Manuel Schmid
GitHub
commit
12dc2396f6
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@ -1 +1,54 @@
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|||
.idea
|
||||
__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
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||||
user_path_config.txt
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||||
user_path_config-deprecated.txt
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||||
/modules/*.png
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/repositories
|
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/fooocus_env
|
||||
/venv
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/tmp
|
||||
/ui-config.json
|
||||
/outputs
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||||
/config.json
|
||||
/log
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||||
/webui.settings.bat
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||||
/embeddings
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/styles.csv
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||||
/params.txt
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||||
/styles.csv.bak
|
||||
/webui-user.bat
|
||||
/webui-user.sh
|
||||
/interrogate
|
||||
/user.css
|
||||
/.idea
|
||||
/notification.ogg
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||||
/notification.mp3
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||||
/SwinIR
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||||
/textual_inversion
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.vscode
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||||
/extensions
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||||
/test/stdout.txt
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/test/stderr.txt
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||||
/cache.json*
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/config_states/
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||||
/node_modules
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||||
/package-lock.json
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||||
/.coverage*
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||||
/auth.json
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||||
.DS_Store
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|
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@ -0,0 +1,3 @@
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# Ensure that shell scripts always use lf line endings, e.g. entrypoint.sh for docker
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* text=auto
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*.sh text eol=lf
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@ -1 +1 @@
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* @lllyasviel
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* @mashb1t
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@ -0,0 +1,6 @@
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version: 2
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updates:
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- package-ecosystem: "github-actions"
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directory: "/"
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schedule:
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interval: "monthly"
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||||
|
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@ -0,0 +1,44 @@
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name: Create and publish a container image
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||||
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on:
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||||
push:
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tags:
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||||
- 'v*'
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||||
|
||||
jobs:
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||||
build-and-push-image:
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runs-on: ubuntu-latest
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|
||||
permissions:
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contents: read
|
||||
packages: write
|
||||
|
||||
steps:
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||||
- name: Checkout repository
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uses: actions/checkout@v4
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||||
|
||||
- name: Log in to the Container registry
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uses: docker/login-action@v3
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with:
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registry: ghcr.io
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username: ${{ github.repository_owner }}
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||||
password: ${{ secrets.GITHUB_TOKEN }}
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||||
|
||||
- name: Extract metadata (tags, labels) for Docker
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id: meta
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||||
uses: docker/metadata-action@v5
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with:
|
||||
images: ghcr.io/${{ github.repository_owner }}/${{ github.event.repository.name }}
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||||
tags: |
|
||||
type=semver,pattern={{version}}
|
||||
type=semver,pattern={{major}}.{{minor}}
|
||||
type=semver,pattern={{major}}
|
||||
|
||||
- name: Build and push Docker image
|
||||
uses: docker/build-push-action@v5
|
||||
with:
|
||||
context: .
|
||||
file: ./Dockerfile
|
||||
push: true
|
||||
tags: ${{ steps.meta.outputs.tags }}
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||||
labels: ${{ steps.meta.outputs.labels }}
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||||
|
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@ -1,4 +1,4 @@
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FROM nvidia/cuda:12.3.1-base-ubuntu22.04
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FROM nvidia/cuda:12.4.1-base-ubuntu22.04
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ENV DEBIAN_FRONTEND noninteractive
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ENV CMDARGS --listen
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|
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@ -23,7 +23,7 @@ RUN chown -R user:user /content
|
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WORKDIR /content
|
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USER user
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||||
|
||||
RUN git clone https://github.com/lllyasviel/Fooocus /content/app
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COPY . /content/app
|
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RUN mv /content/app/models /content/app/models.org
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CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ]
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|
|
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@ -31,6 +31,9 @@ args_parser.parser.add_argument("--disable-metadata", action='store_true',
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args_parser.parser.add_argument("--disable-preset-download", action='store_true',
|
||||
help="Disables downloading models for presets", default=False)
|
||||
|
||||
args_parser.parser.add_argument("--enable-describe-uov-image", action='store_true',
|
||||
help="Disables automatic description of uov images 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)
|
||||
|
||||
|
|
|
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@ -27,6 +27,7 @@ progress {
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|||
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 */
|
||||
|
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@ -69,6 +70,11 @@ progress::after {
|
|||
height: 30px !important;
|
||||
}
|
||||
|
||||
.progress-bar span {
|
||||
text-align: right;
|
||||
width: 215px;
|
||||
}
|
||||
|
||||
.type_row{
|
||||
height: 80px !important;
|
||||
}
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||||
|
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@ -101,10 +107,14 @@ progress::after {
|
|||
overflow: auto !important;
|
||||
}
|
||||
|
||||
.aspect_ratios label {
|
||||
.performance_selection label {
|
||||
width: 140px !important;
|
||||
}
|
||||
|
||||
.aspect_ratios label {
|
||||
flex: calc(50% - 5px) !important;
|
||||
}
|
||||
|
||||
.aspect_ratios label span {
|
||||
white-space: nowrap !important;
|
||||
}
|
||||
|
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@ -391,6 +401,14 @@ progress::after {
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|||
background-color: #fff8;
|
||||
font-family: monospace;
|
||||
text-align: center;
|
||||
border-radius-top: 5px;
|
||||
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;
|
||||
}
|
||||
|
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@ -0,0 +1,11 @@
|
|||
## Running unit tests
|
||||
|
||||
Native python:
|
||||
```
|
||||
python -m unittest tests/
|
||||
```
|
||||
|
||||
Embedded python (Windows zip file installation method):
|
||||
```
|
||||
..\python_embeded\python.exe -m unittest
|
||||
```
|
||||
|
|
@ -1,12 +1,10 @@
|
|||
version: '3.9'
|
||||
|
||||
volumes:
|
||||
fooocus-data:
|
||||
|
||||
services:
|
||||
app:
|
||||
build: .
|
||||
image: fooocus
|
||||
image: ghcr.io/lllyasviel/fooocus
|
||||
ports:
|
||||
- "7865:7865"
|
||||
environment:
|
||||
|
|
|
|||
83
docker.md
83
docker.md
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@ -1,35 +1,99 @@
|
|||
# Fooocus on Docker
|
||||
|
||||
The docker image is based on NVIDIA CUDA 12.3 and PyTorch 2.0, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details.
|
||||
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
|
||||
|
||||
**This is just an easy way for testing. Please find more information in the [notes](#notes).**
|
||||
**More information in the [notes](#notes).**
|
||||
|
||||
### Running with Docker Compose
|
||||
|
||||
1. Clone this repository
|
||||
2. Build the image with `docker compose build`
|
||||
3. Run the docker container with `docker compose up`. Building the image takes some time.
|
||||
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`.
|
||||
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
|
||||
### 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 uncomment the following settings in the [docker-compose.yml](docker-compose.yml):
|
||||
|
||||
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 `docker compose up`, your files will be copied into `/content/data/models` and `/content/data/outputs`
|
||||
Since `/content/data` is a persistent volume folder, your files will be persisted even when you re-run `docker compose up --build` without above volume settings.
|
||||
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
|
||||
|
|
@ -54,6 +118,7 @@ Docker specified environments are there. They are used by 'entrypoint.sh'
|
|||
|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)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,60 @@
|
|||
import os
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import CLIPConfig, CLIPImageProcessor
|
||||
|
||||
import ldm_patched.modules.model_management as model_management
|
||||
import modules.config
|
||||
from extras.safety_checker.models.safety_checker import StableDiffusionSafetyChecker
|
||||
from ldm_patched.modules.model_patcher import ModelPatcher
|
||||
|
||||
safety_checker_repo_root = os.path.join(os.path.dirname(__file__), 'safety_checker')
|
||||
config_path = os.path.join(safety_checker_repo_root, "configs", "config.json")
|
||||
preprocessor_config_path = os.path.join(safety_checker_repo_root, "configs", "preprocessor_config.json")
|
||||
|
||||
|
||||
class Censor:
|
||||
def __init__(self):
|
||||
self.safety_checker_model: ModelPatcher | None = None
|
||||
self.clip_image_processor: CLIPImageProcessor | None = None
|
||||
self.load_device = torch.device('cpu')
|
||||
self.offload_device = torch.device('cpu')
|
||||
|
||||
def init(self):
|
||||
if self.safety_checker_model is None and self.clip_image_processor is None:
|
||||
safety_checker_model = modules.config.downloading_safety_checker_model()
|
||||
self.clip_image_processor = CLIPImageProcessor.from_json_file(preprocessor_config_path)
|
||||
clip_config = CLIPConfig.from_json_file(config_path)
|
||||
model = StableDiffusionSafetyChecker.from_pretrained(safety_checker_model, config=clip_config)
|
||||
model.eval()
|
||||
|
||||
self.load_device = model_management.text_encoder_device()
|
||||
self.offload_device = model_management.text_encoder_offload_device()
|
||||
|
||||
model.to(self.offload_device)
|
||||
|
||||
self.safety_checker_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
|
||||
|
||||
def censor(self, images: list | np.ndarray) -> list | np.ndarray:
|
||||
self.init()
|
||||
model_management.load_model_gpu(self.safety_checker_model)
|
||||
|
||||
single = False
|
||||
if not isinstance(images, list) or isinstance(images, 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
|
||||
|
|
@ -0,0 +1,171 @@
|
|||
{
|
||||
"_name_or_path": "clip-vit-large-patch14/",
|
||||
"architectures": [
|
||||
"SafetyChecker"
|
||||
],
|
||||
"initializer_factor": 1.0,
|
||||
"logit_scale_init_value": 2.6592,
|
||||
"model_type": "clip",
|
||||
"projection_dim": 768,
|
||||
"text_config": {
|
||||
"_name_or_path": "",
|
||||
"add_cross_attention": false,
|
||||
"architectures": null,
|
||||
"attention_dropout": 0.0,
|
||||
"bad_words_ids": null,
|
||||
"bos_token_id": 0,
|
||||
"chunk_size_feed_forward": 0,
|
||||
"cross_attention_hidden_size": null,
|
||||
"decoder_start_token_id": null,
|
||||
"diversity_penalty": 0.0,
|
||||
"do_sample": false,
|
||||
"dropout": 0.0,
|
||||
"early_stopping": false,
|
||||
"encoder_no_repeat_ngram_size": 0,
|
||||
"eos_token_id": 2,
|
||||
"exponential_decay_length_penalty": null,
|
||||
"finetuning_task": null,
|
||||
"forced_bos_token_id": null,
|
||||
"forced_eos_token_id": null,
|
||||
"hidden_act": "quick_gelu",
|
||||
"hidden_size": 768,
|
||||
"id2label": {
|
||||
"0": "LABEL_0",
|
||||
"1": "LABEL_1"
|
||||
},
|
||||
"initializer_factor": 1.0,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 3072,
|
||||
"is_decoder": false,
|
||||
"is_encoder_decoder": false,
|
||||
"label2id": {
|
||||
"LABEL_0": 0,
|
||||
"LABEL_1": 1
|
||||
},
|
||||
"layer_norm_eps": 1e-05,
|
||||
"length_penalty": 1.0,
|
||||
"max_length": 20,
|
||||
"max_position_embeddings": 77,
|
||||
"min_length": 0,
|
||||
"model_type": "clip_text_model",
|
||||
"no_repeat_ngram_size": 0,
|
||||
"num_attention_heads": 12,
|
||||
"num_beam_groups": 1,
|
||||
"num_beams": 1,
|
||||
"num_hidden_layers": 12,
|
||||
"num_return_sequences": 1,
|
||||
"output_attentions": false,
|
||||
"output_hidden_states": false,
|
||||
"output_scores": false,
|
||||
"pad_token_id": 1,
|
||||
"prefix": null,
|
||||
"problem_type": null,
|
||||
"pruned_heads": {},
|
||||
"remove_invalid_values": false,
|
||||
"repetition_penalty": 1.0,
|
||||
"return_dict": true,
|
||||
"return_dict_in_generate": false,
|
||||
"sep_token_id": null,
|
||||
"task_specific_params": null,
|
||||
"temperature": 1.0,
|
||||
"tie_encoder_decoder": false,
|
||||
"tie_word_embeddings": true,
|
||||
"tokenizer_class": null,
|
||||
"top_k": 50,
|
||||
"top_p": 1.0,
|
||||
"torch_dtype": null,
|
||||
"torchscript": false,
|
||||
"transformers_version": "4.21.0.dev0",
|
||||
"typical_p": 1.0,
|
||||
"use_bfloat16": false,
|
||||
"vocab_size": 49408
|
||||
},
|
||||
"text_config_dict": {
|
||||
"hidden_size": 768,
|
||||
"intermediate_size": 3072,
|
||||
"num_attention_heads": 12,
|
||||
"num_hidden_layers": 12
|
||||
},
|
||||
"torch_dtype": "float32",
|
||||
"transformers_version": null,
|
||||
"vision_config": {
|
||||
"_name_or_path": "",
|
||||
"add_cross_attention": false,
|
||||
"architectures": null,
|
||||
"attention_dropout": 0.0,
|
||||
"bad_words_ids": null,
|
||||
"bos_token_id": null,
|
||||
"chunk_size_feed_forward": 0,
|
||||
"cross_attention_hidden_size": null,
|
||||
"decoder_start_token_id": null,
|
||||
"diversity_penalty": 0.0,
|
||||
"do_sample": false,
|
||||
"dropout": 0.0,
|
||||
"early_stopping": false,
|
||||
"encoder_no_repeat_ngram_size": 0,
|
||||
"eos_token_id": null,
|
||||
"exponential_decay_length_penalty": null,
|
||||
"finetuning_task": null,
|
||||
"forced_bos_token_id": null,
|
||||
"forced_eos_token_id": null,
|
||||
"hidden_act": "quick_gelu",
|
||||
"hidden_size": 1024,
|
||||
"id2label": {
|
||||
"0": "LABEL_0",
|
||||
"1": "LABEL_1"
|
||||
},
|
||||
"image_size": 224,
|
||||
"initializer_factor": 1.0,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 4096,
|
||||
"is_decoder": false,
|
||||
"is_encoder_decoder": false,
|
||||
"label2id": {
|
||||
"LABEL_0": 0,
|
||||
"LABEL_1": 1
|
||||
},
|
||||
"layer_norm_eps": 1e-05,
|
||||
"length_penalty": 1.0,
|
||||
"max_length": 20,
|
||||
"min_length": 0,
|
||||
"model_type": "clip_vision_model",
|
||||
"no_repeat_ngram_size": 0,
|
||||
"num_attention_heads": 16,
|
||||
"num_beam_groups": 1,
|
||||
"num_beams": 1,
|
||||
"num_hidden_layers": 24,
|
||||
"num_return_sequences": 1,
|
||||
"output_attentions": false,
|
||||
"output_hidden_states": false,
|
||||
"output_scores": false,
|
||||
"pad_token_id": null,
|
||||
"patch_size": 14,
|
||||
"prefix": null,
|
||||
"problem_type": null,
|
||||
"pruned_heads": {},
|
||||
"remove_invalid_values": false,
|
||||
"repetition_penalty": 1.0,
|
||||
"return_dict": true,
|
||||
"return_dict_in_generate": false,
|
||||
"sep_token_id": null,
|
||||
"task_specific_params": null,
|
||||
"temperature": 1.0,
|
||||
"tie_encoder_decoder": false,
|
||||
"tie_word_embeddings": true,
|
||||
"tokenizer_class": null,
|
||||
"top_k": 50,
|
||||
"top_p": 1.0,
|
||||
"torch_dtype": null,
|
||||
"torchscript": false,
|
||||
"transformers_version": "4.21.0.dev0",
|
||||
"typical_p": 1.0,
|
||||
"use_bfloat16": false
|
||||
},
|
||||
"vision_config_dict": {
|
||||
"hidden_size": 1024,
|
||||
"intermediate_size": 4096,
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 24,
|
||||
"patch_size": 14
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,20 @@
|
|||
{
|
||||
"crop_size": 224,
|
||||
"do_center_crop": true,
|
||||
"do_convert_rgb": true,
|
||||
"do_normalize": true,
|
||||
"do_resize": true,
|
||||
"feature_extractor_type": "CLIPFeatureExtractor",
|
||||
"image_mean": [
|
||||
0.48145466,
|
||||
0.4578275,
|
||||
0.40821073
|
||||
],
|
||||
"image_std": [
|
||||
0.26862954,
|
||||
0.26130258,
|
||||
0.27577711
|
||||
],
|
||||
"resample": 3,
|
||||
"size": 224
|
||||
}
|
||||
|
|
@ -0,0 +1,126 @@
|
|||
# from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py
|
||||
|
||||
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
|
||||
from transformers.utils import logging
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
def cosine_distance(image_embeds, text_embeds):
|
||||
normalized_image_embeds = nn.functional.normalize(image_embeds)
|
||||
normalized_text_embeds = nn.functional.normalize(text_embeds)
|
||||
return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
|
||||
|
||||
|
||||
class StableDiffusionSafetyChecker(PreTrainedModel):
|
||||
config_class = CLIPConfig
|
||||
main_input_name = "clip_input"
|
||||
|
||||
_no_split_modules = ["CLIPEncoderLayer"]
|
||||
|
||||
def __init__(self, config: CLIPConfig):
|
||||
super().__init__(config)
|
||||
|
||||
self.vision_model = CLIPVisionModel(config.vision_config)
|
||||
self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
|
||||
|
||||
self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
|
||||
self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
|
||||
|
||||
self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
|
||||
self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, clip_input, images):
|
||||
pooled_output = self.vision_model(clip_input)[1] # pooled_output
|
||||
image_embeds = self.visual_projection(pooled_output)
|
||||
|
||||
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
||||
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
|
||||
cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
|
||||
|
||||
result = []
|
||||
batch_size = image_embeds.shape[0]
|
||||
for i in range(batch_size):
|
||||
result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
|
||||
|
||||
# increase this value to create a stronger `nfsw` filter
|
||||
# at the cost of increasing the possibility of filtering benign images
|
||||
adjustment = 0.0
|
||||
|
||||
for concept_idx in range(len(special_cos_dist[0])):
|
||||
concept_cos = special_cos_dist[i][concept_idx]
|
||||
concept_threshold = self.special_care_embeds_weights[concept_idx].item()
|
||||
result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
|
||||
if result_img["special_scores"][concept_idx] > 0:
|
||||
result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
|
||||
adjustment = 0.01
|
||||
|
||||
for concept_idx in range(len(cos_dist[0])):
|
||||
concept_cos = cos_dist[i][concept_idx]
|
||||
concept_threshold = self.concept_embeds_weights[concept_idx].item()
|
||||
result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
|
||||
if result_img["concept_scores"][concept_idx] > 0:
|
||||
result_img["bad_concepts"].append(concept_idx)
|
||||
|
||||
result.append(result_img)
|
||||
|
||||
has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
|
||||
|
||||
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
|
||||
if has_nsfw_concept:
|
||||
if torch.is_tensor(images) or torch.is_tensor(images[0]):
|
||||
images[idx] = torch.zeros_like(images[idx]) # black image
|
||||
else:
|
||||
images[idx] = np.zeros(images[idx].shape) # black image
|
||||
|
||||
if any(has_nsfw_concepts):
|
||||
logger.warning(
|
||||
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
|
||||
" Try again with a different prompt and/or seed."
|
||||
)
|
||||
|
||||
return images, has_nsfw_concepts
|
||||
|
||||
@torch.no_grad()
|
||||
def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):
|
||||
pooled_output = self.vision_model(clip_input)[1] # pooled_output
|
||||
image_embeds = self.visual_projection(pooled_output)
|
||||
|
||||
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
|
||||
cos_dist = cosine_distance(image_embeds, self.concept_embeds)
|
||||
|
||||
# increase this value to create a stronger `nsfw` filter
|
||||
# at the cost of increasing the possibility of filtering benign images
|
||||
adjustment = 0.0
|
||||
|
||||
special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
|
||||
# special_scores = special_scores.round(decimals=3)
|
||||
special_care = torch.any(special_scores > 0, dim=1)
|
||||
special_adjustment = special_care * 0.01
|
||||
special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
|
||||
|
||||
concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
|
||||
# concept_scores = concept_scores.round(decimals=3)
|
||||
has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
|
||||
|
||||
images[has_nsfw_concepts] = 0.0 # black image
|
||||
|
||||
return images, has_nsfw_concepts
|
||||
|
|
@ -1,69 +1,85 @@
|
|||
# https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py
|
||||
|
||||
import os
|
||||
import torch
|
||||
import safetensors.torch as sf
|
||||
import torch.nn as nn
|
||||
import ldm_patched.modules.model_management
|
||||
|
||||
import safetensors.torch as sf
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
import ldm_patched.modules.model_management
|
||||
from ldm_patched.modules.model_patcher import ModelPatcher
|
||||
from modules.config import path_vae_approx
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self, size):
|
||||
class ResBlock(nn.Module):
|
||||
"""Block with residuals"""
|
||||
|
||||
def __init__(self, ch):
|
||||
super().__init__()
|
||||
self.join = nn.ReLU()
|
||||
self.norm = nn.BatchNorm2d(ch)
|
||||
self.long = nn.Sequential(
|
||||
nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
|
||||
nn.LeakyReLU(0.1),
|
||||
nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
|
||||
nn.LeakyReLU(0.1),
|
||||
nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
|
||||
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
|
||||
nn.SiLU(),
|
||||
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
|
||||
nn.SiLU(),
|
||||
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
|
||||
nn.Dropout(0.1)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.long(x)
|
||||
z = self.join(y + x)
|
||||
return z
|
||||
x = self.norm(x)
|
||||
return self.join(self.long(x) + x)
|
||||
|
||||
|
||||
class Interposer(nn.Module):
|
||||
def __init__(self):
|
||||
class ExtractBlock(nn.Module):
|
||||
"""Increase no. of channels by [out/in]"""
|
||||
|
||||
def __init__(self, ch_in, ch_out):
|
||||
super().__init__()
|
||||
self.chan = 4
|
||||
self.hid = 128
|
||||
|
||||
self.head_join = nn.ReLU()
|
||||
self.head_short = nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1)
|
||||
self.head_long = nn.Sequential(
|
||||
nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1),
|
||||
nn.LeakyReLU(0.1),
|
||||
nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1),
|
||||
nn.LeakyReLU(0.1),
|
||||
nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1),
|
||||
)
|
||||
self.core = nn.Sequential(
|
||||
Block(self.hid),
|
||||
Block(self.hid),
|
||||
Block(self.hid),
|
||||
)
|
||||
self.tail = nn.Sequential(
|
||||
nn.ReLU(),
|
||||
nn.Conv2d(self.hid, self.chan, kernel_size=3, stride=1, padding=1)
|
||||
self.join = nn.ReLU()
|
||||
self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)
|
||||
self.long = nn.Sequential(
|
||||
nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1),
|
||||
nn.SiLU(),
|
||||
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
|
||||
nn.SiLU(),
|
||||
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
|
||||
nn.Dropout(0.1)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.head_join(
|
||||
self.head_long(x) +
|
||||
self.head_short(x)
|
||||
return self.join(self.long(x) + self.short(x))
|
||||
|
||||
|
||||
class InterposerModel(nn.Module):
|
||||
"""Main neural network"""
|
||||
|
||||
def __init__(self, ch_in=4, ch_out=4, ch_mid=64, scale=1.0, blocks=12):
|
||||
super().__init__()
|
||||
self.ch_in = ch_in
|
||||
self.ch_out = ch_out
|
||||
self.ch_mid = ch_mid
|
||||
self.blocks = blocks
|
||||
self.scale = scale
|
||||
|
||||
self.head = ExtractBlock(self.ch_in, self.ch_mid)
|
||||
self.core = nn.Sequential(
|
||||
nn.Upsample(scale_factor=self.scale, mode="nearest"),
|
||||
*[ResBlock(self.ch_mid) for _ in range(blocks)],
|
||||
nn.BatchNorm2d(self.ch_mid),
|
||||
nn.SiLU(),
|
||||
)
|
||||
self.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.head(x)
|
||||
z = self.core(y)
|
||||
return self.tail(z)
|
||||
|
||||
|
||||
vae_approx_model = None
|
||||
vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v3.1.safetensors')
|
||||
vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v4.0.safetensors')
|
||||
|
||||
|
||||
def parse(x):
|
||||
|
|
@ -72,7 +88,7 @@ def parse(x):
|
|||
x_origin = x.clone()
|
||||
|
||||
if vae_approx_model is None:
|
||||
model = Interposer()
|
||||
model = InterposerModel()
|
||||
model.eval()
|
||||
sd = sf.load_file(vae_approx_filename)
|
||||
model.load_state_dict(sd)
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
version = '2.3.1'
|
||||
version = '2.4.0'
|
||||
|
|
|
|||
|
|
@ -122,6 +122,43 @@ 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
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -4,12 +4,20 @@
|
|||
"Generate": "Generate",
|
||||
"Skip": "Skip",
|
||||
"Stop": "Stop",
|
||||
"Reconnect": "Reconnect",
|
||||
"Input Image": "Input Image",
|
||||
"Advanced": "Advanced",
|
||||
"Upscale or Variation": "Upscale or Variation",
|
||||
"Image Prompt": "Image Prompt",
|
||||
"Inpaint or Outpaint (beta)": "Inpaint or Outpaint (beta)",
|
||||
"Drag above image to here": "Drag above image to here",
|
||||
"Inpaint or Outpaint": "Inpaint or Outpaint",
|
||||
"Outpaint Direction": "Outpaint Direction",
|
||||
"Method": "Method",
|
||||
"Describe": "Describe",
|
||||
"Content Type": "Content Type",
|
||||
"Photograph": "Photograph",
|
||||
"Art/Anime": "Art/Anime",
|
||||
"Describe this Image into Prompt": "Describe this Image into Prompt",
|
||||
"Image Size and Recommended Size": "Image Size and Recommended Size",
|
||||
"Upscale or Variation:": "Upscale or Variation:",
|
||||
"Disabled": "Disabled",
|
||||
"Vary (Subtle)": "Vary (Subtle)",
|
||||
|
|
@ -54,9 +62,12 @@
|
|||
"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.",
|
||||
"\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",
|
||||
|
|
@ -309,6 +320,7 @@
|
|||
"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",
|
||||
"Sampler": "Sampler",
|
||||
"dpmpp_2m_sde_gpu": "dpmpp_2m_sde_gpu",
|
||||
"Only effective in non-inpaint mode.": "Only effective in non-inpaint mode.",
|
||||
|
|
@ -339,6 +351,8 @@
|
|||
"sgm_uniform": "sgm_uniform",
|
||||
"simple": "simple",
|
||||
"ddim_uniform": "ddim_uniform",
|
||||
"VAE": "VAE",
|
||||
"Default (model)": "Default (model)",
|
||||
"Forced Overwrite of Sampling Step": "Forced Overwrite of Sampling Step",
|
||||
"Set as -1 to disable. For developer debugging.": "Set as -1 to disable. For developer debugging.",
|
||||
"Forced Overwrite of Refiner Switch Step": "Forced Overwrite of Refiner Switch Step",
|
||||
|
|
@ -378,7 +392,7 @@
|
|||
"Fooocus Enhance": "Fooocus Enhance",
|
||||
"Fooocus Cinematic": "Fooocus Cinematic",
|
||||
"Fooocus Sharp": "Fooocus Sharp",
|
||||
"Drag any image generated by Fooocus here": "Drag any image generated by Fooocus here",
|
||||
"For images created by Fooocus": "For images created by Fooocus",
|
||||
"Metadata": "Metadata",
|
||||
"Apply Metadata": "Apply Metadata",
|
||||
"Metadata Scheme": "Metadata Scheme",
|
||||
|
|
|
|||
|
|
@ -62,8 +62,8 @@ 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-v3.1.safetensors',
|
||||
'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
|
||||
('xl-to-v1_interposer-v4.0.safetensors',
|
||||
'https://huggingface.co/mashb1t/misc/resolve/main/xl-to-v1_interposer-v4.0.safetensors')
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -80,6 +80,10 @@ 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
|
||||
|
||||
os.environ['GRADIO_TEMP_DIR'] = config.temp_path
|
||||
|
|
|
|||
|
|
@ -0,0 +1,55 @@
|
|||
# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
|
||||
|
||||
#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
def loglinear_interp(t_steps, num_steps):
|
||||
"""
|
||||
Performs log-linear interpolation of a given array of decreasing numbers.
|
||||
"""
|
||||
xs = np.linspace(0, 1, len(t_steps))
|
||||
ys = np.log(t_steps[::-1])
|
||||
|
||||
new_xs = np.linspace(0, 1, num_steps)
|
||||
new_ys = np.interp(new_xs, xs, ys)
|
||||
|
||||
interped_ys = np.exp(new_ys)[::-1].copy()
|
||||
return interped_ys
|
||||
|
||||
NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582],
|
||||
"SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582],
|
||||
"SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}
|
||||
|
||||
class AlignYourStepsScheduler:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required":
|
||||
{"model_type": (["SD1", "SDXL", "SVD"], ),
|
||||
"steps": ("INT", {"default": 10, "min": 10, "max": 10000}),
|
||||
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("SIGMAS",)
|
||||
CATEGORY = "sampling/custom_sampling/schedulers"
|
||||
|
||||
FUNCTION = "get_sigmas"
|
||||
|
||||
def get_sigmas(self, model_type, steps, denoise):
|
||||
total_steps = steps
|
||||
if denoise < 1.0:
|
||||
if denoise <= 0.0:
|
||||
return (torch.FloatTensor([]),)
|
||||
total_steps = round(steps * denoise)
|
||||
|
||||
sigmas = NOISE_LEVELS[model_type][:]
|
||||
if (steps + 1) != len(sigmas):
|
||||
sigmas = loglinear_interp(sigmas, steps + 1)
|
||||
|
||||
sigmas = sigmas[-(total_steps + 1):]
|
||||
sigmas[-1] = 0
|
||||
return (torch.FloatTensor(sigmas), )
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"AlignYourStepsScheduler": AlignYourStepsScheduler,
|
||||
}
|
||||
|
|
@ -230,6 +230,25 @@ class SamplerDPMPP_SDE:
|
|||
sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
|
||||
return (sampler, )
|
||||
|
||||
|
||||
class SamplerTCD:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"eta": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("SAMPLER",)
|
||||
CATEGORY = "sampling/custom_sampling/samplers"
|
||||
|
||||
FUNCTION = "get_sampler"
|
||||
|
||||
def get_sampler(self, eta=0.3):
|
||||
sampler = ldm_patched.modules.samplers.ksampler("tcd", {"eta": eta})
|
||||
return (sampler, )
|
||||
|
||||
|
||||
class SamplerCustom:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
|
|
@ -292,6 +311,7 @@ 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"],),
|
||||
"sampling": (["eps", "v_prediction", "lcm", "tcd"]),
|
||||
"zsnr": ("BOOLEAN", {"default": False}),
|
||||
}}
|
||||
|
||||
|
|
@ -90,6 +90,9 @@ class ModelSamplingDiscrete:
|
|||
elif sampling == "lcm":
|
||||
sampling_type = LCM
|
||||
sampling_base = ModelSamplingDiscreteDistilled
|
||||
elif sampling == "tcd":
|
||||
sampling_type = ldm_patched.modules.model_sampling.EPS
|
||||
sampling_base = ModelSamplingDiscreteDistilled
|
||||
|
||||
class ModelSamplingAdvanced(sampling_base, sampling_type):
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -752,7 +752,6 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
|
|||
return x
|
||||
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
|
||||
# From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
|
||||
|
|
@ -808,3 +807,30 @@ 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()
|
||||
|
||||
return x
|
||||
|
|
@ -37,6 +37,7 @@ parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nar
|
|||
parser.add_argument("--port", type=int, default=8188)
|
||||
parser.add_argument("--disable-header-check", type=str, default=None, metavar="ORIGIN", nargs="?", const="*")
|
||||
parser.add_argument("--web-upload-size", type=float, default=100)
|
||||
parser.add_argument("--hf-mirror", type=str, default=None)
|
||||
|
||||
parser.add_argument("--external-working-path", type=str, default=None, metavar="PATH", nargs='+', action='append')
|
||||
parser.add_argument("--output-path", type=str, default=None)
|
||||
|
|
|
|||
|
|
@ -50,17 +50,17 @@ class ModelSamplingDiscrete(torch.nn.Module):
|
|||
self.linear_start = linear_start
|
||||
self.linear_end = linear_end
|
||||
|
||||
# self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
|
||||
# self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
|
||||
# self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
|
||||
|
||||
sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
|
||||
self.set_sigmas(sigmas)
|
||||
self.set_alphas_cumprod(alphas_cumprod.float())
|
||||
|
||||
def set_sigmas(self, sigmas):
|
||||
self.register_buffer('sigmas', sigmas)
|
||||
self.register_buffer('log_sigmas', sigmas.log())
|
||||
|
||||
def set_alphas_cumprod(self, alphas_cumprod):
|
||||
self.register_buffer("alphas_cumprod", alphas_cumprod.float())
|
||||
|
||||
@property
|
||||
def sigma_min(self):
|
||||
return self.sigmas[0]
|
||||
|
|
|
|||
|
|
@ -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"]
|
||||
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd"]
|
||||
|
||||
class KSAMPLER(Sampler):
|
||||
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
|
||||
|
|
|
|||
|
|
@ -427,12 +427,13 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
|
|||
|
||||
return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
|
||||
|
||||
def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
|
||||
def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, vae_filename_param=None):
|
||||
sd = ldm_patched.modules.utils.load_torch_file(ckpt_path)
|
||||
sd_keys = sd.keys()
|
||||
clip = None
|
||||
clipvision = None
|
||||
vae = None
|
||||
vae_filename = None
|
||||
model = None
|
||||
model_patcher = None
|
||||
clip_target = None
|
||||
|
|
@ -462,8 +463,12 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
|
|||
model.load_model_weights(sd, "model.diffusion_model.")
|
||||
|
||||
if output_vae:
|
||||
vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
|
||||
vae_sd = model_config.process_vae_state_dict(vae_sd)
|
||||
if vae_filename_param is None:
|
||||
vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
|
||||
vae_sd = model_config.process_vae_state_dict(vae_sd)
|
||||
else:
|
||||
vae_sd = ldm_patched.modules.utils.load_torch_file(vae_filename_param)
|
||||
vae_filename = vae_filename_param
|
||||
vae = VAE(sd=vae_sd)
|
||||
|
||||
if output_clip:
|
||||
|
|
@ -485,7 +490,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
|
|||
print("loaded straight to GPU")
|
||||
model_management.load_model_gpu(model_patcher)
|
||||
|
||||
return (model_patcher, clip, vae, clipvision)
|
||||
return model_patcher, clip, vae, vae_filename, clipvision
|
||||
|
||||
|
||||
def load_unet_state_dict(sd): #load unet in diffusers format
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ from modules.patch import PatchSettings, patch_settings, patch_all
|
|||
|
||||
patch_all()
|
||||
|
||||
|
||||
class AsyncTask:
|
||||
def __init__(self, args):
|
||||
self.args = args
|
||||
|
|
@ -43,11 +44,13 @@ def worker():
|
|||
import fooocus_version
|
||||
import args_manager
|
||||
|
||||
from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
|
||||
from extras.censor import default_censor
|
||||
from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name
|
||||
from modules.private_logger import log
|
||||
from extras.expansion import safe_str
|
||||
from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
|
||||
get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras
|
||||
from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil,
|
||||
get_shape_ceil, resample_image, erode_or_dilate, get_enabled_loras,
|
||||
parse_lora_references_from_prompt, apply_wildcards)
|
||||
from modules.upscaler import perform_upscale
|
||||
from modules.flags import Performance
|
||||
from modules.meta_parser import get_metadata_parser, MetadataScheme
|
||||
|
|
@ -68,10 +71,15 @@ def worker():
|
|||
print(f'[Fooocus] {text}')
|
||||
async_task.yields.append(['preview', (number, text, None)])
|
||||
|
||||
def yield_result(async_task, imgs, do_not_show_finished_images=False):
|
||||
def yield_result(async_task, imgs, black_out_nsfw, censor=True, do_not_show_finished_images=False,
|
||||
progressbar_index=flags.preparation_step_count):
|
||||
if not isinstance(imgs, list):
|
||||
imgs = [imgs]
|
||||
|
||||
if censor and (modules.config.default_black_out_nsfw or black_out_nsfw):
|
||||
progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')
|
||||
imgs = default_censor(imgs)
|
||||
|
||||
async_task.results = async_task.results + imgs
|
||||
|
||||
if do_not_show_finished_images:
|
||||
|
|
@ -147,7 +155,8 @@ def worker():
|
|||
base_model_name = args.pop()
|
||||
refiner_model_name = args.pop()
|
||||
refiner_switch = args.pop()
|
||||
loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
|
||||
loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in
|
||||
range(modules.config.default_max_lora_number)])
|
||||
input_image_checkbox = args.pop()
|
||||
current_tab = args.pop()
|
||||
uov_method = args.pop()
|
||||
|
|
@ -160,12 +169,15 @@ def worker():
|
|||
disable_preview = args.pop()
|
||||
disable_intermediate_results = args.pop()
|
||||
disable_seed_increment = args.pop()
|
||||
black_out_nsfw = args.pop()
|
||||
adm_scaler_positive = args.pop()
|
||||
adm_scaler_negative = args.pop()
|
||||
adm_scaler_end = args.pop()
|
||||
adaptive_cfg = args.pop()
|
||||
clip_skip = args.pop()
|
||||
sampler_name = args.pop()
|
||||
scheduler_name = args.pop()
|
||||
vae_name = args.pop()
|
||||
overwrite_step = args.pop()
|
||||
overwrite_switch = args.pop()
|
||||
overwrite_width = args.pop()
|
||||
|
|
@ -195,7 +207,8 @@ def worker():
|
|||
inpaint_erode_or_dilate = args.pop()
|
||||
|
||||
save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
|
||||
metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
|
||||
metadata_scheme = MetadataScheme(
|
||||
args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
|
||||
|
||||
cn_tasks = {x: [] for x in flags.ip_list}
|
||||
for _ in range(flags.controlnet_image_count):
|
||||
|
|
@ -225,10 +238,12 @@ def worker():
|
|||
|
||||
steps = performance_selection.steps()
|
||||
|
||||
performance_loras = []
|
||||
|
||||
if performance_selection == Performance.EXTREME_SPEED:
|
||||
print('Enter LCM mode.')
|
||||
progressbar(async_task, 1, 'Downloading LCM components ...')
|
||||
loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
|
||||
performance_loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
|
||||
|
||||
if refiner_model_name != 'None':
|
||||
print(f'Refiner disabled in LCM mode.')
|
||||
|
|
@ -247,7 +262,7 @@ def worker():
|
|||
elif performance_selection == Performance.LIGHTNING:
|
||||
print('Enter Lightning mode.')
|
||||
progressbar(async_task, 1, 'Downloading Lightning components ...')
|
||||
loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
|
||||
performance_loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
|
||||
|
||||
if refiner_model_name != 'None':
|
||||
print(f'Refiner disabled in Lightning mode.')
|
||||
|
|
@ -263,7 +278,27 @@ def worker():
|
|||
adm_scaler_negative = 1.0
|
||||
adm_scaler_end = 0.0
|
||||
|
||||
elif performance_selection == Performance.HYPER_SD:
|
||||
print('Enter Hyper-SD mode.')
|
||||
progressbar(async_task, 1, 'Downloading Hyper-SD components ...')
|
||||
performance_loras += [(modules.config.downloading_sdxl_hyper_sd_lora(), 0.8)]
|
||||
|
||||
if refiner_model_name != 'None':
|
||||
print(f'Refiner disabled in Hyper-SD mode.')
|
||||
|
||||
refiner_model_name = 'None'
|
||||
sampler_name = 'dpmpp_sde_gpu'
|
||||
scheduler_name = 'karras'
|
||||
sharpness = 0.0
|
||||
guidance_scale = 1.0
|
||||
adaptive_cfg = 1.0
|
||||
refiner_switch = 1.0
|
||||
adm_scaler_positive = 1.0
|
||||
adm_scaler_negative = 1.0
|
||||
adm_scaler_end = 0.0
|
||||
|
||||
print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
|
||||
print(f'[Parameters] CLIP Skip = {clip_skip}')
|
||||
print(f'[Parameters] Sharpness = {sharpness}')
|
||||
print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
|
||||
print(f'[Parameters] ADM Scale = '
|
||||
|
|
@ -425,14 +460,19 @@ def worker():
|
|||
extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
|
||||
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
|
||||
|
||||
progressbar(async_task, 3, 'Loading models ...')
|
||||
progressbar(async_task, 2, 'Loading models ...')
|
||||
|
||||
loras, prompt = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)
|
||||
loras += performance_loras
|
||||
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
|
||||
loras=loras, base_model_additional_loras=base_model_additional_loras,
|
||||
use_synthetic_refiner=use_synthetic_refiner)
|
||||
use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
|
||||
|
||||
pipeline.set_clip_skip(clip_skip)
|
||||
|
||||
progressbar(async_task, 3, 'Processing prompts ...')
|
||||
tasks = []
|
||||
|
||||
|
||||
for i in range(image_number):
|
||||
if disable_seed_increment:
|
||||
task_seed = seed % (constants.MAX_SEED + 1)
|
||||
|
|
@ -443,14 +483,20 @@ def worker():
|
|||
task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
|
||||
task_prompt = apply_arrays(task_prompt, i)
|
||||
task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
|
||||
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
|
||||
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
|
||||
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
|
||||
extra_positive_prompts]
|
||||
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
|
||||
extra_negative_prompts]
|
||||
|
||||
positive_basic_workloads = []
|
||||
negative_basic_workloads = []
|
||||
|
||||
task_styles = style_selections.copy()
|
||||
if use_style:
|
||||
for s in style_selections:
|
||||
for i, s in enumerate(task_styles):
|
||||
if s == random_style_name:
|
||||
s = get_random_style(task_rng)
|
||||
task_styles[i] = s
|
||||
p, n = apply_style(s, positive=task_prompt)
|
||||
positive_basic_workloads = positive_basic_workloads + p
|
||||
negative_basic_workloads = negative_basic_workloads + n
|
||||
|
|
@ -478,29 +524,30 @@ def worker():
|
|||
negative_top_k=len(negative_basic_workloads),
|
||||
log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts),
|
||||
log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts),
|
||||
styles=task_styles
|
||||
))
|
||||
|
||||
if use_expansion:
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...')
|
||||
progressbar(async_task, 4, f'Preparing Fooocus text #{i + 1} ...')
|
||||
expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])
|
||||
print(f'[Prompt Expansion] {expansion}')
|
||||
t['expansion'] = expansion
|
||||
t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy.
|
||||
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(async_task, 7, f'Encoding positive #{i + 1} ...')
|
||||
progressbar(async_task, 5, f'Encoding positive #{i + 1} ...')
|
||||
t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])
|
||||
|
||||
for i, t in enumerate(tasks):
|
||||
if abs(float(cfg_scale) - 1.0) < 1e-4:
|
||||
t['uc'] = pipeline.clone_cond(t['c'])
|
||||
else:
|
||||
progressbar(async_task, 10, f'Encoding negative #{i + 1} ...')
|
||||
progressbar(async_task, 6, f'Encoding negative #{i + 1} ...')
|
||||
t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
|
||||
|
||||
if len(goals) > 0:
|
||||
progressbar(async_task, 13, 'Image processing ...')
|
||||
progressbar(async_task, 7, 'Image processing ...')
|
||||
|
||||
if 'vary' in goals:
|
||||
if 'subtle' in uov_method:
|
||||
|
|
@ -521,7 +568,7 @@ def worker():
|
|||
uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
|
||||
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(async_task, 13, 'VAE encoding ...')
|
||||
progressbar(async_task, 8, 'VAE encoding ...')
|
||||
|
||||
candidate_vae, _ = pipeline.get_candidate_vae(
|
||||
steps=steps,
|
||||
|
|
@ -538,7 +585,7 @@ def worker():
|
|||
|
||||
if 'upscale' in goals:
|
||||
H, W, C = uov_input_image.shape
|
||||
progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...')
|
||||
progressbar(async_task, 9, f'Upscaling image from {str((H, W))} ...')
|
||||
uov_input_image = perform_upscale(uov_input_image)
|
||||
print(f'Image upscaled.')
|
||||
|
||||
|
|
@ -572,8 +619,12 @@ def worker():
|
|||
|
||||
if direct_return:
|
||||
d = [('Upscale (Fast)', 'upscale_fast', '2x')]
|
||||
if modules.config.default_black_out_nsfw or black_out_nsfw:
|
||||
progressbar(async_task, 100, 'Checking for NSFW content ...')
|
||||
uov_input_image = default_censor(uov_input_image)
|
||||
progressbar(async_task, 100, 'Saving image to system ...')
|
||||
uov_input_image_path = log(uov_input_image, d, output_format=output_format)
|
||||
yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True)
|
||||
yield_result(async_task, uov_input_image_path, black_out_nsfw, False, do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
tiled = True
|
||||
|
|
@ -583,7 +634,7 @@ def worker():
|
|||
denoising_strength = overwrite_upscale_strength
|
||||
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(async_task, 13, 'VAE encoding ...')
|
||||
progressbar(async_task, 10, 'VAE encoding ...')
|
||||
|
||||
candidate_vae, _ = pipeline.get_candidate_vae(
|
||||
steps=steps,
|
||||
|
|
@ -637,11 +688,11 @@ def worker():
|
|||
)
|
||||
|
||||
if debugging_inpaint_preprocessor:
|
||||
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
|
||||
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), black_out_nsfw,
|
||||
do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
progressbar(async_task, 13, 'VAE Inpaint encoding ...')
|
||||
progressbar(async_task, 11, 'VAE Inpaint encoding ...')
|
||||
|
||||
inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
|
||||
inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
|
||||
|
|
@ -661,7 +712,7 @@ def worker():
|
|||
|
||||
latent_swap = None
|
||||
if candidate_vae_swap is not None:
|
||||
progressbar(async_task, 13, 'VAE SD15 encoding ...')
|
||||
progressbar(async_task, 12, 'VAE SD15 encoding ...')
|
||||
latent_swap = core.encode_vae(
|
||||
vae=candidate_vae_swap,
|
||||
pixels=inpaint_pixel_fill)['samples']
|
||||
|
|
@ -701,7 +752,7 @@ def worker():
|
|||
cn_img = HWC3(cn_img)
|
||||
task[0] = core.numpy_to_pytorch(cn_img)
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_cpds]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
|
|
@ -713,7 +764,7 @@ def worker():
|
|||
cn_img = HWC3(cn_img)
|
||||
task[0] = core.numpy_to_pytorch(cn_img)
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_ip]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
|
|
@ -724,7 +775,7 @@ def worker():
|
|||
|
||||
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_ip_face]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
|
|
@ -738,7 +789,7 @@ def worker():
|
|||
|
||||
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face]
|
||||
|
|
@ -773,29 +824,31 @@ def worker():
|
|||
final_sampler_name = sampler_name
|
||||
final_scheduler_name = scheduler_name
|
||||
|
||||
if scheduler_name == 'lcm':
|
||||
if scheduler_name in ['lcm', 'tcd']:
|
||||
final_scheduler_name = 'sgm_uniform'
|
||||
if pipeline.final_unet is not None:
|
||||
pipeline.final_unet = core.opModelSamplingDiscrete.patch(
|
||||
pipeline.final_unet,
|
||||
sampling='lcm',
|
||||
sampling=scheduler_name,
|
||||
zsnr=False)[0]
|
||||
if pipeline.final_refiner_unet is not None:
|
||||
pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
|
||||
pipeline.final_refiner_unet,
|
||||
sampling='lcm',
|
||||
sampling=scheduler_name,
|
||||
zsnr=False)[0]
|
||||
print('Using lcm scheduler.')
|
||||
print(f'Using {scheduler_name} scheduler.')
|
||||
|
||||
async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)])
|
||||
async_task.yields.append(['preview', (flags.preparation_step_count, 'Moving model to GPU ...', None)])
|
||||
|
||||
def callback(step, x0, x, total_steps, y):
|
||||
done_steps = current_task_id * steps + step
|
||||
async_task.yields.append(['preview', (
|
||||
int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
|
||||
f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)])
|
||||
int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float(done_steps) / float(all_steps)),
|
||||
f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{image_number} ...', y)])
|
||||
|
||||
for current_task_id, task in enumerate(tasks):
|
||||
current_progress = int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float(current_task_id * steps) / float(all_steps))
|
||||
progressbar(async_task, current_progress, f'Preparing task {current_task_id + 1}/{image_number} ...')
|
||||
execution_start_time = time.perf_counter()
|
||||
|
||||
try:
|
||||
|
|
@ -838,11 +891,18 @@ def worker():
|
|||
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
|
||||
|
||||
img_paths = []
|
||||
current_progress = int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float((current_task_id + 1) * steps) / float(all_steps))
|
||||
if modules.config.default_black_out_nsfw or black_out_nsfw:
|
||||
progressbar(async_task, current_progress, 'Checking for NSFW content ...')
|
||||
imgs = default_censor(imgs)
|
||||
|
||||
progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{image_number} to system ...')
|
||||
for x in imgs:
|
||||
d = [('Prompt', 'prompt', task['log_positive_prompt']),
|
||||
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
|
||||
('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
|
||||
('Styles', 'styles', str(raw_style_selections)),
|
||||
('Styles', 'styles',
|
||||
str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
|
||||
('Performance', 'performance', performance_selection.value)]
|
||||
|
||||
if performance_selection.steps() != steps:
|
||||
|
|
@ -865,10 +925,14 @@ def worker():
|
|||
if refiner_swap_method != flags.refiner_swap_method:
|
||||
d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
|
||||
if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
|
||||
d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
|
||||
d.append(
|
||||
('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
|
||||
|
||||
if clip_skip > 1:
|
||||
d.append(('CLIP Skip', 'clip_skip', clip_skip))
|
||||
d.append(('Sampler', 'sampler', sampler_name))
|
||||
d.append(('Scheduler', 'scheduler', scheduler_name))
|
||||
d.append(('VAE', 'vae', vae_name))
|
||||
d.append(('Seed', 'seed', str(task['task_seed'])))
|
||||
|
||||
if freeu_enabled:
|
||||
|
|
@ -883,12 +947,14 @@ def worker():
|
|||
metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
|
||||
metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
|
||||
task['log_negative_prompt'], task['negative'],
|
||||
steps, base_model_name, refiner_model_name, loras)
|
||||
d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
|
||||
steps, base_model_name, refiner_model_name, loras, vae_name)
|
||||
d.append(('Metadata Scheme', 'metadata_scheme',
|
||||
metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
|
||||
d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
|
||||
img_paths.append(log(x, d, metadata_parser, output_format))
|
||||
img_paths.append(log(x, d, metadata_parser, output_format, task))
|
||||
|
||||
yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
|
||||
yield_result(async_task, img_paths, black_out_nsfw, False,
|
||||
do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
|
||||
except ldm_patched.modules.model_management.InterruptProcessingException as e:
|
||||
if async_task.last_stop == 'skip':
|
||||
print('User skipped')
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ import modules.flags
|
|||
import modules.sdxl_styles
|
||||
|
||||
from modules.model_loader import load_file_from_url
|
||||
from modules.util import get_files_from_folder, makedirs_with_log
|
||||
from modules.extra_utils import makedirs_with_log, get_files_from_folder
|
||||
from modules.flags import OutputFormat, Performance, MetadataScheme
|
||||
|
||||
|
||||
|
|
@ -20,7 +20,7 @@ def get_config_path(key, default_value):
|
|||
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_dict = {}
|
||||
|
|
@ -189,12 +189,14 @@ paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/check
|
|||
paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)
|
||||
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_outputs = get_path_output()
|
||||
|
||||
|
||||
|
|
@ -346,6 +348,11 @@ default_scheduler = get_config_item_or_set_default(
|
|||
default_value='karras',
|
||||
validator=lambda x: x in modules.flags.scheduler_list
|
||||
)
|
||||
default_vae = get_config_item_or_set_default(
|
||||
key='default_vae',
|
||||
default_value=modules.flags.default_vae,
|
||||
validator=lambda x: isinstance(x, str)
|
||||
)
|
||||
default_styles = get_config_item_or_set_default(
|
||||
key='default_styles',
|
||||
default_value=[
|
||||
|
|
@ -409,13 +416,7 @@ embeddings_downloads = get_config_item_or_set_default(
|
|||
)
|
||||
available_aspect_ratios = get_config_item_or_set_default(
|
||||
key='available_aspect_ratios',
|
||||
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'
|
||||
],
|
||||
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
|
||||
)
|
||||
default_aspect_ratio = get_config_item_or_set_default(
|
||||
|
|
@ -433,6 +434,11 @@ default_cfg_tsnr = get_config_item_or_set_default(
|
|||
default_value=7.0,
|
||||
validator=lambda x: isinstance(x, numbers.Number)
|
||||
)
|
||||
default_clip_skip = get_config_item_or_set_default(
|
||||
key='default_clip_skip',
|
||||
default_value=1,
|
||||
validator=lambda x: isinstance(x, numbers.Number)
|
||||
)
|
||||
default_overwrite_step = get_config_item_or_set_default(
|
||||
key='default_overwrite_step',
|
||||
default_value=-1,
|
||||
|
|
@ -450,6 +456,11 @@ example_inpaint_prompts = get_config_item_or_set_default(
|
|||
],
|
||||
validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x)
|
||||
)
|
||||
default_black_out_nsfw = get_config_item_or_set_default(
|
||||
key='default_black_out_nsfw',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool)
|
||||
)
|
||||
default_save_metadata_to_images = get_config_item_or_set_default(
|
||||
key='default_save_metadata_to_images',
|
||||
default_value=False,
|
||||
|
|
@ -481,6 +492,8 @@ possible_preset_keys = {
|
|||
"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",
|
||||
|
|
@ -514,7 +527,7 @@ def add_ratio(x):
|
|||
|
||||
|
||||
default_aspect_ratio = add_ratio(default_aspect_ratio)
|
||||
available_aspect_ratios = [add_ratio(x) for x in available_aspect_ratios]
|
||||
available_aspect_ratios_labels = [add_ratio(x) for x in available_aspect_ratios]
|
||||
|
||||
|
||||
# Only write config in the first launch.
|
||||
|
|
@ -535,26 +548,45 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
|
|||
|
||||
model_filenames = []
|
||||
lora_filenames = []
|
||||
lora_filenames_no_special = []
|
||||
vae_filenames = []
|
||||
wildcard_filenames = []
|
||||
|
||||
sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
|
||||
sdxl_lightning_lora = 'sdxl_lightning_4step_lora.safetensors'
|
||||
loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora]
|
||||
sdxl_hyper_sd_lora = 'sdxl_hyper_sd_4step_lora.safetensors'
|
||||
loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora, sdxl_hyper_sd_lora]
|
||||
|
||||
|
||||
def remove_special_loras(lora_filenames):
|
||||
global loras_metadata_remove
|
||||
|
||||
loras_no_special = lora_filenames.copy()
|
||||
for lora_to_remove in loras_metadata_remove:
|
||||
if lora_to_remove in loras_no_special:
|
||||
loras_no_special.remove(lora_to_remove)
|
||||
return loras_no_special
|
||||
|
||||
|
||||
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 update_files():
|
||||
global model_filenames, lora_filenames, wildcard_filenames, available_presets
|
||||
global model_filenames, lora_filenames, lora_filenames_no_special, vae_filenames, wildcard_filenames, available_presets
|
||||
model_filenames = get_model_filenames(paths_checkpoints)
|
||||
lora_filenames = get_model_filenames(paths_loras)
|
||||
lora_filenames_no_special = remove_special_loras(lora_filenames)
|
||||
vae_filenames = get_model_filenames(path_vae)
|
||||
wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
|
||||
available_presets = get_presets()
|
||||
return
|
||||
|
|
@ -608,13 +640,22 @@ def downloading_sdxl_lcm_lora():
|
|||
|
||||
def downloading_sdxl_lightning_lora():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_4step_lora.safetensors',
|
||||
url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_lightning_4step_lora.safetensors',
|
||||
model_dir=paths_loras[0],
|
||||
file_name=sdxl_lightning_lora
|
||||
)
|
||||
return sdxl_lightning_lora
|
||||
|
||||
|
||||
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=sdxl_hyper_sd_lora
|
||||
)
|
||||
return sdxl_hyper_sd_lora
|
||||
|
||||
|
||||
def downloading_controlnet_canny():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/lllyasviel/misc/resolve/main/control-lora-canny-rank128.safetensors',
|
||||
|
|
@ -679,5 +720,13 @@ 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')
|
||||
|
||||
|
||||
update_files()
|
||||
|
|
|
|||
|
|
@ -35,12 +35,13 @@ opModelSamplingDiscrete = ModelSamplingDiscrete()
|
|||
|
||||
|
||||
class StableDiffusionModel:
|
||||
def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None):
|
||||
def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_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 = ''
|
||||
|
|
@ -142,9 +143,10 @@ def apply_controlnet(positive, negative, control_net, image, strength, start_per
|
|||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
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)
|
||||
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)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ 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
|
||||
|
|
@ -58,17 +59,21 @@ def assert_model_integrity():
|
|||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def refresh_base_model(name):
|
||||
def refresh_base_model(name, vae_name=None):
|
||||
global model_base
|
||||
|
||||
filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
|
||||
|
||||
if model_base.filename == filename:
|
||||
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:
|
||||
return
|
||||
|
||||
model_base = core.StableDiffusionModel()
|
||||
model_base = core.load_model(filename)
|
||||
model_base = core.load_model(filename, vae_filename)
|
||||
print(f'Base model loaded: {model_base.filename}')
|
||||
print(f'VAE loaded: {model_base.vae_filename}')
|
||||
return
|
||||
|
||||
|
||||
|
|
@ -196,6 +201,17 @@ 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():
|
||||
|
|
@ -216,7 +232,7 @@ 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):
|
||||
base_model_additional_loras=None, use_synthetic_refiner=False, vae_name=None):
|
||||
global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion
|
||||
|
||||
final_unet = None
|
||||
|
|
@ -227,11 +243,11 @@ 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)
|
||||
refresh_base_model(base_model_name, vae_name)
|
||||
synthesize_refiner_model()
|
||||
else:
|
||||
refresh_refiner_model(refiner_model_name)
|
||||
refresh_base_model(base_model_name)
|
||||
refresh_base_model(base_model_name, vae_name)
|
||||
|
||||
refresh_loras(loras, base_model_additional_loras=base_model_additional_loras)
|
||||
assert_model_integrity()
|
||||
|
|
@ -254,7 +270,8 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
|
|||
refresh_everything(
|
||||
refiner_model_name=modules.config.default_refiner_model_name,
|
||||
base_model_name=modules.config.default_base_model_name,
|
||||
loras=get_enabled_loras(modules.config.default_loras)
|
||||
loras=get_enabled_loras(modules.config.default_loras),
|
||||
vae_name=modules.config.default_vae,
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,26 @@
|
|||
import os
|
||||
|
||||
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
|
||||
|
|
@ -34,7 +34,8 @@ KSAMPLER = {
|
|||
"dpmpp_3m_sde": "",
|
||||
"dpmpp_3m_sde_gpu": "",
|
||||
"ddpm": "",
|
||||
"lcm": "LCM"
|
||||
"lcm": "LCM",
|
||||
"tcd": "TCD"
|
||||
}
|
||||
|
||||
SAMPLER_EXTRA = {
|
||||
|
|
@ -47,12 +48,14 @@ SAMPLERS = KSAMPLER | SAMPLER_EXTRA
|
|||
|
||||
KSAMPLER_NAMES = list(KSAMPLER.keys())
|
||||
|
||||
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"]
|
||||
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd"]
|
||||
SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
|
||||
|
||||
sampler_list = SAMPLER_NAMES
|
||||
scheduler_list = SCHEDULER_NAMES
|
||||
|
||||
default_vae = 'Default (model)'
|
||||
|
||||
refiner_swap_method = 'joint'
|
||||
|
||||
cn_ip = "ImagePrompt"
|
||||
|
|
@ -78,6 +81,13 @@ inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option
|
|||
desc_type_photo = 'Photograph'
|
||||
desc_type_anime = 'Art/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'
|
||||
|
|
@ -90,6 +100,7 @@ metadata_scheme = [
|
|||
]
|
||||
|
||||
controlnet_image_count = 4
|
||||
preparation_step_count = 13
|
||||
|
||||
|
||||
class OutputFormat(Enum):
|
||||
|
|
@ -107,6 +118,7 @@ class Steps(IntEnum):
|
|||
SPEED = 30
|
||||
EXTREME_SPEED = 8
|
||||
LIGHTNING = 4
|
||||
HYPER_SD = 4
|
||||
|
||||
|
||||
class StepsUOV(IntEnum):
|
||||
|
|
@ -114,6 +126,7 @@ class StepsUOV(IntEnum):
|
|||
SPEED = 18
|
||||
EXTREME_SPEED = 8
|
||||
LIGHTNING = 4
|
||||
HYPER_SD = 4
|
||||
|
||||
|
||||
class Performance(Enum):
|
||||
|
|
@ -121,6 +134,7 @@ class Performance(Enum):
|
|||
SPEED = 'Speed'
|
||||
EXTREME_SPEED = 'Extreme Speed'
|
||||
LIGHTNING = 'Lightning'
|
||||
HYPER_SD = 'Hyper-SD'
|
||||
|
||||
@classmethod
|
||||
def list(cls) -> list:
|
||||
|
|
@ -130,7 +144,7 @@ class Performance(Enum):
|
|||
def has_restricted_features(cls, x) -> bool:
|
||||
if isinstance(x, Performance):
|
||||
x = x.value
|
||||
return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.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 Steps[self.name] else None
|
||||
|
|
|
|||
|
|
@ -34,18 +34,20 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
|
|||
get_list('styles', 'Styles', loaded_parameter_dict, results)
|
||||
get_str('performance', 'Performance', loaded_parameter_dict, results)
|
||||
get_steps('steps', 'Steps', loaded_parameter_dict, results)
|
||||
get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
|
||||
get_number('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
|
||||
get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
|
||||
get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
|
||||
get_float('sharpness', 'Sharpness', loaded_parameter_dict, results)
|
||||
get_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_float('adaptive_cfg', 'CFG Mimicking from TSNR', 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_float('refiner_switch', 'Refiner Switch', 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)
|
||||
|
||||
if is_generating:
|
||||
|
|
@ -82,11 +84,11 @@ def get_list(key: str, fallback: str | None, source_dict: dict, results: list, d
|
|||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
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 = float(h)
|
||||
h = cast_type(h)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
|
@ -123,7 +125,7 @@ def get_resolution(key: str, fallback: str | None, source_dict: dict, results: l
|
|||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
width, height = eval(h)
|
||||
formatted = modules.config.add_ratio(f'{width}*{height}')
|
||||
if formatted in modules.config.available_aspect_ratios:
|
||||
if formatted in modules.config.available_aspect_ratios_labels:
|
||||
results.append(formatted)
|
||||
results.append(-1)
|
||||
results.append(-1)
|
||||
|
|
@ -204,7 +206,6 @@ def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
|
|||
def get_sha256(filepath):
|
||||
global hash_cache
|
||||
if filepath not in hash_cache:
|
||||
# is_safetensors = os.path.splitext(filepath)[1].lower() == '.safetensors'
|
||||
hash_cache[filepath] = sha256(filepath)
|
||||
|
||||
return hash_cache[filepath]
|
||||
|
|
@ -253,6 +254,7 @@ class MetadataParser(ABC):
|
|||
self.refiner_model_name: str = ''
|
||||
self.refiner_model_hash: str = ''
|
||||
self.loras: list = []
|
||||
self.vae_name: str = ''
|
||||
|
||||
@abstractmethod
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
|
|
@ -267,7 +269,7 @@ class MetadataParser(ABC):
|
|||
raise NotImplementedError
|
||||
|
||||
def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
|
||||
refiner_model_name, loras):
|
||||
refiner_model_name, loras, vae_name):
|
||||
self.raw_prompt = raw_prompt
|
||||
self.full_prompt = full_prompt
|
||||
self.raw_negative_prompt = raw_negative_prompt
|
||||
|
|
@ -289,12 +291,7 @@ class MetadataParser(ABC):
|
|||
lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
|
||||
lora_hash = get_sha256(lora_path)
|
||||
self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
|
||||
|
||||
@staticmethod
|
||||
def remove_special_loras(lora_filenames):
|
||||
for lora_to_remove in modules.config.loras_metadata_remove:
|
||||
if lora_to_remove in lora_filenames:
|
||||
lora_filenames.remove(lora_to_remove)
|
||||
self.vae_name = Path(vae_name).stem
|
||||
|
||||
|
||||
class A1111MetadataParser(MetadataParser):
|
||||
|
|
@ -310,6 +307,7 @@ class A1111MetadataParser(MetadataParser):
|
|||
'steps': 'Steps',
|
||||
'sampler': 'Sampler',
|
||||
'scheduler': 'Scheduler',
|
||||
'vae': 'VAE',
|
||||
'guidance_scale': 'CFG scale',
|
||||
'seed': 'Seed',
|
||||
'resolution': 'Size',
|
||||
|
|
@ -317,6 +315,7 @@ class A1111MetadataParser(MetadataParser):
|
|||
'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',
|
||||
|
|
@ -397,13 +396,12 @@ class A1111MetadataParser(MetadataParser):
|
|||
data['sampler'] = k
|
||||
break
|
||||
|
||||
for key in ['base_model', 'refiner_model']:
|
||||
for key in ['base_model', 'refiner_model', 'vae']:
|
||||
if key in data:
|
||||
for filename in modules.config.model_filenames:
|
||||
path = Path(filename)
|
||||
if data[key] == path.stem:
|
||||
data[key] = filename
|
||||
break
|
||||
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'] != '':
|
||||
|
|
@ -412,13 +410,11 @@ class A1111MetadataParser(MetadataParser):
|
|||
lora_data = data['lora_hashes']
|
||||
|
||||
if lora_data != '':
|
||||
lora_filenames = modules.config.lora_filenames.copy()
|
||||
self.remove_special_loras(lora_filenames)
|
||||
for li, lora in enumerate(lora_data.split(', ')):
|
||||
lora_split = lora.split(': ')
|
||||
lora_name = lora_split[0]
|
||||
lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1]
|
||||
for filename in lora_filenames:
|
||||
for filename in modules.config.lora_filenames_no_special:
|
||||
path = Path(filename)
|
||||
if lora_name == path.stem:
|
||||
data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
|
||||
|
|
@ -433,6 +429,7 @@ class A1111MetadataParser(MetadataParser):
|
|||
|
||||
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':
|
||||
|
|
@ -451,6 +448,7 @@ class A1111MetadataParser(MetadataParser):
|
|||
|
||||
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,
|
||||
|
|
@ -462,7 +460,7 @@ class A1111MetadataParser(MetadataParser):
|
|||
self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
|
||||
}
|
||||
|
||||
for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
|
||||
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]
|
||||
|
||||
|
|
@ -491,22 +489,29 @@ class A1111MetadataParser(MetadataParser):
|
|||
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 parse_json(self, metadata: dict) -> dict:
|
||||
model_filenames = modules.config.model_filenames.copy()
|
||||
lora_filenames = modules.config.lora_filenames.copy()
|
||||
self.remove_special_loras(lora_filenames)
|
||||
for key, value in metadata.items():
|
||||
if value in ['', 'None']:
|
||||
continue
|
||||
if key in ['base_model', 'refiner_model']:
|
||||
metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
|
||||
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, lora_filenames)
|
||||
metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames_no_special)
|
||||
elif key == 'vae':
|
||||
metadata[key] = self.replace_value_with_filename(key, value, modules.config.vae_filenames)
|
||||
else:
|
||||
continue
|
||||
|
||||
|
|
@ -533,6 +538,7 @@ class FooocusMetadataParser(MetadataParser):
|
|||
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 != '':
|
||||
|
|
|
|||
|
|
@ -14,6 +14,8 @@ 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)
|
||||
|
|
|
|||
|
|
@ -51,6 +51,8 @@ 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
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ def get_current_html_path(output_format=None):
|
|||
return html_name
|
||||
|
||||
|
||||
def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
|
||||
def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None, task=None) -> str:
|
||||
path_outputs = modules.config.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
|
||||
output_format = output_format if output_format else modules.config.default_output_format
|
||||
date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
|
||||
|
|
@ -111,9 +111,15 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
|
|||
for label, key, value in metadata:
|
||||
value_txt = str(value).replace('\n', ' </br> ')
|
||||
item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
|
||||
|
||||
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 += "</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 metadata}, indent=0), safe='')
|
||||
item += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
|
||||
|
||||
item += "</td>"
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ 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
|
||||
|
|
@ -175,6 +176,9 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
|
|||
sigmas = normal_scheduler(model, steps, sgm=True)
|
||||
elif scheduler_name == "turbo":
|
||||
sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0]
|
||||
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]
|
||||
else:
|
||||
raise TypeError("error invalid scheduler")
|
||||
return sigmas
|
||||
|
|
|
|||
|
|
@ -2,13 +2,12 @@ import os
|
|||
import re
|
||||
import json
|
||||
import math
|
||||
import modules.config
|
||||
|
||||
from modules.util import get_files_from_folder
|
||||
from modules.extra_utils import get_files_from_folder
|
||||
from random import Random
|
||||
|
||||
# cannot use modules.config - validators causing circular imports
|
||||
styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/'))
|
||||
wildcards_max_bfs_depth = 64
|
||||
|
||||
|
||||
def normalize_key(k):
|
||||
|
|
@ -24,7 +23,6 @@ def normalize_key(k):
|
|||
|
||||
|
||||
styles = {}
|
||||
|
||||
styles_files = get_files_from_folder(styles_path, ['.json'])
|
||||
|
||||
for x in ['sdxl_styles_fooocus.json',
|
||||
|
|
@ -50,8 +48,13 @@ for styles_file in styles_files:
|
|||
print(f'Failed to load style file {styles_file}')
|
||||
|
||||
style_keys = list(styles.keys())
|
||||
fooocus_expansion = "Fooocus V2"
|
||||
legal_style_names = [fooocus_expansion] + style_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]
|
||||
|
||||
|
||||
def apply_style(style, positive):
|
||||
|
|
@ -59,34 +62,7 @@ def apply_style(style, positive):
|
|||
return p.replace('{prompt}', positive).splitlines(), n.splitlines()
|
||||
|
||||
|
||||
def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order):
|
||||
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:
|
||||
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_words(arrays, totalMult, index):
|
||||
def get_words(arrays, total_mult, index):
|
||||
if len(arrays) == 1:
|
||||
return [arrays[0].split(',')[index]]
|
||||
else:
|
||||
|
|
@ -95,7 +71,7 @@ def get_words(arrays, totalMult, index):
|
|||
index -= index % len(words)
|
||||
index /= len(words)
|
||||
index = math.floor(index)
|
||||
return [word] + get_words(arrays[1:], math.floor(totalMult/len(words)), index)
|
||||
return [word] + get_words(arrays[1:], math.floor(total_mult / len(words)), index)
|
||||
|
||||
|
||||
def apply_arrays(text, index):
|
||||
|
|
|
|||
|
|
@ -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}"></meta>\n'
|
||||
head += f'<meta name="samples-path" content="{samples_path}">\n'
|
||||
|
||||
if args_manager.args.theme:
|
||||
head += f'<script type="text/javascript">set_theme(\"{args_manager.args.theme}\");</script>\n'
|
||||
|
|
|
|||
156
modules/util.py
156
modules/util.py
|
|
@ -1,4 +1,4 @@
|
|||
import typing
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import datetime
|
||||
|
|
@ -6,16 +6,27 @@ 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
|
||||
|
||||
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)
|
||||
if k > 0:
|
||||
|
|
@ -163,25 +174,6 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
|
|||
return date_string, os.path.abspath(result), filename
|
||||
|
||||
|
||||
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, dirs, 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 sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):
|
||||
print(f"Calculating sha256 for {filename}: ", end='')
|
||||
if use_addnet_hash:
|
||||
|
|
@ -355,7 +347,7 @@ def extract_styles_from_prompt(prompt, negative_prompt):
|
|||
return list(reversed(extracted)), real_prompt, negative_prompt
|
||||
|
||||
|
||||
class PromptStyle(typing.NamedTuple):
|
||||
class PromptStyle(NamedTuple):
|
||||
name: str
|
||||
prompt: str
|
||||
negative_prompt: str
|
||||
|
|
@ -370,7 +362,18 @@ def is_json(data: str) -> bool:
|
|||
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):
|
||||
|
|
@ -378,7 +381,6 @@ def get_file_from_folder_list(name, folders):
|
|||
|
||||
return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))
|
||||
|
||||
|
||||
def ordinal_suffix(number: int) -> str:
|
||||
return 'th' if 10 <= number % 100 <= 20 else {1: 'st', 2: 'nd', 3: 'rd'}.get(number % 10, 'th')
|
||||
|
||||
|
|
@ -390,5 +392,111 @@ def makedirs_with_log(path):
|
|||
print(f'Directory {path} could not be created, reason: {error}')
|
||||
|
||||
|
||||
def get_enabled_loras(loras: list) -> list:
|
||||
return [[lora[1], lora[2]] for lora in loras if lora[0]]
|
||||
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) -> tuple[List[Tuple[AnyStr, float]], str]:
|
||||
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], modules.config.lora_filenames_no_special)
|
||||
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 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}'
|
||||
|
|
|
|||
|
|
@ -368,6 +368,7 @@ 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]
|
||||
|
|
|
|||
|
|
@ -1,5 +1,2 @@
|
|||
torch==2.0.1
|
||||
torchvision==0.15.2
|
||||
torchaudio==2.0.2
|
||||
torchtext==0.15.2
|
||||
torchdata==0.6.1
|
||||
torch==2.1.0
|
||||
torchvision==0.16.0
|
||||
|
|
|
|||
Binary file not shown.
|
After Width: | Height: | Size: 1.4 KiB |
|
|
@ -0,0 +1,4 @@
|
|||
import sys
|
||||
import pathlib
|
||||
|
||||
sys.path.append(pathlib.Path(f'{__file__}/../modules').parent.resolve())
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
import unittest
|
||||
|
||||
from modules import util
|
||||
|
||||
|
||||
class TestUtils(unittest.TestCase):
|
||||
def test_can_parse_tokens_with_lora(self):
|
||||
test_cases = [
|
||||
{
|
||||
"input": ("some prompt, very cool, <lora:hey-lora:0.4>, cool <lora:you-lora:0.2>", [], 5, True),
|
||||
"output": (
|
||||
[('hey-lora.safetensors', 0.4), ('you-lora.safetensors', 0.2)], 'some prompt, very cool, cool'),
|
||||
},
|
||||
# Test can not exceed limit
|
||||
{
|
||||
"input": ("some prompt, very cool, <lora:hey-lora:0.4>, cool <lora:you-lora:0.2>", [], 1, True),
|
||||
"output": (
|
||||
[('hey-lora.safetensors', 0.4)],
|
||||
'some prompt, very cool, cool'
|
||||
),
|
||||
},
|
||||
# test Loras from UI take precedence over prompt
|
||||
{
|
||||
"input": (
|
||||
"some prompt, very cool, <lora:l1:0.4>, <lora:l2:-0.2>, <lora:l3:0.3>, <lora:l4:0.5>, <lora:l6:0.24>, <lora:l7:0.1>",
|
||||
[("hey-lora.safetensors", 0.4)],
|
||||
5,
|
||||
True
|
||||
),
|
||||
"output": (
|
||||
[
|
||||
('hey-lora.safetensors', 0.4),
|
||||
('l1.safetensors', 0.4),
|
||||
('l2.safetensors', -0.2),
|
||||
('l3.safetensors', 0.3),
|
||||
('l4.safetensors', 0.5)
|
||||
],
|
||||
'some prompt, very cool'
|
||||
)
|
||||
},
|
||||
# test correct matching even if there is no space separating loras in the same token
|
||||
{
|
||||
"input": ("some prompt, very cool, <lora:hey-lora:0.4><lora:you-lora:0.2>", [], 3, True),
|
||||
"output": (
|
||||
[
|
||||
('hey-lora.safetensors', 0.4),
|
||||
('you-lora.safetensors', 0.2)
|
||||
],
|
||||
'some prompt, very cool'
|
||||
),
|
||||
},
|
||||
# test deduplication, also selected loras are never overridden with loras in prompt
|
||||
{
|
||||
"input": (
|
||||
"some prompt, very cool, <lora:hey-lora:0.4><lora:hey-lora:0.4><lora:you-lora:0.2>",
|
||||
[('you-lora.safetensors', 0.3)],
|
||||
3,
|
||||
True
|
||||
),
|
||||
"output": (
|
||||
[
|
||||
('you-lora.safetensors', 0.3),
|
||||
('hey-lora.safetensors', 0.4)
|
||||
],
|
||||
'some prompt, very cool'
|
||||
),
|
||||
},
|
||||
{
|
||||
"input": ("<lora:foo:1..2>, <lora:bar:.>, <test:1.0>, <lora:baz:+> and <lora:quux:>", [], 6, True),
|
||||
"output": (
|
||||
[],
|
||||
'<lora:foo:1..2>, <lora:bar:.>, <test:1.0>, <lora:baz:+> and <lora:quux:>'
|
||||
)
|
||||
}
|
||||
]
|
||||
|
||||
for test in test_cases:
|
||||
prompt, loras, loras_limit, skip_file_check = test["input"]
|
||||
expected = test["output"]
|
||||
actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, skip_file_check=skip_file_check)
|
||||
self.assertEqual(expected, actual)
|
||||
|
|
@ -1,3 +1,23 @@
|
|||
# [2.4.0](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.0)
|
||||
|
||||
* Change settings tab elements to be more compact
|
||||
* Add clip skip slider
|
||||
* Add select for custom VAE
|
||||
* Add new style "Random Style"
|
||||
* Update default anime model to animaPencilXL_v310
|
||||
* Add button to reconnect the UI after Fooocus crashed without having to configure everything again (no page reload required)
|
||||
* Add performance "hyper-sd" (based on [Hyper-SDXL 4 step LoRA](https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-4steps-lora.safetensors))
|
||||
* Add [AlignYourSteps](https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/) scheduler by Nvidia, see
|
||||
* Add [TCD](https://github.com/jabir-zheng/TCD) sampler and scheduler (based on sgm_uniform)
|
||||
* Add NSFW image censoring (disables intermediate image preview while generating). Set config value `default_black_out_nsfw` to True to always enable.
|
||||
* Add argument `--enable-describe-uov-image` to automatically describe uploaded images for upscaling
|
||||
* Add inline lora prompt references with subfolder support, example prompt: `colorful bird <lora:toucan:1.2>`
|
||||
* Add size and aspect ratio recommendation on image describe
|
||||
* Add inpaint brush color picker, helpful when image and mask brush have the same color
|
||||
* Add automated Docker image build using Github Actions on each release.
|
||||
* Add full raw prompts to history logs
|
||||
* Change code ownership from @lllyasviel to @mashb1t for automated issue / MR notification
|
||||
|
||||
# [2.3.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.1)
|
||||
|
||||
* Remove positive prompt from anime prefix to not reset prompt after switching presets
|
||||
|
|
|
|||
119
webui.py
119
webui.py
|
|
@ -123,8 +123,9 @@ with shared.gradio_root:
|
|||
|
||||
with gr.Column(scale=3, min_width=0):
|
||||
generate_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', elem_id='generate_button', visible=True)
|
||||
reset_button = gr.Button(label="Reconnect", value="Reconnect", elem_classes='type_row', elem_id='reset_button', visible=False)
|
||||
load_parameter_button = gr.Button(label="Load Parameters", value="Load Parameters", elem_classes='type_row', elem_id='load_parameter_button', visible=False)
|
||||
skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False)
|
||||
skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', elem_id='skip_button', visible=False)
|
||||
stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False)
|
||||
|
||||
def stop_clicked(currentTask):
|
||||
|
|
@ -151,7 +152,7 @@ with shared.gradio_root:
|
|||
with gr.TabItem(label='Upscale or Variation') as uov_tab:
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
uov_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy')
|
||||
uov_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False)
|
||||
with gr.Column():
|
||||
uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=flags.disabled)
|
||||
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/390" target="_blank">\U0001F4D4 Document</a>')
|
||||
|
|
@ -200,7 +201,7 @@ with shared.gradio_root:
|
|||
queue=False, show_progress=False)
|
||||
with gr.TabItem(label='Inpaint or Outpaint') as inpaint_tab:
|
||||
with gr.Row():
|
||||
inpaint_input_image = grh.Image(label='Drag inpaint or outpaint image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas')
|
||||
inpaint_input_image = grh.Image(label='Image', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas', show_label=False)
|
||||
inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', height=500, visible=False)
|
||||
|
||||
with gr.Row():
|
||||
|
|
@ -213,17 +214,26 @@ with shared.gradio_root:
|
|||
with gr.TabItem(label='Describe') as desc_tab:
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
desc_input_image = grh.Image(label='Drag any image to here', source='upload', type='numpy')
|
||||
desc_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False)
|
||||
with gr.Column():
|
||||
desc_method = gr.Radio(
|
||||
label='Content Type',
|
||||
choices=[flags.desc_type_photo, flags.desc_type_anime],
|
||||
value=flags.desc_type_photo)
|
||||
desc_btn = gr.Button(value='Describe this Image into Prompt')
|
||||
desc_image_size = gr.Textbox(label='Image Size and Recommended Size', elem_id='desc_image_size', visible=False)
|
||||
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/1363" target="_blank">\U0001F4D4 Document</a>')
|
||||
with gr.TabItem(label='Metadata') as load_tab:
|
||||
|
||||
def trigger_show_image_properties(image):
|
||||
value = modules.util.get_image_size_info(image, modules.flags.sdxl_aspect_ratios)
|
||||
return gr.update(value=value, visible=True)
|
||||
|
||||
desc_input_image.upload(trigger_show_image_properties, inputs=desc_input_image,
|
||||
outputs=desc_image_size, show_progress=False, queue=False)
|
||||
|
||||
with gr.TabItem(label='Metadata') as metadata_tab:
|
||||
with gr.Column():
|
||||
metadata_input_image = grh.Image(label='Drag any image generated by Fooocus here', source='upload', type='filepath')
|
||||
metadata_input_image = grh.Image(label='For images created by Fooocus', source='upload', type='filepath')
|
||||
metadata_json = gr.JSON(label='Metadata')
|
||||
metadata_import_button = gr.Button(value='Apply Metadata')
|
||||
|
||||
|
|
@ -254,25 +264,40 @@ with shared.gradio_root:
|
|||
inpaint_tab.select(lambda: 'inpaint', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
|
||||
ip_tab.select(lambda: 'ip', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
|
||||
desc_tab.select(lambda: 'desc', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
|
||||
metadata_tab.select(lambda: 'metadata', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
|
||||
|
||||
with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column:
|
||||
with gr.Tab(label='Setting'):
|
||||
if not args_manager.args.disable_preset_selection:
|
||||
preset_selection = gr.Radio(label='Preset',
|
||||
choices=modules.config.available_presets,
|
||||
value=args_manager.args.preset if args_manager.args.preset else "initial",
|
||||
interactive=True)
|
||||
preset_selection = gr.Dropdown(label='Preset',
|
||||
choices=modules.config.available_presets,
|
||||
value=args_manager.args.preset if args_manager.args.preset else "initial",
|
||||
interactive=True)
|
||||
performance_selection = gr.Radio(label='Performance',
|
||||
choices=flags.Performance.list(),
|
||||
value=modules.config.default_performance)
|
||||
aspect_ratios_selection = gr.Radio(label='Aspect Ratios', choices=modules.config.available_aspect_ratios,
|
||||
value=modules.config.default_aspect_ratio, info='width × height',
|
||||
elem_classes='aspect_ratios')
|
||||
value=modules.config.default_performance,
|
||||
elem_classes=['performance_selection'])
|
||||
with gr.Accordion(label='Aspect Ratios', open=False) as aspect_ratios_accordion:
|
||||
aspect_ratios_selection = gr.Radio(label='Aspect Ratios', show_label=False,
|
||||
choices=modules.config.available_aspect_ratios_labels,
|
||||
value=modules.config.default_aspect_ratio,
|
||||
info='width × height',
|
||||
elem_classes='aspect_ratios')
|
||||
|
||||
def change_aspect_ratio(text):
|
||||
import re
|
||||
regex = re.compile('<.*?>')
|
||||
cleaned_text = re.sub(regex, '', text)
|
||||
return gr.update(label='Aspect Ratios ' + cleaned_text)
|
||||
|
||||
aspect_ratios_selection.change(change_aspect_ratio, inputs=aspect_ratios_selection, outputs=aspect_ratios_accordion, queue=False, show_progress=False)
|
||||
shared.gradio_root.load(change_aspect_ratio, inputs=aspect_ratios_selection, outputs=aspect_ratios_accordion, queue=False, show_progress=False)
|
||||
|
||||
image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number)
|
||||
|
||||
output_format = gr.Radio(label='Output Format',
|
||||
choices=flags.OutputFormat.list(),
|
||||
value=modules.config.default_output_format)
|
||||
choices=flags.OutputFormat.list(),
|
||||
value=modules.config.default_output_format)
|
||||
|
||||
negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.",
|
||||
info='Describing what you do not want to see.', lines=2,
|
||||
|
|
@ -402,10 +427,15 @@ with shared.gradio_root:
|
|||
value=modules.config.default_cfg_tsnr,
|
||||
info='Enabling Fooocus\'s implementation of CFG mimicking for TSNR '
|
||||
'(effective when real CFG > mimicked CFG).')
|
||||
clip_skip = gr.Slider(label='CLIP Skip', minimum=1, maximum=10, step=1,
|
||||
value=modules.config.default_clip_skip,
|
||||
info='Bypass CLIP layers to avoid overfitting (use 1 to disable).')
|
||||
sampler_name = gr.Dropdown(label='Sampler', choices=flags.sampler_list,
|
||||
value=modules.config.default_sampler)
|
||||
scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list,
|
||||
value=modules.config.default_scheduler)
|
||||
vae_name = gr.Dropdown(label='VAE', choices=[modules.flags.default_vae] + modules.config.vae_filenames,
|
||||
value=modules.config.default_vae, show_label=True)
|
||||
|
||||
generate_image_grid = gr.Checkbox(label='Generate Image Grid for Each Batch',
|
||||
info='(Experimental) This may cause performance problems on some computers and certain internet conditions.',
|
||||
|
|
@ -433,7 +463,8 @@ with shared.gradio_root:
|
|||
overwrite_upscale_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Upscale"',
|
||||
minimum=-1, maximum=1.0, step=0.001, value=-1,
|
||||
info='Set as negative number to disable. For developer debugging.')
|
||||
disable_preview = gr.Checkbox(label='Disable Preview', value=False,
|
||||
disable_preview = gr.Checkbox(label='Disable Preview', value=modules.config.default_black_out_nsfw,
|
||||
interactive=not modules.config.default_black_out_nsfw,
|
||||
info='Disable preview during generation.')
|
||||
disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results',
|
||||
value=modules.config.default_performance == flags.Performance.EXTREME_SPEED.value,
|
||||
|
|
@ -444,6 +475,15 @@ with shared.gradio_root:
|
|||
value=False)
|
||||
read_wildcards_in_order = gr.Checkbox(label="Read wildcards in order", value=False)
|
||||
|
||||
black_out_nsfw = gr.Checkbox(label='Black Out NSFW',
|
||||
value=modules.config.default_black_out_nsfw,
|
||||
interactive=not modules.config.default_black_out_nsfw,
|
||||
info='Use black image if NSFW is detected.')
|
||||
|
||||
black_out_nsfw.change(lambda x: gr.update(value=x, interactive=not x),
|
||||
inputs=black_out_nsfw, outputs=disable_preview, queue=False,
|
||||
show_progress=False)
|
||||
|
||||
if not args_manager.args.disable_metadata:
|
||||
save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images,
|
||||
info='Adds parameters to generated images allowing manual regeneration.')
|
||||
|
|
@ -502,13 +542,20 @@ with shared.gradio_root:
|
|||
inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False)
|
||||
invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False)
|
||||
|
||||
inpaint_mask_color = gr.ColorPicker(label='Inpaint brush color', value='#FFFFFF', elem_id='inpaint_brush_color')
|
||||
|
||||
inpaint_ctrls = [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]
|
||||
|
||||
inpaint_mask_upload_checkbox.change(lambda x: gr.update(visible=x),
|
||||
inputs=inpaint_mask_upload_checkbox,
|
||||
outputs=inpaint_mask_image, queue=False, show_progress=False)
|
||||
inputs=inpaint_mask_upload_checkbox,
|
||||
outputs=inpaint_mask_image, queue=False,
|
||||
show_progress=False)
|
||||
|
||||
inpaint_mask_color.change(lambda x: gr.update(brush_color=x), inputs=inpaint_mask_color,
|
||||
outputs=inpaint_input_image,
|
||||
queue=False, show_progress=False)
|
||||
|
||||
with gr.Tab(label='FreeU'):
|
||||
freeu_enabled = gr.Checkbox(label='Enabled', value=False)
|
||||
|
|
@ -528,6 +575,7 @@ with shared.gradio_root:
|
|||
modules.config.update_files()
|
||||
results = [gr.update(choices=modules.config.model_filenames)]
|
||||
results += [gr.update(choices=['None'] + modules.config.model_filenames)]
|
||||
results += [gr.update(choices=['None'] + modules.config.vae_filenames)]
|
||||
if not args_manager.args.disable_preset_selection:
|
||||
results += [gr.update(choices=modules.config.available_presets)]
|
||||
for i in range(modules.config.default_max_lora_number):
|
||||
|
|
@ -535,7 +583,7 @@ with shared.gradio_root:
|
|||
gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
|
||||
return results
|
||||
|
||||
refresh_files_output = [base_model, refiner_model]
|
||||
refresh_files_output = [base_model, refiner_model, vae_name]
|
||||
if not args_manager.args.disable_preset_selection:
|
||||
refresh_files_output += [preset_selection]
|
||||
refresh_files.click(refresh_files_clicked, [], refresh_files_output + lora_ctrls,
|
||||
|
|
@ -546,9 +594,9 @@ with shared.gradio_root:
|
|||
load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
|
||||
performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,
|
||||
overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,
|
||||
adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model,
|
||||
refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed,
|
||||
generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
|
||||
adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, clip_skip,
|
||||
base_model, refiner_model, refiner_switch, sampler_name, scheduler_name, vae_name,
|
||||
seed_random, image_seed, generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
|
||||
|
||||
if not args_manager.args.disable_preset_selection:
|
||||
def preset_selection_change(preset, is_generating):
|
||||
|
|
@ -570,7 +618,7 @@ with shared.gradio_root:
|
|||
return modules.meta_parser.load_parameter_button_click(json.dumps(preset_prepared), is_generating)
|
||||
|
||||
preset_selection.change(preset_selection_change, inputs=[preset_selection, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
|
||||
.then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \
|
||||
.then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False)
|
||||
|
||||
performance_selection.change(lambda x: [gr.update(interactive=not flags.Performance.has_restricted_features(x))] * 11 +
|
||||
[gr.update(visible=not flags.Performance.has_restricted_features(x))] * 1 +
|
||||
|
|
@ -632,9 +680,9 @@ with shared.gradio_root:
|
|||
ctrls += [input_image_checkbox, current_tab]
|
||||
ctrls += [uov_method, uov_input_image]
|
||||
ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]
|
||||
ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment]
|
||||
ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
|
||||
ctrls += [sampler_name, scheduler_name]
|
||||
ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment, black_out_nsfw]
|
||||
ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, clip_skip]
|
||||
ctrls += [sampler_name, scheduler_name, vae_name]
|
||||
ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength]
|
||||
ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint]
|
||||
ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold]
|
||||
|
|
@ -688,6 +736,14 @@ with shared.gradio_root:
|
|||
.then(fn=update_history_link, outputs=history_link) \
|
||||
.then(fn=lambda: None, _js='playNotification').then(fn=lambda: None, _js='refresh_grid_delayed')
|
||||
|
||||
reset_button.click(lambda: [worker.AsyncTask(args=[]), False, gr.update(visible=True, interactive=True)] +
|
||||
[gr.update(visible=False)] * 6 +
|
||||
[gr.update(visible=True, value=[])],
|
||||
outputs=[currentTask, state_is_generating, generate_button,
|
||||
reset_button, stop_button, skip_button,
|
||||
progress_html, progress_window, progress_gallery, gallery],
|
||||
queue=False)
|
||||
|
||||
for notification_file in ['notification.ogg', 'notification.mp3']:
|
||||
if os.path.exists(notification_file):
|
||||
gr.Audio(interactive=False, value=notification_file, elem_id='audio_notification', visible=False)
|
||||
|
|
@ -705,6 +761,15 @@ with shared.gradio_root:
|
|||
desc_btn.click(trigger_describe, inputs=[desc_method, desc_input_image],
|
||||
outputs=[prompt, style_selections], show_progress=True, queue=True)
|
||||
|
||||
if args_manager.args.enable_describe_uov_image:
|
||||
def trigger_uov_describe(mode, img, prompt):
|
||||
# keep prompt if not empty
|
||||
if prompt == '':
|
||||
return trigger_describe(mode, img)
|
||||
return gr.update(), gr.update()
|
||||
|
||||
uov_input_image.upload(trigger_uov_describe, inputs=[desc_method, uov_input_image, prompt],
|
||||
outputs=[prompt, style_selections], show_progress=True, queue=True)
|
||||
|
||||
def dump_default_english_config():
|
||||
from modules.localization import dump_english_config
|
||||
|
|
|
|||
|
|
@ -0,0 +1,8 @@
|
|||
*.txt
|
||||
!animal.txt
|
||||
!artist.txt
|
||||
!color.txt
|
||||
!color_flower.txt
|
||||
!extended-color.txt
|
||||
!flower.txt
|
||||
!nationality.txt
|
||||
Loading…
Reference in New Issue