Manuel Schmid
GitHub
commit
4945fc9962
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.idea
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@ -1,18 +0,0 @@
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---
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name: Bug report
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about: Describe a problem
|
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title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Read Troubleshoot**
|
||||
|
||||
[x] I confirm that I have read the [Troubleshoot](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md) guide before making this issue.
|
||||
|
||||
**Describe the problem**
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
**Full Console Log**
|
||||
Paste the **full** console log here. You will make our job easier if you give a **full** log.
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
name: Bug Report
|
||||
description: You think something is broken in Fooocus
|
||||
title: "[Bug]: "
|
||||
labels: ["bug", "triage"]
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
> The title of the bug report should be short and descriptive.
|
||||
> Use relevant keywords for searchability.
|
||||
> Do not leave it blank, but also do not put an entire error log in it.
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Checklist
|
||||
description: |
|
||||
Please perform basic debugging to see if your configuration is the cause of the issue.
|
||||
Basic debug procedure
|
||||
2. Update Fooocus - sometimes things just need to be updated
|
||||
3. Backup and remove your config.txt - check if the issue is caused by bad configuration
|
||||
5. Try a fresh installation of Fooocus in a different directory - see if a clean installation solves the issue
|
||||
Before making a issue report please, check that the issue hasn't been reported recently.
|
||||
options:
|
||||
- label: The issue exists on a clean installation of Fooocus
|
||||
- label: The issue exists in the current version of Fooocus
|
||||
- label: The issue has not been reported before recently
|
||||
- label: The issue has been reported before but has not been fixed yet
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
> Please fill this form with as much information as possible. Don't forget to add information about "What browsers" and provide screenshots if possible
|
||||
- type: textarea
|
||||
id: what-did
|
||||
attributes:
|
||||
label: What happened?
|
||||
description: Tell us what happened in a very clear and simple way
|
||||
placeholder: |
|
||||
image generation is not working as intended.
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: steps
|
||||
attributes:
|
||||
label: Steps to reproduce the problem
|
||||
description: Please provide us with precise step by step instructions on how to reproduce the bug
|
||||
placeholder: |
|
||||
1. Go to ...
|
||||
2. Press ...
|
||||
3. ...
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: what-should
|
||||
attributes:
|
||||
label: What should have happened?
|
||||
description: Tell us what you think the normal behavior should be
|
||||
placeholder: |
|
||||
Fooocus should ...
|
||||
validations:
|
||||
required: true
|
||||
- type: dropdown
|
||||
id: browsers
|
||||
attributes:
|
||||
label: What browsers do you use to access Fooocus?
|
||||
multiple: true
|
||||
options:
|
||||
- Mozilla Firefox
|
||||
- Google Chrome
|
||||
- Brave
|
||||
- Apple Safari
|
||||
- Microsoft Edge
|
||||
- Android
|
||||
- iOS
|
||||
- Other
|
||||
- type: dropdown
|
||||
id: hosting
|
||||
attributes:
|
||||
label: Where are you running Fooocus?
|
||||
multiple: false
|
||||
options:
|
||||
- Locally
|
||||
- Locally with virtualization (e.g. Docker)
|
||||
- Cloud (Google Colab)
|
||||
- Cloud (other)
|
||||
- type: input
|
||||
id: operating-system
|
||||
attributes:
|
||||
label: What operating system are you using?
|
||||
placeholder: |
|
||||
Windows 10
|
||||
- type: textarea
|
||||
id: logs
|
||||
attributes:
|
||||
label: Console logs
|
||||
description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.
|
||||
render: Shell
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: misc
|
||||
attributes:
|
||||
label: Additional information
|
||||
description: |
|
||||
Please provide us with any relevant additional info or context.
|
||||
Examples:
|
||||
I have updated my GPU driver recently.
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
blank_issues_enabled: false
|
||||
contact_links:
|
||||
- name: Ask a question
|
||||
url: https://github.com/lllyasviel/Fooocus/discussions/new?category=q-a
|
||||
about: Ask the community for help
|
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|
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@ -1,14 +0,0 @@
|
|||
---
|
||||
name: Feature request
|
||||
about: Suggest an idea for this project
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Is your feature request related to a problem? Please describe.**
|
||||
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
|
||||
|
||||
**Describe the idea you'd like**
|
||||
A clear and concise description of what you want to happen.
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
name: Feature request
|
||||
description: Suggest an idea for this project
|
||||
title: "[Feature Request]: "
|
||||
labels: ["enhancement", "triage"]
|
||||
|
||||
body:
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Is there an existing issue for this?
|
||||
description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.
|
||||
options:
|
||||
- label: I have searched the existing issues and checked the recent builds/commits
|
||||
required: true
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
*Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
|
||||
- type: textarea
|
||||
id: feature
|
||||
attributes:
|
||||
label: What would your feature do?
|
||||
description: Tell us about your feature in a very clear and simple way, and what problem it would solve
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: workflow
|
||||
attributes:
|
||||
label: Proposed workflow
|
||||
description: Please provide us with step by step information on how you'd like the feature to be accessed and used
|
||||
value: |
|
||||
1. Go to ....
|
||||
2. Press ....
|
||||
3. ...
|
||||
validations:
|
||||
required: true
|
||||
- type: textarea
|
||||
id: misc
|
||||
attributes:
|
||||
label: Additional information
|
||||
description: Add any other context or screenshots about the feature request here.
|
||||
|
|
@ -51,3 +51,4 @@ user_path_config-deprecated.txt
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|||
/package-lock.json
|
||||
/.coverage*
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||||
/auth.json
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||||
.DS_Store
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||||
|
|
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@ -0,0 +1,29 @@
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FROM nvidia/cuda:12.3.1-base-ubuntu22.04
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ENV DEBIAN_FRONTEND noninteractive
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ENV CMDARGS --listen
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||||
RUN apt-get update -y && \
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apt-get install -y curl libgl1 libglib2.0-0 python3-pip python-is-python3 git && \
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apt-get clean && \
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||||
rm -rf /var/lib/apt/lists/*
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||||
|
||||
COPY requirements_docker.txt requirements_versions.txt /tmp/
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||||
RUN pip install --no-cache-dir -r /tmp/requirements_docker.txt -r /tmp/requirements_versions.txt && \
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rm -f /tmp/requirements_docker.txt /tmp/requirements_versions.txt
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||||
RUN pip install --no-cache-dir xformers==0.0.22 --no-dependencies
|
||||
RUN curl -fsL -o /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64 && \
|
||||
chmod +x /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2
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||||
|
||||
RUN adduser --disabled-password --gecos '' user && \
|
||||
mkdir -p /content/app /content/data
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||||
COPY entrypoint.sh /content/
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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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||||
RUN mv /content/app/models /content/app/models.org
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||||
CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ]
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@ -1,5 +1,7 @@
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|||
import ldm_patched.modules.args_parser as args_parser
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||||
import os
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||||
|
||||
from tempfile import gettempdir
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||||
|
||||
args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
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||||
args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.")
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||||
|
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@ -18,7 +20,10 @@ args_parser.parser.add_argument("--disable-image-log", action='store_true',
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|||
help="Prevent writing images and logs to hard drive.")
|
||||
|
||||
args_parser.parser.add_argument("--disable-analytics", action='store_true',
|
||||
help="Disables analytics for Gradio", default=False)
|
||||
help="Disables analytics for Gradio.")
|
||||
|
||||
args_parser.parser.add_argument("--disable-metadata", action='store_true',
|
||||
help="Disables saving metadata to images.")
|
||||
|
||||
args_parser.parser.add_argument("--disable-preset-download", action='store_true',
|
||||
help="Disables downloading models for presets", default=False)
|
||||
|
|
@ -40,7 +45,11 @@ args_parser.args.always_offload_from_vram = not args_parser.args.disable_offload
|
|||
if args_parser.args.disable_analytics:
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||||
import os
|
||||
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
|
||||
|
||||
if args_parser.args.disable_in_browser:
|
||||
args_parser.args.in_browser = False
|
||||
|
||||
if args_parser.args.temp_path is None:
|
||||
args_parser.args.temp_path = os.path.join(gettempdir(), 'Fooocus')
|
||||
|
||||
args = args_parser.args
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||||
|
|
|
|||
|
|
@ -0,0 +1,38 @@
|
|||
version: '3.9'
|
||||
|
||||
volumes:
|
||||
fooocus-data:
|
||||
|
||||
services:
|
||||
app:
|
||||
build: .
|
||||
image: fooocus
|
||||
ports:
|
||||
- "7865:7865"
|
||||
environment:
|
||||
- CMDARGS=--listen # Arguments for launch.py.
|
||||
- DATADIR=/content/data # Directory which stores models, outputs dir
|
||||
- config_path=/content/data/config.txt
|
||||
- config_example_path=/content/data/config_modification_tutorial.txt
|
||||
- path_checkpoints=/content/data/models/checkpoints/
|
||||
- path_loras=/content/data/models/loras/
|
||||
- path_embeddings=/content/data/models/embeddings/
|
||||
- path_vae_approx=/content/data/models/vae_approx/
|
||||
- path_upscale_models=/content/data/models/upscale_models/
|
||||
- path_inpaint=/content/data/models/inpaint/
|
||||
- path_controlnet=/content/data/models/controlnet/
|
||||
- path_clip_vision=/content/data/models/clip_vision/
|
||||
- path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/
|
||||
- path_outputs=/content/app/outputs/ # Warning: If it is not located under '/content/app', you can't see history log!
|
||||
volumes:
|
||||
- fooocus-data:/content/data
|
||||
#- ./models:/import/models # Once you import files, you don't need to mount again.
|
||||
#- ./outputs:/import/outputs # Once you import files, you don't need to mount again.
|
||||
tty: true
|
||||
deploy:
|
||||
resources:
|
||||
reservations:
|
||||
devices:
|
||||
- driver: nvidia
|
||||
device_ids: ['0']
|
||||
capabilities: [compute, utility]
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
# Fooocus on Docker
|
||||
|
||||
The docker image is based on NVIDIA CUDA 12.3 and PyTorch 2.0, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details.
|
||||
|
||||
## Quick start
|
||||
|
||||
**This is just an easy way for testing. Please find more information in the [notes](#notes).**
|
||||
|
||||
1. Clone this repository
|
||||
2. Build the image with `docker compose build`
|
||||
3. Run the docker container with `docker compose up`. Building the image takes some time.
|
||||
|
||||
When you see the message `Use the app with http://0.0.0.0:7865/` in the console, you can access the URL in your browser.
|
||||
|
||||
Your models and outputs are stored in the `fooocus-data` volume, which, depending on OS, is stored in `/var/lib/docker/volumes`.
|
||||
|
||||
## Details
|
||||
|
||||
### Update the container manually
|
||||
|
||||
When you are using `docker compose up` continuously, the container is not updated to the latest version of Fooocus automatically.
|
||||
Run `git pull` before executing `docker compose build --no-cache` to build an image with the latest Fooocus version.
|
||||
You can then start it with `docker compose up`
|
||||
|
||||
### Import models, outputs
|
||||
If you want to import files from models or the outputs folder, you can uncomment the following settings in the [docker-compose.yml](docker-compose.yml):
|
||||
```
|
||||
#- ./models:/import/models # Once you import files, you don't need to mount again.
|
||||
#- ./outputs:/import/outputs # Once you import files, you don't need to mount again.
|
||||
```
|
||||
After running `docker compose up`, your files will be copied into `/content/data/models` and `/content/data/outputs`
|
||||
Since `/content/data` is a persistent volume folder, your files will be persisted even when you re-run `docker compose up --build` without above volume settings.
|
||||
|
||||
|
||||
### Paths inside the container
|
||||
|
||||
|Path|Details|
|
||||
|-|-|
|
||||
|/content/app|The application stored folder|
|
||||
|/content/app/models.org|Original 'models' folder.<br> Files are copied to the '/content/app/models' which is symlinked to '/content/data/models' every time the container boots. (Existing files will not be overwritten.) |
|
||||
|/content/data|Persistent volume mount point|
|
||||
|/content/data/models|The folder is symlinked to '/content/app/models'|
|
||||
|/content/data/outputs|The folder is symlinked to '/content/app/outputs'|
|
||||
|
||||
### Environments
|
||||
|
||||
You can change `config.txt` parameters by using environment variables.
|
||||
**The priority of using the environments is higher than the values defined in `config.txt`, and they will be saved to the `config_modification_tutorial.txt`**
|
||||
|
||||
Docker specified environments are there. They are used by 'entrypoint.sh'
|
||||
|Environment|Details|
|
||||
|-|-|
|
||||
|DATADIR|'/content/data' location.|
|
||||
|CMDARGS|Arguments for [entry_with_update.py](entry_with_update.py) which is called by [entrypoint.sh](entrypoint.sh)|
|
||||
|config_path|'config.txt' location|
|
||||
|config_example_path|'config_modification_tutorial.txt' location|
|
||||
|
||||
You can also use the same json key names and values explained in the 'config_modification_tutorial.txt' as the environments.
|
||||
See examples in the [docker-compose.yml](docker-compose.yml)
|
||||
|
||||
## Notes
|
||||
|
||||
- Please keep 'path_outputs' under '/content/app'. Otherwise, you may get an error when you open the history log.
|
||||
- Docker on Mac/Windows still has issues in the form of slow volume access when you use "bind mount" volumes. Please refer to [this article](https://docs.docker.com/storage/volumes/#use-a-volume-with-docker-compose) for not using "bind mount".
|
||||
- The MPS backend (Metal Performance Shaders, Apple Silicon M1/M2/etc.) is not yet supported in Docker, see https://github.com/pytorch/pytorch/issues/81224
|
||||
- You can also use `docker compose up -d` to start the container detached and connect to the logs with `docker compose logs -f`. This way you can also close the terminal and keep the container running.
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
#!/bin/bash
|
||||
|
||||
ORIGINALDIR=/content/app
|
||||
# Use predefined DATADIR if it is defined
|
||||
[[ x"${DATADIR}" == "x" ]] && DATADIR=/content/data
|
||||
|
||||
# Make persistent dir from original dir
|
||||
function mklink () {
|
||||
mkdir -p $DATADIR/$1
|
||||
ln -s $DATADIR/$1 $ORIGINALDIR
|
||||
}
|
||||
|
||||
# Copy old files from import dir
|
||||
function import () {
|
||||
(test -d /import/$1 && cd /import/$1 && cp -Rpn . $DATADIR/$1/)
|
||||
}
|
||||
|
||||
cd $ORIGINALDIR
|
||||
|
||||
# models
|
||||
mklink models
|
||||
# Copy original files
|
||||
(cd $ORIGINALDIR/models.org && cp -Rpn . $ORIGINALDIR/models/)
|
||||
# Import old files
|
||||
import models
|
||||
|
||||
# outputs
|
||||
mklink outputs
|
||||
# Import old files
|
||||
import outputs
|
||||
|
||||
# Start application
|
||||
python launch.py $*
|
||||
|
|
@ -112,6 +112,9 @@ class FooocusExpansion:
|
|||
max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0))
|
||||
max_new_tokens = max_token_length - current_token_length
|
||||
|
||||
if max_new_tokens == 0:
|
||||
return prompt[:-1]
|
||||
|
||||
# https://huggingface.co/blog/introducing-csearch
|
||||
# https://huggingface.co/docs/transformers/generation_strategies
|
||||
features = self.model.generate(**tokenized_kwargs,
|
||||
|
|
|
|||
|
|
@ -1,27 +1,26 @@
|
|||
import cv2
|
||||
import numpy as np
|
||||
import modules.advanced_parameters as advanced_parameters
|
||||
|
||||
|
||||
def centered_canny(x: np.ndarray):
|
||||
def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
|
||||
assert isinstance(x, np.ndarray)
|
||||
assert x.ndim == 2 and x.dtype == np.uint8
|
||||
|
||||
y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold))
|
||||
y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
|
||||
y = y.astype(np.float32) / 255.0
|
||||
return y
|
||||
|
||||
|
||||
def centered_canny_color(x: np.ndarray):
|
||||
def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
|
||||
assert isinstance(x, np.ndarray)
|
||||
assert x.ndim == 3 and x.shape[2] == 3
|
||||
|
||||
result = [centered_canny(x[..., i]) for i in range(3)]
|
||||
result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
|
||||
result = np.stack(result, axis=2)
|
||||
return result
|
||||
|
||||
|
||||
def pyramid_canny_color(x: np.ndarray):
|
||||
def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
|
||||
assert isinstance(x, np.ndarray)
|
||||
assert x.ndim == 3 and x.shape[2] == 3
|
||||
|
||||
|
|
@ -31,7 +30,7 @@ def pyramid_canny_color(x: np.ndarray):
|
|||
for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
|
||||
Hs, Ws = int(H * k), int(W * k)
|
||||
small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
|
||||
edge = centered_canny_color(small)
|
||||
edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
|
||||
if acc_edge is None:
|
||||
acc_edge = edge
|
||||
else:
|
||||
|
|
@ -54,11 +53,11 @@ def norm255(x, low=4, high=96):
|
|||
return x * 255.0
|
||||
|
||||
|
||||
def canny_pyramid(x):
|
||||
def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
|
||||
# For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
|
||||
# Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
|
||||
|
||||
color_canny = pyramid_canny_color(x)
|
||||
color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
|
||||
result = np.sum(color_canny, axis=2)
|
||||
|
||||
return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
version = '2.1.865'
|
||||
version = '2.2.0'
|
||||
|
|
|
|||
|
|
@ -48,6 +48,8 @@
|
|||
"Describing what you do not want to see.": "Describing what you do not want to see.",
|
||||
"Random": "Random",
|
||||
"Seed": "Seed",
|
||||
"Disable seed increment": "Disable seed increment",
|
||||
"Disable automatic seed increment when image number is > 1.": "Disable automatic seed increment when image number is > 1.",
|
||||
"\ud83d\udcda History Log": "\uD83D\uDCDA History Log",
|
||||
"Image Style": "Image Style",
|
||||
"Fooocus V2": "Fooocus V2",
|
||||
|
|
@ -342,6 +344,10 @@
|
|||
"Forced Overwrite of Denoising Strength of \"Vary\"": "Forced Overwrite of Denoising Strength of \"Vary\"",
|
||||
"Set as negative number to disable. For developer debugging.": "Set as negative number to disable. For developer debugging.",
|
||||
"Forced Overwrite of Denoising Strength of \"Upscale\"": "Forced Overwrite of Denoising Strength of \"Upscale\"",
|
||||
"Disable Preview": "Disable Preview",
|
||||
"Disable preview during generation.": "Disable preview during generation.",
|
||||
"Disable Intermediate Results": "Disable Intermediate Results",
|
||||
"Disable intermediate results during generation, only show final gallery.": "Disable intermediate results during generation, only show final gallery.",
|
||||
"Inpaint Engine": "Inpaint Engine",
|
||||
"v1": "v1",
|
||||
"Version of Fooocus inpaint model": "Version of Fooocus inpaint model",
|
||||
|
|
@ -368,5 +374,12 @@
|
|||
"* Powered by Fooocus Inpaint Engine (beta)": "* Powered by Fooocus Inpaint Engine (beta)",
|
||||
"Fooocus Enhance": "Fooocus Enhance",
|
||||
"Fooocus Cinematic": "Fooocus Cinematic",
|
||||
"Fooocus Sharp": "Fooocus Sharp"
|
||||
"Fooocus Sharp": "Fooocus Sharp",
|
||||
"Drag any image generated by Fooocus here": "Drag any image generated by Fooocus here",
|
||||
"Metadata": "Metadata",
|
||||
"Apply Metadata": "Apply Metadata",
|
||||
"Metadata Scheme": "Metadata Scheme",
|
||||
"Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.",
|
||||
"fooocus (json)": "fooocus (json)",
|
||||
"a1111 (plain text)": "a1111 (plain text)"
|
||||
}
|
||||
|
|
@ -68,7 +68,6 @@ vae_approx_filenames = [
|
|||
'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
|
||||
]
|
||||
|
||||
|
||||
def ini_args():
|
||||
from args_manager import args
|
||||
return args
|
||||
|
|
@ -101,9 +100,9 @@ def download_models():
|
|||
return
|
||||
|
||||
if not args.always_download_new_model:
|
||||
if not os.path.exists(os.path.join(config.path_checkpoints, config.default_base_model_name)):
|
||||
if not os.path.exists(os.path.join(config.paths_checkpoints[0], config.default_base_model_name)):
|
||||
for alternative_model_name in config.previous_default_models:
|
||||
if os.path.exists(os.path.join(config.path_checkpoints, alternative_model_name)):
|
||||
if os.path.exists(os.path.join(config.paths_checkpoints[0], alternative_model_name)):
|
||||
print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].')
|
||||
print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
|
||||
f'but you are not using latest models.')
|
||||
|
|
@ -113,11 +112,11 @@ def download_models():
|
|||
break
|
||||
|
||||
for file_name, url in config.checkpoint_downloads.items():
|
||||
load_file_from_url(url=url, model_dir=config.path_checkpoints, file_name=file_name)
|
||||
load_file_from_url(url=url, model_dir=config.paths_checkpoints[0], file_name=file_name)
|
||||
for file_name, url in config.embeddings_downloads.items():
|
||||
load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
|
||||
for file_name, url in config.lora_downloads.items():
|
||||
load_file_from_url(url=url, model_dir=config.path_loras, file_name=file_name)
|
||||
load_file_from_url(url=url, model_dir=config.paths_loras[0], file_name=file_name)
|
||||
|
||||
return
|
||||
|
||||
|
|
|
|||
|
|
@ -100,8 +100,7 @@ vram_group.add_argument("--always-high-vram", action="store_true")
|
|||
vram_group.add_argument("--always-normal-vram", action="store_true")
|
||||
vram_group.add_argument("--always-low-vram", action="store_true")
|
||||
vram_group.add_argument("--always-no-vram", action="store_true")
|
||||
vram_group.add_argument("--always-cpu", action="store_true")
|
||||
|
||||
vram_group.add_argument("--always-cpu", type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1)
|
||||
|
||||
parser.add_argument("--always-offload-from-vram", action="store_true")
|
||||
parser.add_argument("--pytorch-deterministic", action="store_true")
|
||||
|
|
|
|||
|
|
@ -60,6 +60,9 @@ except:
|
|||
pass
|
||||
|
||||
if args.always_cpu:
|
||||
if args.always_cpu > 0:
|
||||
torch.set_num_threads(args.always_cpu)
|
||||
print(f"Running on {torch.get_num_threads()} CPU threads")
|
||||
cpu_state = CPUState.CPU
|
||||
|
||||
def is_intel_xpu():
|
||||
|
|
|
|||
|
|
@ -1,33 +0,0 @@
|
|||
disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
|
||||
scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
|
||||
overwrite_vary_strength, overwrite_upscale_strength, \
|
||||
mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
|
||||
debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
|
||||
refiner_swap_method, \
|
||||
freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
|
||||
debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
|
||||
inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = [None] * 35
|
||||
|
||||
|
||||
def set_all_advanced_parameters(*args):
|
||||
global disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
|
||||
scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
|
||||
overwrite_vary_strength, overwrite_upscale_strength, \
|
||||
mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
|
||||
debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
|
||||
refiner_swap_method, \
|
||||
freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
|
||||
debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
|
||||
inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate
|
||||
|
||||
disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
|
||||
scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
|
||||
overwrite_vary_strength, overwrite_upscale_strength, \
|
||||
mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
|
||||
debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
|
||||
refiner_swap_method, \
|
||||
freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
|
||||
debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
|
||||
inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = args
|
||||
|
||||
return
|
||||
|
|
@ -1,11 +1,15 @@
|
|||
import threading
|
||||
from modules.patch import PatchSettings, patch_settings, patch_all
|
||||
|
||||
patch_all()
|
||||
|
||||
class AsyncTask:
|
||||
def __init__(self, args):
|
||||
self.args = args
|
||||
self.yields = []
|
||||
self.results = []
|
||||
self.last_stop = False
|
||||
self.processing = False
|
||||
|
||||
|
||||
async_tasks = []
|
||||
|
|
@ -14,6 +18,7 @@ async_tasks = []
|
|||
def worker():
|
||||
global async_tasks
|
||||
|
||||
import os
|
||||
import traceback
|
||||
import math
|
||||
import numpy as np
|
||||
|
|
@ -31,17 +36,22 @@ def worker():
|
|||
import extras.preprocessors as preprocessors
|
||||
import modules.inpaint_worker as inpaint_worker
|
||||
import modules.constants as constants
|
||||
import modules.advanced_parameters as advanced_parameters
|
||||
import extras.ip_adapter as ip_adapter
|
||||
import extras.face_crop
|
||||
import fooocus_version
|
||||
import args_manager
|
||||
|
||||
from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion
|
||||
from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
|
||||
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
|
||||
from modules.upscaler import perform_upscale
|
||||
from modules.flags import Performance
|
||||
from modules.meta_parser import get_metadata_parser, MetadataScheme
|
||||
|
||||
pid = os.getpid()
|
||||
print(f'Started worker with PID {pid}')
|
||||
|
||||
try:
|
||||
async_gradio_app = shared.gradio_root
|
||||
|
|
@ -69,9 +79,6 @@ def worker():
|
|||
return
|
||||
|
||||
def build_image_wall(async_task):
|
||||
if not advanced_parameters.generate_image_grid:
|
||||
return
|
||||
|
||||
results = async_task.results
|
||||
|
||||
if len(results) < 2:
|
||||
|
|
@ -111,10 +118,19 @@ def worker():
|
|||
async_task.results = async_task.results + [wall]
|
||||
return
|
||||
|
||||
def apply_enabled_loras(loras):
|
||||
enabled_loras = []
|
||||
for lora_enabled, lora_model, lora_weight in loras:
|
||||
if lora_enabled:
|
||||
enabled_loras.append([lora_model, lora_weight])
|
||||
|
||||
return enabled_loras
|
||||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def handler(async_task):
|
||||
execution_start_time = time.perf_counter()
|
||||
async_task.processing = True
|
||||
|
||||
args = async_task.args
|
||||
args.reverse()
|
||||
|
|
@ -122,16 +138,17 @@ def worker():
|
|||
prompt = args.pop()
|
||||
negative_prompt = args.pop()
|
||||
style_selections = args.pop()
|
||||
performance_selection = args.pop()
|
||||
performance_selection = Performance(args.pop())
|
||||
aspect_ratios_selection = args.pop()
|
||||
image_number = args.pop()
|
||||
output_format = args.pop()
|
||||
image_seed = args.pop()
|
||||
sharpness = args.pop()
|
||||
guidance_scale = args.pop()
|
||||
base_model_name = args.pop()
|
||||
refiner_model_name = args.pop()
|
||||
refiner_switch = args.pop()
|
||||
loras = [[str(args.pop()), float(args.pop())] for _ in range(5)]
|
||||
loras = apply_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()
|
||||
|
|
@ -141,8 +158,48 @@ def worker():
|
|||
inpaint_additional_prompt = args.pop()
|
||||
inpaint_mask_image_upload = args.pop()
|
||||
|
||||
disable_preview = args.pop()
|
||||
disable_intermediate_results = args.pop()
|
||||
disable_seed_increment = args.pop()
|
||||
adm_scaler_positive = args.pop()
|
||||
adm_scaler_negative = args.pop()
|
||||
adm_scaler_end = args.pop()
|
||||
adaptive_cfg = args.pop()
|
||||
sampler_name = args.pop()
|
||||
scheduler_name = args.pop()
|
||||
overwrite_step = args.pop()
|
||||
overwrite_switch = args.pop()
|
||||
overwrite_width = args.pop()
|
||||
overwrite_height = args.pop()
|
||||
overwrite_vary_strength = args.pop()
|
||||
overwrite_upscale_strength = args.pop()
|
||||
mixing_image_prompt_and_vary_upscale = args.pop()
|
||||
mixing_image_prompt_and_inpaint = args.pop()
|
||||
debugging_cn_preprocessor = args.pop()
|
||||
skipping_cn_preprocessor = args.pop()
|
||||
canny_low_threshold = args.pop()
|
||||
canny_high_threshold = args.pop()
|
||||
refiner_swap_method = args.pop()
|
||||
controlnet_softness = args.pop()
|
||||
freeu_enabled = args.pop()
|
||||
freeu_b1 = args.pop()
|
||||
freeu_b2 = args.pop()
|
||||
freeu_s1 = args.pop()
|
||||
freeu_s2 = args.pop()
|
||||
debugging_inpaint_preprocessor = args.pop()
|
||||
inpaint_disable_initial_latent = args.pop()
|
||||
inpaint_engine = args.pop()
|
||||
inpaint_strength = args.pop()
|
||||
inpaint_respective_field = args.pop()
|
||||
inpaint_mask_upload_checkbox = args.pop()
|
||||
invert_mask_checkbox = args.pop()
|
||||
inpaint_erode_or_dilate = args.pop()
|
||||
|
||||
save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
|
||||
metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
|
||||
|
||||
cn_tasks = {x: [] for x in flags.ip_list}
|
||||
for _ in range(4):
|
||||
for _ in range(flags.controlnet_image_count):
|
||||
cn_img = args.pop()
|
||||
cn_stop = args.pop()
|
||||
cn_weight = args.pop()
|
||||
|
|
@ -167,17 +224,9 @@ def worker():
|
|||
print(f'Refiner disabled because base model and refiner are same.')
|
||||
refiner_model_name = 'None'
|
||||
|
||||
assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
|
||||
steps = performance_selection.steps()
|
||||
|
||||
steps = 30
|
||||
|
||||
if performance_selection == 'Speed':
|
||||
steps = 30
|
||||
|
||||
if performance_selection == 'Quality':
|
||||
steps = 60
|
||||
|
||||
if performance_selection == 'Extreme Speed':
|
||||
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)]
|
||||
|
|
@ -186,30 +235,32 @@ def worker():
|
|||
print(f'Refiner disabled in LCM mode.')
|
||||
|
||||
refiner_model_name = 'None'
|
||||
sampler_name = advanced_parameters.sampler_name = 'lcm'
|
||||
scheduler_name = advanced_parameters.scheduler_name = 'lcm'
|
||||
modules.patch.sharpness = sharpness = 0.0
|
||||
cfg_scale = guidance_scale = 1.0
|
||||
modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0
|
||||
sampler_name = 'lcm'
|
||||
scheduler_name = 'lcm'
|
||||
sharpness = 0.0
|
||||
guidance_scale = 1.0
|
||||
adaptive_cfg = 1.0
|
||||
refiner_switch = 1.0
|
||||
modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0
|
||||
modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0
|
||||
modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
|
||||
steps = 8
|
||||
adm_scaler_positive = 1.0
|
||||
adm_scaler_negative = 1.0
|
||||
adm_scaler_end = 0.0
|
||||
|
||||
modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg
|
||||
print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
|
||||
|
||||
modules.patch.sharpness = sharpness
|
||||
print(f'[Parameters] Sharpness = {modules.patch.sharpness}')
|
||||
|
||||
modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
|
||||
modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
|
||||
modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end
|
||||
print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
|
||||
print(f'[Parameters] Sharpness = {sharpness}')
|
||||
print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
|
||||
print(f'[Parameters] ADM Scale = '
|
||||
f'{modules.patch.positive_adm_scale} : '
|
||||
f'{modules.patch.negative_adm_scale} : '
|
||||
f'{modules.patch.adm_scaler_end}')
|
||||
f'{adm_scaler_positive} : '
|
||||
f'{adm_scaler_negative} : '
|
||||
f'{adm_scaler_end}')
|
||||
|
||||
patch_settings[pid] = PatchSettings(
|
||||
sharpness,
|
||||
adm_scaler_end,
|
||||
adm_scaler_positive,
|
||||
adm_scaler_negative,
|
||||
controlnet_softness,
|
||||
adaptive_cfg
|
||||
)
|
||||
|
||||
cfg_scale = float(guidance_scale)
|
||||
print(f'[Parameters] CFG = {cfg_scale}')
|
||||
|
|
@ -222,10 +273,9 @@ def worker():
|
|||
width, height = int(width), int(height)
|
||||
|
||||
skip_prompt_processing = False
|
||||
refiner_swap_method = advanced_parameters.refiner_swap_method
|
||||
|
||||
inpaint_worker.current_task = None
|
||||
inpaint_parameterized = advanced_parameters.inpaint_engine != 'None'
|
||||
inpaint_parameterized = inpaint_engine != 'None'
|
||||
inpaint_image = None
|
||||
inpaint_mask = None
|
||||
inpaint_head_model_path = None
|
||||
|
|
@ -239,15 +289,12 @@ def worker():
|
|||
seed = int(image_seed)
|
||||
print(f'[Parameters] Seed = {seed}')
|
||||
|
||||
sampler_name = advanced_parameters.sampler_name
|
||||
scheduler_name = advanced_parameters.scheduler_name
|
||||
|
||||
goals = []
|
||||
tasks = []
|
||||
|
||||
if input_image_checkbox:
|
||||
if (current_tab == 'uov' or (
|
||||
current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
|
||||
current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \
|
||||
and uov_method != flags.disabled and uov_input_image is not None:
|
||||
uov_input_image = HWC3(uov_input_image)
|
||||
if 'vary' in uov_method:
|
||||
|
|
@ -257,26 +304,17 @@ def worker():
|
|||
if 'fast' in uov_method:
|
||||
skip_prompt_processing = True
|
||||
else:
|
||||
steps = 18
|
||||
|
||||
if performance_selection == 'Speed':
|
||||
steps = 18
|
||||
|
||||
if performance_selection == 'Quality':
|
||||
steps = 36
|
||||
|
||||
if performance_selection == 'Extreme Speed':
|
||||
steps = 8
|
||||
steps = performance_selection.steps_uov()
|
||||
|
||||
progressbar(async_task, 1, 'Downloading upscale models ...')
|
||||
modules.config.downloading_upscale_model()
|
||||
if (current_tab == 'inpaint' or (
|
||||
current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
|
||||
current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \
|
||||
and isinstance(inpaint_input_image, dict):
|
||||
inpaint_image = inpaint_input_image['image']
|
||||
inpaint_mask = inpaint_input_image['mask'][:, :, 0]
|
||||
|
||||
if advanced_parameters.inpaint_mask_upload_checkbox:
|
||||
|
||||
if inpaint_mask_upload_checkbox:
|
||||
if isinstance(inpaint_mask_image_upload, np.ndarray):
|
||||
if inpaint_mask_image_upload.ndim == 3:
|
||||
H, W, C = inpaint_image.shape
|
||||
|
|
@ -285,10 +323,10 @@ def worker():
|
|||
inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
|
||||
inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload)
|
||||
|
||||
if int(advanced_parameters.inpaint_erode_or_dilate) != 0:
|
||||
inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate)
|
||||
if int(inpaint_erode_or_dilate) != 0:
|
||||
inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate)
|
||||
|
||||
if advanced_parameters.invert_mask_checkbox:
|
||||
if invert_mask_checkbox:
|
||||
inpaint_mask = 255 - inpaint_mask
|
||||
|
||||
inpaint_image = HWC3(inpaint_image)
|
||||
|
|
@ -299,7 +337,7 @@ def worker():
|
|||
if inpaint_parameterized:
|
||||
progressbar(async_task, 1, 'Downloading inpainter ...')
|
||||
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
|
||||
advanced_parameters.inpaint_engine)
|
||||
inpaint_engine)
|
||||
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
|
||||
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
|
||||
if refiner_model_name == 'None':
|
||||
|
|
@ -315,8 +353,8 @@ def worker():
|
|||
prompt = inpaint_additional_prompt + '\n' + prompt
|
||||
goals.append('inpaint')
|
||||
if current_tab == 'ip' or \
|
||||
advanced_parameters.mixing_image_prompt_and_inpaint or \
|
||||
advanced_parameters.mixing_image_prompt_and_vary_upscale:
|
||||
mixing_image_prompt_and_vary_upscale or \
|
||||
mixing_image_prompt_and_inpaint:
|
||||
goals.append('cn')
|
||||
progressbar(async_task, 1, 'Downloading control models ...')
|
||||
if len(cn_tasks[flags.cn_canny]) > 0:
|
||||
|
|
@ -335,19 +373,19 @@ def worker():
|
|||
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
|
||||
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)
|
||||
|
||||
if advanced_parameters.overwrite_step > 0:
|
||||
steps = advanced_parameters.overwrite_step
|
||||
if overwrite_step > 0:
|
||||
steps = overwrite_step
|
||||
|
||||
switch = int(round(steps * refiner_switch))
|
||||
|
||||
if advanced_parameters.overwrite_switch > 0:
|
||||
switch = advanced_parameters.overwrite_switch
|
||||
if overwrite_switch > 0:
|
||||
switch = overwrite_switch
|
||||
|
||||
if advanced_parameters.overwrite_width > 0:
|
||||
width = advanced_parameters.overwrite_width
|
||||
if overwrite_width > 0:
|
||||
width = overwrite_width
|
||||
|
||||
if advanced_parameters.overwrite_height > 0:
|
||||
height = advanced_parameters.overwrite_height
|
||||
if overwrite_height > 0:
|
||||
height = overwrite_height
|
||||
|
||||
print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
|
||||
print(f'[Parameters] Steps = {steps} - {switch}')
|
||||
|
|
@ -376,11 +414,16 @@ def worker():
|
|||
|
||||
progressbar(async_task, 3, 'Processing prompts ...')
|
||||
tasks = []
|
||||
|
||||
for i in range(image_number):
|
||||
task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not
|
||||
task_rng = random.Random(task_seed) # may bind to inpaint noise in the future
|
||||
if disable_seed_increment:
|
||||
task_seed = seed
|
||||
else:
|
||||
task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not
|
||||
|
||||
task_rng = random.Random(task_seed) # may bind to inpaint noise in the future
|
||||
task_prompt = apply_wildcards(prompt, task_rng)
|
||||
task_prompt = apply_arrays(task_prompt, i)
|
||||
task_negative_prompt = apply_wildcards(negative_prompt, task_rng)
|
||||
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts]
|
||||
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts]
|
||||
|
|
@ -446,8 +489,8 @@ def worker():
|
|||
denoising_strength = 0.5
|
||||
if 'strong' in uov_method:
|
||||
denoising_strength = 0.85
|
||||
if advanced_parameters.overwrite_vary_strength > 0:
|
||||
denoising_strength = advanced_parameters.overwrite_vary_strength
|
||||
if overwrite_vary_strength > 0:
|
||||
denoising_strength = overwrite_vary_strength
|
||||
|
||||
shape_ceil = get_image_shape_ceil(uov_input_image)
|
||||
if shape_ceil < 1024:
|
||||
|
|
@ -511,15 +554,15 @@ def worker():
|
|||
|
||||
if direct_return:
|
||||
d = [('Upscale (Fast)', '2x')]
|
||||
log(uov_input_image, d)
|
||||
yield_result(async_task, uov_input_image, do_not_show_finished_images=True)
|
||||
uov_input_image_path = log(uov_input_image, d, output_format)
|
||||
yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
tiled = True
|
||||
denoising_strength = 0.382
|
||||
|
||||
if advanced_parameters.overwrite_upscale_strength > 0:
|
||||
denoising_strength = advanced_parameters.overwrite_upscale_strength
|
||||
if overwrite_upscale_strength > 0:
|
||||
denoising_strength = overwrite_upscale_strength
|
||||
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(async_task, 13, 'VAE encoding ...')
|
||||
|
|
@ -563,19 +606,19 @@ def worker():
|
|||
|
||||
inpaint_image = np.ascontiguousarray(inpaint_image.copy())
|
||||
inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
|
||||
advanced_parameters.inpaint_strength = 1.0
|
||||
advanced_parameters.inpaint_respective_field = 1.0
|
||||
inpaint_strength = 1.0
|
||||
inpaint_respective_field = 1.0
|
||||
|
||||
denoising_strength = advanced_parameters.inpaint_strength
|
||||
denoising_strength = inpaint_strength
|
||||
|
||||
inpaint_worker.current_task = inpaint_worker.InpaintWorker(
|
||||
image=inpaint_image,
|
||||
mask=inpaint_mask,
|
||||
use_fill=denoising_strength > 0.99,
|
||||
k=advanced_parameters.inpaint_respective_field
|
||||
k=inpaint_respective_field
|
||||
)
|
||||
|
||||
if advanced_parameters.debugging_inpaint_preprocessor:
|
||||
if debugging_inpaint_preprocessor:
|
||||
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
|
||||
do_not_show_finished_images=True)
|
||||
return
|
||||
|
|
@ -621,7 +664,7 @@ def worker():
|
|||
model=pipeline.final_unet
|
||||
)
|
||||
|
||||
if not advanced_parameters.inpaint_disable_initial_latent:
|
||||
if not inpaint_disable_initial_latent:
|
||||
initial_latent = {'samples': latent_fill}
|
||||
|
||||
B, C, H, W = latent_fill.shape
|
||||
|
|
@ -634,24 +677,24 @@ def worker():
|
|||
cn_img, cn_stop, cn_weight = task
|
||||
cn_img = resize_image(HWC3(cn_img), width=width, height=height)
|
||||
|
||||
if not advanced_parameters.skipping_cn_preprocessor:
|
||||
cn_img = preprocessors.canny_pyramid(cn_img)
|
||||
if not skipping_cn_preprocessor:
|
||||
cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold)
|
||||
|
||||
cn_img = HWC3(cn_img)
|
||||
task[0] = core.numpy_to_pytorch(cn_img)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_cpds]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
cn_img = resize_image(HWC3(cn_img), width=width, height=height)
|
||||
|
||||
if not advanced_parameters.skipping_cn_preprocessor:
|
||||
if not skipping_cn_preprocessor:
|
||||
cn_img = preprocessors.cpds(cn_img)
|
||||
|
||||
cn_img = HWC3(cn_img)
|
||||
task[0] = core.numpy_to_pytorch(cn_img)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_ip]:
|
||||
|
|
@ -662,21 +705,21 @@ def worker():
|
|||
cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
|
||||
|
||||
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_ip_face]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
cn_img = HWC3(cn_img)
|
||||
|
||||
if not advanced_parameters.skipping_cn_preprocessor:
|
||||
if not skipping_cn_preprocessor:
|
||||
cn_img = extras.face_crop.crop_image(cn_img)
|
||||
|
||||
# https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
|
||||
cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
|
||||
|
||||
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
|
|
@ -685,14 +728,14 @@ def worker():
|
|||
if len(all_ip_tasks) > 0:
|
||||
pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
|
||||
|
||||
if advanced_parameters.freeu_enabled:
|
||||
if freeu_enabled:
|
||||
print(f'FreeU is enabled!')
|
||||
pipeline.final_unet = core.apply_freeu(
|
||||
pipeline.final_unet,
|
||||
advanced_parameters.freeu_b1,
|
||||
advanced_parameters.freeu_b2,
|
||||
advanced_parameters.freeu_s1,
|
||||
advanced_parameters.freeu_s2
|
||||
freeu_b1,
|
||||
freeu_b2,
|
||||
freeu_s1,
|
||||
freeu_s2
|
||||
)
|
||||
|
||||
all_steps = steps * image_number
|
||||
|
|
@ -738,6 +781,8 @@ def worker():
|
|||
execution_start_time = time.perf_counter()
|
||||
|
||||
try:
|
||||
if async_task.last_stop is not False:
|
||||
ldm_patched.model_management.interrupt_current_processing()
|
||||
positive_cond, negative_cond = task['c'], task['uc']
|
||||
|
||||
if 'cn' in goals:
|
||||
|
|
@ -765,7 +810,8 @@ def worker():
|
|||
denoise=denoising_strength,
|
||||
tiled=tiled,
|
||||
cfg_scale=cfg_scale,
|
||||
refiner_swap_method=refiner_swap_method
|
||||
refiner_swap_method=refiner_swap_method,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
|
||||
del task['c'], task['uc'], positive_cond, negative_cond # Save memory
|
||||
|
|
@ -773,37 +819,58 @@ def worker():
|
|||
if inpaint_worker.current_task is not None:
|
||||
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
|
||||
|
||||
img_paths = []
|
||||
for x in imgs:
|
||||
d = [
|
||||
('Prompt', task['log_positive_prompt']),
|
||||
('Negative Prompt', task['log_negative_prompt']),
|
||||
('Fooocus V2 Expansion', task['expansion']),
|
||||
('Styles', str(raw_style_selections)),
|
||||
('Performance', performance_selection),
|
||||
('Resolution', str((width, height))),
|
||||
('Sharpness', sharpness),
|
||||
('Guidance Scale', guidance_scale),
|
||||
('ADM Guidance', str((
|
||||
modules.patch.positive_adm_scale,
|
||||
modules.patch.negative_adm_scale,
|
||||
modules.patch.adm_scaler_end))),
|
||||
('Base Model', base_model_name),
|
||||
('Refiner Model', refiner_model_name),
|
||||
('Refiner Switch', refiner_switch),
|
||||
('Sampler', sampler_name),
|
||||
('Scheduler', scheduler_name),
|
||||
('Seed', task['task_seed']),
|
||||
]
|
||||
d = [('Prompt', 'prompt', task['log_positive_prompt']),
|
||||
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
|
||||
('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
|
||||
('Styles', 'styles', str(raw_style_selections)),
|
||||
('Performance', 'performance', performance_selection.value),
|
||||
('Resolution', 'resolution', str((width, height))),
|
||||
('Guidance Scale', 'guidance_scale', guidance_scale),
|
||||
('Sharpness', 'sharpness', sharpness),
|
||||
('ADM Guidance', 'adm_guidance', str((
|
||||
modules.patch.patch_settings[pid].positive_adm_scale,
|
||||
modules.patch.patch_settings[pid].negative_adm_scale,
|
||||
modules.patch.patch_settings[pid].adm_scaler_end))),
|
||||
('Base Model', 'base_model', base_model_name),
|
||||
('Refiner Model', 'refiner_model', refiner_model_name),
|
||||
('Refiner Switch', 'refiner_switch', refiner_switch)]
|
||||
|
||||
if refiner_model_name != 'None':
|
||||
if overwrite_switch > 0:
|
||||
d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch))
|
||||
if refiner_swap_method != flags.refiner_swap_method:
|
||||
d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
|
||||
if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
|
||||
d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
|
||||
|
||||
d.append(('Sampler', 'sampler', sampler_name))
|
||||
d.append(('Scheduler', 'scheduler', scheduler_name))
|
||||
d.append(('Seed', 'seed', task['task_seed']))
|
||||
|
||||
if freeu_enabled:
|
||||
d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
|
||||
|
||||
metadata_parser = None
|
||||
if save_metadata_to_images:
|
||||
metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
|
||||
metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
|
||||
task['log_negative_prompt'], task['negative'],
|
||||
steps, base_model_name, refiner_model_name, loras)
|
||||
|
||||
for li, (n, w) in enumerate(loras):
|
||||
if n != 'None':
|
||||
d.append((f'LoRA {li + 1}', f'{n} : {w}'))
|
||||
d.append(('Version', 'v' + fooocus_version.version))
|
||||
log(x, d)
|
||||
d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
|
||||
|
||||
yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1)
|
||||
d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
|
||||
img_paths.append(log(x, d, metadata_parser, output_format))
|
||||
|
||||
yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
|
||||
except ldm_patched.modules.model_management.InterruptProcessingException as e:
|
||||
if shared.last_stop == 'skip':
|
||||
if async_task.last_stop == 'skip':
|
||||
print('User skipped')
|
||||
async_task.last_stop = False
|
||||
continue
|
||||
else:
|
||||
print('User stopped')
|
||||
|
|
@ -811,21 +878,27 @@ def worker():
|
|||
|
||||
execution_time = time.perf_counter() - execution_start_time
|
||||
print(f'Generating and saving time: {execution_time:.2f} seconds')
|
||||
|
||||
async_task.processing = False
|
||||
return
|
||||
|
||||
while True:
|
||||
time.sleep(0.01)
|
||||
if len(async_tasks) > 0:
|
||||
task = async_tasks.pop(0)
|
||||
generate_image_grid = task.args.pop(0)
|
||||
|
||||
try:
|
||||
handler(task)
|
||||
build_image_wall(task)
|
||||
if generate_image_grid:
|
||||
build_image_wall(task)
|
||||
task.yields.append(['finish', task.results])
|
||||
pipeline.prepare_text_encoder(async_call=True)
|
||||
except:
|
||||
traceback.print_exc()
|
||||
task.yields.append(['finish', task.results])
|
||||
finally:
|
||||
if pid in modules.patch.patch_settings:
|
||||
del modules.patch.patch_settings[pid]
|
||||
pass
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -7,11 +7,19 @@ import modules.flags
|
|||
import modules.sdxl_styles
|
||||
|
||||
from modules.model_loader import load_file_from_url
|
||||
from modules.util import get_files_from_folder
|
||||
from modules.util import get_files_from_folder, makedirs_with_log
|
||||
from modules.flags import Performance, MetadataScheme
|
||||
|
||||
def get_config_path(key, default_value):
|
||||
env = os.getenv(key)
|
||||
if env is not None and isinstance(env, str):
|
||||
print(f"Environment: {key} = {env}")
|
||||
return env
|
||||
else:
|
||||
return os.path.abspath(default_value)
|
||||
|
||||
config_path = os.path.abspath("./config.txt")
|
||||
config_example_path = os.path.abspath("config_modification_tutorial.txt")
|
||||
config_path = get_config_path('config_path', "./config.txt")
|
||||
config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt")
|
||||
config_dict = {}
|
||||
always_save_keys = []
|
||||
visited_keys = []
|
||||
|
|
@ -107,14 +115,14 @@ def get_path_output() -> str:
|
|||
Checking output path argument and overriding default path.
|
||||
"""
|
||||
global config_dict
|
||||
path_output = get_dir_or_set_default('path_outputs', '../outputs/')
|
||||
path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True)
|
||||
if args_manager.args.output_path:
|
||||
print(f'[CONFIG] Overriding config value path_outputs with {args_manager.args.output_path}')
|
||||
config_dict['path_outputs'] = path_output = args_manager.args.output_path
|
||||
return path_output
|
||||
|
||||
|
||||
def get_dir_or_set_default(key, default_value):
|
||||
def get_dir_or_set_default(key, default_value, as_array=False, make_directory=False):
|
||||
global config_dict, visited_keys, always_save_keys
|
||||
|
||||
if key not in visited_keys:
|
||||
|
|
@ -123,20 +131,44 @@ def get_dir_or_set_default(key, default_value):
|
|||
if key not in always_save_keys:
|
||||
always_save_keys.append(key)
|
||||
|
||||
v = config_dict.get(key, None)
|
||||
if isinstance(v, str) and os.path.exists(v) and os.path.isdir(v):
|
||||
return v
|
||||
v = os.getenv(key)
|
||||
if v is not None:
|
||||
print(f"Environment: {key} = {v}")
|
||||
config_dict[key] = v
|
||||
else:
|
||||
v = config_dict.get(key, None)
|
||||
|
||||
if isinstance(v, str):
|
||||
if make_directory:
|
||||
makedirs_with_log(v)
|
||||
if os.path.exists(v) and os.path.isdir(v):
|
||||
return v if not as_array else [v]
|
||||
elif isinstance(v, list):
|
||||
if make_directory:
|
||||
for d in v:
|
||||
makedirs_with_log(d)
|
||||
if all([os.path.exists(d) and os.path.isdir(d) for d in v]):
|
||||
return v
|
||||
|
||||
if v is not None:
|
||||
print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')
|
||||
if isinstance(default_value, list):
|
||||
dp = []
|
||||
for path in default_value:
|
||||
abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path))
|
||||
dp.append(abs_path)
|
||||
os.makedirs(abs_path, exist_ok=True)
|
||||
else:
|
||||
if v is not None:
|
||||
print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')
|
||||
dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value))
|
||||
os.makedirs(dp, exist_ok=True)
|
||||
config_dict[key] = dp
|
||||
return dp
|
||||
if as_array:
|
||||
dp = [dp]
|
||||
config_dict[key] = dp
|
||||
return dp
|
||||
|
||||
|
||||
path_checkpoints = get_dir_or_set_default('path_checkpoints', '../models/checkpoints/')
|
||||
path_loras = get_dir_or_set_default('path_loras', '../models/loras/')
|
||||
paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True)
|
||||
paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)
|
||||
path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/')
|
||||
path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/')
|
||||
path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')
|
||||
|
|
@ -146,13 +178,17 @@ path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vi
|
|||
path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion')
|
||||
path_outputs = get_path_output()
|
||||
|
||||
|
||||
def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False):
|
||||
global config_dict, visited_keys
|
||||
|
||||
if key not in visited_keys:
|
||||
visited_keys.append(key)
|
||||
|
||||
v = os.getenv(key)
|
||||
if v is not None:
|
||||
print(f"Environment: {key} = {v}")
|
||||
config_dict[key] = v
|
||||
|
||||
if key not in config_dict:
|
||||
config_dict[key] = default_value
|
||||
return default_value
|
||||
|
|
@ -190,6 +226,16 @@ default_refiner_switch = get_config_item_or_set_default(
|
|||
default_value=0.8,
|
||||
validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1
|
||||
)
|
||||
default_loras_min_weight = get_config_item_or_set_default(
|
||||
key='default_loras_min_weight',
|
||||
default_value=-2,
|
||||
validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10
|
||||
)
|
||||
default_loras_max_weight = get_config_item_or_set_default(
|
||||
key='default_loras_max_weight',
|
||||
default_value=2,
|
||||
validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10
|
||||
)
|
||||
default_loras = get_config_item_or_set_default(
|
||||
key='default_loras',
|
||||
default_value=[
|
||||
|
|
@ -216,6 +262,11 @@ default_loras = get_config_item_or_set_default(
|
|||
],
|
||||
validator=lambda x: isinstance(x, list) and all(len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x)
|
||||
)
|
||||
default_max_lora_number = get_config_item_or_set_default(
|
||||
key='default_max_lora_number',
|
||||
default_value=len(default_loras),
|
||||
validator=lambda x: isinstance(x, int) and x >= 1
|
||||
)
|
||||
default_cfg_scale = get_config_item_or_set_default(
|
||||
key='default_cfg_scale',
|
||||
default_value=7.0,
|
||||
|
|
@ -259,8 +310,8 @@ default_prompt = get_config_item_or_set_default(
|
|||
)
|
||||
default_performance = get_config_item_or_set_default(
|
||||
key='default_performance',
|
||||
default_value='Speed',
|
||||
validator=lambda x: x in modules.flags.performance_selections
|
||||
default_value=Performance.SPEED.value,
|
||||
validator=lambda x: x in Performance.list()
|
||||
)
|
||||
default_advanced_checkbox = get_config_item_or_set_default(
|
||||
key='default_advanced_checkbox',
|
||||
|
|
@ -272,6 +323,11 @@ default_max_image_number = get_config_item_or_set_default(
|
|||
default_value=32,
|
||||
validator=lambda x: isinstance(x, int) and x >= 1
|
||||
)
|
||||
default_output_format = get_config_item_or_set_default(
|
||||
key='default_output_format',
|
||||
default_value='png',
|
||||
validator=lambda x: x in modules.flags.output_formats
|
||||
)
|
||||
default_image_number = get_config_item_or_set_default(
|
||||
key='default_image_number',
|
||||
default_value=2,
|
||||
|
|
@ -335,16 +391,34 @@ example_inpaint_prompts = get_config_item_or_set_default(
|
|||
],
|
||||
validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x)
|
||||
)
|
||||
default_save_metadata_to_images = get_config_item_or_set_default(
|
||||
key='default_save_metadata_to_images',
|
||||
default_value=False,
|
||||
validator=lambda x: isinstance(x, bool)
|
||||
)
|
||||
default_metadata_scheme = get_config_item_or_set_default(
|
||||
key='default_metadata_scheme',
|
||||
default_value=MetadataScheme.FOOOCUS.value,
|
||||
validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x]
|
||||
)
|
||||
metadata_created_by = get_config_item_or_set_default(
|
||||
key='metadata_created_by',
|
||||
default_value='',
|
||||
validator=lambda x: isinstance(x, str)
|
||||
)
|
||||
|
||||
example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
|
||||
|
||||
config_dict["default_loras"] = default_loras = default_loras[:5] + [['None', 1.0] for _ in range(5 - len(default_loras))]
|
||||
config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [['None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
|
||||
|
||||
possible_preset_keys = [
|
||||
"default_model",
|
||||
"default_refiner",
|
||||
"default_refiner_switch",
|
||||
"default_loras_min_weight",
|
||||
"default_loras_max_weight",
|
||||
"default_loras",
|
||||
"default_max_lora_number",
|
||||
"default_cfg_scale",
|
||||
"default_sample_sharpness",
|
||||
"default_sampler",
|
||||
|
|
@ -354,6 +428,7 @@ possible_preset_keys = [
|
|||
"default_prompt_negative",
|
||||
"default_styles",
|
||||
"default_aspect_ratio",
|
||||
"default_save_metadata_to_images",
|
||||
"checkpoint_downloads",
|
||||
"embeddings_downloads",
|
||||
"lora_downloads",
|
||||
|
|
@ -397,21 +472,23 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
|
|||
'and there is no "," before the last "}". \n\n\n')
|
||||
json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4)
|
||||
|
||||
|
||||
os.makedirs(path_outputs, exist_ok=True)
|
||||
|
||||
model_filenames = []
|
||||
lora_filenames = []
|
||||
sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
|
||||
|
||||
|
||||
def get_model_filenames(folder_path, name_filter=None):
|
||||
return get_files_from_folder(folder_path, ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch'], name_filter)
|
||||
def get_model_filenames(folder_paths, name_filter=None):
|
||||
extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
|
||||
files = []
|
||||
for folder in folder_paths:
|
||||
files += get_files_from_folder(folder, extensions, name_filter)
|
||||
return files
|
||||
|
||||
|
||||
def update_all_model_names():
|
||||
global model_filenames, lora_filenames
|
||||
model_filenames = get_model_filenames(path_checkpoints)
|
||||
lora_filenames = get_model_filenames(path_loras)
|
||||
model_filenames = get_model_filenames(paths_checkpoints)
|
||||
lora_filenames = get_model_filenames(paths_loras)
|
||||
return
|
||||
|
||||
|
||||
|
|
@ -456,10 +533,10 @@ def downloading_inpaint_models(v):
|
|||
def downloading_sdxl_lcm_lora():
|
||||
load_file_from_url(
|
||||
url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors',
|
||||
model_dir=path_loras,
|
||||
file_name='sdxl_lcm_lora.safetensors'
|
||||
model_dir=paths_loras[0],
|
||||
file_name=sdxl_lcm_lora
|
||||
)
|
||||
return 'sdxl_lcm_lora.safetensors'
|
||||
return sdxl_lcm_lora
|
||||
|
||||
|
||||
def downloading_controlnet_canny():
|
||||
|
|
|
|||
|
|
@ -1,8 +1,3 @@
|
|||
from modules.patch import patch_all
|
||||
|
||||
patch_all()
|
||||
|
||||
|
||||
import os
|
||||
import einops
|
||||
import torch
|
||||
|
|
@ -16,7 +11,6 @@ import ldm_patched.modules.controlnet
|
|||
import modules.sample_hijack
|
||||
import ldm_patched.modules.samplers
|
||||
import ldm_patched.modules.latent_formats
|
||||
import modules.advanced_parameters
|
||||
|
||||
from ldm_patched.modules.sd import load_checkpoint_guess_config
|
||||
from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \
|
||||
|
|
@ -24,6 +18,7 @@ from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode,
|
|||
from ldm_patched.contrib.external_freelunch import FreeU_V2
|
||||
from ldm_patched.modules.sample import prepare_mask
|
||||
from modules.lora import match_lora
|
||||
from modules.util import get_file_from_folder_list
|
||||
from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip
|
||||
from modules.config import path_embeddings
|
||||
from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete
|
||||
|
|
@ -85,7 +80,7 @@ class StableDiffusionModel:
|
|||
if os.path.exists(name):
|
||||
lora_filename = name
|
||||
else:
|
||||
lora_filename = os.path.join(modules.config.path_loras, name)
|
||||
lora_filename = get_file_from_folder_list(name, modules.config.paths_loras)
|
||||
|
||||
if not os.path.exists(lora_filename):
|
||||
print(f'Lora file not found: {lora_filename}')
|
||||
|
|
@ -268,7 +263,7 @@ def get_previewer(model):
|
|||
def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
|
||||
scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
|
||||
force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
|
||||
previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None):
|
||||
previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False):
|
||||
|
||||
if sigmas is not None:
|
||||
sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device())
|
||||
|
|
@ -299,7 +294,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
|
|||
def callback(step, x0, x, total_steps):
|
||||
ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()
|
||||
y = None
|
||||
if previewer is not None and not modules.advanced_parameters.disable_preview:
|
||||
if previewer is not None and not disable_preview:
|
||||
y = previewer(x0, previewer_start + step, previewer_end)
|
||||
if callback_function is not None:
|
||||
callback_function(previewer_start + step, x0, x, previewer_end, y)
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ from extras.expansion import FooocusExpansion
|
|||
|
||||
from ldm_patched.modules.model_base import SDXL, SDXLRefiner
|
||||
from modules.sample_hijack import clip_separate
|
||||
from modules.util import get_file_from_folder_list
|
||||
|
||||
|
||||
model_base = core.StableDiffusionModel()
|
||||
|
|
@ -60,7 +61,7 @@ def assert_model_integrity():
|
|||
def refresh_base_model(name):
|
||||
global model_base
|
||||
|
||||
filename = os.path.abspath(os.path.realpath(os.path.join(modules.config.path_checkpoints, name)))
|
||||
filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
|
||||
|
||||
if model_base.filename == filename:
|
||||
return
|
||||
|
|
@ -76,7 +77,7 @@ def refresh_base_model(name):
|
|||
def refresh_refiner_model(name):
|
||||
global model_refiner
|
||||
|
||||
filename = os.path.abspath(os.path.realpath(os.path.join(modules.config.path_checkpoints, name)))
|
||||
filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
|
||||
|
||||
if model_refiner.filename == filename:
|
||||
return
|
||||
|
|
@ -315,7 +316,7 @@ def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'):
|
|||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'):
|
||||
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False):
|
||||
target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \
|
||||
= final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip
|
||||
|
||||
|
|
@ -374,6 +375,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
refiner_switch=switch,
|
||||
previewer_start=0,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
|
|
@ -392,6 +394,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
scheduler=scheduler_name,
|
||||
previewer_start=0,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
print('Refiner swapped by changing ksampler. Noise preserved.')
|
||||
|
||||
|
|
@ -414,6 +417,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
scheduler=scheduler_name,
|
||||
previewer_start=switch,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
|
||||
target_model = target_refiner_vae
|
||||
|
|
@ -422,7 +426,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
if refiner_swap_method == 'vae':
|
||||
modules.patch.eps_record = 'vae'
|
||||
modules.patch.patch_settings[os.getpid()].eps_record = 'vae'
|
||||
|
||||
if modules.inpaint_worker.current_task is not None:
|
||||
modules.inpaint_worker.current_task.unswap()
|
||||
|
|
@ -440,7 +444,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
sampler_name=sampler_name,
|
||||
scheduler=scheduler_name,
|
||||
previewer_start=0,
|
||||
previewer_end=steps
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
print('Fooocus VAE-based swap.')
|
||||
|
||||
|
|
@ -459,7 +464,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
denoise=denoise)[switch:] * k_sigmas
|
||||
len_sigmas = len(sigmas) - 1
|
||||
|
||||
noise_mean = torch.mean(modules.patch.eps_record, dim=1, keepdim=True)
|
||||
noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)
|
||||
|
||||
if modules.inpaint_worker.current_task is not None:
|
||||
modules.inpaint_worker.current_task.swap()
|
||||
|
|
@ -479,7 +484,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
previewer_start=switch,
|
||||
previewer_end=steps,
|
||||
sigmas=sigmas,
|
||||
noise_mean=noise_mean
|
||||
noise_mean=noise_mean,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
|
||||
target_model = target_refiner_vae
|
||||
|
|
@ -488,5 +494,5 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
images = core.pytorch_to_numpy(decoded_latent)
|
||||
modules.patch.eps_record = None
|
||||
modules.patch.patch_settings[os.getpid()].eps_record = None
|
||||
return images
|
||||
|
|
|
|||
|
|
@ -1,3 +1,5 @@
|
|||
from enum import IntEnum, Enum
|
||||
|
||||
disabled = 'Disabled'
|
||||
enabled = 'Enabled'
|
||||
subtle_variation = 'Vary (Subtle)'
|
||||
|
|
@ -10,16 +12,49 @@ uov_list = [
|
|||
disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast
|
||||
]
|
||||
|
||||
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"]
|
||||
CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"]
|
||||
|
||||
# fooocus: a1111 (Civitai)
|
||||
KSAMPLER = {
|
||||
"euler": "Euler",
|
||||
"euler_ancestral": "Euler a",
|
||||
"heun": "Heun",
|
||||
"heunpp2": "",
|
||||
"dpm_2": "DPM2",
|
||||
"dpm_2_ancestral": "DPM2 a",
|
||||
"lms": "LMS",
|
||||
"dpm_fast": "DPM fast",
|
||||
"dpm_adaptive": "DPM adaptive",
|
||||
"dpmpp_2s_ancestral": "DPM++ 2S a",
|
||||
"dpmpp_sde": "DPM++ SDE",
|
||||
"dpmpp_sde_gpu": "DPM++ SDE",
|
||||
"dpmpp_2m": "DPM++ 2M",
|
||||
"dpmpp_2m_sde": "DPM++ 2M SDE",
|
||||
"dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
|
||||
"dpmpp_3m_sde": "",
|
||||
"dpmpp_3m_sde_gpu": "",
|
||||
"ddpm": "",
|
||||
"lcm": "LCM"
|
||||
}
|
||||
|
||||
SAMPLER_EXTRA = {
|
||||
"ddim": "DDIM",
|
||||
"uni_pc": "UniPC",
|
||||
"uni_pc_bh2": ""
|
||||
}
|
||||
|
||||
SAMPLERS = KSAMPLER | SAMPLER_EXTRA
|
||||
|
||||
KSAMPLER_NAMES = list(KSAMPLER.keys())
|
||||
|
||||
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"]
|
||||
SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
|
||||
SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
|
||||
|
||||
sampler_list = SAMPLER_NAMES
|
||||
scheduler_list = SCHEDULER_NAMES
|
||||
|
||||
refiner_swap_method = 'joint'
|
||||
|
||||
cn_ip = "ImagePrompt"
|
||||
cn_ip_face = "FaceSwap"
|
||||
cn_canny = "PyraCanny"
|
||||
|
|
@ -32,9 +67,9 @@ default_parameters = {
|
|||
cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0)
|
||||
} # stop, weight
|
||||
|
||||
inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
|
||||
performance_selections = ['Speed', 'Quality', 'Extreme Speed']
|
||||
output_formats = ['png', 'jpg', 'webp']
|
||||
|
||||
inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
|
||||
inpaint_option_default = 'Inpaint or Outpaint (default)'
|
||||
inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)'
|
||||
inpaint_option_modify = 'Modify Content (add objects, change background, etc.)'
|
||||
|
|
@ -42,3 +77,49 @@ inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option
|
|||
|
||||
desc_type_photo = 'Photograph'
|
||||
desc_type_anime = 'Art/Anime'
|
||||
|
||||
|
||||
class MetadataScheme(Enum):
|
||||
FOOOCUS = 'fooocus'
|
||||
A1111 = 'a1111'
|
||||
|
||||
|
||||
metadata_scheme = [
|
||||
(f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value),
|
||||
(f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value),
|
||||
]
|
||||
|
||||
lora_count = 5
|
||||
|
||||
controlnet_image_count = 4
|
||||
|
||||
|
||||
class Steps(IntEnum):
|
||||
QUALITY = 60
|
||||
SPEED = 30
|
||||
EXTREME_SPEED = 8
|
||||
|
||||
|
||||
class StepsUOV(IntEnum):
|
||||
QUALITY = 36
|
||||
SPEED = 18
|
||||
EXTREME_SPEED = 8
|
||||
|
||||
|
||||
class Performance(Enum):
|
||||
QUALITY = 'Quality'
|
||||
SPEED = 'Speed'
|
||||
EXTREME_SPEED = 'Extreme Speed'
|
||||
|
||||
@classmethod
|
||||
def list(cls) -> list:
|
||||
return list(map(lambda c: c.value, cls))
|
||||
|
||||
def steps(self) -> int | None:
|
||||
return Steps[self.name].value if Steps[self.name] else None
|
||||
|
||||
def steps_uov(self) -> int | None:
|
||||
return StepsUOV[self.name].value if Steps[self.name] else None
|
||||
|
||||
|
||||
performance_selections = Performance.list()
|
||||
|
|
|
|||
|
|
@ -112,6 +112,30 @@ progress::after {
|
|||
margin-left: -5px !important;
|
||||
}
|
||||
|
||||
.lora_enable {
|
||||
flex-grow: 1 !important;
|
||||
}
|
||||
|
||||
.lora_enable label {
|
||||
height: 100%;
|
||||
}
|
||||
|
||||
.lora_enable label input {
|
||||
margin: auto;
|
||||
}
|
||||
|
||||
.lora_enable label span {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.lora_model {
|
||||
flex-grow: 5 !important;
|
||||
}
|
||||
|
||||
.lora_weight {
|
||||
flex-grow: 5 !important;
|
||||
}
|
||||
|
||||
'''
|
||||
progress_html = '''
|
||||
<div class="loader-container">
|
||||
|
|
|
|||
|
|
@ -1,45 +1,114 @@
|
|||
import json
|
||||
import os
|
||||
import re
|
||||
from abc import ABC, abstractmethod
|
||||
from pathlib import Path
|
||||
|
||||
import gradio as gr
|
||||
from PIL import Image
|
||||
|
||||
import fooocus_version
|
||||
import modules.config
|
||||
import modules.sdxl_styles
|
||||
from modules.flags import MetadataScheme, Performance, Steps
|
||||
from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS
|
||||
from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, calculate_sha256
|
||||
|
||||
re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
|
||||
re_param = re.compile(re_param_code)
|
||||
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
|
||||
|
||||
hash_cache = {}
|
||||
|
||||
|
||||
def load_parameter_button_click(raw_prompt_txt, is_generating):
|
||||
loaded_parameter_dict = json.loads(raw_prompt_txt)
|
||||
def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
|
||||
loaded_parameter_dict = raw_metadata
|
||||
if isinstance(raw_metadata, str):
|
||||
loaded_parameter_dict = json.loads(raw_metadata)
|
||||
assert isinstance(loaded_parameter_dict, dict)
|
||||
|
||||
results = [True, 1]
|
||||
results = [len(loaded_parameter_dict) > 0, 1]
|
||||
|
||||
get_str('prompt', 'Prompt', loaded_parameter_dict, results)
|
||||
get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
|
||||
get_list('styles', 'Styles', loaded_parameter_dict, results)
|
||||
get_str('performance', 'Performance', loaded_parameter_dict, results)
|
||||
get_steps('steps', 'Steps', loaded_parameter_dict, results)
|
||||
get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
|
||||
get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
|
||||
get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
|
||||
get_float('sharpness', 'Sharpness', loaded_parameter_dict, results)
|
||||
get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results)
|
||||
get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results)
|
||||
get_float('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)
|
||||
get_str('base_model', 'Base Model', loaded_parameter_dict, results)
|
||||
get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results)
|
||||
get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
|
||||
get_str('sampler', 'Sampler', loaded_parameter_dict, results)
|
||||
get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
|
||||
get_seed('seed', 'Seed', loaded_parameter_dict, results)
|
||||
|
||||
if is_generating:
|
||||
results.append(gr.update())
|
||||
else:
|
||||
results.append(gr.update(visible=True))
|
||||
|
||||
results.append(gr.update(visible=False))
|
||||
|
||||
get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)
|
||||
|
||||
for i in range(modules.config.default_max_lora_number):
|
||||
get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Prompt', None)
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Negative Prompt', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Styles', None)
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
h = eval(h)
|
||||
assert isinstance(h, list)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Performance', None)
|
||||
assert isinstance(h, str)
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert h is not None
|
||||
h = float(h)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Resolution', None)
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert h is not None
|
||||
h = int(h)
|
||||
# if not in steps or in steps and performance is not the same
|
||||
if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ', '_').casefold():
|
||||
results.append(h)
|
||||
return
|
||||
results.append(-1)
|
||||
except:
|
||||
results.append(-1)
|
||||
|
||||
|
||||
def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
width, height = eval(h)
|
||||
formatted = modules.config.add_ratio(f'{width}*{height}')
|
||||
if formatted in modules.config.available_aspect_ratios:
|
||||
|
|
@ -55,24 +124,22 @@ def load_parameter_button_click(raw_prompt_txt, is_generating):
|
|||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Sharpness', None)
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
assert h is not None
|
||||
h = float(h)
|
||||
h = int(h)
|
||||
results.append(False)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Guidance Scale', None)
|
||||
assert h is not None
|
||||
h = float(h)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = loaded_parameter_dict.get('ADM Guidance', None)
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
p, n, e = eval(h)
|
||||
results.append(float(p))
|
||||
results.append(float(n))
|
||||
|
|
@ -82,67 +149,425 @@ def load_parameter_button_click(raw_prompt_txt, is_generating):
|
|||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Base Model', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Refiner Model', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
h = source_dict.get(key, source_dict.get(fallback, default))
|
||||
b1, b2, s1, s2 = eval(h)
|
||||
results.append(True)
|
||||
results.append(float(b1))
|
||||
results.append(float(b2))
|
||||
results.append(float(s1))
|
||||
results.append(float(s2))
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Refiner Switch', None)
|
||||
assert h is not None
|
||||
h = float(h)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Sampler', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Scheduler', None)
|
||||
assert isinstance(h, str)
|
||||
results.append(h)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
|
||||
try:
|
||||
h = loaded_parameter_dict.get('Seed', None)
|
||||
assert h is not None
|
||||
h = int(h)
|
||||
results.append(False)
|
||||
results.append(h)
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
|
||||
try:
|
||||
n, w = source_dict.get(key, source_dict.get(fallback)).split(' : ')
|
||||
w = float(w)
|
||||
results.append(True)
|
||||
results.append(n)
|
||||
results.append(w)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
results.append(True)
|
||||
results.append('None')
|
||||
results.append(1)
|
||||
|
||||
if is_generating:
|
||||
results.append(gr.update())
|
||||
else:
|
||||
results.append(gr.update(visible=True))
|
||||
|
||||
results.append(gr.update(visible=False))
|
||||
|
||||
for i in range(1, 6):
|
||||
try:
|
||||
n, w = loaded_parameter_dict.get(f'LoRA {i}').split(' : ')
|
||||
w = float(w)
|
||||
results.append(n)
|
||||
results.append(w)
|
||||
except:
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
def get_sha256(filepath):
|
||||
global hash_cache
|
||||
if filepath not in hash_cache:
|
||||
hash_cache[filepath] = calculate_sha256(filepath)
|
||||
|
||||
return results
|
||||
return hash_cache[filepath]
|
||||
|
||||
|
||||
def parse_meta_from_preset(preset_content):
|
||||
assert isinstance(preset_content, dict)
|
||||
preset_prepared = {}
|
||||
items = preset_content
|
||||
|
||||
for settings_key, meta_key in modules.config.possible_preset_keys.items():
|
||||
if settings_key == "default_loras":
|
||||
loras = getattr(modules.config, settings_key)
|
||||
if settings_key in items:
|
||||
loras = items[settings_key]
|
||||
for index, lora in enumerate(loras[:5]):
|
||||
preset_prepared[f'lora_combined_{index + 1}'] = ' : '.join(map(str, lora))
|
||||
elif settings_key == "default_aspect_ratio":
|
||||
if settings_key in items and items[settings_key] is not None:
|
||||
default_aspect_ratio = items[settings_key]
|
||||
width, height = default_aspect_ratio.split('*')
|
||||
else:
|
||||
default_aspect_ratio = getattr(modules.config, settings_key)
|
||||
width, height = default_aspect_ratio.split('×')
|
||||
height = height[:height.index(" ")]
|
||||
preset_prepared[meta_key] = (width, height)
|
||||
else:
|
||||
preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[
|
||||
settings_key] is not None else getattr(modules.config, settings_key)
|
||||
|
||||
if settings_key == "default_styles" or settings_key == "default_aspect_ratio":
|
||||
preset_prepared[meta_key] = str(preset_prepared[meta_key])
|
||||
|
||||
return preset_prepared
|
||||
|
||||
|
||||
class MetadataParser(ABC):
|
||||
def __init__(self):
|
||||
self.raw_prompt: str = ''
|
||||
self.full_prompt: str = ''
|
||||
self.raw_negative_prompt: str = ''
|
||||
self.full_negative_prompt: str = ''
|
||||
self.steps: int = 30
|
||||
self.base_model_name: str = ''
|
||||
self.base_model_hash: str = ''
|
||||
self.refiner_model_name: str = ''
|
||||
self.refiner_model_hash: str = ''
|
||||
self.loras: list = []
|
||||
|
||||
@abstractmethod
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def parse_json(self, metadata: dict | str) -> dict:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def parse_string(self, metadata: dict) -> str:
|
||||
raise NotImplementedError
|
||||
|
||||
def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
|
||||
refiner_model_name, loras):
|
||||
self.raw_prompt = raw_prompt
|
||||
self.full_prompt = full_prompt
|
||||
self.raw_negative_prompt = raw_negative_prompt
|
||||
self.full_negative_prompt = full_negative_prompt
|
||||
self.steps = steps
|
||||
self.base_model_name = Path(base_model_name).stem
|
||||
|
||||
base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints)
|
||||
self.base_model_hash = get_sha256(base_model_path)
|
||||
|
||||
if refiner_model_name not in ['', 'None']:
|
||||
self.refiner_model_name = Path(refiner_model_name).stem
|
||||
refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints)
|
||||
self.refiner_model_hash = get_sha256(refiner_model_path)
|
||||
|
||||
self.loras = []
|
||||
for (lora_name, lora_weight) in loras:
|
||||
if lora_name != 'None':
|
||||
lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
|
||||
lora_hash = get_sha256(lora_path)
|
||||
self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
|
||||
|
||||
|
||||
class A1111MetadataParser(MetadataParser):
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
return MetadataScheme.A1111
|
||||
|
||||
fooocus_to_a1111 = {
|
||||
'raw_prompt': 'Raw prompt',
|
||||
'raw_negative_prompt': 'Raw negative prompt',
|
||||
'negative_prompt': 'Negative prompt',
|
||||
'styles': 'Styles',
|
||||
'performance': 'Performance',
|
||||
'steps': 'Steps',
|
||||
'sampler': 'Sampler',
|
||||
'scheduler': 'Scheduler',
|
||||
'guidance_scale': 'CFG scale',
|
||||
'seed': 'Seed',
|
||||
'resolution': 'Size',
|
||||
'sharpness': 'Sharpness',
|
||||
'adm_guidance': 'ADM Guidance',
|
||||
'refiner_swap_method': 'Refiner Swap Method',
|
||||
'adaptive_cfg': 'Adaptive CFG',
|
||||
'overwrite_switch': 'Overwrite Switch',
|
||||
'freeu': 'FreeU',
|
||||
'base_model': 'Model',
|
||||
'base_model_hash': 'Model hash',
|
||||
'refiner_model': 'Refiner',
|
||||
'refiner_model_hash': 'Refiner hash',
|
||||
'lora_hashes': 'Lora hashes',
|
||||
'lora_weights': 'Lora weights',
|
||||
'created_by': 'User',
|
||||
'version': 'Version'
|
||||
}
|
||||
|
||||
def parse_json(self, metadata: str) -> dict:
|
||||
metadata_prompt = ''
|
||||
metadata_negative_prompt = ''
|
||||
|
||||
done_with_prompt = False
|
||||
|
||||
*lines, lastline = metadata.strip().split("\n")
|
||||
if len(re_param.findall(lastline)) < 3:
|
||||
lines.append(lastline)
|
||||
lastline = ''
|
||||
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"):
|
||||
done_with_prompt = True
|
||||
line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip()
|
||||
if done_with_prompt:
|
||||
metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line
|
||||
else:
|
||||
metadata_prompt += ('' if metadata_prompt == '' else "\n") + line
|
||||
|
||||
found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt)
|
||||
|
||||
data = {
|
||||
'prompt': prompt,
|
||||
'negative_prompt': negative_prompt
|
||||
}
|
||||
|
||||
for k, v in re_param.findall(lastline):
|
||||
try:
|
||||
if v != '' and v[0] == '"' and v[-1] == '"':
|
||||
v = unquote(v)
|
||||
|
||||
m = re_imagesize.match(v)
|
||||
if m is not None:
|
||||
data['resolution'] = str((m.group(1), m.group(2)))
|
||||
else:
|
||||
data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v
|
||||
except Exception:
|
||||
print(f"Error parsing \"{k}: {v}\"")
|
||||
|
||||
# workaround for multiline prompts
|
||||
if 'raw_prompt' in data:
|
||||
data['prompt'] = data['raw_prompt']
|
||||
raw_prompt = data['raw_prompt'].replace("\n", ', ')
|
||||
if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles:
|
||||
found_styles.append(modules.sdxl_styles.fooocus_expansion)
|
||||
|
||||
if 'raw_negative_prompt' in data:
|
||||
data['negative_prompt'] = data['raw_negative_prompt']
|
||||
|
||||
data['styles'] = str(found_styles)
|
||||
|
||||
# try to load performance based on steps, fallback for direct A1111 imports
|
||||
if 'steps' in data and 'performance' not in data:
|
||||
try:
|
||||
data['performance'] = Performance[Steps(int(data['steps'])).name].value
|
||||
except ValueError | KeyError:
|
||||
pass
|
||||
|
||||
if 'sampler' in data:
|
||||
data['sampler'] = data['sampler'].replace(' Karras', '')
|
||||
# get key
|
||||
for k, v in SAMPLERS.items():
|
||||
if v == data['sampler']:
|
||||
data['sampler'] = k
|
||||
break
|
||||
|
||||
for key in ['base_model', 'refiner_model']:
|
||||
if key in data:
|
||||
for filename in modules.config.model_filenames:
|
||||
path = Path(filename)
|
||||
if data[key] == path.stem:
|
||||
data[key] = filename
|
||||
break
|
||||
|
||||
if 'lora_hashes' in data:
|
||||
lora_filenames = modules.config.lora_filenames.copy()
|
||||
if modules.config.sdxl_lcm_lora in lora_filenames:
|
||||
lora_filenames.remove(modules.config.sdxl_lcm_lora)
|
||||
for li, lora in enumerate(data['lora_hashes'].split(', ')):
|
||||
lora_name, lora_hash, lora_weight = lora.split(': ')
|
||||
for filename in lora_filenames:
|
||||
path = Path(filename)
|
||||
if lora_name == path.stem:
|
||||
data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
|
||||
break
|
||||
|
||||
return data
|
||||
|
||||
def parse_string(self, metadata: dict) -> str:
|
||||
data = {k: v for _, k, v in metadata}
|
||||
|
||||
width, height = eval(data['resolution'])
|
||||
|
||||
sampler = data['sampler']
|
||||
scheduler = data['scheduler']
|
||||
if sampler in SAMPLERS and SAMPLERS[sampler] != '':
|
||||
sampler = SAMPLERS[sampler]
|
||||
if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
|
||||
sampler += f' Karras'
|
||||
|
||||
generation_params = {
|
||||
self.fooocus_to_a1111['steps']: self.steps,
|
||||
self.fooocus_to_a1111['sampler']: sampler,
|
||||
self.fooocus_to_a1111['seed']: data['seed'],
|
||||
self.fooocus_to_a1111['resolution']: f'{width}x{height}',
|
||||
self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'],
|
||||
self.fooocus_to_a1111['sharpness']: data['sharpness'],
|
||||
self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'],
|
||||
self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem,
|
||||
self.fooocus_to_a1111['base_model_hash']: self.base_model_hash,
|
||||
|
||||
self.fooocus_to_a1111['performance']: data['performance'],
|
||||
self.fooocus_to_a1111['scheduler']: scheduler,
|
||||
# workaround for multiline prompts
|
||||
self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
|
||||
self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
|
||||
}
|
||||
|
||||
if self.refiner_model_name not in ['', 'None']:
|
||||
generation_params |= {
|
||||
self.fooocus_to_a1111['refiner_model']: self.refiner_model_name,
|
||||
self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
|
||||
}
|
||||
|
||||
for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
|
||||
if key in data:
|
||||
generation_params[self.fooocus_to_a1111[key]] = data[key]
|
||||
|
||||
lora_hashes = []
|
||||
for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
|
||||
# workaround for Fooocus not knowing LoRA name in LoRA metadata
|
||||
lora_hashes.append(f'{lora_name}: {lora_hash}: {lora_weight}')
|
||||
lora_hashes_string = ', '.join(lora_hashes)
|
||||
|
||||
generation_params |= {
|
||||
self.fooocus_to_a1111['lora_hashes']: lora_hashes_string,
|
||||
self.fooocus_to_a1111['version']: data['version']
|
||||
}
|
||||
|
||||
if modules.config.metadata_created_by != '':
|
||||
generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by
|
||||
|
||||
generation_params_text = ", ".join(
|
||||
[k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if
|
||||
v is not None])
|
||||
positive_prompt_resolved = ', '.join(self.full_prompt)
|
||||
negative_prompt_resolved = ', '.join(self.full_negative_prompt)
|
||||
negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
|
||||
return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
|
||||
class FooocusMetadataParser(MetadataParser):
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
return MetadataScheme.FOOOCUS
|
||||
|
||||
def parse_json(self, metadata: dict) -> dict:
|
||||
model_filenames = modules.config.model_filenames.copy()
|
||||
lora_filenames = modules.config.lora_filenames.copy()
|
||||
if modules.config.sdxl_lcm_lora in lora_filenames:
|
||||
lora_filenames.remove(modules.config.sdxl_lcm_lora)
|
||||
|
||||
for key, value in metadata.items():
|
||||
if value in ['', 'None']:
|
||||
continue
|
||||
if key in ['base_model', 'refiner_model']:
|
||||
metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
|
||||
elif key.startswith('lora_combined_'):
|
||||
metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
|
||||
else:
|
||||
continue
|
||||
|
||||
return metadata
|
||||
|
||||
def parse_string(self, metadata: list) -> str:
|
||||
for li, (label, key, value) in enumerate(metadata):
|
||||
# remove model folder paths from metadata
|
||||
if key.startswith('lora_combined_'):
|
||||
name, weight = value.split(' : ')
|
||||
name = Path(name).stem
|
||||
value = f'{name} : {weight}'
|
||||
metadata[li] = (label, key, value)
|
||||
|
||||
res = {k: v for _, k, v in metadata}
|
||||
|
||||
res['full_prompt'] = self.full_prompt
|
||||
res['full_negative_prompt'] = self.full_negative_prompt
|
||||
res['steps'] = self.steps
|
||||
res['base_model'] = self.base_model_name
|
||||
res['base_model_hash'] = self.base_model_hash
|
||||
|
||||
if self.refiner_model_name not in ['', 'None']:
|
||||
res['refiner_model'] = self.refiner_model_name
|
||||
res['refiner_model_hash'] = self.refiner_model_hash
|
||||
|
||||
res['loras'] = self.loras
|
||||
|
||||
if modules.config.metadata_created_by != '':
|
||||
res['created_by'] = modules.config.metadata_created_by
|
||||
|
||||
return json.dumps(dict(sorted(res.items())))
|
||||
|
||||
@staticmethod
|
||||
def replace_value_with_filename(key, value, filenames):
|
||||
for filename in filenames:
|
||||
path = Path(filename)
|
||||
if key.startswith('lora_combined_'):
|
||||
name, weight = value.split(' : ')
|
||||
if name == path.stem:
|
||||
return f'{filename} : {weight}'
|
||||
elif value == path.stem:
|
||||
return filename
|
||||
|
||||
|
||||
def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
|
||||
match metadata_scheme:
|
||||
case MetadataScheme.FOOOCUS:
|
||||
return FooocusMetadataParser()
|
||||
case MetadataScheme.A1111:
|
||||
return A1111MetadataParser()
|
||||
case _:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def read_info_from_image(filepath) -> tuple[str | None, MetadataScheme | None]:
|
||||
with Image.open(filepath) as image:
|
||||
items = (image.info or {}).copy()
|
||||
|
||||
parameters = items.pop('parameters', None)
|
||||
metadata_scheme = items.pop('fooocus_scheme', None)
|
||||
exif = items.pop('exif', None)
|
||||
|
||||
if parameters is not None and is_json(parameters):
|
||||
parameters = json.loads(parameters)
|
||||
elif exif is not None:
|
||||
exif = image.getexif()
|
||||
# 0x9286 = UserComment
|
||||
parameters = exif.get(0x9286, None)
|
||||
# 0x927C = MakerNote
|
||||
metadata_scheme = exif.get(0x927C, None)
|
||||
|
||||
if is_json(parameters):
|
||||
parameters = json.loads(parameters)
|
||||
|
||||
try:
|
||||
metadata_scheme = MetadataScheme(metadata_scheme)
|
||||
except ValueError:
|
||||
metadata_scheme = None
|
||||
|
||||
# broad fallback
|
||||
if isinstance(parameters, dict):
|
||||
metadata_scheme = MetadataScheme.FOOOCUS
|
||||
|
||||
if isinstance(parameters, str):
|
||||
metadata_scheme = MetadataScheme.A1111
|
||||
|
||||
return parameters, metadata_scheme
|
||||
|
||||
|
||||
def get_exif(metadata: str | None, metadata_scheme: str):
|
||||
exif = Image.Exif()
|
||||
# tags see see https://github.com/python-pillow/Pillow/blob/9.2.x/src/PIL/ExifTags.py
|
||||
# 0x9286 = UserComment
|
||||
exif[0x9286] = metadata
|
||||
# 0x0131 = Software
|
||||
exif[0x0131] = 'Fooocus v' + fooocus_version.version
|
||||
# 0x927C = MakerNote
|
||||
exif[0x927C] = metadata_scheme
|
||||
return exif
|
||||
|
|
@ -17,7 +17,6 @@ import ldm_patched.controlnet.cldm
|
|||
import ldm_patched.modules.model_patcher
|
||||
import ldm_patched.modules.samplers
|
||||
import ldm_patched.modules.args_parser
|
||||
import modules.advanced_parameters as advanced_parameters
|
||||
import warnings
|
||||
import safetensors.torch
|
||||
import modules.constants as constants
|
||||
|
|
@ -29,15 +28,25 @@ from modules.patch_precision import patch_all_precision
|
|||
from modules.patch_clip import patch_all_clip
|
||||
|
||||
|
||||
sharpness = 2.0
|
||||
class PatchSettings:
|
||||
def __init__(self,
|
||||
sharpness=2.0,
|
||||
adm_scaler_end=0.3,
|
||||
positive_adm_scale=1.5,
|
||||
negative_adm_scale=0.8,
|
||||
controlnet_softness=0.25,
|
||||
adaptive_cfg=7.0):
|
||||
self.sharpness = sharpness
|
||||
self.adm_scaler_end = adm_scaler_end
|
||||
self.positive_adm_scale = positive_adm_scale
|
||||
self.negative_adm_scale = negative_adm_scale
|
||||
self.controlnet_softness = controlnet_softness
|
||||
self.adaptive_cfg = adaptive_cfg
|
||||
self.global_diffusion_progress = 0
|
||||
self.eps_record = None
|
||||
|
||||
adm_scaler_end = 0.3
|
||||
positive_adm_scale = 1.5
|
||||
negative_adm_scale = 0.8
|
||||
|
||||
adaptive_cfg = 7.0
|
||||
global_diffusion_progress = 0
|
||||
eps_record = None
|
||||
patch_settings = {}
|
||||
|
||||
|
||||
def calculate_weight_patched(self, patches, weight, key):
|
||||
|
|
@ -201,14 +210,13 @@ class BrownianTreeNoiseSamplerPatched:
|
|||
|
||||
|
||||
def compute_cfg(uncond, cond, cfg_scale, t):
|
||||
global adaptive_cfg
|
||||
|
||||
mimic_cfg = float(adaptive_cfg)
|
||||
pid = os.getpid()
|
||||
mimic_cfg = float(patch_settings[pid].adaptive_cfg)
|
||||
real_cfg = float(cfg_scale)
|
||||
|
||||
real_eps = uncond + real_cfg * (cond - uncond)
|
||||
|
||||
if cfg_scale > adaptive_cfg:
|
||||
if cfg_scale > patch_settings[pid].adaptive_cfg:
|
||||
mimicked_eps = uncond + mimic_cfg * (cond - uncond)
|
||||
return real_eps * t + mimicked_eps * (1 - t)
|
||||
else:
|
||||
|
|
@ -216,13 +224,13 @@ def compute_cfg(uncond, cond, cfg_scale, t):
|
|||
|
||||
|
||||
def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
|
||||
global eps_record
|
||||
pid = os.getpid()
|
||||
|
||||
if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False):
|
||||
final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
|
||||
|
||||
if eps_record is not None:
|
||||
eps_record = ((x - final_x0) / timestep).cpu()
|
||||
if patch_settings[pid].eps_record is not None:
|
||||
patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu()
|
||||
|
||||
return final_x0
|
||||
|
||||
|
|
@ -231,16 +239,16 @@ def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
|
|||
positive_eps = x - positive_x0
|
||||
negative_eps = x - negative_x0
|
||||
|
||||
alpha = 0.001 * sharpness * global_diffusion_progress
|
||||
alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress
|
||||
|
||||
positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
|
||||
positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
|
||||
|
||||
final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,
|
||||
cfg_scale=cond_scale, t=global_diffusion_progress)
|
||||
cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress)
|
||||
|
||||
if eps_record is not None:
|
||||
eps_record = (final_eps / timestep).cpu()
|
||||
if patch_settings[pid].eps_record is not None:
|
||||
patch_settings[pid].eps_record = (final_eps / timestep).cpu()
|
||||
|
||||
return x - final_eps
|
||||
|
||||
|
|
@ -255,20 +263,19 @@ def round_to_64(x):
|
|||
|
||||
|
||||
def sdxl_encode_adm_patched(self, **kwargs):
|
||||
global positive_adm_scale, negative_adm_scale
|
||||
|
||||
clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
|
||||
width = kwargs.get("width", 1024)
|
||||
height = kwargs.get("height", 1024)
|
||||
target_width = width
|
||||
target_height = height
|
||||
pid = os.getpid()
|
||||
|
||||
if kwargs.get("prompt_type", "") == "negative":
|
||||
width = float(width) * negative_adm_scale
|
||||
height = float(height) * negative_adm_scale
|
||||
width = float(width) * patch_settings[pid].negative_adm_scale
|
||||
height = float(height) * patch_settings[pid].negative_adm_scale
|
||||
elif kwargs.get("prompt_type", "") == "positive":
|
||||
width = float(width) * positive_adm_scale
|
||||
height = float(height) * positive_adm_scale
|
||||
width = float(width) * patch_settings[pid].positive_adm_scale
|
||||
height = float(height) * patch_settings[pid].positive_adm_scale
|
||||
|
||||
def embedder(number_list):
|
||||
h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
|
||||
|
|
@ -322,7 +329,7 @@ def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale,
|
|||
|
||||
def timed_adm(y, timesteps):
|
||||
if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
|
||||
y_mask = (timesteps > 999.0 * (1.0 - float(adm_scaler_end))).to(y)[..., None]
|
||||
y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]
|
||||
y_with_adm = y[..., :2816].clone()
|
||||
y_without_adm = y[..., 2816:].clone()
|
||||
return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
|
||||
|
|
@ -332,6 +339,7 @@ def timed_adm(y, timesteps):
|
|||
def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
|
||||
t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
|
||||
emb = self.time_embed(t_emb)
|
||||
pid = os.getpid()
|
||||
|
||||
guided_hint = self.input_hint_block(hint, emb, context)
|
||||
|
||||
|
|
@ -357,19 +365,17 @@ def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
|
|||
h = self.middle_block(h, emb, context)
|
||||
outs.append(self.middle_block_out(h, emb, context))
|
||||
|
||||
if advanced_parameters.controlnet_softness > 0:
|
||||
if patch_settings[pid].controlnet_softness > 0:
|
||||
for i in range(10):
|
||||
k = 1.0 - float(i) / 9.0
|
||||
outs[i] = outs[i] * (1.0 - advanced_parameters.controlnet_softness * k)
|
||||
outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k)
|
||||
|
||||
return outs
|
||||
|
||||
|
||||
def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
||||
global global_diffusion_progress
|
||||
|
||||
self.current_step = 1.0 - timesteps.to(x) / 999.0
|
||||
global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
|
||||
patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
|
||||
|
||||
y = timed_adm(y, timesteps)
|
||||
|
||||
|
|
@ -483,7 +489,7 @@ def patch_all():
|
|||
if ldm_patched.modules.model_management.directml_enabled:
|
||||
ldm_patched.modules.model_management.lowvram_available = True
|
||||
ldm_patched.modules.model_management.OOM_EXCEPTION = Exception
|
||||
|
||||
|
||||
patch_all_precision()
|
||||
patch_all_clip()
|
||||
|
||||
|
|
|
|||
|
|
@ -5,26 +5,48 @@ import json
|
|||
import urllib.parse
|
||||
|
||||
from PIL import Image
|
||||
from PIL.PngImagePlugin import PngInfo
|
||||
from modules.util import generate_temp_filename
|
||||
|
||||
from modules.meta_parser import MetadataParser, get_exif
|
||||
|
||||
log_cache = {}
|
||||
|
||||
|
||||
def get_current_html_path():
|
||||
def get_current_html_path(output_format=None):
|
||||
output_format = output_format if output_format else modules.config.default_output_format
|
||||
date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs,
|
||||
extension='png')
|
||||
extension=output_format)
|
||||
html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
|
||||
return html_name
|
||||
|
||||
|
||||
def log(img, dic):
|
||||
if args_manager.args.disable_image_log:
|
||||
return
|
||||
|
||||
date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs, extension='png')
|
||||
def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
|
||||
path_outputs = args_manager.args.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
|
||||
output_format = output_format if output_format else modules.config.default_output_format
|
||||
date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
|
||||
os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
|
||||
Image.fromarray(img).save(local_temp_filename)
|
||||
|
||||
parsed_parameters = metadata_parser.parse_string(metadata) if metadata_parser is not None else ''
|
||||
image = Image.fromarray(img)
|
||||
|
||||
if output_format == 'png':
|
||||
if parsed_parameters != '':
|
||||
pnginfo = PngInfo()
|
||||
pnginfo.add_text('parameters', parsed_parameters)
|
||||
pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value)
|
||||
else:
|
||||
pnginfo = None
|
||||
image.save(local_temp_filename, pnginfo=pnginfo)
|
||||
elif output_format == 'jpg':
|
||||
image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
|
||||
elif output_format == 'webp':
|
||||
image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
|
||||
else:
|
||||
image.save(local_temp_filename)
|
||||
|
||||
if args_manager.args.disable_image_log:
|
||||
return local_temp_filename
|
||||
|
||||
html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
|
||||
|
||||
css_styles = (
|
||||
|
|
@ -32,7 +54,7 @@ def log(img, dic):
|
|||
"body { background-color: #121212; color: #E0E0E0; } "
|
||||
"a { color: #BB86FC; } "
|
||||
".metadata { border-collapse: collapse; width: 100%; } "
|
||||
".metadata .key { width: 15%; } "
|
||||
".metadata .label { width: 15%; } "
|
||||
".metadata .value { width: 85%; font-weight: bold; } "
|
||||
".metadata th, .metadata td { border: 1px solid #4d4d4d; padding: 4px; } "
|
||||
".image-container img { height: auto; max-width: 512px; display: block; padding-right:10px; } "
|
||||
|
|
@ -85,13 +107,13 @@ def log(img, dic):
|
|||
item = f"<div id=\"{div_name}\" class=\"image-container\"><hr><table><tr>\n"
|
||||
item += f"<td><a href=\"{only_name}\" target=\"_blank\"><img src='{only_name}' onerror=\"this.closest('.image-container').style.display='none';\" loading='lazy'/></a><div>{only_name}</div></td>"
|
||||
item += "<td><table class='metadata'>"
|
||||
for key, value in dic:
|
||||
value_txt = str(value).replace('\n', ' <br/> ')
|
||||
item += f"<tr><td class='key'>{key}</td><td class='value'>{value_txt}</td></tr>\n"
|
||||
for label, key, value in metadata:
|
||||
value_txt = str(value).replace('\n', ' </br> ')
|
||||
item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
|
||||
item += "</table>"
|
||||
|
||||
js_txt = urllib.parse.quote(json.dumps({k: v for k, v in dic}, indent=0), safe='')
|
||||
item += f"<br/><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
|
||||
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>"
|
||||
item += "</tr></table></div>\n\n"
|
||||
|
|
@ -105,4 +127,4 @@ def log(img, dic):
|
|||
|
||||
log_cache[html_name] = middle_part
|
||||
|
||||
return
|
||||
return local_temp_filename
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import os
|
||||
import re
|
||||
import json
|
||||
import math
|
||||
|
||||
from modules.util import get_files_from_folder
|
||||
|
||||
|
|
@ -80,3 +81,38 @@ def apply_wildcards(wildcard_text, rng, directory=wildcards_path):
|
|||
|
||||
print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
|
||||
return wildcard_text
|
||||
|
||||
def get_words(arrays, totalMult, index):
|
||||
if(len(arrays) == 1):
|
||||
return [arrays[0].split(',')[index]]
|
||||
else:
|
||||
words = arrays[0].split(',')
|
||||
word = words[index % len(words)]
|
||||
index -= index % len(words)
|
||||
index /= len(words)
|
||||
index = math.floor(index)
|
||||
return [word] + get_words(arrays[1:], math.floor(totalMult/len(words)), index)
|
||||
|
||||
|
||||
|
||||
def apply_arrays(text, index):
|
||||
arrays = re.findall(r'\[\[([\s,\w-]+)\]\]', text)
|
||||
if len(arrays) == 0:
|
||||
return text
|
||||
|
||||
print(f'[Arrays] processing: {text}')
|
||||
mult = 1
|
||||
for arr in arrays:
|
||||
words = arr.split(',')
|
||||
mult *= len(words)
|
||||
|
||||
index %= mult
|
||||
chosen_words = get_words(arrays, mult, index)
|
||||
|
||||
i = 0
|
||||
for arr in arrays:
|
||||
text = text.replace(f'[[{arr}]]', chosen_words[i], 1)
|
||||
i = i+1
|
||||
|
||||
return text
|
||||
|
||||
|
|
|
|||
189
modules/util.py
189
modules/util.py
|
|
@ -1,15 +1,20 @@
|
|||
import typing
|
||||
|
||||
import numpy as np
|
||||
import datetime
|
||||
import random
|
||||
import math
|
||||
import os
|
||||
import cv2
|
||||
import json
|
||||
|
||||
from PIL import Image
|
||||
from hashlib import sha256
|
||||
|
||||
import modules.sdxl_styles
|
||||
|
||||
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
|
||||
|
||||
HASH_SHA256_LENGTH = 10
|
||||
|
||||
def erode_or_dilate(x, k):
|
||||
k = int(k)
|
||||
|
|
@ -155,7 +160,7 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
|
|||
random_number = random.randint(1000, 9999)
|
||||
filename = f"{time_string}_{random_number}.{extension}"
|
||||
result = os.path.join(folder, date_string, filename)
|
||||
return date_string, os.path.abspath(os.path.realpath(result)), filename
|
||||
return date_string, os.path.abspath(result), filename
|
||||
|
||||
|
||||
def get_files_from_folder(folder_path, exensions=None, name_filter=None):
|
||||
|
|
@ -168,14 +173,190 @@ def get_files_from_folder(folder_path, exensions=None, name_filter=None):
|
|||
relative_path = os.path.relpath(root, folder_path)
|
||||
if relative_path == ".":
|
||||
relative_path = ""
|
||||
for filename in sorted(files):
|
||||
for filename in sorted(files, key=lambda s: s.casefold()):
|
||||
_, file_extension = os.path.splitext(filename)
|
||||
if (exensions == None or file_extension.lower() in exensions) and (name_filter == None or name_filter in _):
|
||||
if (exensions is None or file_extension.lower() in exensions) and (name_filter is None or name_filter in _):
|
||||
path = os.path.join(relative_path, filename)
|
||||
filenames.append(path)
|
||||
|
||||
return filenames
|
||||
|
||||
|
||||
def calculate_sha256(filename, length=HASH_SHA256_LENGTH) -> str:
|
||||
hash_sha256 = sha256()
|
||||
blksize = 1024 * 1024
|
||||
|
||||
with open(filename, "rb") as f:
|
||||
for chunk in iter(lambda: f.read(blksize), b""):
|
||||
hash_sha256.update(chunk)
|
||||
|
||||
res = hash_sha256.hexdigest()
|
||||
return res[:length] if length else res
|
||||
|
||||
|
||||
def quote(text):
|
||||
if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
|
||||
return text
|
||||
|
||||
return json.dumps(text, ensure_ascii=False)
|
||||
|
||||
|
||||
def unquote(text):
|
||||
if len(text) == 0 or text[0] != '"' or text[-1] != '"':
|
||||
return text
|
||||
|
||||
try:
|
||||
return json.loads(text)
|
||||
except Exception:
|
||||
return text
|
||||
|
||||
|
||||
def unwrap_style_text_from_prompt(style_text, prompt):
|
||||
"""
|
||||
Checks the prompt to see if the style text is wrapped around it. If so,
|
||||
returns True plus the prompt text without the style text. Otherwise, returns
|
||||
False with the original prompt.
|
||||
|
||||
Note that the "cleaned" version of the style text is only used for matching
|
||||
purposes here. It isn't returned; the original style text is not modified.
|
||||
"""
|
||||
stripped_prompt = prompt
|
||||
stripped_style_text = style_text
|
||||
if "{prompt}" in stripped_style_text:
|
||||
# Work out whether the prompt is wrapped in the style text. If so, we
|
||||
# return True and the "inner" prompt text that isn't part of the style.
|
||||
try:
|
||||
left, right = stripped_style_text.split("{prompt}", 2)
|
||||
except ValueError as e:
|
||||
# If the style text has multple "{prompt}"s, we can't split it into
|
||||
# two parts. This is an error, but we can't do anything about it.
|
||||
print(f"Unable to compare style text to prompt:\n{style_text}")
|
||||
print(f"Error: {e}")
|
||||
return False, prompt, ''
|
||||
|
||||
left_pos = stripped_prompt.find(left)
|
||||
right_pos = stripped_prompt.find(right)
|
||||
if 0 <= left_pos < right_pos:
|
||||
real_prompt = stripped_prompt[left_pos + len(left):right_pos]
|
||||
prompt = stripped_prompt.replace(left + real_prompt + right, '', 1)
|
||||
if prompt.startswith(", "):
|
||||
prompt = prompt[2:]
|
||||
if prompt.endswith(", "):
|
||||
prompt = prompt[:-2]
|
||||
return True, prompt, real_prompt
|
||||
else:
|
||||
# Work out whether the given prompt starts with the style text. If so, we
|
||||
# return True and the prompt text up to where the style text starts.
|
||||
if stripped_prompt.endswith(stripped_style_text):
|
||||
prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]
|
||||
if prompt.endswith(", "):
|
||||
prompt = prompt[:-2]
|
||||
return True, prompt, prompt
|
||||
|
||||
return False, prompt, ''
|
||||
|
||||
|
||||
def extract_original_prompts(style, prompt, negative_prompt):
|
||||
"""
|
||||
Takes a style and compares it to the prompt and negative prompt. If the style
|
||||
matches, returns True plus the prompt and negative prompt with the style text
|
||||
removed. Otherwise, returns False with the original prompt and negative prompt.
|
||||
"""
|
||||
if not style.prompt and not style.negative_prompt:
|
||||
return False, prompt, negative_prompt
|
||||
|
||||
match_positive, extracted_positive, real_prompt = unwrap_style_text_from_prompt(
|
||||
style.prompt, prompt
|
||||
)
|
||||
if not match_positive:
|
||||
return False, prompt, negative_prompt, ''
|
||||
|
||||
match_negative, extracted_negative, _ = unwrap_style_text_from_prompt(
|
||||
style.negative_prompt, negative_prompt
|
||||
)
|
||||
if not match_negative:
|
||||
return False, prompt, negative_prompt, ''
|
||||
|
||||
return True, extracted_positive, extracted_negative, real_prompt
|
||||
|
||||
|
||||
def extract_styles_from_prompt(prompt, negative_prompt):
|
||||
extracted = []
|
||||
applicable_styles = []
|
||||
|
||||
for style_name, (style_prompt, style_negative_prompt) in modules.sdxl_styles.styles.items():
|
||||
applicable_styles.append(PromptStyle(name=style_name, prompt=style_prompt, negative_prompt=style_negative_prompt))
|
||||
|
||||
real_prompt = ''
|
||||
|
||||
while True:
|
||||
found_style = None
|
||||
|
||||
for style in applicable_styles:
|
||||
is_match, new_prompt, new_neg_prompt, new_real_prompt = extract_original_prompts(
|
||||
style, prompt, negative_prompt
|
||||
)
|
||||
if is_match:
|
||||
found_style = style
|
||||
prompt = new_prompt
|
||||
negative_prompt = new_neg_prompt
|
||||
if real_prompt == '' and new_real_prompt != '' and new_real_prompt != prompt:
|
||||
real_prompt = new_real_prompt
|
||||
break
|
||||
|
||||
if not found_style:
|
||||
break
|
||||
|
||||
applicable_styles.remove(found_style)
|
||||
extracted.append(found_style.name)
|
||||
|
||||
# add prompt expansion if not all styles could be resolved
|
||||
if prompt != '':
|
||||
if real_prompt != '':
|
||||
extracted.append(modules.sdxl_styles.fooocus_expansion)
|
||||
else:
|
||||
# find real_prompt when only prompt expansion is selected
|
||||
first_word = prompt.split(', ')[0]
|
||||
first_word_positions = [i for i in range(len(prompt)) if prompt.startswith(first_word, i)]
|
||||
if len(first_word_positions) > 1:
|
||||
real_prompt = prompt[:first_word_positions[-1]]
|
||||
extracted.append(modules.sdxl_styles.fooocus_expansion)
|
||||
if real_prompt.endswith(', '):
|
||||
real_prompt = real_prompt[:-2]
|
||||
|
||||
return list(reversed(extracted)), real_prompt, negative_prompt
|
||||
|
||||
|
||||
class PromptStyle(typing.NamedTuple):
|
||||
name: str
|
||||
prompt: str
|
||||
negative_prompt: str
|
||||
|
||||
|
||||
def is_json(data: str) -> bool:
|
||||
try:
|
||||
loaded_json = json.loads(data)
|
||||
assert isinstance(loaded_json, dict)
|
||||
except (ValueError, AssertionError):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_file_from_folder_list(name, folders):
|
||||
for folder in folders:
|
||||
filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))
|
||||
if os.path.isfile(filename):
|
||||
return filename
|
||||
|
||||
return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))
|
||||
|
||||
|
||||
def ordinal_suffix(number: int) -> str:
|
||||
return 'th' if 10 <= number % 100 <= 20 else {1: 'st', 2: 'nd', 3: 'rd'}.get(number % 10, 'th')
|
||||
|
||||
|
||||
def makedirs_with_log(path):
|
||||
try:
|
||||
os.makedirs(path, exist_ok=True)
|
||||
except OSError as error:
|
||||
print(f'Directory {path} could not be created, reason: {error}')
|
||||
|
|
|
|||
|
|
@ -237,6 +237,10 @@ You can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or
|
|||
|
||||
Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.
|
||||
|
||||
### Docker
|
||||
|
||||
See [docker.md](docker.md)
|
||||
|
||||
### Download Previous Version
|
||||
|
||||
See the guidelines [here](https://github.com/lllyasviel/Fooocus/discussions/1405).
|
||||
|
|
@ -293,7 +297,7 @@ In both ways the access is unauthenticated by default. You can add basic authent
|
|||
|
||||
The below things are already inside the software, and **users do not need to do anything about these**.
|
||||
|
||||
1. GPT2-based [prompt expansion as a dynamic style "Fooocus V2".](https://github.com/lllyasviel/Fooocus/discussions/117#raw) (similar to Midjourney's hidden pre-processsing and "raw" mode, or the LeonardoAI's Prompt Magic).
|
||||
1. GPT2-based [prompt expansion as a dynamic style "Fooocus V2".](https://github.com/lllyasviel/Fooocus/discussions/117#raw) (similar to Midjourney's hidden pre-processing and "raw" mode, or the LeonardoAI's Prompt Magic).
|
||||
2. Native refiner swap inside one single k-sampler. The advantage is that the refiner model can now reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually, I discussed this with Automatic1111 several days ago, and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!)
|
||||
3. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results to look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Draw Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!)
|
||||
4. We implemented a carefully tuned variation of Section 5.1 of ["Improving Sample Quality of Diffusion Models Using Self-Attention Guidance"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure that the XL will never yield an overly smooth or plastic appearance (examples [here](https://github.com/lllyasviel/Fooocus/discussions/117#sharpness)). This can almost eliminate all cases for which XL still occasionally produces overly smooth results, even with negative ADM guidance. (Update 2023 Aug 18, the Gaussian kernel of SAG is changed to an anisotropic kernel for better structure preservation and fewer artifacts.)
|
||||
|
|
@ -370,7 +374,7 @@ entry_with_update.py [-h] [--listen [IP]] [--port PORT]
|
|||
[--attention-split | --attention-quad | --attention-pytorch]
|
||||
[--disable-xformers]
|
||||
[--always-gpu | --always-high-vram | --always-normal-vram |
|
||||
--always-low-vram | --always-no-vram | --always-cpu]
|
||||
--always-low-vram | --always-no-vram | --always-cpu [CPU_NUM_THREADS]]
|
||||
[--always-offload-from-vram] [--disable-server-log]
|
||||
[--debug-mode] [--is-windows-embedded-python]
|
||||
[--disable-server-info] [--share] [--preset PRESET]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,5 @@
|
|||
torch==2.0.1
|
||||
torchvision==0.15.2
|
||||
torchaudio==2.0.2
|
||||
torchtext==0.15.2
|
||||
torchdata==0.6.1
|
||||
|
|
@ -1,3 +1,16 @@
|
|||
# [2.2.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.0)
|
||||
|
||||
* Isolate every image generation to truly allow multi-user usage
|
||||
* Add array support, changes the main prompt when increasing the image number. Syntax: `[[red, green, blue]] flower`
|
||||
* Add optional metadata to images, allowing you to regenerate and modify them later with the same parameters
|
||||
* Now supports native PNG, JPG and WEBP image generation
|
||||
* Add Docker support
|
||||
|
||||
# [2.1.865](https://github.com/lllyasviel/Fooocus/releases/tag/2.1.865)
|
||||
|
||||
* Various bugfixes
|
||||
* Add authentication to --listen
|
||||
|
||||
# 2.1.864
|
||||
|
||||
* New model list. See also discussions.
|
||||
|
|
|
|||
209
webui.py
209
webui.py
|
|
@ -11,7 +11,6 @@ import modules.async_worker as worker
|
|||
import modules.constants as constants
|
||||
import modules.flags as flags
|
||||
import modules.gradio_hijack as grh
|
||||
import modules.advanced_parameters as advanced_parameters
|
||||
import modules.style_sorter as style_sorter
|
||||
import modules.meta_parser
|
||||
import args_manager
|
||||
|
|
@ -21,18 +20,21 @@ from modules.sdxl_styles import legal_style_names
|
|||
from modules.private_logger import get_current_html_path
|
||||
from modules.ui_gradio_extensions import reload_javascript
|
||||
from modules.auth import auth_enabled, check_auth
|
||||
from modules.util import is_json
|
||||
|
||||
def get_task(*args):
|
||||
args = list(args)
|
||||
args.pop(0)
|
||||
|
||||
def generate_clicked(*args):
|
||||
return worker.AsyncTask(args=args)
|
||||
|
||||
def generate_clicked(task):
|
||||
import ldm_patched.modules.model_management as model_management
|
||||
|
||||
with model_management.interrupt_processing_mutex:
|
||||
model_management.interrupt_processing = False
|
||||
|
||||
# outputs=[progress_html, progress_window, progress_gallery, gallery]
|
||||
|
||||
execution_start_time = time.perf_counter()
|
||||
task = worker.AsyncTask(args=list(args))
|
||||
finished = False
|
||||
|
||||
yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Waiting for task to start ...')), \
|
||||
|
|
@ -71,6 +73,11 @@ def generate_clicked(*args):
|
|||
gr.update(visible=True, value=product)
|
||||
finished = True
|
||||
|
||||
# delete Fooocus temp images, only keep gradio temp images
|
||||
if args_manager.args.disable_image_log:
|
||||
for filepath in product:
|
||||
os.remove(filepath)
|
||||
|
||||
execution_time = time.perf_counter() - execution_start_time
|
||||
print(f'Total time: {execution_time:.2f} seconds')
|
||||
return
|
||||
|
|
@ -88,6 +95,7 @@ shared.gradio_root = gr.Blocks(
|
|||
css=modules.html.css).queue()
|
||||
|
||||
with shared.gradio_root:
|
||||
currentTask = gr.State(worker.AsyncTask(args=[]))
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
with gr.Row():
|
||||
|
|
@ -115,21 +123,22 @@ with shared.gradio_root:
|
|||
skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False)
|
||||
stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False)
|
||||
|
||||
def stop_clicked():
|
||||
def stop_clicked(currentTask):
|
||||
import ldm_patched.modules.model_management as model_management
|
||||
shared.last_stop = 'stop'
|
||||
model_management.interrupt_current_processing()
|
||||
return [gr.update(interactive=False)] * 2
|
||||
currentTask.last_stop = 'stop'
|
||||
if (currentTask.processing):
|
||||
model_management.interrupt_current_processing()
|
||||
return currentTask
|
||||
|
||||
def skip_clicked():
|
||||
def skip_clicked(currentTask):
|
||||
import ldm_patched.modules.model_management as model_management
|
||||
shared.last_stop = 'skip'
|
||||
model_management.interrupt_current_processing()
|
||||
return
|
||||
currentTask.last_stop = 'skip'
|
||||
if (currentTask.processing):
|
||||
model_management.interrupt_current_processing()
|
||||
return currentTask
|
||||
|
||||
stop_button.click(stop_clicked, outputs=[skip_button, stop_button],
|
||||
queue=False, show_progress=False, _js='cancelGenerateForever')
|
||||
skip_button.click(skip_clicked, queue=False, show_progress=False)
|
||||
stop_button.click(stop_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False, _js='cancelGenerateForever')
|
||||
skip_button.click(skip_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False)
|
||||
with gr.Row(elem_classes='advanced_check_row'):
|
||||
input_image_checkbox = gr.Checkbox(label='Input Image', value=False, container=False, elem_classes='min_check')
|
||||
advanced_checkbox = gr.Checkbox(label='Advanced', value=modules.config.default_advanced_checkbox, container=False, elem_classes='min_check')
|
||||
|
|
@ -150,7 +159,7 @@ with shared.gradio_root:
|
|||
ip_weights = []
|
||||
ip_ctrls = []
|
||||
ip_ad_cols = []
|
||||
for _ in range(4):
|
||||
for _ in range(flags.controlnet_image_count):
|
||||
with gr.Column():
|
||||
ip_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False, height=300)
|
||||
ip_images.append(ip_image)
|
||||
|
|
@ -208,6 +217,27 @@ with shared.gradio_root:
|
|||
value=flags.desc_type_photo)
|
||||
desc_btn = gr.Button(value='Describe this Image into Prompt')
|
||||
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/1363" target="_blank">\U0001F4D4 Document</a>')
|
||||
with gr.TabItem(label='Metadata') as load_tab:
|
||||
with gr.Column():
|
||||
metadata_input_image = grh.Image(label='Drag any image generated by Fooocus here', source='upload', type='filepath')
|
||||
metadata_json = gr.JSON(label='Metadata')
|
||||
metadata_import_button = gr.Button(value='Apply Metadata')
|
||||
|
||||
def trigger_metadata_preview(filepath):
|
||||
parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
|
||||
|
||||
results = {}
|
||||
if parameters is not None:
|
||||
results['parameters'] = parameters
|
||||
|
||||
if isinstance(metadata_scheme, flags.MetadataScheme):
|
||||
results['metadata_scheme'] = metadata_scheme.value
|
||||
|
||||
return results
|
||||
|
||||
metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image,
|
||||
outputs=metadata_json, queue=False, show_progress=True)
|
||||
|
||||
switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}"
|
||||
down_js = "() => {viewer_to_bottom();}"
|
||||
|
||||
|
|
@ -230,6 +260,11 @@ with shared.gradio_root:
|
|||
value=modules.config.default_aspect_ratio, info='width × height',
|
||||
elem_classes='aspect_ratios')
|
||||
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=modules.flags.output_formats,
|
||||
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,
|
||||
elem_id='negative_prompt',
|
||||
|
|
@ -259,7 +294,7 @@ with shared.gradio_root:
|
|||
if args_manager.args.disable_image_log:
|
||||
return gr.update(value='')
|
||||
|
||||
return gr.update(value=f'<a href="file={get_current_html_path()}" target="_blank">\U0001F4DA History Log</a>')
|
||||
return gr.update(value=f'<a href="file={get_current_html_path(output_format)}" target="_blank">\U0001F4DA History Log</a>')
|
||||
|
||||
history_link = gr.HTML()
|
||||
shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False)
|
||||
|
|
@ -319,11 +354,15 @@ with shared.gradio_root:
|
|||
|
||||
for i, (n, v) in enumerate(modules.config.default_loras):
|
||||
with gr.Row():
|
||||
lora_enabled = gr.Checkbox(label='Enable', value=True,
|
||||
elem_classes=['lora_enable', 'min_check'])
|
||||
lora_model = gr.Dropdown(label=f'LoRA {i + 1}',
|
||||
choices=['None'] + modules.config.lora_filenames, value=n)
|
||||
lora_weight = gr.Slider(label='Weight', minimum=-2, maximum=2, step=0.01, value=v,
|
||||
choices=['None'] + modules.config.lora_filenames, value=n,
|
||||
elem_classes='lora_model')
|
||||
lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight,
|
||||
maximum=modules.config.default_loras_max_weight, step=0.01, value=v,
|
||||
elem_classes='lora_weight')
|
||||
lora_ctrls += [lora_model, lora_weight]
|
||||
lora_ctrls += [lora_enabled, lora_model, lora_weight]
|
||||
|
||||
with gr.Row():
|
||||
model_refresh = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
|
||||
|
|
@ -347,7 +386,7 @@ with shared.gradio_root:
|
|||
step=0.001, value=0.3,
|
||||
info='When to end the guidance from positive/negative ADM. ')
|
||||
|
||||
refiner_swap_method = gr.Dropdown(label='Refiner swap method', value='joint',
|
||||
refiner_swap_method = gr.Dropdown(label='Refiner swap method', value=flags.refiner_swap_method,
|
||||
choices=['joint', 'separate', 'vae'])
|
||||
|
||||
adaptive_cfg = gr.Slider(label='CFG Mimicking from TSNR', minimum=1.0, maximum=30.0, step=0.01,
|
||||
|
|
@ -387,6 +426,23 @@ with shared.gradio_root:
|
|||
info='Set as negative number to disable. For developer debugging.')
|
||||
disable_preview = gr.Checkbox(label='Disable Preview', value=False,
|
||||
info='Disable preview during generation.')
|
||||
disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results',
|
||||
value=modules.config.default_performance == 'Extreme Speed',
|
||||
interactive=modules.config.default_performance != 'Extreme Speed',
|
||||
info='Disable intermediate results during generation, only show final gallery.')
|
||||
disable_seed_increment = gr.Checkbox(label='Disable seed increment',
|
||||
info='Disable automatic seed increment when image number is > 1.',
|
||||
value=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.')
|
||||
metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme,
|
||||
info='Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.',
|
||||
visible=modules.config.default_save_metadata_to_images)
|
||||
|
||||
save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme],
|
||||
queue=False, show_progress=False)
|
||||
|
||||
with gr.Tab(label='Control'):
|
||||
debugging_cn_preprocessor = gr.Checkbox(label='Debug Preprocessors', value=False,
|
||||
|
|
@ -435,7 +491,7 @@ with shared.gradio_root:
|
|||
'(default is 0, always process before any mask invert)')
|
||||
inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False)
|
||||
invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False)
|
||||
|
||||
|
||||
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]
|
||||
|
|
@ -452,15 +508,6 @@ with shared.gradio_root:
|
|||
freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)
|
||||
freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2]
|
||||
|
||||
adps = [disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,
|
||||
scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height,
|
||||
overwrite_vary_strength, overwrite_upscale_strength,
|
||||
mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint,
|
||||
debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness,
|
||||
canny_low_threshold, canny_high_threshold, refiner_swap_method]
|
||||
adps += freeu_ctrls
|
||||
adps += inpaint_ctrls
|
||||
|
||||
def dev_mode_checked(r):
|
||||
return gr.update(visible=r)
|
||||
|
||||
|
|
@ -470,24 +517,27 @@ with shared.gradio_root:
|
|||
|
||||
def model_refresh_clicked():
|
||||
modules.config.update_all_model_names()
|
||||
results = []
|
||||
results += [gr.update(choices=modules.config.model_filenames), gr.update(choices=['None'] + modules.config.model_filenames)]
|
||||
for i in range(5):
|
||||
results += [gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
|
||||
results = [gr.update(choices=modules.config.model_filenames)]
|
||||
results += [gr.update(choices=['None'] + modules.config.model_filenames)]
|
||||
for i in range(modules.config.default_max_lora_number):
|
||||
results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
|
||||
return results
|
||||
|
||||
model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls,
|
||||
queue=False, show_progress=False)
|
||||
|
||||
performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11 +
|
||||
[gr.update(visible=x != 'Extreme Speed')] * 1,
|
||||
[gr.update(visible=x != 'Extreme Speed')] * 1 +
|
||||
[gr.update(interactive=x != 'Extreme Speed', value=x == 'Extreme Speed', )] * 1,
|
||||
inputs=performance_selection,
|
||||
outputs=[
|
||||
guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,
|
||||
adm_scaler_negative, refiner_switch, refiner_model, sampler_name,
|
||||
scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt
|
||||
scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt, disable_intermediate_results
|
||||
], queue=False, show_progress=False)
|
||||
|
||||
|
||||
output_format.input(lambda x: gr.update(output_format=x), inputs=output_format)
|
||||
|
||||
advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, advanced_column,
|
||||
queue=False, show_progress=False) \
|
||||
.then(fn=lambda: None, _js='refresh_grid_delayed', queue=False, show_progress=False)
|
||||
|
|
@ -525,29 +575,37 @@ with shared.gradio_root:
|
|||
inpaint_strength, inpaint_respective_field
|
||||
], show_progress=False, queue=False)
|
||||
|
||||
ctrls = [
|
||||
ctrls = [currentTask, generate_image_grid]
|
||||
ctrls += [
|
||||
prompt, negative_prompt, style_selections,
|
||||
performance_selection, aspect_ratios_selection, image_number, image_seed, sharpness, guidance_scale
|
||||
performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, sharpness, guidance_scale
|
||||
]
|
||||
|
||||
ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls
|
||||
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 += [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]
|
||||
ctrls += [refiner_swap_method, controlnet_softness]
|
||||
ctrls += freeu_ctrls
|
||||
ctrls += inpaint_ctrls
|
||||
|
||||
if not args_manager.args.disable_metadata:
|
||||
ctrls += [save_metadata_to_images, metadata_scheme]
|
||||
|
||||
ctrls += ip_ctrls
|
||||
|
||||
state_is_generating = gr.State(False)
|
||||
|
||||
def parse_meta(raw_prompt_txt, is_generating):
|
||||
loaded_json = None
|
||||
try:
|
||||
if '{' in raw_prompt_txt:
|
||||
if '}' in raw_prompt_txt:
|
||||
if ':' in raw_prompt_txt:
|
||||
loaded_json = json.loads(raw_prompt_txt)
|
||||
assert isinstance(loaded_json, dict)
|
||||
except:
|
||||
loaded_json = None
|
||||
if is_json(raw_prompt_txt):
|
||||
loaded_json = json.loads(raw_prompt_txt)
|
||||
|
||||
if loaded_json is None:
|
||||
if is_generating:
|
||||
|
|
@ -559,37 +617,35 @@ with shared.gradio_root:
|
|||
|
||||
prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)
|
||||
|
||||
load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=[
|
||||
advanced_checkbox,
|
||||
image_number,
|
||||
prompt,
|
||||
negative_prompt,
|
||||
style_selections,
|
||||
performance_selection,
|
||||
aspect_ratios_selection,
|
||||
overwrite_width,
|
||||
overwrite_height,
|
||||
sharpness,
|
||||
guidance_scale,
|
||||
adm_scaler_positive,
|
||||
adm_scaler_negative,
|
||||
adm_scaler_end,
|
||||
base_model,
|
||||
refiner_model,
|
||||
refiner_switch,
|
||||
sampler_name,
|
||||
scheduler_name,
|
||||
seed_random,
|
||||
image_seed,
|
||||
generate_button,
|
||||
load_parameter_button
|
||||
] + lora_ctrls, queue=False, show_progress=False)
|
||||
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
|
||||
|
||||
load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
|
||||
|
||||
def trigger_metadata_import(filepath, state_is_generating):
|
||||
parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
|
||||
if parameters is None:
|
||||
print('Could not find metadata in the image!')
|
||||
parsed_parameters = {}
|
||||
else:
|
||||
metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
|
||||
parsed_parameters = metadata_parser.parse_json(parameters)
|
||||
|
||||
return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
|
||||
|
||||
|
||||
metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
|
||||
.then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False)
|
||||
|
||||
generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True),
|
||||
outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \
|
||||
.then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \
|
||||
.then(advanced_parameters.set_all_advanced_parameters, inputs=adps) \
|
||||
.then(fn=generate_clicked, inputs=ctrls, outputs=[progress_html, progress_window, progress_gallery, gallery]) \
|
||||
.then(fn=get_task, inputs=ctrls, outputs=currentTask) \
|
||||
.then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \
|
||||
.then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False),
|
||||
outputs=[generate_button, stop_button, skip_button, state_is_generating]) \
|
||||
.then(fn=update_history_link, outputs=history_link) \
|
||||
|
|
@ -626,5 +682,6 @@ shared.gradio_root.launch(
|
|||
server_port=args_manager.args.port,
|
||||
share=args_manager.args.share,
|
||||
auth=check_auth if (args_manager.args.share or args_manager.args.listen) and auth_enabled else None,
|
||||
allowed_paths=[modules.config.path_outputs],
|
||||
blocked_paths=[constants.AUTH_FILENAME]
|
||||
)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,100 @@
|
|||
Alligator
|
||||
Ant
|
||||
Antelope
|
||||
Armadillo
|
||||
Badger
|
||||
Bat
|
||||
Bear
|
||||
Beaver
|
||||
Bison
|
||||
Boar
|
||||
Bobcat
|
||||
Bull
|
||||
Camel
|
||||
Chameleon
|
||||
Cheetah
|
||||
Chicken
|
||||
Chihuahua
|
||||
Chimpanzee
|
||||
Chinchilla
|
||||
Chipmunk
|
||||
Comodo Dragon
|
||||
Cow
|
||||
Coyote
|
||||
Crocodile
|
||||
Crow
|
||||
Deer
|
||||
Dinosaur
|
||||
Dolphin
|
||||
Donkey
|
||||
Duck
|
||||
Eagle
|
||||
Eel
|
||||
Elephant
|
||||
Elk
|
||||
Emu
|
||||
Falcon
|
||||
Ferret
|
||||
Flamingo
|
||||
Flying Squirrel
|
||||
Giraffe
|
||||
Goose
|
||||
Guinea pig
|
||||
Hawk
|
||||
Hedgehog
|
||||
Hippopotamus
|
||||
Horse
|
||||
Hummingbird
|
||||
Hyena
|
||||
Jackal
|
||||
Jaguar
|
||||
Jellyfish
|
||||
Kangaroo
|
||||
King Cobra
|
||||
Koala bear
|
||||
Leopard
|
||||
Lion
|
||||
Lizard
|
||||
Magpie
|
||||
Marten
|
||||
Meerkat
|
||||
Mole
|
||||
Monkey
|
||||
Moose
|
||||
Mouse
|
||||
Octopus
|
||||
Okapi
|
||||
Orangutan
|
||||
Ostrich
|
||||
Otter
|
||||
Owl
|
||||
Panda
|
||||
Pangolin
|
||||
Panther
|
||||
Penguin
|
||||
Pig
|
||||
Porcupine
|
||||
Possum
|
||||
Puma
|
||||
Quokka
|
||||
Rabbit
|
||||
Raccoon
|
||||
Raven
|
||||
Reindeer
|
||||
Rhinoceros
|
||||
Seal
|
||||
Shark
|
||||
Sheep
|
||||
Snail
|
||||
Snake
|
||||
Sparrow
|
||||
Spider
|
||||
Squirrel
|
||||
Swallow
|
||||
Tiger
|
||||
Walrus
|
||||
Whale
|
||||
Wolf
|
||||
Wombat
|
||||
Yak
|
||||
Zebra
|
||||
Loading…
Reference in New Issue