Compare commits
No commits in common. "main" and "2.1.864" have entirely different histories.
|
|
@ -1,54 +0,0 @@
|
|||
__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
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
# Ensure that shell scripts always use lf line endings, e.g. entrypoint.sh for docker
|
||||
* text=auto
|
||||
*.sh text eol=lf
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
---
|
||||
name: Bug report
|
||||
about: Describe a problem
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Read Troubleshoot**
|
||||
|
||||
[x] I admit that I have read the [Troubleshoot](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md) before making this issue.
|
||||
|
||||
**Describe the problem**
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
**Full Console Log**
|
||||
Paste **full** console log here. You will make our job easier if you give a **full** log.
|
||||
|
|
@ -1,107 +0,0 @@
|
|||
name: Bug Report
|
||||
description: You think something is broken in Fooocus
|
||||
title: "[Bug]: "
|
||||
labels: ["bug", "triage"]
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
> The title of the bug report should be short and descriptive.
|
||||
> Use relevant keywords for searchability.
|
||||
> Do not leave it blank, but also do not put an entire error log in it.
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Checklist
|
||||
description: |
|
||||
Please perform basic debugging to see if your configuration is the cause of the issue.
|
||||
Basic debug procedure
|
||||
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.
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
blank_issues_enabled: false
|
||||
contact_links:
|
||||
- name: Ask a question
|
||||
url: https://github.com/lllyasviel/Fooocus/discussions/new?category=q-a
|
||||
about: Ask the community for help
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
---
|
||||
name: Feature request
|
||||
about: Suggest an idea for this project
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Is your feature request related to a problem? Please describe.**
|
||||
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
|
||||
|
||||
**Describe the idea you'd like**
|
||||
A clear and concise description of what you want to happen.
|
||||
|
|
@ -1,40 +0,0 @@
|
|||
name: Feature request
|
||||
description: Suggest an idea for this project
|
||||
title: "[Feature Request]: "
|
||||
labels: ["enhancement", "triage"]
|
||||
|
||||
body:
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Is there an existing issue for this?
|
||||
description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.
|
||||
options:
|
||||
- label: I have searched the existing issues and checked the recent builds/commits
|
||||
required: true
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
*Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
|
||||
- type: textarea
|
||||
id: feature
|
||||
attributes:
|
||||
label: What would your feature do?
|
||||
description: Tell us about your feature in a very clear and simple way, and what problem it would solve
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: workflow
|
||||
attributes:
|
||||
label: Proposed workflow
|
||||
description: Please provide us with step by step information on how you'd like the feature to be accessed and used
|
||||
value: |
|
||||
1. Go to ....
|
||||
2. Press ....
|
||||
3. ...
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: misc
|
||||
attributes:
|
||||
label: Additional information
|
||||
description: Add any other context or screenshots about the feature request here.
|
||||
|
|
@ -1,6 +0,0 @@
|
|||
version: 2
|
||||
updates:
|
||||
- package-ecosystem: "github-actions"
|
||||
directory: "/"
|
||||
schedule:
|
||||
interval: "monthly"
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
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@v5
|
||||
|
||||
- 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 }}
|
||||
|
|
@ -10,7 +10,6 @@ __pycache__
|
|||
*.partial
|
||||
*.onnx
|
||||
sorted_styles.json
|
||||
hash_cache.txt
|
||||
/input
|
||||
/cache
|
||||
/language/default.json
|
||||
|
|
@ -52,4 +51,3 @@ user_path_config-deprecated.txt
|
|||
/package-lock.json
|
||||
/.coverage*
|
||||
/auth.json
|
||||
.DS_Store
|
||||
|
|
|
|||
29
Dockerfile
29
Dockerfile
|
|
@ -1,29 +0,0 @@
|
|||
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}" ]
|
||||
|
|
@ -1,10 +1,8 @@
|
|||
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("--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. "
|
||||
|
|
@ -17,28 +15,16 @@ 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 the outputs folder.")
|
||||
help="Prevent writing images and logs to hard drive.")
|
||||
|
||||
args_parser.parser.add_argument("--disable-analytics", action='store_true',
|
||||
help="Disables analytics for Gradio.")
|
||||
|
||||
args_parser.parser.add_argument("--disable-metadata", action='store_true',
|
||||
help="Disables saving metadata to images.")
|
||||
help="Disables analytics for Gradio", default=False)
|
||||
|
||||
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)
|
||||
help="Always download newer models ", default=False)
|
||||
|
||||
args_parser.parser.set_defaults(
|
||||
disable_cuda_malloc=True,
|
||||
|
|
@ -54,7 +40,6 @@ args_parser.args.always_offload_from_vram = not args_parser.args.disable_offload
|
|||
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
|
||||
|
||||
|
|
|
|||
198
css/style.css
198
css/style.css
|
|
@ -1,150 +1,5 @@
|
|||
/* based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/style.css */
|
||||
|
||||
.loader-container {
|
||||
display: flex; /* Use flex to align items horizontally */
|
||||
align-items: center; /* Center items vertically within the container */
|
||||
white-space: nowrap; /* Prevent line breaks within the container */
|
||||
}
|
||||
|
||||
.loader {
|
||||
border: 8px solid #f3f3f3; /* Light grey */
|
||||
border-top: 8px solid #3498db; /* Blue */
|
||||
border-radius: 50%;
|
||||
width: 30px;
|
||||
height: 30px;
|
||||
animation: spin 2s linear infinite;
|
||||
}
|
||||
|
||||
@keyframes spin {
|
||||
0% { transform: rotate(0deg); }
|
||||
100% { transform: rotate(360deg); }
|
||||
}
|
||||
|
||||
/* Style the progress bar */
|
||||
progress {
|
||||
appearance: none; /* Remove default styling */
|
||||
height: 20px; /* Set the height of the progress bar */
|
||||
border-radius: 5px; /* Round the corners of the progress bar */
|
||||
background-color: #f3f3f3; /* Light grey background */
|
||||
width: 100%;
|
||||
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;
|
||||
|
|
@ -363,56 +218,3 @@ div:has(> #positive_prompt) {
|
|||
#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;
|
||||
}
|
||||
|
|
@ -1,11 +0,0 @@
|
|||
## Running unit tests
|
||||
|
||||
Native python:
|
||||
```
|
||||
python -m unittest tests/
|
||||
```
|
||||
|
||||
Embedded python (Windows zip file installation method):
|
||||
```
|
||||
..\python_embeded\python.exe -m unittest
|
||||
```
|
||||
|
|
@ -1,36 +0,0 @@
|
|||
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]
|
||||
131
docker.md
131
docker.md
|
|
@ -1,131 +0,0 @@
|
|||
# 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.
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
ORIGINALDIR=/content/app
|
||||
# Use predefined DATADIR if it is defined
|
||||
[[ x"${DATADIR}" == "x" ]] && DATADIR=/content/data
|
||||
|
||||
# Make persistent dir from original dir
|
||||
function mklink () {
|
||||
mkdir -p $DATADIR/$1
|
||||
ln -s $DATADIR/$1 $ORIGINALDIR
|
||||
}
|
||||
|
||||
# Copy old files from import dir
|
||||
function import () {
|
||||
(test -d /import/$1 && cd /import/$1 && cp -Rpn . $DATADIR/$1/)
|
||||
}
|
||||
|
||||
cd $ORIGINALDIR
|
||||
|
||||
# models
|
||||
mklink models
|
||||
# Copy original files
|
||||
(cd $ORIGINALDIR/models.org && cp -Rpn . $ORIGINALDIR/models/)
|
||||
# Import old files
|
||||
import models
|
||||
|
||||
# outputs
|
||||
mklink outputs
|
||||
# Import old files
|
||||
import outputs
|
||||
|
||||
# Start application
|
||||
python launch.py $*
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
# 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()
|
||||
|
|
@ -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', weights_only=True)
|
||||
checkpoint = torch.load(cached_file, map_location='cpu')
|
||||
elif os.path.isfile(url_or_filename):
|
||||
checkpoint = torch.load(url_or_filename, map_location='cpu', weights_only=True)
|
||||
checkpoint = torch.load(url_or_filename, map_location='cpu')
|
||||
else:
|
||||
raise RuntimeError('checkpoint url or path is invalid')
|
||||
|
||||
|
|
|
|||
|
|
@ -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', weights_only=True)
|
||||
checkpoint = torch.load(cached_file, map_location='cpu')
|
||||
elif os.path.isfile(url_or_filename):
|
||||
checkpoint = torch.load(url_or_filename, map_location='cpu', weights_only=True)
|
||||
checkpoint = torch.load(url_or_filename, map_location='cpu')
|
||||
else:
|
||||
raise RuntimeError('checkpoint url or path is invalid')
|
||||
state_dict = checkpoint['model']
|
||||
|
|
|
|||
|
|
@ -1,43 +0,0 @@
|
|||
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
|
||||
|
|
@ -1,100 +0,0 @@
|
|||
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
|
||||
|
|
@ -1,60 +0,0 @@
|
|||
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
|
||||
|
|
@ -112,9 +112,6 @@ class FooocusExpansion:
|
|||
max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0))
|
||||
max_new_tokens = max_token_length - current_token_length
|
||||
|
||||
if max_new_tokens == 0:
|
||||
return prompt[:-1]
|
||||
|
||||
# https://huggingface.co/blog/introducing-csearch
|
||||
# https://huggingface.co/docs/transformers/generation_strategies
|
||||
features = self.model.generate(**tokenized_kwargs,
|
||||
|
|
|
|||
|
|
@ -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, weights_only=True)
|
||||
load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
|
||||
# remove unnecessary 'module.'
|
||||
for k, v in deepcopy(load_net).items():
|
||||
if k.startswith('module.'):
|
||||
|
|
|
|||
|
|
@ -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, weights_only=True)
|
||||
load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
|
||||
model.load_state_dict(load_net, strict=True)
|
||||
model.eval()
|
||||
model = model.to(device)
|
||||
|
|
|
|||
|
|
@ -1,130 +0,0 @@
|
|||
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
|
||||
|
|
@ -104,7 +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", weights_only=True)
|
||||
ip_state_dict = torch.load(ip_adapter_path, map_location="cpu")
|
||||
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
|
||||
|
|
|
|||
|
|
@ -1,26 +1,27 @@
|
|||
import cv2
|
||||
import numpy as np
|
||||
import modules.advanced_parameters as advanced_parameters
|
||||
|
||||
|
||||
def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
|
||||
def centered_canny(x: np.ndarray):
|
||||
assert isinstance(x, np.ndarray)
|
||||
assert x.ndim == 2 and x.dtype == np.uint8
|
||||
|
||||
y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
|
||||
y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold))
|
||||
y = y.astype(np.float32) / 255.0
|
||||
return y
|
||||
|
||||
|
||||
def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
|
||||
def centered_canny_color(x: np.ndarray):
|
||||
assert isinstance(x, np.ndarray)
|
||||
assert x.ndim == 3 and x.shape[2] == 3
|
||||
|
||||
result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
|
||||
result = [centered_canny(x[..., i]) for i in range(3)]
|
||||
result = np.stack(result, axis=2)
|
||||
return result
|
||||
|
||||
|
||||
def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
|
||||
def pyramid_canny_color(x: np.ndarray):
|
||||
assert isinstance(x, np.ndarray)
|
||||
assert x.ndim == 3 and x.shape[2] == 3
|
||||
|
||||
|
|
@ -30,7 +31,7 @@ def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold
|
|||
for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
|
||||
Hs, Ws = int(H * k), int(W * k)
|
||||
small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
|
||||
edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
|
||||
edge = centered_canny_color(small)
|
||||
if acc_edge is None:
|
||||
acc_edge = edge
|
||||
else:
|
||||
|
|
@ -53,11 +54,11 @@ def norm255(x, low=4, high=96):
|
|||
return x * 255.0
|
||||
|
||||
|
||||
def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
|
||||
def canny_pyramid(x):
|
||||
# For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
|
||||
# Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
|
||||
|
||||
color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
|
||||
color_canny = pyramid_canny_color(x)
|
||||
result = np.sum(color_canny, axis=2)
|
||||
|
||||
return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)
|
||||
|
|
|
|||
|
|
@ -1,171 +0,0 @@
|
|||
{
|
||||
"_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
|
||||
}
|
||||
}
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
{
|
||||
"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
|
||||
}
|
||||
|
|
@ -1,126 +0,0 @@
|
|||
# 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
|
||||
|
|
@ -1,288 +0,0 @@
|
|||
# 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
|
||||
|
|
@ -1,85 +1,69 @@
|
|||
# https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py
|
||||
|
||||
import os
|
||||
|
||||
import safetensors.torch as sf
|
||||
import torch
|
||||
import safetensors.torch as sf
|
||||
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 ResBlock(nn.Module):
|
||||
"""Block with residuals"""
|
||||
|
||||
def __init__(self, ch):
|
||||
class Block(nn.Module):
|
||||
def __init__(self, size):
|
||||
super().__init__()
|
||||
self.join = nn.ReLU()
|
||||
self.norm = nn.BatchNorm2d(ch)
|
||||
self.long = nn.Sequential(
|
||||
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)
|
||||
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),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.norm(x)
|
||||
return self.join(self.long(x) + x)
|
||||
y = self.long(x)
|
||||
z = self.join(y + x)
|
||||
return z
|
||||
|
||||
|
||||
class ExtractBlock(nn.Module):
|
||||
"""Increase no. of channels by [out/in]"""
|
||||
|
||||
def __init__(self, ch_in, ch_out):
|
||||
class Interposer(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
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)
|
||||
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),
|
||||
)
|
||||
|
||||
def forward(self, 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(),
|
||||
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.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.head(x)
|
||||
y = self.head_join(
|
||||
self.head_long(x) +
|
||||
self.head_short(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-v4.0.safetensors')
|
||||
vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v3.1.safetensors')
|
||||
|
||||
|
||||
def parse(x):
|
||||
|
|
@ -88,7 +72,7 @@ def parse(x):
|
|||
x_origin = x.clone()
|
||||
|
||||
if vae_approx_model is None:
|
||||
model = InterposerModel()
|
||||
model = Interposer()
|
||||
model.eval()
|
||||
sd = sf.load_file(vae_approx_filename)
|
||||
model.load_state_dict(sd)
|
||||
|
|
|
|||
|
|
@ -8,11 +8,11 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install pygit2==1.15.1\n",
|
||||
"!pip install pygit2==1.12.2\n",
|
||||
"%cd /content\n",
|
||||
"!git clone https://github.com/lllyasviel/Fooocus.git\n",
|
||||
"%cd /content/Fooocus\n",
|
||||
"!python entry_with_update.py --share --always-high-vram\n"
|
||||
"!python entry_with_update.py --share\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
version = '2.5.5'
|
||||
version = '2.1.864'
|
||||
|
|
|
|||
|
|
@ -154,8 +154,12 @@ let cancelGenerateForever = function() {
|
|||
let generateOnRepeatForButtons = function() {
|
||||
generateOnRepeat('#generate_button', '#stop_button');
|
||||
};
|
||||
appendContextMenuOption('#generate_button', 'Generate forever', generateOnRepeatForButtons);
|
||||
|
||||
appendContextMenuOption('#generate_button', 'Generate forever', generateOnRepeatForButtons);
|
||||
// appendContextMenuOption('#stop_button', 'Generate forever', generateOnRepeatForButtons);
|
||||
|
||||
// appendContextMenuOption('#stop_button', 'Cancel generate forever', cancelGenerateForever);
|
||||
// appendContextMenuOption('#generate_button', 'Cancel generate forever', cancelGenerateForever);
|
||||
})();
|
||||
//End example Context Menu Items
|
||||
|
||||
|
|
|
|||
|
|
@ -80,15 +80,6 @@ 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());
|
||||
|
||||
|
|
|
|||
|
|
@ -122,43 +122,6 @@ document.addEventListener("DOMContentLoaded", function() {
|
|||
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");
|
||||
});
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
/**
|
||||
* Add a ctrl+enter as a shortcut to start a generation
|
||||
*/
|
||||
|
|
@ -187,12 +150,9 @@ 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) {
|
||||
document.addEventListener('mouseover', function(e) {
|
||||
const label = e.target.closest('.style_selections label');
|
||||
if (!label) return;
|
||||
label.removeEventListener("mouseout", onMouseLeave);
|
||||
|
|
@ -202,12 +162,9 @@ function initStylePreviewOverlay() {
|
|||
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(" ", "_")
|
||||
"fooocus_v2",
|
||||
name.toLowerCase().replaceAll(" ", "_")
|
||||
).replaceAll("\\", "\\\\")}")`;
|
||||
|
||||
tooltip.textContent = name;
|
||||
|
||||
function onMouseLeave() {
|
||||
overlayVisible = false;
|
||||
overlay.style.opacity = "0";
|
||||
|
|
@ -215,8 +172,8 @@ function initStylePreviewOverlay() {
|
|||
label.removeEventListener("mouseout", onMouseLeave);
|
||||
}
|
||||
});
|
||||
document.addEventListener('mousemove', function (e) {
|
||||
if (!overlayVisible) return;
|
||||
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";
|
||||
|
|
@ -256,8 +213,3 @@ 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;
|
||||
}
|
||||
|
|
@ -642,5 +642,4 @@ onUiLoaded(async() => {
|
|||
}
|
||||
|
||||
applyZoomAndPan("#inpaint_canvas");
|
||||
applyZoomAndPan("#inpaint_mask_canvas");
|
||||
});
|
||||
|
|
|
|||
129
language/en.json
129
language/en.json
|
|
@ -4,22 +4,12 @@
|
|||
"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": "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",
|
||||
"Inpaint or Outpaint (beta)": "Inpaint or Outpaint (beta)",
|
||||
"Drag above image to here": "Drag above image to here",
|
||||
"Upscale or Variation:": "Upscale or Variation:",
|
||||
"Disabled": "Disabled",
|
||||
"Vary (Subtle)": "Vary (Subtle)",
|
||||
|
|
@ -27,7 +17,7 @@
|
|||
"Upscale (1.5x)": "Upscale (1.5x)",
|
||||
"Upscale (2x)": "Upscale (2x)",
|
||||
"Upscale (Fast 2x)": "Upscale (Fast 2x)",
|
||||
"\ud83d\udcd4 Documentation": "\uD83D\uDCD4 Documentation",
|
||||
"\ud83d\udcd4 Document": "\uD83D\uDCD4 Document",
|
||||
"Image": "Image",
|
||||
"Stop At": "Stop At",
|
||||
"Weight": "Weight",
|
||||
|
|
@ -46,17 +36,11 @@
|
|||
"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)",
|
||||
"Advanced options": "Advanced options",
|
||||
"Generate mask from image": "Generate mask from image",
|
||||
"Settings": "Settings",
|
||||
"Setting": "Setting",
|
||||
"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",
|
||||
|
|
@ -64,18 +48,9 @@
|
|||
"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",
|
||||
|
|
@ -287,7 +262,7 @@
|
|||
"Volumetric Lighting": "Volumetric Lighting",
|
||||
"Watercolor 2": "Watercolor 2",
|
||||
"Whimsical And Playful": "Whimsical And Playful",
|
||||
"Models": "Models",
|
||||
"Model": "Model",
|
||||
"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",
|
||||
|
|
@ -328,8 +303,6 @@
|
|||
"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.",
|
||||
|
|
@ -360,8 +333,6 @@
|
|||
"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",
|
||||
|
|
@ -371,18 +342,10 @@
|
|||
"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",
|
||||
|
|
@ -398,88 +361,12 @@
|
|||
"B2": "B2",
|
||||
"S1": "S1",
|
||||
"S2": "S2",
|
||||
"Extreme Speed": "Extreme Speed",
|
||||
"\uD83D\uDD0E Type here to search styles ...": "\uD83D\uDD0E Type here to search styles ...",
|
||||
"Type prompt here.": "Type prompt here.",
|
||||
"Outpaint Expansion Direction:": "Outpaint Expansion Direction:",
|
||||
"* Powered by Fooocus Inpaint Engine (beta)": "* Powered by Fooocus Inpaint Engine (beta)",
|
||||
"Fooocus Enhance": "Fooocus Enhance",
|
||||
"Fooocus Cinematic": "Fooocus Cinematic",
|
||||
"Fooocus Sharp": "Fooocus Sharp",
|
||||
"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"
|
||||
"Fooocus Sharp": "Fooocus Sharp"
|
||||
}
|
||||
78
launch.py
78
launch.py
|
|
@ -1,6 +1,6 @@
|
|||
import os
|
||||
import ssl
|
||||
import sys
|
||||
import ssl
|
||||
|
||||
print('[System ARGV] ' + str(sys.argv))
|
||||
|
||||
|
|
@ -10,17 +10,19 @@ os.chdir(root)
|
|||
|
||||
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
|
||||
os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
|
||||
if "GRADIO_SERVER_PORT" not in os.environ:
|
||||
os.environ["GRADIO_SERVER_PORT"] = "7865"
|
||||
os.environ["GRADIO_SERVER_PORT"] = "7865"
|
||||
|
||||
ssl._create_default_https_context = ssl._create_unverified_context
|
||||
|
||||
|
||||
import platform
|
||||
import fooocus_version
|
||||
|
||||
from build_launcher import build_launcher
|
||||
from modules.launch_util import is_installed, run, python, run_pip, requirements_met, delete_folder_content
|
||||
from modules.launch_util import is_installed, run, python, run_pip, requirements_met
|
||||
from modules.model_loader import load_file_from_url
|
||||
from modules import config
|
||||
|
||||
|
||||
REINSTALL_ALL = False
|
||||
TRY_INSTALL_XFORMERS = False
|
||||
|
|
@ -40,7 +42,7 @@ def prepare_environment():
|
|||
|
||||
if TRY_INSTALL_XFORMERS:
|
||||
if REINSTALL_ALL or not is_installed("xformers"):
|
||||
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23')
|
||||
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
|
||||
if platform.system() == "Windows":
|
||||
if platform.python_version().startswith("3.10"):
|
||||
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
|
||||
|
|
@ -62,8 +64,8 @@ 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-v4.0.safetensors',
|
||||
'https://huggingface.co/mashb1t/misc/resolve/main/xl-to-v1_interposer-v4.0.safetensors')
|
||||
('xl-to-v1_interposer-v3.1.safetensors',
|
||||
'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -76,33 +78,13 @@ 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)
|
||||
|
||||
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
|
||||
|
||||
def download_models():
|
||||
for file_name, url in vae_approx_filenames:
|
||||
load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)
|
||||
|
||||
|
|
@ -114,39 +96,31 @@ def download_models(default_model, previous_default_models, checkpoint_downloads
|
|||
|
||||
if args.disable_preset_download:
|
||||
print('Skipped model download.')
|
||||
return default_model, checkpoint_downloads
|
||||
return
|
||||
|
||||
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}].')
|
||||
if not os.path.exists(os.path.join(config.path_checkpoints, config.default_base_model_name)):
|
||||
for alternative_model_name in config.previous_default_models:
|
||||
if os.path.exists(os.path.join(config.path_checkpoints, alternative_model_name)):
|
||||
print(f'You do not have [{config.default_base_model_name}] 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.')
|
||||
f'but you are not using latest models.')
|
||||
print('Use --always-download-new-model to avoid fallback and always get new models.')
|
||||
checkpoint_downloads = {}
|
||||
default_model = alternative_model_name
|
||||
config.checkpoint_downloads = {}
|
||||
config.default_base_model_name = 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():
|
||||
for file_name, url in config.checkpoint_downloads.items():
|
||||
load_file_from_url(url=url, model_dir=config.path_checkpoints, file_name=file_name)
|
||||
for file_name, url in config.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)
|
||||
for file_name, url in config.lora_downloads.items():
|
||||
load_file_from_url(url=url, model_dir=config.path_loras, file_name=file_name)
|
||||
|
||||
return default_model, checkpoint_downloads
|
||||
return
|
||||
|
||||
|
||||
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)
|
||||
download_models()
|
||||
|
||||
config.update_files()
|
||||
init_cache(config.model_filenames, config.paths_checkpoints, config.lora_filenames, config.paths_loras)
|
||||
|
||||
from webui import *
|
||||
|
|
|
|||
|
|
@ -1,55 +0,0 @@
|
|||
# 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,
|
||||
}
|
||||
|
|
@ -78,7 +78,7 @@ def spatial_gradient(input, normalized: bool = True):
|
|||
Return:
|
||||
the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`.
|
||||
.. note::
|
||||
See a working example `here <https://kornia.readthedocs.io/en/latest/
|
||||
See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
|
||||
filtering_edges.html>`__.
|
||||
Examples:
|
||||
>>> input = torch.rand(1, 3, 4, 4)
|
||||
|
|
@ -120,7 +120,7 @@ def rgb_to_grayscale(image, rgb_weights = None):
|
|||
grayscale version of the image with shape :math:`(*,1,H,W)`.
|
||||
|
||||
.. note::
|
||||
See a working example `here <https://kornia.readthedocs.io/en/latest/
|
||||
See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
|
||||
color_conversions.html>`__.
|
||||
|
||||
Example:
|
||||
|
|
@ -176,7 +176,7 @@ def canny(
|
|||
- the canny edge magnitudes map, shape of :math:`(B,1,H,W)`.
|
||||
- the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`.
|
||||
.. note::
|
||||
See a working example `here <https://kornia.readthedocs.io/en/latest/
|
||||
See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
|
||||
canny.html>`__.
|
||||
Example:
|
||||
>>> input = torch.rand(5, 3, 4, 4)
|
||||
|
|
|
|||
|
|
@ -107,7 +107,8 @@ class SDTurboScheduler:
|
|||
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)
|
||||
ldm_patched.modules.model_management.load_models_gpu([model])
|
||||
sigmas = model.model.model_sampling.sigma(timesteps)
|
||||
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
|
||||
return (sigmas, )
|
||||
|
||||
|
|
@ -229,25 +230,6 @@ 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):
|
||||
|
|
@ -310,7 +292,6 @@ NODE_CLASS_MAPPINGS = {
|
|||
"KSamplerSelect": KSamplerSelect,
|
||||
"SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
|
||||
"SamplerDPMPP_SDE": SamplerDPMPP_SDE,
|
||||
"SamplerTCD": SamplerTCD,
|
||||
"SplitSigmas": SplitSigmas,
|
||||
"FlipSigmas": FlipSigmas,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -70,7 +70,7 @@ class ModelSamplingDiscrete:
|
|||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "model": ("MODEL",),
|
||||
"sampling": (["eps", "v_prediction", "lcm", "tcd"]),
|
||||
"sampling": (["eps", "v_prediction", "lcm"],),
|
||||
"zsnr": ("BOOLEAN", {"default": False}),
|
||||
}}
|
||||
|
||||
|
|
@ -90,9 +90,6 @@ 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
|
||||
|
|
@ -108,7 +105,7 @@ class ModelSamplingContinuousEDM:
|
|||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "model": ("MODEL",),
|
||||
"sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
|
||||
"sampling": (["v_prediction", "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}),
|
||||
}}
|
||||
|
|
@ -121,25 +118,17 @@ 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_parameters(sigma_min, sigma_max, sigma_data)
|
||||
model_sampling.set_sigma_range(sigma_min, sigma_max)
|
||||
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:
|
||||
|
|
|
|||
|
|
@ -752,6 +752,7 @@ 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/
|
||||
|
|
@ -807,102 +808,3 @@ 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
|
||||
|
|
@ -8,7 +8,7 @@ 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", weights_only=True)
|
||||
clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
|
||||
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)
|
||||
|
|
|
|||
|
|
@ -37,7 +37,6 @@ 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)
|
||||
|
|
@ -101,7 +100,8 @@ vram_group.add_argument("--always-high-vram", action="store_true")
|
|||
vram_group.add_argument("--always-normal-vram", action="store_true")
|
||||
vram_group.add_argument("--always-low-vram", action="store_true")
|
||||
vram_group.add_argument("--always-no-vram", action="store_true")
|
||||
vram_group.add_argument("--always-cpu", type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1)
|
||||
vram_group.add_argument("--always-cpu", action="store_true")
|
||||
|
||||
|
||||
parser.add_argument("--always-offload-from-vram", action="store_true")
|
||||
parser.add_argument("--pytorch-deterministic", action="store_true")
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@ import math
|
|||
import ldm_patched.modules.utils
|
||||
|
||||
|
||||
def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
|
||||
return abs(a*b) // math.gcd(a, b)
|
||||
|
||||
class CONDRegular:
|
||||
def __init__(self, cond):
|
||||
|
|
@ -39,7 +41,7 @@ class CONDCrossAttn(CONDRegular):
|
|||
if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
|
||||
return False
|
||||
|
||||
mult_min = math.lcm(s1[1], s2[1])
|
||||
mult_min = lcm(s1[1], s2[1])
|
||||
diff = mult_min // min(s1[1], s2[1])
|
||||
if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
|
||||
return False
|
||||
|
|
@ -50,7 +52,7 @@ class CONDCrossAttn(CONDRegular):
|
|||
crossattn_max_len = self.cond.shape[1]
|
||||
for x in others:
|
||||
c = x.cond
|
||||
crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
|
||||
crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
|
||||
conds.append(c)
|
||||
|
||||
out = []
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
import torch
|
||||
|
||||
class LatentFormat:
|
||||
scale_factor = 1.0
|
||||
|
|
@ -35,70 +34,6 @@ class SDXL(LatentFormat):
|
|||
]
|
||||
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]
|
||||
]
|
||||
|
|
@ -60,9 +60,6 @@ except:
|
|||
pass
|
||||
|
||||
if args.always_cpu:
|
||||
if args.always_cpu > 0:
|
||||
torch.set_num_threads(args.always_cpu)
|
||||
print(f"Running on {torch.get_num_threads()} CPU threads")
|
||||
cpu_state = CPUState.CPU
|
||||
|
||||
def is_intel_xpu():
|
||||
|
|
|
|||
|
|
@ -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):
|
||||
|
|
@ -12,28 +12,12 @@ 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):
|
||||
|
|
@ -58,7 +42,8 @@ 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.cumprod(alphas, dim=0)
|
||||
alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
|
||||
# alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
|
||||
|
||||
timesteps, = betas.shape
|
||||
self.num_timesteps = int(timesteps)
|
||||
|
|
@ -70,16 +55,11 @@ 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.float())
|
||||
self.register_buffer('log_sigmas', sigmas.log().float())
|
||||
|
||||
def set_alphas_cumprod(self, alphas_cumprod):
|
||||
self.register_buffer("alphas_cumprod", alphas_cumprod.float())
|
||||
self.register_buffer('sigmas', sigmas)
|
||||
self.register_buffer('log_sigmas', sigmas.log())
|
||||
|
||||
@property
|
||||
def sigma_min(self):
|
||||
|
|
@ -114,6 +94,8 @@ 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:
|
||||
|
|
@ -121,11 +103,9 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
|
|||
|
||||
sigma_min = sampling_settings.get("sigma_min", 0.002)
|
||||
sigma_max = sampling_settings.get("sigma_max", 120.0)
|
||||
sigma_data = sampling_settings.get("sigma_data", 1.0)
|
||||
self.set_parameters(sigma_min, sigma_max, sigma_data)
|
||||
self.set_sigma_range(sigma_min, sigma_max)
|
||||
|
||||
def set_parameters(self, sigma_min, sigma_max, sigma_data):
|
||||
self.sigma_data = sigma_data
|
||||
def set_sigma_range(self, sigma_min, sigma_max):
|
||||
sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
|
||||
|
||||
self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
|
||||
|
|
@ -154,56 +134,3 @@ 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))
|
||||
|
|
@ -523,7 +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", "tcd", "edm_playground_v2.5", "restart"]
|
||||
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
|
||||
|
||||
class KSAMPLER(Sampler):
|
||||
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
|
||||
|
|
|
|||
|
|
@ -427,13 +427,12 @@ 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, vae_filename_param=None):
|
||||
def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
|
||||
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
|
||||
|
|
@ -463,12 +462,8 @@ 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:
|
||||
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_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)
|
||||
vae = VAE(sd=vae_sd)
|
||||
|
||||
if output_clip:
|
||||
|
|
@ -490,7 +485,7 @@ 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, vae_filename, clipvision
|
||||
return (model_patcher, clip, vae, clipvision)
|
||||
|
||||
|
||||
def load_unet_state_dict(sd): #load unet in diffusers format
|
||||
|
|
|
|||
|
|
@ -326,7 +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", weights_only=True)
|
||||
embed = torch.load(embed_path, map_location="cpu")
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
print()
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ from .timm.weight_init import trunc_normal_
|
|||
|
||||
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py
|
||||
From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
|
||||
"""
|
||||
if drop_prob == 0.0 or not training:
|
||||
return x
|
||||
|
|
@ -30,7 +30,7 @@ def drop_path(x, drop_prob: float = 0.0, training: bool = False):
|
|||
|
||||
class DropPath(nn.Module):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py
|
||||
From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
|
||||
"""
|
||||
|
||||
def __init__(self, drop_prob=None):
|
||||
|
|
|
|||
|
|
@ -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__(
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
Modified from https://github.com/sczhou/CodeFormer
|
||||
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
|
||||
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
|
||||
This version of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.
|
||||
This verison of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.
|
||||
"""
|
||||
import math
|
||||
from typing import Optional
|
||||
|
|
@ -377,15 +377,15 @@ class VQAutoEncoder(nn.Module):
|
|||
)
|
||||
|
||||
if model_path is not None:
|
||||
chkpt = torch.load(model_path, map_location="cpu", weights_only=True)
|
||||
chkpt = torch.load(model_path, map_location="cpu")
|
||||
if "params_ema" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu", weights_only=True)["params_ema"]
|
||||
torch.load(model_path, map_location="cpu")["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", weights_only=True)["params"]
|
||||
torch.load(model_path, map_location="cpu")["params"]
|
||||
)
|
||||
logger.info(f"vqgan is loaded from: {model_path} [params]")
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -273,8 +273,8 @@ class GFPGANBilinear(nn.Module):
|
|||
if decoder_load_path:
|
||||
self.stylegan_decoder.load_state_dict(
|
||||
torch.load(
|
||||
decoder_load_path, map_location=lambda storage, loc: storage,
|
||||
weights_only=True)["params_ema"]
|
||||
decoder_load_path, map_location=lambda storage, loc: storage
|
||||
)["params_ema"]
|
||||
)
|
||||
# fix decoder without updating params
|
||||
if fix_decoder:
|
||||
|
|
|
|||
|
|
@ -373,8 +373,8 @@ class GFPGANv1(nn.Module):
|
|||
if decoder_load_path:
|
||||
self.stylegan_decoder.load_state_dict(
|
||||
torch.load(
|
||||
decoder_load_path, map_location=lambda storage, loc: storage,
|
||||
weights_only=True)["params_ema"]
|
||||
decoder_load_path, map_location=lambda storage, loc: storage
|
||||
)["params_ema"]
|
||||
)
|
||||
# fix decoder without updating params
|
||||
if fix_decoder:
|
||||
|
|
|
|||
|
|
@ -284,8 +284,8 @@ class GFPGANv1Clean(nn.Module):
|
|||
if decoder_load_path:
|
||||
self.stylegan_decoder.load_state_dict(
|
||||
torch.load(
|
||||
decoder_load_path, map_location=lambda storage, loc: storage,
|
||||
weights_only=True)["params_ema"]
|
||||
decoder_load_path, map_location=lambda storage, loc: storage
|
||||
)["params_ema"]
|
||||
)
|
||||
# fix decoder without updating params
|
||||
if fix_decoder:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,33 @@
|
|||
disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
|
||||
scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
|
||||
overwrite_vary_strength, overwrite_upscale_strength, \
|
||||
mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
|
||||
debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
|
||||
refiner_swap_method, \
|
||||
freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
|
||||
debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
|
||||
inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = [None] * 35
|
||||
|
||||
|
||||
def set_all_advanced_parameters(*args):
|
||||
global disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
|
||||
scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
|
||||
overwrite_vary_strength, overwrite_upscale_strength, \
|
||||
mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
|
||||
debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
|
||||
refiner_swap_method, \
|
||||
freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
|
||||
debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
|
||||
inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate
|
||||
|
||||
disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
|
||||
scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
|
||||
overwrite_vary_strength, overwrite_upscale_strength, \
|
||||
mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
|
||||
debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
|
||||
refiner_swap_method, \
|
||||
freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
|
||||
debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
|
||||
inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = args
|
||||
|
||||
return
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -2,43 +2,24 @@ import os
|
|||
import json
|
||||
import math
|
||||
import numbers
|
||||
|
||||
import args_manager
|
||||
import tempfile
|
||||
import modules.flags
|
||||
import modules.sdxl_styles
|
||||
|
||||
from modules.model_loader import load_file_from_url
|
||||
from modules.extra_utils import makedirs_with_log, get_files_from_folder, try_eval_env_var
|
||||
from modules.flags import OutputFormat, Performance, MetadataScheme
|
||||
from modules.util import get_files_from_folder
|
||||
|
||||
|
||||
def get_config_path(key, default_value):
|
||||
env = os.getenv(key)
|
||||
if env is not None and isinstance(env, str):
|
||||
print(f"Environment: {key} = {env}")
|
||||
return env
|
||||
else:
|
||||
return os.path.abspath(default_value)
|
||||
|
||||
wildcards_max_bfs_depth = 64
|
||||
config_path = get_config_path('config_path', "./config.txt")
|
||||
config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt")
|
||||
config_path = os.path.abspath("./config.txt")
|
||||
config_example_path = os.path.abspath("config_modification_tutorial.txt")
|
||||
config_dict = {}
|
||||
always_save_keys = []
|
||||
visited_keys = []
|
||||
|
||||
try:
|
||||
with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file:
|
||||
config_dict.update(json.load(json_file))
|
||||
except Exception as e:
|
||||
print(f'Load default preset failed.')
|
||||
print(e)
|
||||
|
||||
try:
|
||||
if os.path.exists(config_path):
|
||||
with open(config_path, "r", encoding="utf-8") as json_file:
|
||||
config_dict.update(json.load(json_file))
|
||||
config_dict = json.load(json_file)
|
||||
always_save_keys = list(config_dict.keys())
|
||||
except Exception as e:
|
||||
print(f'Failed to load config file "{config_path}" . The reason is: {str(e)}')
|
||||
|
|
@ -98,52 +79,30 @@ def try_load_deprecated_user_path_config():
|
|||
|
||||
try_load_deprecated_user_path_config()
|
||||
|
||||
def get_presets():
|
||||
preset_folder = 'presets'
|
||||
presets = ['initial']
|
||||
if not os.path.exists(preset_folder):
|
||||
print('No presets found.')
|
||||
return presets
|
||||
try:
|
||||
with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file:
|
||||
config_dict.update(json.load(json_file))
|
||||
except Exception as e:
|
||||
print(f'Load default preset failed.')
|
||||
print(e)
|
||||
|
||||
return presets + [f[:f.index(".json")] for f in os.listdir(preset_folder) if f.endswith('.json')]
|
||||
|
||||
def update_presets():
|
||||
global available_presets
|
||||
available_presets = get_presets()
|
||||
|
||||
def try_get_preset_content(preset):
|
||||
if isinstance(preset, str):
|
||||
preset_path = os.path.abspath(f'./presets/{preset}.json')
|
||||
try:
|
||||
if os.path.exists(preset_path):
|
||||
with open(preset_path, "r", encoding="utf-8") as json_file:
|
||||
json_content = json.load(json_file)
|
||||
print(f'Loaded preset: {preset_path}')
|
||||
return json_content
|
||||
else:
|
||||
raise FileNotFoundError
|
||||
except Exception as e:
|
||||
print(f'Load preset [{preset_path}] failed')
|
||||
print(e)
|
||||
return {}
|
||||
|
||||
available_presets = get_presets()
|
||||
preset = args_manager.args.preset
|
||||
config_dict.update(try_get_preset_content(preset))
|
||||
|
||||
def get_path_output() -> str:
|
||||
"""
|
||||
Checking output path argument and overriding default path.
|
||||
"""
|
||||
global config_dict
|
||||
path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True)
|
||||
if args_manager.args.output_path:
|
||||
print(f'Overriding config value path_outputs with {args_manager.args.output_path}')
|
||||
config_dict['path_outputs'] = path_output = args_manager.args.output_path
|
||||
return path_output
|
||||
if isinstance(preset, str):
|
||||
preset_path = os.path.abspath(f'./presets/{preset}.json')
|
||||
try:
|
||||
if os.path.exists(preset_path):
|
||||
with open(preset_path, "r", encoding="utf-8") as json_file:
|
||||
config_dict.update(json.load(json_file))
|
||||
print(f'Loaded preset: {preset_path}')
|
||||
else:
|
||||
raise FileNotFoundError
|
||||
except Exception as e:
|
||||
print(f'Load preset [{preset_path}] failed')
|
||||
print(e)
|
||||
|
||||
|
||||
def get_dir_or_set_default(key, default_value, as_array=False, make_directory=False):
|
||||
def get_dir_or_set_default(key, default_value):
|
||||
global config_dict, visited_keys, always_save_keys
|
||||
|
||||
if key not in visited_keys:
|
||||
|
|
@ -152,70 +111,36 @@ def get_dir_or_set_default(key, default_value, as_array=False, make_directory=Fa
|
|||
if key not in always_save_keys:
|
||||
always_save_keys.append(key)
|
||||
|
||||
v = os.getenv(key)
|
||||
if v is not None:
|
||||
print(f"Environment: {key} = {v}")
|
||||
config_dict[key] = v
|
||||
else:
|
||||
v = config_dict.get(key, None)
|
||||
|
||||
if isinstance(v, str):
|
||||
if make_directory:
|
||||
makedirs_with_log(v)
|
||||
if os.path.exists(v) and os.path.isdir(v):
|
||||
return v if not as_array else [v]
|
||||
elif isinstance(v, list):
|
||||
if make_directory:
|
||||
for d in v:
|
||||
makedirs_with_log(d)
|
||||
if all([os.path.exists(d) and os.path.isdir(d) for d in v]):
|
||||
return v
|
||||
|
||||
if v is not None:
|
||||
print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')
|
||||
if isinstance(default_value, list):
|
||||
dp = []
|
||||
for path in default_value:
|
||||
abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path))
|
||||
dp.append(abs_path)
|
||||
os.makedirs(abs_path, exist_ok=True)
|
||||
v = config_dict.get(key, None)
|
||||
if isinstance(v, str) and os.path.exists(v) and os.path.isdir(v):
|
||||
return v
|
||||
else:
|
||||
if v is not None:
|
||||
print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')
|
||||
dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value))
|
||||
os.makedirs(dp, exist_ok=True)
|
||||
if as_array:
|
||||
dp = [dp]
|
||||
config_dict[key] = dp
|
||||
return dp
|
||||
config_dict[key] = dp
|
||||
return dp
|
||||
|
||||
|
||||
paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True)
|
||||
paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)
|
||||
path_checkpoints = get_dir_or_set_default('path_checkpoints', '../models/checkpoints/')
|
||||
path_loras = get_dir_or_set_default('path_loras', '../models/loras/')
|
||||
path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/')
|
||||
path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/')
|
||||
path_vae = get_dir_or_set_default('path_vae', '../models/vae/')
|
||||
path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')
|
||||
path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/')
|
||||
path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/')
|
||||
path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/')
|
||||
path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion')
|
||||
path_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/')
|
||||
path_safety_checker = get_dir_or_set_default('path_safety_checker', '../models/safety_checker/')
|
||||
path_sam = get_dir_or_set_default('path_sam', '../models/sam/')
|
||||
path_outputs = get_path_output()
|
||||
path_outputs = get_dir_or_set_default('path_outputs', '../outputs/')
|
||||
|
||||
|
||||
def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False, expected_type=None):
|
||||
def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False):
|
||||
global config_dict, visited_keys
|
||||
|
||||
if key not in visited_keys:
|
||||
visited_keys.append(key)
|
||||
|
||||
v = os.getenv(key)
|
||||
if v is not None:
|
||||
v = try_eval_env_var(v, expected_type)
|
||||
print(f"Environment: {key} = {v}")
|
||||
config_dict[key] = v
|
||||
|
||||
if key not in config_dict:
|
||||
config_dict[key] = default_value
|
||||
return default_value
|
||||
|
|
@ -233,145 +158,71 @@ def get_config_item_or_set_default(key, default_value, validator, disable_empty_
|
|||
return default_value
|
||||
|
||||
|
||||
def init_temp_path(path: str | None, default_path: str) -> str:
|
||||
if args_manager.args.temp_path:
|
||||
path = args_manager.args.temp_path
|
||||
|
||||
if path != '' and path != default_path:
|
||||
try:
|
||||
if not os.path.isabs(path):
|
||||
path = os.path.abspath(path)
|
||||
os.makedirs(path, exist_ok=True)
|
||||
print(f'Using temp path {path}')
|
||||
return path
|
||||
except Exception as e:
|
||||
print(f'Could not create temp path {path}. Reason: {e}')
|
||||
print(f'Using default temp path {default_path} instead.')
|
||||
|
||||
os.makedirs(default_path, exist_ok=True)
|
||||
return default_path
|
||||
|
||||
|
||||
default_temp_path = os.path.join(tempfile.gettempdir(), 'fooocus')
|
||||
temp_path = init_temp_path(get_config_item_or_set_default(
|
||||
key='temp_path',
|
||||
default_value=default_temp_path,
|
||||
validator=lambda x: isinstance(x, str),
|
||||
expected_type=str
|
||||
), default_temp_path)
|
||||
temp_path_cleanup_on_launch = get_config_item_or_set_default(
|
||||
key='temp_path_cleanup_on_launch',
|
||||
default_value=True,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_base_model_name = default_model = get_config_item_or_set_default(
|
||||
default_base_model_name = get_config_item_or_set_default(
|
||||
key='default_model',
|
||||
default_value='model.safetensors',
|
||||
validator=lambda x: isinstance(x, str),
|
||||
expected_type=str
|
||||
validator=lambda x: isinstance(x, str)
|
||||
)
|
||||
previous_default_models = get_config_item_or_set_default(
|
||||
key='previous_default_models',
|
||||
default_value=[],
|
||||
validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x),
|
||||
expected_type=list
|
||||
validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x)
|
||||
)
|
||||
default_refiner_model_name = default_refiner = get_config_item_or_set_default(
|
||||
default_refiner_model_name = get_config_item_or_set_default(
|
||||
key='default_refiner',
|
||||
default_value='None',
|
||||
validator=lambda x: isinstance(x, str),
|
||||
expected_type=str
|
||||
validator=lambda x: isinstance(x, str)
|
||||
)
|
||||
default_refiner_switch = get_config_item_or_set_default(
|
||||
key='default_refiner_switch',
|
||||
default_value=0.8,
|
||||
validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1,
|
||||
expected_type=numbers.Number
|
||||
)
|
||||
default_loras_min_weight = get_config_item_or_set_default(
|
||||
key='default_loras_min_weight',
|
||||
default_value=-2,
|
||||
validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10,
|
||||
expected_type=numbers.Number
|
||||
)
|
||||
default_loras_max_weight = get_config_item_or_set_default(
|
||||
key='default_loras_max_weight',
|
||||
default_value=2,
|
||||
validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10,
|
||||
expected_type=numbers.Number
|
||||
validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1
|
||||
)
|
||||
default_loras = get_config_item_or_set_default(
|
||||
key='default_loras',
|
||||
default_value=[
|
||||
[
|
||||
True,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
True,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
True,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
True,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
True,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
],
|
||||
validator=lambda x: isinstance(x, list) and all(
|
||||
len(y) == 3 and isinstance(y[0], bool) and isinstance(y[1], str) and isinstance(y[2], numbers.Number)
|
||||
or len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number)
|
||||
for y in x),
|
||||
expected_type=list
|
||||
)
|
||||
default_loras = [(y[0], y[1], y[2]) if len(y) == 3 else (True, y[0], y[1]) for y in default_loras]
|
||||
default_max_lora_number = get_config_item_or_set_default(
|
||||
key='default_max_lora_number',
|
||||
default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5,
|
||||
validator=lambda x: isinstance(x, int) and x >= 1,
|
||||
expected_type=int
|
||||
validator=lambda x: isinstance(x, list) and all(len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x)
|
||||
)
|
||||
default_cfg_scale = get_config_item_or_set_default(
|
||||
key='default_cfg_scale',
|
||||
default_value=7.0,
|
||||
validator=lambda x: isinstance(x, numbers.Number),
|
||||
expected_type=numbers.Number
|
||||
validator=lambda x: isinstance(x, numbers.Number)
|
||||
)
|
||||
default_sample_sharpness = get_config_item_or_set_default(
|
||||
key='default_sample_sharpness',
|
||||
default_value=2.0,
|
||||
validator=lambda x: isinstance(x, numbers.Number),
|
||||
expected_type=numbers.Number
|
||||
validator=lambda x: isinstance(x, numbers.Number)
|
||||
)
|
||||
default_sampler = get_config_item_or_set_default(
|
||||
key='default_sampler',
|
||||
default_value='dpmpp_2m_sde_gpu',
|
||||
validator=lambda x: x in modules.flags.sampler_list,
|
||||
expected_type=str
|
||||
validator=lambda x: x in modules.flags.sampler_list
|
||||
)
|
||||
default_scheduler = get_config_item_or_set_default(
|
||||
key='default_scheduler',
|
||||
default_value='karras',
|
||||
validator=lambda x: x in modules.flags.scheduler_list,
|
||||
expected_type=str
|
||||
)
|
||||
default_vae = get_config_item_or_set_default(
|
||||
key='default_vae',
|
||||
default_value=modules.flags.default_vae,
|
||||
validator=lambda x: isinstance(x, str),
|
||||
expected_type=str
|
||||
validator=lambda x: x in modules.flags.scheduler_list
|
||||
)
|
||||
default_styles = get_config_item_or_set_default(
|
||||
key='default_styles',
|
||||
|
|
@ -380,379 +231,122 @@ default_styles = get_config_item_or_set_default(
|
|||
"Fooocus Enhance",
|
||||
"Fooocus Sharp"
|
||||
],
|
||||
validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x),
|
||||
expected_type=list
|
||||
validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x)
|
||||
)
|
||||
default_prompt_negative = get_config_item_or_set_default(
|
||||
key='default_prompt_negative',
|
||||
default_value='',
|
||||
validator=lambda x: isinstance(x, str),
|
||||
disable_empty_as_none=True,
|
||||
expected_type=str
|
||||
disable_empty_as_none=True
|
||||
)
|
||||
default_prompt = get_config_item_or_set_default(
|
||||
key='default_prompt',
|
||||
default_value='',
|
||||
validator=lambda x: isinstance(x, str),
|
||||
disable_empty_as_none=True,
|
||||
expected_type=str
|
||||
disable_empty_as_none=True
|
||||
)
|
||||
default_performance = get_config_item_or_set_default(
|
||||
key='default_performance',
|
||||
default_value=Performance.SPEED.value,
|
||||
validator=lambda x: x in Performance.values(),
|
||||
expected_type=str
|
||||
)
|
||||
default_image_prompt_checkbox = get_config_item_or_set_default(
|
||||
key='default_image_prompt_checkbox',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_enhance_checkbox = get_config_item_or_set_default(
|
||||
key='default_enhance_checkbox',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
default_value='Speed',
|
||||
validator=lambda x: x in modules.flags.performance_selections
|
||||
)
|
||||
default_advanced_checkbox = get_config_item_or_set_default(
|
||||
key='default_advanced_checkbox',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_developer_debug_mode_checkbox = get_config_item_or_set_default(
|
||||
key='default_developer_debug_mode_checkbox',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_image_prompt_advanced_checkbox = get_config_item_or_set_default(
|
||||
key='default_image_prompt_advanced_checkbox',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
validator=lambda x: isinstance(x, bool)
|
||||
)
|
||||
default_max_image_number = get_config_item_or_set_default(
|
||||
key='default_max_image_number',
|
||||
default_value=32,
|
||||
validator=lambda x: isinstance(x, int) and x >= 1,
|
||||
expected_type=int
|
||||
)
|
||||
default_output_format = get_config_item_or_set_default(
|
||||
key='default_output_format',
|
||||
default_value='png',
|
||||
validator=lambda x: x in OutputFormat.list(),
|
||||
expected_type=str
|
||||
validator=lambda x: isinstance(x, int) and x >= 1
|
||||
)
|
||||
default_image_number = get_config_item_or_set_default(
|
||||
key='default_image_number',
|
||||
default_value=2,
|
||||
validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number,
|
||||
expected_type=int
|
||||
validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number
|
||||
)
|
||||
checkpoint_downloads = get_config_item_or_set_default(
|
||||
key='checkpoint_downloads',
|
||||
default_value={},
|
||||
validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
|
||||
expected_type=dict
|
||||
validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
|
||||
)
|
||||
lora_downloads = get_config_item_or_set_default(
|
||||
key='lora_downloads',
|
||||
default_value={},
|
||||
validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
|
||||
expected_type=dict
|
||||
validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
|
||||
)
|
||||
embeddings_downloads = get_config_item_or_set_default(
|
||||
key='embeddings_downloads',
|
||||
default_value={},
|
||||
validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
|
||||
expected_type=dict
|
||||
)
|
||||
vae_downloads = get_config_item_or_set_default(
|
||||
key='vae_downloads',
|
||||
default_value={},
|
||||
validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
|
||||
expected_type=dict
|
||||
validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
|
||||
)
|
||||
available_aspect_ratios = get_config_item_or_set_default(
|
||||
key='available_aspect_ratios',
|
||||
default_value=modules.flags.sdxl_aspect_ratios,
|
||||
validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1,
|
||||
expected_type=list
|
||||
default_value=[
|
||||
'704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152',
|
||||
'896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960',
|
||||
'1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768',
|
||||
'1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640',
|
||||
'1664*576', '1728*576'
|
||||
],
|
||||
validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1
|
||||
)
|
||||
default_aspect_ratio = get_config_item_or_set_default(
|
||||
key='default_aspect_ratio',
|
||||
default_value='1152*896' if '1152*896' in available_aspect_ratios else available_aspect_ratios[0],
|
||||
validator=lambda x: x in available_aspect_ratios,
|
||||
expected_type=str
|
||||
validator=lambda x: x in available_aspect_ratios
|
||||
)
|
||||
default_inpaint_engine_version = get_config_item_or_set_default(
|
||||
key='default_inpaint_engine_version',
|
||||
default_value='v2.6',
|
||||
validator=lambda x: x in modules.flags.inpaint_engine_versions,
|
||||
expected_type=str
|
||||
)
|
||||
default_selected_image_input_tab_id = get_config_item_or_set_default(
|
||||
key='default_selected_image_input_tab_id',
|
||||
default_value=modules.flags.default_input_image_tab,
|
||||
validator=lambda x: x in modules.flags.input_image_tab_ids,
|
||||
expected_type=str
|
||||
)
|
||||
default_uov_method = get_config_item_or_set_default(
|
||||
key='default_uov_method',
|
||||
default_value=modules.flags.disabled,
|
||||
validator=lambda x: x in modules.flags.uov_list,
|
||||
expected_type=str
|
||||
)
|
||||
default_controlnet_image_count = get_config_item_or_set_default(
|
||||
key='default_controlnet_image_count',
|
||||
default_value=4,
|
||||
validator=lambda x: isinstance(x, int) and x > 0,
|
||||
expected_type=int
|
||||
)
|
||||
default_ip_images = {}
|
||||
default_ip_stop_ats = {}
|
||||
default_ip_weights = {}
|
||||
default_ip_types = {}
|
||||
|
||||
for image_count in range(default_controlnet_image_count):
|
||||
image_count += 1
|
||||
default_ip_images[image_count] = get_config_item_or_set_default(
|
||||
key=f'default_ip_image_{image_count}',
|
||||
default_value='None',
|
||||
validator=lambda x: x == 'None' or isinstance(x, str) and os.path.exists(x),
|
||||
expected_type=str
|
||||
)
|
||||
|
||||
if default_ip_images[image_count] == 'None':
|
||||
default_ip_images[image_count] = None
|
||||
|
||||
default_ip_types[image_count] = get_config_item_or_set_default(
|
||||
key=f'default_ip_type_{image_count}',
|
||||
default_value=modules.flags.default_ip,
|
||||
validator=lambda x: x in modules.flags.ip_list,
|
||||
expected_type=str
|
||||
)
|
||||
|
||||
default_end, default_weight = modules.flags.default_parameters[default_ip_types[image_count]]
|
||||
|
||||
default_ip_stop_ats[image_count] = get_config_item_or_set_default(
|
||||
key=f'default_ip_stop_at_{image_count}',
|
||||
default_value=default_end,
|
||||
validator=lambda x: isinstance(x, float) and 0 <= x <= 1,
|
||||
expected_type=float
|
||||
)
|
||||
default_ip_weights[image_count] = get_config_item_or_set_default(
|
||||
key=f'default_ip_weight_{image_count}',
|
||||
default_value=default_weight,
|
||||
validator=lambda x: isinstance(x, float) and 0 <= x <= 2,
|
||||
expected_type=float
|
||||
)
|
||||
|
||||
default_inpaint_advanced_masking_checkbox = get_config_item_or_set_default(
|
||||
key='default_inpaint_advanced_masking_checkbox',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_inpaint_method = get_config_item_or_set_default(
|
||||
key='default_inpaint_method',
|
||||
default_value=modules.flags.inpaint_option_default,
|
||||
validator=lambda x: x in modules.flags.inpaint_options,
|
||||
expected_type=str
|
||||
validator=lambda x: x in modules.flags.inpaint_engine_versions
|
||||
)
|
||||
default_cfg_tsnr = get_config_item_or_set_default(
|
||||
key='default_cfg_tsnr',
|
||||
default_value=7.0,
|
||||
validator=lambda x: isinstance(x, numbers.Number),
|
||||
expected_type=numbers.Number
|
||||
)
|
||||
default_clip_skip = get_config_item_or_set_default(
|
||||
key='default_clip_skip',
|
||||
default_value=2,
|
||||
validator=lambda x: isinstance(x, int) and 1 <= x <= modules.flags.clip_skip_max,
|
||||
expected_type=int
|
||||
validator=lambda x: isinstance(x, numbers.Number)
|
||||
)
|
||||
default_overwrite_step = get_config_item_or_set_default(
|
||||
key='default_overwrite_step',
|
||||
default_value=-1,
|
||||
validator=lambda x: isinstance(x, int),
|
||||
expected_type=int
|
||||
validator=lambda x: isinstance(x, int)
|
||||
)
|
||||
default_overwrite_switch = get_config_item_or_set_default(
|
||||
key='default_overwrite_switch',
|
||||
default_value=-1,
|
||||
validator=lambda x: isinstance(x, int),
|
||||
expected_type=int
|
||||
)
|
||||
default_overwrite_upscale = get_config_item_or_set_default(
|
||||
key='default_overwrite_upscale',
|
||||
default_value=-1,
|
||||
validator=lambda x: isinstance(x, numbers.Number)
|
||||
validator=lambda x: isinstance(x, int)
|
||||
)
|
||||
example_inpaint_prompts = get_config_item_or_set_default(
|
||||
key='example_inpaint_prompts',
|
||||
default_value=[
|
||||
'highly detailed face', 'detailed girl face', 'detailed man face', 'detailed hand', 'beautiful eyes'
|
||||
],
|
||||
validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x),
|
||||
expected_type=list
|
||||
)
|
||||
example_enhance_detection_prompts = get_config_item_or_set_default(
|
||||
key='example_enhance_detection_prompts',
|
||||
default_value=[
|
||||
'face', 'eye', 'mouth', 'hair', 'hand', 'body'
|
||||
],
|
||||
validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x),
|
||||
expected_type=list
|
||||
)
|
||||
default_enhance_tabs = get_config_item_or_set_default(
|
||||
key='default_enhance_tabs',
|
||||
default_value=3,
|
||||
validator=lambda x: isinstance(x, int) and 1 <= x <= 5,
|
||||
expected_type=int
|
||||
)
|
||||
default_enhance_uov_method = get_config_item_or_set_default(
|
||||
key='default_enhance_uov_method',
|
||||
default_value=modules.flags.disabled,
|
||||
validator=lambda x: x in modules.flags.uov_list,
|
||||
expected_type=int
|
||||
)
|
||||
default_enhance_uov_processing_order = get_config_item_or_set_default(
|
||||
key='default_enhance_uov_processing_order',
|
||||
default_value=modules.flags.enhancement_uov_before,
|
||||
validator=lambda x: x in modules.flags.enhancement_uov_processing_order,
|
||||
expected_type=int
|
||||
)
|
||||
default_enhance_uov_prompt_type = get_config_item_or_set_default(
|
||||
key='default_enhance_uov_prompt_type',
|
||||
default_value=modules.flags.enhancement_uov_prompt_type_original,
|
||||
validator=lambda x: x in modules.flags.enhancement_uov_prompt_types,
|
||||
expected_type=int
|
||||
)
|
||||
default_sam_max_detections = get_config_item_or_set_default(
|
||||
key='default_sam_max_detections',
|
||||
default_value=0,
|
||||
validator=lambda x: isinstance(x, int) and 0 <= x <= 10,
|
||||
expected_type=int
|
||||
)
|
||||
default_black_out_nsfw = get_config_item_or_set_default(
|
||||
key='default_black_out_nsfw',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_save_only_final_enhanced_image = get_config_item_or_set_default(
|
||||
key='default_save_only_final_enhanced_image',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_save_metadata_to_images = get_config_item_or_set_default(
|
||||
key='default_save_metadata_to_images',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_metadata_scheme = get_config_item_or_set_default(
|
||||
key='default_metadata_scheme',
|
||||
default_value=MetadataScheme.FOOOCUS.value,
|
||||
validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x],
|
||||
expected_type=str
|
||||
)
|
||||
metadata_created_by = get_config_item_or_set_default(
|
||||
key='metadata_created_by',
|
||||
default_value='',
|
||||
validator=lambda x: isinstance(x, str),
|
||||
expected_type=str
|
||||
validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x)
|
||||
)
|
||||
|
||||
example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
|
||||
example_enhance_detection_prompts = [[x] for x in example_enhance_detection_prompts]
|
||||
|
||||
default_invert_mask_checkbox = get_config_item_or_set_default(
|
||||
key='default_invert_mask_checkbox',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
config_dict["default_loras"] = default_loras = default_loras[:5] + [['None', 1.0] for _ in range(5 - len(default_loras))]
|
||||
|
||||
default_inpaint_mask_model = get_config_item_or_set_default(
|
||||
key='default_inpaint_mask_model',
|
||||
default_value='isnet-general-use',
|
||||
validator=lambda x: x in modules.flags.inpaint_mask_models,
|
||||
expected_type=str
|
||||
)
|
||||
possible_preset_keys = [
|
||||
"default_model",
|
||||
"default_refiner",
|
||||
"default_refiner_switch",
|
||||
"default_loras",
|
||||
"default_cfg_scale",
|
||||
"default_sample_sharpness",
|
||||
"default_sampler",
|
||||
"default_scheduler",
|
||||
"default_performance",
|
||||
"default_prompt",
|
||||
"default_prompt_negative",
|
||||
"default_styles",
|
||||
"default_aspect_ratio",
|
||||
"checkpoint_downloads",
|
||||
"embeddings_downloads",
|
||||
"lora_downloads",
|
||||
]
|
||||
|
||||
default_enhance_inpaint_mask_model = get_config_item_or_set_default(
|
||||
key='default_enhance_inpaint_mask_model',
|
||||
default_value='sam',
|
||||
validator=lambda x: x in modules.flags.inpaint_mask_models,
|
||||
expected_type=str
|
||||
)
|
||||
|
||||
default_inpaint_mask_cloth_category = get_config_item_or_set_default(
|
||||
key='default_inpaint_mask_cloth_category',
|
||||
default_value='full',
|
||||
validator=lambda x: x in modules.flags.inpaint_mask_cloth_category,
|
||||
expected_type=str
|
||||
)
|
||||
|
||||
default_inpaint_mask_sam_model = get_config_item_or_set_default(
|
||||
key='default_inpaint_mask_sam_model',
|
||||
default_value='vit_b',
|
||||
validator=lambda x: x in modules.flags.inpaint_mask_sam_model,
|
||||
expected_type=str
|
||||
)
|
||||
|
||||
default_describe_apply_prompts_checkbox = get_config_item_or_set_default(
|
||||
key='default_describe_apply_prompts_checkbox',
|
||||
default_value=True,
|
||||
validator=lambda x: isinstance(x, bool),
|
||||
expected_type=bool
|
||||
)
|
||||
default_describe_content_type = get_config_item_or_set_default(
|
||||
key='default_describe_content_type',
|
||||
default_value=[modules.flags.describe_type_photo],
|
||||
validator=lambda x: all(k in modules.flags.describe_types for k in x),
|
||||
expected_type=list
|
||||
)
|
||||
|
||||
config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
|
||||
|
||||
# mapping config to meta parameter
|
||||
possible_preset_keys = {
|
||||
"default_model": "base_model",
|
||||
"default_refiner": "refiner_model",
|
||||
"default_refiner_switch": "refiner_switch",
|
||||
"previous_default_models": "previous_default_models",
|
||||
"default_loras_min_weight": "default_loras_min_weight",
|
||||
"default_loras_max_weight": "default_loras_max_weight",
|
||||
"default_loras": "<processed>",
|
||||
"default_cfg_scale": "guidance_scale",
|
||||
"default_sample_sharpness": "sharpness",
|
||||
"default_cfg_tsnr": "adaptive_cfg",
|
||||
"default_clip_skip": "clip_skip",
|
||||
"default_sampler": "sampler",
|
||||
"default_scheduler": "scheduler",
|
||||
"default_overwrite_step": "steps",
|
||||
"default_overwrite_switch": "overwrite_switch",
|
||||
"default_performance": "performance",
|
||||
"default_image_number": "image_number",
|
||||
"default_prompt": "prompt",
|
||||
"default_prompt_negative": "negative_prompt",
|
||||
"default_styles": "styles",
|
||||
"default_aspect_ratio": "resolution",
|
||||
"default_save_metadata_to_images": "default_save_metadata_to_images",
|
||||
"checkpoint_downloads": "checkpoint_downloads",
|
||||
"embeddings_downloads": "embeddings_downloads",
|
||||
"lora_downloads": "lora_downloads",
|
||||
"vae_downloads": "vae_downloads",
|
||||
"default_vae": "vae",
|
||||
# "default_inpaint_method": "inpaint_method", # disabled so inpaint mode doesn't refresh after every preset change
|
||||
"default_inpaint_engine_version": "inpaint_engine_version",
|
||||
}
|
||||
|
||||
REWRITE_PRESET = False
|
||||
|
||||
|
|
@ -772,7 +366,7 @@ def add_ratio(x):
|
|||
|
||||
|
||||
default_aspect_ratio = add_ratio(default_aspect_ratio)
|
||||
available_aspect_ratios_labels = [add_ratio(x) for x in available_aspect_ratios]
|
||||
available_aspect_ratios = [add_ratio(x) for x in available_aspect_ratios]
|
||||
|
||||
|
||||
# Only write config in the first launch.
|
||||
|
|
@ -791,32 +385,21 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
|
|||
'and there is no "," before the last "}". \n\n\n')
|
||||
json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4)
|
||||
|
||||
|
||||
os.makedirs(path_outputs, exist_ok=True)
|
||||
|
||||
model_filenames = []
|
||||
lora_filenames = []
|
||||
vae_filenames = []
|
||||
wildcard_filenames = []
|
||||
|
||||
|
||||
def get_model_filenames(folder_paths, extensions=None, name_filter=None):
|
||||
if extensions is None:
|
||||
extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
|
||||
files = []
|
||||
|
||||
if not isinstance(folder_paths, list):
|
||||
folder_paths = [folder_paths]
|
||||
for folder in folder_paths:
|
||||
files += get_files_from_folder(folder, extensions, name_filter)
|
||||
|
||||
return files
|
||||
def get_model_filenames(folder_path, name_filter=None):
|
||||
return get_files_from_folder(folder_path, ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch'], name_filter)
|
||||
|
||||
|
||||
def update_files():
|
||||
global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets
|
||||
model_filenames = get_model_filenames(paths_checkpoints)
|
||||
lora_filenames = get_model_filenames(paths_loras)
|
||||
vae_filenames = get_model_filenames(path_vae)
|
||||
wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
|
||||
available_presets = get_presets()
|
||||
def update_all_model_names():
|
||||
global model_filenames, lora_filenames
|
||||
model_filenames = get_model_filenames(path_checkpoints)
|
||||
lora_filenames = get_model_filenames(path_loras)
|
||||
return
|
||||
|
||||
|
||||
|
|
@ -861,28 +444,10 @@ def downloading_inpaint_models(v):
|
|||
def downloading_sdxl_lcm_lora():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors',
|
||||
model_dir=paths_loras[0],
|
||||
file_name=modules.flags.PerformanceLoRA.EXTREME_SPEED.value
|
||||
model_dir=path_loras,
|
||||
file_name='sdxl_lcm_lora.safetensors'
|
||||
)
|
||||
return modules.flags.PerformanceLoRA.EXTREME_SPEED.value
|
||||
|
||||
|
||||
def downloading_sdxl_lightning_lora():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_lightning_4step_lora.safetensors',
|
||||
model_dir=paths_loras[0],
|
||||
file_name=modules.flags.PerformanceLoRA.LIGHTNING.value
|
||||
)
|
||||
return modules.flags.PerformanceLoRA.LIGHTNING.value
|
||||
|
||||
|
||||
def downloading_sdxl_hyper_sd_lora():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_hyper_sd_4step_lora.safetensors',
|
||||
model_dir=paths_loras[0],
|
||||
file_name=modules.flags.PerformanceLoRA.HYPER_SD.value
|
||||
)
|
||||
return modules.flags.PerformanceLoRA.HYPER_SD.value
|
||||
return 'sdxl_lcm_lora.safetensors'
|
||||
|
||||
|
||||
def downloading_controlnet_canny():
|
||||
|
|
@ -949,49 +514,5 @@ def downloading_upscale_model():
|
|||
)
|
||||
return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin')
|
||||
|
||||
def downloading_safety_checker_model():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/mashb1t/misc/resolve/main/stable-diffusion-safety-checker.bin',
|
||||
model_dir=path_safety_checker,
|
||||
file_name='stable-diffusion-safety-checker.bin'
|
||||
)
|
||||
return os.path.join(path_safety_checker, 'stable-diffusion-safety-checker.bin')
|
||||
|
||||
|
||||
def download_sam_model(sam_model: str) -> str:
|
||||
match sam_model:
|
||||
case 'vit_b':
|
||||
return downloading_sam_vit_b()
|
||||
case 'vit_l':
|
||||
return downloading_sam_vit_l()
|
||||
case 'vit_h':
|
||||
return downloading_sam_vit_h()
|
||||
case _:
|
||||
raise ValueError(f"sam model {sam_model} does not exist.")
|
||||
|
||||
|
||||
def downloading_sam_vit_b():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_b_01ec64.pth',
|
||||
model_dir=path_sam,
|
||||
file_name='sam_vit_b_01ec64.pth'
|
||||
)
|
||||
return os.path.join(path_sam, 'sam_vit_b_01ec64.pth')
|
||||
|
||||
|
||||
def downloading_sam_vit_l():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_l_0b3195.pth',
|
||||
model_dir=path_sam,
|
||||
file_name='sam_vit_l_0b3195.pth'
|
||||
)
|
||||
return os.path.join(path_sam, 'sam_vit_l_0b3195.pth')
|
||||
|
||||
|
||||
def downloading_sam_vit_h():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_h_4b8939.pth',
|
||||
model_dir=path_sam,
|
||||
file_name='sam_vit_h_4b8939.pth'
|
||||
)
|
||||
return os.path.join(path_sam, 'sam_vit_h_4b8939.pth')
|
||||
update_all_model_names()
|
||||
|
|
|
|||
|
|
@ -1,3 +1,8 @@
|
|||
from modules.patch import patch_all
|
||||
|
||||
patch_all()
|
||||
|
||||
|
||||
import os
|
||||
import einops
|
||||
import torch
|
||||
|
|
@ -11,6 +16,7 @@ import ldm_patched.modules.controlnet
|
|||
import modules.sample_hijack
|
||||
import ldm_patched.modules.samplers
|
||||
import ldm_patched.modules.latent_formats
|
||||
import modules.advanced_parameters
|
||||
|
||||
from ldm_patched.modules.sd import load_checkpoint_guess_config
|
||||
from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \
|
||||
|
|
@ -18,10 +24,10 @@ from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode,
|
|||
from ldm_patched.contrib.external_freelunch import FreeU_V2
|
||||
from ldm_patched.modules.sample import prepare_mask
|
||||
from modules.lora import match_lora
|
||||
from modules.util import get_file_from_folder_list
|
||||
from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip
|
||||
from modules.config import path_embeddings
|
||||
from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete, ModelSamplingContinuousEDM
|
||||
from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete
|
||||
|
||||
|
||||
opEmptyLatentImage = EmptyLatentImage()
|
||||
opVAEDecode = VAEDecode()
|
||||
|
|
@ -31,17 +37,15 @@ opVAEEncodeTiled = VAEEncodeTiled()
|
|||
opControlNetApplyAdvanced = ControlNetApplyAdvanced()
|
||||
opFreeU = FreeU_V2()
|
||||
opModelSamplingDiscrete = ModelSamplingDiscrete()
|
||||
opModelSamplingContinuousEDM = ModelSamplingContinuousEDM()
|
||||
|
||||
|
||||
class StableDiffusionModel:
|
||||
def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_filename=None):
|
||||
def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None):
|
||||
self.unet = unet
|
||||
self.vae = vae
|
||||
self.clip = clip
|
||||
self.clip_vision = clip_vision
|
||||
self.filename = filename
|
||||
self.vae_filename = vae_filename
|
||||
self.unet_with_lora = unet
|
||||
self.clip_with_lora = clip
|
||||
self.visited_loras = ''
|
||||
|
|
@ -74,14 +78,14 @@ class StableDiffusionModel:
|
|||
|
||||
loras_to_load = []
|
||||
|
||||
for filename, weight in loras:
|
||||
if filename == 'None':
|
||||
for name, weight in loras:
|
||||
if name == 'None':
|
||||
continue
|
||||
|
||||
if os.path.exists(filename):
|
||||
lora_filename = filename
|
||||
if os.path.exists(name):
|
||||
lora_filename = name
|
||||
else:
|
||||
lora_filename = get_file_from_folder_list(filename, modules.config.paths_loras)
|
||||
lora_filename = os.path.join(modules.config.path_loras, name)
|
||||
|
||||
if not os.path.exists(lora_filename):
|
||||
print(f'Lora file not found: {lora_filename}')
|
||||
|
|
@ -143,10 +147,9 @@ def apply_controlnet(positive, negative, control_net, image, strength, start_per
|
|||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def load_model(ckpt_filename, vae_filename=None):
|
||||
unet, clip, vae, vae_filename, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings,
|
||||
vae_filename_param=vae_filename)
|
||||
return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename, vae_filename=vae_filename)
|
||||
def load_model(ckpt_filename):
|
||||
unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings)
|
||||
return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
|
|
@ -231,7 +234,7 @@ def get_previewer(model):
|
|||
if vae_approx_filename in VAE_approx_models:
|
||||
VAE_approx_model = VAE_approx_models[vae_approx_filename]
|
||||
else:
|
||||
sd = torch.load(vae_approx_filename, map_location='cpu', weights_only=True)
|
||||
sd = torch.load(vae_approx_filename, map_location='cpu')
|
||||
VAE_approx_model = VAEApprox()
|
||||
VAE_approx_model.load_state_dict(sd)
|
||||
del sd
|
||||
|
|
@ -265,7 +268,7 @@ def get_previewer(model):
|
|||
def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
|
||||
scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
|
||||
force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
|
||||
previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False):
|
||||
previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None):
|
||||
|
||||
if sigmas is not None:
|
||||
sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device())
|
||||
|
|
@ -296,7 +299,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
|
|||
def callback(step, x0, x, total_steps):
|
||||
ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()
|
||||
y = None
|
||||
if previewer is not None and not disable_preview:
|
||||
if previewer is not None and not modules.advanced_parameters.disable_preview:
|
||||
y = previewer(x0, previewer_start + step, previewer_end)
|
||||
if callback_function is not None:
|
||||
callback_function(previewer_start + step, x0, x, previewer_end, y)
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ import os
|
|||
import torch
|
||||
import modules.patch
|
||||
import modules.config
|
||||
import modules.flags
|
||||
import ldm_patched.modules.model_management
|
||||
import ldm_patched.modules.latent_formats
|
||||
import modules.inpaint_worker
|
||||
|
|
@ -12,7 +11,6 @@ from extras.expansion import FooocusExpansion
|
|||
|
||||
from ldm_patched.modules.model_base import SDXL, SDXLRefiner
|
||||
from modules.sample_hijack import clip_separate
|
||||
from modules.util import get_file_from_folder_list, get_enabled_loras
|
||||
|
||||
|
||||
model_base = core.StableDiffusionModel()
|
||||
|
|
@ -59,21 +57,17 @@ def assert_model_integrity():
|
|||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def refresh_base_model(name, vae_name=None):
|
||||
def refresh_base_model(name):
|
||||
global model_base
|
||||
|
||||
filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
|
||||
filename = os.path.abspath(os.path.realpath(os.path.join(modules.config.path_checkpoints, name)))
|
||||
|
||||
vae_filename = None
|
||||
if vae_name is not None and vae_name != modules.flags.default_vae:
|
||||
vae_filename = get_file_from_folder_list(vae_name, modules.config.path_vae)
|
||||
|
||||
if model_base.filename == filename and model_base.vae_filename == vae_filename:
|
||||
if model_base.filename == filename:
|
||||
return
|
||||
|
||||
model_base = core.load_model(filename, vae_filename)
|
||||
model_base = core.StableDiffusionModel()
|
||||
model_base = core.load_model(filename)
|
||||
print(f'Base model loaded: {model_base.filename}')
|
||||
print(f'VAE loaded: {model_base.vae_filename}')
|
||||
return
|
||||
|
||||
|
||||
|
|
@ -82,7 +76,7 @@ def refresh_base_model(name, vae_name=None):
|
|||
def refresh_refiner_model(name):
|
||||
global model_refiner
|
||||
|
||||
filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
|
||||
filename = os.path.abspath(os.path.realpath(os.path.join(modules.config.path_checkpoints, name)))
|
||||
|
||||
if model_refiner.filename == filename:
|
||||
return
|
||||
|
|
@ -201,17 +195,6 @@ def clip_encode(texts, pool_top_k=1):
|
|||
return [[torch.cat(cond_list, dim=1), {"pooled_output": pooled_acc}]]
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def set_clip_skip(clip_skip: int):
|
||||
global final_clip
|
||||
|
||||
if final_clip is None:
|
||||
return
|
||||
|
||||
final_clip.clip_layer(-abs(clip_skip))
|
||||
return
|
||||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def clear_all_caches():
|
||||
|
|
@ -232,7 +215,7 @@ def prepare_text_encoder(async_call=True):
|
|||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def refresh_everything(refiner_model_name, base_model_name, loras,
|
||||
base_model_additional_loras=None, use_synthetic_refiner=False, vae_name=None):
|
||||
base_model_additional_loras=None, use_synthetic_refiner=False):
|
||||
global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion
|
||||
|
||||
final_unet = None
|
||||
|
|
@ -243,11 +226,11 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
|
|||
|
||||
if use_synthetic_refiner and refiner_model_name == 'None':
|
||||
print('Synthetic Refiner Activated')
|
||||
refresh_base_model(base_model_name, vae_name)
|
||||
refresh_base_model(base_model_name)
|
||||
synthesize_refiner_model()
|
||||
else:
|
||||
refresh_refiner_model(refiner_model_name)
|
||||
refresh_base_model(base_model_name, vae_name)
|
||||
refresh_base_model(base_model_name)
|
||||
|
||||
refresh_loras(loras, base_model_additional_loras=base_model_additional_loras)
|
||||
assert_model_integrity()
|
||||
|
|
@ -270,8 +253,7 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
|
|||
refresh_everything(
|
||||
refiner_model_name=modules.config.default_refiner_model_name,
|
||||
base_model_name=modules.config.default_base_model_name,
|
||||
loras=get_enabled_loras(modules.config.default_loras),
|
||||
vae_name=modules.config.default_vae,
|
||||
loras=modules.config.default_loras
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -333,7 +315,7 @@ def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'):
|
|||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False):
|
||||
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'):
|
||||
target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \
|
||||
= final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip
|
||||
|
||||
|
|
@ -392,7 +374,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
refiner_switch=switch,
|
||||
previewer_start=0,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
|
|
@ -411,7 +392,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
scheduler=scheduler_name,
|
||||
previewer_start=0,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
print('Refiner swapped by changing ksampler. Noise preserved.')
|
||||
|
||||
|
|
@ -434,7 +414,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
scheduler=scheduler_name,
|
||||
previewer_start=switch,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
|
||||
target_model = target_refiner_vae
|
||||
|
|
@ -443,7 +422,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
if refiner_swap_method == 'vae':
|
||||
modules.patch.patch_settings[os.getpid()].eps_record = 'vae'
|
||||
modules.patch.eps_record = 'vae'
|
||||
|
||||
if modules.inpaint_worker.current_task is not None:
|
||||
modules.inpaint_worker.current_task.unswap()
|
||||
|
|
@ -461,8 +440,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
sampler_name=sampler_name,
|
||||
scheduler=scheduler_name,
|
||||
previewer_start=0,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
previewer_end=steps
|
||||
)
|
||||
print('Fooocus VAE-based swap.')
|
||||
|
||||
|
|
@ -481,7 +459,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
denoise=denoise)[switch:] * k_sigmas
|
||||
len_sigmas = len(sigmas) - 1
|
||||
|
||||
noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)
|
||||
noise_mean = torch.mean(modules.patch.eps_record, dim=1, keepdim=True)
|
||||
|
||||
if modules.inpaint_worker.current_task is not None:
|
||||
modules.inpaint_worker.current_task.swap()
|
||||
|
|
@ -501,8 +479,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
previewer_start=switch,
|
||||
previewer_end=steps,
|
||||
sigmas=sigmas,
|
||||
noise_mean=noise_mean,
|
||||
disable_preview=disable_preview
|
||||
noise_mean=noise_mean
|
||||
)
|
||||
|
||||
target_model = target_refiner_vae
|
||||
|
|
@ -511,5 +488,5 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
images = core.pytorch_to_numpy(decoded_latent)
|
||||
modules.patch.patch_settings[os.getpid()].eps_record = None
|
||||
modules.patch.eps_record = None
|
||||
return images
|
||||
|
|
|
|||
|
|
@ -1,41 +0,0 @@
|
|||
import os
|
||||
from ast import literal_eval
|
||||
|
||||
|
||||
def makedirs_with_log(path):
|
||||
try:
|
||||
os.makedirs(path, exist_ok=True)
|
||||
except OSError as error:
|
||||
print(f'Directory {path} could not be created, reason: {error}')
|
||||
|
||||
|
||||
def get_files_from_folder(folder_path, extensions=None, name_filter=None):
|
||||
if not os.path.isdir(folder_path):
|
||||
raise ValueError("Folder path is not a valid directory.")
|
||||
|
||||
filenames = []
|
||||
|
||||
for root, _, files in os.walk(folder_path, topdown=False):
|
||||
relative_path = os.path.relpath(root, folder_path)
|
||||
if relative_path == ".":
|
||||
relative_path = ""
|
||||
for filename in sorted(files, key=lambda s: s.casefold()):
|
||||
_, file_extension = os.path.splitext(filename)
|
||||
if (extensions is None or file_extension.lower() in extensions) and (name_filter is None or name_filter in _):
|
||||
path = os.path.join(relative_path, filename)
|
||||
filenames.append(path)
|
||||
|
||||
return filenames
|
||||
|
||||
|
||||
def try_eval_env_var(value: str, expected_type=None):
|
||||
try:
|
||||
value_eval = value
|
||||
if expected_type is bool:
|
||||
value_eval = value.title()
|
||||
value_eval = literal_eval(value_eval)
|
||||
if expected_type is not None and not isinstance(value_eval, expected_type):
|
||||
return value
|
||||
return value_eval
|
||||
except:
|
||||
return value
|
||||
171
modules/flags.py
171
modules/flags.py
|
|
@ -1,5 +1,3 @@
|
|||
from enum import IntEnum, Enum
|
||||
|
||||
disabled = 'Disabled'
|
||||
enabled = 'Enabled'
|
||||
subtle_variation = 'Vary (Subtle)'
|
||||
|
|
@ -8,68 +6,20 @@ upscale_15 = 'Upscale (1.5x)'
|
|||
upscale_2 = 'Upscale (2x)'
|
||||
upscale_fast = 'Upscale (Fast 2x)'
|
||||
|
||||
uov_list = [disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast]
|
||||
uov_list = [
|
||||
disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast
|
||||
]
|
||||
|
||||
enhancement_uov_before = "Before First Enhancement"
|
||||
enhancement_uov_after = "After Last Enhancement"
|
||||
enhancement_uov_processing_order = [enhancement_uov_before, enhancement_uov_after]
|
||||
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"]
|
||||
|
||||
enhancement_uov_prompt_type_original = 'Original Prompts'
|
||||
enhancement_uov_prompt_type_last_filled = 'Last Filled Enhancement Prompts'
|
||||
enhancement_uov_prompt_types = [enhancement_uov_prompt_type_original, enhancement_uov_prompt_type_last_filled]
|
||||
|
||||
CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"]
|
||||
|
||||
# fooocus: a1111 (Civitai)
|
||||
KSAMPLER = {
|
||||
"euler": "Euler",
|
||||
"euler_ancestral": "Euler a",
|
||||
"heun": "Heun",
|
||||
"heunpp2": "",
|
||||
"dpm_2": "DPM2",
|
||||
"dpm_2_ancestral": "DPM2 a",
|
||||
"lms": "LMS",
|
||||
"dpm_fast": "DPM fast",
|
||||
"dpm_adaptive": "DPM adaptive",
|
||||
"dpmpp_2s_ancestral": "DPM++ 2S a",
|
||||
"dpmpp_sde": "DPM++ SDE",
|
||||
"dpmpp_sde_gpu": "DPM++ SDE",
|
||||
"dpmpp_2m": "DPM++ 2M",
|
||||
"dpmpp_2m_sde": "DPM++ 2M SDE",
|
||||
"dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
|
||||
"dpmpp_3m_sde": "",
|
||||
"dpmpp_3m_sde_gpu": "",
|
||||
"ddpm": "",
|
||||
"lcm": "LCM",
|
||||
"tcd": "TCD",
|
||||
"restart": "Restart"
|
||||
}
|
||||
|
||||
SAMPLER_EXTRA = {
|
||||
"ddim": "DDIM",
|
||||
"uni_pc": "UniPC",
|
||||
"uni_pc_bh2": ""
|
||||
}
|
||||
|
||||
SAMPLERS = KSAMPLER | SAMPLER_EXTRA
|
||||
|
||||
KSAMPLER_NAMES = list(KSAMPLER.keys())
|
||||
|
||||
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd", "edm_playground_v2.5"]
|
||||
SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
|
||||
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"]
|
||||
SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
|
||||
|
||||
sampler_list = SAMPLER_NAMES
|
||||
scheduler_list = SCHEDULER_NAMES
|
||||
|
||||
clip_skip_max = 12
|
||||
|
||||
default_vae = 'Default (model)'
|
||||
|
||||
refiner_swap_method = 'joint'
|
||||
|
||||
default_input_image_tab = 'uov_tab'
|
||||
input_image_tab_ids = ['uov_tab', 'ip_tab', 'inpaint_tab', 'describe_tab', 'enhance_tab', 'metadata_tab']
|
||||
|
||||
cn_ip = "ImagePrompt"
|
||||
cn_ip_face = "FaceSwap"
|
||||
cn_canny = "PyraCanny"
|
||||
|
|
@ -82,110 +32,13 @@ default_parameters = {
|
|||
cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0)
|
||||
} # stop, weight
|
||||
|
||||
output_formats = ['png', 'jpeg', 'webp']
|
||||
|
||||
inpaint_mask_models = ['u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg', 'silueta', 'isnet-general-use', 'isnet-anime', 'sam']
|
||||
inpaint_mask_cloth_category = ['full', 'upper', 'lower']
|
||||
inpaint_mask_sam_model = ['vit_b', 'vit_l', 'vit_h']
|
||||
|
||||
inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
|
||||
performance_selections = ['Speed', 'Quality', 'Extreme Speed']
|
||||
|
||||
inpaint_option_default = 'Inpaint or Outpaint (default)'
|
||||
inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)'
|
||||
inpaint_option_modify = 'Modify Content (add objects, change background, etc.)'
|
||||
inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option_modify]
|
||||
|
||||
describe_type_photo = 'Photograph'
|
||||
describe_type_anime = 'Art/Anime'
|
||||
describe_types = [describe_type_photo, describe_type_anime]
|
||||
|
||||
sdxl_aspect_ratios = [
|
||||
'704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152',
|
||||
'896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960',
|
||||
'1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768',
|
||||
'1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640',
|
||||
'1664*576', '1728*576'
|
||||
]
|
||||
|
||||
|
||||
class MetadataScheme(Enum):
|
||||
FOOOCUS = 'fooocus'
|
||||
A1111 = 'a1111'
|
||||
|
||||
|
||||
metadata_scheme = [
|
||||
(f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value),
|
||||
(f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value),
|
||||
]
|
||||
|
||||
|
||||
class OutputFormat(Enum):
|
||||
PNG = 'png'
|
||||
JPEG = 'jpeg'
|
||||
WEBP = 'webp'
|
||||
|
||||
@classmethod
|
||||
def list(cls) -> list:
|
||||
return list(map(lambda c: c.value, cls))
|
||||
|
||||
|
||||
class PerformanceLoRA(Enum):
|
||||
QUALITY = None
|
||||
SPEED = None
|
||||
EXTREME_SPEED = 'sdxl_lcm_lora.safetensors'
|
||||
LIGHTNING = 'sdxl_lightning_4step_lora.safetensors'
|
||||
HYPER_SD = 'sdxl_hyper_sd_4step_lora.safetensors'
|
||||
|
||||
|
||||
class Steps(IntEnum):
|
||||
QUALITY = 60
|
||||
SPEED = 30
|
||||
EXTREME_SPEED = 8
|
||||
LIGHTNING = 4
|
||||
HYPER_SD = 4
|
||||
|
||||
@classmethod
|
||||
def keys(cls) -> list:
|
||||
return list(map(lambda c: c, Steps.__members__))
|
||||
|
||||
|
||||
class StepsUOV(IntEnum):
|
||||
QUALITY = 36
|
||||
SPEED = 18
|
||||
EXTREME_SPEED = 8
|
||||
LIGHTNING = 4
|
||||
HYPER_SD = 4
|
||||
|
||||
|
||||
class Performance(Enum):
|
||||
QUALITY = 'Quality'
|
||||
SPEED = 'Speed'
|
||||
EXTREME_SPEED = 'Extreme Speed'
|
||||
LIGHTNING = 'Lightning'
|
||||
HYPER_SD = 'Hyper-SD'
|
||||
|
||||
@classmethod
|
||||
def list(cls) -> list:
|
||||
return list(map(lambda c: (c.name, c.value), cls))
|
||||
|
||||
@classmethod
|
||||
def values(cls) -> list:
|
||||
return list(map(lambda c: c.value, cls))
|
||||
|
||||
@classmethod
|
||||
def by_steps(cls, steps: int | str):
|
||||
return cls[Steps(int(steps)).name]
|
||||
|
||||
@classmethod
|
||||
def has_restricted_features(cls, x) -> bool:
|
||||
if isinstance(x, Performance):
|
||||
x = x.value
|
||||
return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value, cls.HYPER_SD.value]
|
||||
|
||||
def steps(self) -> int | None:
|
||||
return Steps[self.name].value if self.name in Steps.__members__ else None
|
||||
|
||||
def steps_uov(self) -> int | None:
|
||||
return StepsUOV[self.name].value if self.name in StepsUOV.__members__ else None
|
||||
|
||||
def lora_filename(self) -> str | None:
|
||||
return PerformanceLoRA[self.name].value if self.name in PerformanceLoRA.__members__ else None
|
||||
desc_type_photo = 'Photograph'
|
||||
desc_type_anime = 'Art/Anime'
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ from gradio_client.documentation import document, set_documentation_group
|
|||
from gradio_client.serializing import ImgSerializable
|
||||
from PIL import Image as _Image # using _ to minimize namespace pollution
|
||||
|
||||
from gradio import processing_utils, utils, Error
|
||||
from gradio import processing_utils, utils
|
||||
from gradio.components.base import IOComponent, _Keywords, Block
|
||||
from gradio.deprecation import warn_style_method_deprecation
|
||||
from gradio.events import (
|
||||
|
|
@ -275,10 +275,7 @@ class Image(
|
|||
x, mask = x["image"], x["mask"]
|
||||
|
||||
assert isinstance(x, str)
|
||||
try:
|
||||
im = processing_utils.decode_base64_to_image(x)
|
||||
except PIL.UnidentifiedImageError:
|
||||
raise Error("Unsupported image type in input")
|
||||
im = processing_utils.decode_base64_to_image(x)
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
im = im.convert(self.image_mode)
|
||||
|
|
|
|||
|
|
@ -1,83 +0,0 @@
|
|||
import json
|
||||
import os
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from multiprocessing import cpu_count
|
||||
|
||||
import args_manager
|
||||
from modules.util import sha256, HASH_SHA256_LENGTH, get_file_from_folder_list
|
||||
|
||||
hash_cache_filename = 'hash_cache.txt'
|
||||
hash_cache = {}
|
||||
|
||||
|
||||
def sha256_from_cache(filepath):
|
||||
global hash_cache
|
||||
if filepath not in hash_cache:
|
||||
print(f"[Cache] Calculating sha256 for {filepath}")
|
||||
hash_value = sha256(filepath)
|
||||
print(f"[Cache] sha256 for {filepath}: {hash_value}")
|
||||
hash_cache[filepath] = hash_value
|
||||
save_cache_to_file(filepath, hash_value)
|
||||
|
||||
return hash_cache[filepath]
|
||||
|
||||
|
||||
def load_cache_from_file():
|
||||
global hash_cache
|
||||
|
||||
try:
|
||||
if os.path.exists(hash_cache_filename):
|
||||
with open(hash_cache_filename, 'rt', encoding='utf-8') as fp:
|
||||
for line in fp:
|
||||
entry = json.loads(line)
|
||||
for filepath, hash_value in entry.items():
|
||||
if not os.path.exists(filepath) or not isinstance(hash_value, str) and len(hash_value) != HASH_SHA256_LENGTH:
|
||||
print(f'[Cache] Skipping invalid cache entry: {filepath}')
|
||||
continue
|
||||
hash_cache[filepath] = hash_value
|
||||
except Exception as e:
|
||||
print(f'[Cache] Loading failed: {e}')
|
||||
|
||||
|
||||
def save_cache_to_file(filename=None, hash_value=None):
|
||||
global hash_cache
|
||||
|
||||
if filename is not None and hash_value is not None:
|
||||
items = [(filename, hash_value)]
|
||||
mode = 'at'
|
||||
else:
|
||||
items = sorted(hash_cache.items())
|
||||
mode = 'wt'
|
||||
|
||||
try:
|
||||
with open(hash_cache_filename, mode, encoding='utf-8') as fp:
|
||||
for filepath, hash_value in items:
|
||||
json.dump({filepath: hash_value}, fp)
|
||||
fp.write('\n')
|
||||
except Exception as e:
|
||||
print(f'[Cache] Saving failed: {e}')
|
||||
|
||||
|
||||
def init_cache(model_filenames, paths_checkpoints, lora_filenames, paths_loras):
|
||||
load_cache_from_file()
|
||||
|
||||
if args_manager.args.rebuild_hash_cache:
|
||||
max_workers = args_manager.args.rebuild_hash_cache if args_manager.args.rebuild_hash_cache > 0 else cpu_count()
|
||||
rebuild_cache(lora_filenames, model_filenames, paths_checkpoints, paths_loras, max_workers)
|
||||
|
||||
# write cache to file again for sorting and cleanup of invalid cache entries
|
||||
save_cache_to_file()
|
||||
|
||||
|
||||
def rebuild_cache(lora_filenames, model_filenames, paths_checkpoints, paths_loras, max_workers=cpu_count()):
|
||||
def thread(filename, paths):
|
||||
filepath = get_file_from_folder_list(filename, paths)
|
||||
sha256_from_cache(filepath)
|
||||
|
||||
print('[Cache] Rebuilding hash cache')
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||||
for model_filename in model_filenames:
|
||||
executor.submit(thread, model_filename, paths_checkpoints)
|
||||
for lora_filename in lora_filenames:
|
||||
executor.submit(thread, lora_filename, paths_loras)
|
||||
print('[Cache] Done')
|
||||
115
modules/html.py
115
modules/html.py
|
|
@ -1,3 +1,118 @@
|
|||
css = '''
|
||||
.loader-container {
|
||||
display: flex; /* Use flex to align items horizontally */
|
||||
align-items: center; /* Center items vertically within the container */
|
||||
white-space: nowrap; /* Prevent line breaks within the container */
|
||||
}
|
||||
|
||||
.loader {
|
||||
border: 8px solid #f3f3f3; /* Light grey */
|
||||
border-top: 8px solid #3498db; /* Blue */
|
||||
border-radius: 50%;
|
||||
width: 30px;
|
||||
height: 30px;
|
||||
animation: spin 2s linear infinite;
|
||||
}
|
||||
|
||||
@keyframes spin {
|
||||
0% { transform: rotate(0deg); }
|
||||
100% { transform: rotate(360deg); }
|
||||
}
|
||||
|
||||
/* Style the progress bar */
|
||||
progress {
|
||||
appearance: none; /* Remove default styling */
|
||||
height: 20px; /* Set the height of the progress bar */
|
||||
border-radius: 5px; /* Round the corners of the progress bar */
|
||||
background-color: #f3f3f3; /* Light grey background */
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
/* Style the progress bar container */
|
||||
.progress-container {
|
||||
margin-left: 20px;
|
||||
margin-right: 20px;
|
||||
flex-grow: 1; /* Allow the progress container to take up remaining space */
|
||||
}
|
||||
|
||||
/* Set the color of the progress bar fill */
|
||||
progress::-webkit-progress-value {
|
||||
background-color: #3498db; /* Blue color for the fill */
|
||||
}
|
||||
|
||||
progress::-moz-progress-bar {
|
||||
background-color: #3498db; /* Blue color for the fill in Firefox */
|
||||
}
|
||||
|
||||
/* Style the text on the progress bar */
|
||||
progress::after {
|
||||
content: attr(value '%'); /* Display the progress value followed by '%' */
|
||||
position: absolute;
|
||||
top: 50%;
|
||||
left: 50%;
|
||||
transform: translate(-50%, -50%);
|
||||
color: white; /* Set text color */
|
||||
font-size: 14px; /* Set font size */
|
||||
}
|
||||
|
||||
/* Style other texts */
|
||||
.loader-container > span {
|
||||
margin-left: 5px; /* Add spacing between the progress bar and the text */
|
||||
}
|
||||
|
||||
.progress-bar > .generating {
|
||||
display: none !important;
|
||||
}
|
||||
|
||||
.progress-bar{
|
||||
height: 30px !important;
|
||||
}
|
||||
|
||||
.type_row{
|
||||
height: 80px !important;
|
||||
}
|
||||
|
||||
.type_row_half{
|
||||
height: 32px !important;
|
||||
}
|
||||
|
||||
.scroll-hide{
|
||||
resize: none !important;
|
||||
}
|
||||
|
||||
.refresh_button{
|
||||
border: none !important;
|
||||
background: none !important;
|
||||
font-size: none !important;
|
||||
box-shadow: none !important;
|
||||
}
|
||||
|
||||
.advanced_check_row{
|
||||
width: 250px !important;
|
||||
}
|
||||
|
||||
.min_check{
|
||||
min-width: min(1px, 100%) !important;
|
||||
}
|
||||
|
||||
.resizable_area {
|
||||
resize: vertical;
|
||||
overflow: auto !important;
|
||||
}
|
||||
|
||||
.aspect_ratios label {
|
||||
width: 140px !important;
|
||||
}
|
||||
|
||||
.aspect_ratios label span {
|
||||
white-space: nowrap !important;
|
||||
}
|
||||
|
||||
.aspect_ratios label input {
|
||||
margin-left: -5px !important;
|
||||
}
|
||||
|
||||
'''
|
||||
progress_html = '''
|
||||
<div class="loader-container">
|
||||
<div class="loader"></div>
|
||||
|
|
|
|||
|
|
@ -196,7 +196,7 @@ class InpaintWorker:
|
|||
|
||||
if inpaint_head_model is None:
|
||||
inpaint_head_model = InpaintHead()
|
||||
sd = torch.load(inpaint_head_model_path, map_location='cpu', weights_only=True)
|
||||
sd = torch.load(inpaint_head_model_path, map_location='cpu')
|
||||
inpaint_head_model.load_state_dict(sd)
|
||||
|
||||
feed = torch.cat([
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import os
|
||||
import importlib
|
||||
import importlib.util
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import re
|
||||
|
|
@ -10,10 +9,13 @@ import importlib.metadata
|
|||
import packaging.version
|
||||
from packaging.requirements import Requirement
|
||||
|
||||
|
||||
|
||||
|
||||
logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR) # sshh...
|
||||
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
|
||||
|
||||
re_requirement = re.compile(r"\s*([-\w]+)\s*(?:==\s*([-+.\w]+))?\s*")
|
||||
re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
|
||||
|
||||
python = sys.executable
|
||||
default_command_live = (os.environ.get('LAUNCH_LIVE_OUTPUT') == "1")
|
||||
|
|
@ -99,19 +101,3 @@ def requirements_met(requirements_file):
|
|||
|
||||
return True
|
||||
|
||||
|
||||
def delete_folder_content(folder, prefix=None):
|
||||
result = True
|
||||
|
||||
for filename in os.listdir(folder):
|
||||
file_path = os.path.join(folder, filename)
|
||||
try:
|
||||
if os.path.isfile(file_path) or os.path.islink(file_path):
|
||||
os.unlink(file_path)
|
||||
elif os.path.isdir(file_path):
|
||||
shutil.rmtree(file_path)
|
||||
except Exception as e:
|
||||
print(f'{prefix}Failed to delete {file_path}. Reason: {e}')
|
||||
result = False
|
||||
|
||||
return result
|
||||
|
|
@ -1,199 +1,78 @@
|
|||
import json
|
||||
import re
|
||||
from abc import ABC, abstractmethod
|
||||
from pathlib import Path
|
||||
|
||||
import gradio as gr
|
||||
from PIL import Image
|
||||
|
||||
import fooocus_version
|
||||
import modules.config
|
||||
import modules.sdxl_styles
|
||||
from modules.flags import MetadataScheme, Performance, Steps
|
||||
from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS
|
||||
from modules.hash_cache import sha256_from_cache
|
||||
from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list
|
||||
|
||||
re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
|
||||
re_param = re.compile(re_param_code)
|
||||
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
|
||||
|
||||
|
||||
def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool, inpaint_mode: str):
|
||||
loaded_parameter_dict = raw_metadata
|
||||
if isinstance(raw_metadata, str):
|
||||
loaded_parameter_dict = json.loads(raw_metadata)
|
||||
def load_parameter_button_click(raw_prompt_txt, is_generating):
|
||||
loaded_parameter_dict = json.loads(raw_prompt_txt)
|
||||
assert isinstance(loaded_parameter_dict, dict)
|
||||
|
||||
results = [len(loaded_parameter_dict) > 0]
|
||||
results = [True, 1]
|
||||
|
||||
get_image_number('image_number', 'Image Number', loaded_parameter_dict, results)
|
||||
get_str('prompt', 'Prompt', loaded_parameter_dict, results)
|
||||
get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
|
||||
get_list('styles', 'Styles', loaded_parameter_dict, results)
|
||||
performance = get_str('performance', 'Performance', loaded_parameter_dict, results)
|
||||
get_steps('steps', 'Steps', loaded_parameter_dict, results)
|
||||
get_number('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
|
||||
get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
|
||||
get_number('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
|
||||
get_number('sharpness', 'Sharpness', loaded_parameter_dict, results)
|
||||
get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results)
|
||||
get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results)
|
||||
get_number('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)
|
||||
get_number('clip_skip', 'CLIP Skip', loaded_parameter_dict, results, cast_type=int)
|
||||
get_str('base_model', 'Base Model', loaded_parameter_dict, results)
|
||||
get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results)
|
||||
get_number('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
|
||||
get_str('sampler', 'Sampler', loaded_parameter_dict, results)
|
||||
get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
|
||||
get_str('vae', 'VAE', loaded_parameter_dict, results)
|
||||
get_seed('seed', 'Seed', loaded_parameter_dict, results)
|
||||
get_inpaint_engine_version('inpaint_engine_version', 'Inpaint Engine Version', loaded_parameter_dict, results, inpaint_mode)
|
||||
get_inpaint_method('inpaint_method', 'Inpaint Mode', loaded_parameter_dict, results)
|
||||
|
||||
if is_generating:
|
||||
results.append(gr.update())
|
||||
else:
|
||||
results.append(gr.update(visible=True))
|
||||
|
||||
results.append(gr.update(visible=False))
|
||||
|
||||
get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)
|
||||
|
||||
# prevent performance LoRAs to be added twice, by performance and by lora
|
||||
performance_filename = None
|
||||
if performance is not None and performance in Performance.values():
|
||||
performance = Performance(performance)
|
||||
performance_filename = performance.lora_filename()
|
||||
|
||||
for i in range(modules.config.default_max_lora_number):
|
||||
get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results, performance_filename)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None:
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
h = loaded_parameter_dict.get('Prompt', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
return h
|
||||
except:
|
||||
results.append(gr.update())
|
||||
return None
|
||||
|
||||
|
||||
def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
h = loaded_parameter_dict.get('Negative Prompt', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Styles', None)
|
||||
h = eval(h)
|
||||
assert isinstance(h, list)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None, cast_type=float):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert h is not None
|
||||
h = cast_type(h)
|
||||
h = loaded_parameter_dict.get('Performance', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_image_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert h is not None
|
||||
h = int(h)
|
||||
h = min(h, modules.config.default_max_image_number)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(1)
|
||||
|
||||
|
||||
def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert h is not None
|
||||
h = int(h)
|
||||
# if not in steps or in steps and performance is not the same
|
||||
performance_name = source_dict.get('performance', '').replace(' ', '_').replace('-', '_').casefold()
|
||||
performance_candidates = [key for key in Steps.keys() if key.casefold() == performance_name and Steps[key] == h]
|
||||
if len(performance_candidates) == 0:
|
||||
results.append(h)
|
||||
return
|
||||
results.append(-1)
|
||||
except:
|
||||
results.append(-1)
|
||||
|
||||
|
||||
def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
h = loaded_parameter_dict.get('Resolution', None)
|
||||
width, height = eval(h)
|
||||
formatted = modules.config.add_ratio(f'{width}*{height}')
|
||||
if formatted in modules.config.available_aspect_ratios_labels:
|
||||
if formatted in modules.config.available_aspect_ratios:
|
||||
results.append(formatted)
|
||||
results.append(-1)
|
||||
results.append(-1)
|
||||
else:
|
||||
results.append(gr.update())
|
||||
results.append(int(width))
|
||||
results.append(int(height))
|
||||
results.append(width)
|
||||
results.append(height)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
h = loaded_parameter_dict.get('Sharpness', None)
|
||||
assert h is not None
|
||||
h = int(h)
|
||||
results.append(False)
|
||||
h = float(h)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_inpaint_engine_version(key: str, fallback: str | None, source_dict: dict, results: list, inpaint_mode: str, default=None) -> str | None:
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert isinstance(h, str) and h in modules.flags.inpaint_engine_versions
|
||||
if inpaint_mode != modules.flags.inpaint_option_detail:
|
||||
results.append(h)
|
||||
else:
|
||||
results.append(gr.update())
|
||||
h = loaded_parameter_dict.get('Guidance Scale', None)
|
||||
assert h is not None
|
||||
h = float(h)
|
||||
results.append(h)
|
||||
return h
|
||||
except:
|
||||
results.append(gr.update())
|
||||
results.append('empty')
|
||||
return None
|
||||
|
||||
|
||||
def get_inpaint_method(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None:
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert isinstance(h, str) and h in modules.flags.inpaint_options
|
||||
results.append(h)
|
||||
for i in range(modules.config.default_enhance_tabs):
|
||||
results.append(h)
|
||||
return h
|
||||
except:
|
||||
results.append(gr.update())
|
||||
for i in range(modules.config.default_enhance_tabs):
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
h = loaded_parameter_dict.get('ADM Guidance', None)
|
||||
p, n, e = eval(h)
|
||||
results.append(float(p))
|
||||
results.append(float(n))
|
||||
|
|
@ -203,448 +82,67 @@ def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results:
|
|||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
b1, b2, s1, s2 = eval(h)
|
||||
results.append(True)
|
||||
results.append(float(b1))
|
||||
results.append(float(b2))
|
||||
results.append(float(s1))
|
||||
results.append(float(s2))
|
||||
h = loaded_parameter_dict.get('Base Model', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Refiner Model', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Refiner Switch', None)
|
||||
assert h is not None
|
||||
h = float(h)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Sampler', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Scheduler', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Seed', None)
|
||||
assert h is not None
|
||||
h = int(h)
|
||||
results.append(False)
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_lora(key: str, fallback: str | None, source_dict: dict, results: list, performance_filename: str | None):
|
||||
try:
|
||||
split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ')
|
||||
enabled = True
|
||||
name = split_data[0]
|
||||
weight = split_data[1]
|
||||
|
||||
if len(split_data) == 3:
|
||||
enabled = split_data[0] == 'True'
|
||||
name = split_data[1]
|
||||
weight = split_data[2]
|
||||
|
||||
if name == performance_filename:
|
||||
raise Exception
|
||||
|
||||
weight = float(weight)
|
||||
results.append(enabled)
|
||||
results.append(name)
|
||||
results.append(weight)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(True)
|
||||
results.append('None')
|
||||
results.append(1)
|
||||
|
||||
|
||||
def parse_meta_from_preset(preset_content):
|
||||
assert isinstance(preset_content, dict)
|
||||
preset_prepared = {}
|
||||
items = preset_content
|
||||
|
||||
for settings_key, meta_key in modules.config.possible_preset_keys.items():
|
||||
if settings_key == "default_loras":
|
||||
loras = getattr(modules.config, settings_key)
|
||||
if settings_key in items:
|
||||
loras = items[settings_key]
|
||||
for index, lora in enumerate(loras[:modules.config.default_max_lora_number]):
|
||||
preset_prepared[f'lora_combined_{index + 1}'] = ' : '.join(map(str, lora))
|
||||
elif settings_key == "default_aspect_ratio":
|
||||
if settings_key in items and items[settings_key] is not None:
|
||||
default_aspect_ratio = items[settings_key]
|
||||
width, height = default_aspect_ratio.split('*')
|
||||
else:
|
||||
default_aspect_ratio = getattr(modules.config, settings_key)
|
||||
width, height = default_aspect_ratio.split('×')
|
||||
height = height[:height.index(" ")]
|
||||
preset_prepared[meta_key] = (width, height)
|
||||
else:
|
||||
preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[settings_key] is not None else getattr(modules.config, settings_key)
|
||||
|
||||
if settings_key == "default_styles" or settings_key == "default_aspect_ratio":
|
||||
preset_prepared[meta_key] = str(preset_prepared[meta_key])
|
||||
|
||||
return preset_prepared
|
||||
|
||||
|
||||
class MetadataParser(ABC):
|
||||
def __init__(self):
|
||||
self.raw_prompt: str = ''
|
||||
self.full_prompt: str = ''
|
||||
self.raw_negative_prompt: str = ''
|
||||
self.full_negative_prompt: str = ''
|
||||
self.steps: int = Steps.SPEED.value
|
||||
self.base_model_name: str = ''
|
||||
self.base_model_hash: str = ''
|
||||
self.refiner_model_name: str = ''
|
||||
self.refiner_model_hash: str = ''
|
||||
self.loras: list = []
|
||||
self.vae_name: str = ''
|
||||
|
||||
@abstractmethod
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def to_json(self, metadata: dict | str) -> dict:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def to_string(self, metadata: dict) -> str:
|
||||
raise NotImplementedError
|
||||
|
||||
def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
|
||||
refiner_model_name, loras, vae_name):
|
||||
self.raw_prompt = raw_prompt
|
||||
self.full_prompt = full_prompt
|
||||
self.raw_negative_prompt = raw_negative_prompt
|
||||
self.full_negative_prompt = full_negative_prompt
|
||||
self.steps = steps
|
||||
self.base_model_name = Path(base_model_name).stem
|
||||
|
||||
base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints)
|
||||
self.base_model_hash = sha256_from_cache(base_model_path)
|
||||
|
||||
if refiner_model_name not in ['', 'None']:
|
||||
self.refiner_model_name = Path(refiner_model_name).stem
|
||||
refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints)
|
||||
self.refiner_model_hash = sha256_from_cache(refiner_model_path)
|
||||
|
||||
self.loras = []
|
||||
for (lora_name, lora_weight) in loras:
|
||||
if lora_name != 'None':
|
||||
lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
|
||||
lora_hash = sha256_from_cache(lora_path)
|
||||
self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
|
||||
self.vae_name = Path(vae_name).stem
|
||||
|
||||
|
||||
class A1111MetadataParser(MetadataParser):
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
return MetadataScheme.A1111
|
||||
|
||||
fooocus_to_a1111 = {
|
||||
'raw_prompt': 'Raw prompt',
|
||||
'raw_negative_prompt': 'Raw negative prompt',
|
||||
'negative_prompt': 'Negative prompt',
|
||||
'styles': 'Styles',
|
||||
'performance': 'Performance',
|
||||
'steps': 'Steps',
|
||||
'sampler': 'Sampler',
|
||||
'scheduler': 'Scheduler',
|
||||
'vae': 'VAE',
|
||||
'guidance_scale': 'CFG scale',
|
||||
'seed': 'Seed',
|
||||
'resolution': 'Size',
|
||||
'sharpness': 'Sharpness',
|
||||
'adm_guidance': 'ADM Guidance',
|
||||
'refiner_swap_method': 'Refiner Swap Method',
|
||||
'adaptive_cfg': 'Adaptive CFG',
|
||||
'clip_skip': 'Clip skip',
|
||||
'overwrite_switch': 'Overwrite Switch',
|
||||
'freeu': 'FreeU',
|
||||
'base_model': 'Model',
|
||||
'base_model_hash': 'Model hash',
|
||||
'refiner_model': 'Refiner',
|
||||
'refiner_model_hash': 'Refiner hash',
|
||||
'lora_hashes': 'Lora hashes',
|
||||
'lora_weights': 'Lora weights',
|
||||
'created_by': 'User',
|
||||
'version': 'Version'
|
||||
}
|
||||
|
||||
def to_json(self, metadata: str) -> dict:
|
||||
metadata_prompt = ''
|
||||
metadata_negative_prompt = ''
|
||||
|
||||
done_with_prompt = False
|
||||
|
||||
*lines, lastline = metadata.strip().split("\n")
|
||||
if len(re_param.findall(lastline)) < 3:
|
||||
lines.append(lastline)
|
||||
lastline = ''
|
||||
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"):
|
||||
done_with_prompt = True
|
||||
line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip()
|
||||
if done_with_prompt:
|
||||
metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line
|
||||
else:
|
||||
metadata_prompt += ('' if metadata_prompt == '' else "\n") + line
|
||||
|
||||
found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt)
|
||||
|
||||
data = {
|
||||
'prompt': prompt,
|
||||
'negative_prompt': negative_prompt
|
||||
}
|
||||
|
||||
for k, v in re_param.findall(lastline):
|
||||
try:
|
||||
if v != '' and v[0] == '"' and v[-1] == '"':
|
||||
v = unquote(v)
|
||||
|
||||
m = re_imagesize.match(v)
|
||||
if m is not None:
|
||||
data['resolution'] = str((m.group(1), m.group(2)))
|
||||
else:
|
||||
data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v
|
||||
except Exception:
|
||||
print(f"Error parsing \"{k}: {v}\"")
|
||||
|
||||
# workaround for multiline prompts
|
||||
if 'raw_prompt' in data:
|
||||
data['prompt'] = data['raw_prompt']
|
||||
raw_prompt = data['raw_prompt'].replace("\n", ', ')
|
||||
if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles:
|
||||
found_styles.append(modules.sdxl_styles.fooocus_expansion)
|
||||
|
||||
if 'raw_negative_prompt' in data:
|
||||
data['negative_prompt'] = data['raw_negative_prompt']
|
||||
|
||||
data['styles'] = str(found_styles)
|
||||
|
||||
# try to load performance based on steps, fallback for direct A1111 imports
|
||||
if 'steps' in data and 'performance' in data is None:
|
||||
try:
|
||||
data['performance'] = Performance.by_steps(data['steps']).value
|
||||
except ValueError | KeyError:
|
||||
pass
|
||||
|
||||
if 'sampler' in data:
|
||||
data['sampler'] = data['sampler'].replace(' Karras', '')
|
||||
# get key
|
||||
for k, v in SAMPLERS.items():
|
||||
if v == data['sampler']:
|
||||
data['sampler'] = k
|
||||
break
|
||||
|
||||
for key in ['base_model', 'refiner_model', 'vae']:
|
||||
if key in data:
|
||||
if key == 'vae':
|
||||
self.add_extension_to_filename(data, modules.config.vae_filenames, 'vae')
|
||||
else:
|
||||
self.add_extension_to_filename(data, modules.config.model_filenames, key)
|
||||
|
||||
lora_data = ''
|
||||
if 'lora_weights' in data and data['lora_weights'] != '':
|
||||
lora_data = data['lora_weights']
|
||||
elif 'lora_hashes' in data and data['lora_hashes'] != '' and data['lora_hashes'].split(', ')[0].count(':') == 2:
|
||||
lora_data = data['lora_hashes']
|
||||
|
||||
if lora_data != '':
|
||||
for li, lora in enumerate(lora_data.split(', ')):
|
||||
lora_split = lora.split(': ')
|
||||
lora_name = lora_split[0]
|
||||
lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1]
|
||||
for filename in modules.config.lora_filenames:
|
||||
path = Path(filename)
|
||||
if lora_name == path.stem:
|
||||
data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
|
||||
break
|
||||
|
||||
return data
|
||||
|
||||
def to_string(self, metadata: dict) -> str:
|
||||
data = {k: v for _, k, v in metadata}
|
||||
|
||||
width, height = eval(data['resolution'])
|
||||
|
||||
sampler = data['sampler']
|
||||
scheduler = data['scheduler']
|
||||
|
||||
if sampler in SAMPLERS and SAMPLERS[sampler] != '':
|
||||
sampler = SAMPLERS[sampler]
|
||||
if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
|
||||
sampler += f' Karras'
|
||||
|
||||
generation_params = {
|
||||
self.fooocus_to_a1111['steps']: self.steps,
|
||||
self.fooocus_to_a1111['sampler']: sampler,
|
||||
self.fooocus_to_a1111['seed']: data['seed'],
|
||||
self.fooocus_to_a1111['resolution']: f'{width}x{height}',
|
||||
self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'],
|
||||
self.fooocus_to_a1111['sharpness']: data['sharpness'],
|
||||
self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'],
|
||||
self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem,
|
||||
self.fooocus_to_a1111['base_model_hash']: self.base_model_hash,
|
||||
|
||||
self.fooocus_to_a1111['performance']: data['performance'],
|
||||
self.fooocus_to_a1111['scheduler']: scheduler,
|
||||
self.fooocus_to_a1111['vae']: Path(data['vae']).stem,
|
||||
# workaround for multiline prompts
|
||||
self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
|
||||
self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
|
||||
}
|
||||
|
||||
if self.refiner_model_name not in ['', 'None']:
|
||||
generation_params |= {
|
||||
self.fooocus_to_a1111['refiner_model']: self.refiner_model_name,
|
||||
self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
|
||||
}
|
||||
|
||||
for key in ['adaptive_cfg', 'clip_skip', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
|
||||
if key in data:
|
||||
generation_params[self.fooocus_to_a1111[key]] = data[key]
|
||||
|
||||
if len(self.loras) > 0:
|
||||
lora_hashes = []
|
||||
lora_weights = []
|
||||
for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
|
||||
# workaround for Fooocus not knowing LoRA name in LoRA metadata
|
||||
lora_hashes.append(f'{lora_name}: {lora_hash}')
|
||||
lora_weights.append(f'{lora_name}: {lora_weight}')
|
||||
lora_hashes_string = ', '.join(lora_hashes)
|
||||
lora_weights_string = ', '.join(lora_weights)
|
||||
generation_params[self.fooocus_to_a1111['lora_hashes']] = lora_hashes_string
|
||||
generation_params[self.fooocus_to_a1111['lora_weights']] = lora_weights_string
|
||||
|
||||
generation_params[self.fooocus_to_a1111['version']] = data['version']
|
||||
|
||||
if modules.config.metadata_created_by != '':
|
||||
generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by
|
||||
|
||||
generation_params_text = ", ".join(
|
||||
[k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if
|
||||
v is not None])
|
||||
positive_prompt_resolved = ', '.join(self.full_prompt)
|
||||
negative_prompt_resolved = ', '.join(self.full_negative_prompt)
|
||||
negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
|
||||
return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
@staticmethod
|
||||
def add_extension_to_filename(data, filenames, key):
|
||||
for filename in filenames:
|
||||
path = Path(filename)
|
||||
if data[key] == path.stem:
|
||||
data[key] = filename
|
||||
break
|
||||
|
||||
|
||||
class FooocusMetadataParser(MetadataParser):
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
return MetadataScheme.FOOOCUS
|
||||
|
||||
def to_json(self, metadata: dict) -> dict:
|
||||
for key, value in metadata.items():
|
||||
if value in ['', 'None']:
|
||||
continue
|
||||
if key in ['base_model', 'refiner_model']:
|
||||
metadata[key] = self.replace_value_with_filename(key, value, modules.config.model_filenames)
|
||||
elif key.startswith('lora_combined_'):
|
||||
metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames)
|
||||
elif key == 'vae':
|
||||
metadata[key] = self.replace_value_with_filename(key, value, modules.config.vae_filenames)
|
||||
else:
|
||||
continue
|
||||
|
||||
return metadata
|
||||
|
||||
def to_string(self, metadata: list) -> str:
|
||||
for li, (label, key, value) in enumerate(metadata):
|
||||
# remove model folder paths from metadata
|
||||
if key.startswith('lora_combined_'):
|
||||
name, weight = value.split(' : ')
|
||||
name = Path(name).stem
|
||||
value = f'{name} : {weight}'
|
||||
metadata[li] = (label, key, value)
|
||||
|
||||
res = {k: v for _, k, v in metadata}
|
||||
|
||||
res['full_prompt'] = self.full_prompt
|
||||
res['full_negative_prompt'] = self.full_negative_prompt
|
||||
res['steps'] = self.steps
|
||||
res['base_model'] = self.base_model_name
|
||||
res['base_model_hash'] = self.base_model_hash
|
||||
|
||||
if self.refiner_model_name not in ['', 'None']:
|
||||
res['refiner_model'] = self.refiner_model_name
|
||||
res['refiner_model_hash'] = self.refiner_model_hash
|
||||
|
||||
res['vae'] = self.vae_name
|
||||
res['loras'] = self.loras
|
||||
|
||||
if modules.config.metadata_created_by != '':
|
||||
res['created_by'] = modules.config.metadata_created_by
|
||||
|
||||
return json.dumps(dict(sorted(res.items())))
|
||||
|
||||
@staticmethod
|
||||
def replace_value_with_filename(key, value, filenames):
|
||||
for filename in filenames:
|
||||
path = Path(filename)
|
||||
if key.startswith('lora_combined_'):
|
||||
name, weight = value.split(' : ')
|
||||
if name == path.stem:
|
||||
return f'{filename} : {weight}'
|
||||
elif value == path.stem:
|
||||
return filename
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
|
||||
match metadata_scheme:
|
||||
case MetadataScheme.FOOOCUS:
|
||||
return FooocusMetadataParser()
|
||||
case MetadataScheme.A1111:
|
||||
return A1111MetadataParser()
|
||||
case _:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def read_info_from_image(file) -> tuple[str | None, MetadataScheme | None]:
|
||||
items = (file.info or {}).copy()
|
||||
|
||||
parameters = items.pop('parameters', None)
|
||||
metadata_scheme = items.pop('fooocus_scheme', None)
|
||||
exif = items.pop('exif', None)
|
||||
|
||||
if parameters is not None and is_json(parameters):
|
||||
parameters = json.loads(parameters)
|
||||
elif exif is not None:
|
||||
exif = file.getexif()
|
||||
# 0x9286 = UserComment
|
||||
parameters = exif.get(0x9286, None)
|
||||
# 0x927C = MakerNote
|
||||
metadata_scheme = exif.get(0x927C, None)
|
||||
|
||||
if is_json(parameters):
|
||||
parameters = json.loads(parameters)
|
||||
|
||||
try:
|
||||
metadata_scheme = MetadataScheme(metadata_scheme)
|
||||
except ValueError:
|
||||
metadata_scheme = None
|
||||
|
||||
# broad fallback
|
||||
if isinstance(parameters, dict):
|
||||
metadata_scheme = MetadataScheme.FOOOCUS
|
||||
|
||||
if isinstance(parameters, str):
|
||||
metadata_scheme = MetadataScheme.A1111
|
||||
|
||||
return parameters, metadata_scheme
|
||||
|
||||
|
||||
def get_exif(metadata: str | None, metadata_scheme: str):
|
||||
exif = Image.Exif()
|
||||
# tags see see https://github.com/python-pillow/Pillow/blob/9.2.x/src/PIL/ExifTags.py
|
||||
# 0x9286 = UserComment
|
||||
exif[0x9286] = metadata
|
||||
# 0x0131 = Software
|
||||
exif[0x0131] = 'Fooocus v' + fooocus_version.version
|
||||
# 0x927C = MakerNote
|
||||
exif[0x927C] = metadata_scheme
|
||||
return exif
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
if is_generating:
|
||||
results.append(gr.update())
|
||||
else:
|
||||
results.append(gr.update(visible=True))
|
||||
|
||||
results.append(gr.update(visible=False))
|
||||
|
||||
for i in range(1, 6):
|
||||
try:
|
||||
n, w = loaded_parameter_dict.get(f'LoRA {i}').split(' : ')
|
||||
w = float(w)
|
||||
results.append(n)
|
||||
results.append(w)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
return results
|
||||
|
|
|
|||
|
|
@ -14,8 +14,6 @@ def load_file_from_url(
|
|||
|
||||
Returns the path to the downloaded file.
|
||||
"""
|
||||
domain = os.environ.get("HF_MIRROR", "https://huggingface.co").rstrip('/')
|
||||
url = str.replace(url, "https://huggingface.co", domain, 1)
|
||||
os.makedirs(model_dir, exist_ok=True)
|
||||
if not file_name:
|
||||
parts = urlparse(url)
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ import ldm_patched.controlnet.cldm
|
|||
import ldm_patched.modules.model_patcher
|
||||
import ldm_patched.modules.samplers
|
||||
import ldm_patched.modules.args_parser
|
||||
import modules.advanced_parameters as advanced_parameters
|
||||
import warnings
|
||||
import safetensors.torch
|
||||
import modules.constants as constants
|
||||
|
|
@ -28,25 +29,15 @@ from modules.patch_precision import patch_all_precision
|
|||
from modules.patch_clip import patch_all_clip
|
||||
|
||||
|
||||
class PatchSettings:
|
||||
def __init__(self,
|
||||
sharpness=2.0,
|
||||
adm_scaler_end=0.3,
|
||||
positive_adm_scale=1.5,
|
||||
negative_adm_scale=0.8,
|
||||
controlnet_softness=0.25,
|
||||
adaptive_cfg=7.0):
|
||||
self.sharpness = sharpness
|
||||
self.adm_scaler_end = adm_scaler_end
|
||||
self.positive_adm_scale = positive_adm_scale
|
||||
self.negative_adm_scale = negative_adm_scale
|
||||
self.controlnet_softness = controlnet_softness
|
||||
self.adaptive_cfg = adaptive_cfg
|
||||
self.global_diffusion_progress = 0
|
||||
self.eps_record = None
|
||||
sharpness = 2.0
|
||||
|
||||
adm_scaler_end = 0.3
|
||||
positive_adm_scale = 1.5
|
||||
negative_adm_scale = 0.8
|
||||
|
||||
patch_settings = {}
|
||||
adaptive_cfg = 7.0
|
||||
global_diffusion_progress = 0
|
||||
eps_record = None
|
||||
|
||||
|
||||
def calculate_weight_patched(self, patches, weight, key):
|
||||
|
|
@ -210,13 +201,14 @@ class BrownianTreeNoiseSamplerPatched:
|
|||
|
||||
|
||||
def compute_cfg(uncond, cond, cfg_scale, t):
|
||||
pid = os.getpid()
|
||||
mimic_cfg = float(patch_settings[pid].adaptive_cfg)
|
||||
global adaptive_cfg
|
||||
|
||||
mimic_cfg = float(adaptive_cfg)
|
||||
real_cfg = float(cfg_scale)
|
||||
|
||||
real_eps = uncond + real_cfg * (cond - uncond)
|
||||
|
||||
if cfg_scale > patch_settings[pid].adaptive_cfg:
|
||||
if cfg_scale > adaptive_cfg:
|
||||
mimicked_eps = uncond + mimic_cfg * (cond - uncond)
|
||||
return real_eps * t + mimicked_eps * (1 - t)
|
||||
else:
|
||||
|
|
@ -224,13 +216,13 @@ def compute_cfg(uncond, cond, cfg_scale, t):
|
|||
|
||||
|
||||
def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
|
||||
pid = os.getpid()
|
||||
global eps_record
|
||||
|
||||
if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False):
|
||||
final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
|
||||
|
||||
if patch_settings[pid].eps_record is not None:
|
||||
patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu()
|
||||
if eps_record is not None:
|
||||
eps_record = ((x - final_x0) / timestep).cpu()
|
||||
|
||||
return final_x0
|
||||
|
||||
|
|
@ -239,16 +231,16 @@ def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
|
|||
positive_eps = x - positive_x0
|
||||
negative_eps = x - negative_x0
|
||||
|
||||
alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress
|
||||
alpha = 0.001 * sharpness * global_diffusion_progress
|
||||
|
||||
positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
|
||||
positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
|
||||
|
||||
final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,
|
||||
cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress)
|
||||
cfg_scale=cond_scale, t=global_diffusion_progress)
|
||||
|
||||
if patch_settings[pid].eps_record is not None:
|
||||
patch_settings[pid].eps_record = (final_eps / timestep).cpu()
|
||||
if eps_record is not None:
|
||||
eps_record = (final_eps / timestep).cpu()
|
||||
|
||||
return x - final_eps
|
||||
|
||||
|
|
@ -263,19 +255,20 @@ def round_to_64(x):
|
|||
|
||||
|
||||
def sdxl_encode_adm_patched(self, **kwargs):
|
||||
global positive_adm_scale, negative_adm_scale
|
||||
|
||||
clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
|
||||
width = kwargs.get("width", 1024)
|
||||
height = kwargs.get("height", 1024)
|
||||
target_width = width
|
||||
target_height = height
|
||||
pid = os.getpid()
|
||||
|
||||
if kwargs.get("prompt_type", "") == "negative":
|
||||
width = float(width) * patch_settings[pid].negative_adm_scale
|
||||
height = float(height) * patch_settings[pid].negative_adm_scale
|
||||
width = float(width) * negative_adm_scale
|
||||
height = float(height) * negative_adm_scale
|
||||
elif kwargs.get("prompt_type", "") == "positive":
|
||||
width = float(width) * patch_settings[pid].positive_adm_scale
|
||||
height = float(height) * patch_settings[pid].positive_adm_scale
|
||||
width = float(width) * positive_adm_scale
|
||||
height = float(height) * positive_adm_scale
|
||||
|
||||
def embedder(number_list):
|
||||
h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
|
||||
|
|
@ -329,7 +322,7 @@ def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale,
|
|||
|
||||
def timed_adm(y, timesteps):
|
||||
if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
|
||||
y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]
|
||||
y_mask = (timesteps > 999.0 * (1.0 - float(adm_scaler_end))).to(y)[..., None]
|
||||
y_with_adm = y[..., :2816].clone()
|
||||
y_without_adm = y[..., 2816:].clone()
|
||||
return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
|
||||
|
|
@ -339,7 +332,6 @@ def timed_adm(y, timesteps):
|
|||
def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
|
||||
t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
|
||||
emb = self.time_embed(t_emb)
|
||||
pid = os.getpid()
|
||||
|
||||
guided_hint = self.input_hint_block(hint, emb, context)
|
||||
|
||||
|
|
@ -365,17 +357,19 @@ def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
|
|||
h = self.middle_block(h, emb, context)
|
||||
outs.append(self.middle_block_out(h, emb, context))
|
||||
|
||||
if patch_settings[pid].controlnet_softness > 0:
|
||||
if advanced_parameters.controlnet_softness > 0:
|
||||
for i in range(10):
|
||||
k = 1.0 - float(i) / 9.0
|
||||
outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k)
|
||||
outs[i] = outs[i] * (1.0 - advanced_parameters.controlnet_softness * k)
|
||||
|
||||
return outs
|
||||
|
||||
|
||||
def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
||||
global global_diffusion_progress
|
||||
|
||||
self.current_step = 1.0 - timesteps.to(x) / 999.0
|
||||
patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
|
||||
global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
|
||||
|
||||
y = timed_adm(y, timesteps)
|
||||
|
||||
|
|
@ -489,7 +483,7 @@ def patch_all():
|
|||
if ldm_patched.modules.model_management.directml_enabled:
|
||||
ldm_patched.modules.model_management.lowvram_available = True
|
||||
ldm_patched.modules.model_management.OOM_EXCEPTION = Exception
|
||||
|
||||
|
||||
patch_all_precision()
|
||||
patch_all_clip()
|
||||
|
||||
|
|
|
|||
|
|
@ -51,8 +51,6 @@ def patched_register_schedule(self, given_betas=None, beta_schedule="linear", ti
|
|||
self.linear_end = linear_end
|
||||
sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32)
|
||||
self.set_sigmas(sigmas)
|
||||
alphas_cumprod = torch.tensor(alphas_cumprod, dtype=torch.float32)
|
||||
self.set_alphas_cumprod(alphas_cumprod)
|
||||
return
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -5,49 +5,26 @@ import json
|
|||
import urllib.parse
|
||||
|
||||
from PIL import Image
|
||||
from PIL.PngImagePlugin import PngInfo
|
||||
from modules.flags import OutputFormat
|
||||
from modules.meta_parser import MetadataParser, get_exif
|
||||
from modules.util import generate_temp_filename
|
||||
|
||||
|
||||
log_cache = {}
|
||||
|
||||
|
||||
def get_current_html_path(output_format=None):
|
||||
output_format = output_format if output_format else modules.config.default_output_format
|
||||
def get_current_html_path():
|
||||
date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs,
|
||||
extension=output_format)
|
||||
extension='png')
|
||||
html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
|
||||
return html_name
|
||||
|
||||
|
||||
def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None, task=None, persist_image=True) -> str:
|
||||
path_outputs = modules.config.temp_path if args_manager.args.disable_image_log or not persist_image else modules.config.path_outputs
|
||||
output_format = output_format if output_format else modules.config.default_output_format
|
||||
date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
|
||||
os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
|
||||
|
||||
parsed_parameters = metadata_parser.to_string(metadata.copy()) if metadata_parser is not None else ''
|
||||
image = Image.fromarray(img)
|
||||
|
||||
if output_format == OutputFormat.PNG.value:
|
||||
if parsed_parameters != '':
|
||||
pnginfo = PngInfo()
|
||||
pnginfo.add_text('parameters', parsed_parameters)
|
||||
pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value)
|
||||
else:
|
||||
pnginfo = None
|
||||
image.save(local_temp_filename, pnginfo=pnginfo)
|
||||
elif output_format == OutputFormat.JPEG.value:
|
||||
image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
|
||||
elif output_format == OutputFormat.WEBP.value:
|
||||
image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
|
||||
else:
|
||||
image.save(local_temp_filename)
|
||||
|
||||
def log(img, dic):
|
||||
if args_manager.args.disable_image_log:
|
||||
return local_temp_filename
|
||||
return
|
||||
|
||||
date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs, extension='png')
|
||||
os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
|
||||
Image.fromarray(img).save(local_temp_filename)
|
||||
html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
|
||||
|
||||
css_styles = (
|
||||
|
|
@ -55,7 +32,7 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
|
|||
"body { background-color: #121212; color: #E0E0E0; } "
|
||||
"a { color: #BB86FC; } "
|
||||
".metadata { border-collapse: collapse; width: 100%; } "
|
||||
".metadata .label { width: 15%; } "
|
||||
".metadata .key { width: 15%; } "
|
||||
".metadata .value { width: 85%; font-weight: bold; } "
|
||||
".metadata th, .metadata td { border: 1px solid #4d4d4d; padding: 4px; } "
|
||||
".image-container img { height: auto; max-width: 512px; display: block; padding-right:10px; } "
|
||||
|
|
@ -91,7 +68,7 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
|
|||
</script>"""
|
||||
)
|
||||
|
||||
begin_part = f"<!DOCTYPE html><html><head><title>Fooocus Log {date_string}</title>{css_styles}</head><body>{js}<p>Fooocus Log {date_string} (private)</p>\n<p>Metadata is embedded if enabled in the config or developer debug mode. You can find the information for each image in line Metadata Scheme.</p><!--fooocus-log-split-->\n\n"
|
||||
begin_part = f"<html><head><title>Fooocus Log {date_string}</title>{css_styles}</head><body>{js}<p>Fooocus Log {date_string} (private)</p>\n<p>All images are clean, without any hidden data/meta, and safe to share with others.</p><!--fooocus-log-split-->\n\n"
|
||||
end_part = f'\n<!--fooocus-log-split--></body></html>'
|
||||
|
||||
middle_part = log_cache.get(html_name, "")
|
||||
|
|
@ -106,20 +83,14 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
|
|||
|
||||
div_name = only_name.replace('.', '_')
|
||||
item = f"<div id=\"{div_name}\" class=\"image-container\"><hr><table><tr>\n"
|
||||
item += f"<td><a href=\"{only_name}\" target=\"_blank\"><img src='{only_name}' onerror=\"this.closest('.image-container').style.display='none';\" loading='lazy'/></a><div>{only_name}</div></td>"
|
||||
item += f"<td><a href=\"{only_name}\" target=\"_blank\"><img src='{only_name}' onerror=\"this.closest('.image-container').style.display='none';\" loading='lazy'></img></a><div>{only_name}</div></td>"
|
||||
item += "<td><table class='metadata'>"
|
||||
for label, key, value in metadata:
|
||||
for key, value in dic:
|
||||
value_txt = str(value).replace('\n', ' </br> ')
|
||||
item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
|
||||
|
||||
if task is not None and 'positive' in task and 'negative' in task:
|
||||
full_prompt_details = f"""<details><summary>Positive</summary>{', '.join(task['positive'])}</details>
|
||||
<details><summary>Negative</summary>{', '.join(task['negative'])}</details>"""
|
||||
item += f"<tr><td class='label'>Full raw prompt</td><td class='value'>{full_prompt_details}</td></tr>\n"
|
||||
|
||||
item += f"<tr><td class='key'>{key}</td><td class='value'>{value_txt}</td></tr>\n"
|
||||
item += "</table>"
|
||||
|
||||
js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v, in metadata}, indent=0), safe='')
|
||||
js_txt = urllib.parse.quote(json.dumps({k: v for k, v in dic}, indent=0), safe='')
|
||||
item += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
|
||||
|
||||
item += "</td>"
|
||||
|
|
@ -134,4 +105,4 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
|
|||
|
||||
log_cache[html_name] = middle_part
|
||||
|
||||
return local_temp_filename
|
||||
return
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ import ldm_patched.modules.samplers
|
|||
import ldm_patched.modules.model_management
|
||||
|
||||
from collections import namedtuple
|
||||
from ldm_patched.contrib.external_align_your_steps import AlignYourStepsScheduler
|
||||
from ldm_patched.contrib.external_custom_sampler import SDTurboScheduler
|
||||
from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
|
||||
from ldm_patched.modules.samplers import normal_scheduler, simple_scheduler, ddim_scheduler
|
||||
|
|
@ -175,10 +174,7 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
|
|||
elif scheduler_name == "sgm_uniform":
|
||||
sigmas = normal_scheduler(model, steps, sgm=True)
|
||||
elif scheduler_name == "turbo":
|
||||
sigmas = SDTurboScheduler().get_sigmas(model=model, steps=steps, denoise=1.0)[0]
|
||||
elif scheduler_name == "align_your_steps":
|
||||
model_type = 'SDXL' if isinstance(model.latent_format, ldm_patched.modules.latent_formats.SDXL) else 'SD1'
|
||||
sigmas = AlignYourStepsScheduler().get_sigmas(model_type=model_type, steps=steps, denoise=1.0)[0]
|
||||
sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0]
|
||||
else:
|
||||
raise TypeError("error invalid scheduler")
|
||||
return sigmas
|
||||
|
|
|
|||
|
|
@ -1,13 +1,14 @@
|
|||
import os
|
||||
import re
|
||||
import json
|
||||
import math
|
||||
|
||||
from modules.extra_utils import get_files_from_folder
|
||||
from random import Random
|
||||
from modules.util import get_files_from_folder
|
||||
|
||||
|
||||
# cannot use modules.config - validators causing circular imports
|
||||
styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/'))
|
||||
wildcards_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../wildcards/'))
|
||||
wildcards_max_bfs_depth = 64
|
||||
|
||||
|
||||
def normalize_key(k):
|
||||
|
|
@ -23,6 +24,7 @@ def normalize_key(k):
|
|||
|
||||
|
||||
styles = {}
|
||||
|
||||
styles_files = get_files_from_folder(styles_path, ['.json'])
|
||||
|
||||
for x in ['sdxl_styles_fooocus.json',
|
||||
|
|
@ -48,50 +50,33 @@ for styles_file in styles_files:
|
|||
print(f'Failed to load style file {styles_file}')
|
||||
|
||||
style_keys = list(styles.keys())
|
||||
fooocus_expansion = 'Fooocus V2'
|
||||
random_style_name = 'Random Style'
|
||||
legal_style_names = [fooocus_expansion, random_style_name] + style_keys
|
||||
|
||||
|
||||
def get_random_style(rng: Random) -> str:
|
||||
return rng.choice(list(styles.items()))[0]
|
||||
fooocus_expansion = "Fooocus V2"
|
||||
legal_style_names = [fooocus_expansion] + style_keys
|
||||
|
||||
|
||||
def apply_style(style, positive):
|
||||
p, n = styles[style]
|
||||
return p.replace('{prompt}', positive).splitlines(), n.splitlines(), '{prompt}' in p
|
||||
return p.replace('{prompt}', positive).splitlines(), n.splitlines()
|
||||
|
||||
|
||||
def get_words(arrays, total_mult, index):
|
||||
if len(arrays) == 1:
|
||||
return [arrays[0].split(',')[index]]
|
||||
else:
|
||||
words = arrays[0].split(',')
|
||||
word = words[index % len(words)]
|
||||
index -= index % len(words)
|
||||
index /= len(words)
|
||||
index = math.floor(index)
|
||||
return [word] + get_words(arrays[1:], math.floor(total_mult / len(words)), index)
|
||||
def apply_wildcards(wildcard_text, rng, directory=wildcards_path):
|
||||
for _ in range(wildcards_max_bfs_depth):
|
||||
placeholders = re.findall(r'__([\w-]+)__', wildcard_text)
|
||||
if len(placeholders) == 0:
|
||||
return wildcard_text
|
||||
|
||||
print(f'[Wildcards] processing: {wildcard_text}')
|
||||
for placeholder in placeholders:
|
||||
try:
|
||||
words = open(os.path.join(directory, f'{placeholder}.txt'), encoding='utf-8').read().splitlines()
|
||||
words = [x for x in words if x != '']
|
||||
assert len(words) > 0
|
||||
wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
|
||||
except:
|
||||
print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '
|
||||
f'Using "{placeholder}" as a normal word.')
|
||||
wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder)
|
||||
print(f'[Wildcards] {wildcard_text}')
|
||||
|
||||
def apply_arrays(text, index):
|
||||
arrays = re.findall(r'\[\[(.*?)\]\]', text)
|
||||
if len(arrays) == 0:
|
||||
return text
|
||||
|
||||
print(f'[Arrays] processing: {text}')
|
||||
mult = 1
|
||||
for arr in arrays:
|
||||
words = arr.split(',')
|
||||
mult *= len(words)
|
||||
|
||||
index %= mult
|
||||
chosen_words = get_words(arrays, mult, index)
|
||||
|
||||
i = 0
|
||||
for arr in arrays:
|
||||
text = text.replace(f'[[{arr}]]', chosen_words[i], 1)
|
||||
i = i+1
|
||||
|
||||
return text
|
||||
|
||||
print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
|
||||
return wildcard_text
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ def javascript_html():
|
|||
head += f'<script type="text/javascript" src="{edit_attention_js_path}"></script>\n'
|
||||
head += f'<script type="text/javascript" src="{viewer_js_path}"></script>\n'
|
||||
head += f'<script type="text/javascript" src="{image_viewer_js_path}"></script>\n'
|
||||
head += f'<meta name="samples-path" content="{samples_path}">\n'
|
||||
head += f'<meta name="samples-path" content="{samples_path}"></meta>\n'
|
||||
|
||||
if args_manager.args.theme:
|
||||
head += f'<script type="text/javascript">set_theme(\"{args_manager.args.theme}\");</script>\n'
|
||||
|
|
|
|||
|
|
@ -1,11 +1,13 @@
|
|||
from collections import OrderedDict
|
||||
|
||||
import modules.core as core
|
||||
import os
|
||||
import torch
|
||||
from ldm_patched.contrib.external_upscale_model import ImageUpscaleWithModel
|
||||
from ldm_patched.pfn.architecture.RRDB import RRDBNet as ESRGAN
|
||||
from modules.config import downloading_upscale_model
|
||||
import modules.core as core
|
||||
|
||||
from ldm_patched.pfn.architecture.RRDB import RRDBNet as ESRGAN
|
||||
from ldm_patched.contrib.external_upscale_model import ImageUpscaleWithModel
|
||||
from collections import OrderedDict
|
||||
from modules.config import path_upscale_models
|
||||
|
||||
model_filename = os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin')
|
||||
opImageUpscaleWithModel = ImageUpscaleWithModel()
|
||||
model = None
|
||||
|
||||
|
|
@ -16,8 +18,7 @@ def perform_upscale(img):
|
|||
print(f'Upscaling image with shape {str(img.shape)} ...')
|
||||
|
||||
if model is None:
|
||||
model_filename = downloading_upscale_model()
|
||||
sd = torch.load(model_filename, weights_only=True)
|
||||
sd = torch.load(model_filename)
|
||||
sdo = OrderedDict()
|
||||
for k, v in sd.items():
|
||||
sdo[k.replace('residual_block_', 'RDB')] = v
|
||||
|
|
|
|||
368
modules/util.py
368
modules/util.py
|
|
@ -1,32 +1,15 @@
|
|||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import datetime
|
||||
import random
|
||||
import math
|
||||
import os
|
||||
import cv2
|
||||
import re
|
||||
from typing import List, Tuple, AnyStr, NamedTuple
|
||||
|
||||
import json
|
||||
import hashlib
|
||||
|
||||
from PIL import Image
|
||||
|
||||
import modules.config
|
||||
import modules.sdxl_styles
|
||||
from modules.flags import Performance
|
||||
|
||||
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
|
||||
|
||||
# Regexp compiled once. Matches entries with the following pattern:
|
||||
# <lora:some_lora:1>
|
||||
# <lora:aNotherLora:-1.6>
|
||||
LORAS_PROMPT_PATTERN = re.compile(r"(<lora:([^:]+):([+-]?(?:\d+(?:\.\d*)?|\.\d+))>)", re.X)
|
||||
|
||||
HASH_SHA256_LENGTH = 10
|
||||
|
||||
|
||||
def erode_or_dilate(x, k):
|
||||
k = int(k)
|
||||
|
|
@ -172,344 +155,23 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
|
|||
random_number = random.randint(1000, 9999)
|
||||
filename = f"{time_string}_{random_number}.{extension}"
|
||||
result = os.path.join(folder, date_string, filename)
|
||||
return date_string, os.path.abspath(result), filename
|
||||
return date_string, os.path.abspath(os.path.realpath(result)), filename
|
||||
|
||||
|
||||
def sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):
|
||||
if use_addnet_hash:
|
||||
with open(filename, "rb") as file:
|
||||
sha256_value = addnet_hash_safetensors(file)
|
||||
else:
|
||||
sha256_value = calculate_sha256(filename)
|
||||
def get_files_from_folder(folder_path, exensions=None, name_filter=None):
|
||||
if not os.path.isdir(folder_path):
|
||||
raise ValueError("Folder path is not a valid directory.")
|
||||
|
||||
return sha256_value[:length] if length is not None else sha256_value
|
||||
filenames = []
|
||||
|
||||
for root, dirs, files in os.walk(folder_path):
|
||||
relative_path = os.path.relpath(root, folder_path)
|
||||
if relative_path == ".":
|
||||
relative_path = ""
|
||||
for filename in files:
|
||||
_, file_extension = os.path.splitext(filename)
|
||||
if (exensions == None or file_extension.lower() in exensions) and (name_filter == None or name_filter in _):
|
||||
path = os.path.join(relative_path, filename)
|
||||
filenames.append(path)
|
||||
|
||||
def addnet_hash_safetensors(b):
|
||||
"""kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"""
|
||||
hash_sha256 = hashlib.sha256()
|
||||
blksize = 1024 * 1024
|
||||
|
||||
b.seek(0)
|
||||
header = b.read(8)
|
||||
n = int.from_bytes(header, "little")
|
||||
|
||||
offset = n + 8
|
||||
b.seek(offset)
|
||||
for chunk in iter(lambda: b.read(blksize), b""):
|
||||
hash_sha256.update(chunk)
|
||||
|
||||
return hash_sha256.hexdigest()
|
||||
|
||||
|
||||
def calculate_sha256(filename) -> str:
|
||||
hash_sha256 = hashlib.sha256()
|
||||
blksize = 1024 * 1024
|
||||
|
||||
with open(filename, "rb") as f:
|
||||
for chunk in iter(lambda: f.read(blksize), b""):
|
||||
hash_sha256.update(chunk)
|
||||
|
||||
return hash_sha256.hexdigest()
|
||||
|
||||
|
||||
def quote(text):
|
||||
if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
|
||||
return text
|
||||
|
||||
return json.dumps(text, ensure_ascii=False)
|
||||
|
||||
|
||||
def unquote(text):
|
||||
if len(text) == 0 or text[0] != '"' or text[-1] != '"':
|
||||
return text
|
||||
|
||||
try:
|
||||
return json.loads(text)
|
||||
except Exception:
|
||||
return text
|
||||
|
||||
|
||||
def unwrap_style_text_from_prompt(style_text, prompt):
|
||||
"""
|
||||
Checks the prompt to see if the style text is wrapped around it. If so,
|
||||
returns True plus the prompt text without the style text. Otherwise, returns
|
||||
False with the original prompt.
|
||||
|
||||
Note that the "cleaned" version of the style text is only used for matching
|
||||
purposes here. It isn't returned; the original style text is not modified.
|
||||
"""
|
||||
stripped_prompt = prompt
|
||||
stripped_style_text = style_text
|
||||
if "{prompt}" in stripped_style_text:
|
||||
# Work out whether the prompt is wrapped in the style text. If so, we
|
||||
# return True and the "inner" prompt text that isn't part of the style.
|
||||
try:
|
||||
left, right = stripped_style_text.split("{prompt}", 2)
|
||||
except ValueError as e:
|
||||
# If the style text has multple "{prompt}"s, we can't split it into
|
||||
# two parts. This is an error, but we can't do anything about it.
|
||||
print(f"Unable to compare style text to prompt:\n{style_text}")
|
||||
print(f"Error: {e}")
|
||||
return False, prompt, ''
|
||||
|
||||
left_pos = stripped_prompt.find(left)
|
||||
right_pos = stripped_prompt.find(right)
|
||||
if 0 <= left_pos < right_pos:
|
||||
real_prompt = stripped_prompt[left_pos + len(left):right_pos]
|
||||
prompt = stripped_prompt.replace(left + real_prompt + right, '', 1)
|
||||
if prompt.startswith(", "):
|
||||
prompt = prompt[2:]
|
||||
if prompt.endswith(", "):
|
||||
prompt = prompt[:-2]
|
||||
return True, prompt, real_prompt
|
||||
else:
|
||||
# Work out whether the given prompt starts with the style text. If so, we
|
||||
# return True and the prompt text up to where the style text starts.
|
||||
if stripped_prompt.endswith(stripped_style_text):
|
||||
prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]
|
||||
if prompt.endswith(", "):
|
||||
prompt = prompt[:-2]
|
||||
return True, prompt, prompt
|
||||
|
||||
return False, prompt, ''
|
||||
|
||||
|
||||
def extract_original_prompts(style, prompt, negative_prompt):
|
||||
"""
|
||||
Takes a style and compares it to the prompt and negative prompt. If the style
|
||||
matches, returns True plus the prompt and negative prompt with the style text
|
||||
removed. Otherwise, returns False with the original prompt and negative prompt.
|
||||
"""
|
||||
if not style.prompt and not style.negative_prompt:
|
||||
return False, prompt, negative_prompt
|
||||
|
||||
match_positive, extracted_positive, real_prompt = unwrap_style_text_from_prompt(
|
||||
style.prompt, prompt
|
||||
)
|
||||
if not match_positive:
|
||||
return False, prompt, negative_prompt, ''
|
||||
|
||||
match_negative, extracted_negative, _ = unwrap_style_text_from_prompt(
|
||||
style.negative_prompt, negative_prompt
|
||||
)
|
||||
if not match_negative:
|
||||
return False, prompt, negative_prompt, ''
|
||||
|
||||
return True, extracted_positive, extracted_negative, real_prompt
|
||||
|
||||
|
||||
def extract_styles_from_prompt(prompt, negative_prompt):
|
||||
extracted = []
|
||||
applicable_styles = []
|
||||
|
||||
for style_name, (style_prompt, style_negative_prompt) in modules.sdxl_styles.styles.items():
|
||||
applicable_styles.append(PromptStyle(name=style_name, prompt=style_prompt, negative_prompt=style_negative_prompt))
|
||||
|
||||
real_prompt = ''
|
||||
|
||||
while True:
|
||||
found_style = None
|
||||
|
||||
for style in applicable_styles:
|
||||
is_match, new_prompt, new_neg_prompt, new_real_prompt = extract_original_prompts(
|
||||
style, prompt, negative_prompt
|
||||
)
|
||||
if is_match:
|
||||
found_style = style
|
||||
prompt = new_prompt
|
||||
negative_prompt = new_neg_prompt
|
||||
if real_prompt == '' and new_real_prompt != '' and new_real_prompt != prompt:
|
||||
real_prompt = new_real_prompt
|
||||
break
|
||||
|
||||
if not found_style:
|
||||
break
|
||||
|
||||
applicable_styles.remove(found_style)
|
||||
extracted.append(found_style.name)
|
||||
|
||||
# add prompt expansion if not all styles could be resolved
|
||||
if prompt != '':
|
||||
if real_prompt != '':
|
||||
extracted.append(modules.sdxl_styles.fooocus_expansion)
|
||||
else:
|
||||
# find real_prompt when only prompt expansion is selected
|
||||
first_word = prompt.split(', ')[0]
|
||||
first_word_positions = [i for i in range(len(prompt)) if prompt.startswith(first_word, i)]
|
||||
if len(first_word_positions) > 1:
|
||||
real_prompt = prompt[:first_word_positions[-1]]
|
||||
extracted.append(modules.sdxl_styles.fooocus_expansion)
|
||||
if real_prompt.endswith(', '):
|
||||
real_prompt = real_prompt[:-2]
|
||||
|
||||
return list(reversed(extracted)), real_prompt, negative_prompt
|
||||
|
||||
|
||||
class PromptStyle(NamedTuple):
|
||||
name: str
|
||||
prompt: str
|
||||
negative_prompt: str
|
||||
|
||||
|
||||
def is_json(data: str) -> bool:
|
||||
try:
|
||||
loaded_json = json.loads(data)
|
||||
assert isinstance(loaded_json, dict)
|
||||
except (ValueError, AssertionError):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_filname_by_stem(lora_name, filenames: List[str]) -> str | None:
|
||||
for filename in filenames:
|
||||
path = Path(filename)
|
||||
if lora_name == path.stem:
|
||||
return filename
|
||||
return None
|
||||
|
||||
|
||||
def get_file_from_folder_list(name, folders):
|
||||
if not isinstance(folders, list):
|
||||
folders = [folders]
|
||||
|
||||
for folder in folders:
|
||||
filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))
|
||||
if os.path.isfile(filename):
|
||||
return filename
|
||||
|
||||
return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))
|
||||
|
||||
|
||||
def get_enabled_loras(loras: list, remove_none=True) -> list:
|
||||
return [(lora[1], lora[2]) for lora in loras if lora[0] and (lora[1] != 'None' if remove_none else True)]
|
||||
|
||||
|
||||
def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5,
|
||||
skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True,
|
||||
lora_filenames=None) -> tuple[List[Tuple[AnyStr, float]], str]:
|
||||
# prevent unintended side effects when returning without detection
|
||||
loras = loras.copy()
|
||||
|
||||
if lora_filenames is None:
|
||||
lora_filenames = []
|
||||
|
||||
found_loras = []
|
||||
prompt_without_loras = ''
|
||||
cleaned_prompt = ''
|
||||
|
||||
for token in prompt.split(','):
|
||||
matches = LORAS_PROMPT_PATTERN.findall(token)
|
||||
|
||||
if len(matches) == 0:
|
||||
prompt_without_loras += token + ', '
|
||||
continue
|
||||
for match in matches:
|
||||
lora_name = match[1] + '.safetensors'
|
||||
if not skip_file_check:
|
||||
lora_name = get_filname_by_stem(match[1], lora_filenames)
|
||||
if lora_name is not None:
|
||||
found_loras.append((lora_name, float(match[2])))
|
||||
token = token.replace(match[0], '')
|
||||
prompt_without_loras += token + ', '
|
||||
|
||||
if prompt_without_loras != '':
|
||||
cleaned_prompt = prompt_without_loras[:-2]
|
||||
|
||||
if prompt_cleanup:
|
||||
cleaned_prompt = cleanup_prompt(prompt_without_loras)
|
||||
|
||||
new_loras = []
|
||||
lora_names = [lora[0] for lora in loras]
|
||||
for found_lora in found_loras:
|
||||
if deduplicate_loras and (found_lora[0] in lora_names or found_lora in new_loras):
|
||||
continue
|
||||
new_loras.append(found_lora)
|
||||
|
||||
if len(new_loras) == 0:
|
||||
return loras, cleaned_prompt
|
||||
|
||||
updated_loras = []
|
||||
for lora in loras + new_loras:
|
||||
if lora[0] != "None":
|
||||
updated_loras.append(lora)
|
||||
|
||||
return updated_loras[:loras_limit], cleaned_prompt
|
||||
|
||||
|
||||
def remove_performance_lora(filenames: list, performance: Performance | None):
|
||||
loras_without_performance = filenames.copy()
|
||||
|
||||
if performance is None:
|
||||
return loras_without_performance
|
||||
|
||||
performance_lora = performance.lora_filename()
|
||||
|
||||
for filename in filenames:
|
||||
path = Path(filename)
|
||||
if performance_lora == path.name:
|
||||
loras_without_performance.remove(filename)
|
||||
|
||||
return loras_without_performance
|
||||
|
||||
|
||||
def cleanup_prompt(prompt):
|
||||
prompt = re.sub(' +', ' ', prompt)
|
||||
prompt = re.sub(',+', ',', prompt)
|
||||
cleaned_prompt = ''
|
||||
for token in prompt.split(','):
|
||||
token = token.strip()
|
||||
if token == '':
|
||||
continue
|
||||
cleaned_prompt += token + ', '
|
||||
return cleaned_prompt[:-2]
|
||||
|
||||
|
||||
def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order) -> str:
|
||||
for _ in range(modules.config.wildcards_max_bfs_depth):
|
||||
placeholders = re.findall(r'__([\w-]+)__', wildcard_text)
|
||||
if len(placeholders) == 0:
|
||||
return wildcard_text
|
||||
|
||||
print(f'[Wildcards] processing: {wildcard_text}')
|
||||
for placeholder in placeholders:
|
||||
try:
|
||||
matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder]
|
||||
words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines()
|
||||
words = [x for x in words if x != '']
|
||||
assert len(words) > 0
|
||||
if read_wildcards_in_order:
|
||||
wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1)
|
||||
else:
|
||||
wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
|
||||
except:
|
||||
print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '
|
||||
f'Using "{placeholder}" as a normal word.')
|
||||
wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder)
|
||||
print(f'[Wildcards] {wildcard_text}')
|
||||
|
||||
print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
|
||||
return wildcard_text
|
||||
|
||||
|
||||
def get_image_size_info(image: np.ndarray, aspect_ratios: list) -> str:
|
||||
try:
|
||||
image = Image.fromarray(np.uint8(image))
|
||||
width, height = image.size
|
||||
ratio = round(width / height, 2)
|
||||
gcd = math.gcd(width, height)
|
||||
lcm_ratio = f'{width // gcd}:{height // gcd}'
|
||||
size_info = f'Image Size: {width} x {height}, Ratio: {ratio}, {lcm_ratio}'
|
||||
|
||||
closest_ratio = min(aspect_ratios, key=lambda x: abs(ratio - float(x.split('*')[0]) / float(x.split('*')[1])))
|
||||
recommended_width, recommended_height = map(int, closest_ratio.split('*'))
|
||||
recommended_ratio = round(recommended_width / recommended_height, 2)
|
||||
recommended_gcd = math.gcd(recommended_width, recommended_height)
|
||||
recommended_lcm_ratio = f'{recommended_width // recommended_gcd}:{recommended_height // recommended_gcd}'
|
||||
|
||||
size_info = f'{width} x {height}, {ratio}, {lcm_ratio}'
|
||||
size_info += f'\n{recommended_width} x {recommended_height}, {recommended_ratio}, {recommended_lcm_ratio}'
|
||||
|
||||
return size_info
|
||||
except Exception as e:
|
||||
return f'Error reading image: {e}'
|
||||
return sorted(filenames, key=lambda x: -1 if os.sep in x else 1)
|
||||
|
|
|
|||
Binary file not shown.
Binary file not shown.
|
|
@ -1,8 +0,0 @@
|
|||
*.json
|
||||
!anime.json
|
||||
!default.json
|
||||
!lcm.json
|
||||
!playground_v2.5.json
|
||||
!pony_v6.json
|
||||
!realistic.json
|
||||
!sai.json
|
||||
|
|
@ -1,60 +1,45 @@
|
|||
{
|
||||
"default_model": "animaPencilXL_v500.safetensors",
|
||||
"default_model": "animaPencilXL_v100.safetensors",
|
||||
"default_refiner": "None",
|
||||
"default_refiner_switch": 0.5,
|
||||
"default_loras": [
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
],
|
||||
"default_cfg_scale": 6.0,
|
||||
"default_cfg_scale": 7.0,
|
||||
"default_sample_sharpness": 2.0,
|
||||
"default_sampler": "dpmpp_2m_sde_gpu",
|
||||
"default_scheduler": "karras",
|
||||
"default_performance": "Speed",
|
||||
"default_prompt": "",
|
||||
"default_prompt": "1girl, ",
|
||||
"default_prompt_negative": "",
|
||||
"default_styles": [
|
||||
"Fooocus V2",
|
||||
"Fooocus Semi Realistic",
|
||||
"Fooocus Negative",
|
||||
"Fooocus Masterpiece"
|
||||
],
|
||||
"default_aspect_ratio": "896*1152",
|
||||
"default_overwrite_step": -1,
|
||||
"checkpoint_downloads": {
|
||||
"animaPencilXL_v500.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/animaPencilXL_v500.safetensors"
|
||||
"animaPencilXL_v100.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/animaPencilXL_v100.safetensors"
|
||||
},
|
||||
"embeddings_downloads": {},
|
||||
"lora_downloads": {},
|
||||
"previous_default_models": [
|
||||
"animaPencilXL_v400.safetensors",
|
||||
"animaPencilXL_v310.safetensors",
|
||||
"animaPencilXL_v300.safetensors",
|
||||
"animaPencilXL_v260.safetensors",
|
||||
"animaPencilXL_v210.safetensors",
|
||||
"animaPencilXL_v200.safetensors",
|
||||
"animaPencilXL_v100.safetensors"
|
||||
]
|
||||
}
|
||||
"lora_downloads": {}
|
||||
}
|
||||
|
|
@ -4,27 +4,22 @@
|
|||
"default_refiner_switch": 0.5,
|
||||
"default_loras": [
|
||||
[
|
||||
true,
|
||||
"sd_xl_offset_example-lora_1.0.safetensors",
|
||||
0.1
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
|
|
@ -42,7 +37,6 @@
|
|||
"Fooocus Sharp"
|
||||
],
|
||||
"default_aspect_ratio": "1152*896",
|
||||
"default_overwrite_step": -1,
|
||||
"checkpoint_downloads": {
|
||||
"juggernautXL_v8Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors"
|
||||
},
|
||||
|
|
|
|||
|
|
@ -1,30 +1,25 @@
|
|||
{
|
||||
"default_model": "juggernautXL_v8Rundiffusion.safetensors",
|
||||
"default_model": "juggernautXL_version6Rundiffusion.safetensors",
|
||||
"default_refiner": "None",
|
||||
"default_refiner_switch": 0.5,
|
||||
"default_loras": [
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
|
|
@ -42,17 +37,9 @@
|
|||
"Fooocus Sharp"
|
||||
],
|
||||
"default_aspect_ratio": "1152*896",
|
||||
"default_overwrite_step": -1,
|
||||
"checkpoint_downloads": {
|
||||
"juggernautXL_v8Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors"
|
||||
"juggernautXL_version6Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_version6Rundiffusion.safetensors"
|
||||
},
|
||||
"embeddings_downloads": {},
|
||||
"lora_downloads": {},
|
||||
"previous_default_models": [
|
||||
"juggernautXL_version8Rundiffusion.safetensors",
|
||||
"juggernautXL_version7Rundiffusion.safetensors",
|
||||
"juggernautXL_v7Rundiffusion.safetensors",
|
||||
"juggernautXL_version6Rundiffusion.safetensors",
|
||||
"juggernautXL_v6Rundiffusion.safetensors"
|
||||
]
|
||||
"lora_downloads": {}
|
||||
}
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
{
|
||||
"default_model": "juggernautXL_v8Rundiffusion.safetensors",
|
||||
"default_refiner": "None",
|
||||
"default_refiner_switch": 0.5,
|
||||
"default_loras": [
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
],
|
||||
"default_cfg_scale": 4.0,
|
||||
"default_sample_sharpness": 2.0,
|
||||
"default_sampler": "dpmpp_2m_sde_gpu",
|
||||
"default_scheduler": "karras",
|
||||
"default_performance": "Lightning",
|
||||
"default_prompt": "",
|
||||
"default_prompt_negative": "",
|
||||
"default_styles": [
|
||||
"Fooocus V2",
|
||||
"Fooocus Enhance",
|
||||
"Fooocus Sharp"
|
||||
],
|
||||
"default_aspect_ratio": "1152*896",
|
||||
"checkpoint_downloads": {
|
||||
"juggernautXL_v8Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors"
|
||||
},
|
||||
"embeddings_downloads": {},
|
||||
"lora_downloads": {},
|
||||
"previous_default_models": [
|
||||
"juggernautXL_version8Rundiffusion.safetensors",
|
||||
"juggernautXL_version7Rundiffusion.safetensors",
|
||||
"juggernautXL_v7Rundiffusion.safetensors",
|
||||
"juggernautXL_version6Rundiffusion.safetensors",
|
||||
"juggernautXL_v6Rundiffusion.safetensors"
|
||||
]
|
||||
}
|
||||
|
|
@ -1,51 +0,0 @@
|
|||
{
|
||||
"default_model": "playground-v2.5-1024px-aesthetic.fp16.safetensors",
|
||||
"default_refiner": "None",
|
||||
"default_refiner_switch": 0.5,
|
||||
"default_loras": [
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
],
|
||||
"default_cfg_scale": 2.0,
|
||||
"default_sample_sharpness": 2.0,
|
||||
"default_sampler": "dpmpp_2m",
|
||||
"default_scheduler": "edm_playground_v2.5",
|
||||
"default_performance": "Speed",
|
||||
"default_prompt": "",
|
||||
"default_prompt_negative": "",
|
||||
"default_styles": [
|
||||
"Fooocus V2"
|
||||
],
|
||||
"default_aspect_ratio": "1024*1024",
|
||||
"default_overwrite_step": -1,
|
||||
"default_inpaint_engine_version": "None",
|
||||
"checkpoint_downloads": {
|
||||
"playground-v2.5-1024px-aesthetic.fp16.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/playground-v2.5-1024px-aesthetic.fp16.safetensors"
|
||||
},
|
||||
"embeddings_downloads": {},
|
||||
"lora_downloads": {},
|
||||
"previous_default_models": []
|
||||
}
|
||||
|
|
@ -1,54 +0,0 @@
|
|||
{
|
||||
"default_model": "ponyDiffusionV6XL.safetensors",
|
||||
"default_refiner": "None",
|
||||
"default_refiner_switch": 0.5,
|
||||
"default_vae": "ponyDiffusionV6XL_vae.safetensors",
|
||||
"default_loras": [
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
],
|
||||
"default_cfg_scale": 7.0,
|
||||
"default_sample_sharpness": 2.0,
|
||||
"default_sampler": "dpmpp_2m_sde_gpu",
|
||||
"default_scheduler": "karras",
|
||||
"default_performance": "Speed",
|
||||
"default_prompt": "",
|
||||
"default_prompt_negative": "",
|
||||
"default_styles": [
|
||||
"Fooocus Pony"
|
||||
],
|
||||
"default_aspect_ratio": "896*1152",
|
||||
"default_overwrite_step": -1,
|
||||
"default_inpaint_engine_version": "None",
|
||||
"checkpoint_downloads": {
|
||||
"ponyDiffusionV6XL.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/ponyDiffusionV6XL.safetensors"
|
||||
},
|
||||
"embeddings_downloads": {},
|
||||
"lora_downloads": {},
|
||||
"vae_downloads": {
|
||||
"ponyDiffusionV6XL_vae.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/ponyDiffusionV6XL_vae.safetensors"
|
||||
}
|
||||
}
|
||||
|
|
@ -1,30 +1,25 @@
|
|||
{
|
||||
"default_model": "realisticStockPhoto_v20.safetensors",
|
||||
"default_refiner": "None",
|
||||
"default_refiner": "",
|
||||
"default_refiner_switch": 0.5,
|
||||
"default_loras": [
|
||||
[
|
||||
true,
|
||||
"SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors",
|
||||
"SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors",
|
||||
0.25
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
|
|
@ -42,13 +37,12 @@
|
|||
"Fooocus Negative"
|
||||
],
|
||||
"default_aspect_ratio": "896*1152",
|
||||
"default_overwrite_step": -1,
|
||||
"checkpoint_downloads": {
|
||||
"realisticStockPhoto_v20.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/realisticStockPhoto_v20.safetensors"
|
||||
},
|
||||
"embeddings_downloads": {},
|
||||
"lora_downloads": {
|
||||
"SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors"
|
||||
"SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors"
|
||||
},
|
||||
"previous_default_models": ["realisticStockPhoto_v10.safetensors"]
|
||||
}
|
||||
|
|
@ -4,27 +4,22 @@
|
|||
"default_refiner_switch": 0.75,
|
||||
"default_loras": [
|
||||
[
|
||||
true,
|
||||
"sd_xl_offset_example-lora_1.0.safetensors",
|
||||
0.5
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
|
|
@ -41,7 +36,6 @@
|
|||
"Fooocus Cinematic"
|
||||
],
|
||||
"default_aspect_ratio": "1152*896",
|
||||
"default_overwrite_step": -1,
|
||||
"checkpoint_downloads": {
|
||||
"sd_xl_base_1.0_0.9vae.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors",
|
||||
"sd_xl_refiner_1.0_0.9vae.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors"
|
||||
|
|
@ -49,6 +43,5 @@
|
|||
"embeddings_downloads": {},
|
||||
"lora_downloads": {
|
||||
"sd_xl_offset_example-lora_1.0.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors"
|
||||
},
|
||||
"previous_default_models": []
|
||||
}
|
||||
}
|
||||
175
readme.md
175
readme.md
|
|
@ -1,30 +1,40 @@
|
|||
<div align=center>
|
||||
<img src="https://github.com/lllyasviel/Fooocus/assets/19834515/483fb86d-c9a2-4c20-997c-46dafc124f25">
|
||||
|
||||
**Non-cherry-picked** random batch by just typing two words "forest elf",
|
||||
|
||||
without any parameter tweaking, without any strange prompt tags.
|
||||
|
||||
See also **non-cherry-picked** generalization and diversity tests [here](https://github.com/lllyasviel/Fooocus/discussions/2067) and [here](https://github.com/lllyasviel/Fooocus/discussions/808) and [here](https://github.com/lllyasviel/Fooocus/discussions/679) and [here](https://github.com/lllyasviel/Fooocus/discussions/679#realistic).
|
||||
|
||||
In the entire open source community, only Fooocus can achieve this level of **non-cherry-picked** quality.
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
# Fooocus
|
||||
|
||||
[>>> Click Here to Install Fooocus <<<](#download)
|
||||
Fooocus is an image generating software (based on [Gradio](https://www.gradio.app/)).
|
||||
|
||||
Fooocus is an image generating software (based on [Gradio](https://www.gradio.app/) <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>).
|
||||
Fooocus is a rethinking of Stable Diffusion and Midjourney’s designs:
|
||||
|
||||
Fooocus presents a rethinking of image generator designs. The software is offline, open source, and free, while at the same time, similar to many online image generators like Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images. Fooocus has also simplified the installation: between pressing "download" and generating the first image, the number of needed mouse clicks is strictly limited to less than 3. Minimal GPU memory requirement is 4GB (Nvidia).
|
||||
* Learned from Stable Diffusion, the software is offline, open source, and free.
|
||||
|
||||
* Learned from Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images.
|
||||
|
||||
Fooocus has included and automated [lots of inner optimizations and quality improvements](#tech_list). Users can forget all those difficult technical parameters, and just enjoy the interaction between human and computer to "explore new mediums of thought and expanding the imaginative powers of the human species" `[1]`.
|
||||
|
||||
Fooocus has simplified the installation. Between pressing "download" and generating the first image, the number of needed mouse clicks is strictly limited to less than 3. Minimal GPU memory requirement is 4GB (Nvidia).
|
||||
|
||||
`[1]` David Holz, 2019.
|
||||
|
||||
**Recently many fake websites exist on Google when you search “fooocus”. Do not trust those – here is the only official source of Fooocus.**
|
||||
|
||||
# Project Status: Limited Long-Term Support (LTS) with Bug Fixes Only
|
||||
## [Installing Fooocus](#download)
|
||||
|
||||
The Fooocus project, built entirely on the **Stable Diffusion XL** architecture, is now in a state of limited long-term support (LTS) with bug fixes only. As the existing functionalities are considered as nearly free of programmartic issues (Thanks to [mashb1t](https://github.com/mashb1t)'s huge efforts), future updates will focus exclusively on addressing any bugs that may arise.
|
||||
# Moving from Midjourney to Fooocus
|
||||
|
||||
**There are no current plans to migrate to or incorporate newer model architectures.** However, this may change during time with the development of open-source community. For example, if the community converge to one single dominant method for image generation (which may really happen in half or one years given the current status), Fooocus may also migrate to that exact method.
|
||||
|
||||
For those interested in utilizing newer models such as **Flux**, we recommend exploring alternative platforms such as [WebUI Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge) (also from us), [ComfyUI/SwarmUI](https://github.com/comfyanonymous/ComfyUI). Additionally, several [excellent forks of Fooocus](https://github.com/lllyasviel/Fooocus?tab=readme-ov-file#forks) are available for experimentation.
|
||||
|
||||
Again, recently many fake websites exist on Google when you search “fooocus”. Do **NOT** get Fooocus from those websites – this page is the only official source of Fooocus. We never have any website like such as “fooocus.com”, “fooocus.net”, “fooocus.co”, “fooocus.ai”, “fooocus.org”, “fooocus.pro”, “fooocus.one”. Those websites are ALL FAKE. **They have ABSOLUTLY no relationship to us. Fooocus is a 100% non-commercial offline open-source software.**
|
||||
|
||||
# Features
|
||||
|
||||
Below is a quick list using Midjourney's examples:
|
||||
Using Fooocus is as easy as (probably easier than) Midjourney – but this does not mean we lack functionality. Below are the details.
|
||||
|
||||
| Midjourney | Fooocus |
|
||||
| - | - |
|
||||
|
|
@ -45,7 +55,7 @@ Below is a quick list using Midjourney's examples:
|
|||
| InsightFace | Input Image -> Image Prompt -> Advanced -> FaceSwap |
|
||||
| Describe | Input Image -> Describe |
|
||||
|
||||
Below is a quick list using LeonardoAI's examples:
|
||||
We also have a few things borrowed from the best parts of LeonardoAI:
|
||||
|
||||
| LeonardoAI | Fooocus |
|
||||
| - | - |
|
||||
|
|
@ -53,7 +63,7 @@ Below is a quick list using LeonardoAI's examples:
|
|||
| Advanced Sampler Parameters (like Contrast/Sharpness/etc) | Advanced -> Advanced -> Sampling Sharpness / etc |
|
||||
| User-friendly ControlNets | Input Image -> Image Prompt -> Advanced |
|
||||
|
||||
Also, [click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117)
|
||||
Fooocus also developed many "fooocus-only" features for advanced users to get perfect results. [Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117)
|
||||
|
||||
# Download
|
||||
|
||||
|
|
@ -61,7 +71,7 @@ Also, [click here to browse the advanced features.](https://github.com/lllyasvie
|
|||
|
||||
You can directly download Fooocus with:
|
||||
|
||||
**[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/v2.5.0/Fooocus_win64_2-5-0.7z)**
|
||||
**[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/release/Fooocus_win64_2-1-831.7z)**
|
||||
|
||||
After you download the file, please uncompress it and then run the "run.bat".
|
||||
|
||||
|
|
@ -74,10 +84,6 @@ The first time you launch the software, it will automatically download models:
|
|||
|
||||
After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and require different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679).
|
||||
|
||||
After Fooocus 2.3.0 you can also switch presets directly in the browser. Keep in mind to add these arguments if you want to change the default behavior:
|
||||
* Use `--disable-preset-selection` to disable preset selection in the browser.
|
||||
* Use `--always-download-new-model` to download missing models on preset switch. Default is fallback to `previous_default_models` defined in the corresponding preset, also see terminal output.
|
||||
|
||||
|
||||
|
||||
If you already have these files, you can copy them to the above locations to speed up installation.
|
||||
|
|
@ -109,21 +115,17 @@ See also the common problems and troubleshoots [here](troubleshoot.md).
|
|||
|
||||
### Colab
|
||||
|
||||
(Last tested - 2024 Aug 12 by [mashb1t](https://github.com/mashb1t))
|
||||
(Last tested - 2023 Dec 12)
|
||||
|
||||
| Colab | Info
|
||||
| --- | --- |
|
||||
[](https://colab.research.google.com/github/lllyasviel/Fooocus/blob/main/fooocus_colab.ipynb) | Fooocus Official
|
||||
|
||||
In Colab, you can modify the last line to `!python entry_with_update.py --share --always-high-vram` or `!python entry_with_update.py --share --always-high-vram --preset anime` or `!python entry_with_update.py --share --always-high-vram --preset realistic` for Fooocus Default/Anime/Realistic Edition.
|
||||
|
||||
You can also change the preset in the UI. Please be aware that this may lead to timeouts after 60 seconds. If this is the case, please wait until the download has finished, change the preset to initial and back to the one you've selected or reload the page.
|
||||
In Colab, you can modify the last line to `!python entry_with_update.py --share` or `!python entry_with_update.py --preset anime --share` or `!python entry_with_update.py --preset realistic --share` for Fooocus Default/Anime/Realistic Edition.
|
||||
|
||||
Note that this Colab will disable refiner by default because Colab free's resources are relatively limited (and some "big" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab.
|
||||
|
||||
Using `--always-high-vram` shifts resource allocation from RAM to VRAM and achieves the overall best balance between performance, flexibility and stability on the default T4 instance. Please find more information [here](https://github.com/lllyasviel/Fooocus/pull/1710#issuecomment-1989185346).
|
||||
|
||||
Thanks to [camenduru](https://github.com/camenduru) for the template!
|
||||
Thanks to [camenduru](https://github.com/camenduru)!
|
||||
|
||||
### Linux (Using Anaconda)
|
||||
|
||||
|
|
@ -200,7 +202,7 @@ AMD is not intensively tested, however. The AMD support is in beta.
|
|||
|
||||
Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.
|
||||
|
||||
### Windows (AMD GPUs)
|
||||
### Windows(AMD GPUs)
|
||||
|
||||
Note that the [minimal requirement](#minimal-requirement) for different platforms is different.
|
||||
|
||||
|
|
@ -215,7 +217,7 @@ Then run the `run.bat`.
|
|||
|
||||
AMD is not intensively tested, however. The AMD support is in beta.
|
||||
|
||||
For AMD, use `.\python_embeded\python.exe Fooocus\entry_with_update.py --directml --preset anime` or `.\python_embeded\python.exe Fooocus\entry_with_update.py --directml --preset realistic` for Fooocus Anime/Realistic Edition.
|
||||
For AMD, use `.\python_embeded\python.exe entry_with_update.py --directml --preset anime` or `.\python_embeded\python.exe entry_with_update.py --directml --preset realistic` for Fooocus Anime/Realistic Edition.
|
||||
|
||||
### Mac
|
||||
|
||||
|
|
@ -235,10 +237,6 @@ You can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or
|
|||
|
||||
Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.
|
||||
|
||||
### Docker
|
||||
|
||||
See [docker.md](docker.md)
|
||||
|
||||
### Download Previous Version
|
||||
|
||||
See the guidelines [here](https://github.com/lllyasviel/Fooocus/discussions/1405).
|
||||
|
|
@ -275,30 +273,22 @@ See the common problems [here](troubleshoot.md).
|
|||
|
||||
Given different goals, the default models and configs of Fooocus are different:
|
||||
|
||||
| Task | Windows | Linux args | Main Model | Refiner | Config |
|
||||
|-----------| --- | --- |-----------------------------| --- |--------------------------------------------------------------------------------|
|
||||
| General | run.bat | | juggernautXL_v8Rundiffusion | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/default.json) |
|
||||
| Realistic | run_realistic.bat | --preset realistic | realisticStockPhoto_v20 | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/realistic.json) |
|
||||
| Anime | run_anime.bat | --preset anime | animaPencilXL_v500 | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/anime.json) |
|
||||
| Task | Windows | Linux args | Main Model | Refiner | Config |
|
||||
| --- | --- | --- | --- | --- |--------------------------------------------------------------------------------|
|
||||
| General | run.bat | | juggernautXL_v8Rundiffusion | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/default.json) |
|
||||
| Realistic | run_realistic.bat | --preset realistic | realisticStockPhoto_v20 | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/realistic.json) |
|
||||
| Anime | run_anime.bat | --preset anime | animaPencilXL_v100 | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/anime.json) |
|
||||
|
||||
Note that the download is **automatic** - you do not need to do anything if the internet connection is okay. However, you can download them manually if you (or move them from somewhere else) have your own preparation.
|
||||
|
||||
## UI Access and Authentication
|
||||
In addition to running on localhost, Fooocus can also expose its UI in two ways:
|
||||
* Local UI listener: use `--listen` (specify port e.g. with `--port 8888`).
|
||||
* API access: use `--share` (registers an endpoint at `.gradio.live`).
|
||||
|
||||
In both ways the access is unauthenticated by default. You can add basic authentication by creating a file called `auth.json` in the main directory, which contains a list of JSON objects with the keys `user` and `pass` (see example in [auth-example.json](./auth-example.json)).
|
||||
|
||||
## List of "Hidden" Tricks
|
||||
<a name="tech_list"></a>
|
||||
|
||||
<details>
|
||||
<summary>Click to see a list of tricks. Those are based on SDXL and are not very up-to-date with latest models.</summary>
|
||||
The below things are already inside the software, and **users do not need to do anything about these**.
|
||||
|
||||
1. GPT2-based [prompt expansion as a dynamic style "Fooocus V2".](https://github.com/lllyasviel/Fooocus/discussions/117#raw) (similar to Midjourney's hidden pre-processing and "raw" mode, or the LeonardoAI's Prompt Magic).
|
||||
1. GPT2-based [prompt expansion as a dynamic style "Fooocus V2".](https://github.com/lllyasviel/Fooocus/discussions/117#raw) (similar to Midjourney's hidden pre-processsing and "raw" mode, or the LeonardoAI's Prompt Magic).
|
||||
2. Native refiner swap inside one single k-sampler. The advantage is that the refiner model can now reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually, I discussed this with Automatic1111 several days ago, and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!)
|
||||
3. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results to look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Draw Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!)
|
||||
3. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results to look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Drawing Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!)
|
||||
4. We implemented a carefully tuned variation of Section 5.1 of ["Improving Sample Quality of Diffusion Models Using Self-Attention Guidance"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure that the XL will never yield an overly smooth or plastic appearance (examples [here](https://github.com/lllyasviel/Fooocus/discussions/117#sharpness)). This can almost eliminate all cases for which XL still occasionally produces overly smooth results, even with negative ADM guidance. (Update 2023 Aug 18, the Gaussian kernel of SAG is changed to an anisotropic kernel for better structure preservation and fewer artifacts.)
|
||||
5. We modified the style templates a bit and added the "cinematic-default".
|
||||
6. We tested the "sd_xl_offset_example-lora_1.0.safetensors" and it seems that when the lora weight is below 0.5, the results are always better than XL without lora.
|
||||
|
|
@ -310,7 +300,6 @@ In both ways the access is unauthenticated by default. You can add basic authent
|
|||
12. Using automatic1111's method to normalize prompt emphasizing. This significantly improves results when users directly copy prompts from civitai.
|
||||
13. The joint swap system of the refiner now also supports img2img and upscale in a seamless way.
|
||||
14. CFG Scale and TSNR correction (tuned for SDXL) when CFG is bigger than 10.
|
||||
</details>
|
||||
|
||||
## Customization
|
||||
|
||||
|
|
@ -360,93 +349,43 @@ A safer way is just to try "run_anime.bat" or "run_realistic.bat" - they should
|
|||
entry_with_update.py [-h] [--listen [IP]] [--port PORT]
|
||||
[--disable-header-check [ORIGIN]]
|
||||
[--web-upload-size WEB_UPLOAD_SIZE]
|
||||
[--hf-mirror HF_MIRROR]
|
||||
[--external-working-path PATH [PATH ...]]
|
||||
[--output-path OUTPUT_PATH]
|
||||
[--temp-path TEMP_PATH] [--cache-path CACHE_PATH]
|
||||
[--in-browser] [--disable-in-browser]
|
||||
[--gpu-device-id DEVICE_ID]
|
||||
[--output-path OUTPUT_PATH] [--temp-path TEMP_PATH]
|
||||
[--cache-path CACHE_PATH] [--in-browser]
|
||||
[--disable-in-browser] [--gpu-device-id DEVICE_ID]
|
||||
[--async-cuda-allocation | --disable-async-cuda-allocation]
|
||||
[--disable-attention-upcast]
|
||||
[--all-in-fp32 | --all-in-fp16]
|
||||
[--disable-attention-upcast] [--all-in-fp32 | --all-in-fp16]
|
||||
[--unet-in-bf16 | --unet-in-fp16 | --unet-in-fp8-e4m3fn | --unet-in-fp8-e5m2]
|
||||
[--vae-in-fp16 | --vae-in-fp32 | --vae-in-bf16]
|
||||
[--vae-in-cpu]
|
||||
[--clip-in-fp8-e4m3fn | --clip-in-fp8-e5m2 | --clip-in-fp16 | --clip-in-fp32]
|
||||
[--directml [DIRECTML_DEVICE]]
|
||||
[--disable-ipex-hijack]
|
||||
[--directml [DIRECTML_DEVICE]] [--disable-ipex-hijack]
|
||||
[--preview-option [none,auto,fast,taesd]]
|
||||
[--attention-split | --attention-quad | --attention-pytorch]
|
||||
[--disable-xformers]
|
||||
[--always-gpu | --always-high-vram | --always-normal-vram | --always-low-vram | --always-no-vram | --always-cpu [CPU_NUM_THREADS]]
|
||||
[--always-offload-from-vram]
|
||||
[--pytorch-deterministic] [--disable-server-log]
|
||||
[--always-gpu | --always-high-vram | --always-normal-vram |
|
||||
--always-low-vram | --always-no-vram | --always-cpu]
|
||||
[--always-offload-from-vram] [--disable-server-log]
|
||||
[--debug-mode] [--is-windows-embedded-python]
|
||||
[--disable-server-info] [--multi-user] [--share]
|
||||
[--preset PRESET] [--disable-preset-selection]
|
||||
[--language LANGUAGE]
|
||||
[--disable-offload-from-vram] [--theme THEME]
|
||||
[--disable-image-log] [--disable-analytics]
|
||||
[--disable-metadata] [--disable-preset-download]
|
||||
[--disable-enhance-output-sorting]
|
||||
[--enable-auto-describe-image]
|
||||
[--always-download-new-model]
|
||||
[--rebuild-hash-cache [CPU_NUM_THREADS]]
|
||||
[--disable-server-info] [--share] [--preset PRESET]
|
||||
[--language LANGUAGE] [--disable-offload-from-vram]
|
||||
[--theme THEME] [--disable-image-log]
|
||||
```
|
||||
|
||||
## Inline Prompt Features
|
||||
|
||||
### Wildcards
|
||||
|
||||
Example prompt: `__color__ flower`
|
||||
|
||||
Processed for positive and negative prompt.
|
||||
|
||||
Selects a random wildcard from a predefined list of options, in this case the `wildcards/color.txt` file.
|
||||
The wildcard will be replaced with a random color (randomness based on seed).
|
||||
You can also disable randomness and process a wildcard file from top to bottom by enabling the checkbox `Read wildcards in order` in Developer Debug Mode.
|
||||
|
||||
Wildcards can be nested and combined, and multiple wildcards can be used in the same prompt (example see `wildcards/color_flower.txt`).
|
||||
|
||||
### Array Processing
|
||||
|
||||
Example prompt: `[[red, green, blue]] flower`
|
||||
|
||||
Processed only for positive prompt.
|
||||
|
||||
Processes the array from left to right, generating a separate image for each element in the array. In this case 3 images would be generated, one for each color.
|
||||
Increase the image number to 3 to generate all 3 variants.
|
||||
|
||||
Arrays can not be nested, but multiple arrays can be used in the same prompt.
|
||||
Does support inline LoRAs as array elements!
|
||||
|
||||
### Inline LoRAs
|
||||
|
||||
Example prompt: `flower <lora:sunflowers:1.2>`
|
||||
|
||||
Processed only for positive prompt.
|
||||
|
||||
Applies a LoRA to the prompt. The LoRA file must be located in the `models/loras` directory.
|
||||
|
||||
## Advanced Features
|
||||
|
||||
[Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117)
|
||||
|
||||
## Forks
|
||||
|
||||
Below are some Forks to Fooocus:
|
||||
Fooocus also has many community forks, just like SD-WebUI's [vladmandic/automatic](https://github.com/vladmandic/automatic) and [anapnoe/stable-diffusion-webui-ux](https://github.com/anapnoe/stable-diffusion-webui-ux), for enthusiastic users who want to try!
|
||||
|
||||
| Fooocus' forks |
|
||||
| - |
|
||||
| [fenneishi/Fooocus-Control](https://github.com/fenneishi/Fooocus-Control) </br>[runew0lf/RuinedFooocus](https://github.com/runew0lf/RuinedFooocus) </br> [MoonRide303/Fooocus-MRE](https://github.com/MoonRide303/Fooocus-MRE) </br> [mashb1t/Fooocus](https://github.com/mashb1t/Fooocus) </br> and so on ... |
|
||||
| [fenneishi/Fooocus-Control](https://github.com/fenneishi/Fooocus-Control) </br>[runew0lf/RuinedFooocus](https://github.com/runew0lf/RuinedFooocus) </br> [MoonRide303/Fooocus-MRE](https://github.com/MoonRide303/Fooocus-MRE) </br> [metercai/SimpleSDXL](https://github.com/metercai/SimpleSDXL) </br> and so on ... |
|
||||
|
||||
See also [About Forking and Promotion of Forks](https://github.com/lllyasviel/Fooocus/discussions/699).
|
||||
|
||||
## Thanks
|
||||
|
||||
Many thanks to [twri](https://github.com/twri) and [3Diva](https://github.com/3Diva) and [Marc K3nt3L](https://github.com/K3nt3L) for creating additional SDXL styles available in Fooocus.
|
||||
|
||||
The project starts from a mixture of [Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) and [ComfyUI](https://github.com/comfyanonymous/ComfyUI) codebases.
|
||||
|
||||
Also, thanks [daswer123](https://github.com/daswer123) for contributing the Canvas Zoom!
|
||||
Special thanks to [twri](https://github.com/twri) and [3Diva](https://github.com/3Diva) and [Marc K3nt3L](https://github.com/K3nt3L) for creating additional SDXL styles available in Fooocus. Thanks [daswer123](https://github.com/daswer123) for contributing the Canvas Zoom!
|
||||
|
||||
## Update Log
|
||||
|
||||
|
|
@ -454,6 +393,8 @@ The log is [here](update_log.md).
|
|||
|
||||
## Localization/Translation/I18N
|
||||
|
||||
**We need your help!** Please help translate Fooocus into international languages.
|
||||
|
||||
You can put json files in the `language` folder to translate the user interface.
|
||||
|
||||
For example, below is the content of `Fooocus/language/example.json`:
|
||||
|
|
|
|||
|
|
@ -1,2 +0,0 @@
|
|||
torch==2.1.0
|
||||
torchvision==0.16.0
|
||||
|
|
@ -1,24 +1,18 @@
|
|||
torchsde==0.2.6
|
||||
einops==0.8.0
|
||||
transformers==4.42.4
|
||||
safetensors==0.4.3
|
||||
accelerate==0.32.1
|
||||
pyyaml==6.0.1
|
||||
pillow==10.4.0
|
||||
scipy==1.14.0
|
||||
tqdm==4.66.4
|
||||
psutil==6.0.0
|
||||
pytorch_lightning==2.3.3
|
||||
omegaconf==2.3.0
|
||||
torchsde==0.2.5
|
||||
einops==0.4.1
|
||||
transformers==4.30.2
|
||||
safetensors==0.3.1
|
||||
accelerate==0.21.0
|
||||
pyyaml==6.0
|
||||
Pillow==9.2.0
|
||||
scipy==1.9.3
|
||||
tqdm==4.64.1
|
||||
psutil==5.9.5
|
||||
pytorch_lightning==1.9.4
|
||||
omegaconf==2.2.3
|
||||
gradio==3.41.2
|
||||
pygit2==1.15.1
|
||||
opencv-contrib-python-headless==4.10.0.84
|
||||
httpx==0.27.0
|
||||
onnxruntime==1.18.1
|
||||
timm==1.0.7
|
||||
numpy==1.26.4
|
||||
tokenizers==0.19.1
|
||||
packaging==24.1
|
||||
rembg==2.0.57
|
||||
groundingdino-py==0.4.0
|
||||
segment_anything==1.0
|
||||
pygit2==1.12.2
|
||||
opencv-contrib-python==4.8.0.74
|
||||
httpx==0.24.1
|
||||
onnxruntime==1.16.3
|
||||
timm==0.9.2
|
||||
|
|
|
|||
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