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Merge branch 'comfyanonymous:master' into feature/blockweights

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Dr.Lt.Data 2023-05-02 22:57:45 +09:00 committed by GitHub
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3 changed files with 17 additions and 10 deletions
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2
.ci/nightly/update_windows/update_comfyui_and_python_dependencies.bat
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@ -1,3 +1,3 @@
..\python_embeded\python.exe .\update.py ..\ComfyUI\ ..\python_embeded\python.exe .\update.py ..\ComfyUI\
..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu118 -r ../ComfyUI/requirements.txt pygit2 ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -r ../ComfyUI/requirements.txt pygit2
pause pause

10
.github/workflows/windows_release_nightly_pytorch.yml vendored
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@ -19,21 +19,21 @@ jobs:
fetch-depth: 0 fetch-depth: 0
- uses: actions/setup-python@v4 - uses: actions/setup-python@v4
with: with:
python-version: '3.10.9' python-version: '3.11.3'
- shell: bash - shell: bash
run: | run: |
cd .. cd ..
cp -r ComfyUI ComfyUI_copy cp -r ComfyUI ComfyUI_copy
curl https://www.python.org/ftp/python/3.10.9/python-3.10.9-embed-amd64.zip -o python_embeded.zip curl https://www.python.org/ftp/python/3.11.3/python-3.11.3-embed-amd64.zip -o python_embeded.zip
unzip python_embeded.zip -d python_embeded unzip python_embeded.zip -d python_embeded
cd python_embeded cd python_embeded
echo 'import site' >> ./python310._pth echo 'import site' >> ./python311._pth
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
./python.exe get-pip.py ./python.exe get-pip.py
python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu118 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
ls ../temp_wheel_dir ls ../temp_wheel_dir
./python.exe -s -m pip install --pre ../temp_wheel_dir/* ./python.exe -s -m pip install --pre ../temp_wheel_dir/*
sed -i '1i../ComfyUI' ./python310._pth sed -i '1i../ComfyUI' ./python311._pth
cd .. cd ..

15
nodes.py
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@ -5,6 +5,7 @@ import sys
import json import json
import hashlib import hashlib
import traceback import traceback
import math
from PIL import Image from PIL import Image
from PIL.PngImagePlugin import PngInfo from PIL.PngImagePlugin import PngInfo
@ -223,13 +224,13 @@ class VAEEncodeForInpaint:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", )}} return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}}
RETURN_TYPES = ("LATENT",) RETURN_TYPES = ("LATENT",)
FUNCTION = "encode" FUNCTION = "encode"
CATEGORY = "latent/inpaint" CATEGORY = "latent/inpaint"
def encode(self, vae, pixels, mask): def encode(self, vae, pixels, mask, grow_mask_by=6):
x = (pixels.shape[1] // 64) * 64 x = (pixels.shape[1] // 64) * 64
y = (pixels.shape[2] // 64) * 64 y = (pixels.shape[2] // 64) * 64
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
@ -240,8 +241,14 @@ class VAEEncodeForInpaint:
mask = mask[:,:,:x,:y] mask = mask[:,:,:x,:y]
#grow mask by a few pixels to keep things seamless in latent space #grow mask by a few pixels to keep things seamless in latent space
kernel_tensor = torch.ones((1, 1, 6, 6)) if grow_mask_by == 0:
mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=3), 0, 1) mask_erosion = mask
else:
kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
padding = math.ceil((grow_mask_by - 1) / 2)
mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
m = (1.0 - mask.round()).squeeze(1) m = (1.0 - mask.round()).squeeze(1)
for i in range(3): for i in range(3):
pixels[:,:,:,i] -= 0.5 pixels[:,:,:,i] -= 0.5