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Merge branch 'beta' into serverip

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1
CHANGES.md
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@ -23,6 +23,7 @@
### Detailed changelog ### Detailed changelog
* 2.4.14 - 23 Nov 2022 - Button to retrieve the network addresses of the server in the systems setting dialog * 2.4.14 - 23 Nov 2022 - Button to retrieve the network addresses of the server in the systems setting dialog
* 2.4.14 - 22 Nov 2022 - Change the backend to a custom fork of Stable Diffusion
* 2.4.13 - 21 Nov 2022 - Change the modifier weight via mouse wheel, drag to reorder selected modifiers, and some more modifier-related fixes. Thanks @patriceac * 2.4.13 - 21 Nov 2022 - Change the modifier weight via mouse wheel, drag to reorder selected modifiers, and some more modifier-related fixes. Thanks @patriceac
* 2.4.12 - 21 Nov 2022 - Another fix for improving how long images take to generate. Reduces the time taken for an enqueued task to start processing. * 2.4.12 - 21 Nov 2022 - Another fix for improving how long images take to generate. Reduces the time taken for an enqueued task to start processing.
* 2.4.11 - 21 Nov 2022 - Installer improvements: avoid crashing if the username contains a space or special characters, allow moving/renaming the folder after installation on Windows, whitespace fix on git apply * 2.4.11 - 21 Nov 2022 - Installer improvements: avoid crashing if the username contains a space or special characters, allow moving/renaming the folder after installation on Windows, whitespace fix on git apply

2
README.md
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@ -71,7 +71,7 @@ Useful for judging (and stopping) an image quickly, without waiting for it to fi
You don't need to install or struggle with Python, Anaconda, Docker etc. The installer will take care of whatever is needed. You don't need to install or struggle with Python, Anaconda, Docker etc. The installer will take care of whatever is needed.
# Installation # Installation
1. **Download** [for Windows](https://github.com/cmdr2/stable-diffusion-ui/releases/download/v2.3.5/stable-diffusion-ui-windows.zip) or [for Linux](https://github.com/cmdr2/stable-diffusion-ui/releases/download/v2.3.5/stable-diffusion-ui-linux.zip). 1. **Download** [for Windows](https://github.com/cmdr2/stable-diffusion-ui/releases/download/v2.4.13/stable-diffusion-ui-windows.zip) or [for Linux](https://github.com/cmdr2/stable-diffusion-ui/releases/download/v2.4.13/stable-diffusion-ui-linux.zip).
2. **Extract**: 2. **Extract**:
- For Windows: After unzipping the file, please move the `stable-diffusion-ui` folder to your `C:` (or any drive like D:, at the top root level), e.g. `C:\stable-diffusion-ui`. This will avoid a common problem with Windows (file path length limits). - For Windows: After unzipping the file, please move the `stable-diffusion-ui` folder to your `C:` (or any drive like D:, at the top root level), e.g. `C:\stable-diffusion-ui`. This will avoid a common problem with Windows (file path length limits).

10
scripts/bootstrap.bat
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@ -42,11 +42,11 @@ if "%PACKAGES_TO_INSTALL%" NEQ "" (
mkdir "%MAMBA_ROOT_PREFIX%" mkdir "%MAMBA_ROOT_PREFIX%"
call curl -Lk "%MICROMAMBA_DOWNLOAD_URL%" > "%MAMBA_ROOT_PREFIX%\micromamba.exe" call curl -Lk "%MICROMAMBA_DOWNLOAD_URL%" > "%MAMBA_ROOT_PREFIX%\micromamba.exe"
@REM if "%ERRORLEVEL%" NEQ "0" ( if "%ERRORLEVEL%" NEQ "0" (
@REM echo "There was a problem downloading micromamba. Cannot continue." echo "There was a problem downloading micromamba. Cannot continue."
@REM pause pause
@REM exit /b exit /b
@REM ) )
mkdir "%APPDATA%" mkdir "%APPDATA%"
mkdir "%USERPROFILE%" mkdir "%USERPROFILE%"

48
scripts/on_sd_start.bat
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@ -16,11 +16,11 @@ if exist "%cd%\profile" (
@rem activate the installer env @rem activate the installer env
call conda activate call conda activate
@rem @if "%ERRORLEVEL%" NEQ "0" ( @if "%ERRORLEVEL%" NEQ "0" (
@rem @echo. & echo "Error activating conda for Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo. @echo. & echo "Error activating conda for Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
@rem pause pause
@rem exit /b exit /b
@rem ) )
@REM remove the old version of the dev console script, if it's still present @REM remove the old version of the dev console script, if it's still present
if exist "Open Developer Console.cmd" del "Open Developer Console.cmd" if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
@ -33,18 +33,19 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
@cd stable-diffusion @cd stable-diffusion
@call git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
@call git reset --hard @call git reset --hard
@call git pull @call git pull
@call git -c advice.detachedHead=false checkout f6cfebffa752ee11a7b07497b8529d5971de916c @call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
@call git apply --whitespace=nowarn ..\ui\sd_internal\ddim_callback.patch @call git apply --whitespace=warn ..\ui\sd_internal\ddim_callback.patch
@call git apply --whitespace=nowarn ..\ui\sd_internal\env_yaml.patch
@cd .. @cd ..
) else ( ) else (
@echo. & echo "Downloading Stable Diffusion.." & echo. @echo. & echo "Downloading Stable Diffusion.." & echo.
@call git clone https://github.com/basujindal/stable-diffusion.git && ( @call git clone https://github.com/easydiffusion/diffusion-kit.git stable-diffusion && (
@echo sd_git_cloned >> scripts\install_status.txt @echo sd_git_cloned >> scripts\install_status.txt
) || ( ) || (
@echo "Error downloading Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" @echo "Error downloading Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
@ -53,10 +54,9 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
) )
@cd stable-diffusion @cd stable-diffusion
@call git -c advice.detachedHead=false checkout f6cfebffa752ee11a7b07497b8529d5971de916c @call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
@call git apply --whitespace=nowarn ..\ui\sd_internal\ddim_callback.patch @call git apply --whitespace=warn ..\ui\sd_internal\ddim_callback.patch
@call git apply --whitespace=nowarn ..\ui\sd_internal\env_yaml.patch
@cd .. @cd ..
) )
@ -88,12 +88,6 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
@call conda activate .\env @call conda activate .\env
@call conda install -c conda-forge -y --prefix env antlr4-python3-runtime=4.8 || (
@echo. & echo "Error installing antlr4-python3-runtime for Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
pause
exit /b
)
for /f "tokens=*" %%a in ('python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"') do if "%%a" NEQ "42" ( for /f "tokens=*" %%a in ('python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"') do if "%%a" NEQ "42" (
@echo. & echo "Dependency test failed! Error installing the packages necessary for Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo. @echo. & echo "Dependency test failed! Error installing the packages necessary for Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
pause pause
@ -117,18 +111,6 @@ set PATH=C:\Windows\System32;%PATH%
set PYTHONPATH=%cd%;%cd%\env\lib\site-packages set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
@call pip install -e git+https://github.com/TencentARC/GFPGAN#egg=GFPGAN || (
@echo. & echo "Error installing the packages necessary for GFPGAN (Face Correction). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
pause
exit /b
)
@call pip install basicsr==1.4.2 || (
@echo. & echo "Error installing the basicsr package necessary for GFPGAN (Face Correction). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
pause
exit /b
)
for /f "tokens=*" %%a in ('python -c "from gfpgan import GFPGANer; print(42)"') do if "%%a" NEQ "42" ( for /f "tokens=*" %%a in ('python -c "from gfpgan import GFPGANer; print(42)"') do if "%%a" NEQ "42" (
@echo. & echo "Dependency test failed! Error installing the packages necessary for GFPGAN (Face Correction). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo. @echo. & echo "Dependency test failed! Error installing the packages necessary for GFPGAN (Face Correction). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
pause pause
@ -150,12 +132,6 @@ set PATH=C:\Windows\System32;%PATH%
set PYTHONPATH=%cd%;%cd%\env\lib\site-packages set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
@call pip install -e git+https://github.com/xinntao/Real-ESRGAN#egg=realesrgan || (
@echo. & echo "Error installing the packages necessary for ESRGAN (Resolution Upscaling). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
pause
exit /b
)
for /f "tokens=*" %%a in ('python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"') do if "%%a" NEQ "42" ( for /f "tokens=*" %%a in ('python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"') do if "%%a" NEQ "42" (
@echo. & echo "Dependency test failed! Error installing the packages necessary for ESRGAN (Resolution Upscaling). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo. @echo. & echo "Dependency test failed! Error installing the packages necessary for ESRGAN (Resolution Upscaling). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
pause pause

32
scripts/on_sd_start.sh Executable file → Normal file
View File

@ -26,28 +26,28 @@ if [ -e "scripts/install_status.txt" ] && [ `grep -c sd_git_cloned scripts/insta
cd stable-diffusion cd stable-diffusion
git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
git reset --hard git reset --hard
git pull git pull
git -c advice.detachedHead=false checkout f6cfebffa752ee11a7b07497b8529d5971de916c git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
git apply --whitespace=nowarn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed" git apply --whitespace=warn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
git apply --whitespace=nowarn ../ui/sd_internal/env_yaml.patch || fail "yaml patch failed"
cd .. cd ..
else else
printf "\n\nDownloading Stable Diffusion..\n\n" printf "\n\nDownloading Stable Diffusion..\n\n"
if git clone https://github.com/basujindal/stable-diffusion.git ; then if git clone https://github.com/easydiffusion/diffusion-kit.git stable-diffusion ; then
echo sd_git_cloned >> scripts/install_status.txt echo sd_git_cloned >> scripts/install_status.txt
else else
fail "git clone of basujindal/stable-diffusion.git failed" fail "git clone of basujindal/stable-diffusion.git failed"
fi fi
cd stable-diffusion cd stable-diffusion
git -c advice.detachedHead=false checkout f6cfebffa752ee11a7b07497b8529d5971de916c git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
git apply --whitespace=nowarn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed" git apply --whitespace=warn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
git apply --whitespace=nowarn ../ui/sd_internal/env_yaml.patch || fail "yaml patch failed"
cd .. cd ..
fi fi
@ -74,12 +74,6 @@ else
conda activate ./env || fail "conda activate failed" conda activate ./env || fail "conda activate failed"
if conda install -c conda-forge --prefix ./env -y antlr4-python3-runtime=4.8 ; then
echo "Installed. Testing.."
else
fail "Error installing antlr4-python3-runtime"
fi
out_test=`python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"` out_test=`python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"`
if [ "$out_test" != "42" ]; then if [ "$out_test" != "42" ]; then
fail "Dependency test failed" fail "Dependency test failed"
@ -96,12 +90,6 @@ else
export PYTHONNOUSERSITE=1 export PYTHONNOUSERSITE=1
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages" export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
if pip install -e git+https://github.com/TencentARC/GFPGAN#egg=GFPGAN ; then
echo "Installed. Testing.."
else
fail "Error installing the packages necessary for GFPGAN (Face Correction)."
fi
out_test=`python -c "from gfpgan import GFPGANer; print(42)"` out_test=`python -c "from gfpgan import GFPGANer; print(42)"`
if [ "$out_test" != "42" ]; then if [ "$out_test" != "42" ]; then
echo "EE The dependency check has failed. This usually means that some system libraries are missing." echo "EE The dependency check has failed. This usually means that some system libraries are missing."
@ -121,12 +109,6 @@ else
export PYTHONNOUSERSITE=1 export PYTHONNOUSERSITE=1
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages" export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
if pip install -e git+https://github.com/xinntao/Real-ESRGAN#egg=realesrgan ; then
echo "Installed. Testing.."
else
fail "Error installing the packages necessary for ESRGAN"
fi
out_test=`python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"` out_test=`python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"`
if [ "$out_test" != "42" ]; then if [ "$out_test" != "42" ]; then
fail "ESRGAN dependency test failed" fail "ESRGAN dependency test failed"

5
ui/index.html
View File

@ -20,7 +20,10 @@
<div id="container"> <div id="container">
<div id="top-nav"> <div id="top-nav">
<div id="logo"> <div id="logo">
<h1>Stable Diffusion UI <small>v2.4.13 <span id="updateBranchLabel"></span></small></h1> <h1>
Stable Diffusion UI
<small>v2.4.14 <span id="updateBranchLabel"></span></small>
</h1>
</div> </div>
<div id="server-status"> <div id="server-status">
<div id="server-status-color">●</div> <div id="server-status-color">●</div>

14
ui/media/js/dnd.js
View File

@ -51,6 +51,13 @@ const TASK_MAPPING = {
readUI: () => negativePromptField.value, readUI: () => negativePromptField.value,
parse: (val) => val parse: (val) => val
}, },
active_tags: { name: "Image Modifiers",
setUI: (active_tags) => {
refreshModifiersState(active_tags)
},
readUI: () => activeTags.map(x => x.name),
parse: (val) => val
},
width: { name: 'Width', width: { name: 'Width',
setUI: (width) => { setUI: (width) => {
const oldVal = widthField.value const oldVal = widthField.value
@ -267,11 +274,6 @@ function restoreTaskToUI(task, fieldsToSkip) {
// restore the original tag // restore the original tag
promptField.value = task.reqBody.original_prompt || task.reqBody.prompt promptField.value = task.reqBody.original_prompt || task.reqBody.prompt
// Restore modifiers
if (task.reqBody.active_tags) {
refreshModifiersState(task.reqBody.active_tags)
}
// properly reset checkboxes // properly reset checkboxes
if (!('use_face_correction' in task.reqBody)) { if (!('use_face_correction' in task.reqBody)) {
useFaceCorrectionField.checked = false useFaceCorrectionField.checked = false
@ -406,7 +408,7 @@ async function parseContent(text) {
} }
async function readFile(file, i) { async function readFile(file, i) {
console.log(`Event %o reading file[${i}]:${file.name}...`, e) console.log(`Event %o reading file[${i}]:${file.name}...`)
const fileContent = (await file.text()).trim() const fileContent = (await file.text()).trim()
return await parseContent(fileContent) return await parseContent(fileContent)
} }

252
ui/sd_internal/ddim_callback.patch
View File

@ -1,72 +1,13 @@
diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py
index b967b55..35ef520 100644 index 79058bc..a473411 100644
--- a/optimizedSD/ddpm.py --- a/optimizedSD/ddpm.py
+++ b/optimizedSD/ddpm.py +++ b/optimizedSD/ddpm.py
@@ -22,7 +22,7 @@ from ldm.util import exists, default, instantiate_from_config @@ -564,12 +564,12 @@ class UNet(DDPM):
from ldm.modules.diffusionmodules.util import make_beta_schedule unconditional_guidance_scale=unconditional_guidance_scale,
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like callback=callback, img_callback=img_callback)
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from samplers import CompVisDenoiser, get_ancestral_step, to_d, append_dims,linear_multistep_coeff
+from .samplers import CompVisDenoiser, get_ancestral_step, to_d, append_dims,linear_multistep_coeff
def disabled_train(self):
"""Overwrite model.train with this function to make sure train/eval mode
@@ -506,6 +506,8 @@ class UNet(DDPM):
x_latent = noise if x0 is None else x0
# sampling
+ if sampler in ('ddim', 'dpm2', 'heun', 'dpm2_a', 'lms') and not hasattr(self, 'ddim_timesteps'):
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
if sampler == "plms":
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
@@ -528,39 +530,46 @@ class UNet(DDPM):
elif sampler == "ddim":
samples = self.ddim_sampling(x_latent, conditioning, S, unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
- mask = mask,init_latent=x_T,use_original_steps=False)
+ mask = mask,init_latent=x_T,use_original_steps=False,
+ callback=callback, img_callback=img_callback)
elif sampler == "euler":
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
samples = self.euler_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
- unconditional_guidance_scale=unconditional_guidance_scale)
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ img_callback=img_callback)
elif sampler == "euler_a":
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
samples = self.euler_ancestral_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
- unconditional_guidance_scale=unconditional_guidance_scale)
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ img_callback=img_callback)
elif sampler == "dpm2":
samples = self.dpm_2_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
- unconditional_guidance_scale=unconditional_guidance_scale)
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ img_callback=img_callback)
elif sampler == "heun":
samples = self.heun_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
- unconditional_guidance_scale=unconditional_guidance_scale)
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ img_callback=img_callback)
elif sampler == "dpm2_a":
samples = self.dpm_2_ancestral_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
- unconditional_guidance_scale=unconditional_guidance_scale)
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ img_callback=img_callback)
elif sampler == "lms":
samples = self.lms_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
- unconditional_guidance_scale=unconditional_guidance_scale)
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ img_callback=img_callback)
+
+ yield from samples + yield from samples
+
if(self.turbo): if(self.turbo):
self.model1.to("cpu") self.model1.to("cpu")
self.model2.to("cpu") self.model2.to("cpu")
@ -76,7 +17,7 @@ index b967b55..35ef520 100644
@torch.no_grad() @torch.no_grad()
def plms_sampling(self, cond,b, img, def plms_sampling(self, cond,b, img,
ddim_use_original_steps=False, ddim_use_original_steps=False,
@@ -599,10 +608,10 @@ class UNet(DDPM): @@ -608,10 +608,10 @@ class UNet(DDPM):
old_eps.append(e_t) old_eps.append(e_t)
if len(old_eps) >= 4: if len(old_eps) >= 4:
old_eps.pop(0) old_eps.pop(0)
@ -90,23 +31,15 @@ index b967b55..35ef520 100644
@torch.no_grad() @torch.no_grad()
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
@@ -706,7 +715,8 @@ class UNet(DDPM): @@ -740,13 +740,13 @@ class UNet(DDPM):
@torch.no_grad()
def ddim_sampling(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
- mask = None,init_latent=None,use_original_steps=False):
+ mask = None,init_latent=None,use_original_steps=False,
+ callback=None, img_callback=None):
timesteps = self.ddim_timesteps
timesteps = timesteps[:t_start]
@@ -730,10 +740,13 @@ class UNet(DDPM):
unconditional_guidance_scale=unconditional_guidance_scale, unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning) unconditional_conditioning=unconditional_conditioning)
- if callback: callback(i)
- if img_callback: img_callback(x_dec, i)
+ if callback: yield from callback(i) + if callback: yield from callback(i)
+ if img_callback: yield from img_callback(x_dec, i) + if img_callback: yield from img_callback(x_dec, i)
+
if mask is not None: if mask is not None:
- return x0 * mask + (1. - mask) * x_dec - return x0 * mask + (1. - mask) * x_dec
+ x_dec = x0 * mask + (1. - mask) * x_dec + x_dec = x0 * mask + (1. - mask) * x_dec
@ -116,217 +49,114 @@ index b967b55..35ef520 100644
@torch.no_grad() @torch.no_grad()
@@ -779,13 +792,16 @@ class UNet(DDPM): @@ -820,12 +820,12 @@ class UNet(DDPM):
@torch.no_grad()
- def euler_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None,callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ def euler_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None,callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
+ img_callback=None):
"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
cvd = CompVisDenoiser(ac)
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
+ print(f"Running Euler Sampling with {len(sigmas) - 1} timesteps")
+
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
@@ -807,13 +823,18 @@ class UNet(DDPM):
d = to_d(x, sigma_hat, denoised) d = to_d(x, sigma_hat, denoised)
if callback is not None: - if callback: callback(i)
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - if img_callback: img_callback(x, i)
+ + if callback: yield from callback(i)
+ if img_callback: yield from img_callback(x, i) + if img_callback: yield from img_callback(x, i)
+
dt = sigmas[i + 1] - sigma_hat dt = sigmas[i + 1] - sigma_hat
# Euler method # Euler method
x = x + d * dt x = x + d * dt
- return x - return x
+
+ yield from img_callback(x, len(sigmas)-1) + yield from img_callback(x, len(sigmas)-1)
@torch.no_grad() @torch.no_grad()
- def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None): def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, img_callback=None):
+ def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, @@ -852,14 +852,14 @@ class UNet(DDPM):
+ img_callback=None): denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
"""Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args
@@ -822,6 +843,8 @@ class UNet(DDPM):
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
+ print(f"Running Euler Ancestral Sampling with {len(sigmas) - 1} timesteps")
+
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
@@ -837,17 +860,22 @@ class UNet(DDPM):
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1]) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
if callback is not None: - if callback: callback(i)
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if img_callback: img_callback(x, i)
+ + if callback: yield from callback(i)
+ if img_callback: yield from img_callback(x, i) + if img_callback: yield from img_callback(x, i)
+
d = to_d(x, sigmas[i], denoised) d = to_d(x, sigmas[i], denoised)
# Euler method # Euler method
dt = sigma_down - sigmas[i] dt = sigma_down - sigmas[i]
x = x + d * dt x = x + d * dt
x = x + torch.randn_like(x) * sigma_up x = x + torch.randn_like(x) * sigma_up
- return x - return x
+
+ yield from img_callback(x, len(sigmas)-1) + yield from img_callback(x, len(sigmas)-1)
@torch.no_grad() @@ -892,8 +892,8 @@ class UNet(DDPM):
- def heun_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+ def heun_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
+ img_callback=None):
"""Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
@@ -855,6 +883,8 @@ class UNet(DDPM):
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
+ print(f"Running Heun Sampling with {len(sigmas) - 1} timesteps")
+
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
@@ -876,6 +906,9 @@ class UNet(DDPM):
d = to_d(x, sigma_hat, denoised) d = to_d(x, sigma_hat, denoised)
if callback is not None: - if callback: callback(i)
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - if img_callback: img_callback(x, i)
+ + if callback: yield from callback(i)
+ if img_callback: yield from img_callback(x, i) + if img_callback: yield from img_callback(x, i)
+
dt = sigmas[i + 1] - sigma_hat dt = sigmas[i + 1] - sigma_hat
if sigmas[i + 1] == 0: if sigmas[i + 1] == 0:
# Euler method # Euler method
@@ -895,11 +928,13 @@ class UNet(DDPM): @@ -913,7 +913,7 @@ class UNet(DDPM):
d_2 = to_d(x_2, sigmas[i + 1], denoised_2) d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
d_prime = (d + d_2) / 2 d_prime = (d + d_2) / 2
x = x + d_prime * dt x = x + d_prime * dt
- return x - return x
+
+ yield from img_callback(x, len(sigmas)-1) + yield from img_callback(x, len(sigmas)-1)
@torch.no_grad() @torch.no_grad()
- def dpm_2_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): @@ -944,8 +944,8 @@ class UNet(DDPM):
+ def dpm_2_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
+ img_callback=None):
"""A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
@@ -907,6 +942,8 @@ class UNet(DDPM):
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
+ print(f"Running DPM2 Sampling with {len(sigmas) - 1} timesteps")
+
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
@@ -924,7 +961,7 @@ class UNet(DDPM):
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2) e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
- - if callback: callback(i)
- if img_callback: img_callback(x, i)
+ if callback: yield from callback(i)
+ if img_callback: yield from img_callback(x, i) + if img_callback: yield from img_callback(x, i)
d = to_d(x, sigma_hat, denoised) d = to_d(x, sigma_hat, denoised)
# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
@@ -945,11 +982,13 @@ class UNet(DDPM): @@ -966,7 +966,7 @@ class UNet(DDPM):
d_2 = to_d(x_2, sigma_mid, denoised_2) d_2 = to_d(x_2, sigma_mid, denoised_2)
x = x + d_2 * dt_2 x = x + d_2 * dt_2
- return x - return x
+
+ yield from img_callback(x, len(sigmas)-1) + yield from img_callback(x, len(sigmas)-1)
@torch.no_grad() @torch.no_grad()
- def dpm_2_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None): @@ -994,8 +994,8 @@ class UNet(DDPM):
+ def dpm_2_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None,
+ img_callback=None):
"""Ancestral sampling with DPM-Solver inspired second-order steps."""
extra_args = {} if extra_args is None else extra_args
@@ -957,6 +996,8 @@ class UNet(DDPM):
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
+ print(f"Running DPM2 Ancestral Sampling with {len(sigmas) - 1} timesteps")
+
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
@@ -973,6 +1014,9 @@ class UNet(DDPM):
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1]) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
if callback is not None: - if callback: callback(i)
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if img_callback: img_callback(x, i)
+ + if callback: yield from callback(i)
+ if img_callback: yield from img_callback(x, i) + if img_callback: yield from img_callback(x, i)
+
d = to_d(x, sigmas[i], denoised) d = to_d(x, sigmas[i], denoised)
# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3 sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3
@@ -993,11 +1037,13 @@ class UNet(DDPM): @@ -1016,7 +1016,7 @@ class UNet(DDPM):
d_2 = to_d(x_2, sigma_mid, denoised_2) d_2 = to_d(x_2, sigma_mid, denoised_2)
x = x + d_2 * dt_2 x = x + d_2 * dt_2
x = x + torch.randn_like(x) * sigma_up x = x + torch.randn_like(x) * sigma_up
- return x - return x
+
+ yield from img_callback(x, len(sigmas)-1) + yield from img_callback(x, len(sigmas)-1)
@torch.no_grad() @torch.no_grad()
- def lms_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, order=4): @@ -1042,8 +1042,8 @@ class UNet(DDPM):
+ def lms_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, order=4,
+ img_callback=None):
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
@@ -1005,6 +1051,8 @@ class UNet(DDPM):
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
+ print(f"Running LMS Sampling with {len(sigmas) - 1} timesteps")
+
ds = []
for i in trange(len(sigmas) - 1, disable=disable):
@@ -1017,6 +1065,7 @@ class UNet(DDPM):
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2) e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
- if callback: callback(i)
- if img_callback: img_callback(x, i)
+ if callback: yield from callback(i)
+ if img_callback: yield from img_callback(x, i) + if img_callback: yield from img_callback(x, i)
d = to_d(x, sigmas[i], denoised) d = to_d(x, sigmas[i], denoised)
ds.append(d) ds.append(d)
@@ -1027,4 +1076,5 @@ class UNet(DDPM): @@ -1054,4 +1054,4 @@ class UNet(DDPM):
cur_order = min(i + 1, order) cur_order = min(i + 1, order)
coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)] coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)]
x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
- return x - return x
+
+ yield from img_callback(x, len(sigmas)-1) + yield from img_callback(x, len(sigmas)-1)
diff --git a/optimizedSD/openaimodelSplit.py b/optimizedSD/openaimodelSplit.py
index abc3098..7a32ffe 100644
--- a/optimizedSD/openaimodelSplit.py
+++ b/optimizedSD/openaimodelSplit.py
@@ -13,7 +13,7 @@ from ldm.modules.diffusionmodules.util import (
normalization,
timestep_embedding,
)
-from splitAttention import SpatialTransformer
+from .splitAttention import SpatialTransformer
class AttentionPool2d(nn.Module):

13
ui/sd_internal/env_yaml.patch
View File

@ -1,13 +0,0 @@
diff --git a/environment.yaml b/environment.yaml
index 7f25da8..306750f 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -23,6 +23,8 @@ dependencies:
- torch-fidelity==0.3.0
- transformers==4.19.2
- torchmetrics==0.6.0
+ - pywavelets==1.3.0
+ - pandas==1.4.4
- kornia==0.6
- -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
- -e git+https://github.com/openai/CLIP.git@main#egg=clip