, mode="holes")[0])
+    masks = np.stack(fine_masks, axis=0)[:, np.newaxis]
+    return torch.from_numpy(masks)
+
+
+def generate_mask_from_image(image: np.ndarray, mask_model: str = 'sam', extras=None,
+                             sam_options: SAMOptions | None = SAMOptions) -> tuple[np.ndarray | None, int | None, int | None, int | None]:
+    dino_detection_count = 0
+    sam_detection_count = 0
+    sam_detection_on_mask_count = 0
+
+    if image is None:
+        return None, dino_detection_count, sam_detection_count, sam_detection_on_mask_count
+
+    if extras is None:
+        extras = {}
+
+    if 'image' in image:
+        image = image['image']
+
+    if mask_model != 'sam' or sam_options is None:
+        result = remove(
+            image,
+            session=new_session(mask_model, **extras),
+            only_mask=True,
+            **extras
+        )
+
+        return result, dino_detection_count, sam_detection_count, sam_detection_on_mask_count
+
+    detections, boxes, logits, phrases = default_groundingdino(
+        image=image,
+        caption=sam_options.dino_prompt,
+        box_threshold=sam_options.dino_box_threshold,
+        text_threshold=sam_options.dino_text_threshold
+    )
+
+    H, W = image.shape[0], image.shape[1]
+    boxes = boxes * torch.Tensor([W, H, W, H])
+    boxes[:, :2] = boxes[:, :2] - boxes[:, 2:] / 2
+    boxes[:, 2:] = boxes[:, 2:] + boxes[:, :2]
+
+    sam_checkpoint = modules.config.download_sam_model(sam_options.model_type)
+    sam = sam_model_registry[sam_options.model_type](checkpoint=sam_checkpoint)
+
+    sam_predictor = SamPredictor(sam)
+    final_mask_tensor = torch.zeros((image.shape[0], image.shape[1]))
+    dino_detection_count = boxes.size(0)
+
+    if dino_detection_count > 0:
+        sam_predictor.set_image(image)
+
+        if sam_options.dino_erode_or_dilate != 0:
+            for index in range(boxes.size(0)):
+                assert boxes.size(1) == 4
+                boxes[index][0] -= sam_options.dino_erode_or_dilate
+                boxes[index][1] -= sam_options.dino_erode_or_dilate
+                boxes[index][2] += sam_options.dino_erode_or_dilate
+                boxes[index][3] += sam_options.dino_erode_or_dilate
+
+        if sam_options.dino_debug:
+            from PIL import ImageDraw, Image
+            debug_dino_image = Image.new("RGB", (image.shape[1], image.shape[0]), color="black")
+            draw = ImageDraw.Draw(debug_dino_image)
+            for box in boxes.numpy():
+                draw.rectangle(box.tolist(), fill="white")
+            return np.array(debug_dino_image), dino_detection_count, sam_detection_count, sam_detection_on_mask_count
+
+        transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes, image.shape[:2])
+        masks, _, _ = sam_predictor.predict_torch(
+            point_coords=None,
+            point_labels=None,
+            boxes=transformed_boxes,
+            multimask_output=False,
+        )
+
+        masks = optimize_masks(masks)
+        sam_detection_count = len(masks)
+        if sam_options.max_detections == 0:
+            sam_options.max_detections = sys.maxsize
+        sam_objects = min(len(logits), sam_options.max_detections)
+        for obj_ind in range(sam_objects):
+            mask_tensor = masks[obj_ind][0]
+            final_mask_tensor += mask_tensor
+            sam_detection_on_mask_count += 1
+
+    final_mask_tensor = (final_mask_tensor > 0).to('cpu').numpy()
+    mask_image = np.dstack((final_mask_tensor, final_mask_tensor, final_mask_tensor)) * 255
+    mask_image = np.array(mask_image, dtype=np.uint8)
+    return mask_image, dino_detection_count, sam_detection_count, sam_detection_on_mask_count

extras/ip_adapter.py

@@ -2,12 +2,13 @@ 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
@@ -103,7 +104,7 @@ def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path):
     offload_device = torch.device('cpu')
 
     use_fp16 = model_management.should_use_fp16(device=load_device)
-    ip_state_dict = torch.load(ip_adapter_path, map_location="cpu")
+    ip_state_dict = torch.load(ip_adapter_path, map_location="cpu", weights_only=True)
     plus = "latents" in ip_state_dict["image_proj"]
     cross_attention_dim = ip_state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[1]
     sdxl = cross_attention_dim == 2048
@@ -116,14 +117,16 @@ 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
 
-    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
-    )
+    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.sdxl = sdxl
     ip_adapter.load_device = load_device
     ip_adapter.offload_device = offload_device

extras/preprocessors.py

@@ -1,27 +1,26 @@
 import cv2
 import numpy as np
-import modules.advanced_parameters as advanced_parameters
 
 
-def centered_canny(x: np.ndarray):
+def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 2 and x.dtype == np.uint8
 
-    y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold))
+    y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
     y = y.astype(np.float32) / 255.0
     return y
 
 
-def centered_canny_color(x: np.ndarray):
+def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
-    result = [centered_canny(x[..., i]) for i in range(3)]
+    result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
     result = np.stack(result, axis=2)
     return result
 
 
-def pyramid_canny_color(x: np.ndarray):
+def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
@@ -31,7 +30,7 @@ def pyramid_canny_color(x: np.ndarray):
     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)
+        edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
         if acc_edge is None:
             acc_edge = edge
         else:
@@ -54,11 +53,11 @@ def norm255(x, low=4, high=96):
     return x * 255.0
 
 
-def canny_pyramid(x):
+def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
     # 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)
+    color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
     result = np.sum(color_canny, axis=2)
 
     return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)

extras/resampler.py

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

extras/safety_checker/configs/config.json

@@ -0,0 +1,171 @@
+{
+  "_name_or_path": "clip-vit-large-patch14/",
+  "architectures": [
+    "SafetyChecker"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 77,
+    "min_length": 0,
+    "model_type": "clip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "vocab_size": 49408
+  },
+  "text_config_dict": {
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12
+  },
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "clip_vision_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "vision_config_dict": {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14
+  }
+}

extras/safety_checker/configs/preprocessor_config.json

@@ -0,0 +1,20 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

extras/safety_checker/models/safety_checker.py

@@ -0,0 +1,126 @@
+# from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py
+
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+def cosine_distance(image_embeds, text_embeds):
+    normalized_image_embeds = nn.functional.normalize(image_embeds)
+    normalized_text_embeds = nn.functional.normalize(text_embeds)
+    return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
+
+
+class StableDiffusionSafetyChecker(PreTrainedModel):
+    config_class = CLIPConfig
+    main_input_name = "clip_input"
+
+    _no_split_modules = ["CLIPEncoderLayer"]
+
+    def __init__(self, config: CLIPConfig):
+        super().__init__(config)
+
+        self.vision_model = CLIPVisionModel(config.vision_config)
+        self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
+
+        self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
+        self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
+
+        self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
+        self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
+
+    @torch.no_grad()
+    def forward(self, clip_input, images):
+        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
+        image_embeds = self.visual_projection(pooled_output)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
+        cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
+
+        result = []
+        batch_size = image_embeds.shape[0]
+        for i in range(batch_size):
+            result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
+
+            # increase this value to create a stronger `nfsw` filter
+            # at the cost of increasing the possibility of filtering benign images
+            adjustment = 0.0
+
+            for concept_idx in range(len(special_cos_dist[0])):
+                concept_cos = special_cos_dist[i][concept_idx]
+                concept_threshold = self.special_care_embeds_weights[concept_idx].item()
+                result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
+                if result_img["special_scores"][concept_idx] > 0:
+                    result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
+                    adjustment = 0.01
+
+            for concept_idx in range(len(cos_dist[0])):
+                concept_cos = cos_dist[i][concept_idx]
+                concept_threshold = self.concept_embeds_weights[concept_idx].item()
+                result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
+                if result_img["concept_scores"][concept_idx] > 0:
+                    result_img["bad_concepts"].append(concept_idx)
+
+            result.append(result_img)
+
+        has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
+
+        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
+            if has_nsfw_concept:
+                if torch.is_tensor(images) or torch.is_tensor(images[0]):
+                    images[idx] = torch.zeros_like(images[idx])  # black image
+                else:
+                    images[idx] = np.zeros(images[idx].shape)  # black image
+
+        if any(has_nsfw_concepts):
+            logger.warning(
+                "Potential NSFW content was detected in one or more images. A black image will be returned instead."
+                " Try again with a different prompt and/or seed."
+            )
+
+        return images, has_nsfw_concepts
+
+    @torch.no_grad()
+    def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):
+        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
+        image_embeds = self.visual_projection(pooled_output)
+
+        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
+        cos_dist = cosine_distance(image_embeds, self.concept_embeds)
+
+        # increase this value to create a stronger `nsfw` filter
+        # at the cost of increasing the possibility of filtering benign images
+        adjustment = 0.0
+
+        special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
+        # special_scores = special_scores.round(decimals=3)
+        special_care = torch.any(special_scores > 0, dim=1)
+        special_adjustment = special_care * 0.01
+        special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
+
+        concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
+        # concept_scores = concept_scores.round(decimals=3)
+        has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
+
+        images[has_nsfw_concepts] = 0.0  # black image
+
+        return images, has_nsfw_concepts

extras/sam/predictor.py

@@ -0,0 +1,288 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from ldm_patched.modules import model_management
+from ldm_patched.modules.model_patcher import ModelPatcher
+
+from segment_anything.modeling import Sam
+
+from typing import Optional, Tuple
+
+from segment_anything.utils.transforms import ResizeLongestSide
+
+
+class SamPredictor:
+    def __init__(
+        self,
+        model: Sam,
+        load_device=model_management.text_encoder_device(),
+        offload_device=model_management.text_encoder_offload_device()
+    ) -> None:
+        """
+        Uses SAM to calculate the image embedding for an image, and then
+        allow repeated, efficient mask prediction given prompts.
+
+        Arguments:
+          model (Sam): The model to use for mask prediction.
+        """
+        super().__init__()
+
+        self.load_device = load_device
+        self.offload_device = offload_device
+        # can't use model.half() here as slow_conv2d_cpu is not implemented for half
+        model.to(self.offload_device)
+
+        self.patcher = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
+
+        self.transform = ResizeLongestSide(model.image_encoder.img_size)
+        self.reset_image()
+
+    def set_image(
+        self,
+        image: np.ndarray,
+        image_format: str = "RGB",
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method.
+
+        Arguments:
+          image (np.ndarray): The image for calculating masks. Expects an
+            image in HWC uint8 format, with pixel values in [0, 255].
+          image_format (str): The color format of the image, in ['RGB', 'BGR'].
+        """
+        assert image_format in [
+            "RGB",
+            "BGR",
+        ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
+        if image_format != self.patcher.model.image_format:
+            image = image[..., ::-1]
+
+        # Transform the image to the form expected by the model
+        input_image = self.transform.apply_image(image)
+        input_image_torch = torch.as_tensor(input_image, device=self.load_device)
+        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
+
+        self.set_torch_image(input_image_torch, image.shape[:2])
+
+    @torch.no_grad()
+    def set_torch_image(
+        self,
+        transformed_image: torch.Tensor,
+        original_image_size: Tuple[int, ...],
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method. Expects the input
+        image to be already transformed to the format expected by the model.
+
+        Arguments:
+          transformed_image (torch.Tensor): The input image, with shape
+            1x3xHxW, which has been transformed with ResizeLongestSide.
+          original_image_size (tuple(int, int)): The size of the image
+            before transformation, in (H, W) format.
+        """
+        assert (
+            len(transformed_image.shape) == 4
+            and transformed_image.shape[1] == 3
+            and max(*transformed_image.shape[2:]) == self.patcher.model.image_encoder.img_size
+        ), f"set_torch_image input must be BCHW with long side {self.patcher.model.image_encoder.img_size}."
+        self.reset_image()
+
+        self.original_size = original_image_size
+        self.input_size = tuple(transformed_image.shape[-2:])
+        model_management.load_model_gpu(self.patcher)
+        input_image = self.patcher.model.preprocess(transformed_image.to(self.load_device))
+        self.features = self.patcher.model.image_encoder(input_image)
+        self.is_image_set = True
+
+    def predict(
+        self,
+        point_coords: Optional[np.ndarray] = None,
+        point_labels: Optional[np.ndarray] = None,
+        box: Optional[np.ndarray] = None,
+        mask_input: Optional[np.ndarray] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+
+        Arguments:
+          point_coords (np.ndarray or None): A Nx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (np.ndarray or None): A length N array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A length 4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form 1xHxW, where
+            for SAM, H=W=256.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (np.ndarray): The output masks in CxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (np.ndarray): An array of length C containing the model's
+            predictions for the quality of each mask.
+          (np.ndarray): An array of shape CxHxW, where C is the number
+            of masks and H=W=256. These low resolution logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self.is_image_set:
+            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
+
+        # Transform input prompts
+        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
+        if point_coords is not None:
+            assert (
+                point_labels is not None
+            ), "point_labels must be supplied if point_coords is supplied."
+            point_coords = self.transform.apply_coords(point_coords, self.original_size)
+            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.load_device)
+            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.load_device)
+            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
+        if box is not None:
+            box = self.transform.apply_boxes(box, self.original_size)
+            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.load_device)
+            box_torch = box_torch[None, :]
+        if mask_input is not None:
+            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.load_device)
+            mask_input_torch = mask_input_torch[None, :, :, :]
+
+        masks, iou_predictions, low_res_masks = self.predict_torch(
+            coords_torch,
+            labels_torch,
+            box_torch,
+            mask_input_torch,
+            multimask_output,
+            return_logits=return_logits,
+        )
+
+        masks = masks[0].detach().cpu().numpy()
+        iou_predictions = iou_predictions[0].detach().cpu().numpy()
+        low_res_masks = low_res_masks[0].detach().cpu().numpy()
+        return masks, iou_predictions, low_res_masks
+
+    @torch.no_grad()
+    def predict_torch(
+        self,
+        point_coords: Optional[torch.Tensor],
+        point_labels: Optional[torch.Tensor],
+        boxes: Optional[torch.Tensor] = None,
+        mask_input: Optional[torch.Tensor] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+        Input prompts are batched torch tensors and are expected to already be
+        transformed to the input frame using ResizeLongestSide.
+
+        Arguments:
+          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (torch.Tensor or None): A BxN array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A Bx4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form Bx1xHxW, where
+            for SAM, H=W=256. Masks returned by a previous iteration of the
+            predict method do not need further transformation.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (torch.Tensor): The output masks in BxCxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (torch.Tensor): An array of shape BxC containing the model's
+            predictions for the quality of each mask.
+          (torch.Tensor): An array of shape BxCxHxW, where C is the number
+            of masks and H=W=256. These low res logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self.is_image_set:
+            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
+
+        if point_coords is not None:
+            points = (point_coords.to(self.load_device), point_labels.to(self.load_device))
+        else:
+            points = None
+
+        # load
+        if boxes is not None:
+            boxes = boxes.to(self.load_device)
+        if mask_input is not None:
+            mask_input = mask_input.to(self.load_device)
+        model_management.load_model_gpu(self.patcher)
+
+        # Embed prompts
+        sparse_embeddings, dense_embeddings = self.patcher.model.prompt_encoder(
+            points=points,
+            boxes=boxes,
+            masks=mask_input,
+        )
+
+        # Predict masks
+        low_res_masks, iou_predictions = self.patcher.model.mask_decoder(
+            image_embeddings=self.features,
+            image_pe=self.patcher.model.prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embeddings,
+            dense_prompt_embeddings=dense_embeddings,
+            multimask_output=multimask_output,
+        )
+
+        # Upscale the masks to the original image resolution
+        masks = self.patcher.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
+
+        if not return_logits:
+            masks = masks > self.patcher.model.mask_threshold
+
+        return masks, iou_predictions, low_res_masks
+
+    def get_image_embedding(self) -> torch.Tensor:
+        """
+        Returns the image embeddings for the currently set image, with
+        shape 1xCxHxW, where C is the embedding dimension and (H,W) are
+        the embedding spatial dimension of SAM (typically C=256, H=W=64).
+        """
+        if not self.is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) to generate an embedding."
+            )
+        assert self.features is not None, "Features must exist if an image has been set."
+        return self.features
+
+    @property
+    def device(self) -> torch.device:
+        return self.patcher.model.device
+
+    def reset_image(self) -> None:
+        """Resets the currently set image."""
+        self.is_image_set = False
+        self.features = None
+        self.orig_h = None
+        self.orig_w = None
+        self.input_h = None
+        self.input_w = None

extras/vae_interpose.py

@@ -1,69 +1,85 @@
 # https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py
 
 import os
-import torch
-import safetensors.torch as sf
-import torch.nn as nn
-import ldm_patched.modules.model_management
 
+import safetensors.torch as sf
+import torch
+import torch.nn as nn
+
+import ldm_patched.modules.model_management
 from ldm_patched.modules.model_patcher import ModelPatcher
 from modules.config import path_vae_approx
 
 
-class Block(nn.Module):
-    def __init__(self, size):
+class ResBlock(nn.Module):
+    """Block with residuals"""
+
+    def __init__(self, ch):
         super().__init__()
         self.join = nn.ReLU()
+        self.norm = nn.BatchNorm2d(ch)
         self.long = nn.Sequential(
-            nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
-            nn.LeakyReLU(0.1),
-            nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
-            nn.LeakyReLU(0.1),
-            nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
+            nn.Dropout(0.1)
         )
 
     def forward(self, x):
-        y = self.long(x)
-        z = self.join(y + x)
-        return z
+        x = self.norm(x)
+        return self.join(self.long(x) + x)
 
 
-class Interposer(nn.Module):
-    def __init__(self):
+class ExtractBlock(nn.Module):
+    """Increase no. of channels by [out/in]"""
+
+    def __init__(self, ch_in, ch_out):
         super().__init__()
-        self.chan = 4
-        self.hid = 128
-
-        self.head_join = nn.ReLU()
-        self.head_short = nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1)
-        self.head_long = nn.Sequential(
-            nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1),
-            nn.LeakyReLU(0.1),
-            nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1),
-            nn.LeakyReLU(0.1),
-            nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1),
-        )
-        self.core = nn.Sequential(
-            Block(self.hid),
-            Block(self.hid),
-            Block(self.hid),
-        )
-        self.tail = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv2d(self.hid, self.chan, kernel_size=3, stride=1, padding=1)
+        self.join = nn.ReLU()
+        self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)
+        self.long = nn.Sequential(
+            nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.Dropout(0.1)
         )
 
     def forward(self, x):
-        y = self.head_join(
-            self.head_long(x) +
-            self.head_short(x)
+        return self.join(self.long(x) + self.short(x))
+
+
+class InterposerModel(nn.Module):
+    """Main neural network"""
+
+    def __init__(self, ch_in=4, ch_out=4, ch_mid=64, scale=1.0, blocks=12):
+        super().__init__()
+        self.ch_in = ch_in
+        self.ch_out = ch_out
+        self.ch_mid = ch_mid
+        self.blocks = blocks
+        self.scale = scale
+
+        self.head = ExtractBlock(self.ch_in, self.ch_mid)
+        self.core = nn.Sequential(
+            nn.Upsample(scale_factor=self.scale, mode="nearest"),
+            *[ResBlock(self.ch_mid) for _ in range(blocks)],
+            nn.BatchNorm2d(self.ch_mid),
+            nn.SiLU(),
         )
+        self.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        y = self.head(x)
         z = self.core(y)
         return self.tail(z)
 
 
 vae_approx_model = None
-vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v3.1.safetensors')
+vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v4.0.safetensors')
 
 
 def parse(x):
@@ -72,7 +88,7 @@ def parse(x):
     x_origin = x.clone()
 
     if vae_approx_model is None:
-        model = Interposer()
+        model = InterposerModel()
         model.eval()
         sd = sf.load_file(vae_approx_filename)
         model.load_state_dict(sd)

fooocus_colab.ipynb

@@ -8,11 +8,11 @@
    },
    "outputs": [],
    "source": [
-    "!pip install pygit2==1.12.2\n",
+    "!pip install pygit2==1.15.1\n",
     "%cd /content\n",
     "!git clone https://github.com/lllyasviel/Fooocus.git\n",
     "%cd /content/Fooocus\n",
-    "!python entry_with_update.py --share\n"
+    "!python entry_with_update.py --share --always-high-vram\n"
    ]
   }
  ],

fooocus_version.py

@@ -1 +1 @@
-version = '2.1.851'
+version = '2.5.5'

javascript/contextMenus.js

@@ -154,12 +154,8 @@ let cancelGenerateForever = function() {
     let generateOnRepeatForButtons = function() {
         generateOnRepeat('#generate_button', '#stop_button');
     };
-
     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

@@ -45,6 +45,9 @@ function processTextNode(node) {
     var tl = getTranslation(text);
     if (tl !== undefined) {
         node.textContent = tl;
+        if (text && node.parentElement) {
+          node.parentElement.setAttribute("data-original-text", text);
+        }
     }
 }
 
@@ -77,6 +80,15 @@ function refresh_style_localization() {
     processNode(document.querySelector('.style_selections'));
 }
 
+function refresh_aspect_ratios_label(value) {
+    label = document.querySelector('#aspect_ratios_accordion div span');
+    translation = getTranslation("Aspect Ratios");
+    if (typeof translation == "undefined") {
+        translation = "Aspect Ratios";
+    }
+    label.textContent = translation + " " + htmlDecode(value);
+}
+
 function localizeWholePage() {
     processNode(gradioApp());
 

javascript/script.js

@@ -119,6 +119,44 @@ document.addEventListener("DOMContentLoaded", function() {
         }
     });
     mutationObserver.observe(gradioApp(), {childList: true, subtree: true});
+    initStylePreviewOverlay();
+});
+
+var onAppend = function(elem, f) {
+    var observer = new MutationObserver(function(mutations) {
+        mutations.forEach(function(m) {
+            if (m.addedNodes.length) {
+                f(m.addedNodes);
+            }
+        });
+    });
+    observer.observe(elem, {childList: true});
+}
+
+function addObserverIfDesiredNodeAvailable(querySelector, callback) {
+    var elem = document.querySelector(querySelector);
+    if (!elem) {
+        window.setTimeout(() => addObserverIfDesiredNodeAvailable(querySelector, callback), 1000);
+        return;
+    }
+
+    onAppend(elem, callback);
+}
+
+/**
+ * Show reset button on toast "Connection errored out."
+ */
+addObserverIfDesiredNodeAvailable(".toast-wrap", function(added) {
+    added.forEach(function(element) {
+         if (element.innerText.includes("Connection errored out.")) {
+             window.setTimeout(function() {
+                document.getElementById("reset_button").classList.remove("hidden");
+                document.getElementById("generate_button").classList.add("hidden");
+                document.getElementById("skip_button").classList.add("hidden");
+                document.getElementById("stop_button").classList.add("hidden");
+            });
+         }
+    });
 });
 
 /**
@@ -145,6 +183,46 @@ 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
  */
@@ -178,3 +256,8 @@ function set_theme(theme) {
         window.location.replace(gradioURL + '?__theme=' + theme);
     }
 }
+
+function htmlDecode(input) {
+  var doc = new DOMParser().parseFromString(input, "text/html");
+  return doc.documentElement.textContent;
+}

javascript/zoom.js

@@ -642,4 +642,5 @@ onUiLoaded(async() => {
     }
 
     applyZoomAndPan("#inpaint_canvas");
+    applyZoomAndPan("#inpaint_mask_canvas");
 });

language/en.json

@@ -4,12 +4,22 @@
     "Generate": "Generate",
     "Skip": "Skip",
     "Stop": "Stop",
+    "Reconnect": "Reconnect",
     "Input Image": "Input Image",
     "Advanced": "Advanced",
     "Upscale or Variation": "Upscale or Variation",
     "Image Prompt": "Image Prompt",
-    "Inpaint or Outpaint (beta)": "Inpaint or Outpaint (beta)",
-    "Drag above image to here": "Drag above image to here",
+    "Inpaint or Outpaint": "Inpaint or Outpaint",
+    "Outpaint Direction": "Outpaint Direction",
+    "Enable Advanced Masking Features": "Enable Advanced Masking Features",
+    "Method": "Method",
+    "Describe": "Describe",
+    "Content Type": "Content Type",
+    "Photograph": "Photograph",
+    "Art/Anime": "Art/Anime",
+    "Apply Styles": "Apply Styles",
+    "Describe this Image into Prompt": "Describe this Image into Prompt",
+    "Image Size and Recommended Size": "Image Size and Recommended Size",
     "Upscale or Variation:": "Upscale or Variation:",
     "Disabled": "Disabled",
     "Vary (Subtle)": "Vary (Subtle)",
@@ -17,7 +27,7 @@
     "Upscale (1.5x)": "Upscale (1.5x)",
     "Upscale (2x)": "Upscale (2x)",
     "Upscale (Fast 2x)": "Upscale (Fast 2x)",
-    "\ud83d\udcd4 Document": "\uD83D\uDCD4 Document",
+    "\ud83d\udcd4 Documentation": "\uD83D\uDCD4 Documentation",
     "Image": "Image",
     "Stop At": "Stop At",
     "Weight": "Weight",
@@ -36,11 +46,17 @@
     "Top": "Top",
     "Bottom": "Bottom",
     "* \"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",
+    "Advanced options": "Advanced options",
+    "Generate mask from image": "Generate mask from image",
+    "Settings": "Settings",
     "Style": "Style",
+    "Styles": "Styles",
+    "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",
@@ -48,9 +64,18 @@
     "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",
+    "Black Out NSFW": "Black Out NSFW",
+    "Use black image if NSFW is detected.": "Use black image if NSFW is detected.",
+    "Save only final enhanced image": "Save only final enhanced image",
+    "Save Metadata to Images": "Save Metadata to Images",
+    "Adds parameters to generated images allowing manual regeneration.": "Adds parameters to generated images allowing manual regeneration.",
     "\ud83d\udcda History Log": "\uD83D\uDCDA History Log",
     "Image Style": "Image Style",
     "Fooocus V2": "Fooocus V2",
+    "Random Style": "Random Style",
     "Default (Slightly Cinematic)": "Default (Slightly Cinematic)",
     "Fooocus Masterpiece": "Fooocus Masterpiece",
     "Fooocus Photograph": "Fooocus Photograph",
@@ -262,7 +287,7 @@
     "Volumetric Lighting": "Volumetric Lighting",
     "Watercolor 2": "Watercolor 2",
     "Whimsical And Playful": "Whimsical And Playful",
-    "Model": "Model",
+    "Models": "Models",
     "Base Model (SDXL only)": "Base Model (SDXL only)",
     "sd_xl_base_1.0_0.9vae.safetensors": "sd_xl_base_1.0_0.9vae.safetensors",
     "bluePencilXL_v009.safetensors": "bluePencilXL_v009.safetensors",
@@ -303,6 +328,8 @@
     "vae": "vae",
     "CFG Mimicking from TSNR": "CFG Mimicking from TSNR",
     "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).": "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).",
+    "CLIP Skip": "CLIP Skip",
+    "Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).": "Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).",
     "Sampler": "Sampler",
     "dpmpp_2m_sde_gpu": "dpmpp_2m_sde_gpu",
     "Only effective in non-inpaint mode.": "Only effective in non-inpaint mode.",
@@ -333,6 +360,8 @@
     "sgm_uniform": "sgm_uniform",
     "simple": "simple",
     "ddim_uniform": "ddim_uniform",
+    "VAE": "VAE",
+    "Default (model)": "Default (model)",
     "Forced Overwrite of Sampling Step": "Forced Overwrite of Sampling Step",
     "Set as -1 to disable. For developer debugging.": "Set as -1 to disable. For developer debugging.",
     "Forced Overwrite of Refiner Switch Step": "Forced Overwrite of Refiner Switch Step",
@@ -342,10 +371,18 @@
     "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.",
+    "Debug Inpaint Preprocessing": "Debug Inpaint Preprocessing",
+    "Debug GroundingDINO": "Debug GroundingDINO",
+    "Used for SAM object detection and box generation": "Used for SAM object detection and box generation",
+    "GroundingDINO Box Erode or Dilate": "GroundingDINO Box Erode or Dilate",
     "Inpaint Engine": "Inpaint Engine",
     "v1": "v1",
-    "Version of Fooocus inpaint model": "Version of Fooocus inpaint model",
     "v2.5": "v2.5",
+    "v2.6": "v2.6",
     "Control Debug": "Control Debug",
     "Debug Preprocessors": "Debug Preprocessors",
     "Mixing Image Prompt and Vary/Upscale": "Mixing Image Prompt and Vary/Upscale",
@@ -361,12 +398,88 @@
     "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"
+    "Fooocus Sharp": "Fooocus Sharp",
+    "For images created by Fooocus": "For images created by Fooocus",
+    "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",
+    "Enhance": "Enhance",
+    "Detection prompt": "Detection prompt",
+    "Detection Prompt Quick List": "Detection Prompt Quick List",
+    "Maximum number of detections": "Maximum number of detections",
+    "Use with Enhance, skips image generation": "Use with Enhance, skips image generation",
+    "Order of Processing": "Order of Processing",
+    "Use before to enhance small details and after to enhance large areas.": "Use before to enhance small details and after to enhance large areas.",
+    "Before First Enhancement": "Before First Enhancement",
+    "After Last Enhancement": "After Last Enhancement",
+    "Prompt Type": "Prompt Type",
+    "Choose which prompt to use for Upscale or Variation.": "Choose which prompt to use for Upscale or Variation.",
+    "Original Prompts": "Original Prompts",
+    "Last Filled Enhancement Prompts": "Last Filled Enhancement Prompts",
+    "Enable": "Enable",
+    "Describe what you want to detect.": "Describe what you want to detect.",
+    "Enhancement positive prompt": "Enhancement positive prompt",
+    "Uses original prompt instead if empty.": "Uses original prompt instead if empty.",
+    "Enhancement negative prompt": "Enhancement negative prompt",
+    "Uses original negative prompt instead if empty.": "Uses original negative prompt instead if empty.",
+    "Detection": "Detection",
+    "u2net": "u2net",
+    "u2netp": "u2netp",
+    "u2net_human_seg": "u2net_human_seg",
+    "u2net_cloth_seg": "u2net_cloth_seg",
+    "silueta": "silueta",
+    "isnet-general-use": "isnet-general-use",
+    "isnet-anime": "isnet-anime",
+    "sam": "sam",
+    "Mask generation model": "Mask generation model",
+    "Cloth category": "Cloth category",
+    "Use singular whenever possible": "Use singular whenever possible",
+    "full": "full",
+    "upper": "upper",
+    "lower": "lower",
+    "SAM Options": "SAM Options",
+    "SAM model": "SAM model",
+    "vit_b": "vit_b",
+    "vit_l": "vit_l",
+    "vit_h": "vit_h",
+    "Box Threshold": "Box Threshold",
+    "Text Threshold": "Text Threshold",
+    "Set to 0 to detect all": "Set to 0 to detect all",
+    "Inpaint": "Inpaint",
+    "Inpaint or Outpaint (default)": "Inpaint or Outpaint (default)",
+    "Improve Detail (face, hand, eyes, etc.)": "Improve Detail (face, hand, eyes, etc.)",
+    "Modify Content (add objects, change background, etc.)": "Modify Content (add objects, change background, etc.)",
+    "Disable initial latent in inpaint": "Disable initial latent in inpaint",
+    "Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.": "Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.",
+    "Inpaint Denoising Strength": "Inpaint Denoising Strength",
+    "Same as the denoising strength in A1111 inpaint. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)": "Same as the denoising strength in A1111 inpaint. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)",
+    "Inpaint Respective Field": "Inpaint Respective Field",
+    "The area to inpaint. Value 0 is same as \"Only Masked\" in A1111. Value 1 is same as \"Whole Image\" in A1111. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)": "The area to inpaint. Value 0 is same as \"Only Masked\" in A1111. Value 1 is same as \"Whole Image\" in A1111. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)",
+    "Mask Erode or Dilate": "Mask Erode or Dilate",
+    "Positive value will make white area in the mask larger, negative value will make white area smaller. (default is 0, always processed before any mask invert)": "Positive value will make white area in the mask larger, negative value will make white area smaller. (default is 0, always processed before any mask invert)",
+    "Invert Mask When Generating": "Invert Mask When Generating",
+    "Debug Enhance Masks": "Debug Enhance Masks",
+    "Show enhance masks in preview and final results": "Show enhance masks in preview and final results",
+    "Use GroundingDINO boxes instead of more detailed SAM masks": "Use GroundingDINO boxes instead of more detailed SAM masks",
+    "highly detailed face": "highly detailed face",
+    "detailed girl face": "detailed girl face",
+    "detailed man face": "detailed man face",
+    "detailed hand": "detailed hand",
+    "beautiful eyes": "beautiful eyes",
+    "face": "face",
+    "eye": "eye",
+    "mouth": "mouth",
+    "hair": "hair",
+    "hand": "hand",
+    "body": "body"
 }

launch.py

@@ -1,6 +1,6 @@
 import os
-import sys
 import ssl
+import sys
 
 print('[System ARGV] ' + str(sys.argv))
 
@@ -10,20 +10,17 @@ os.chdir(root)
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
-os.environ["GRADIO_SERVER_PORT"] = "7865"
+if "GRADIO_SERVER_PORT" not in os.environ:
+    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
+from modules.launch_util import is_installed, run, python, run_pip, requirements_met, delete_folder_content
 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
@@ -43,7 +40,7 @@ def prepare_environment():
 
     if TRY_INSTALL_XFORMERS:
         if REINSTALL_ALL or not is_installed("xformers"):
-            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
+            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23')
             if platform.system() == "Windows":
                 if platform.python_version().startswith("3.10"):
                     run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
@@ -65,30 +62,11 @@ def prepare_environment():
 vae_approx_filenames = [
     ('xlvaeapp.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth'),
     ('vaeapp_sd15.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt'),
-    ('xl-to-v1_interposer-v3.1.safetensors',
-     'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
+    ('xl-to-v1_interposer-v4.0.safetensors',
+     'https://huggingface.co/mashb1t/misc/resolve/main/xl-to-v1_interposer-v4.0.safetensors')
 ]
 
 
-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
@@ -98,12 +76,77 @@ 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)
 
+if args.hf_mirror is not None:
+    os.environ['HF_MIRROR'] = str(args.hf_mirror)
+    print("Set hf_mirror to:", args.hf_mirror)
 
-download_models()
+from modules import config
+from modules.hash_cache import init_cache
+
+os.environ["U2NET_HOME"] = config.path_inpaint
+
+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, vae_downloads):
+    from modules.util import get_file_from_folder_list
+
+    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.isfile(get_file_from_folder_list(default_model, config.paths_checkpoints)):
+            for alternative_model_name in previous_default_models:
+                if os.path.isfile(get_file_from_folder_list(alternative_model_name, config.paths_checkpoints)):
+                    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():
+        model_dir = os.path.dirname(get_file_from_folder_list(file_name, config.paths_checkpoints))
+        load_file_from_url(url=url, model_dir=model_dir, 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():
+        model_dir = os.path.dirname(get_file_from_folder_list(file_name, config.paths_loras))
+        load_file_from_url(url=url, model_dir=model_dir, file_name=file_name)
+    for file_name, url in vae_downloads.items():
+        load_file_from_url(url=url, model_dir=config.path_vae, 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, config.vae_downloads)
+
+config.update_files()
+init_cache(config.model_filenames, config.paths_checkpoints, config.lora_filenames, config.paths_loras)
 
 from webui import *

ldm_patched/contrib/external.py

@@ -11,7 +11,7 @@ import math
 import time
 import random
 
-from PIL import Image, ImageOps
+from PIL import Image, ImageOps, ImageSequence
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
 import safetensors.torch
@@ -361,6 +361,62 @@ 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()
@@ -1412,17 +1468,32 @@ class LoadImage:
     FUNCTION = "load_image"
     def load_image(self, image):
         image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)
-        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)
+        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)
         else:
-            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-        return (image, mask.unsqueeze(0))
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return (output_image, output_mask)
 
     @classmethod
     def IS_CHANGED(s, image):
@@ -1459,6 +1530,8 @@ 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()
@@ -1480,13 +1553,10 @@ class LoadImageMask:
         return m.digest().hex()
 
     @classmethod
-    def VALIDATE_INPUTS(s, image, channel):
+    def VALIDATE_INPUTS(s, image):
         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:
@@ -1616,10 +1686,11 @@ class ImagePadForOutpaint:
     def expand_image(self, image, left, top, right, bottom, feathering):
         d1, d2, d3, d4 = image.size()
 
-        new_image = torch.zeros(
+        new_image = torch.ones(
             (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(
@@ -1711,6 +1782,7 @@ NODE_CLASS_MAPPINGS = {
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
     "GLIGENLoader": GLIGENLoader,
     "GLIGENTextBoxApply": GLIGENTextBoxApply,
+    "InpaintModelConditioning": InpaintModelConditioning,
 
     "CheckpointLoader": CheckpointLoader,
     "DiffusersLoader": DiffusersLoader,
@@ -1871,6 +1943,9 @@ 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_align_your_steps.py

@@ -0,0 +1,55 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
+
+#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+import numpy as np
+import torch
+
+def loglinear_interp(t_steps, num_steps):
+    """
+    Performs log-linear interpolation of a given array of decreasing numbers.
+    """
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    interped_ys = np.exp(new_ys)[::-1].copy()
+    return interped_ys
+
+NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956,  3.8636745985,  2.6946151520, 1.8841921177,  1.3943805092,  0.9642583904,  0.6523686016, 0.3977456272,  0.1515232662,  0.0291671582],
+                "SDXL":[14.6146412293, 6.3184485287,  3.7681790315,  2.1811480769, 1.3405244945,  0.8620721141,  0.5550693289,  0.3798540708, 0.2332364134,  0.1114188177,  0.0291671582],
+                "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}
+
+class AlignYourStepsScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model_type": (["SD1", "SDXL", "SVD"], ),
+                     "steps": ("INT", {"default": 10, "min": 10, "max": 10000}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                      }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, model_type, steps, denoise):
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return (torch.FloatTensor([]),)
+            total_steps = round(steps * denoise)
+
+        sigmas = NOISE_LEVELS[model_type][:]
+        if (steps + 1) != len(sigmas):
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return (torch.FloatTensor(sigmas), )
+
+NODE_CLASS_MAPPINGS = {
+    "AlignYourStepsScheduler": AlignYourStepsScheduler,
+}

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-tutorials.readthedocs.io/en/latest/
+       See a working example `here <https://kornia.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-tutorials.readthedocs.io/en/latest/
+       See a working example `here <https://kornia.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-tutorials.readthedocs.io/en/latest/
+       See a working example `here <https://kornia.readthedocs.io/en/latest/
        canny.html>`__.
     Example:
         >>> input = torch.rand(5, 3, 4, 4)

ldm_patched/contrib/external_custom_sampler.py

@@ -15,6 +15,7 @@ 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",)
@@ -22,8 +23,14 @@ class BasicScheduler:
 
     FUNCTION = "get_sigmas"
 
-    def get_sigmas(self, model, scheduler, steps):
-        sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, steps).cpu()
+    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):]
         return (sigmas, )
 
 
@@ -89,6 +96,7 @@ 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",)
@@ -96,9 +104,10 @@ class SDTurboScheduler:
 
     FUNCTION = "get_sigmas"
 
-    def get_sigmas(self, model, steps):
-        timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[:steps]
-        sigmas = model.model.model_sampling.sigma(timesteps)
+    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]
+        sigmas = model.model_sampling.sigma(timesteps)
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )
 
@@ -220,6 +229,25 @@ class SamplerDPMPP_SDE:
         sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
         return (sampler, )
 
+
+class SamplerTCD:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "eta": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
+            }
+        }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta=0.3):
+        sampler = ldm_patched.modules.samplers.ksampler("tcd", {"eta": eta})
+        return (sampler, )
+
+
 class SamplerCustom:
     @classmethod
     def INPUT_TYPES(s):
@@ -282,6 +310,7 @@ NODE_CLASS_MAPPINGS = {
     "KSamplerSelect": KSamplerSelect,
     "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
     "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
+    "SamplerTCD": SamplerTCD,
     "SplitSigmas": SplitSigmas,
     "FlipSigmas": FlipSigmas,
 }

ldm_patched/contrib/external_freelunch.py

@@ -36,7 +36,7 @@ class FreeU:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches"
 
     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 = "_for_testing"
+    CATEGORY = "model_patches"
 
     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 = "_for_testing"
+    CATEGORY = "model_patches"
 
     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):
-            if q.shape[-1] in apply_to:
+            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:
                 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**((q.shape[-1] // model_channels) - 1) if scale_depth else 1
+                factor = (2 ** apply_to.index(model_chans)) 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

@@ -124,10 +124,34 @@ 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

@@ -8,6 +8,7 @@ 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")
 
@@ -22,7 +23,7 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
     if mask is None:
         mask = torch.ones_like(source)
     else:
-        mask = mask.clone()
+        mask = mask.to(destination.device, copy=True)
         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_advanced.py

@@ -70,7 +70,7 @@ class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["eps", "v_prediction", "lcm"],),
+                              "sampling": (["eps", "v_prediction", "lcm", "tcd"]),
                               "zsnr": ("BOOLEAN", {"default": False}),
                               }}
 
@@ -90,6 +90,9 @@ class ModelSamplingDiscrete:
         elif sampling == "lcm":
             sampling_type = LCM
             sampling_base = ModelSamplingDiscreteDistilled
+        elif sampling == "tcd":
+            sampling_type = ldm_patched.modules.model_sampling.EPS
+            sampling_base = ModelSamplingDiscreteDistilled
 
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass
@@ -105,7 +108,7 @@ class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["v_prediction", "eps"],),
+                              "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
                               "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                               "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                               }}
@@ -118,17 +121,25 @@ class ModelSamplingContinuousEDM:
     def patch(self, model, sampling, sigma_max, sigma_min):
         m = model.clone()
 
+        latent_format = None
+        sigma_data = 1.0
         if sampling == "eps":
             sampling_type = ldm_patched.modules.model_sampling.EPS
         elif sampling == "v_prediction":
             sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION
+        elif sampling == "edm_playground_v2.5":
+            sampling_type = ldm_patched.modules.model_sampling.EDM
+            sigma_data = 0.5
+            latent_format = ldm_patched.modules.latent_formats.SDXL_Playground_2_5()
 
         class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type):
             pass
 
         model_sampling = ModelSamplingAdvanced(model.model.model_config)
-        model_sampling.set_sigma_range(sigma_min, sigma_max)
+        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
         m.add_object_patch("model_sampling", model_sampling)
+        if latent_format is not None:
+            m.add_object_patch("latent_format", latent_format)
         return (m, )
 
 class RescaleCFG:

ldm_patched/contrib/external_model_merging.py

@@ -121,6 +121,48 @@ 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()
@@ -139,46 +181,7 @@ class CheckpointSave:
     CATEGORY = "advanced/model_merging"
 
     def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
-        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)
+        save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
         return {}
 
 class CLIPSave:

ldm_patched/contrib/external_photomaker.py

@@ -0,0 +1,189 @@
+# 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

@@ -35,6 +35,7 @@ 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

@@ -101,10 +101,40 @@ 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 = math.ceil(math.sqrt(lh * lw / hw1))
+    ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length()
     mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
 
     # Reshape
@@ -145,6 +145,8 @@ 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)
@@ -153,7 +155,7 @@ 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)
+        m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True)
 
         # from diffusers:
         # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch

ldm_patched/contrib/external_sdupscale.py

@@ -0,0 +1,49 @@
+# 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

@@ -0,0 +1,104 @@
+# 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

@@ -5,6 +5,7 @@ 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:
@@ -80,10 +81,26 @@ 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/k_diffusion/sampling.py

@@ -752,7 +752,6 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
     return x
 
 
-
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
@@ -808,3 +807,102 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
             d_prime = w1 * d + w2 * d_2 + w3 * d_3
             x = x + d_prime * dt
     return x
+
+
+@torch.no_grad()
+def sample_tcd(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, eta=0.3):
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    model_sampling = model.inner_model.inner_model.model_sampling
+    timesteps_s = torch.floor((1 - eta) * model_sampling.timestep(sigmas)).to(dtype=torch.long).detach().cpu()
+    timesteps_s[-1] = 0
+    alpha_prod_s = model_sampling.alphas_cumprod[timesteps_s]
+    beta_prod_s = 1 - alpha_prod_s
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)  # predicted_original_sample
+        eps = (x - denoised) / sigmas[i]
+        denoised = alpha_prod_s[i + 1].sqrt() * denoised + beta_prod_s[i + 1].sqrt() * eps
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+
+        x = denoised
+        if eta > 0 and sigmas[i + 1] > 0:
+            noise = noise_sampler(sigmas[i], sigmas[i + 1])
+            x = x / alpha_prod_s[i+1].sqrt() + noise * (sigmas[i+1]**2 + 1 - 1/alpha_prod_s[i+1]).sqrt()
+        else:
+            x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2)
+
+    return x
+
+
+@torch.no_grad()
+def sample_restart(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):
+    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)
+    Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
+    If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
+    """
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    step_id = 0
+
+    def heun_step(x, old_sigma, new_sigma, second_order=True):
+        nonlocal step_id
+        denoised = model(x, old_sigma * s_in, **extra_args)
+        d = to_d(x, old_sigma, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
+        dt = new_sigma - old_sigma
+        if new_sigma == 0 or not second_order:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+            d_2 = to_d(x_2, new_sigma, denoised_2)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+        step_id += 1
+        return x
+
+    steps = sigmas.shape[0] - 1
+    if restart_list is None:
+        if steps >= 20:
+            restart_steps = 9
+            restart_times = 1
+            if steps >= 36:
+                restart_steps = steps // 4
+                restart_times = 2
+            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
+            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+        else:
+            restart_list = {}
+
+    restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}
+
+    step_list = []
+    for i in range(len(sigmas) - 1):
+        step_list.append((sigmas[i], sigmas[i + 1]))
+        if i + 1 in restart_list:
+            restart_steps, restart_times, restart_max = restart_list[i + 1]
+            min_idx = i + 1
+            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+            if max_idx < min_idx:
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
+                while restart_times > 0:
+                    restart_times -= 1
+                    step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))
+
+    last_sigma = None
+    for old_sigma, new_sigma in tqdm(step_list, disable=disable):
+        if last_sigma is None:
+            last_sigma = old_sigma
+        elif last_sigma < old_sigma:
+            x = x + torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5
+        x = heun_step(x, old_sigma, new_sigma)
+        last_sigma = new_sigma
+
+    return x

ldm_patched/ldm/models/autoencoder.py

@@ -8,6 +8,7 @@ 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):
@@ -161,12 +162,12 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
             },
             **kwargs,
         )
-        self.quant_conv = torch.nn.Conv2d(
+        self.quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(
             (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
             (1 + ddconfig["double_z"]) * embed_dim,
             1,
         )
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.post_quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
         self.embed_dim = embed_dim
 
     def get_autoencoder_params(self) -> list:

ldm_patched/ldm/modules/attention.py

@@ -1,12 +1,9 @@
-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
@@ -177,6 +174,7 @@ 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)
@@ -239,6 +237,12 @@ 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
@@ -294,11 +298,14 @@ def attention_xformers(q, k, v, heads, mask=None):
         (q, k, v),
     )
 
-    # actually compute the attention, what we cannot get enough of
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
+    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)
 
-    if exists(mask):
-        raise NotImplementedError
     out = (
         out.unsqueeze(0)
         .reshape(b, heads, -1, dim_head)
@@ -323,7 +330,6 @@ 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")
@@ -339,15 +345,18 @@ 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
 
-if model_management.pytorch_attention_enabled():
-    optimized_attention_masked = attention_pytorch
+optimized_attention_masked = optimized_attention
 
-def optimized_attention_for_device(device, mask=False):
-    if device == torch.device("cpu"): #TODO
+def optimized_attention_for_device(device, mask=False, small_input=False):
+    if small_input:
         if model_management.pytorch_attention_enabled():
-            return attention_pytorch
+            return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases
         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 torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+    return ops.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

@@ -1,12 +1,9 @@
 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,
@@ -437,9 +434,6 @@ 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...'
@@ -456,7 +450,6 @@ 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
@@ -502,7 +495,7 @@ 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)
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)
             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

@@ -41,10 +41,14 @@ 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):
-        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 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 forward(self, x):
         return x, None
@@ -69,12 +73,12 @@ 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):
+    def forward(self, x, noise_level=None, seed=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)
+        z = self.q_sample(x, noise_level, seed=seed)
         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
+            alpha = self.mix_factor.to(image_only_indicator.device)
         elif self.merge_strategy == "learned":
-            alpha = torch.sigmoid(self.mix_factor)
+            alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device))
             # 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), "... -> ... 1"),
+                rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
             )
             alpha = rearrange(alpha, self.rearrange_pattern)
             # make shape compatible

ldm_patched/ldm/modules/encoders/noise_aug_modules.py

@@ -8,28 +8,28 @@ class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
         if clip_stats_path is None:
             clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
         else:
-            clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
+            clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu", weights_only=True)
         self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
         self.register_buffer("data_std", clip_std[None, :], persistent=False)
         self.time_embed = Timestep(timestep_dim)
 
     def scale(self, x):
         # re-normalize to centered mean and unit variance
-        x = (x - self.data_mean) * 1. / self.data_std
+        x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device)
         return x
 
     def unscale(self, x):
         # back to original data stats
-        x = (x * self.data_std) + self.data_mean
+        x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device)
         return x
 
-    def forward(self, x, noise_level=None):
+    def forward(self, x, noise_level=None, seed=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)
+        z = self.q_sample(x, noise_level, seed=seed)
         z = self.unscale(z)
         noise_level = self.time_embed(noise_level)
         return z, noise_level

ldm_patched/ldm/modules/sub_quadratic_attention.py

@@ -61,6 +61,7 @@ def _summarize_chunk(
     value: Tensor,
     scale: float,
     upcast_attention: bool,
+    mask,
 ) -> AttnChunk:
     if upcast_attention:
         with torch.autocast(enabled=False, device_type = 'cuda'):
@@ -84,6 +85,8 @@ 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)
@@ -96,11 +99,12 @@ 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) -> AttnChunk:
+    def chunk_scanner(chunk_idx: int, mask) -> AttnChunk:
         key_chunk = dynamic_slice(
             key_t,
             (0, 0, chunk_idx),
@@ -111,10 +115,13 @@ def _query_chunk_attention(
             (0, chunk_idx, 0),
             (batch_x_heads, kv_chunk_size, v_channels_per_head)
         )
-        return summarize_chunk(query, key_chunk, value_chunk)
+        if mask is not None:
+            mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size]
+
+        return summarize_chunk(query, key_chunk, value_chunk, mask=mask)
 
     chunks: List[AttnChunk] = [
-        chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
+        chunk_scanner(chunk, mask) 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
@@ -135,6 +142,7 @@ 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'):
@@ -156,6 +164,8 @@ 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
@@ -183,6 +193,7 @@ 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
@@ -209,13 +220,22 @@ 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(
@@ -237,6 +257,7 @@ 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,
@@ -246,6 +267,7 @@ 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)
+            alpha = self.get_alpha(bs=b // timesteps).to(x.device)
             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(torch.nn.Conv2d):
+class AE3DConv(ops.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(torch.nn.Conv2d):
         else:
             padding = int(video_kernel_size // 2)
 
-        self.time_mix_conv = torch.nn.Conv3d(
+        self.time_mix_conv = ops.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()
+        alpha = self.get_alpha().to(x.device)
         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

@@ -0,0 +1,20 @@
+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

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
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+know their rights.
+
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+
+  7. Additional Terms.
+
+  "Additional permissions" are terms that supplement the terms of this
+License by making exceptions from one or more of its conditions.
+Additional permissions that are applicable to the entire Program shall
+be treated as though they were included in this License, to the extent
+that they are valid under applicable law.  If additional permissions
+apply only to part of the Program, that part may be used separately
+under those permissions, but the entire Program remains governed by
+this License without regard to the additional permissions.
+
+  When you convey a copy of a covered work, you may at your option
+remove any additional permissions from that copy, or from any part of
+it.  (Additional permissions may be written to require their own
+removal in certain cases when you modify the work.)  You may place
+additional permissions on material, added by you to a covered work,
+for which you have or can give appropriate copyright permission.
+
+  Notwithstanding any other provision of this License, for material you
+add to a covered work, you may (if authorized by the copyright holders of
+that material) supplement the terms of this License with terms:
+
+    a) Disclaiming warranty or limiting liability differently from the
+    terms of sections 15 and 16 of this License; or
+
+    b) Requiring preservation of specified reasonable legal notices or
+    author attributions in that material or in the Appropriate Legal
+    Notices displayed by works containing it; or
+
+    c) Prohibiting misrepresentation of the origin of that material, or
+    requiring that modified versions of such material be marked in
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+
+    d) Limiting the use for publicity purposes of names of licensors or
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+
+    e) Declining to grant rights under trademark law for use of some
+    trade names, trademarks, or service marks; or
+
+    f) Requiring indemnification of licensors and authors of that
+    material by anyone who conveys the material (or modified versions of
+    it) with contractual assumptions of liability to the recipient, for
+    any liability that these contractual assumptions directly impose on
+    those licensors and authors.
+
+  All other non-permissive additional terms are considered "further
+restrictions" within the meaning of section 10.  If the Program as you
+received it, or any part of it, contains a notice stating that it is
+governed by this License along with a term that is a further
+restriction, you may remove that term.  If a license document contains
+a further restriction but permits relicensing or conveying under this
+License, you may add to a covered work material governed by the terms
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+not survive such relicensing or conveying.
+
+  If you add terms to a covered work in accord with this section, you
+must place, in the relevant source files, a statement of the
+additional terms that apply to those files, or a notice indicating
+where to find the applicable terms.
+
+  Additional terms, permissive or non-permissive, may be stated in the
+form of a separately written license, or stated as exceptions;
+the above requirements apply either way.
+
+  8. Termination.
+
+  You may not propagate or modify a covered work except as expressly
+provided under this License.  Any attempt otherwise to propagate or
+modify it is void, and will automatically terminate your rights under
+this License (including any patent licenses granted under the third
+paragraph of section 11).
+
+  However, if you cease all violation of this License, then your
+license from a particular copyright holder is reinstated (a)
+provisionally, unless and until the copyright holder explicitly and
+finally terminates your license, and (b) permanently, if the copyright
+holder fails to notify you of the violation by some reasonable means
+prior to 60 days after the cessation.
+
+  Moreover, your license from a particular copyright holder is
+reinstated permanently if the copyright holder notifies you of the
+violation by some reasonable means, this is the first time you have
+received notice of violation of this License (for any work) from that
+copyright holder, and you cure the violation prior to 30 days after
+your receipt of the notice.
+
+  Termination of your rights under this section does not terminate the
+licenses of parties who have received copies or rights from you under
+this License.  If your rights have been terminated and not permanently
+reinstated, you do not qualify to receive new licenses for the same
+material under section 10.
+
+  9. Acceptance Not Required for Having Copies.
+
+  You are not required to accept this License in order to receive or
+run a copy of the Program.  Ancillary propagation of a covered work
+occurring solely as a consequence of using peer-to-peer transmission
+to receive a copy likewise does not require acceptance.  However,
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+modify any covered work.  These actions infringe copyright if you do
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+covered work, you indicate your acceptance of this License to do so.
+
+  10. Automatic Licensing of Downstream Recipients.
+
+  Each time you convey a covered work, the recipient automatically
+receives a license from the original licensors, to run, modify and
+propagate that work, subject to this License.  You are not responsible
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+  An "entity transaction" is a transaction transferring control of an
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+the predecessor has it or can get it with reasonable efforts.
+
+  You may not impose any further restrictions on the exercise of the
+rights granted or affirmed under this License.  For example, you may
+not impose a license fee, royalty, or other charge for exercise of
+rights granted under this License, and you may not initiate litigation
+(including a cross-claim or counterclaim in a lawsuit) alleging that
+any patent claim is infringed by making, using, selling, offering for
+sale, or importing the Program or any portion of it.
+
+  11. Patents.
+
+  A "contributor" is a copyright holder who authorizes use under this
+License of the Program or a work on which the Program is based.  The
+work thus licensed is called the contributor's "contributor version".
+
+  A contributor's "essential patent claims" are all patent claims
+owned or controlled by the contributor, whether already acquired or
+hereafter acquired, that would be infringed by some manner, permitted
+by this License, of making, using, or selling its contributor version,
+but do not include claims that would be infringed only as a
+consequence of further modification of the contributor version.  For
+purposes of this definition, "control" includes the right to grant
+patent sublicenses in a manner consistent with the requirements of
+this License.
+
+  Each contributor grants you a non-exclusive, worldwide, royalty-free
+patent license under the contributor's essential patent claims, to
+make, use, sell, offer for sale, import and otherwise run, modify and
+propagate the contents of its contributor version.
+
+  In the following three paragraphs, a "patent license" is any express
+agreement or commitment, however denominated, not to enforce a patent
+(such as an express permission to practice a patent or covenant not to
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+
+  If you convey a covered work, knowingly relying on a patent license,
+and the Corresponding Source of the work is not available for anyone
+to copy, free of charge and under the terms of this License, through a
+publicly available network server or other readily accessible means,
+then you must either (1) cause the Corresponding Source to be so
+available, or (2) arrange to deprive yourself of the benefit of the
+patent license for this particular work, or (3) arrange, in a manner
+consistent with the requirements of this License, to extend the patent
+license to downstream recipients.  "Knowingly relying" means you have
+actual knowledge that, but for the patent license, your conveying the
+covered work in a country, or your recipient's use of the covered work
+in a country, would infringe one or more identifiable patents in that
+country that you have reason to believe are valid.
+
+  If, pursuant to or in connection with a single transaction or
+arrangement, you convey, or propagate by procuring conveyance of, a
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+receiving the covered work authorizing them to use, propagate, modify
+or convey a specific copy of the covered work, then the patent license
+you grant is automatically extended to all recipients of the covered
+work and works based on it.
+
+  A patent license is "discriminatory" if it does not include within
+the scope of its coverage, prohibits the exercise of, or is
+conditioned on the non-exercise of one or more of the rights that are
+specifically granted under this License.  You may not convey a covered
+work if you are a party to an arrangement with a third party that is
+in the business of distributing software, under which you make payment
+to the third party based on the extent of your activity of conveying
+the work, and under which the third party grants, to any of the
+parties who would receive the covered work from you, a discriminatory
+patent license (a) in connection with copies of the covered work
+conveyed by you (or copies made from those copies), or (b) primarily
+for and in connection with specific products or compilations that
+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  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
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  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
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    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
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  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

@@ -0,0 +1,201 @@
+                                 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
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+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
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+
+   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,
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+      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
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+
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+      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
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+
+      (c) You must retain, in the Source form of any Derivative Works
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+
+      (d) If the Work includes a "NOTICE" text file as part of its
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+          include a readable copy of the attribution notices contained
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+          of the following places: within a NOTICE text file distributed
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+          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
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+          that such additional attribution notices cannot be construed
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+
+      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,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      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.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

ldm_patched/licenses-3rd/kdiffusion

@@ -0,0 +1,19 @@
+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

@@ -0,0 +1,21 @@
+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

@@ -0,0 +1,21 @@
+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

@@ -0,0 +1,203 @@
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+
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ldm_patched/modules/args_parser.py

@@ -37,6 +37,7 @@ parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nar
 parser.add_argument("--port", type=int, default=8188)
 parser.add_argument("--disable-header-check", type=str, default=None, metavar="ORIGIN", nargs="?", const="*")
 parser.add_argument("--web-upload-size", type=float, default=100)
+parser.add_argument("--hf-mirror", type=str, default=None)
 
 parser.add_argument("--external-working-path", type=str, default=None, metavar="PATH", nargs='+', action='append')
 parser.add_argument("--output-path", type=str, default=None)
@@ -66,6 +67,8 @@ 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")
@@ -98,8 +101,7 @@ 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", action="store_true")
-
+vram_group.add_argument("--always-cpu", type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1)
 
 parser.add_argument("--always-offload-from-vram", action="store_true")
 parser.add_argument("--pytorch-deterministic", action="store_true")
@@ -110,6 +112,8 @@ 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)
+        optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
 
         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.expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight
+        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)
 
 
 class CLIPVision(torch.nn.Module):

ldm_patched/modules/clip_vision.py

@@ -1,7 +1,6 @@
-from .utils import load_torch_file, transformers_convert, common_upscale
+from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
 import os
 import torch
-import contextlib
 import json
 
 import ldm_patched.modules.ops
@@ -41,9 +40,13 @@ 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()
@@ -76,6 +79,9 @@ 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

@@ -1,11 +1,8 @@
-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):
@@ -42,7 +39,7 @@ class CONDCrossAttn(CONDRegular):
             if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
                 return False
 
-            mult_min = lcm(s1[1], s2[1])
+            mult_min = math.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
@@ -53,7 +50,7 @@ class CONDCrossAttn(CONDRegular):
         crossattn_max_len = self.cond.shape[1]
         for x in others:
             c = x.cond
-            crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
             conds.append(c)
 
         out = []

ldm_patched/modules/controlnet.py

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

ldm_patched/modules/diffusers_load.py

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

ldm_patched/modules/gligen.py

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

ldm_patched/modules/latent_formats.py

@@ -1,3 +1,4 @@
+import torch
 
 class LatentFormat:
     scale_factor = 1.0
@@ -33,3 +34,71 @@ class SDXL(LatentFormat):
                     [-0.3112, -0.2359, -0.2076]
                 ]
         self.taesd_decoder_name = "taesdxl_decoder"
+
+class SDXL_Playground_2_5(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 0.5
+        self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
+        self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
+
+        self.latent_rgb_factors = [
+                    #   R        G        B
+                    [ 0.3920,  0.4054,  0.4549],
+                    [-0.2634, -0.0196,  0.0653],
+                    [ 0.0568,  0.1687, -0.0755],
+                    [-0.3112, -0.2359, -0.2076]
+                ]
+        self.taesd_decoder_name = "taesdxl_decoder"
+
+    def process_in(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return (latent - latents_mean) * self.scale_factor / latents_std
+
+    def process_out(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return latent * latents_std / self.scale_factor + latents_mean
+
+
+class SD_X4(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 0.08333
+        self.latent_rgb_factors = [
+            [-0.2340, -0.3863, -0.3257],
+            [ 0.0994,  0.0885, -0.0908],
+            [-0.2833, -0.2349, -0.3741],
+            [ 0.2523, -0.0055, -0.1651]
+        ]
+
+class SC_Prior(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 1.0
+        self.latent_rgb_factors = [
+            [-0.0326, -0.0204, -0.0127],
+            [-0.1592, -0.0427,  0.0216],
+            [ 0.0873,  0.0638, -0.0020],
+            [-0.0602,  0.0442,  0.1304],
+            [ 0.0800, -0.0313, -0.1796],
+            [-0.0810, -0.0638, -0.1581],
+            [ 0.1791,  0.1180,  0.0967],
+            [ 0.0740,  0.1416,  0.0432],
+            [-0.1745, -0.1888, -0.1373],
+            [ 0.2412,  0.1577,  0.0928],
+            [ 0.1908,  0.0998,  0.0682],
+            [ 0.0209,  0.0365, -0.0092],
+            [ 0.0448, -0.0650, -0.1728],
+            [-0.1658, -0.1045, -0.1308],
+            [ 0.0542,  0.1545,  0.1325],
+            [-0.0352, -0.1672, -0.2541]
+        ]
+
+class SC_B(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 1.0 / 0.43
+        self.latent_rgb_factors = [
+            [ 0.1121,  0.2006,  0.1023],
+            [-0.2093, -0.0222, -0.0195],
+            [-0.3087, -0.1535,  0.0366],
+            [ 0.0290, -0.1574, -0.4078]
+        ]

ldm_patched/modules/model_base.py

@@ -1,12 +1,11 @@
 import torch
-from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel
+from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
 from ldm_patched.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
-from ldm_patched.ldm.modules.diffusionmodules.openaimodel import Timestep
+from ldm_patched.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
 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):
@@ -78,8 +77,9 @@ class BaseModel(torch.nn.Module):
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
-            if hasattr(extra, "to"):
-                extra = extra.to(dtype)
+            if hasattr(extra, "dtype"):
+                if extra.dtype != torch.int and extra.dtype != torch.long:
+                    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()
@@ -99,11 +99,29 @@ class BaseModel(torch.nn.Module):
         if self.inpaint_model:
             concat_keys = ("mask", "masked_image")
             cond_concat = []
-            denoise_mask = kwargs.get("denoise_mask", None)
-            latent_image = kwargs.get("latent_image", None)
+            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)
+
             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
@@ -116,9 +134,9 @@ class BaseModel(torch.nn.Module):
             for ck in concat_keys:
                 if denoise_mask is not None:
                     if ck == "mask":
-                        cond_concat.append(denoise_mask[:,:1].to(device))
+                        cond_concat.append(denoise_mask.to(device))
                     elif ck == "masked_image":
-                        cond_concat.append(latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
+                        cond_concat.append(concat_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])
@@ -126,9 +144,15 @@ 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=""):
@@ -154,23 +178,28 @@ 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, 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)
+    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))
 
+        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:
-            clip_state_dict = utils.convert_sd_to(clip_state_dict, torch.float16)
-            vae_state_dict = utils.convert_sd_to(vae_state_dict, torch.float16)
+            extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds)
 
         if self.model_type == ModelType.V_PREDICTION:
             unet_state_dict["v_pred"] = torch.tensor([])
 
-        return {**unet_state_dict, **vae_state_dict, **clip_state_dict}
+        for sd in extra_sds:
+            unet_state_dict.update(sd)
+
+        return unet_state_dict
 
     def set_inpaint(self):
         self.inpaint_model = True
@@ -189,7 +218,7 @@ 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):
+def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
     adm_inputs = []
     weights = []
     noise_aug = []
@@ -198,7 +227,7 @@ 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))
+            c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed)
             adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
             weights.append(weight)
             noise_aug.append(noise_augment)
@@ -224,11 +253,11 @@ 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))
+            return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10)
 
 def sdxl_pooled(args, noise_augmentor):
     if "unclip_conditioning" in args:
-        return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor)[:,:1280]
+        return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280]
     else:
         return args["pooled_output"]
 
@@ -322,9 +351,75 @@ 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

@@ -34,7 +34,6 @@ 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
     }
@@ -50,6 +49,12 @@ 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 = []
@@ -122,6 +127,7 @@ 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

@@ -60,6 +60,9 @@ 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():
@@ -175,7 +178,7 @@ 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():
+            if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
                 VAE_DTYPE = torch.bfloat16
     if is_intel_xpu():
         if args.attention_split == False and args.attention_quad == False:
@@ -186,6 +189,9 @@ 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:
@@ -218,15 +224,8 @@ if args.all_in_fp16:
     FORCE_FP16 = True
 
 if lowvram_available:
-    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 set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
+        vram_state = set_vram_to
 
 
 if cpu_state != CPUState.GPU:
@@ -266,6 +265,14 @@ 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
@@ -298,8 +305,20 @@ class LoadedModel:
 
         if lowvram_model_memory > 0:
             print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024))
-            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)
+            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)
+
             self.model_accelerated = True
 
         if is_intel_xpu() and not args.disable_ipex_hijack:
@@ -309,7 +328,11 @@ class LoadedModel:
 
     def model_unload(self):
         if self.model_accelerated:
-            accelerate.hooks.remove_hook_from_submodules(self.real_model)
+            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
+
             self.model_accelerated = False
 
         self.model.unpatch_model(self.model.offload_device)
@@ -402,14 +425,14 @@ 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(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
+            lowvram_model_memory = int(max(64 * (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 = 256 * 1024 * 1024
+            lowvram_model_memory = 64 * 1024 * 1024
 
         cur_loaded_model = loaded_model.model_load(lowvram_model_memory)
         current_loaded_models.insert(0, loaded_model)
@@ -538,6 +561,8 @@ 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():
@@ -566,6 +591,11 @@ 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:
@@ -576,9 +606,7 @@ def cast_to_device(tensor, device, dtype, copy=False):
         elif is_intel_xpu():
             device_supports_cast = True
 
-    non_blocking = True
-    if is_device_mps(device):
-        non_blocking = False #pytorch bug? mps doesn't support non blocking
+    non_blocking = device_supports_non_blocking(device)
 
     if device_supports_cast:
         if copy:
@@ -742,11 +770,11 @@ def soft_empty_cache(force=False):
             torch.cuda.empty_cache()
             torch.cuda.ipc_collect()
 
-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]]
+def unload_all_models():
+    free_memory(1e30, get_torch_device())
+
+
+def resolve_lowvram_weight(weight, model, key): #TODO: remove
     return weight
 
 #TODO: might be cleaner to put this somewhere else

ldm_patched/modules/model_patcher.py

@@ -28,13 +28,9 @@ class ModelPatcher:
         if self.size > 0:
             return self.size
         model_sd = self.model.state_dict()
-        size = 0
-        for k in model_sd:
-            t = model_sd[k]
-            size += t.nelement() * t.element_size()
-        self.size = size
+        self.size = ldm_patched.modules.model_management.module_size(self.model)
         self.model_keys = set(model_sd.keys())
-        return size
+        return self.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)
@@ -55,14 +51,18 @@ 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):
+    def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False):
         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):
+    def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
         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
@@ -174,40 +174,41 @@ class ModelPatcher:
                     sd.pop(k)
         return sd
 
-    def patch_model(self, device_to=None):
+    def patch_model(self, device_to=None, patch_weights=True):
         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])
 
-        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
+        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
 
-            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 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 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 device_to is not None:
-            self.model.to(device_to)
-            self.current_device = device_to
+                self.model.to(device_to)
+                self.current_device = device_to
 
         return self.model
 

ldm_patched/modules/model_sampling.py

@@ -1,7 +1,7 @@
 import torch
-import numpy as np
 from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
+import numpy as np
 
 class EPS:
     def calculate_input(self, sigma, noise):
@@ -12,12 +12,28 @@ class EPS:
         sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
         return model_input - model_output * sigma
 
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        if max_denoise:
+            noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
+        else:
+            noise = noise * sigma
+
+        noise += latent_image
+        return noise
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent
 
 class V_PREDICTION(EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
         sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
+class EDM(V_PREDICTION):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+
 
 class ModelSamplingDiscrete(torch.nn.Module):
     def __init__(self, model_config=None):
@@ -42,8 +58,7 @@ class ModelSamplingDiscrete(torch.nn.Module):
         else:
             betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
         alphas = 1. - betas
-        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
-        # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+        alphas_cumprod = torch.cumprod(alphas, dim=0)
 
         timesteps, = betas.shape
         self.num_timesteps = int(timesteps)
@@ -55,11 +70,16 @@ class ModelSamplingDiscrete(torch.nn.Module):
         # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
 
         sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
         self.set_sigmas(sigmas)
+        self.set_alphas_cumprod(alphas_cumprod.float())
 
     def set_sigmas(self, sigmas):
-        self.register_buffer('sigmas', sigmas)
-        self.register_buffer('log_sigmas', sigmas.log())
+        self.register_buffer('sigmas', sigmas.float())
+        self.register_buffer('log_sigmas', sigmas.log().float())
+
+    def set_alphas_cumprod(self, alphas_cumprod):
+        self.register_buffer("alphas_cumprod", alphas_cumprod.float())
 
     @property
     def sigma_min(self):
@@ -94,8 +114,6 @@ class ModelSamplingDiscrete(torch.nn.Module):
 class ModelSamplingContinuousEDM(torch.nn.Module):
     def __init__(self, model_config=None):
         super().__init__()
-        self.sigma_data = 1.0
-
         if model_config is not None:
             sampling_settings = model_config.sampling_settings
         else:
@@ -103,9 +121,11 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
 
         sigma_min = sampling_settings.get("sigma_min", 0.002)
         sigma_max = sampling_settings.get("sigma_max", 120.0)
-        self.set_sigma_range(sigma_min, sigma_max)
+        sigma_data = sampling_settings.get("sigma_data", 1.0)
+        self.set_parameters(sigma_min, sigma_max, sigma_data)
 
-    def set_sigma_range(self, sigma_min, sigma_max):
+    def set_parameters(self, sigma_min, sigma_max, sigma_data):
+        self.sigma_data = sigma_data
         sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
 
         self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
@@ -134,3 +154,56 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
 
         log_sigma_min = math.log(self.sigma_min)
         return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
+
+class StableCascadeSampling(ModelSamplingDiscrete):
+    def __init__(self, model_config=None):
+        super().__init__()
+
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(sampling_settings.get("shift", 1.0))
+
+    def set_parameters(self, shift=1.0, cosine_s=8e-3):
+        self.shift = shift
+        self.cosine_s = torch.tensor(cosine_s)
+        self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
+
+        #This part is just for compatibility with some schedulers in the codebase
+        self.num_timesteps = 10000
+        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
+        for x in range(self.num_timesteps):
+            t = (x + 1) / self.num_timesteps
+            sigmas[x] = self.sigma(t)
+
+        self.set_sigmas(sigmas)
+
+    def sigma(self, timestep):
+        alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)
+
+        if self.shift != 1.0:
+            var = alpha_cumprod
+            logSNR = (var/(1-var)).log()
+            logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
+            alpha_cumprod = logSNR.sigmoid()
+
+        alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
+        return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5
+
+    def timestep(self, sigma):
+        var = 1 / ((sigma * sigma) + 1)
+        var = var.clamp(0, 1.0)
+        s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
+        t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
+        return t
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 999999999.9
+        if percent >= 1.0:
+            return 0.0
+
+        percent = 1.0 - percent
+        return self.sigma(torch.tensor(percent))

ldm_patched/modules/ops.py

@@ -1,27 +1,92 @@
 import torch
-from contextlib import contextmanager
+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
+
 
 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:
@@ -31,35 +96,19 @@ 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):
-        def forward(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.linear(input, weight, bias)
+        ldm_patched_cast_weights = True
 
     class Conv2d(disable_weight_init.Conv2d):
-        def forward(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
+        ldm_patched_cast_weights = True
 
     class Conv3d(disable_weight_init.Conv3d):
-        def forward(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
+        ldm_patched_cast_weights = True
 
     class GroupNorm(disable_weight_init.GroupNorm):
-        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)
+        ldm_patched_cast_weights = True
 
     class LayerNorm(disable_weight_init.LayerNorm):
-        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_cast_weights = True

ldm_patched/modules/sample.py

@@ -28,7 +28,6 @@ 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)
@@ -47,7 +46,8 @@ 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])
+            model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) #TODO: remove
+            temp["cross_attn"] = c[0]
         temp["model_conds"] = model_conds
         out.append(temp)
     return out

ldm_patched/modules/samplers.py

@@ -1,13 +1,9 @@
 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)
@@ -244,7 +240,7 @@ 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):
+        if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
             uncond_ = None
         else:
             uncond_ = uncond
@@ -527,7 +523,7 @@ class UNIPCBH2(Sampler):
 
 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"]
+                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd", "edm_playground_v2.5", "restart"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
@@ -599,6 +595,13 @@ 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)
@@ -610,13 +613,6 @@ 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)
@@ -639,7 +635,7 @@ 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", self.scheduler)
+        print("error invalid scheduler", scheduler_name)
     return sigmas
 
 def sampler_object(name):

ldm_patched/modules/sd.py

@@ -1,9 +1,6 @@
 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
 
@@ -157,6 +154,8 @@ 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:
@@ -172,6 +171,11 @@ 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']))
@@ -204,9 +208,9 @@ 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 = 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))
+            (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))
             / 3.0) / 2.0, min=0.0, max=1.0)
         return output
 
@@ -217,9 +221,9 @@ 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 = (1/8), 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 = 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 /= 3.0
         return samples
 
@@ -231,7 +235,7 @@ 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] * 8), round(samples_in.shape[3] * 8)), device=self.output_device)
+            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)
             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)
@@ -255,7 +259,7 @@ 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], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device=self.output_device)
+            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)
             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()
@@ -423,12 +427,13 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
 
     return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
 
-def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
+def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, vae_filename_param=None):
     sd = ldm_patched.modules.utils.load_torch_file(ckpt_path)
     sd_keys = sd.keys()
     clip = None
     clipvision = None
     vae = None
+    vae_filename = None
     model = None
     model_patcher = None
     clip_target = None
@@ -458,8 +463,12 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         model.load_model_weights(sd, "model.diffusion_model.")
 
     if output_vae:
-        vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
-        vae_sd = model_config.process_vae_state_dict(vae_sd)
+        if vae_filename_param is None:
+            vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
+            vae_sd = model_config.process_vae_state_dict(vae_sd)
+        else:
+            vae_sd = ldm_patched.modules.utils.load_torch_file(vae_filename_param)
+            vae_filename = vae_filename_param
         vae = VAE(sd=vae_sd)
 
     if output_clip:
@@ -481,7 +490,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
             print("loaded straight to GPU")
             model_management.load_model_gpu(model_patcher)
 
-    return (model_patcher, clip, vae, clipvision)
+    return model_patcher, clip, vae, vae_filename, clipvision
 
 
 def load_unet_state_dict(sd): #load unet in diffusers format
@@ -527,7 +536,14 @@ 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, 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())
+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)
     ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata)

ldm_patched/modules/sd1_clip.py

@@ -6,7 +6,6 @@ import torch
 import traceback
 import zipfile
 from . import model_management
-import contextlib
 import ldm_patched.modules.clip_model
 import json
 
@@ -327,7 +326,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
                 except:
                     embed_out = safe_load_embed_zip(embed_path)
             else:
-                embed = torch.load(embed_path, map_location="cpu")
+                embed = torch.load(embed_path, map_location="cpu", weights_only=True)
     except Exception as e:
         print(traceback.format_exc())
         print()

ldm_patched/modules/supported_models.py

@@ -252,5 +252,59 @@ class SVD_img2vid(supported_models_base.BASE):
     def clip_target(self):
         return None
 
-models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega]
+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 += [SVD_img2vid]

ldm_patched/modules/supported_models_base.py

@@ -65,6 +65,12 @@ 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/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
+    From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/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/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
+    From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/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 verison of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.
+This version 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
@@ -377,15 +377,15 @@ class VQAutoEncoder(nn.Module):
         )
 
         if model_path is not None:
-            chkpt = torch.load(model_path, map_location="cpu")
+            chkpt = torch.load(model_path, map_location="cpu", weights_only=True)
             if "params_ema" in chkpt:
                 self.load_state_dict(
-                    torch.load(model_path, map_location="cpu")["params_ema"]
+                    torch.load(model_path, map_location="cpu", weights_only=True)["params_ema"]
                 )
                 logger.info(f"vqgan is loaded from: {model_path} [params_ema]")
             elif "params" in chkpt:
                 self.load_state_dict(
-                    torch.load(model_path, map_location="cpu")["params"]
+                    torch.load(model_path, map_location="cpu", weights_only=True)["params"]
                 )
                 logger.info(f"vqgan is loaded from: {model_path} [params]")
             else: