sd-webui/stable-diffusion-webui
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Merge pull request #1210 from codedealer/the-merge

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The merge
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hlky 2022-09-18 14:15:24 +01:00 committed by GitHub
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39 changed files with 9911 additions and 2109 deletions
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3
.dockerignore Normal file
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@ -0,0 +1,3 @@
models/
outputs/
src/

10
.env_docker.example
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@ -6,9 +6,13 @@ CONDA_FORCE_UPDATE=false
# (useful to set to false after you're sure the model files are already in place)
VALIDATE_MODELS=true
#Automatically relaunch the webui on crashes
# Automatically relaunch the webui on crashes
WEBUI_RELAUNCH=true
#Pass cli arguments to webui.py e.g:
#WEBUI_ARGS=--gpu=1 --esrgan-gpu=1 --gfpgan-gpu=1
# Which webui to launch
# WEBUI_SCRIPT=webui_streamlit.py
WEBUI_SCRIPT=webui.py
# Pass cli arguments to webui.py e.g:
# WEBUI_ARGS=--optimized --extra-models-cpu --gpu=1 --esrgan-gpu=1 --gfpgan-gpu=1
WEBUI_ARGS=

9
.gitignore vendored
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@ -47,16 +47,21 @@ MANIFEST
.env_updated
condaenv.*.requirements.txt
# Visual Studio directories
.vs/
.vscode/
# =========================================================================== #
# Repo-specific
# =========================================================================== #
/configs/webui/userconfig_streamlit.yaml
/custom-conda-path.txt
/src/*
/outputs/*
/outputs
/model_cache
/log/**/*.png
/log/log.csv
/flagged/*
/gfpgan/*
/models/*
z_version_env.tmp
z_version_env.tmp

9
Dockerfile
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@ -1,6 +1,10 @@
FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
ENV DEBIAN_FRONTEND=noninteractive
ENV DEBIAN_FRONTEND=noninteractive \
PYTHONUNBUFFERED=1 \
PYTHONIOENCODING=UTF-8 \
CONDA_DIR=/opt/conda
WORKDIR /sd
SHELL ["/bin/bash", "-c"]
@ -11,7 +15,6 @@ RUN apt-get update && \
rm -rf /var/lib/apt/lists/*
# Install miniconda
ENV CONDA_DIR /opt/conda
RUN wget -O ~/miniconda.sh -q --show-progress --progress=bar:force https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
/bin/bash ~/miniconda.sh -b -p $CONDA_DIR && \
rm ~/miniconda.sh
@ -20,7 +23,7 @@ ENV PATH=$CONDA_DIR/bin:$PATH
# Install font for prompt matrix
COPY /data/DejaVuSans.ttf /usr/share/fonts/truetype/
EXPOSE 7860
EXPOSE 7860 8501
COPY ./entrypoint.sh /sd/
ENTRYPOINT /sd/entrypoint.sh

6
README.md
View File

@ -46,8 +46,8 @@ Features:
* Gradio GUI: Idiot-proof, fully featured frontend for both txt2img and img2img generation
* No more manually typing parameters, now all you have to do is write your prompt and adjust sliders
* GFPGAN Face Correction 🔥: [Download the model](https://github.com/sd-webui/stable-diffusion-webui#gfpgan)Automatically correct distorted faces with a built-in GFPGAN option, fixes them in less than half a second
* RealESRGAN Upscaling 🔥: [Download the models](https://github.com/sd-webui/stable-diffusion-webui#realesrgan) Boosts the resolution of images with a built-in RealESRGAN option
* GFPGAN Face Correction 🔥: [Download the model](https://github.com/sd-webui/stable-diffusion-webui/wiki/Installation#optional-additional-models) Automatically correct distorted faces with a built-in GFPGAN option, fixes them in less than half a second
* RealESRGAN Upscaling 🔥: [Download the models](https://github.com/sd-webui/stable-diffusion-webui/wiki/Installation#optional-additional-models) Boosts the resolution of images with a built-in RealESRGAN option
* :computer: esrgan/gfpgan on cpu support :computer:
* Textual inversion 🔥: [info](https://textual-inversion.github.io/) - requires enabling, see [here](https://github.com/hlky/sd-enable-textual-inversion), script works as usual without it enabled
* Advanced img2img editor :art: :fire: :art:
@ -106,7 +106,7 @@ that are not in original script.
### GFPGAN
Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and
also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strongthe effect is.
also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strong the effect is.
![](images/GFPGAN.png)

11
configs/webui/webui.yaml
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@ -12,8 +12,9 @@ txt2img:
# 5: Write sample info files
# 6: write sample info to log file
# 7: jpg samples
# 8: Fix faces using GFPGAN
# 9: Upscale images using RealESRGAN
# 8: Filter NSFW content
# 9: Fix faces using GFPGAN
# 10: Upscale images using RealESRGAN
toggles: [1, 2, 3, 4, 5]
sampler_name: k_lms
ddim_eta: 0.0 # legacy name, applies to all algorithms.
@ -40,8 +41,10 @@ img2img:
# 6: Sort samples by prompt
# 7: Write sample info files
# 8: jpg samples
# 9: Fix faces using GFPGAN
# 10: Upscale images using Real-ESRGAN
# 9: Color correction
# 10: Filter NSFW content
# 11: Fix faces using GFPGAN
# 12: Upscale images using Real-ESRGAN
toggles: [1, 4, 5, 6, 7]
sampler_name: k_lms
ddim_eta: 0.0

190
configs/webui/webui_streamlit.yaml
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@ -1,14 +1,19 @@
# UI defaults configuration file. It is automatically loaded if located at configs/webui/webui_streamlit.yaml.
# Any changes made here will be available automatically on the web app without having to stop it.
# You may add overrides in a file named "userconfig_streamlit.yaml" in this folder, which can contain any subset
# of the properties below.
general:
gpu: 0
outdir: outputs
ckpt: "models/ldm/stable-diffusion-v1/model.ckpt"
fp:
name: 'embeddings/alex/embeddings_gs-11000.pt'
default_model: "Stable Diffusion v1.4"
default_model_config: "configs/stable-diffusion/v1-inference.yaml"
default_model_path: "models/ldm/stable-diffusion-v1/model.ckpt"
use_sd_concepts_library: True
sd_concepts_library_folder: "models/custom/sd-concepts-library"
GFPGAN_dir: "./src/gfpgan"
RealESRGAN_dir: "./src/realesrgan"
RealESRGAN_model: "RealESRGAN_x4plus"
LDSR_dir: "./src/latent-diffusion"
outdir_txt2img: outputs/txt2img-samples
outdir_img2img: outputs/img2img-samples
gfpgan_cpu: False
@ -16,88 +21,161 @@ general:
extra_models_cpu: False
extra_models_gpu: False
save_metadata: True
save_format: "png"
skip_grid: False
skip_save: False
grid_format: "jpg:95"
n_rows: -1
no_verify_input: False
no_half: False
use_float16: False
precision: "autocast"
optimized: False
optimized_turbo: False
optimized_config: "optimizedSD/v1-inference.yaml"
enable_attention_slicing: False
enable_minimal_memory_usage : False
update_preview: True
update_preview_frequency: 1
update_preview_frequency: 5
txt2img:
prompt:
height: 512
width: 512
cfg_scale: 5.0
cfg_scale: 7.5
seed: ""
batch_count: 1
batch_size: 1
sampling_steps: 50
default_sampler: "k_lms"
sampling_steps: 30
default_sampler: "k_euler"
separate_prompts: False
update_preview: True
update_preview_frequency: 5
normalize_prompt_weights: True
save_individual_images: True
save_grid: True
group_by_prompt: True
save_as_jpg: False
use_GFPGAN: True
use_RealESRGAN: True
use_GFPGAN: False
use_RealESRGAN: False
RealESRGAN_model: "RealESRGAN_x4plus"
variant_amount: 0.0
variant_seed: ""
write_info_files: True
slider_steps: {
sampling: 1
}
slider_bounds: {
sampling: {
lower: 1,
upper: 150
}
}
txt2vid:
default_model: "CompVis/stable-diffusion-v1-4"
custom_models_list: ["CompVis/stable-diffusion-v1-4", "naclbit/trinart_stable_diffusion_v2", "hakurei/waifu-diffusion", "osanseviero/BigGAN-deep-128"]
prompt:
height: 512
width: 512
cfg_scale: 7.5
seed: ""
batch_count: 1
batch_size: 1
sampling_steps: 30
num_inference_steps: 200
default_sampler: "k_euler"
scheduler_name: "klms"
separate_prompts: False
update_preview: True
update_preview_frequency: 5
dynamic_preview_frequency: True
normalize_prompt_weights: True
save_individual_images: True
save_video: True
group_by_prompt: True
write_info_files: True
do_loop: False
save_as_jpg: False
use_GFPGAN: False
use_RealESRGAN: False
RealESRGAN_model: "RealESRGAN_x4plus"
variant_amount: 0.0
variant_seed: ""
beta_start: 0.00085
beta_end: 0.012
beta_scheduler_type: "linear"
max_frames: 1000
slider_steps: {
sampling: 1
}
slider_bounds: {
sampling: {
lower: 1,
upper: 150
}
}
img2img:
prompt:
sampling_steps: 50
# Adding an int to toggles enables the corresponding feature.
# 0: Create prompt matrix (separate multiple prompts using |, and get all combinations of them)
# 1: Normalize Prompt Weights (ensure sum of weights add up to 1.0)
# 2: Loopback (use images from previous batch when creating next batch)
# 3: Random loopback seed
# 4: Save individual images
# 5: Save grid
# 6: Sort samples by prompt
# 7: Write sample info files
# 8: jpg samples
# 9: Fix faces using GFPGAN
# 10: Upscale images using Real-ESRGAN
sampler_name: k_lms
denoising_strength: 0.45
# 0: Keep masked area
# 1: Regenerate only masked area
mask_mode: 0
# 0: Just resize
# 1: Crop and resize
# 2: Resize and fill
resize_mode: 0
# Leave blank for random seed:
seed: ""
ddim_eta: 0.0
cfg_scale: 5.0
batch_count: 1
batch_size: 1
height: 512
width: 512
# Textual inversion embeddings file path:
fp: ""
loopback: True
random_seed_loopback: True
separate_prompts: False
normalize_prompt_weights: True
save_individual_images: True
save_grid: True
group_by_prompt: True
save_as_jpg: False
use_GFPGAN: True
use_RealESRGAN: True
RealESRGAN_model: "RealESRGAN_x4plus"
variant_amount: 0.0
variant_seed: ""
prompt:
sampling_steps: 30
# Adding an int to toggles enables the corresponding feature.
# 0: Create prompt matrix (separate multiple prompts using |, and get all combinations of them)
# 1: Normalize Prompt Weights (ensure sum of weights add up to 1.0)
# 2: Loopback (use images from previous batch when creating next batch)
# 3: Random loopback seed
# 4: Save individual images
# 5: Save grid
# 6: Sort samples by prompt
# 7: Write sample info files
# 8: jpg samples
# 9: Fix faces using GFPGAN
# 10: Upscale images using Real-ESRGAN
sampler_name: "k_euler"
denoising_strength: 0.75
# 0: Keep masked area
# 1: Regenerate only masked area
mask_mode: 0
mask_restore: False
# 0: Just resize
# 1: Crop and resize
# 2: Resize and fill
resize_mode: 0
# Leave blank for random seed:
seed: ""
ddim_eta: 0.0
cfg_scale: 7.5
batch_count: 1
batch_size: 1
height: 512
width: 512
# Textual inversion embeddings file path:
fp: ""
loopback: True
random_seed_loopback: True
separate_prompts: False
update_preview: True
update_preview_frequency: 5
normalize_prompt_weights: True
save_individual_images: True
save_grid: True
group_by_prompt: True
save_as_jpg: False
use_GFPGAN: False
use_RealESRGAN: False
RealESRGAN_model: "RealESRGAN_x4plus"
variant_amount: 0.0
variant_seed: ""
write_info_files: True
slider_steps: {
sampling: 1
}
slider_bounds: {
sampling: {
lower: 1,
upper: 150
}
}
gfpgan:
strength: 100

5
docker-compose.yml
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@ -2,7 +2,7 @@ version: '3.3'
services:
stable-diffusion:
container_name: sd
container_name: sd-webui
build:
context: .
dockerfile: Dockerfile
@ -12,6 +12,7 @@ services:
volumes:
- .:/sd
- ./outputs:/sd/outputs
- ./model_cache:/sd/model_cache
- conda_env:/opt/conda
- root_profile:/root
ports:
@ -21,7 +22,7 @@ services:
resources:
reservations:
devices:
- capabilities: [gpu]
- capabilities: [ gpu ]
volumes:
conda_env:

9
docker-reset.sh Normal file → Executable file
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@ -10,12 +10,13 @@ echo $(pwd)
read -p "Is the directory above correct to run reset on? (y/n) " -n 1 DIRCONFIRM
if [[ $DIRCONFIRM =~ ^[Yy]$ ]]; then
docker compose down
docker image rm stable-diffusion_stable-diffusion:latest
docker volume rm stable-diffusion_conda_env
docker volume rm stable-diffusion_root_profile
docker image rm stable-diffusion-webui_stable-diffusion:latest
docker volume rm stable-diffusion-webui_conda_env
docker volume rm stable-diffusion-webui_root_profile
echo "Remove ./src"
sudo rm -rf src
sudo rm -rf latent_diffusion.egg-info
sudo rm -rf gfpgan
sudo rm -rf sd_webui.egg-info
sudo rm .env_updated
else
echo "Exited without resetting"

73
entrypoint.sh
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@ -3,26 +3,36 @@
# Starts the gui inside the docker container using the conda env
#
# set -x
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
cd $SCRIPT_DIR
export PYTHONPATH=$SCRIPT_DIR
MODEL_DIR="${SCRIPT_DIR}/model_cache"
# Array of model files to pre-download
# local filename
# local path in container (no trailing slash)
# download URL
# sha256sum
MODEL_FILES=(
'model.ckpt /sd/models/ldm/stable-diffusion-v1 https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media fe4efff1e174c627256e44ec2991ba279b3816e364b49f9be2abc0b3ff3f8556'
'GFPGANv1.3.pth /sd/src/gfpgan/experiments/pretrained_models https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth c953a88f2727c85c3d9ae72e2bd4846bbaf59fe6972ad94130e23e7017524a70'
'RealESRGAN_x4plus.pth /sd/src/realesrgan/experiments/pretrained_models https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth 4fa0d38905f75ac06eb49a7951b426670021be3018265fd191d2125df9d682f1'
'RealESRGAN_x4plus_anime_6B.pth /sd/src/realesrgan/experiments/pretrained_models https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth f872d837d3c90ed2e05227bed711af5671a6fd1c9f7d7e91c911a61f155e99da'
'model.ckpt models/ldm/stable-diffusion-v1 https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media fe4efff1e174c627256e44ec2991ba279b3816e364b49f9be2abc0b3ff3f8556'
'GFPGANv1.3.pth src/gfpgan/experiments/pretrained_models https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth c953a88f2727c85c3d9ae72e2bd4846bbaf59fe6972ad94130e23e7017524a70'
'RealESRGAN_x4plus.pth src/realesrgan/experiments/pretrained_models https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth 4fa0d38905f75ac06eb49a7951b426670021be3018265fd191d2125df9d682f1'
'RealESRGAN_x4plus_anime_6B.pth src/realesrgan/experiments/pretrained_models https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth f872d837d3c90ed2e05227bed711af5671a6fd1c9f7d7e91c911a61f155e99da'
'project.yaml src/latent-diffusion/experiments/pretrained_models https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1 9d6ad53c5dafeb07200fb712db14b813b527edd262bc80ea136777bdb41be2ba'
'model.ckpt src/latent-diffusion/experiments/pretrained_models https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1 c209caecac2f97b4bb8f4d726b70ac2ac9b35904b7fc99801e1f5e61f9210c13'
)
# Conda environment installs/updates
# @see https://github.com/ContinuumIO/docker-images/issues/89#issuecomment-467287039
ENV_NAME="ldm"
ENV_FILE="/sd/environment.yaml"
ENV_FILE="${SCRIPT_DIR}/environment.yaml"
ENV_UPDATED=0
ENV_MODIFIED=$(date -r $ENV_FILE "+%s")
ENV_MODIFED_FILE="/sd/.env_updated"
ENV_MODIFED_FILE="${SCRIPT_DIR}/.env_updated"
if [[ -f $ENV_MODIFED_FILE ]]; then ENV_MODIFIED_CACHED=$(<${ENV_MODIFED_FILE}); else ENV_MODIFIED_CACHED=0; fi
export PIP_EXISTS_ACTION=w
# Create/update conda env if needed
if ! conda env list | grep ".*${ENV_NAME}.*" >/dev/null 2>&1; then
@ -51,54 +61,67 @@ conda info | grep active
# Function to checks for valid hash for model files and download/replaces if invalid or does not exist
validateDownloadModel() {
local file=$1
local path=$2
local path="${SCRIPT_DIR}/${2}"
local url=$3
local hash=$4
echo "checking ${file}..."
sha256sum --check --status <<< "${hash} ${path}/${file}"
sha256sum --check --status <<< "${hash} ${MODEL_DIR}/${file}.${hash}"
if [[ $? == "1" ]]; then
echo "Downloading: ${url} please wait..."
mkdir -p ${path}
wget --output-document=${path}/${file} --no-verbose --show-progress --progress=dot:giga ${url}
echo "saved ${file}"
wget --output-document=${MODEL_DIR}/${file}.${hash} --no-verbose --show-progress --progress=dot:giga ${url}
ln -sf ${MODEL_DIR}/${file}.${hash} ${path}/${file}
if [[ -e "${path}/${file}" ]]; then
echo "saved ${file}"
else
echo "error saving ${path}/${file}!"
exit 1
fi
else
echo -e "${file} is valid!\n"
if [[ ! -e ${path}/${file} || ! -L ${path}/${file} ]]; then
mkdir -p ${path}
ln -sf ${MODEL_DIR}/${file}.${hash} ${path}/${file}
echo -e "linked valid ${file}\n"
else
echo -e "${file} is valid!\n"
fi
fi
}
# Validate model files
if [[ -z $VALIDATE_MODELS || $VALIDATE_MODELS == "true" ]]; then
echo "Validating model files..."
for models in "${MODEL_FILES[@]}"; do
model=($models)
echo "Validating model files..."
for models in "${MODEL_FILES[@]}"; do
model=($models)
if [[ ! -e ${model[1]}/${model[0]} || ! -L ${model[1]}/${model[0]} || -z $VALIDATE_MODELS || $VALIDATE_MODELS == "true" ]]; then
validateDownloadModel ${model[0]} ${model[1]} ${model[2]} ${model[3]}
done
fi
fi
done
# Launch web gui
cd /sd
if [[ -z $WEBUI_ARGS ]]; then
launch_message="entrypoint.sh: Launching..."
if [[ ! -z $WEBUI_SCRIPT && $WEBUI_SCRIPT == "webui_streamlit.py" ]]; then
launch_command="streamlit run scripts/${WEBUI_SCRIPT:-webui.py} $WEBUI_ARGS"
else
launch_message="entrypoint.sh: Launching with arguments ${WEBUI_ARGS}"
launch_command="python scripts/${WEBUI_SCRIPT:-webui.py} $WEBUI_ARGS"
fi
launch_message="entrypoint.sh: Run ${launch_command}..."
if [[ -z $WEBUI_RELAUNCH || $WEBUI_RELAUNCH == "true" ]]; then
n=0
while true; do
echo $launch_message
if (( $n > 0 )); then
echo "Relaunch count: ${n}"
fi
python -u scripts/webui.py $WEBUI_ARGS
$launch_command
echo "entrypoint.sh: Process is ending. Relaunching in 0.5s..."
((n++))
sleep 0.5
done
else
echo $launch_message
python -u scripts/webui.py $WEBUI_ARGS
$launch_command
fi

62
environment.yaml
View File

@ -3,39 +3,47 @@ channels:
- pytorch
- defaults
dependencies:
- git
- python=3.8.5
- pip=20.3
- cudatoolkit=11.3
- git
- numpy=1.22.3
- pip=20.3
- python=3.8.5
- pytorch=1.11.0
- scikit-image=0.19.2
- torchvision=0.12.0
- numpy=1.19.2
- pip:
- albumentations==0.4.3
- opencv-python==4.1.2.30
- opencv-python-headless==4.1.2.30
- pudb==2019.2
- imageio==2.9.0
- imageio-ffmpeg==0.4.2
- pytorch-lightning==1.4.2
- omegaconf==2.1.1
- test-tube>=0.7.5
- einops==0.3.0
- torch-fidelity==0.3.0
- transformers==4.19.2
- torchmetrics==0.6.0
- kornia==0.6
- gradio==3.1.6
- accelerate==0.12.0
- pynvml==11.4.1
- basicsr>=1.3.4.0
- facexlib>=0.2.3
- python-slugify>=6.1.2
- streamlit>=1.12.2
- retry>=0.9.2
- -e .
- -e git+https://github.com/CompVis/taming-transformers#egg=taming-transformers
- -e git+https://github.com/openai/CLIP#egg=clip
- -e git+https://github.com/TencentARC/GFPGAN#egg=GFPGAN
- -e git+https://github.com/xinntao/Real-ESRGAN#egg=realesrgan
- -e git+https://github.com/hlky/k-diffusion-sd#egg=k_diffusion
- -e .
- -e git+https://github.com/devilismyfriend/latent-diffusion#egg=latent-diffusion
- accelerate==0.12.0
- albumentations==0.4.3
- basicsr>=1.3.4.0
- diffusers==0.3.0
- einops==0.3.0
- facexlib>=0.2.3
- gradio==3.1.6
- imageio-ffmpeg==0.4.2
- imageio==2.9.0
- kornia==0.6
- omegaconf==2.1.1
- opencv-python-headless==4.6.0.66
- pandas==1.4.3
- piexif==1.1.3
- pudb==2019.2
- pynvml==11.4.1
- python-slugify>=6.1.2
- pytorch-lightning==1.4.2
- retry>=0.9.2
- streamlit>=1.12.2
- streamlit-on-Hover-tabs==1.0.1
- streamlit-option-menu==0.3.2
- streamlit_nested_layout
- test-tube>=0.7.5
- tensorboard
- torch-fidelity==0.3.0
- torchmetrics==0.6.0
- transformers==4.19.2

122
frontend/css/streamlit.main.css
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@ -1,15 +1,111 @@
.css-18e3th9 {
padding-top: 2rem;
padding-bottom: 10rem;
padding-left: 5rem;
padding-right: 5rem;
}
.css-1d391kg {
padding-top: 3.5rem;
padding-right: 1rem;
padding-bottom: 3.5rem;
padding-left: 1rem;
}
/***********************************************************
* Additional CSS for streamlit builtin components *
************************************************************/
/* Tab name (e.g. Text-to-Image) */
button[data-baseweb="tab"] {
font-size: 25px;
font-size: 25px; //improve legibility
}
/* Image Container (only appear after run finished) */
.css-du1fp8 {
justify-content: center; //center the image, especially better looks in wide screen
}
/* Streamlit header */
.css-1avcm0n {
background-color: transparent;
}
/* Main streamlit container (below header) */
.css-18e3th9 {
padding-top: 2rem; //reduce the empty spaces
}
/* @media only for widescreen, to ensure enough space to see all */
@media (min-width: 1024px) {
/* Main streamlit container (below header) */
.css-18e3th9 {
padding-top: 0px; //reduce the empty spaces, can go fully to the top on widescreen devices
}
}
/***********************************************************
* Additional CSS for streamlit custom/3rd party components *
************************************************************/
/* For stream_on_hover */
section[data-testid="stSidebar"] > div:nth-of-type(1) {
background-color: #111;
}
button[kind="header"] {
background-color: transparent;
color: rgb(180, 167, 141);
}
@media (hover) {
/* header element */
header[data-testid="stHeader"] {
/* display: none;*/ /*suggested behavior by streamlit hover components*/
pointer-events: none; /* disable interaction of the transparent background */
}
/* The button on the streamlit navigation menu */
button[kind="header"] {
/* display: none;*/ /*suggested behavior by streamlit hover components*/
pointer-events: auto; /* enable interaction of the button even if parents intereaction disabled */
}
/* added to avoid main sectors (all element to the right of sidebar from) moving */
section[data-testid="stSidebar"] {
width: 3.5% !important;
min-width: 3.5% !important;
}
/* The navigation menu specs and size */
section[data-testid="stSidebar"] > div {
height: 100%;
width: 2% !important;
min-width: 100% !important;
position: relative;
z-index: 1;
top: 0;
left: 0;
background-color: #111;
overflow-x: hidden;
transition: 0.5s ease-in-out;
padding-top: 0px;
white-space: nowrap;
}
/* The navigation menu open and close on hover and size */
section[data-testid="stSidebar"] > div:hover {
width: 300px !important;
}
}
@media (max-width: 272px) {
section[data-testid="stSidebar"] > div {
width: 15rem;
}
}
/***********************************************************
* Additional CSS for other elements
************************************************************/
button[data-baseweb="tab"] {
font-size: 20px;
}
@media (min-width: 1200px){
h1 {
font-size: 1.75rem;
}
}
#tabs-1-tabpanel-0 > div:nth-child(1) > div > div.stTabs.css-0.exp6ofz0 {
width: 50rem;
align-self: center;
}
div.gallery:hover {
border: 1px solid #777;
}

80
frontend/frontend.py
View File

@ -3,6 +3,8 @@ from frontend.css_and_js import css, js, call_JS, js_parse_prompt, js_copy_txt2i
from frontend.job_manager import JobManager
import frontend.ui_functions as uifn
import uuid
import torch
def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda x: x, txt2img_defaults={},
@ -36,8 +38,11 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
value=txt2img_defaults['cfg_scale'], elem_id='cfg_slider')
txt2img_seed = gr.Textbox(label="Seed (blank to randomize)", lines=1, max_lines=1,
value=txt2img_defaults["seed"])
txt2img_batch_size = gr.Slider(minimum=1, maximum=50, step=1,
label='Images per batch',
value=txt2img_defaults['batch_size'])
txt2img_batch_count = gr.Slider(minimum=1, maximum=50, step=1,
label='Number of images to generate',
label='Number of batches to generate',
value=txt2img_defaults['n_iter'])
txt2img_job_ui = job_manager.draw_gradio_ui() if job_manager else None
@ -51,11 +56,15 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
gr.Markdown(
"Select an image from the gallery, then click one of the buttons below to perform an action.")
with gr.Row(elem_id='txt2img_actions_row'):
gr.Button("Copy to clipboard").click(fn=None,
inputs=output_txt2img_gallery,
outputs=[],
# _js=js_copy_to_clipboard( 'txt2img_gallery_output')
)
gr.Button("Copy to clipboard").click(
fn=None,
inputs=output_txt2img_gallery,
outputs=[],
_js=call_JS(
"copyImageFromGalleryToClipboard",
fromId="txt2img_gallery_output"
)
)
output_txt2img_copy_to_input_btn = gr.Button("Push to img2img")
output_txt2img_to_imglab = gr.Button("Send to Lab", visible=True)
@ -91,9 +100,6 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
with gr.TabItem('Advanced'):
txt2img_toggles = gr.CheckboxGroup(label='', choices=txt2img_toggles,
value=txt2img_toggle_defaults, type="index")
txt2img_batch_size = gr.Slider(minimum=1, maximum=8, step=1,
label='Batch size (how many images are in a batch; memory-hungry)',
value=txt2img_defaults['batch_size'])
txt2img_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model',
choices=['RealESRGAN_x4plus',
'RealESRGAN_x4plus_anime_6B'],
@ -124,20 +130,27 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
inputs=txt2img_inputs,
outputs=txt2img_outputs
)
use_queue = False
else:
use_queue = True
txt2img_btn.click(
txt2img_func,
txt2img_inputs,
txt2img_outputs
txt2img_outputs,
api_name='txt2img',
queue=use_queue
)
txt2img_prompt.submit(
txt2img_func,
txt2img_inputs,
txt2img_outputs
txt2img_outputs,
queue=use_queue
)
# txt2img_width.change(fn=uifn.update_dimensions_info, inputs=[txt2img_width, txt2img_height], outputs=txt2img_dimensions_info_text_box)
# txt2img_height.change(fn=uifn.update_dimensions_info, inputs=[txt2img_width, txt2img_height], outputs=txt2img_dimensions_info_text_box)
txt2img_width.change(fn=uifn.update_dimensions_info, inputs=[txt2img_width, txt2img_height], outputs=txt2img_dimensions_info_text_box)
txt2img_height.change(fn=uifn.update_dimensions_info, inputs=[txt2img_width, txt2img_height], outputs=txt2img_dimensions_info_text_box)
txt2img_dimensions_info_text_box.value = uifn.update_dimensions_info(txt2img_width.value, txt2img_height.value)
# Temporarily disable prompt parsing until memory issues could be solved
# See #676
@ -189,8 +202,9 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
with gr.TabItem("Editor Options"):
with gr.Row():
# disable Uncrop for now
# choices=["Mask", "Crop", "Uncrop"]
img2img_image_editor_mode = gr.Radio(choices=["Mask", "Crop"],
choices=["Mask", "Crop", "Uncrop"]
#choices=["Mask", "Crop"]
img2img_image_editor_mode = gr.Radio(choices=choices,
label="Image Editor Mode",
value="Mask", elem_id='edit_mode_select',
visible=True)
@ -199,9 +213,13 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
value=img2img_mask_modes[img2img_defaults['mask_mode']],
visible=True)
img2img_mask_blur_strength = gr.Slider(minimum=1, maximum=10, step=1,
img2img_mask_restore = gr.Checkbox(label="Only modify regenerated parts of image",
value=img2img_defaults['mask_restore'],
visible=True)
img2img_mask_blur_strength = gr.Slider(minimum=1, maximum=100, step=1,
label="How much blurry should the mask be? (to avoid hard edges)",
value=3, visible=False)
value=3, visible=True)
img2img_resize = gr.Radio(label="Resize mode",
choices=["Just resize", "Crop and resize",
@ -293,7 +311,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
img2img_height
],
[img2img_image_editor, img2img_image_mask, img2img_btn_editor, img2img_btn_mask,
img2img_painterro_btn, img2img_mask, img2img_mask_blur_strength]
img2img_painterro_btn, img2img_mask, img2img_mask_blur_strength, img2img_mask_restore]
)
# img2img_image_editor_mode.change(
@ -334,8 +352,8 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
)
img2img_func = img2img
img2img_inputs = [img2img_prompt, img2img_image_editor_mode, img2img_mask,
img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
img2img_inputs = [img2img_prompt, img2img_image_editor_mode, img2img_mask, img2img_mask_blur_strength,
img2img_mask_restore, img2img_steps, img2img_sampling, img2img_toggles,
img2img_realesrgan_model_name, img2img_batch_count, img2img_cfg,
img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
img2img_image_editor, img2img_image_mask, img2img_embeddings]
@ -349,11 +367,16 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
inputs=img2img_inputs,
outputs=img2img_outputs,
)
use_queue = False
else:
use_queue = True
img2img_btn_mask.click(
img2img_func,
img2img_inputs,
img2img_outputs
img2img_outputs,
api_name="img2img",
queue=use_queue
)
def img2img_submit_params():
@ -383,6 +406,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
outputs=img2img_dimensions_info_text_box)
img2img_height.change(fn=uifn.update_dimensions_info, inputs=[img2img_width, img2img_height],
outputs=img2img_dimensions_info_text_box)
img2img_dimensions_info_text_box.value = uifn.update_dimensions_info(img2img_width.value, img2img_height.value)
with gr.TabItem("Image Lab", id='imgproc_tab'):
gr.Markdown("Post-process results")
@ -397,8 +421,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
# value=gfpgan_defaults['strength'])
# select folder with images to process
with gr.TabItem('Batch Process'):
imgproc_folder = gr.File(label="Batch Process", file_count="multiple", source="upload",
interactive=True, type="file")
imgproc_folder = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
imgproc_pngnfo = gr.Textbox(label="PNG Metadata", placeholder="PngNfo", visible=False,
max_lines=5)
with gr.Row():
@ -540,7 +563,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
imgproc_width, imgproc_cfg, imgproc_denoising, imgproc_seed,
imgproc_gfpgan_strength, imgproc_ldsr_steps, imgproc_ldsr_pre_downSample,
imgproc_ldsr_post_downSample],
[imgproc_output])
[imgproc_output], api_name="imgproc")
imgproc_source.change(
uifn.get_png_nfo,
@ -631,11 +654,12 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
"""
gr.HTML("""
<div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
<p>For help and advanced usage guides, visit the <a href="https://github.com/sd-webui/stable-diffusion-webui/wiki" target="_blank">Project Wiki</a></p>
<p><a href="https://github.com/sd-webui/stable-diffusion-webui">Stable Diffusion WebUI</a> is an open-source project.
If you would like to contribute to development or test bleeding edge builds, use the <a href="https://github.com/sd-webui/stable-diffusion-webui/tree/dev" target="_blank">dev branch</a>.</p>
<p>For help and advanced usage guides, visit the <a href="https://github.com/hlky/stable-diffusion-webui/wiki" target="_blank">Project Wiki</a></p>
<p>Stable Diffusion WebUI is an open-source project. You can find the latest stable builds on the <a href="https://github.com/hlky/stable-diffusion" target="_blank">main repository</a>.
If you would like to contribute to development or test bleeding edge builds, you can visit the <a href="https://github.com/hlky/stable-diffusion-webui" target="_blank">developement repository</a>.</p>
<p>Device ID {current_device_index}: {current_device_name}<br/>{total_device_count} total devices</p>
</div>
""")
""".format(current_device_name=torch.cuda.get_device_name(), current_device_index=torch.cuda.current_device(), total_device_count=torch.cuda.device_count()))
# Hack: Detect the load event on the frontend
# Won't be needed in the next version of gradio
# See the relevant PR: https://github.com/gradio-app/gradio/pull/2108

57
frontend/image_metadata.py Normal file
View File

@ -0,0 +1,57 @@
''' Class to store image generation parameters to be stored as metadata in the image'''
from __future__ import annotations
from dataclasses import dataclass, asdict
from typing import Dict, Optional
from PIL import Image
from PIL.PngImagePlugin import PngInfo
import copy
@dataclass
class ImageMetadata:
prompt: str = None
seed: str = None
width: str = None
height: str = None
steps: str = None
cfg_scale: str = None
normalize_prompt_weights: str = None
denoising_strength: str = None
GFPGAN: str = None
def as_png_info(self) -> PngInfo:
info = PngInfo()
for key, value in self.as_dict().items():
info.add_text(key, value)
return info
def as_dict(self) -> Dict[str, str]:
return {f"SD:{key}": str(value) for key, value in asdict(self).items() if value is not None}
@classmethod
def set_on_image(cls, image: Image, metadata: ImageMetadata) -> None:
''' Sets metadata on image, in both text form and as an ImageMetadata object '''
if metadata:
image.info = metadata.as_dict()
else:
metadata = ImageMetadata()
image.info["ImageMetadata"] = copy.copy(metadata)
@classmethod
def get_from_image(cls, image: Image) -> Optional[ImageMetadata]:
''' Gets metadata from an image, first looking for an ImageMetadata,
then if not found tries to construct one from the info '''
metadata = image.info.get("ImageMetadata", None)
if not metadata:
found_metadata = False
metadata = ImageMetadata()
for key, value in image.info.items():
if key.lower().startswith("sd:"):
key = key[3:]
if f"{key}" in metadata.__dict__:
metadata.__dict__[key] = value
found_metadata = True
if not found_metadata:
metadata = None
if not metadata:
print("Couldn't find metadata on image")
return metadata

221
frontend/job_manager.py
View File

@ -1,7 +1,7 @@
''' Provides simple job management for gradio, allowing viewing and stopping in-progress multi-batch generations '''
from __future__ import annotations
import gradio as gr
from gradio.components import Component, Gallery
from gradio.components import Component, Gallery, Slider
from threading import Event, Timer
from typing import Callable, List, Dict, Tuple, Optional, Any
from dataclasses import dataclass, field
@ -9,6 +9,7 @@ from functools import partial
from PIL.Image import Image
import uuid
import traceback
import time
@dataclass(eq=True, frozen=True)
@ -30,9 +31,21 @@ class JobInfo:
session_key: str
job_token: Optional[int] = None
images: List[Image] = field(default_factory=list)
active_image: Image = None
rec_steps_enabled: bool = False
rec_steps_imgs: List[Image] = field(default_factory=list)
rec_steps_intrvl: int = None
rec_steps_to_gallery: bool = False
rec_steps_to_file: bool = False
should_stop: Event = field(default_factory=Event)
refresh_active_image_requested: Event = field(default_factory=Event)
refresh_active_image_done: Event = field(default_factory=Event)
stop_cur_iter: Event = field(default_factory=Event)
active_iteration_cnt: int = field(default_factory=int)
job_status: str = field(default_factory=str)
finished: bool = False
started: bool = False
timestamp: float = None
removed_output_idxs: List[int] = field(default_factory=list)
@ -76,7 +89,7 @@ class JobManagerUi:
'''
return self._job_manager._wrap_func(
func=func, inputs=inputs, outputs=outputs,
refresh_btn=self._refresh_btn, stop_btn=self._stop_btn, status_text=self._status_text
job_ui=self
)
_refresh_btn: gr.Button
@ -84,10 +97,19 @@ class JobManagerUi:
_status_text: gr.Textbox
_stop_all_session_btn: gr.Button
_free_done_sessions_btn: gr.Button
_active_image: gr.Image
_active_image_stop_btn: gr.Button
_active_image_refresh_btn: gr.Button
_rec_steps_intrvl_sldr: gr.Slider
_rec_steps_checkbox: gr.Checkbox
_save_rec_steps_to_gallery_chkbx: gr.Checkbox
_save_rec_steps_to_file_chkbx: gr.Checkbox
_job_manager: JobManager
class JobManager:
JOB_MAX_START_TIME = 5.0 # How long can a job be stuck 'starting' before assuming it isn't running
def __init__(self, max_jobs: int):
self._max_jobs: int = max_jobs
self._avail_job_tokens: List[Any] = list(range(max_jobs))
@ -102,11 +124,23 @@ class JobManager:
'''
assert gr.context.Context.block is not None, "draw_gradio_ui must be called within a 'gr.Blocks' 'with' context"
with gr.Tabs():
with gr.TabItem("Current Session"):
with gr.TabItem("Job Controls"):
with gr.Row():
stop_btn = gr.Button("Stop", elem_id="stop", variant="secondary")
refresh_btn = gr.Button("Refresh", elem_id="refresh", variant="secondary")
stop_btn = gr.Button("Stop All Batches", elem_id="stop", variant="secondary")
refresh_btn = gr.Button("Refresh Finished Batches", elem_id="refresh", variant="secondary")
status_text = gr.Textbox(placeholder="Job Status", interactive=False, show_label=False)
with gr.Row():
active_image_stop_btn = gr.Button("Skip Active Batch", variant="secondary")
active_image_refresh_btn = gr.Button("View Batch Progress", variant="secondary")
active_image = gr.Image(type="pil", interactive=False, visible=False, elem_id="active_iteration_image")
with gr.TabItem("Batch Progress Settings"):
with gr.Row():
record_steps_checkbox = gr.Checkbox(value=False, label="Enable Batch Progress Grid")
record_steps_interval_slider = gr.Slider(
value=3, label="Record Interval (steps)", minimum=1, maximum=25, step=1)
with gr.Row() as record_steps_box:
steps_to_gallery_checkbox = gr.Checkbox(value=False, label="Save Progress Grid to Gallery")
steps_to_file_checkbox = gr.Checkbox(value=False, label="Save Progress Grid to File")
with gr.TabItem("Maintenance"):
with gr.Row():
gr.Markdown(
@ -118,9 +152,15 @@ class JobManager:
free_done_sessions_btn = gr.Button(
"Clear Finished Jobs", elem_id="clear_finished", variant="secondary"
)
return JobManagerUi(_refresh_btn=refresh_btn, _stop_btn=stop_btn, _status_text=status_text,
_stop_all_session_btn=stop_all_sessions_btn, _free_done_sessions_btn=free_done_sessions_btn,
_job_manager=self)
_active_image=active_image, _active_image_stop_btn=active_image_stop_btn,
_active_image_refresh_btn=active_image_refresh_btn,
_rec_steps_checkbox=record_steps_checkbox,
_save_rec_steps_to_gallery_chkbx=steps_to_gallery_checkbox,
_save_rec_steps_to_file_chkbx=steps_to_file_checkbox,
_rec_steps_intrvl_sldr=record_steps_interval_slider, _job_manager=self)
def clear_all_finished_jobs(self):
''' Removes all currently finished jobs, across all sessions.
@ -134,6 +174,7 @@ class JobManager:
for session in self._sessions.values():
for job in session.jobs.values():
job.should_stop.set()
job.stop_cur_iter.set()
def _get_job_token(self, block: bool = False) -> Optional[int]:
''' Attempts to acquire a job token, optionally blocking until available '''
@ -175,6 +216,26 @@ class JobManager:
job_info.should_stop.set()
return "Stopping after current batch finishes"
def _refresh_cur_iter_func(self, func_key: FuncKey, session_key: str) -> List[Component]:
''' Updates information from the active iteration '''
session_info, job_info = self._get_call_info(func_key, session_key)
if job_info is None:
return [None, f"Session {session_key} was not running function {func_key}"]
job_info.refresh_active_image_requested.set()
if job_info.refresh_active_image_done.wait(timeout=20.0):
job_info.refresh_active_image_done.clear()
return [gr.Image.update(value=job_info.active_image, visible=True), f"Sample iteration {job_info.active_iteration_cnt}"]
return [gr.Image.update(visible=False), "Timed out getting image"]
def _stop_cur_iter_func(self, func_key: FuncKey, session_key: str) -> List[Component]:
''' Marks that the active iteration should be stopped'''
session_info, job_info = self._get_call_info(func_key, session_key)
if job_info is None:
return [None, f"Session {session_key} was not running function {func_key}"]
job_info.stop_cur_iter.set()
return [gr.Image.update(visible=False), "Stopping current iteration"]
def _get_call_info(self, func_key: FuncKey, session_key: str) -> Tuple[SessionInfo, JobInfo]:
''' Helper to get the SessionInfo and JobInfo. '''
session_info = self._sessions.get(session_key, None)
@ -207,19 +268,22 @@ class JobManager:
def _pre_call_func(
self, func_key: FuncKey, output_dummy_obj: Component, refresh_btn: gr.Button, stop_btn: gr.Button,
status_text: gr.Textbox, session_key: str) -> List[Component]:
status_text: gr.Textbox, active_image: gr.Image, active_refresh_btn: gr.Button, active_stop_btn: gr.Button,
session_key: str) -> List[Component]:
''' Called when a job is about to start '''
session_info, job_info = self._get_call_info(func_key, session_key)
# If we didn't already get a token then queue up for one
if job_info.job_token is None:
job_info.token = self._get_job_token(block=True)
job_info.job_token = self._get_job_token(block=True)
# Buttons don't seem to update unless value is set on them as well...
return {output_dummy_obj: triggerChangeEvent(),
refresh_btn: gr.Button.update(variant="primary", value=refresh_btn.value),
stop_btn: gr.Button.update(variant="primary", value=stop_btn.value),
status_text: gr.Textbox.update(value="Generation has started. Click 'Refresh' for updates")
status_text: gr.Textbox.update(value="Generation has started. Click 'Refresh' to see finished images, 'View Batch Progress' for active images"),
active_refresh_btn: gr.Button.update(variant="primary", value=active_refresh_btn.value),
active_stop_btn: gr.Button.update(variant="primary", value=active_stop_btn.value),
}
def _call_func(self, func_key: FuncKey, session_key: str) -> List[Component]:
@ -228,12 +292,19 @@ class JobManager:
if session_info is None or job_info is None:
return []
job_info.started = True
try:
if job_info.should_stop.is_set():
raise Exception(f"Job {job_info} requested a stop before execution began")
outputs = job_info.func(*job_info.inputs, job_info=job_info)
except Exception as e:
job_info.job_status = f"Error: {e}"
print(f"Exception processing job {job_info}: {e}\n{traceback.format_exc()}")
outputs = []
raise
finally:
job_info.finished = True
session_info.finished_jobs[func_key] = session_info.jobs.pop(func_key)
self._release_job_token(job_info.job_token)
# Filter the function output for any removed outputs
filtered_output = []
@ -241,11 +312,6 @@ class JobManager:
if idx not in job_info.removed_output_idxs:
filtered_output.append(output)
job_info.finished = True
session_info.finished_jobs[func_key] = session_info.jobs.pop(func_key)
self._release_job_token(job_info.job_token)
# The wrapper added a dummy JSON output. Append a random text string
# to fire the dummy objects 'change' event to notify that the job is done
filtered_output.append(triggerChangeEvent())
@ -254,12 +320,16 @@ class JobManager:
def _post_call_func(
self, func_key: FuncKey, output_dummy_obj: Component, refresh_btn: gr.Button, stop_btn: gr.Button,
status_text: gr.Textbox, session_key: str) -> List[Component]:
status_text: gr.Textbox, active_image: gr.Image, active_refresh_btn: gr.Button, active_stop_btn: gr.Button,
session_key: str) -> List[Component]:
''' Called when a job completes '''
return {output_dummy_obj: triggerChangeEvent(),
refresh_btn: gr.Button.update(variant="secondary", value=refresh_btn.value),
stop_btn: gr.Button.update(variant="secondary", value=stop_btn.value),
status_text: gr.Textbox.update(value="Generation has finished!")
status_text: gr.Textbox.update(value="Generation has finished!"),
active_refresh_btn: gr.Button.update(variant="secondary", value=active_refresh_btn.value),
active_stop_btn: gr.Button.update(variant="secondary", value=active_stop_btn.value),
active_image: gr.Image.update(visible=False)
}
def _update_gallery_event(self, func_key: FuncKey, session_key: str) -> List[Component]:
@ -270,21 +340,17 @@ class JobManager:
if session_info is None or job_info is None:
return []
if job_info.finished:
session_info.finished_jobs.pop(func_key)
return job_info.images
def _wrap_func(
self, func: Callable, inputs: List[Component], outputs: List[Component],
refresh_btn: gr.Button = None, stop_btn: gr.Button = None,
status_text: Optional[gr.Textbox] = None) -> Tuple[Callable, List[Component]]:
def _wrap_func(self, func: Callable, inputs: List[Component],
outputs: List[Component],
job_ui: JobManagerUi) -> Tuple[Callable, List[Component]]:
''' handles JobManageUI's wrap_func'''
assert gr.context.Context.block is not None, "wrap_func must be called within a 'gr.Blocks' 'with' context"
# Create a unique key for this job
func_key = FuncKey(job_id=uuid.uuid4(), func=func)
func_key = FuncKey(job_id=uuid.uuid4().hex, func=func)
# Create a unique session key (next gradio release can use gr.State, see https://gradio.app/state_in_blocks/)
if self._session_key is None:
@ -302,31 +368,59 @@ class JobManager:
del outputs[idx]
break
# Add the session key to the inputs
inputs += [self._session_key]
# Create dummy objects
update_gallery_obj = gr.JSON(visible=False, elem_id="JobManagerDummyObject")
update_gallery_obj.change(
partial(self._update_gallery_event, func_key),
[self._session_key],
[gallery_comp]
[gallery_comp],
queue=False
)
if refresh_btn:
refresh_btn.variant = 'secondary'
refresh_btn.click(
if job_ui._refresh_btn:
job_ui._refresh_btn.variant = 'secondary'
job_ui._refresh_btn.click(
partial(self._refresh_func, func_key),
[self._session_key],
[update_gallery_obj, status_text]
[update_gallery_obj, job_ui._status_text],
queue=False
)
if stop_btn:
stop_btn.variant = 'secondary'
stop_btn.click(
if job_ui._stop_btn:
job_ui._stop_btn.variant = 'secondary'
job_ui._stop_btn.click(
partial(self._stop_wrapped_func, func_key),
[self._session_key],
[status_text]
[job_ui._status_text],
queue=False
)
if job_ui._active_image and job_ui._active_image_refresh_btn:
job_ui._active_image_refresh_btn.click(
partial(self._refresh_cur_iter_func, func_key),
[self._session_key],
[job_ui._active_image, job_ui._status_text],
queue=False
)
if job_ui._active_image_stop_btn:
job_ui._active_image_stop_btn.click(
partial(self._stop_cur_iter_func, func_key),
[self._session_key],
[job_ui._active_image, job_ui._status_text],
queue=False
)
if job_ui._stop_all_session_btn:
job_ui._stop_all_session_btn.click(
self.stop_all_jobs, [], [],
queue=False
)
if job_ui._free_done_sessions_btn:
job_ui._free_done_sessions_btn.click(
self.clear_all_finished_jobs, [], [],
queue=False
)
# (ab)use gr.JSON to forward events.
@ -343,7 +437,8 @@ class JobManager:
# Since some parameters are optional it makes sense to use the 'dict' return value type, which requires
# the Component as a key... so group together the UI components that the event listeners are going to update
# to make it easy to append to function calls and outputs
job_ui_params = [refresh_btn, stop_btn, status_text]
job_ui_params = [job_ui._refresh_btn, job_ui._stop_btn, job_ui._status_text,
job_ui._active_image, job_ui._active_image_refresh_btn, job_ui._active_image_stop_btn]
job_ui_outputs = [comp for comp in job_ui_params if comp is not None]
# Here a chain is constructed that will make a 'pre' call, a 'run' call, and a 'post' call,
@ -352,44 +447,70 @@ class JobManager:
post_call_dummyobj.change(
partial(self._post_call_func, func_key, update_gallery_obj, *job_ui_params),
[self._session_key],
[update_gallery_obj] + job_ui_outputs
[update_gallery_obj] + job_ui_outputs,
queue=False
)
call_dummyobj = gr.JSON(visible=False, elem_id="JobManagerDummyObject_runCall")
call_dummyobj.change(
partial(self._call_func, func_key),
[self._session_key],
outputs + [post_call_dummyobj]
outputs + [post_call_dummyobj],
queue=False
)
pre_call_dummyobj = gr.JSON(visible=False, elem_id="JobManagerDummyObject_preCall")
pre_call_dummyobj.change(
partial(self._pre_call_func, func_key, call_dummyobj, *job_ui_params),
[self._session_key],
[call_dummyobj] + job_ui_outputs
[call_dummyobj] + job_ui_outputs,
queue=False
)
# Now replace the original function with one that creates a JobInfo and triggers the dummy obj
# Add any components that we want the runtime values for
added_inputs = [self._session_key, job_ui._rec_steps_checkbox, job_ui._save_rec_steps_to_gallery_chkbx,
job_ui._save_rec_steps_to_file_chkbx, job_ui._rec_steps_intrvl_sldr]
def wrapped_func(*inputs):
session_key = inputs[-1]
inputs = inputs[:-1]
# Now replace the original function with one that creates a JobInfo and triggers the dummy obj
def wrapped_func(*wrapped_inputs):
# Remove the added_inputs (pop opposite order of list)
wrapped_inputs = list(wrapped_inputs)
rec_steps_interval: int = wrapped_inputs.pop()
save_rec_steps_file: bool = wrapped_inputs.pop()
save_rec_steps_grid: bool = wrapped_inputs.pop()
record_steps_enabled: bool = wrapped_inputs.pop()
session_key: str = wrapped_inputs.pop()
job_inputs = tuple(wrapped_inputs)
# Get or create a session for this key
session_info = self._sessions.setdefault(session_key, SessionInfo())
# Is this session already running this job?
if func_key in session_info.jobs:
return {status_text: "This session is already running that function!"}
job_info = session_info.jobs[func_key]
# If the job seems stuck in 'starting' then go ahead and toss it
if not job_info.started and time.time() > job_info.timestamp + JobManager.JOB_MAX_START_TIME:
job_info.should_stop.set()
job_info.stop_cur_iter.set()
session_info.jobs.pop(func_key)
return {job_ui._status_text: "Canceled possibly hung job. Try again"}
return {job_ui._status_text: "This session is already running that function!"}
# Is this a new run of a previously finished job? Clear old info
if func_key in session_info.finished_jobs:
session_info.finished_jobs.pop(func_key)
job_token = self._get_job_token(block=False)
job = JobInfo(inputs=inputs, func=func, removed_output_idxs=removed_idxs, session_key=session_key,
job_token=job_token)
job = JobInfo(
inputs=job_inputs, func=func, removed_output_idxs=removed_idxs, session_key=session_key,
job_token=job_token, rec_steps_enabled=record_steps_enabled, rec_steps_intrvl=rec_steps_interval,
rec_steps_to_gallery=save_rec_steps_grid, rec_steps_to_file=save_rec_steps_file, timestamp=time.time())
session_info.jobs[func_key] = job
ret = {pre_call_dummyobj: triggerChangeEvent()}
if job_token is None:
ret[status_text] = "Job is queued"
ret[job_ui._status_text] = "Job is queued"
return ret
return wrapped_func, inputs, [pre_call_dummyobj, status_text]
return wrapped_func, inputs + added_inputs, [pre_call_dummyobj, job_ui._status_text]

4
frontend/ui_functions.py
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@ -9,10 +9,10 @@ import re
def change_image_editor_mode(choice, cropped_image, masked_image, resize_mode, width, height):
if choice == "Mask":
update_image_result = update_image_mask(cropped_image, resize_mode, width, height)
return [gr.update(visible=False), update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)]
return [gr.update(visible=False), update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)]
update_image_result = update_image_mask(masked_image["image"] if masked_image is not None else None, resize_mode, width, height)
return [update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
return [update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)]
def update_image_mask(cropped_image, resize_mode, width, height):
resized_cropped_image = resize_image(resize_mode, cropped_image, width, height) if cropped_image else None

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105
ldm/modules/attention.py
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@ -7,6 +7,8 @@ from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
import psutil
def exists(val):
return val is not None
@ -167,30 +169,98 @@ class CrossAttention(nn.Module):
nn.Dropout(dropout)
)
if torch.cuda.is_available():
self.einsum_op = self.einsum_op_cuda
else:
self.mem_total = psutil.virtual_memory().total / (1024**3)
self.einsum_op = self.einsum_op_mps_v1 if self.mem_total >= 32 else self.einsum_op_mps_v2
def einsum_op_compvis(self, q, k, v, r1):
s1 = einsum('b i d, b j d -> b i j', q, k) * self.scale # faster
s2 = s1.softmax(dim=-1, dtype=q.dtype)
del s1
r1 = einsum('b i j, b j d -> b i d', s2, v)
del s2
return r1
def einsum_op_mps_v1(self, q, k, v, r1):
if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
r1 = self.einsum_op_compvis(q, k, v, r1)
else:
slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
return r1
def einsum_op_mps_v2(self, q, k, v, r1):
if self.mem_total >= 8 and q.shape[1] <= 4096:
r1 = self.einsum_op_compvis(q, k, v, r1)
else:
slice_size = 1
for i in range(0, q.shape[0], slice_size):
end = min(q.shape[0], i + slice_size)
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
s1 *= self.scale
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
del s1
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
del s2
return r1
def einsum_op_cuda(self, q, k, v, r1):
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * 4
mem_required = tensor_size * 2.5
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
if steps > 64:
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = min(q.shape[1], i + slice_size)
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
return r1
def forward(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
del x
k = self.to_k(context)
v = self.to_v(context)
del context
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
out = einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return self.to_out(out)
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
r1 = self.einsum_op(q, k, v, r1)
del q, k, v
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return self.to_out(r2)
class BasicTransformerBlock(nn.Module):
@ -209,9 +279,10 @@ class BasicTransformerBlock(nn.Module):
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
def _forward(self, x, context=None):
x = self.attn1(self.norm1(x)) + x
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
x = x.contiguous() if x.device.type == 'mps' else x
x += self.attn1(self.norm1(x))
x += self.attn2(self.norm2(x), context=context)
x += self.ff(self.norm3(x))
return x

145
ldm/modules/diffusionmodules/model.py
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@ -1,4 +1,5 @@
# pytorch_diffusion + derived encoder decoder
import gc
import math
import torch
import torch.nn as nn
@ -119,18 +120,30 @@ class ResnetBlock(nn.Module):
padding=0)
def forward(self, x, temb):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
h1 = x
h2 = self.norm1(h1)
del h1
h3 = nonlinearity(h2)
del h2
h4 = self.conv1(h3)
del h3
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
h4 = h4 + self.temb_proj(nonlinearity(temb))[:,:,None,None]
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
h5 = self.norm2(h4)
del h4
h6 = nonlinearity(h5)
del h5
h7 = self.dropout(h6)
del h6
h8 = self.conv2(h7)
del h7
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
@ -138,7 +151,7 @@ class ResnetBlock(nn.Module):
else:
x = self.nin_shortcut(x)
return x+h
return x + h8
class LinAttnBlock(LinearAttention):
@ -178,28 +191,65 @@ class AttnBlock(nn.Module):
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
q1 = self.q(h_)
k1 = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
b, c, h, w = q1.shape
# attend to values
v = v.reshape(b,c,h*w)
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b,c,h,w)
q2 = q1.reshape(b, c, h*w)
del q1
h_ = self.proj_out(h_)
q = q2.permute(0, 2, 1) # b,hw,c
del q2
return x+h_
k = k1.reshape(b, c, h*w) # b,c,hw
del k1
h_ = torch.zeros_like(k, device=q.device)
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * 4
mem_required = tensor_size * 2.5
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w2 = w1 * (int(c)**(-0.5))
del w1
w3 = torch.nn.functional.softmax(w2, dim=2)
del w2
# attend to values
v1 = v.reshape(b, c, h*w)
w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
del w3
h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
del v1, w4
h2 = h_.reshape(b, c, h, w)
del h_
h3 = self.proj_out(h2)
del h2
h3 += x
return h3
def make_attn(in_channels, attn_type="vanilla"):
@ -540,31 +590,54 @@ class Decoder(nn.Module):
temb = None
# z to block_in
h = self.conv_in(z)
h1 = self.conv_in(z)
# middle
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
h2 = self.mid.block_1(h1, temb)
del h1
h3 = self.mid.attn_1(h2)
del h2
h = self.mid.block_2(h3, temb)
del h3
# prepare for up sampling
gc.collect()
torch.cuda.empty_cache()
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](h, temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
t = h
h = self.up[i_level].attn[i_block](t)
del t
if i_level != 0:
h = self.up[i_level].upsample(h)
t = h
h = self.up[i_level].upsample(t)
del t
# end
if self.give_pre_end:
return h
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
h1 = self.norm_out(h)
del h
h2 = nonlinearity(h1)
del h1
h = self.conv_out(h2)
del h2
if self.tanh_out:
h = torch.tanh(h)
t = h
h = torch.tanh(t)
del t
return h

5
ldm/modules/diffusionmodules/util.py
View File

@ -54,7 +54,8 @@ def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timestep
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
# add one to get the final alpha values right (the ones from first scale to data during sampling)
steps_out = ddim_timesteps + 1
# steps_out = ddim_timesteps + 1 # removed due to some issues when reaching 1000
steps_out = np.where(ddim_timesteps != 999, ddim_timesteps+1, ddim_timesteps)
if verbose:
print(f'Selected timesteps for ddim sampler: {steps_out}')
return steps_out
@ -264,4 +265,4 @@ class HybridConditioner(nn.Module):
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
return repeat_noise() if repeat else noise()

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scripts/DeforumStableDiffusion.py Normal file
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46
scripts/ModelManager.py Normal file
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@ -0,0 +1,46 @@
# base webui import and utils.
from webui_streamlit import st
from sd_utils import *
# streamlit imports
#other imports
import pandas as pd
from io import StringIO
# Temp imports
# end of imports
#---------------------------------------------------------------------------------------------------------------
def layout():
#search = st.text_input(label="Search", placeholder="Type the name of the model you want to search for.", help="")
csvString = f"""
,Stable Diffusion v1.4 , ./models/ldm/stable-diffusion-v1 , https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media
,GFPGAN v1.3 , ./src/gfpgan/experiments/pretrained_models , https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth
,RealESRGAN_x4plus , ./src/realesrgan/experiments/pretrained_models , https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth
,RealESRGAN_x4plus_anime_6B , ./src/realesrgan/experiments/pretrained_models , https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth
,Waifu Diffusion v1.2 , ./models/custom , http://wd.links.sd:8880/wd-v1-2-full-ema.ckpt
,TrinArt Stable Diffusion v2 , ./models/custom , https://huggingface.co/naclbit/trinart_stable_diffusion_v2/resolve/main/trinart2_step115000.ckpt
,Stable Diffusion Concept Library , ./models/customsd-concepts-library , https://github.com/sd-webui/sd-concepts-library
"""
colms = st.columns((1, 3, 5, 5))
columns = ["№",'Model Name','Save Location','Download Link']
# Convert String into StringIO
csvStringIO = StringIO(csvString)
df = pd.read_csv(csvStringIO, sep=",", header=None, names=columns)
for col, field_name in zip(colms, columns):
# table header
col.write(field_name)
for x, model_name in enumerate(df["Model Name"]):
col1, col2, col3, col4 = st.columns((1, 3, 4, 6))
col1.write(x) # index
col2.write(df['Model Name'][x])
col3.write(df['Save Location'][x])
col4.write(df['Download Link'][x])

5
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from webui_streamlit import st
# The global settings section will be moved to the Settings page.
#with st.expander("Global Settings:"):
st.write("Global Settings:")

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# base webui import and utils.
from webui_streamlit import st
from sd_utils import *
# streamlit imports
#other imports
# Temp imports
# end of imports
#---------------------------------------------------------------------------------------------------------------
import os
from PIL import Image
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
class plugin_info():
plugname = "home"
description = "Home"
isTab = True
displayPriority = 0
def getLatestGeneratedImagesFromPath():
#get the latest images from the generated images folder
#get the path to the generated images folder
generatedImagesPath = os.path.join(os.getcwd(),'outputs')
#get all the files from the folders and subfolders
files = []
#get the latest 10 images from the output folder without walking the subfolders
for r, d, f in os.walk(generatedImagesPath):
for file in f:
if '.png' in file:
files.append(os.path.join(r, file))
#sort the files by date
files.sort(reverse=True, key=os.path.getmtime)
latest = files[:90]
latest.reverse()
# reverse the list so the latest images are first and truncate to
# a reasonable number of images, 10 pages worth
return [Image.open(f) for f in latest]
def get_images_from_lexica():
#scrape images from lexica.art
#get the html from the page
#get the html with cookies and javascript
apiEndpoint = r'https://lexica.art/api/trpc/prompts.infinitePrompts?batch=1&input=%7B%220%22%3A%7B%22json%22%3A%7B%22limit%22%3A10%2C%22text%22%3A%22%22%2C%22cursor%22%3A10%7D%7D%7D'
#REST API call
#
from requests_html import HTMLSession
session = HTMLSession()
response = session.get(apiEndpoint)
#req = requests.Session()
#req.headers['user-agent'] = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'
#response = req.get(apiEndpoint)
print(response.status_code)
print(response.text)
#get the json from the response
#json = response.json()
#get the prompts from the json
print(response)
#session = requests.Session()
#parseEndpointJson = session.get(apiEndpoint,headers=headers,verify=False)
#print(parseEndpointJson)
#print('test2')
#page = requests.get("https://lexica.art/", headers={'User-Agent': 'Mozilla/5.0'})
#parse the html
#soup = BeautifulSoup(page.content, 'html.parser')
#find all the images
#print(soup)
#images = soup.find_all('alt-image')
#create a list to store the image urls
image_urls = []
#loop through the images
for image in images:
#get the url
image_url = image['src']
#add it to the list
image_urls.append('http://www.lexica.art/'+image_url)
#return the list
print(image_urls)
return image_urls
def layout():
#streamlit home page layout
#center the title
st.markdown("<h1 style='text-align: center; color: white;'>Welcome, let's make some 🎨</h1>", unsafe_allow_html=True)
#make a gallery of images
#st.markdown("<h2 style='text-align: center; color: white;'>Gallery</h2>", unsafe_allow_html=True)
#create a gallery of images using columns
#col1, col2, col3 = st.columns(3)
#load the images
#create 3 columns
# create a tab for the gallery
#st.markdown("<h2 style='text-align: center; color: white;'>Gallery</h2>", unsafe_allow_html=True)
#st.markdown("<h2 style='text-align: center; color: white;'>Gallery</h2>", unsafe_allow_html=True)
history_tab, discover_tabs = st.tabs(["History","Discover"])
latestImages = getLatestGeneratedImagesFromPath()
st.session_state['latestImages'] = latestImages
with history_tab:
##---------------------------------------------------------
## image slideshow test
## Number of entries per screen
#slideshow_N = 9
#slideshow_page_number = 0
#slideshow_last_page = len(latestImages) // slideshow_N
## Add a next button and a previous button
#slideshow_prev, slideshow_image_col , slideshow_next = st.columns([1, 10, 1])
#with slideshow_image_col:
#slideshow_image = st.empty()
#slideshow_image.image(st.session_state['latestImages'][0])
#current_image = 0
#if slideshow_next.button("Next", key=1):
##print (current_image+1)
#current_image = current_image+1
#slideshow_image.image(st.session_state['latestImages'][current_image+1])
#if slideshow_prev.button("Previous", key=0):
##print ([current_image-1])
#current_image = current_image-1
#slideshow_image.image(st.session_state['latestImages'][current_image - 1])
#---------------------------------------------------------
# image gallery
# Number of entries per screen
gallery_N = 9
if not "galleryPage" in st.session_state:
st.session_state["galleryPage"] = 0
gallery_last_page = len(latestImages) // gallery_N
# Add a next button and a previous button
gallery_prev, gallery_refresh, gallery_pagination , gallery_next = st.columns([2, 2, 8, 1])
# the pagination doesnt work for now so its better to disable the buttons.
if gallery_refresh.button("Refresh", key=4):
st.session_state["galleryPage"] = 0
if gallery_next.button("Next", key=3):
if st.session_state["galleryPage"] + 1 > gallery_last_page:
st.session_state["galleryPage"] = 0
else:
st.session_state["galleryPage"] += 1
if gallery_prev.button("Previous", key=2):
if st.session_state["galleryPage"] - 1 < 0:
st.session_state["galleryPage"] = gallery_last_page
else:
st.session_state["galleryPage"] -= 1
print(st.session_state["galleryPage"])
# Get start and end indices of the next page of the dataframe
gallery_start_idx = st.session_state["galleryPage"] * gallery_N
gallery_end_idx = (1 + st.session_state["galleryPage"]) * gallery_N
#---------------------------------------------------------
placeholder = st.empty()
#populate the 3 images per column
with placeholder.container():
col1, col2, col3 = st.columns(3)
col1_cont = st.container()
col2_cont = st.container()
col3_cont = st.container()
print (len(st.session_state['latestImages']))
images = list(reversed(st.session_state['latestImages']))[gallery_start_idx:(gallery_start_idx+gallery_N)]
with col1_cont:
with col1:
[st.image(images[index]) for index in [0, 3, 6] if index < len(images)]
with col2_cont:
with col2:
[st.image(images[index]) for index in [1, 4, 7] if index < len(images)]
with col3_cont:
with col3:
[st.image(images[index]) for index in [2, 5, 8] if index < len(images)]
st.session_state['historyTab'] = [history_tab,col1,col2,col3,placeholder,col1_cont,col2_cont,col3_cont]
with discover_tabs:
st.markdown("<h1 style='text-align: center; color: white;'>Soon :)</h1>", unsafe_allow_html=True)
#display the images
#add a button to the gallery
#st.markdown("<h2 style='text-align: center; color: white;'>Try it out</h2>", unsafe_allow_html=True)
#create a button to the gallery
#if st.button("Try it out"):
#if the button is clicked, go to the gallery
#st.experimental_rerun()

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# base webui import and utils.
from webui_streamlit import st
from sd_utils import *
# streamlit imports
from streamlit import StopException
#other imports
import cv2
from PIL import Image, ImageOps
import torch
import k_diffusion as K
import numpy as np
import time
import torch
import skimage
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
# Temp imports
# end of imports
#---------------------------------------------------------------------------------------------------------------
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None, mask_mode: int = 0, mask_blur_strength: int = 3,
mask_restore: bool = False, ddim_steps: int = 50, sampler_name: str = 'DDIM',
n_iter: int = 1, cfg_scale: float = 7.5, denoising_strength: float = 0.8,
seed: int = -1, noise_mode: int = 0, find_noise_steps: str = "", height: int = 512, width: int = 512, resize_mode: int = 0, fp = None,
variant_amount: float = None, variant_seed: int = None, ddim_eta:float = 0.0,
write_info_files:bool = True, RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B",
separate_prompts:bool = False, normalize_prompt_weights:bool = True,
save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
save_as_jpg: bool = True, use_GFPGAN: bool = True, use_RealESRGAN: bool = True, loopback: bool = False,
random_seed_loopback: bool = False
):
outpath = st.session_state['defaults'].general.outdir_img2img or st.session_state['defaults'].general.outdir or "outputs/img2img-samples"
#err = False
#loopback = False
#skip_save = False
seed = seed_to_int(seed)
batch_size = 1
#prompt_matrix = 0
#normalize_prompt_weights = 1 in toggles
#loopback = 2 in toggles
#random_seed_loopback = 3 in toggles
#skip_save = 4 not in toggles
#save_grid = 5 in toggles
#sort_samples = 6 in toggles
#write_info_files = 7 in toggles
#write_sample_info_to_log_file = 8 in toggles
#jpg_sample = 9 in toggles
#use_GFPGAN = 10 in toggles
#use_RealESRGAN = 11 in toggles
if sampler_name == 'PLMS':
sampler = PLMSSampler(st.session_state["model"])
elif sampler_name == 'DDIM':
sampler = DDIMSampler(st.session_state["model"])
elif sampler_name == 'k_dpm_2_a':
sampler = KDiffusionSampler(st.session_state["model"],'dpm_2_ancestral')
elif sampler_name == 'k_dpm_2':
sampler = KDiffusionSampler(st.session_state["model"],'dpm_2')
elif sampler_name == 'k_euler_a':
sampler = KDiffusionSampler(st.session_state["model"],'euler_ancestral')
elif sampler_name == 'k_euler':
sampler = KDiffusionSampler(st.session_state["model"],'euler')
elif sampler_name == 'k_heun':
sampler = KDiffusionSampler(st.session_state["model"],'heun')
elif sampler_name == 'k_lms':
sampler = KDiffusionSampler(st.session_state["model"],'lms')
else:
raise Exception("Unknown sampler: " + sampler_name)
def process_init_mask(init_mask: Image):
if init_mask.mode == "RGBA":
init_mask = init_mask.convert('RGBA')
background = Image.new('RGBA', init_mask.size, (0, 0, 0))
init_mask = Image.alpha_composite(background, init_mask)
init_mask = init_mask.convert('RGB')
return init_mask
init_img = init_info
init_mask = None
if mask_mode == 0:
if init_info_mask:
init_mask = process_init_mask(init_info_mask)
elif mask_mode == 1:
if init_info_mask:
init_mask = process_init_mask(init_info_mask)
init_mask = ImageOps.invert(init_mask)
elif mask_mode == 2:
init_img_transparency = init_img.split()[-1].convert('L')#.point(lambda x: 255 if x > 0 else 0, mode='1')
init_mask = init_img_transparency
init_mask = init_mask.convert("RGB")
init_mask = resize_image(resize_mode, init_mask, width, height)
init_mask = init_mask.convert("RGB")
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(denoising_strength * ddim_steps)
if init_mask is not None and (noise_mode == 2 or noise_mode == 3) and init_img is not None:
noise_q = 0.99
color_variation = 0.0
mask_blend_factor = 1.0
np_init = (np.asarray(init_img.convert("RGB"))/255.0).astype(np.float64) # annoyingly complex mask fixing
np_mask_rgb = 1. - (np.asarray(ImageOps.invert(init_mask).convert("RGB"))/255.0).astype(np.float64)
np_mask_rgb -= np.min(np_mask_rgb)
np_mask_rgb /= np.max(np_mask_rgb)
np_mask_rgb = 1. - np_mask_rgb
np_mask_rgb_hardened = 1. - (np_mask_rgb < 0.99).astype(np.float64)
blurred = skimage.filters.gaussian(np_mask_rgb_hardened[:], sigma=16., channel_axis=2, truncate=32.)
blurred2 = skimage.filters.gaussian(np_mask_rgb_hardened[:], sigma=16., channel_axis=2, truncate=32.)
#np_mask_rgb_dilated = np_mask_rgb + blurred # fixup mask todo: derive magic constants
#np_mask_rgb = np_mask_rgb + blurred
np_mask_rgb_dilated = np.clip((np_mask_rgb + blurred2) * 0.7071, 0., 1.)
np_mask_rgb = np.clip((np_mask_rgb + blurred) * 0.7071, 0., 1.)
noise_rgb = get_matched_noise(np_init, np_mask_rgb, noise_q, color_variation)
blend_mask_rgb = np.clip(np_mask_rgb_dilated,0.,1.) ** (mask_blend_factor)
noised = noise_rgb[:]
blend_mask_rgb **= (2.)
noised = np_init[:] * (1. - blend_mask_rgb) + noised * blend_mask_rgb
np_mask_grey = np.sum(np_mask_rgb, axis=2)/3.
ref_mask = np_mask_grey < 1e-3
all_mask = np.ones((height, width), dtype=bool)
noised[all_mask,:] = skimage.exposure.match_histograms(noised[all_mask,:]**1., noised[ref_mask,:], channel_axis=1)
init_img = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
st.session_state["editor_image"].image(init_img) # debug
def init():
image = init_img.convert('RGB')
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
mask_channel = None
if init_mask:
alpha = resize_image(resize_mode, init_mask, width // 8, height // 8)
mask_channel = alpha.split()[-1]
mask = None
if mask_channel is not None:
mask = np.array(mask_channel).astype(np.float32) / 255.0
mask = (1 - mask)
mask = np.tile(mask, (4, 1, 1))
mask = mask[None].transpose(0, 1, 2, 3)
mask = torch.from_numpy(mask).to(st.session_state["device"])
if st.session_state['defaults'].general.optimized:
st.session_state.modelFS.to(st.session_state["device"] )
init_image = 2. * image - 1.
init_image = init_image.to(st.session_state["device"])
init_latent = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).get_first_stage_encoding((st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
if st.session_state['defaults'].general.optimized:
mem = torch.cuda.memory_allocated()/1e6
st.session_state.modelFS.to("cpu")
while(torch.cuda.memory_allocated()/1e6 >= mem):
time.sleep(1)
return init_latent, mask,
def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
t_enc_steps = t_enc
obliterate = False
if ddim_steps == t_enc_steps:
t_enc_steps = t_enc_steps - 1
obliterate = True
if sampler_name != 'DDIM':
x0, z_mask = init_data
sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
noise = x * sigmas[ddim_steps - t_enc_steps - 1]
xi = x0 + noise
# Obliterate masked image
if z_mask is not None and obliterate:
random = torch.randn(z_mask.shape, device=xi.device)
xi = (z_mask * noise) + ((1-z_mask) * xi)
sigma_sched = sigmas[ddim_steps - t_enc_steps - 1:]
model_wrap_cfg = CFGMaskedDenoiser(sampler.model_wrap)
samples_ddim = K.sampling.__dict__[f'sample_{sampler.get_sampler_name()}'](model_wrap_cfg, xi, sigma_sched,
extra_args={'cond': conditioning, 'uncond': unconditional_conditioning,
'cond_scale': cfg_scale, 'mask': z_mask, 'x0': x0, 'xi': xi}, disable=False,
callback=generation_callback)
else:
x0, z_mask = init_data
sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=0.0, verbose=False)
z_enc = sampler.stochastic_encode(x0, torch.tensor([t_enc_steps]*batch_size).to(st.session_state["device"] ))
# Obliterate masked image
if z_mask is not None and obliterate:
random = torch.randn(z_mask.shape, device=z_enc.device)
z_enc = (z_mask * random) + ((1-z_mask) * z_enc)
# decode it
samples_ddim = sampler.decode(z_enc, conditioning, t_enc_steps,
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=unconditional_conditioning,
z_mask=z_mask, x0=x0)
return samples_ddim
if loopback:
output_images, info = None, None
history = []
initial_seed = None
do_color_correction = False
try:
from skimage import exposure
do_color_correction = True
except:
print("Install scikit-image to perform color correction on loopback")
for i in range(n_iter):
if do_color_correction and i == 0:
correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB)
output_images, seed, info, stats = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_name=sampler_name,
save_grid=save_grid,
batch_size=1,
n_iter=1,
steps=ddim_steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=separate_prompts,
use_GFPGAN=use_GFPGAN,
use_RealESRGAN=use_RealESRGAN, # Forcefully disable upscaling when using loopback
realesrgan_model_name=RealESRGAN_model,
normalize_prompt_weights=normalize_prompt_weights,
save_individual_images=save_individual_images,
init_img=init_img,
init_mask=init_mask,
mask_blur_strength=mask_blur_strength,
mask_restore=mask_restore,
denoising_strength=denoising_strength,
noise_mode=noise_mode,
find_noise_steps=find_noise_steps,
resize_mode=resize_mode,
uses_loopback=loopback,
uses_random_seed_loopback=random_seed_loopback,
sort_samples=group_by_prompt,
write_info_files=write_info_files,
jpg_sample=save_as_jpg
)
if initial_seed is None:
initial_seed = seed
input_image = init_img
init_img = output_images[0]
if do_color_correction and correction_target is not None:
init_img = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
cv2.cvtColor(
np.asarray(init_img),
cv2.COLOR_RGB2LAB
),
correction_target,
channel_axis=2
), cv2.COLOR_LAB2RGB).astype("uint8"))
if mask_restore is True and init_mask is not None:
color_mask = init_mask.filter(ImageFilter.GaussianBlur(mask_blur_strength))
color_mask = color_mask.convert('L')
source_image = input_image.convert('RGB')
target_image = init_img.convert('RGB')
init_img = Image.composite(source_image, target_image, color_mask)
if not random_seed_loopback:
seed = seed + 1
else:
seed = seed_to_int(None)
denoising_strength = max(denoising_strength * 0.95, 0.1)
history.append(init_img)
output_images = history
seed = initial_seed
else:
output_images, seed, info, stats = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_name=sampler_name,
save_grid=save_grid,
batch_size=batch_size,
n_iter=n_iter,
steps=ddim_steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=separate_prompts,
use_GFPGAN=use_GFPGAN,
use_RealESRGAN=use_RealESRGAN,
realesrgan_model_name=RealESRGAN_model,
normalize_prompt_weights=normalize_prompt_weights,
save_individual_images=save_individual_images,
init_img=init_img,
init_mask=init_mask,
mask_blur_strength=mask_blur_strength,
denoising_strength=denoising_strength,
noise_mode=noise_mode,
find_noise_steps=find_noise_steps,
mask_restore=mask_restore,
resize_mode=resize_mode,
uses_loopback=loopback,
sort_samples=group_by_prompt,
write_info_files=write_info_files,
jpg_sample=save_as_jpg
)
del sampler
return output_images, seed, info, stats
#
def layout():
with st.form("img2img-inputs"):
st.session_state["generation_mode"] = "img2img"
img2img_input_col, img2img_generate_col = st.columns([10,1])
with img2img_input_col:
#prompt = st.text_area("Input Text","")
prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
img2img_generate_col.write("")
img2img_generate_col.write("")
generate_button = img2img_generate_col.form_submit_button("Generate")
# creating the page layout using columns
col1_img2img_layout, col2_img2img_layout, col3_img2img_layout = st.columns([1,2,2], gap="small")
with col1_img2img_layout:
# If we have custom models available on the "models/custom"
#folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
if st.session_state["CustomModel_available"]:
st.session_state["custom_model"] = st.selectbox("Custom Model:", st.session_state["custom_models"],
index=st.session_state["custom_models"].index(st.session_state['defaults'].general.default_model),
help="Select the model you want to use. This option is only available if you have custom models \
on your 'models/custom' folder. The model name that will be shown here is the same as the name\
the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
else:
st.session_state["custom_model"] = "Stable Diffusion v1.4"
st.session_state["sampling_steps"] = st.slider("Sampling Steps",
value=st.session_state['defaults'].img2img.sampling_steps,
min_value=st.session_state['defaults'].img2img.slider_bounds.sampling.lower,
max_value=st.session_state['defaults'].img2img.slider_bounds.sampling.upper,
step=st.session_state['defaults'].img2img.slider_steps.sampling)
sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
st.session_state["sampler_name"] = st.selectbox("Sampling method",sampler_name_list,
index=sampler_name_list.index(st.session_state['defaults'].img2img.sampler_name), help="Sampling method to use.")
mask_mode_list = ["Mask", "Inverted mask", "Image alpha"]
mask_mode = st.selectbox("Mask Mode", mask_mode_list,
help="Select how you want your image to be masked.\"Mask\" modifies the image where the mask is white.\n\
\"Inverted mask\" modifies the image where the mask is black. \"Image alpha\" modifies the image where the image is transparent."
)
mask_mode = mask_mode_list.index(mask_mode)
width = st.slider("Width:", min_value=64, max_value=1024, value=st.session_state['defaults'].img2img.width, step=64)
height = st.slider("Height:", min_value=64, max_value=1024, value=st.session_state['defaults'].img2img.height, step=64)
seed = st.text_input("Seed:", value=st.session_state['defaults'].img2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
noise_mode_list = ["Seed", "Find Noise", "Matched Noise", "Find+Matched Noise"]
noise_mode = st.selectbox(
"Noise Mode", noise_mode_list,
help=""
)
noise_mode = noise_mode_list.index(noise_mode)
find_noise_steps = st.slider("Find Noise Steps", value=100, min_value=1, max_value=500)
batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].img2img.batch_count, step=1,
help="How many iterations or batches of images to generate in total.")
#
with st.expander("Advanced"):
separate_prompts = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].img2img.separate_prompts,
help="Separate multiple prompts using the `|` character, and get all combinations of them.")
normalize_prompt_weights = st.checkbox("Normalize Prompt Weights.", value=st.session_state['defaults'].img2img.normalize_prompt_weights,
help="Ensure the sum of all weights add up to 1.0")
loopback = st.checkbox("Loopback.", value=st.session_state['defaults'].img2img.loopback, help="Use images from previous batch when creating next batch.")
random_seed_loopback = st.checkbox("Random loopback seed.", value=st.session_state['defaults'].img2img.random_seed_loopback, help="Random loopback seed")
img2img_mask_restore = st.checkbox("Only modify regenerated parts of image",
value=st.session_state['defaults'].img2img.mask_restore,
help="Enable to restore the unmasked parts of the image with the input, may not blend as well but preserves detail")
save_individual_images = st.checkbox("Save individual images.", value=st.session_state['defaults'].img2img.save_individual_images,
help="Save each image generated before any filter or enhancement is applied.")
save_grid = st.checkbox("Save grid",value=st.session_state['defaults'].img2img.save_grid, help="Save a grid with all the images generated into a single image.")
group_by_prompt = st.checkbox("Group results by prompt", value=st.session_state['defaults'].img2img.group_by_prompt,
help="Saves all the images with the same prompt into the same folder. \
When using a prompt matrix each prompt combination will have its own folder.")
write_info_files = st.checkbox("Write Info file", value=st.session_state['defaults'].img2img.write_info_files,
help="Save a file next to the image with informartion about the generation.")
save_as_jpg = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].img2img.save_as_jpg, help="Saves the images as jpg instead of png.")
if st.session_state["GFPGAN_available"]:
use_GFPGAN = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].img2img.use_GFPGAN, help="Uses the GFPGAN model to improve faces after the generation.\
This greatly improve the quality and consistency of faces but uses extra VRAM. Disable if you need the extra VRAM.")
else:
use_GFPGAN = False
if st.session_state["RealESRGAN_available"]:
st.session_state["use_RealESRGAN"] = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].img2img.use_RealESRGAN,
help="Uses the RealESRGAN model to upscale the images after the generation.\
This greatly improve the quality and lets you have high resolution images but uses extra VRAM. Disable if you need the extra VRAM.")
st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", ["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"], index=0)
else:
st.session_state["use_RealESRGAN"] = False
st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].img2img.variant_amount, min_value=0.0, max_value=1.0, step=0.01)
variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].img2img.variant_seed,
help="The seed to use when generating a variant, if left blank a random seed will be generated.")
cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].img2img.cfg_scale, step=0.5,
help="How strongly the image should follow the prompt.")
batch_size = st.slider("Batch size", min_value=1, max_value=100, value=st.session_state['defaults'].img2img.batch_size, step=1,
help="How many images are at once in a batch.\
It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish \
generation as more images are generated at once.\
Default: 1")
st.session_state["denoising_strength"] = st.slider("Denoising Strength:", value=st.session_state['defaults'].img2img.denoising_strength,
min_value=0.01, max_value=1.0, step=0.01)
with st.expander("Preview Settings"):
st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].img2img.update_preview,
help="If enabled the image preview will be updated during the generation instead of at the end. \
You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
By default this is enabled and the frequency is set to 1 step.")
st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].img2img.update_preview_frequency,
help="Frequency in steps at which the the preview image is updated. By default the frequency \
is set to 1 step.")
with col2_img2img_layout:
editor_tab = st.tabs(["Editor"])
editor_image = st.empty()
st.session_state["editor_image"] = editor_image
st.form_submit_button("Refresh")
masked_image_holder = st.empty()
image_holder = st.empty()
uploaded_images = st.file_uploader(
"Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"],
help="Upload an image which will be used for the image to image generation.",
)
if uploaded_images:
image = Image.open(uploaded_images).convert('RGBA')
new_img = image.resize((width, height))
image_holder.image(new_img)
mask_holder = st.empty()
uploaded_masks = st.file_uploader(
"Upload Mask", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"],
help="Upload an mask image which will be used for masking the image to image generation.",
)
if uploaded_masks:
mask = Image.open(uploaded_masks)
if mask.mode == "RGBA":
mask = mask.convert('RGBA')
background = Image.new('RGBA', mask.size, (0, 0, 0))
mask = Image.alpha_composite(background, mask)
mask = mask.resize((width, height))
mask_holder.image(mask)
if uploaded_images and uploaded_masks:
if mask_mode != 2:
final_img = new_img.copy()
alpha_layer = mask.convert('L')
strength = st.session_state["denoising_strength"]
if mask_mode == 0:
alpha_layer = ImageOps.invert(alpha_layer)
alpha_layer = alpha_layer.point(lambda a: a * strength)
alpha_layer = ImageOps.invert(alpha_layer)
elif mask_mode == 1:
alpha_layer = alpha_layer.point(lambda a: a * strength)
alpha_layer = ImageOps.invert(alpha_layer)
final_img.putalpha(alpha_layer)
with masked_image_holder.container():
st.text("Masked Image Preview")
st.image(final_img)
with col3_img2img_layout:
result_tab = st.tabs(["Result"])
# create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
preview_image = st.empty()
st.session_state["preview_image"] = preview_image
#st.session_state["loading"] = st.empty()
st.session_state["progress_bar_text"] = st.empty()
st.session_state["progress_bar"] = st.empty()
message = st.empty()
#if uploaded_images:
#image = Image.open(uploaded_images).convert('RGB')
##img_array = np.array(image) # if you want to pass it to OpenCV
#new_img = image.resize((width, height))
#st.image(new_img, use_column_width=True)
if generate_button:
#print("Loading models")
# load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
load_models(False, use_GFPGAN, st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"], st.session_state["CustomModel_available"],
st.session_state["custom_model"])
if uploaded_images:
image = Image.open(uploaded_images).convert('RGBA')
new_img = image.resize((width, height))
#img_array = np.array(image) # if you want to pass it to OpenCV
new_mask = None
if uploaded_masks:
mask = Image.open(uploaded_masks).convert('RGBA')
new_mask = mask.resize((width, height))
try:
output_images, seed, info, stats = img2img(prompt=prompt, init_info=new_img, init_info_mask=new_mask, mask_mode=mask_mode,
mask_restore=img2img_mask_restore, ddim_steps=st.session_state["sampling_steps"],
sampler_name=st.session_state["sampler_name"], n_iter=batch_count,
cfg_scale=cfg_scale, denoising_strength=st.session_state["denoising_strength"], variant_seed=variant_seed,
seed=seed, noise_mode=noise_mode, find_noise_steps=find_noise_steps, width=width,
height=height, variant_amount=variant_amount,
ddim_eta=0.0, write_info_files=write_info_files, RealESRGAN_model=st.session_state["RealESRGAN_model"],
separate_prompts=separate_prompts, normalize_prompt_weights=normalize_prompt_weights,
save_individual_images=save_individual_images, save_grid=save_grid,
group_by_prompt=group_by_prompt, save_as_jpg=save_as_jpg, use_GFPGAN=use_GFPGAN,
use_RealESRGAN=st.session_state["use_RealESRGAN"] if not loopback else False, loopback=loopback
)
#show a message when the generation is complete.
message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
except (StopException, KeyError):
print(f"Received Streamlit StopException")
# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
# use the current col2 first tab to show the preview_img and update it as its generated.
#preview_image.image(output_images, width=750)
#on import run init

161
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@ -0,0 +1,161 @@
# base webui import and utils.
from webui_streamlit import st
from sd_utils import *
#home plugin
import os
from PIL import Image
#from bs4 import BeautifulSoup
from streamlit.runtime.in_memory_file_manager import in_memory_file_manager
from streamlit.elements import image as STImage
# Temp imports
# end of imports
#---------------------------------------------------------------------------------------------------------------
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
class plugin_info():
plugname = "imglab"
description = "Image Lab"
isTab = True
displayPriority = 3
def getLatestGeneratedImagesFromPath():
#get the latest images from the generated images folder
#get the path to the generated images folder
generatedImagesPath = os.path.join(os.getcwd(),'outputs')
#get all the files from the folders and subfolders
files = []
#get the laest 10 images from the output folder without walking the subfolders
for r, d, f in os.walk(generatedImagesPath):
for file in f:
if '.png' in file:
files.append(os.path.join(r, file))
#sort the files by date
files.sort(key=os.path.getmtime)
#reverse the list so the latest images are first
for f in files:
img = Image.open(f)
files[files.index(f)] = img
#get the latest 10 files
#get all the files with the .png or .jpg extension
#sort files by date
#get the latest 10 files
latestFiles = files[-10:]
#reverse the list
latestFiles.reverse()
return latestFiles
def getImagesFromLexica():
#scrape images from lexica.art
#get the html from the page
#get the html with cookies and javascript
apiEndpoint = r'https://lexica.art/api/trpc/prompts.infinitePrompts?batch=1&input=%7B%220%22%3A%7B%22json%22%3A%7B%22limit%22%3A10%2C%22text%22%3A%22%22%2C%22cursor%22%3A10%7D%7D%7D'
#REST API call
#
from requests_html import HTMLSession
session = HTMLSession()
response = session.get(apiEndpoint)
#req = requests.Session()
#req.headers['user-agent'] = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'
#response = req.get(apiEndpoint)
print(response.status_code)
print(response.text)
#get the json from the response
#json = response.json()
#get the prompts from the json
print(response)
#session = requests.Session()
#parseEndpointJson = session.get(apiEndpoint,headers=headers,verify=False)
#print(parseEndpointJson)
#print('test2')
#page = requests.get("https://lexica.art/", headers={'User-Agent': 'Mozilla/5.0'})
#parse the html
#soup = BeautifulSoup(page.content, 'html.parser')
#find all the images
#print(soup)
#images = soup.find_all('alt-image')
#create a list to store the image urls
image_urls = []
#loop through the images
for image in images:
#get the url
image_url = image['src']
#add it to the list
image_urls.append('http://www.lexica.art/'+image_url)
#return the list
print(image_urls)
return image_urls
def changeImage():
#change the image in the image holder
#check if the file is not empty
if len(st.session_state['uploaded_file']) > 0:
#read the file
print('test2')
uploaded = st.session_state['uploaded_file'][0].read()
#show the image in the image holder
st.session_state['previewImg'].empty()
st.session_state['previewImg'].image(uploaded,use_column_width=True)
def createHTMLGallery(images):
html3 = """
<div class="gallery-history" style="
display: flex;
flex-wrap: wrap;
align-items: flex-start;">
"""
mkdwn_array = []
for i in images:
bImg = i.read()
i = Image.save(bImg, 'PNG')
width, height = i.size
#get random number for the id
image_id = "%s" % (str(images.index(i)))
(data, mimetype) = STImage._normalize_to_bytes(bImg.getvalue(), width, 'auto')
this_file = in_memory_file_manager.add(data, mimetype, image_id)
img_str = this_file.url
#img_str = 'data:image/png;base64,' + b64encode(image_io.getvalue()).decode('ascii')
#get image size
#make sure the image is not bigger then 150px but keep the aspect ratio
if width > 150:
height = int(height * (150/width))
width = 150
if height > 150:
width = int(width * (150/height))
height = 150
#mkdwn = f"""<img src="{img_str}" alt="Image" with="200" height="200" />"""
mkdwn = f'''<div class="gallery" style="margin: 3px;" >
<a href="{img_str}">
<img src="{img_str}" alt="Image" width="{width}" height="{height}">
</a>
</div>
'''
mkdwn_array.append(mkdwn)
html3 += "".join(mkdwn_array)
html3 += '</div>'
return html3
def layout():
col1, col2 = st.columns(2)
with col1:
st.session_state['uploaded_file'] = st.file_uploader("Choose an image or images", type=["png", "jpg", "jpeg", "webp"],accept_multiple_files=True,on_change=changeImage)
if 'previewImg' not in st.session_state:
st.session_state['previewImg'] = st.empty()
else:
if len(st.session_state['uploaded_file']) > 0:
st.session_state['previewImg'].empty()
st.session_state['previewImg'].image(st.session_state['uploaded_file'][0],use_column_width=True)
else:
st.session_state['previewImg'] = st.empty()

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import numpy as np
def perlin(x, y, seed=0):
# permutation table
np.random.seed(seed)
p = np.arange(256, dtype=int)
np.random.shuffle(p)
p = np.stack([p, p]).flatten()
# coordinates of the top-left
xi, yi = x.astype(int), y.astype(int)
# internal coordinates
xf, yf = x - xi, y - yi
# fade factors
u, v = fade(xf), fade(yf)
# noise components
n00 = gradient(p[p[xi] + yi], xf, yf)
n01 = gradient(p[p[xi] + yi + 1], xf, yf - 1)
n11 = gradient(p[p[xi + 1] + yi + 1], xf - 1, yf - 1)
n10 = gradient(p[p[xi + 1] + yi], xf - 1, yf)
# combine noises
x1 = lerp(n00, n10, u)
x2 = lerp(n01, n11, u) # FIX1: I was using n10 instead of n01
return lerp(x1, x2, v) # FIX2: I also had to reverse x1 and x2 here
def lerp(a, b, x):
"linear interpolation"
return a + x * (b - a)
def fade(t):
"6t^5 - 15t^4 + 10t^3"
return 6 * t**5 - 15 * t**4 + 10 * t**3
def gradient(h, x, y):
"grad converts h to the right gradient vector and return the dot product with (x,y)"
vectors = np.array([[0, 1], [0, -1], [1, 0], [-1, 0]])
g = vectors[h % 4]
return g[:, :, 0] * x + g[:, :, 1] * y
lin = np.linspace(0, 5, 100, endpoint=False)
x, y = np.meshgrid(lin, lin)
def perlinNoise(height,width,octavesx=5,octavesy=5,seed=None):
linx = np.linspace(0,octavesx,width,endpoint=False)
liny = np.linspace(0,octavesy,height,endpoint=False)
x,y = np.meshgrid(linx,liny)
return perlin(x,y,seed=seed)

4
scripts/relauncher.py
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@ -19,6 +19,8 @@ optimized_turbo = False
# Creates a public xxxxx.gradio.app share link to allow others to use your interface (requires properly forwarded ports to work correctly)
share = False
# Generate tiling images
tiling = False
# Enter other `--arguments` you wish to use - Must be entered as a `--argument ` syntax
additional_arguments = ""
@ -37,6 +39,8 @@ if optimized_turbo == True:
common_arguments += "--optimized-turbo "
if optimized == True:
common_arguments += "--optimized "
if tiling == True:
common_arguments += "--tiling "
if share == True:
common_arguments += "--share "

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import inspect
import warnings
from tqdm.auto import tqdm
from typing import List, Optional, Union
import torch
from diffusers import ModelMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import \
StableDiffusionSafetyChecker
from diffusers.schedulers import (DDIMScheduler, LMSDiscreteScheduler,
PNDMScheduler)
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
class StableDiffusionPipeline(DiffusionPipeline):
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
@torch.no_grad()
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
text_embeddings: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
**kwargs,
):
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and"
" will be removed in v0.3.0. Consider using `pipe.to(torch_device)`"
" instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
if text_embeddings is None:
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(
f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(
"`height` and `width` have to be divisible by 8 but are"
f" {height} and {width}."
)
# get prompt text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
else:
batch_size = text_embeddings.shape[0]
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
# max_length = text_input.input_ids.shape[-1]
max_length = 77 # self.tokenizer.model_max_length
uncond_input = self.tokenizer(
[""] * batch_size,
padding="max_length",
max_length=max_length,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(self.device)
)[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
if latents is None:
latents = torch.randn(
latents_shape,
generator=generator,
device=self.device,
)
else:
if latents.shape != latents_shape:
raise ValueError(
f"Unexpected latents shape, got {latents.shape}, expected"
f" {latents_shape}"
)
latents = latents.to(self.device)
# set timesteps
accepts_offset = "offset" in set(
inspect.signature(self.scheduler.set_timesteps).parameters.keys()
)
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = latents * self.scheduler.sigmas[0]
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(
inspect.signature(self.scheduler.step).parameters.keys()
)
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([latents] * 2) if do_classifier_free_guidance else latents
)
if isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[i]
# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
# predict the noise residual
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=text_embeddings
)["sample"]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
# compute the previous noisy sample x_t -> x_t-1
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = self.scheduler.step(
noise_pred, i, latents, **extra_step_kwargs
)["prev_sample"]
else:
latents = self.scheduler.step(
noise_pred, t, latents, **extra_step_kwargs
)["prev_sample"]
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
safety_cheker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="pt"
).to(self.device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_cheker_input.pixel_values
)
if output_type == "pil":
image = self.numpy_to_pil(image)
return {"sample": image, "nsfw_content_detected": has_nsfw_concept}
def embed_text(self, text):
"""Helper to embed some text"""
with torch.autocast("cuda"):
text_input = self.tokenizer(
text,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
with torch.no_grad():
embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
return embed
class NoCheck(ModelMixin):
"""Can be used in place of safety checker. Use responsibly and at your own risk."""
def __init__(self):
super().__init__()
self.register_parameter(name='asdf', param=torch.nn.Parameter(torch.randn(3)))
def forward(self, images=None, **kwargs):
return images, [False]

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import json
import subprocess
from pathlib import Path
import numpy as np
import torch
from diffusers.schedulers import (DDIMScheduler, LMSDiscreteScheduler,
PNDMScheduler)
from diffusers import ModelMixin
from stable_diffusion_pipeline import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
use_auth_token=True,
torch_dtype=torch.float16,
revision="fp16",
).to("cuda")
default_scheduler = PNDMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
ddim_scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
klms_scheduler = LMSDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler, klms=klms_scheduler)
def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
"""helper function to spherically interpolate two arrays v1 v2"""
if not isinstance(v0, np.ndarray):
inputs_are_torch = True
input_device = v0.device
v0 = v0.cpu().numpy()
v1 = v1.cpu().numpy()
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
if np.abs(dot) > DOT_THRESHOLD:
v2 = (1 - t) * v0 + t * v1
else:
theta_0 = np.arccos(dot)
sin_theta_0 = np.sin(theta_0)
theta_t = theta_0 * t
sin_theta_t = np.sin(theta_t)
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
s1 = sin_theta_t / sin_theta_0
v2 = s0 * v0 + s1 * v1
if inputs_are_torch:
v2 = torch.from_numpy(v2).to(input_device)
return v2
def make_video_ffmpeg(frame_dir, output_file_name='output.mp4', frame_filename="frame%06d.jpg", fps=30):
frame_ref_path = str(frame_dir / frame_filename)
video_path = str(frame_dir / output_file_name)
subprocess.call(
f"ffmpeg -r {fps} -i {frame_ref_path} -vcodec libx264 -crf 10 -pix_fmt yuv420p"
f" {video_path}".split()
)
return video_path
def walk(
prompts=["blueberry spaghetti", "strawberry spaghetti"],
seeds=[42, 123],
num_steps=5,
output_dir="dreams",
name="berry_good_spaghetti",
height=512,
width=512,
guidance_scale=7.5,
eta=0.0,
num_inference_steps=50,
do_loop=False,
make_video=False,
use_lerp_for_text=False,
scheduler="klms", # choices: default, ddim, klms
disable_tqdm=False,
upsample=False,
fps=30,
):
"""Generate video frames/a video given a list of prompts and seeds.
Args:
prompts (List[str], optional): List of . Defaults to ["blueberry spaghetti", "strawberry spaghetti"].
seeds (List[int], optional): List of random seeds corresponding to given prompts.
num_steps (int, optional): Number of steps to walk. Increase this value to 60-200 for good results. Defaults to 5.
output_dir (str, optional): Root dir where images will be saved. Defaults to "dreams".
name (str, optional): Sub directory of output_dir to save this run's files. Defaults to "berry_good_spaghetti".
height (int, optional): Height of image to generate. Defaults to 512.
width (int, optional): Width of image to generate. Defaults to 512.
guidance_scale (float, optional): Higher = more adherance to prompt. Lower = let model take the wheel. Defaults to 7.5.
eta (float, optional): ETA. Defaults to 0.0.
num_inference_steps (int, optional): Number of diffusion steps. Defaults to 50.
do_loop (bool, optional): Whether to loop from last prompt back to first. Defaults to False.
make_video (bool, optional): Whether to make a video or just save the images. Defaults to False.
use_lerp_for_text (bool, optional): Use LERP instead of SLERP for text embeddings when walking. Defaults to False.
scheduler (str, optional): Which scheduler to use. Defaults to "klms". Choices are "default", "ddim", "klms".
disable_tqdm (bool, optional): Whether to turn off the tqdm progress bars. Defaults to False.
upsample (bool, optional): If True, uses Real-ESRGAN to upsample images 4x. Requires it to be installed
which you can do by running: `pip install git+https://github.com/xinntao/Real-ESRGAN.git`. Defaults to False.
fps (int, optional): The frames per second (fps) that you want the video to use. Does nothing if make_video is False. Defaults to 30.
Returns:
str: Path to video file saved if make_video=True, else None.
"""
if upsample:
from .upsampling import PipelineRealESRGAN
upsampling_pipeline = PipelineRealESRGAN.from_pretrained('nateraw/real-esrgan')
pipeline.set_progress_bar_config(disable=disable_tqdm)
pipeline.scheduler = SCHEDULERS[scheduler]
output_path = Path(output_dir) / name
output_path.mkdir(exist_ok=True, parents=True)
# Write prompt info to file in output dir so we can keep track of what we did
prompt_config_path = output_path / 'prompt_config.json'
prompt_config_path.write_text(
json.dumps(
dict(
prompts=prompts,
seeds=seeds,
num_steps=num_steps,
name=name,
guidance_scale=guidance_scale,
eta=eta,
num_inference_steps=num_inference_steps,
do_loop=do_loop,
make_video=make_video,
use_lerp_for_text=use_lerp_for_text,
scheduler=scheduler
),
indent=2,
sort_keys=False,
)
)
assert len(prompts) == len(seeds)
first_prompt, *prompts = prompts
embeds_a = pipeline.embed_text(first_prompt)
first_seed, *seeds = seeds
latents_a = torch.randn(
(1, pipeline.unet.in_channels, height // 8, width // 8),
device=pipeline.device,
generator=torch.Generator(device=pipeline.device).manual_seed(first_seed),
)
if do_loop:
prompts.append(first_prompt)
seeds.append(first_seed)
frame_index = 0
for prompt, seed in zip(prompts, seeds):
# Text
embeds_b = pipeline.embed_text(prompt)
# Latent Noise
latents_b = torch.randn(
(1, pipeline.unet.in_channels, height // 8, width // 8),
device=pipeline.device,
generator=torch.Generator(device=pipeline.device).manual_seed(seed),
)
for i, t in enumerate(np.linspace(0, 1, num_steps)):
do_print_progress = (i == 0) or ((frame_index + 1) % 20 == 0)
if do_print_progress:
print(f"COUNT: {frame_index+1}/{len(seeds)*num_steps}")
if use_lerp_for_text:
embeds = torch.lerp(embeds_a, embeds_b, float(t))
else:
embeds = slerp(float(t), embeds_a, embeds_b)
latents = slerp(float(t), latents_a, latents_b)
with torch.autocast("cuda"):
im = pipeline(
latents=latents,
text_embeddings=embeds,
height=height,
width=width,
guidance_scale=guidance_scale,
eta=eta,
num_inference_steps=num_inference_steps,
output_type='pil' if not upsample else 'numpy'
)["sample"][0]
if upsample:
im = upsampling_pipeline(im)
im.save(output_path / ("frame%06d.jpg" % frame_index))
frame_index += 1
embeds_a = embeds_b
latents_a = latents_b
if make_video:
return make_video_ffmpeg(output_path, f"{name}.mp4", fps=fps)
if __name__ == "__main__":
import fire
fire.Fire(walk)

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# base webui import and utils.
from webui_streamlit import st
from sd_utils import *
# streamlit imports
#other imports
#from transformers import CLIPTextModel, CLIPTokenizer
# Temp imports
# end of imports
#---------------------------------------------------------------------------------------------------------------
#def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
#loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
## separate token and the embeds
#print (loaded_learned_embeds)
#trained_token = list(loaded_learned_embeds.keys())[0]
#embeds = loaded_learned_embeds[trained_token]
## cast to dtype of text_encoder
#dtype = text_encoder.get_input_embeddings().weight.dtype
#embeds.to(dtype)
## add the token in tokenizer
#token = token if token is not None else trained_token
#num_added_tokens = tokenizer.add_tokens(token)
#i = 1
#while(num_added_tokens == 0):
#print(f"The tokenizer already contains the token {token}.")
#token = f"{token[:-1]}-{i}>"
#print(f"Attempting to add the token {token}.")
#num_added_tokens = tokenizer.add_tokens(token)
#i+=1
## resize the token embeddings
#text_encoder.resize_token_embeddings(len(tokenizer))
## get the id for the token and assign the embeds
#token_id = tokenizer.convert_tokens_to_ids(token)
#text_encoder.get_input_embeddings().weight.data[token_id] = embeds
#return token
##def token_loader()
#learned_token = load_learned_embed_in_clip(f"models/custom/embeddings/Custom Ami.pt", st.session_state.pipe.text_encoder, st.session_state.pipe.tokenizer, "*")
#model_content["token"] = learned_token
#models.append(model_content)
model_id = "./models/custom/embeddings/"
def layout():
st.write("Textual Inversion")

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# base webui import and utils.
from webui_streamlit import st
from sd_utils import *
# streamlit imports
from streamlit import StopException
from streamlit.runtime.in_memory_file_manager import in_memory_file_manager
from streamlit.elements import image as STImage
#other imports
import os
from typing import Union
from io import BytesIO
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
# Temp imports
# end of imports
#---------------------------------------------------------------------------------------------------------------
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
class plugin_info():
plugname = "txt2img"
description = "Text to Image"
isTab = True
displayPriority = 1
if os.path.exists(os.path.join(st.session_state['defaults'].general.GFPGAN_dir, "experiments", "pretrained_models", "GFPGANv1.3.pth")):
GFPGAN_available = True
else:
GFPGAN_available = False
if os.path.exists(os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, "experiments","pretrained_models", f"{st.session_state['defaults'].general.RealESRGAN_model}.pth")):
RealESRGAN_available = True
else:
RealESRGAN_available = False
#
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, realesrgan_model_name: str,
n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
height: int, width: int, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
save_as_jpg: bool = True, use_GFPGAN: bool = True, use_RealESRGAN: bool = True,
RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B", fp = None, variant_amount: float = None,
variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True):
outpath = st.session_state['defaults'].general.outdir_txt2img or st.session_state['defaults'].general.outdir or "outputs/txt2img-samples"
seed = seed_to_int(seed)
#prompt_matrix = 0 in toggles
#normalize_prompt_weights = 1 in toggles
#skip_save = 2 not in toggles
#save_grid = 3 not in toggles
#sort_samples = 4 in toggles
#write_info_files = 5 in toggles
#jpg_sample = 6 in toggles
#use_GFPGAN = 7 in toggles
#use_RealESRGAN = 8 in toggles
if sampler_name == 'PLMS':
sampler = PLMSSampler(st.session_state["model"])
elif sampler_name == 'DDIM':
sampler = DDIMSampler(st.session_state["model"])
elif sampler_name == 'k_dpm_2_a':
sampler = KDiffusionSampler(st.session_state["model"],'dpm_2_ancestral')
elif sampler_name == 'k_dpm_2':
sampler = KDiffusionSampler(st.session_state["model"],'dpm_2')
elif sampler_name == 'k_euler_a':
sampler = KDiffusionSampler(st.session_state["model"],'euler_ancestral')
elif sampler_name == 'k_euler':
sampler = KDiffusionSampler(st.session_state["model"],'euler')
elif sampler_name == 'k_heun':
sampler = KDiffusionSampler(st.session_state["model"],'heun')
elif sampler_name == 'k_lms':
sampler = KDiffusionSampler(st.session_state["model"],'lms')
else:
raise Exception("Unknown sampler: " + sampler_name)
def init():
pass
def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x, img_callback=generation_callback,
log_every_t=int(st.session_state.update_preview_frequency))
return samples_ddim
#try:
output_images, seed, info, stats = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_name=sampler_name,
save_grid=save_grid,
batch_size=batch_size,
n_iter=n_iter,
steps=ddim_steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=separate_prompts,
use_GFPGAN=st.session_state["use_GFPGAN"],
use_RealESRGAN=st.session_state["use_RealESRGAN"],
realesrgan_model_name=realesrgan_model_name,
ddim_eta=ddim_eta,
normalize_prompt_weights=normalize_prompt_weights,
save_individual_images=save_individual_images,
sort_samples=group_by_prompt,
write_info_files=write_info_files,
jpg_sample=save_as_jpg,
variant_amount=variant_amount,
variant_seed=variant_seed,
)
del sampler
return output_images, seed, info, stats
#except RuntimeError as e:
#err = e
#err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
#stats = err_msg
#return [], seed, 'err', stats
def layout():
with st.form("txt2img-inputs"):
st.session_state["generation_mode"] = "txt2img"
input_col1, generate_col1 = st.columns([10,1])
with input_col1:
#prompt = st.text_area("Input Text","")
prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
generate_col1.write("")
generate_col1.write("")
generate_button = generate_col1.form_submit_button("Generate")
# creating the page layout using columns
col1, col2, col3 = st.columns([1,2,1], gap="large")
with col1:
width = st.slider("Width:", min_value=64, max_value=4096, value=st.session_state['defaults'].txt2img.width, step=64)
height = st.slider("Height:", min_value=64, max_value=4096, value=st.session_state['defaults'].txt2img.height, step=64)
cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].txt2img.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.")
seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
bs_slider_max_value = 5
if st.session_state.defaults.general.optimized:
bs_slider_max_value = 100
batch_size = st.slider(
"Batch size",
min_value=1,
max_value=bs_slider_max_value,
value=st.session_state.defaults.txt2img.batch_size,
step=1,
help="How many images are at once in a batch.\
It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
Default: 1")
with st.expander("Preview Settings"):
st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview,
help="If enabled the image preview will be updated during the generation instead of at the end. \
You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
By default this is enabled and the frequency is set to 1 step.")
st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
help="Frequency in steps at which the the preview image is updated. By default the frequency \
is set to 1 step.")
with col2:
preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
with preview_tab:
#st.write("Image")
#Image for testing
#image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
#new_image = image.resize((175, 240))
#preview_image = st.image(image)
# create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
st.session_state["preview_image"] = st.empty()
st.session_state["loading"] = st.empty()
st.session_state["progress_bar_text"] = st.empty()
st.session_state["progress_bar"] = st.empty()
message = st.empty()
with col3:
# If we have custom models available on the "models/custom"
#folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
if st.session_state.CustomModel_available:
st.session_state.custom_model = st.selectbox("Custom Model:", st.session_state.custom_models,
index=st.session_state["custom_models"].index(st.session_state['defaults'].general.default_model),
help="Select the model you want to use. This option is only available if you have custom models \
on your 'models/custom' folder. The model name that will be shown here is the same as the name\
the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
st.session_state.sampling_steps = st.slider("Sampling Steps",
value=st.session_state['defaults'].txt2img.sampling_steps,
min_value=st.session_state['defaults'].txt2img.slider_bounds.sampling.lower,
max_value=st.session_state['defaults'].txt2img.slider_bounds.sampling.upper,
step=st.session_state['defaults'].txt2img.slider_steps.sampling)
sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
sampler_name = st.selectbox("Sampling method", sampler_name_list,
index=sampler_name_list.index(st.session_state['defaults'].txt2img.default_sampler), help="Sampling method to use. Default: k_euler")
#basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"])
#with basic_tab:
#summit_on_enter = st.radio("Submit on enter?", ("Yes", "No"), horizontal=True,
#help="Press the Enter key to summit, when 'No' is selected you can use the Enter key to write multiple lines.")
with st.expander("Advanced"):
separate_prompts = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].txt2img.separate_prompts, help="Separate multiple prompts using the `|` character, and get all combinations of them.")
normalize_prompt_weights = st.checkbox("Normalize Prompt Weights.", value=st.session_state['defaults'].txt2img.normalize_prompt_weights, help="Ensure the sum of all weights add up to 1.0")
save_individual_images = st.checkbox("Save individual images.", value=st.session_state['defaults'].txt2img.save_individual_images, help="Save each image generated before any filter or enhancement is applied.")
save_grid = st.checkbox("Save grid",value=st.session_state['defaults'].txt2img.save_grid, help="Save a grid with all the images generated into a single image.")
group_by_prompt = st.checkbox("Group results by prompt", value=st.session_state['defaults'].txt2img.group_by_prompt,
help="Saves all the images with the same prompt into the same folder. When using a prompt matrix each prompt combination will have its own folder.")
write_info_files = st.checkbox("Write Info file", value=st.session_state['defaults'].txt2img.write_info_files, help="Save a file next to the image with informartion about the generation.")
save_as_jpg = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].txt2img.save_as_jpg, help="Saves the images as jpg instead of png.")
if st.session_state["GFPGAN_available"]:
st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2img.use_GFPGAN, help="Uses the GFPGAN model to improve faces after the generation.\
This greatly improve the quality and consistency of faces but uses extra VRAM. Disable if you need the extra VRAM.")
else:
st.session_state["use_GFPGAN"] = False
if st.session_state["RealESRGAN_available"]:
st.session_state["use_RealESRGAN"] = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].txt2img.use_RealESRGAN,
help="Uses the RealESRGAN model to upscale the images after the generation.\
This greatly improve the quality and lets you have high resolution images but uses extra VRAM. Disable if you need the extra VRAM.")
st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", ["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"], index=0)
else:
st.session_state["use_RealESRGAN"] = False
st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2img.variant_amount, min_value=0.0, max_value=1.0, step=0.01)
variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2img.seed, help="The seed to use when generating a variant, if left blank a random seed will be generated.")
galleryCont = st.empty()
if generate_button:
#print("Loading models")
# load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
load_models(False, st.session_state["use_GFPGAN"], st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"], st.session_state["CustomModel_available"],
st.session_state["custom_model"])
try:
#
output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, st.session_state["RealESRGAN_model"], batch_count, batch_size,
cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"],
variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
#if 'latestImages' in st.session_state:
#for i in output_images:
##push the new image to the list of latest images and remove the oldest one
##remove the last index from the list\
#st.session_state['latestImages'].pop()
##add the new image to the start of the list
#st.session_state['latestImages'].insert(0, i)
#PlaceHolder.empty()
#with PlaceHolder.container():
#col1, col2, col3 = st.columns(3)
#col1_cont = st.container()
#col2_cont = st.container()
#col3_cont = st.container()
#images = st.session_state['latestImages']
#with col1_cont:
#with col1:
#[st.image(images[index]) for index in [0, 3, 6] if index < len(images)]
#with col2_cont:
#with col2:
#[st.image(images[index]) for index in [1, 4, 7] if index < len(images)]
#with col3_cont:
#with col3:
#[st.image(images[index]) for index in [2, 5, 8] if index < len(images)]
#historyGallery = st.empty()
## check if output_images length is the same as seeds length
#with gallery_tab:
#st.markdown(createHTMLGallery(output_images,seeds), unsafe_allow_html=True)
#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
except (StopException, KeyError):
print(f"Received Streamlit StopException")
# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
# use the current col2 first tab to show the preview_img and update it as its generated.
#preview_image.image(output_images)
#on import run init
def createHTMLGallery(images,info):
html3 = """
<div class="gallery-history" style="
display: flex;
flex-wrap: wrap;
align-items: flex-start;">
"""
mkdwn_array = []
for i in images:
try:
seed = info[images.index(i)]
except:
seed = ' '
image_io = BytesIO()
i.save(image_io, 'PNG')
width, height = i.size
#get random number for the id
image_id = "%s" % (str(images.index(i)))
(data, mimetype) = STImage._normalize_to_bytes(image_io.getvalue(), width, 'auto')
this_file = in_memory_file_manager.add(data, mimetype, image_id)
img_str = this_file.url
#img_str = 'data:image/png;base64,' + b64encode(image_io.getvalue()).decode('ascii')
#get image size
#make sure the image is not bigger then 150px but keep the aspect ratio
if width > 150:
height = int(height * (150/width))
width = 150
if height > 150:
width = int(width * (150/height))
height = 150
#mkdwn = f"""<img src="{img_str}" alt="Image" with="200" height="200" />"""
mkdwn = f'''<div class="gallery" style="margin: 3px;" >
<a href="{img_str}">
<img src="{img_str}" alt="Image" width="{width}" height="{height}">
</a>
<div class="desc" style="text-align: center; opacity: 40%;">{seed}</div>
</div>
'''
mkdwn_array.append(mkdwn)
html3 += "".join(mkdwn_array)
html3 += '</div>'
return html3

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# base webui import and utils.
from webui_streamlit import st
from sd_utils import *
# streamlit imports
from streamlit import StopException
from streamlit.runtime.in_memory_file_manager import in_memory_file_manager
from streamlit.elements import image as STImage
#other imports
import os
from PIL import Image
import torch
import numpy as np
import time, inspect, timeit
import torch
from torch import autocast
from io import BytesIO
import imageio
from slugify import slugify
# Temp imports
# these are for testing txt2vid, should be removed and we should use things from our own code.
from diffusers import StableDiffusionPipeline
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
# end of imports
#---------------------------------------------------------------------------------------------------------------
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
class plugin_info():
plugname = "txt2img"
description = "Text to Image"
isTab = True
displayPriority = 1
if os.path.exists(os.path.join(st.session_state['defaults'].general.GFPGAN_dir, "experiments", "pretrained_models", "GFPGANv1.3.pth")):
GFPGAN_available = True
else:
GFPGAN_available = False
if os.path.exists(os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, "experiments","pretrained_models", f"{st.session_state['defaults'].txt2vid.RealESRGAN_model}.pth")):
RealESRGAN_available = True
else:
RealESRGAN_available = False
#
# -----------------------------------------------------------------------------
@torch.no_grad()
def diffuse(
pipe,
cond_embeddings, # text conditioning, should be (1, 77, 768)
cond_latents, # image conditioning, should be (1, 4, 64, 64)
num_inference_steps,
cfg_scale,
eta,
):
torch_device = cond_latents.get_device()
# classifier guidance: add the unconditional embedding
max_length = cond_embeddings.shape[1] # 77
uncond_input = pipe.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0]
text_embeddings = torch.cat([uncond_embeddings, cond_embeddings])
# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
if isinstance(pipe.scheduler, LMSDiscreteScheduler):
cond_latents = cond_latents * pipe.scheduler.sigmas[0]
# init the scheduler
accepts_offset = "offset" in set(inspect.signature(pipe.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
pipe.scheduler.set_timesteps(num_inference_steps + st.session_state.sampling_steps, **extra_set_kwargs)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(pipe.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
step_counter = 0
inference_counter = 0
if "current_chunk_speed" not in st.session_state:
st.session_state["current_chunk_speed"] = 0
if "previous_chunk_speed_list" not in st.session_state:
st.session_state["previous_chunk_speed_list"] = [0]
st.session_state["previous_chunk_speed_list"].append(st.session_state["current_chunk_speed"])
if "update_preview_frequency_list" not in st.session_state:
st.session_state["update_preview_frequency_list"] = [0]
st.session_state["update_preview_frequency_list"].append(st.session_state['defaults'].txt2vid.update_preview_frequency)
# diffuse!
for i, t in enumerate(pipe.scheduler.timesteps):
start = timeit.default_timer()
#status_text.text(f"Running step: {step_counter}{total_number_steps} {percent} | {duration:.2f}{speed}")
# expand the latents for classifier free guidance
latent_model_input = torch.cat([cond_latents] * 2)
if isinstance(pipe.scheduler, LMSDiscreteScheduler):
sigma = pipe.scheduler.sigmas[i]
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
# predict the noise residual
noise_pred = pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
# cfg
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + cfg_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if isinstance(pipe.scheduler, LMSDiscreteScheduler):
cond_latents = pipe.scheduler.step(noise_pred, i, cond_latents, **extra_step_kwargs)["prev_sample"]
else:
cond_latents = pipe.scheduler.step(noise_pred, t, cond_latents, **extra_step_kwargs)["prev_sample"]
#print (st.session_state["update_preview_frequency"])
#update the preview image if it is enabled and the frequency matches the step_counter
if st.session_state['defaults'].txt2vid.update_preview:
step_counter += 1
if st.session_state['defaults'].txt2vid.update_preview_frequency == step_counter or step_counter == st.session_state.sampling_steps:
if st.session_state.dynamic_preview_frequency:
st.session_state["current_chunk_speed"], st.session_state["previous_chunk_speed_list"], st.session_state['defaults'].txt2vid.update_preview_frequency, st.session_state["avg_update_preview_frequency"] = optimize_update_preview_frequency(st.session_state["current_chunk_speed"], st.session_state["previous_chunk_speed_list"], st.session_state['defaults'].txt2vid.update_preview_frequency, st.session_state["update_preview_frequency_list"])
#scale and decode the image latents with vae
cond_latents_2 = 1 / 0.18215 * cond_latents
image = pipe.vae.decode(cond_latents_2)
# generate output numpy image as uint8
image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
image = transforms.ToPILImage()(image.squeeze_(0))
st.session_state["preview_image"].image(image)
step_counter = 0
duration = timeit.default_timer() - start
st.session_state["current_chunk_speed"] = duration
if duration >= 1:
speed = "s/it"
else:
speed = "it/s"
duration = 1 / duration
if i > st.session_state.sampling_steps:
inference_counter += 1
inference_percent = int(100 * float(inference_counter + 1 if inference_counter < num_inference_steps else num_inference_steps)/float(num_inference_steps))
inference_progress = f"{inference_counter + 1 if inference_counter < num_inference_steps else num_inference_steps}/{num_inference_steps} {inference_percent}% "
else:
inference_progress = ""
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
frames_percent = int(100 * float(st.session_state.current_frame if st.session_state.current_frame < st.session_state.max_frames else st.session_state.max_frames)/float(st.session_state.max_frames))
st.session_state["progress_bar_text"].text(
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} "
f"{percent if percent < 100 else 100}% {inference_progress}{duration:.2f}{speed} | "
f"Frame: {st.session_state.current_frame + 1 if st.session_state.current_frame < st.session_state.max_frames else st.session_state.max_frames}/{st.session_state.max_frames} "
f"{frames_percent if frames_percent < 100 else 100}% {st.session_state.frame_duration:.2f}{st.session_state.frame_speed}"
)
st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
return image
#
def txt2vid(
# --------------------------------------
# args you probably want to change
prompts = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about
gpu:int = st.session_state['defaults'].general.gpu, # id of the gpu to run on
#name:str = 'test', # name of this project, for the output directory
#rootdir:str = st.session_state['defaults'].general.outdir,
num_steps:int = 200, # number of steps between each pair of sampled points
max_frames:int = 10000, # number of frames to write and then exit the script
num_inference_steps:int = 50, # more (e.g. 100, 200 etc) can create slightly better images
cfg_scale:float = 5.0, # can depend on the prompt. usually somewhere between 3-10 is good
do_loop = False,
use_lerp_for_text = False,
seeds = None,
quality:int = 100, # for jpeg compression of the output images
eta:float = 0.0,
width:int = 256,
height:int = 256,
weights_path = "CompVis/stable-diffusion-v1-4",
scheduler="klms", # choices: default, ddim, klms
disable_tqdm = False,
#-----------------------------------------------
beta_start = 0.0001,
beta_end = 0.00012,
beta_schedule = "scaled_linear",
starting_image=None
):
"""
prompt = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about
gpu:int = st.session_state['defaults'].general.gpu, # id of the gpu to run on
#name:str = 'test', # name of this project, for the output directory
#rootdir:str = st.session_state['defaults'].general.outdir,
num_steps:int = 200, # number of steps between each pair of sampled points
max_frames:int = 10000, # number of frames to write and then exit the script
num_inference_steps:int = 50, # more (e.g. 100, 200 etc) can create slightly better images
cfg_scale:float = 5.0, # can depend on the prompt. usually somewhere between 3-10 is good
do_loop = False,
use_lerp_for_text = False,
seed = None,
quality:int = 100, # for jpeg compression of the output images
eta:float = 0.0,
width:int = 256,
height:int = 256,
weights_path = "CompVis/stable-diffusion-v1-4",
scheduler="klms", # choices: default, ddim, klms
disable_tqdm = False,
beta_start = 0.0001,
beta_end = 0.00012,
beta_schedule = "scaled_linear"
"""
mem_mon = MemUsageMonitor('MemMon')
mem_mon.start()
seeds = seed_to_int(seeds)
# We add an extra frame because most
# of the time the first frame is just the noise.
#max_frames +=1
assert torch.cuda.is_available()
assert height % 8 == 0 and width % 8 == 0
torch.manual_seed(seeds)
torch_device = f"cuda:{gpu}"
# init the output dir
sanitized_prompt = slugify(prompts)
full_path = os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid-samples", "samples", sanitized_prompt)
if len(full_path) > 220:
sanitized_prompt = sanitized_prompt[:220-len(full_path)]
full_path = os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid-samples", "samples", sanitized_prompt)
os.makedirs(full_path, exist_ok=True)
# Write prompt info to file in output dir so we can keep track of what we did
if st.session_state.write_info_files:
with open(os.path.join(full_path , f'{slugify(str(seeds))}_config.json' if len(prompts) > 1 else "prompts_config.json"), "w") as outfile:
outfile.write(json.dumps(
dict(
prompts = prompts,
gpu = gpu,
num_steps = num_steps,
max_frames = max_frames,
num_inference_steps = num_inference_steps,
cfg_scale = cfg_scale,
do_loop = do_loop,
use_lerp_for_text = use_lerp_for_text,
seeds = seeds,
quality = quality,
eta = eta,
width = width,
height = height,
weights_path = weights_path,
scheduler=scheduler,
disable_tqdm = disable_tqdm,
beta_start = beta_start,
beta_end = beta_end,
beta_schedule = beta_schedule
),
indent=2,
sort_keys=False,
))
#print(scheduler)
default_scheduler = PNDMScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
)
# ------------------------------------------------------------------------------
#Schedulers
ddim_scheduler = DDIMScheduler(
beta_start=beta_start,
beta_end=beta_end,
beta_schedule=beta_schedule,
clip_sample=False,
set_alpha_to_one=False,
)
klms_scheduler = LMSDiscreteScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
)
SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler, klms=klms_scheduler)
# ------------------------------------------------------------------------------
st.session_state["progress_bar_text"].text("Loading models...")
try:
if "model" in st.session_state:
del st.session_state["model"]
except:
pass
#print (st.session_state["weights_path"] != weights_path)
try:
if not "pipe" in st.session_state or st.session_state["weights_path"] != weights_path:
if st.session_state["weights_path"] != weights_path:
del st.session_state["weights_path"]
st.session_state["weights_path"] = weights_path
st.session_state["pipe"] = StableDiffusionPipeline.from_pretrained(
weights_path,
use_local_file=True,
use_auth_token=True,
torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
revision="fp16" if not st.session_state['defaults'].general.no_half else None
)
st.session_state["pipe"].unet.to(torch_device)
st.session_state["pipe"].vae.to(torch_device)
st.session_state["pipe"].text_encoder.to(torch_device)
if st.session_state.defaults.general.enable_attention_slicing:
st.session_state["pipe"].enable_attention_slicing()
if st.session_state.defaults.general.enable_minimal_memory_usage:
st.session_state["pipe"].enable_minimal_memory_usage()
print("Tx2Vid Model Loaded")
else:
print("Tx2Vid Model already Loaded")
except:
#del st.session_state["weights_path"]
#del st.session_state["pipe"]
st.session_state["weights_path"] = weights_path
st.session_state["pipe"] = StableDiffusionPipeline.from_pretrained(
weights_path,
use_local_file=True,
use_auth_token=True,
torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
revision="fp16" if not st.session_state['defaults'].general.no_half else None
)
st.session_state["pipe"].unet.to(torch_device)
st.session_state["pipe"].vae.to(torch_device)
st.session_state["pipe"].text_encoder.to(torch_device)
if st.session_state.defaults.general.enable_attention_slicing:
st.session_state["pipe"].enable_attention_slicing()
print("Tx2Vid Model Loaded")
st.session_state["pipe"].scheduler = SCHEDULERS[scheduler]
# get the conditional text embeddings based on the prompt
text_input = st.session_state["pipe"].tokenizer(prompts, padding="max_length", max_length=st.session_state["pipe"].tokenizer.model_max_length, truncation=True, return_tensors="pt")
cond_embeddings = st.session_state["pipe"].text_encoder(text_input.input_ids.to(torch_device))[0] # shape [1, 77, 768]
#
if st.session_state.defaults.general.use_sd_concepts_library:
prompt_tokens = re.findall('<([a-zA-Z0-9-]+)>', prompts)
if prompt_tokens:
# compviz
#tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer
#text_encoder = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.transformer
# diffusers
tokenizer = st.session_state.pipe.tokenizer
text_encoder = st.session_state.pipe.text_encoder
ext = ('pt', 'bin')
#print (prompt_tokens)
if len(prompt_tokens) > 1:
for token_name in prompt_tokens:
embedding_path = os.path.join(st.session_state['defaults'].general.sd_concepts_library_folder, token_name)
if os.path.exists(embedding_path):
for files in os.listdir(embedding_path):
if files.endswith(ext):
load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{token_name}>")
else:
embedding_path = os.path.join(st.session_state['defaults'].general.sd_concepts_library_folder, prompt_tokens[0])
if os.path.exists(embedding_path):
for files in os.listdir(embedding_path):
if files.endswith(ext):
load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{prompt_tokens[0]}>")
# sample a source
init1 = torch.randn((1, st.session_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device)
if do_loop:
prompts = [prompts, prompts]
seeds = [seeds, seeds]
#first_seed, *seeds = seeds
#prompts.append(prompts)
#seeds.append(first_seed)
# iterate the loop
frames = []
frame_index = 0
st.session_state["total_frames_avg_duration"] = []
st.session_state["total_frames_avg_speed"] = []
try:
while frame_index < max_frames:
st.session_state["frame_duration"] = 0
st.session_state["frame_speed"] = 0
st.session_state["current_frame"] = frame_index
# sample the destination
init2 = torch.randn((1, st.session_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device)
for i, t in enumerate(np.linspace(0, 1, max_frames)):
start = timeit.default_timer()
print(f"COUNT: {frame_index+1}/{max_frames}")
#if use_lerp_for_text:
#init = torch.lerp(init1, init2, float(t))
#else:
#init = slerp(gpu, float(t), init1, init2)
init = slerp(gpu, float(t), init1, init2)
with autocast("cuda"):
image = diffuse(st.session_state["pipe"], cond_embeddings, init, num_inference_steps, cfg_scale, eta)
#im = Image.fromarray(image)
outpath = os.path.join(full_path, 'frame%06d.png' % frame_index)
image.save(outpath, quality=quality)
# send the image to the UI to update it
#st.session_state["preview_image"].image(im)
#append the frames to the frames list so we can use them later.
frames.append(np.asarray(image))
#increase frame_index counter.
frame_index += 1
st.session_state["current_frame"] = frame_index
duration = timeit.default_timer() - start
if duration >= 1:
speed = "s/it"
else:
speed = "it/s"
duration = 1 / duration
st.session_state["frame_duration"] = duration
st.session_state["frame_speed"] = speed
init1 = init2
except StopException:
pass
if st.session_state['save_video']:
# write video to memory
#output = io.BytesIO()
#writer = imageio.get_writer(os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid-samples"), im, extension=".mp4", fps=30)
try:
video_path = os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid-samples","temp.mp4")
writer = imageio.get_writer(video_path, fps=24)
for frame in frames:
writer.append_data(frame)
writer.close()
except:
print("Can't save video, skipping.")
# show video preview on the UI
st.session_state["preview_video"].video(open(video_path, 'rb').read())
mem_max_used, mem_total = mem_mon.read_and_stop()
time_diff = time.time()- start
info = f"""
{prompts}
Sampling Steps: {num_steps}, Sampler: {scheduler}, CFG scale: {cfg_scale}, Seed: {seeds}, Max Frames: {max_frames}""".strip()
stats = f'''
Took { round(time_diff, 2) }s total ({ round(time_diff/(max_frames),2) }s per image)
Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%'''
return video_path, seeds, info, stats
#on import run init
def createHTMLGallery(images,info):
html3 = """
<div class="gallery-history" style="
display: flex;
flex-wrap: wrap;
align-items: flex-start;">
"""
mkdwn_array = []
for i in images:
try:
seed = info[images.index(i)]
except:
seed = ' '
image_io = BytesIO()
i.save(image_io, 'PNG')
width, height = i.size
#get random number for the id
image_id = "%s" % (str(images.index(i)))
(data, mimetype) = STImage._normalize_to_bytes(image_io.getvalue(), width, 'auto')
this_file = in_memory_file_manager.add(data, mimetype, image_id)
img_str = this_file.url
#img_str = 'data:image/png;base64,' + b64encode(image_io.getvalue()).decode('ascii')
#get image size
#make sure the image is not bigger then 150px but keep the aspect ratio
if width > 150:
height = int(height * (150/width))
width = 150
if height > 150:
width = int(width * (150/height))
height = 150
#mkdwn = f"""<img src="{img_str}" alt="Image" with="200" height="200" />"""
mkdwn = f'''<div class="gallery" style="margin: 3px;" >
<a href="{img_str}">
<img src="{img_str}" alt="Image" width="{width}" height="{height}">
</a>
<div class="desc" style="text-align: center; opacity: 40%;">{seed}</div>
</div>
'''
mkdwn_array.append(mkdwn)
html3 += "".join(mkdwn_array)
html3 += '</div>'
return html3
#
def layout():
with st.form("txt2vid-inputs"):
st.session_state["generation_mode"] = "txt2vid"
input_col1, generate_col1 = st.columns([10,1])
with input_col1:
#prompt = st.text_area("Input Text","")
prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
generate_col1.write("")
generate_col1.write("")
generate_button = generate_col1.form_submit_button("Generate")
# creating the page layout using columns
col1, col2, col3 = st.columns([1,2,1], gap="large")
with col1:
width = st.slider("Width:", min_value=64, max_value=2048, value=st.session_state['defaults'].txt2vid.width, step=64)
height = st.slider("Height:", min_value=64, max_value=2048, value=st.session_state['defaults'].txt2vid.height, step=64)
cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].txt2vid.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.")
#uploaded_images = st.file_uploader("Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"],
#help="Upload an image which will be used for the image to image generation.")
seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2vid.seed, help=" The seed to use, if left blank a random seed will be generated.")
#batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2vid.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
#batch_size = st.slider("Batch size", min_value=1, max_value=250, value=st.session_state['defaults'].txt2vid.batch_size, step=1,
#help="How many images are at once in a batch.\
#It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
#Default: 1")
st.session_state["max_frames"] = int(st.text_input("Max Frames:", value=st.session_state['defaults'].txt2vid.max_frames, help="Specify the max number of frames you want to generate."))
with st.expander("Preview Settings"):
st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2vid.update_preview,
help="If enabled the image preview will be updated during the generation instead of at the end. \
You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
By default this is enabled and the frequency is set to 1 step.")
st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2vid.update_preview_frequency,
help="Frequency in steps at which the the preview image is updated. By default the frequency \
is set to 1 step.")
#
with col2:
preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
with preview_tab:
#st.write("Image")
#Image for testing
#image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
#new_image = image.resize((175, 240))
#preview_image = st.image(image)
# create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
st.session_state["preview_image"] = st.empty()
st.session_state["loading"] = st.empty()
st.session_state["progress_bar_text"] = st.empty()
st.session_state["progress_bar"] = st.empty()
#generate_video = st.empty()
st.session_state["preview_video"] = st.empty()
message = st.empty()
with gallery_tab:
st.write('Here should be the image gallery, if I could make a grid in streamlit.')
with col3:
# If we have custom models available on the "models/custom"
#folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
if st.session_state["CustomModel_available"]:
custom_model = st.selectbox("Custom Model:", st.session_state["defaults"].txt2vid.custom_models_list,
index=st.session_state["defaults"].txt2vid.custom_models_list.index(st.session_state["defaults"].txt2vid.default_model),
help="Select the model you want to use. This option is only available if you have custom models \
on your 'models/custom' folder. The model name that will be shown here is the same as the name\
the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
else:
custom_model = "CompVis/stable-diffusion-v1-4"
#st.session_state["weights_path"] = custom_model
#else:
#custom_model = "CompVis/stable-diffusion-v1-4"
#st.session_state["weights_path"] = f"CompVis/{slugify(custom_model.lower())}"
st.session_state.sampling_steps = st.slider("Sampling Steps",
value=st.session_state['defaults'].txt2vid.sampling_steps,
min_value=st.session_state['defaults'].txt2vid.slider_bounds.sampling.lower,
max_value=st.session_state['defaults'].txt2vid.slider_bounds.sampling.upper,
step=st.session_state['defaults'].txt2vid.slider_steps.sampling,
help="Number of steps between each pair of sampled points")
st.session_state.num_inference_steps = st.slider("Inference Steps:", value=st.session_state['defaults'].txt2vid.num_inference_steps, min_value=10,step=10, max_value=500,
help="Higher values (e.g. 100, 200 etc) can create better images.")
#sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
#sampler_name = st.selectbox("Sampling method", sampler_name_list,
#index=sampler_name_list.index(st.session_state['defaults'].txt2vid.default_sampler), help="Sampling method to use. Default: k_euler")
scheduler_name_list = ["klms", "ddim"]
scheduler_name = st.selectbox("Scheduler:", scheduler_name_list,
index=scheduler_name_list.index(st.session_state['defaults'].txt2vid.scheduler_name), help="Scheduler to use. Default: klms")
beta_scheduler_type_list = ["scaled_linear", "linear"]
beta_scheduler_type = st.selectbox("Beta Schedule Type:", beta_scheduler_type_list,
index=beta_scheduler_type_list.index(st.session_state['defaults'].txt2vid.beta_scheduler_type), help="Schedule Type to use. Default: linear")
#basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"])
#with basic_tab:
#summit_on_enter = st.radio("Submit on enter?", ("Yes", "No"), horizontal=True,
#help="Press the Enter key to summit, when 'No' is selected you can use the Enter key to write multiple lines.")
with st.expander("Advanced"):
st.session_state["separate_prompts"] = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].txt2vid.separate_prompts,
help="Separate multiple prompts using the `|` character, and get all combinations of them.")
st.session_state["normalize_prompt_weights"] = st.checkbox("Normalize Prompt Weights.",
value=st.session_state['defaults'].txt2vid.normalize_prompt_weights, help="Ensure the sum of all weights add up to 1.0")
st.session_state["save_individual_images"] = st.checkbox("Save individual images.",
value=st.session_state['defaults'].txt2vid.save_individual_images, help="Save each image generated before any filter or enhancement is applied.")
st.session_state["save_video"] = st.checkbox("Save video",value=st.session_state['defaults'].txt2vid.save_video, help="Save a video with all the images generated as frames at the end of the generation.")
st.session_state["group_by_prompt"] = st.checkbox("Group results by prompt", value=st.session_state['defaults'].txt2vid.group_by_prompt,
help="Saves all the images with the same prompt into the same folder. When using a prompt matrix each prompt combination will have its own folder.")
st.session_state["write_info_files"] = st.checkbox("Write Info file", value=st.session_state['defaults'].txt2vid.write_info_files,
help="Save a file next to the image with informartion about the generation.")
st.session_state["dynamic_preview_frequency"] = st.checkbox("Dynamic Preview Frequency", value=st.session_state['defaults'].txt2vid.dynamic_preview_frequency,
help="This option tries to find the best value at which we can update \
the preview image during generation while minimizing the impact it has in performance. Default: True")
st.session_state["do_loop"] = st.checkbox("Do Loop", value=st.session_state['defaults'].txt2vid.do_loop,
help="Do loop")
st.session_state["save_as_jpg"] = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].txt2vid.save_as_jpg, help="Saves the images as jpg instead of png.")
if GFPGAN_available:
st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2vid.use_GFPGAN, help="Uses the GFPGAN model to improve faces after the generation. This greatly improve the quality and consistency of faces but uses extra VRAM. Disable if you need the extra VRAM.")
else:
st.session_state["use_GFPGAN"] = False
if RealESRGAN_available:
st.session_state["use_RealESRGAN"] = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].txt2vid.use_RealESRGAN,
help="Uses the RealESRGAN model to upscale the images after the generation. This greatly improve the quality and lets you have high resolution images but uses extra VRAM. Disable if you need the extra VRAM.")
st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", ["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"], index=0)
else:
st.session_state["use_RealESRGAN"] = False
st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
st.session_state["variant_amount"] = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2vid.variant_amount, min_value=0.0, max_value=1.0, step=0.01)
st.session_state["variant_seed"] = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2vid.seed, help="The seed to use when generating a variant, if left blank a random seed will be generated.")
st.session_state["beta_start"] = st.slider("Beta Start:", value=st.session_state['defaults'].txt2vid.beta_start, min_value=0.0001, max_value=0.03, step=0.0001, format="%.4f")
st.session_state["beta_end"] = st.slider("Beta End:", value=st.session_state['defaults'].txt2vid.beta_end, min_value=0.0001, max_value=0.03, step=0.0001, format="%.4f")
if generate_button:
#print("Loading models")
# load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
#load_models(False, False, False, st.session_state["RealESRGAN_model"], CustomModel_available=st.session_state["CustomModel_available"], custom_model=custom_model)
try:
# run video generation
video, seed, info, stats = txt2vid(prompts=prompt, gpu=st.session_state["defaults"].general.gpu,
num_steps=st.session_state.sampling_steps, max_frames=int(st.session_state.max_frames),
num_inference_steps=st.session_state.num_inference_steps,
cfg_scale=cfg_scale,do_loop=st.session_state["do_loop"],
seeds=seed, quality=100, eta=0.0, width=width,
height=height, weights_path=custom_model, scheduler=scheduler_name,
disable_tqdm=False, beta_start=st.session_state["beta_start"], beta_end=st.session_state["beta_end"],
beta_schedule=beta_scheduler_type, starting_image=None)
#message.success('Done!', icon="✅")
message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
#if 'latestVideos' in st.session_state:
#for i in video:
##push the new image to the list of latest images and remove the oldest one
##remove the last index from the list\
#st.session_state['latestVideos'].pop()
##add the new image to the start of the list
#st.session_state['latestVideos'].insert(0, i)
#PlaceHolder.empty()
#with PlaceHolder.container():
#col1, col2, col3 = st.columns(3)
#col1_cont = st.container()
#col2_cont = st.container()
#col3_cont = st.container()
#with col1_cont:
#with col1:
#st.image(st.session_state['latestVideos'][0])
#st.image(st.session_state['latestVideos'][3])
#st.image(st.session_state['latestVideos'][6])
#with col2_cont:
#with col2:
#st.image(st.session_state['latestVideos'][1])
#st.image(st.session_state['latestVideos'][4])
#st.image(st.session_state['latestVideos'][7])
#with col3_cont:
#with col3:
#st.image(st.session_state['latestVideos'][2])
#st.image(st.session_state['latestVideos'][5])
#st.image(st.session_state['latestVideos'][8])
#historyGallery = st.empty()
## check if output_images length is the same as seeds length
#with gallery_tab:
#st.markdown(createHTMLGallery(video,seed), unsafe_allow_html=True)
#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
except (StopException, KeyError):
print(f"Received Streamlit StopException")

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scripts/webui.py
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scripts/webui_streamlit.py
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scripts/webui_streamlit_old.py Normal file
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2
setup.py
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@ -1,7 +1,7 @@
from setuptools import setup, find_packages
setup(
name='latent-diffusion',
name='sd-webui',
version='0.0.1',
description='',
packages=find_packages(),

4
webui.sh
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@ -37,7 +37,7 @@ if ! conda env list | grep ".*${ENV_NAME}.*" >/dev/null 2>&1; then
ENV_UPDATED=1
elif [[ ! -z $CONDA_FORCE_UPDATE && $CONDA_FORCE_UPDATE == "true" ]] || (( $ENV_MODIFIED > $ENV_MODIFIED_CACHED )); then
echo "Updating conda env: ${ENV_NAME} ..."
conda env update --file $ENV_FILE --prune
PIP_EXISTS_ACTION=w conda env update --file $ENV_FILE --prune
ENV_UPDATED=1
fi
@ -56,4 +56,4 @@ if [ ! -e "models/ldm/stable-diffusion-v1/model.ckpt" ]; then
exit 1
fi
python scripts/relauncher.py
python scripts/relauncher.py