mirror of
https://github.com/easydiffusion/easydiffusion.git
synced 2024-11-22 16:23:28 +01:00
Merge branch 'beta' into serverip
This commit is contained in:
JeLuF
GitHub
commit
3452d7852a
@ -23,6 +23,7 @@
|
||||
|
||||
### 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 - 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
|
||||
* 2.4.12 - 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
|
||||
|
||||
@ -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.
|
||||
|
||||
# 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**:
|
||||
- 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).
|
||||
|
||||
@ -42,11 +42,11 @@ if "%PACKAGES_TO_INSTALL%" NEQ "" (
|
||||
mkdir "%MAMBA_ROOT_PREFIX%"
|
||||
call curl -Lk "%MICROMAMBA_DOWNLOAD_URL%" > "%MAMBA_ROOT_PREFIX%\micromamba.exe"
|
||||
|
||||
@REM if "%ERRORLEVEL%" NEQ "0" (
|
||||
@REM echo "There was a problem downloading micromamba. Cannot continue."
|
||||
@REM pause
|
||||
@REM exit /b
|
||||
@REM )
|
||||
if "%ERRORLEVEL%" NEQ "0" (
|
||||
echo "There was a problem downloading micromamba. Cannot continue."
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
|
||||
mkdir "%APPDATA%"
|
||||
mkdir "%USERPROFILE%"
|
||||
|
||||
@ -16,11 +16,11 @@ if exist "%cd%\profile" (
|
||||
|
||||
@rem activate the installer env
|
||||
call conda activate
|
||||
@rem @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.
|
||||
@rem pause
|
||||
@rem exit /b
|
||||
@rem )
|
||||
@if "%ERRORLEVEL%" NEQ "0" (
|
||||
@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.
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
|
||||
@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"
|
||||
@ -33,18 +33,19 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
|
||||
|
||||
@cd stable-diffusion
|
||||
|
||||
@call git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
|
||||
|
||||
@call git reset --hard
|
||||
@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=nowarn ..\ui\sd_internal\env_yaml.patch
|
||||
@call git apply --whitespace=warn ..\ui\sd_internal\ddim_callback.patch
|
||||
|
||||
@cd ..
|
||||
) else (
|
||||
@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 "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
|
||||
@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=nowarn ..\ui\sd_internal\env_yaml.patch
|
||||
@call git apply --whitespace=warn ..\ui\sd_internal\ddim_callback.patch
|
||||
|
||||
@cd ..
|
||||
)
|
||||
@ -88,12 +88,6 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
|
||||
|
||||
@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" (
|
||||
@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
|
||||
@ -117,18 +111,6 @@ set PATH=C:\Windows\System32;%PATH%
|
||||
|
||||
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" (
|
||||
@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
|
||||
@ -150,12 +132,6 @@ set PATH=C:\Windows\System32;%PATH%
|
||||
|
||||
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" (
|
||||
@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
|
||||
|
||||
32
scripts/on_sd_start.sh
Executable file → Normal file
32
scripts/on_sd_start.sh
Executable file → Normal file
@ -26,28 +26,28 @@ if [ -e "scripts/install_status.txt" ] && [ `grep -c sd_git_cloned scripts/insta
|
||||
|
||||
cd stable-diffusion
|
||||
|
||||
git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
|
||||
|
||||
git reset --hard
|
||||
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=nowarn ../ui/sd_internal/env_yaml.patch || fail "yaml patch failed"
|
||||
git apply --whitespace=warn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
|
||||
|
||||
cd ..
|
||||
else
|
||||
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
|
||||
else
|
||||
fail "git clone of basujindal/stable-diffusion.git failed"
|
||||
fi
|
||||
|
||||
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=nowarn ../ui/sd_internal/env_yaml.patch || fail "yaml patch failed"
|
||||
git apply --whitespace=warn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
|
||||
|
||||
cd ..
|
||||
fi
|
||||
@ -74,12 +74,6 @@ else
|
||||
|
||||
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)"`
|
||||
if [ "$out_test" != "42" ]; then
|
||||
fail "Dependency test failed"
|
||||
@ -96,12 +90,6 @@ else
|
||||
export PYTHONNOUSERSITE=1
|
||||
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
|
||||
|
||||
if pip install -e git+https://github.com/TencentARC/GFPGAN#egg=GFPGAN ; then
|
||||
echo "Installed. Testing.."
|
||||
else
|
||||
fail "Error installing the packages necessary for GFPGAN (Face Correction)."
|
||||
fi
|
||||
|
||||
out_test=`python -c "from gfpgan import GFPGANer; print(42)"`
|
||||
if [ "$out_test" != "42" ]; then
|
||||
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 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)"`
|
||||
if [ "$out_test" != "42" ]; then
|
||||
fail "ESRGAN dependency test failed"
|
||||
|
||||
@ -20,7 +20,10 @@
|
||||
<div id="container">
|
||||
<div id="top-nav">
|
||||
<div id="logo">
|
||||
<h1>Stable Diffusion UI <small>v2.4.13 <span id="updateBranchLabel"></span></small></h1>
|
||||
<h1>
|
||||
Stable Diffusion UI
|
||||
<small>v2.4.14 <span id="updateBranchLabel"></span></small>
|
||||
</h1>
|
||||
</div>
|
||||
<div id="server-status">
|
||||
<div id="server-status-color">●</div>
|
||||
|
||||
@ -51,6 +51,13 @@ const TASK_MAPPING = {
|
||||
readUI: () => negativePromptField.value,
|
||||
parse: (val) => val
|
||||
},
|
||||
active_tags: { name: "Image Modifiers",
|
||||
setUI: (active_tags) => {
|
||||
refreshModifiersState(active_tags)
|
||||
},
|
||||
readUI: () => activeTags.map(x => x.name),
|
||||
parse: (val) => val
|
||||
},
|
||||
width: { name: 'Width',
|
||||
setUI: (width) => {
|
||||
const oldVal = widthField.value
|
||||
@ -267,11 +274,6 @@ function restoreTaskToUI(task, fieldsToSkip) {
|
||||
// restore the original tag
|
||||
promptField.value = task.reqBody.original_prompt || task.reqBody.prompt
|
||||
|
||||
// Restore modifiers
|
||||
if (task.reqBody.active_tags) {
|
||||
refreshModifiersState(task.reqBody.active_tags)
|
||||
}
|
||||
|
||||
// properly reset checkboxes
|
||||
if (!('use_face_correction' in task.reqBody)) {
|
||||
useFaceCorrectionField.checked = false
|
||||
@ -406,7 +408,7 @@ async function parseContent(text) {
|
||||
}
|
||||
|
||||
async function readFile(file, i) {
|
||||
console.log(`Event %o reading file[${i}]:${file.name}...`, e)
|
||||
console.log(`Event %o reading file[${i}]:${file.name}...`)
|
||||
const fileContent = (await file.text()).trim()
|
||||
return await parseContent(fileContent)
|
||||
}
|
||||
|
||||
@ -1,72 +1,13 @@
|
||||
diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py
|
||||
index b967b55..35ef520 100644
|
||||
index 79058bc..a473411 100644
|
||||
--- a/optimizedSD/ddpm.py
|
||||
+++ b/optimizedSD/ddpm.py
|
||||
@@ -22,7 +22,7 @@ from ldm.util import exists, default, instantiate_from_config
|
||||
from ldm.modules.diffusionmodules.util import make_beta_schedule
|
||||
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
|
||||
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
|
||||
@@ -564,12 +564,12 @@ class UNet(DDPM):
|
||||
unconditional_guidance_scale=unconditional_guidance_scale,
|
||||
callback=callback, img_callback=img_callback)
|
||||
|
||||
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
|
||||
|
||||
+
|
||||
if(self.turbo):
|
||||
self.model1.to("cpu")
|
||||
self.model2.to("cpu")
|
||||
@ -76,7 +17,7 @@ index b967b55..35ef520 100644
|
||||
@torch.no_grad()
|
||||
def plms_sampling(self, cond,b, img,
|
||||
ddim_use_original_steps=False,
|
||||
@@ -599,10 +608,10 @@ class UNet(DDPM):
|
||||
@@ -608,10 +608,10 @@ class UNet(DDPM):
|
||||
old_eps.append(e_t)
|
||||
if len(old_eps) >= 4:
|
||||
old_eps.pop(0)
|
||||
@ -90,23 +31,15 @@ index b967b55..35ef520 100644
|
||||
|
||||
@torch.no_grad()
|
||||
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):
|
||||
|
||||
@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):
|
||||
@@ -740,13 +740,13 @@ class UNet(DDPM):
|
||||
unconditional_guidance_scale=unconditional_guidance_scale,
|
||||
unconditional_conditioning=unconditional_conditioning)
|
||||
|
||||
- if callback: callback(i)
|
||||
- if img_callback: img_callback(x_dec, i)
|
||||
+ if callback: yield from callback(i)
|
||||
+ if img_callback: yield from img_callback(x_dec, i)
|
||||
+
|
||||
|
||||
if mask is not None:
|
||||
- return 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()
|
||||
@@ -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)
|
||||
if callback is not None:
|
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
|
||||
+
|
||||
- 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)
|
||||
+
|
||||
dt = sigmas[i + 1] - sigma_hat
|
||||
# Euler method
|
||||
x = x + d * dt
|
||||
- return x
|
||||
+
|
||||
+ yield from img_callback(x, len(sigmas)-1)
|
||||
|
||||
@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):
|
||||
"""Ancestral sampling with Euler method steps."""
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
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):
|
||||
@@ -852,14 +852,14 @@ class UNet(DDPM):
|
||||
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
||||
|
||||
@@ -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])
|
||||
if callback is not None:
|
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||||
+
|
||||
- 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)
|
||||
+
|
||||
d = to_d(x, sigmas[i], denoised)
|
||||
# Euler method
|
||||
dt = sigma_down - sigmas[i]
|
||||
x = x + d * dt
|
||||
x = x + torch.randn_like(x) * sigma_up
|
||||
- return x
|
||||
+
|
||||
+ yield from img_callback(x, len(sigmas)-1)
|
||||
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
- 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.):
|
||||
+ 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
|
||||
@@ -892,8 +892,8 @@ class UNet(DDPM):
|
||||
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
||||
|
||||
@@ -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)
|
||||
if callback is not None:
|
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
|
||||
+
|
||||
- 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)
|
||||
+
|
||||
dt = sigmas[i + 1] - sigma_hat
|
||||
if sigmas[i + 1] == 0:
|
||||
# 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_prime = (d + d_2) / 2
|
||||
x = x + d_prime * dt
|
||||
- return x
|
||||
+
|
||||
+ yield from img_callback(x, len(sigmas)-1)
|
||||
|
||||
|
||||
@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.):
|
||||
+ 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):
|
||||
@@ -944,8 +944,8 @@ class UNet(DDPM):
|
||||
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
|
||||
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)
|
||||
|
||||
d = to_d(x, sigma_hat, denoised)
|
||||
# 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)
|
||||
x = x + d_2 * dt_2
|
||||
- return x
|
||||
+
|
||||
+ yield from img_callback(x, len(sigmas)-1)
|
||||
|
||||
|
||||
@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):
|
||||
+ 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
|
||||
@@ -994,8 +994,8 @@ class UNet(DDPM):
|
||||
|
||||
@@ -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])
|
||||
if callback is not None:
|
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||||
+
|
||||
- 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)
|
||||
+
|
||||
d = to_d(x, sigmas[i], denoised)
|
||||
# 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
|
||||
@@ -993,11 +1037,13 @@ class UNet(DDPM):
|
||||
@@ -1016,7 +1016,7 @@ class UNet(DDPM):
|
||||
d_2 = to_d(x_2, sigma_mid, denoised_2)
|
||||
x = x + d_2 * dt_2
|
||||
x = x + torch.randn_like(x) * sigma_up
|
||||
- return x
|
||||
+
|
||||
+ yield from img_callback(x, len(sigmas)-1)
|
||||
|
||||
|
||||
@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):
|
||||
+ 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):
|
||||
@@ -1042,8 +1042,8 @@ class UNet(DDPM):
|
||||
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
|
||||
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)
|
||||
|
||||
d = to_d(x, sigmas[i], denoised)
|
||||
ds.append(d)
|
||||
@@ -1027,4 +1076,5 @@ class UNet(DDPM):
|
||||
@@ -1054,4 +1054,4 @@ class UNet(DDPM):
|
||||
cur_order = min(i + 1, 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)))
|
||||
- return x
|
||||
+
|
||||
+ 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):
|
||||
|
||||
@ -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
|
||||
Loading…
Reference in New Issue
Block a user