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ComfyUI
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Merge remote-tracking branch 'origin/master' into a1111-meta-v2

This commit is contained in:
pythongosssss 2023-03-16 21:30:23 +00:00
parent 85989c74e5 bf1dc1d9bc
commit ba4a754a53
32 changed files with 865 additions and 121 deletions
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65
.ci/nightly/update_windows/update.py Executable file
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@ -0,0 +1,65 @@
import pygit2
from datetime import datetime
import sys
def pull(repo, remote_name='origin', branch='master'):
for remote in repo.remotes:
if remote.name == remote_name:
remote.fetch()
remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target
merge_result, _ = repo.merge_analysis(remote_master_id)
# Up to date, do nothing
if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
return
# We can just fastforward
elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:
repo.checkout_tree(repo.get(remote_master_id))
try:
master_ref = repo.lookup_reference('refs/heads/%s' % (branch))
master_ref.set_target(remote_master_id)
except KeyError:
repo.create_branch(branch, repo.get(remote_master_id))
repo.head.set_target(remote_master_id)
elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:
repo.merge(remote_master_id)
if repo.index.conflicts is not None:
for conflict in repo.index.conflicts:
print('Conflicts found in:', conflict[0].path)
raise AssertionError('Conflicts, ahhhhh!!')
user = repo.default_signature
tree = repo.index.write_tree()
commit = repo.create_commit('HEAD',
user,
user,
'Merge!',
tree,
[repo.head.target, remote_master_id])
# We need to do this or git CLI will think we are still merging.
repo.state_cleanup()
else:
raise AssertionError('Unknown merge analysis result')
repo = pygit2.Repository(str(sys.argv[1]))
ident = pygit2.Signature('comfyui', 'comfy@ui')
try:
print("stashing current changes")
repo.stash(ident)
except KeyError:
print("nothing to stash")
backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
print("creating backup branch: {}".format(backup_branch_name))
repo.branches.local.create(backup_branch_name, repo.head.peel())
print("checking out master branch")
branch = repo.lookup_branch('master')
ref = repo.lookup_reference(branch.name)
repo.checkout(ref)
print("pulling latest changes")
pull(repo)
print("Done!")

2
.ci/nightly/update_windows/update_comfyui.bat Executable file
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@ -0,0 +1,2 @@
..\python_embeded\python.exe .\update.py ..\ComfyUI\
pause

3
.ci/nightly/update_windows/update_comfyui_and_python_dependencies.bat Executable file
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@ -0,0 +1,3 @@
..\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/cu118 -r ../ComfyUI/requirements.txt pygit2
pause

27
.ci/nightly/windows_base_files/README_VERY_IMPORTANT.txt Executable file
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@ -0,0 +1,27 @@
HOW TO RUN:
if you have a NVIDIA gpu:
run_nvidia_gpu.bat
To run it in slow CPU mode:
run_cpu.bat
IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
You can download the stable diffusion 1.5 one from: https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt
RECOMMENDED WAY TO UPDATE:
To update the ComfyUI code: update\update_comfyui.bat
To update ComfyUI with the python dependencies:
update\update_comfyui_and_python_dependencies.bat

2
.ci/nightly/windows_base_files/run_cpu.bat Executable file
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@ -0,0 +1,2 @@
.\python_embeded\python.exe -s ComfyUI\main.py --cpu --windows-standalone-build
pause

2
.ci/nightly/windows_base_files/run_nvidia_gpu.bat Executable file
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@ -0,0 +1,2 @@
.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --use-pytorch-cross-attention
pause

65
.ci/update_windows/update.py Executable file
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@ -0,0 +1,65 @@
import pygit2
from datetime import datetime
import sys
def pull(repo, remote_name='origin', branch='master'):
for remote in repo.remotes:
if remote.name == remote_name:
remote.fetch()
remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target
merge_result, _ = repo.merge_analysis(remote_master_id)
# Up to date, do nothing
if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
return
# We can just fastforward
elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:
repo.checkout_tree(repo.get(remote_master_id))
try:
master_ref = repo.lookup_reference('refs/heads/%s' % (branch))
master_ref.set_target(remote_master_id)
except KeyError:
repo.create_branch(branch, repo.get(remote_master_id))
repo.head.set_target(remote_master_id)
elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:
repo.merge(remote_master_id)
if repo.index.conflicts is not None:
for conflict in repo.index.conflicts:
print('Conflicts found in:', conflict[0].path)
raise AssertionError('Conflicts, ahhhhh!!')
user = repo.default_signature
tree = repo.index.write_tree()
commit = repo.create_commit('HEAD',
user,
user,
'Merge!',
tree,
[repo.head.target, remote_master_id])
# We need to do this or git CLI will think we are still merging.
repo.state_cleanup()
else:
raise AssertionError('Unknown merge analysis result')
repo = pygit2.Repository(str(sys.argv[1]))
ident = pygit2.Signature('comfyui', 'comfy@ui')
try:
print("stashing current changes")
repo.stash(ident)
except KeyError:
print("nothing to stash")
backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
print("creating backup branch: {}".format(backup_branch_name))
repo.branches.local.create(backup_branch_name, repo.head.peel())
print("checking out master branch")
branch = repo.lookup_branch('master')
ref = repo.lookup_reference(branch.name)
repo.checkout(ref)
print("pulling latest changes")
pull(repo)
print("Done!")

2
.ci/update_windows/update_comfyui.bat Executable file
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@ -0,0 +1,2 @@
..\python_embeded\python.exe .\update.py ..\ComfyUI\
pause

3
.ci/update_windows/update_comfyui_and_python_dependencies.bat Executable file
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@ -0,0 +1,3 @@
..\python_embeded\python.exe .\update.py ..\ComfyUI\
..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 xformers -r ../ComfyUI/requirements.txt pygit2
pause

4
.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat Executable file
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@ -0,0 +1,4 @@
..\python_embeded\python.exe .\update.py ..\ComfyUI\
..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 xformers -r ../ComfyUI/requirements.txt pygit2
echo NOTE If you get an error with pip you can ignore it, it's pip being pip as usual, your ComfyUI should have updated anyways.
pause

27
.ci/windows_base_files/README_VERY_IMPORTANT.txt Executable file
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@ -0,0 +1,27 @@
HOW TO RUN:
if you have a NVIDIA gpu:
run_nvidia_gpu.bat
To run it in slow CPU mode:
run_cpu.bat
IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
You can download the stable diffusion 1.5 one from: https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt
RECOMMENDED WAY TO UPDATE:
To update the ComfyUI code: update\update_comfyui.bat
To update ComfyUI with the python dependencies, note that you should ONLY run this if you have issues with python dependencies.
update\update_comfyui_and_python_dependencies.bat

2
.ci/windows_base_files/run_cpu.bat Executable file
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@ -0,0 +1,2 @@
.\python_embeded\python.exe -s ComfyUI\main.py --cpu --windows-standalone-build
pause

2
.ci/windows_base_files/run_nvidia_gpu.bat Executable file
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@ -0,0 +1,2 @@
.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
pause

64
.github/workflows/windows_release.yml vendored Normal file
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@ -0,0 +1,64 @@
name: "Windows Release"
on:
workflow_dispatch:
# push:
# branches:
# - master
jobs:
build:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
runs-on: windows-latest
steps:
- uses: actions/checkout@v3
with:
fetch-depth: 0
- shell: bash
run: |
cd ..
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
unzip python_embeded.zip -d python_embeded
cd python_embeded
echo 'import site' >> ./python310._pth
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
./python.exe get-pip.py
./python.exe -s -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 xformers -r ../ComfyUI/requirements.txt pygit2
sed -i '1i../ComfyUI' ./python310._pth
cd ..
mkdir ComfyUI_windows_portable
mv python_embeded ComfyUI_windows_portable
mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI
cd ComfyUI_windows_portable
mkdir update
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on ComfyUI_windows_portable.7z ComfyUI_windows_portable
mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu.7z
cd ComfyUI_windows_portable
python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
ls
- name: Upload binaries to release
uses: svenstaro/upload-release-action@v2
with:
repo_token: ${{ secrets.GITHUB_TOKEN }}
file: ComfyUI_windows_portable_nvidia_or_cpu.7z
tag: "latest"
overwrite: true

141
.github/workflows/windows_release_cu118.yml vendored Normal file
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name: "Windows Release cu118"
on:
workflow_dispatch:
# push:
# branches:
# - master
jobs:
build_dependencies:
env:
# you need at least cuda 5.0 for some of the stuff compiled here.
TORCH_CUDA_ARCH_LIST: "5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6 8.9"
FORCE_CUDA: 1
MAX_JOBS: 1 # will crash otherwise
DISTUTILS_USE_SDK: 1 # otherwise distutils will complain on windows about multiple versions of msvc
XFORMERS_BUILD_TYPE: "Release"
runs-on: windows-latest
steps:
- name: Cache Built Dependencies
uses: actions/cache@v3
id: cache-cu118_python_stuff
with:
path: cu118_python_deps.tar
key: ${{ runner.os }}-build-cu118
- if: ${{ steps.cache-cu118_python_stuff.cache-hit != 'true' }}
uses: actions/checkout@v3
- if: ${{ steps.cache-cu118_python_stuff.cache-hit != 'true' }}
uses: actions/setup-python@v4
with:
python-version: '3.10.9'
- if: ${{ steps.cache-cu118_python_stuff.cache-hit != 'true' }}
uses: comfyanonymous/cuda-toolkit@test
id: cuda-toolkit
with:
cuda: '11.8.0'
# copied from xformers github
- name: Setup MSVC
uses: ilammy/msvc-dev-cmd@v1
- name: Configure Pagefile
# windows runners will OOM with many CUDA architectures
# we cheat here with a page file
uses: al-cheb/configure-pagefile-action@v1.3
with:
minimum-size: 2GB
# really unfortunate: https://github.com/ilammy/msvc-dev-cmd#name-conflicts-with-shell-bash
- name: Remove link.exe
shell: bash
run: rm /usr/bin/link
- if: ${{ steps.cache-cu118_python_stuff.cache-hit != 'true' }}
shell: bash
run: |
python -m pip wheel --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
python -m pip install --no-cache-dir ./temp_wheel_dir/*
echo installed basic
git clone --recurse-submodules https://github.com/facebookresearch/xformers.git
cd xformers
python -m pip install --no-cache-dir wheel setuptools twine
echo building xformers
python setup.py bdist_wheel -d ../temp_wheel_dir/
cd ..
rm -rf xformers
ls -lah temp_wheel_dir
mv temp_wheel_dir cu118_python_deps
tar cf cu118_python_deps.tar cu118_python_deps
- uses: actions/upload-artifact@v3
with:
name: cu118_python_deps
path: cu118_python_deps.tar
package_comfyui:
needs: build_dependencies
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
runs-on: windows-latest
steps:
- uses: actions/download-artifact@v3
with:
name: cu118_python_deps
- shell: bash
run: |
mv cu118_python_deps.tar ../
cd ..
tar xf cu118_python_deps.tar
pwd
ls
- uses: actions/checkout@v3
with:
fetch-depth: 0
- shell: bash
run: |
cd ..
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
unzip python_embeded.zip -d python_embeded
cd python_embeded
echo 'import site' >> ./python310._pth
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
./python.exe get-pip.py
./python.exe -s -m pip install ../cu118_python_deps/*
sed -i '1i../ComfyUI' ./python310._pth
cd ..
mkdir ComfyUI_windows_portable
mv python_embeded ComfyUI_windows_portable
mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI
cd ComfyUI_windows_portable
mkdir update
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/update_windows_cu118/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on ComfyUI_windows_portable.7z ComfyUI_windows_portable
mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z
cd ComfyUI_windows_portable
python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
ls
- name: Upload binaries to release
uses: svenstaro/upload-release-action@v2
with:
repo_token: ${{ secrets.GITHUB_TOKEN }}
file: ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z
tag: "latest"
overwrite: true

66
.github/workflows/windows_release_nightly_pytorch.yml vendored Normal file
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@ -0,0 +1,66 @@
name: "Windows Release Nightly pytorch"
on:
workflow_dispatch:
# push:
# branches:
# - master
jobs:
build:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
runs-on: windows-latest
steps:
- uses: actions/checkout@v3
with:
fetch-depth: 0
- uses: actions/setup-python@v4
with:
python-version: '3.10.9'
- shell: bash
run: |
cd ..
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
unzip python_embeded.zip -d python_embeded
cd python_embeded
echo 'import site' >> ./python310._pth
curl https://bootstrap.pypa.io/get-pip.py -o 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
ls ../temp_wheel_dir
./python.exe -s -m pip install --pre ../temp_wheel_dir/*
sed -i '1i../ComfyUI' ./python310._pth
cd ..
mkdir ComfyUI_windows_portable_nightly_pytorch
mv python_embeded ComfyUI_windows_portable_nightly_pytorch
mv ComfyUI_copy ComfyUI_windows_portable_nightly_pytorch/ComfyUI
cd ComfyUI_windows_portable_nightly_pytorch
mkdir update
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
cd ComfyUI_windows_portable_nightly_pytorch
python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
ls
- name: Upload binaries to release
uses: svenstaro/upload-release-action@v2
with:
repo_token: ${{ secrets.GITHUB_TOKEN }}
file: ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
tag: "latest"
overwrite: true

33
README.md
View File

@ -31,6 +31,20 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
# Installing
## Windows
There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_or_cpu.7z)
Just download, extract and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
## Colab Notebook
To run it on colab or paperspace you can use my [Colab Notebook](notebooks/comfyui_colab.ipynb) here: [Link to open with google colab](https://colab.research.google.com/github/comfyanonymous/ComfyUI/blob/master/notebooks/comfyui_colab.ipynb)
## Manual Install (Windows, Linux)
Git clone this repo.
Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
@ -39,20 +53,17 @@ Put your VAE in: models/vae
At the time of writing this pytorch has issues with python versions higher than 3.10 so make sure your python/pip versions are 3.10.
### AMD
AMD users can install rocm and pytorch with pip if you don't have it already installed:
### AMD (Linux only)
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.4.2```
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2```
### NVIDIA
Nvidia users should install torch using this command:
Nvidia users should install torch and xformers using this command:
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117```
Nvidia users should also install Xformers for a speed boost but can still run the software without it.
```pip install xformers```
```pip install torch==1.13.1 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 xformers```
#### Troubleshooting
@ -112,10 +123,6 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the
```embedding:embedding_filename.pt```
### Colab Notebook
To run it on colab or paperspace you can use my [Colab Notebook](notebooks/comfyui_colab.ipynb) here: [Link to open with google colab](https://colab.research.google.com/github/comfyanonymous/ComfyUI/blob/master/notebooks/comfyui_colab.ipynb)
### Fedora
To get python 3.10 on fedora:

6
comfy/cldm/cldm.py
View File

@ -59,9 +59,9 @@ class ControlNet(nn.Module):
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
# from omegaconf.listconfig import ListConfig
# if type(context_dim) == ListConfig:
# context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads

5
comfy/ldm/models/diffusion/ddpm.py
View File

@ -18,7 +18,6 @@ import itertools
from tqdm import tqdm
from torchvision.utils import make_grid
# from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
@ -1124,8 +1123,8 @@ class LatentDiffusion(DDPM):
def get_unconditional_conditioning(self, batch_size, null_label=None):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
# if isinstance(xc, ListConfig):
# xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:

28
comfy/ldm/modules/attention.py
View File

@ -11,12 +11,10 @@ from .sub_quadratic_attention import efficient_dot_product_attention
import model_management
try:
if model_management.xformers_enabled():
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True
except:
XFORMERS_IS_AVAILBLE = False
# CrossAttn precision handling
import os
@ -481,23 +479,19 @@ class CrossAttentionPytorch(nn.Module):
return self.to_out(out)
import sys
if XFORMERS_IS_AVAILBLE == False or "--disable-xformers" in sys.argv:
if model_management.xformers_enabled():
print("Using xformers cross attention")
CrossAttention = MemoryEfficientCrossAttention
elif model_management.pytorch_attention_enabled():
print("Using pytorch cross attention")
CrossAttention = CrossAttentionPytorch
else:
if "--use-split-cross-attention" in sys.argv:
print("Using split optimization for cross attention")
CrossAttention = CrossAttentionDoggettx
else:
if "--use-pytorch-cross-attention" in sys.argv:
print("Using pytorch cross attention")
torch.backends.cuda.enable_math_sdp(False)
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(True)
CrossAttention = CrossAttentionPytorch
else:
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
CrossAttention = CrossAttentionBirchSan
else:
print("Using xformers cross attention")
CrossAttention = MemoryEfficientCrossAttention
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
CrossAttention = CrossAttentionBirchSan
class BasicTransformerBlock(nn.Module):

70
comfy/ldm/modules/diffusionmodules/model.py
View File

@ -9,13 +9,9 @@ from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention
import model_management
try:
if model_management.xformers_enabled():
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True
except:
XFORMERS_IS_AVAILBLE = False
print("No module 'xformers'. Proceeding without it.")
try:
OOM_EXCEPTION = torch.cuda.OutOfMemoryError
@ -303,6 +299,64 @@ class MemoryEfficientAttnBlock(nn.Module):
out = self.proj_out(out)
return x+out
class MemoryEfficientAttnBlockPytorch(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.attention_op: Optional[Any] = None
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
B, C, H, W = q.shape
q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(B, t.shape[1], 1, C)
.permute(0, 2, 1, 3)
.reshape(B * 1, t.shape[1], C)
.contiguous(),
(q, k, v),
)
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = (
out.unsqueeze(0)
.reshape(B, 1, out.shape[1], C)
.permute(0, 2, 1, 3)
.reshape(B, out.shape[1], C)
)
out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
out = self.proj_out(out)
return x+out
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
def forward(self, x, context=None, mask=None):
@ -315,8 +369,10 @@ class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
if model_management.xformers_enabled() and attn_type == "vanilla":
attn_type = "vanilla-xformers"
if model_management.pytorch_attention_enabled() and attn_type == "vanilla":
attn_type = "vanilla-pytorch"
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
if attn_type == "vanilla":
assert attn_kwargs is None
@ -324,6 +380,8 @@ def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
elif attn_type == "vanilla-xformers":
print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
return MemoryEfficientAttnBlock(in_channels)
elif attn_type == "vanilla-pytorch":
return MemoryEfficientAttnBlockPytorch(in_channels)
elif type == "memory-efficient-cross-attn":
attn_kwargs["query_dim"] = in_channels
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)

6
comfy/ldm/modules/diffusionmodules/openaimodel.py
View File

@ -477,9 +477,9 @@ class UNetModel(nn.Module):
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
# from omegaconf.listconfig import ListConfig
# if type(context_dim) == ListConfig:
# context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads

31
comfy/model_management.py
View File

@ -31,8 +31,25 @@ try:
except:
pass
if "--cpu" in sys.argv:
vram_state = CPU
if "--disable-xformers" in sys.argv:
XFORMERS_IS_AVAILBLE = False
else:
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True
except:
XFORMERS_IS_AVAILBLE = False
ENABLE_PYTORCH_ATTENTION = False
if "--use-pytorch-cross-attention" in sys.argv:
torch.backends.cuda.enable_math_sdp(True)
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(True)
ENABLE_PYTORCH_ATTENTION = True
XFORMERS_IS_AVAILBLE = False
if "--lowvram" in sys.argv:
set_vram_to = LOW_VRAM
if "--novram" in sys.argv:
@ -54,6 +71,8 @@ if set_vram_to == LOW_VRAM or set_vram_to == NO_VRAM:
total_vram_available_mb = (total_vram - 1024) // 2
total_vram_available_mb = int(max(256, total_vram_available_mb))
if "--cpu" in sys.argv:
vram_state = CPU
print("Set vram state to:", ["CPU", "NO VRAM", "LOW VRAM", "NORMAL VRAM", "HIGH VRAM"][vram_state])
@ -159,6 +178,14 @@ def get_autocast_device(dev):
return dev.type
return "cuda"
def xformers_enabled():
if vram_state == CPU:
return False
return XFORMERS_IS_AVAILBLE
def pytorch_attention_enabled():
return ENABLE_PYTORCH_ATTENTION
def get_free_memory(dev=None, torch_free_too=False):
if dev is None:
dev = get_torch_device()

20
comfy/sd.py
View File

@ -6,7 +6,7 @@ import sd2_clip
import model_management
from .ldm.util import instantiate_from_config
from .ldm.models.autoencoder import AutoencoderKL
from omegaconf import OmegaConf
import yaml
from .cldm import cldm
from .t2i_adapter import adapter
@ -726,12 +726,19 @@ def load_clip(ckpt_path, embedding_directory=None):
return clip
def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=None):
config = OmegaConf.load(config_path)
with open(config_path, 'r') as stream:
config = yaml.safe_load(stream)
model_config_params = config['model']['params']
clip_config = model_config_params['cond_stage_config']
scale_factor = model_config_params['scale_factor']
vae_config = model_config_params['first_stage_config']
fp16 = False
if "unet_config" in model_config_params:
if "params" in model_config_params["unet_config"]:
if "use_fp16" in model_config_params["unet_config"]["params"]:
fp16 = model_config_params["unet_config"]["params"]["use_fp16"]
clip = None
vae = None
@ -750,9 +757,13 @@ def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, e
w.cond_stage_model = clip.cond_stage_model
load_state_dict_to = [w]
model = instantiate_from_config(config.model)
model = instantiate_from_config(config["model"])
sd = load_torch_file(ckpt_path)
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
if fp16:
model = model.half()
return (ModelPatcher(model), clip, vae)
@ -853,4 +864,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, e
model = instantiate_from_config(model_config)
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
if fp16:
model = model.half()
return (ModelPatcher(model), clip, vae)

4
comfy_extras/nodes_upscale_model.py
View File

@ -1,7 +1,7 @@
import os
from comfy_extras.chainner_models import model_loading
from comfy.sd import load_torch_file
import comfy.model_management
import model_management
from nodes import filter_files_extensions, recursive_search, supported_ckpt_extensions
import torch
import comfy.utils
@ -38,7 +38,7 @@ class ImageUpscaleWithModel:
CATEGORY = "image/upscaling"
def upscale(self, upscale_model, image):
device = comfy.model_management.get_torch_device()
device = model_management.get_torch_device()
upscale_model.to(device)
in_img = image.movedim(-1,-3).to(device)
s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=128 + 64, tile_y=128 + 64, overlap = 8, upscale_amount=upscale_model.scale)

35
main.py
View File

@ -1,5 +1,6 @@
import os
import sys
import shutil
import threading
import asyncio
@ -8,9 +9,6 @@ if os.name == "nt":
import logging
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
import execution
import server
if __name__ == "__main__":
if '--help' in sys.argv:
print("Valid Command line Arguments:")
@ -18,6 +16,8 @@ if __name__ == "__main__":
print("\t--port 8188\t\t\tSet the listen port.")
print("\t--dont-upcast-attention\t\tDisable upcasting of attention \n\t\t\t\t\tcan boost speed but increase the chances of black images.\n")
print("\t--use-split-cross-attention\tUse the split cross attention optimization instead of the sub-quadratic one.\n\t\t\t\t\tIgnored when xformers is used.")
print("\t--use-pytorch-cross-attention\tUse the new pytorch 2.0 cross attention function.")
print("\t--disable-xformers\t\tdisables xformers")
print()
print("\t--highvram\t\t\tBy default models will be unloaded to CPU memory after being used.\n\t\t\t\t\tThis option keeps them in GPU memory.\n")
print("\t--normalvram\t\t\tUsed to force normal vram use if lowvram gets automatically enabled.")
@ -31,6 +31,9 @@ if __name__ == "__main__":
print("disabling upcasting of attention")
os.environ['ATTN_PRECISION'] = "fp16"
import execution
import server
def prompt_worker(q, server):
e = execution.PromptExecutor(server)
while True:
@ -38,8 +41,8 @@ def prompt_worker(q, server):
e.execute(item[-2], item[-1])
q.task_done(item_id, e.outputs)
async def run(server, address='', port=8188, verbose=True):
await asyncio.gather(server.start(address, port, verbose), server.publish_loop())
async def run(server, address='', port=8188, verbose=True, call_on_start=None):
await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop())
def hijack_progress(server):
from tqdm.auto import tqdm
@ -51,7 +54,14 @@ def hijack_progress(server):
return v
setattr(tqdm, "update", wrapped_func)
def cleanup_temp():
temp_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
if os.path.exists(temp_dir):
shutil.rmtree(temp_dir, ignore_errors=True)
if __name__ == "__main__":
cleanup_temp()
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
server = server.PromptServer(loop)
@ -76,11 +86,22 @@ if __name__ == "__main__":
except:
pass
if '--quick-test-for-ci' in sys.argv:
exit(0)
call_on_start = None
if "--windows-standalone-build" in sys.argv:
def startup_server(address, port):
import webbrowser
webbrowser.open("http://{}:{}".format(address, port))
call_on_start = startup_server
if os.name == "nt":
try:
loop.run_until_complete(run(server, address=address, port=port, verbose=not dont_print))
loop.run_until_complete(run(server, address=address, port=port, verbose=not dont_print, call_on_start=call_on_start))
except KeyboardInterrupt:
pass
else:
loop.run_until_complete(run(server, address=address, port=port, verbose=not dont_print))
loop.run_until_complete(run(server, address=address, port=port, verbose=not dont_print, call_on_start=call_on_start))
cleanup_temp()

24
nodes.py
View File

@ -189,6 +189,7 @@ class VAEEncodeForInpaint:
y = (pixels.shape[2] // 64) * 64
mask = torch.nn.functional.interpolate(mask[None,None,], size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")[0][0]
pixels = pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
pixels = pixels[:,:x,:y,:]
mask = mask[:x,:y]
@ -691,8 +692,8 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
if t.shape[0] < noise.shape[0]:
t = torch.cat([t] * noise.shape[0])
t = t.to(device)
if 'control' in p[1]:
control_nets += [p[1]['control']]
if 'control' in n[1]:
control_nets += [n[1]['control']]
negative_copy += [[t] + n[1:]]
control_net_models = []
@ -775,6 +776,7 @@ class KSamplerAdvanced:
class SaveImage:
def __init__(self):
self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
self.url_suffix = ""
@classmethod
def INPUT_TYPES(s):
@ -808,6 +810,9 @@ class SaveImage:
os.mkdir(self.output_dir)
counter = 1
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
paths = list()
for image in images:
i = 255. * image.cpu().numpy()
@ -820,10 +825,22 @@ class SaveImage:
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
file = f"{filename_prefix}_{counter:05}_.png"
img.save(os.path.join(self.output_dir, file), pnginfo=metadata, optimize=True)
paths.append(file)
paths.append(file + self.url_suffix)
counter += 1
return { "ui": { "images": paths } }
class PreviewImage(SaveImage):
def __init__(self):
self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
self.url_suffix = "?type=temp"
@classmethod
def INPUT_TYPES(s):
return {"required":
{"images": ("IMAGE", ), },
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
class LoadImage:
input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
@classmethod
@ -944,6 +961,7 @@ NODE_CLASS_MAPPINGS = {
"EmptyLatentImage": EmptyLatentImage,
"LatentUpscale": LatentUpscale,
"SaveImage": SaveImage,
"PreviewImage": PreviewImage,
"LoadImage": LoadImage,
"LoadImageMask": LoadImageMask,
"ImageScale": ImageScale,

2
requirements.txt
View File

@ -1,7 +1,6 @@
torch
torchdiffeq
torchsde
omegaconf
einops
open-clip-torch
transformers
@ -9,3 +8,4 @@ safetensors
pytorch_lightning
aiohttp
accelerate
pyyaml

7
server.py
View File

@ -121,7 +121,7 @@ class PromptServer():
async def view_image(request):
if "file" in request.match_info:
type = request.rel_url.query.get("type", "output")
if type != "output" and type != "input":
if type not in ["output", "input", "temp"]:
return web.Response(status=400)
output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), type)
@ -268,7 +268,7 @@ class PromptServer():
msg = await self.messages.get()
await self.send(*msg)
async def start(self, address, port, verbose=True):
async def start(self, address, port, verbose=True, call_on_start=None):
runner = web.AppRunner(self.app)
await runner.setup()
site = web.TCPSite(runner, address, port)
@ -279,3 +279,6 @@ class PromptServer():
if verbose:
print("Starting server\n")
print("To see the GUI go to: http://{}:{}".format(address, port))
if call_on_start is not None:
call_on_start(address, port)

126
web/scripts/app.js
View File

@ -142,7 +142,14 @@ class ComfyApp {
if (numImages === 1 && !imageIndex) {
this.imageIndex = imageIndex = 0;
}
let shiftY = this.type === "SaveImage" ? 55 : this.imageOffset || 0;
let shiftY;
if (this.imageOffset != null) {
shiftY = this.imageOffset;
} else {
shiftY = this.computeSize()[1];
}
let dw = this.size[0];
let dh = this.size[1];
dh -= shiftY;
@ -284,9 +291,47 @@ class ComfyApp {
document.addEventListener("drop", async (event) => {
event.preventDefault();
event.stopPropagation();
const file = event.dataTransfer.files[0];
await this.handleFile(file);
const n = this.dragOverNode;
this.dragOverNode = null;
// Node handles file drop, we dont use the built in onDropFile handler as its buggy
// If you drag multiple files it will call it multiple times with the same file
if (n && n.onDragDrop && (await n.onDragDrop(event))) {
return;
}
await this.handleFile(event.dataTransfer.files[0]);
});
// Always clear over node on drag leave
this.canvasEl.addEventListener("dragleave", async () => {
if (this.dragOverNode) {
this.dragOverNode = null;
this.graph.setDirtyCanvas(false, true);
}
});
// Add handler for dropping onto a specific node
this.canvasEl.addEventListener(
"dragover",
(e) => {
this.canvas.adjustMouseEvent(e);
const node = this.graph.getNodeOnPos(e.canvasX, e.canvasY);
if (node) {
if (node.onDragOver && node.onDragOver(e)) {
this.dragOverNode = node;
// dragover event is fired very frequently, run this on an animation frame
requestAnimationFrame(() => {
this.graph.setDirtyCanvas(false, true);
});
return;
}
}
this.dragOverNode = null;
},
false
);
}
/**
@ -314,15 +359,22 @@ class ComfyApp {
}
/**
* Draws currently executing node highlight and progress bar
* Draws node highlights (executing, drag drop) and progress bar
*/
#addDrawNodeProgressHandler() {
#addDrawNodeHandler() {
const orig = LGraphCanvas.prototype.drawNodeShape;
const self = this;
LGraphCanvas.prototype.drawNodeShape = function (node, ctx, size, fgcolor, bgcolor, selected, mouse_over) {
const res = orig.apply(this, arguments);
if (node.id + "" === self.runningNodeId) {
let color = null;
if (node.id === +self.runningNodeId) {
color = "#0f0";
} else if (self.dragOverNode && node.id === self.dragOverNode.id) {
color = "dodgerblue";
}
if (color) {
const shape = node._shape || node.constructor.shape || LiteGraph.ROUND_SHAPE;
ctx.lineWidth = 1;
ctx.globalAlpha = 0.8;
@ -348,7 +400,7 @@ class ComfyApp {
);
else if (shape == LiteGraph.CIRCLE_SHAPE)
ctx.arc(size[0] * 0.5, size[1] * 0.5, size[0] * 0.5 + 6, 0, Math.PI * 2);
ctx.strokeStyle = "#0f0";
ctx.strokeStyle = color;
ctx.stroke();
ctx.strokeStyle = fgcolor;
ctx.globalAlpha = 1;
@ -398,6 +450,15 @@ class ComfyApp {
api.init();
}
#addKeyboardHandler() {
window.addEventListener("keydown", (e) => {
// Queue prompt using ctrl or command + enter
if ((e.ctrlKey || e.metaKey) && (e.key === "Enter" || e.keyCode === 13 || e.keyCode === 10)) {
this.queuePrompt(e.shiftKey ? -1 : 0);
}
});
}
/**
* Loads all extensions from the API into the window
*/
@ -419,7 +480,7 @@ class ComfyApp {
await this.#loadExtensions();
// Create and mount the LiteGraph in the DOM
const canvasEl = Object.assign(document.createElement("canvas"), { id: "graph-canvas" });
const canvasEl = (this.canvasEl = Object.assign(document.createElement("canvas"), { id: "graph-canvas" }));
document.body.prepend(canvasEl);
this.graph = new LGraph();
@ -460,10 +521,11 @@ class ComfyApp {
// Save current workflow automatically
setInterval(() => localStorage.setItem("workflow", JSON.stringify(this.graph.serialize())), 1000);
this.#addDrawNodeProgressHandler();
this.#addDrawNodeHandler();
this.#addApiUpdateHandlers();
this.#addDropHandler();
this.#addPasteHandler();
this.#addKeyboardHandler();
await this.#invokeExtensionsAsync("setup");
}
@ -497,7 +559,11 @@ class ComfyApp {
if (Array.isArray(type)) {
// Enums e.g. latent rotation
this.addWidget("combo", inputName, type[0], () => {}, { values: type });
let defaultValue = type[0];
if (inputData[1] && inputData[1].default) {
defaultValue = inputData[1].default;
}
this.addWidget("combo", inputName, defaultValue, () => {}, { values: type });
} else if (`${type}:${inputName}` in widgets) {
// Support custom widgets by Type:Name
Object.assign(config, widgets[`${type}:${inputName}`](this, inputName, inputData, app) || {});
@ -641,31 +707,33 @@ class ComfyApp {
return { workflow, output };
}
async queuePrompt(number) {
const p = await this.graphToPrompt();
async queuePrompt(number, batchCount = 1) {
for (let i = 0; i < batchCount; i++) {
const p = await this.graphToPrompt();
try {
await api.queuePrompt(number, p);
} catch (error) {
this.ui.dialog.show(error.response || error.toString());
return;
}
try {
await api.queuePrompt(number, p);
} catch (error) {
this.ui.dialog.show(error.response || error.toString());
return;
}
for (const n of p.workflow.nodes) {
const node = graph.getNodeById(n.id);
if (node.widgets) {
for (const widget of node.widgets) {
// Allow widgets to run callbacks after a prompt has been queued
// e.g. random seed after every gen
if (widget.afterQueued) {
widget.afterQueued();
for (const n of p.workflow.nodes) {
const node = graph.getNodeById(n.id);
if (node.widgets) {
for (const widget of node.widgets) {
// Allow widgets to run callbacks after a prompt has been queued
// e.g. random seed after every gen
if (widget.afterQueued) {
widget.afterQueued();
}
}
}
}
}
this.canvas.draw(true, true);
await this.ui.queue.update();
this.canvas.draw(true, true);
await this.ui.queue.update();
}
}
/**

31
web/scripts/ui.js
View File

@ -231,6 +231,7 @@ export class ComfyUI {
this.dialog = new ComfyDialog();
this.settings = new ComfySettingsDialog();
this.batchCount = 1;
this.queue = new ComfyList("Queue");
this.history = new ComfyList("History");
@ -254,9 +255,35 @@ export class ComfyUI {
$el("span", { $: (q) => (this.queueSize = q) }),
$el("button.comfy-settings-btn", { textContent: "⚙️", onclick: () => this.settings.show() }),
]),
$el("button.comfy-queue-btn", { textContent: "Queue Prompt", onclick: () => app.queuePrompt(0) }),
$el("button.comfy-queue-btn", { textContent: "Queue Prompt", onclick: () => app.queuePrompt(0, this.batchCount) }),
$el("div", {}, [
$el("label", { innerHTML: "Extra options"}, [
$el("input", { type: "checkbox",
onchange: (i) => {
document.getElementById('extraOptions').style.display = i.srcElement.checked ? "block" : "none";
this.batchCount = i.srcElement.checked ? document.getElementById('batchCountInputRange').value : 1;
}
})
])
]),
$el("div", { id: "extraOptions", style: { width: "100%", display: "none" }}, [
$el("label", { innerHTML: "Batch count" }, [
$el("input", { id: "batchCountInputNumber", type: "number", value: this.batchCount, min: "1", style: { width: "35%", "margin-left": "0.4em" },
oninput: (i) => {
this.batchCount = i.target.value;
document.getElementById('batchCountInputRange').value = this.batchCount;
}
}),
$el("input", { id: "batchCountInputRange", type: "range", min: "1", max: "100", value: this.batchCount,
oninput: (i) => {
this.batchCount = i.srcElement.value;
document.getElementById('batchCountInputNumber').value = i.srcElement.value;
}
}),
]),
]),
$el("div.comfy-menu-btns", [
$el("button", { textContent: "Queue Front", onclick: () => app.queuePrompt(-1) }),
$el("button", { textContent: "Queue Front", onclick: () => app.queuePrompt(-1, this.batchCount) }),
$el("button", {
$: (b) => (this.queue.button = b),
textContent: "View Queue",

81
web/scripts/widgets.js
View File

@ -132,7 +132,7 @@ export const ComfyWidgets = {
function showImage(name) {
// Position the image somewhere sensible
if(!node.imageOffset) {
if (!node.imageOffset) {
node.imageOffset = uploadWidget.last_y ? uploadWidget.last_y + 25 : 75;
}
@ -162,6 +162,36 @@ export const ComfyWidgets = {
}
});
async function uploadFile(file, updateNode) {
try {
// Wrap file in formdata so it includes filename
const body = new FormData();
body.append("image", file);
const resp = await fetch("/upload/image", {
method: "POST",
body,
});
if (resp.status === 200) {
const data = await resp.json();
// Add the file as an option and update the widget value
if (!imageWidget.options.values.includes(data.name)) {
imageWidget.options.values.push(data.name);
}
if (updateNode) {
showImage(data.name);
imageWidget.value = data.name;
}
} else {
alert(resp.status + " - " + resp.statusText);
}
} catch (error) {
alert(error);
}
}
const fileInput = document.createElement("input");
Object.assign(fileInput, {
type: "file",
@ -169,30 +199,7 @@ export const ComfyWidgets = {
style: "display: none",
onchange: async () => {
if (fileInput.files.length) {
try {
// Wrap file in formdata so it includes filename
const body = new FormData();
body.append("image", fileInput.files[0]);
const resp = await fetch("/upload/image", {
method: "POST",
body,
});
if (resp.status === 200) {
const data = await resp.json();
showImage(data.name);
// Add the file as an option and update the widget value
if (!imageWidget.options.values.includes(data.name)) {
imageWidget.options.values.push(data.name);
}
imageWidget.value = data.name;
} else {
alert(resp.status + " - " + resp.statusText);
}
} catch (error) {
alert(error);
}
await uploadFile(fileInput.files[0], true);
}
},
});
@ -204,6 +211,30 @@ export const ComfyWidgets = {
});
uploadWidget.serialize = false;
// Add handler to check if an image is being dragged over our node
node.onDragOver = function (e) {
if (e.dataTransfer && e.dataTransfer.items) {
const image = [...e.dataTransfer.items].find((f) => f.kind === "file" && f.type.startsWith("image/"));
return !!image;
}
return false;
};
// On drop upload files
node.onDragDrop = function (e) {
console.log("onDragDrop called");
let handled = false;
for (const file of e.dataTransfer.files) {
if (file.type.startsWith("image/")) {
uploadFile(file, !handled); // Dont await these, any order is fine, only update on first one
handled = true;
}
}
return handled;
};
return { widget: uploadWidget };
},
};