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Merge upstream/master, keep local README.md

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GitHub Actions 2025-10-01 05:03:19 +00:00
commit 2c099490d4
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24
.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt Executable file
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@ -0,0 +1,24 @@
As of the time of writing this you need this preview driver for best results:
https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-PREVIEW.html
HOW TO RUN:
if you have a AMD gpu:
run_amd_gpu.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 XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
RECOMMENDED WAY TO UPDATE:
To update the ComfyUI code: update\update_comfyui.bat
TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.

0
.ci/windows_base_files/run_nvidia_gpu.bat → .ci/windows_amd_base_files/run_amd_gpu.bat
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0
.ci/windows_base_files/README_VERY_IMPORTANT.txt → .ci/windows_nvidia_base_files/README_VERY_IMPORTANT.txt
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0
.ci/windows_base_files/run_cpu.bat → .ci/windows_nvidia_base_files/run_cpu.bat
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2
.ci/windows_nvidia_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

0
.ci/windows_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat → .ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat
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61
.github/workflows/release-stable-all.yml vendored Normal file
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@ -0,0 +1,61 @@
name: "Release Stable All Portable Versions"
on:
workflow_dispatch:
inputs:
git_tag:
description: 'Git tag'
required: true
type: string
jobs:
release_nvidia_default:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release NVIDIA Default (cu129)"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "cu129"
python_minor: "13"
python_patch: "6"
rel_name: "nvidia"
rel_extra_name: ""
test_release: true
secrets: inherit
release_nvidia_cu128:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release NVIDIA cu128"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "cu128"
python_minor: "12"
python_patch: "10"
rel_name: "nvidia"
rel_extra_name: "_cu128"
test_release: true
secrets: inherit
release_amd_rocm:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release AMD ROCm 6.4.4"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "rocm644"
python_minor: "12"
python_patch: "10"
rel_name: "amd"
rel_extra_name: ""
test_release: false
secrets: inherit

100
.github/workflows/stable-release.yml vendored
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@ -2,17 +2,17 @@
name: "Release Stable Version"
on:
workflow_dispatch:
workflow_call:
inputs:
git_tag:
description: 'Git tag'
required: true
type: string
cu:
description: 'CUDA version'
cache_tag:
description: 'Cached dependencies tag'
required: true
type: string
default: "129"
default: "cu129"
python_minor:
description: 'Python minor version'
required: true
@ -23,7 +23,57 @@ on:
required: true
type: string
default: "6"
rel_name:
description: 'Release name'
required: true
type: string
default: "nvidia"
rel_extra_name:
description: 'Release extra name'
required: false
type: string
default: ""
test_release:
description: 'Test Release'
required: true
type: boolean
default: true
workflow_dispatch:
inputs:
git_tag:
description: 'Git tag'
required: true
type: string
cache_tag:
description: 'Cached dependencies tag'
required: true
type: string
default: "cu129"
python_minor:
description: 'Python minor version'
required: true
type: string
default: "13"
python_patch:
description: 'Python patch version'
required: true
type: string
default: "6"
rel_name:
description: 'Release name'
required: true
type: string
default: "nvidia"
rel_extra_name:
description: 'Release extra name'
required: false
type: string
default: ""
test_release:
description: 'Test Release'
required: true
type: boolean
default: true
jobs:
package_comfy_windows:
@ -42,15 +92,15 @@ jobs:
id: cache
with:
path: |
cu${{ inputs.cu }}_python_deps.tar
${{ inputs.cache_tag }}_python_deps.tar
update_comfyui_and_python_dependencies.bat
key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
- shell: bash
run: |
mv cu${{ inputs.cu }}_python_deps.tar ../
mv ${{ inputs.cache_tag }}_python_deps.tar ../
mv update_comfyui_and_python_dependencies.bat ../
cd ..
tar xf cu${{ inputs.cu }}_python_deps.tar
tar xf ${{ inputs.cache_tag }}_python_deps.tar
pwd
ls
@ -65,12 +115,19 @@ jobs:
echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
./python.exe get-pip.py
./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*
grep comfyui ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
./python.exe -s -m pip install -r requirements_comfyui.txt
rm requirements_comfyui.txt
sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
rm ./Lib/site-packages/torch/lib/libprotoc.lib
rm ./Lib/site-packages/torch/lib/libprotobuf.lib
if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then
rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
rm ./Lib/site-packages/torch/lib/libprotoc.lib
rm ./Lib/site-packages/torch/lib/libprotobuf.lib
fi
cd ..
@ -85,14 +142,18 @@ jobs:
mkdir update
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
cp ../update_comfyui_and_python_dependencies.bat ./update/
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z
mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
- shell: bash
if: ${{ inputs.test_release }}
run: |
cd ..
cd ComfyUI_windows_portable
python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
@ -101,10 +162,9 @@ jobs:
ls
- name: Upload binaries to release
uses: svenstaro/upload-release-action@v2
uses: softprops/action-gh-release@v2
with:
repo_token: ${{ secrets.GITHUB_TOKEN }}
file: ComfyUI_windows_portable_nvidia.7z
tag: ${{ inputs.git_tag }}
overwrite: true
files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
tag_name: ${{ inputs.git_tag }}
draft: true
overwrite_files: true

2
.github/workflows/test-unit.yml vendored
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@ -10,7 +10,7 @@ jobs:
test:
strategy:
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
os: [ubuntu-latest, windows-2022, macos-latest]
runs-on: ${{ matrix.os }}
continue-on-error: true
steps:

3
.github/workflows/windows_release_dependencies.yml vendored
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@ -56,7 +56,8 @@ jobs:
..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
pause" > update_comfyui_and_python_dependencies.bat
python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
grep -v comfyui requirements.txt > requirements_nocomfyui.txt
python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
python -m pip install --no-cache-dir ./temp_wheel_dir/*
echo installed basic
ls -lah temp_wheel_dir

64
.github/workflows/windows_release_dependencies_manual.yml vendored Normal file
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@ -0,0 +1,64 @@
name: "Windows Release dependencies Manual"
on:
workflow_dispatch:
inputs:
torch_dependencies:
description: 'torch dependencies'
required: false
type: string
default: "torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128"
cache_tag:
description: 'Cached dependencies tag'
required: true
type: string
default: "cu128"
python_minor:
description: 'python minor version'
required: true
type: string
default: "12"
python_patch:
description: 'python patch version'
required: true
type: string
default: "10"
jobs:
build_dependencies:
runs-on: windows-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
- shell: bash
run: |
echo "@echo off
call update_comfyui.bat nopause
echo -
echo This will try to update pytorch and all python dependencies.
echo -
echo If you just want to update normally, close this and run update_comfyui.bat instead.
echo -
pause
..\python_embeded\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../ComfyUI/requirements.txt pygit2
pause" > update_comfyui_and_python_dependencies.bat
grep -v comfyui requirements.txt > requirements_nocomfyui.txt
python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
python -m pip install --no-cache-dir ./temp_wheel_dir/*
echo installed basic
ls -lah temp_wheel_dir
mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps
tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps
- uses: actions/cache/save@v4
with:
path: |
${{ inputs.cache_tag }}_python_deps.tar
update_comfyui_and_python_dependencies.bat
key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}

2
.github/workflows/windows_release_nightly_pytorch.yml vendored
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@ -68,7 +68,7 @@ jobs:
mkdir update
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
cp -r ComfyUI/.ci/windows_nightly_base_files/* ./
echo "call update_comfyui.bat nopause

2
.github/workflows/windows_release_package.yml vendored
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@ -81,7 +81,7 @@ jobs:
mkdir update
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
cp ../update_comfyui_and_python_dependencies.bat ./update/
cd ..

24
CODEOWNERS
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@ -1,25 +1,3 @@
# Admins
* @comfyanonymous
# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org.
# Inlined the team members for now.
# Maintainers
*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
# Python web server
/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
# Node developers
/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
/comfy_api_nodes/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
* @kosinkadink

33
app/frontend_management.py
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@ -42,6 +42,7 @@ def get_installed_frontend_version():
frontend_version_str = version("comfyui-frontend-package")
return frontend_version_str
def get_required_frontend_version():
"""Get the required frontend version from requirements.txt."""
try:
@ -63,6 +64,7 @@ def get_required_frontend_version():
logging.error(f"Error reading requirements.txt: {e}")
return None
def check_frontend_version():
"""Check if the frontend version is up to date."""
@ -203,6 +205,37 @@ class FrontendManager:
"""Get the required frontend package version."""
return get_required_frontend_version()
@classmethod
def get_installed_templates_version(cls) -> str:
"""Get the currently installed workflow templates package version."""
try:
templates_version_str = version("comfyui-workflow-templates")
return templates_version_str
except Exception:
return None
@classmethod
def get_required_templates_version(cls) -> str:
"""Get the required workflow templates version from requirements.txt."""
try:
with open(requirements_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line.startswith("comfyui-workflow-templates=="):
version_str = line.split("==")[-1]
if not is_valid_version(version_str):
logging.error(f"Invalid templates version format in requirements.txt: {version_str}")
return None
return version_str
logging.error("comfyui-workflow-templates not found in requirements.txt")
return None
except FileNotFoundError:
logging.error("requirements.txt not found. Cannot determine required templates version.")
return None
except Exception as e:
logging.error(f"Error reading requirements.txt: {e}")
return None
@classmethod
def default_frontend_path(cls) -> str:
try:

5
comfy/ldm/flux/math.py
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@ -37,7 +37,10 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
def apply_rope1(x: Tensor, freqs_cis: Tensor):
x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
x_out = freqs_cis[..., 0] * x_[..., 0] + freqs_cis[..., 1] * x_[..., 1]
x_out = freqs_cis[..., 0] * x_[..., 0]
x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
return x_out.reshape(*x.shape).type_as(x)
def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):

3
comfy/ldm/wan/model.py
View File

@ -237,6 +237,7 @@ class WanAttentionBlock(nn.Module):
freqs, transformer_options=transformer_options)
x = torch.addcmul(x, y, repeat_e(e[2], x))
del y
# cross-attention & ffn
x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
@ -1355,7 +1356,7 @@ class WanT2VCrossAttentionGather(WanSelfAttention):
x = optimized_attention(q, k, v, heads=self.num_heads, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
x = x.transpose(1, 2).view(b, -1, n, d).flatten(2)
x = x.transpose(1, 2).reshape(b, -1, n * d)
x = self.o(x)
return x

4
comfy/model_management.py
View File

@ -645,7 +645,9 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
if loaded_model.model.is_clone(current_loaded_models[i].model):
to_unload = [i] + to_unload
for i in to_unload:
current_loaded_models.pop(i).model.detach(unpatch_all=False)
model_to_unload = current_loaded_models.pop(i)
model_to_unload.model.detach(unpatch_all=False)
model_to_unload.model_finalizer.detach()
total_memory_required = {}
for loaded_model in models_to_load:

4
comfy/samplers.py
View File

@ -360,7 +360,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
if "sampler_cfg_function" in model_options:
args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
"cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
"cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options, "input_cond": cond, "input_uncond": uncond}
cfg_result = x - model_options["sampler_cfg_function"](args)
else:
cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
@ -390,7 +390,7 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
for fn in model_options.get("sampler_pre_cfg_function", []):
args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
"input": x, "sigma": timestep, "model": model, "model_options": model_options}
out = fn(args)
out = fn(args)
return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)

8
comfy/text_encoders/hunyuan_image.py
View File

@ -63,7 +63,13 @@ class HunyuanImageTEModel(QwenImageTEModel):
self.byt5_small = None
def encode_token_weights(self, token_weight_pairs):
cond, p, extra = super().encode_token_weights(token_weight_pairs)
tok_pairs = token_weight_pairs["qwen25_7b"][0]
template_end = -1
if tok_pairs[0][0] == 27:
if len(tok_pairs) > 36: # refiner prompt uses a fixed 36 template_end
template_end = 36
cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=template_end)
if self.byt5_small is not None and "byt5" in token_weight_pairs:
out = self.byt5_small.encode_token_weights(token_weight_pairs["byt5"])
extra["conditioning_byt5small"] = out[0]

46
comfy/text_encoders/qwen_image.py
View File

@ -18,13 +18,22 @@ class QwenImageTokenizer(sd1_clip.SD1Tokenizer):
self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs):
if llama_template is None:
if len(images) > 0:
llama_text = self.llama_template_images.format(text)
else:
llama_text = self.llama_template.format(text)
skip_template = False
if text.startswith('<|im_start|>'):
skip_template = True
if text.startswith('<|start_header_id|>'):
skip_template = True
if skip_template:
llama_text = text
else:
llama_text = llama_template.format(text)
if llama_template is None:
if len(images) > 0:
llama_text = self.llama_template_images.format(text)
else:
llama_text = self.llama_template.format(text)
else:
llama_text = llama_template.format(text)
tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
key_name = next(iter(tokens))
embed_count = 0
@ -47,22 +56,23 @@ class QwenImageTEModel(sd1_clip.SD1ClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options)
def encode_token_weights(self, token_weight_pairs):
def encode_token_weights(self, token_weight_pairs, template_end=-1):
out, pooled, extra = super().encode_token_weights(token_weight_pairs)
tok_pairs = token_weight_pairs["qwen25_7b"][0]
count_im_start = 0
for i, v in enumerate(tok_pairs):
elem = v[0]
if not torch.is_tensor(elem):
if isinstance(elem, numbers.Integral):
if elem == 151644 and count_im_start < 2:
template_end = i
count_im_start += 1
if template_end == -1:
for i, v in enumerate(tok_pairs):
elem = v[0]
if not torch.is_tensor(elem):
if isinstance(elem, numbers.Integral):
if elem == 151644 and count_im_start < 2:
template_end = i
count_im_start += 1
if out.shape[1] > (template_end + 3):
if tok_pairs[template_end + 1][0] == 872:
if tok_pairs[template_end + 2][0] == 198:
template_end += 3
if out.shape[1] > (template_end + 3):
if tok_pairs[template_end + 1][0] == 872:
if tok_pairs[template_end + 2][0] == 198:
template_end += 3
out = out[:, template_end:]

3
comfy_api_nodes/apis/rodin_api.py
View File

@ -9,8 +9,9 @@ class Rodin3DGenerateRequest(BaseModel):
seed: int = Field(..., description="seed_")
tier: str = Field(..., description="Tier of generation.")
material: str = Field(..., description="The material type.")
quality: str = Field(..., description="The generation quality of the mesh.")
quality_override: int = Field(..., description="The poly count of the mesh.")
mesh_mode: str = Field(..., description="It controls the type of faces of generated models.")
TAPose: Optional[bool] = Field(None, description="")
class GenerateJobsData(BaseModel):
uuids: List[str] = Field(..., description="str LIST")

1056
comfy_api_nodes/nodes_bfl.py
View File

File diff suppressed because it is too large Load Diff

2
comfy_api_nodes/nodes_bytedance.py
View File

@ -920,7 +920,7 @@ class ByteDanceFirstLastFrameNode(comfy_io.ComfyNode):
inputs=[
comfy_io.Combo.Input(
"model",
options=[Image2VideoModelName.seedance_1_lite.value],
options=[model.value for model in Image2VideoModelName],
default=Image2VideoModelName.seedance_1_lite.value,
tooltip="Model name",
),

774
comfy_api_nodes/nodes_luma.py
View File

@ -1,7 +1,8 @@
from __future__ import annotations
from inspect import cleandoc
from typing import Optional
from comfy.comfy_types.node_typing import IO, ComfyNodeABC
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io as comfy_io
from comfy_api.input_impl.video_types import VideoFromFile
from comfy_api_nodes.apis.luma_api import (
LumaImageModel,
@ -51,174 +52,186 @@ def image_result_url_extractor(response: LumaGeneration):
def video_result_url_extractor(response: LumaGeneration):
return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None
class LumaReferenceNode(ComfyNodeABC):
class LumaReferenceNode(comfy_io.ComfyNode):
"""
Holds an image and weight for use with Luma Generate Image node.
"""
RETURN_TYPES = (LumaIO.LUMA_REF,)
RETURN_NAMES = ("luma_ref",)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "create_luma_reference"
CATEGORY = "api node/image/Luma"
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="LumaReferenceNode",
display_name="Luma Reference",
category="api node/image/Luma",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.Image.Input(
"image",
tooltip="Image to use as reference.",
),
comfy_io.Float.Input(
"weight",
default=1.0,
min=0.0,
max=1.0,
step=0.01,
tooltip="Weight of image reference.",
),
comfy_io.Custom(LumaIO.LUMA_REF).Input(
"luma_ref",
optional=True,
),
],
outputs=[comfy_io.Custom(LumaIO.LUMA_REF).Output(display_name="luma_ref")],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
comfy_io.Hidden.unique_id,
],
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": (
IO.IMAGE,
{
"tooltip": "Image to use as reference.",
},
),
"weight": (
IO.FLOAT,
{
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.01,
"tooltip": "Weight of image reference.",
},
),
},
"optional": {"luma_ref": (LumaIO.LUMA_REF,)},
}
def create_luma_reference(
self, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None
):
def execute(
cls, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None
) -> comfy_io.NodeOutput:
if luma_ref is not None:
luma_ref = luma_ref.clone()
else:
luma_ref = LumaReferenceChain()
luma_ref.add(LumaReference(image=image, weight=round(weight, 2)))
return (luma_ref,)
return comfy_io.NodeOutput(luma_ref)
class LumaConceptsNode(ComfyNodeABC):
class LumaConceptsNode(comfy_io.ComfyNode):
"""
Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.
"""
RETURN_TYPES = (LumaIO.LUMA_CONCEPTS,)
RETURN_NAMES = ("luma_concepts",)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "create_concepts"
CATEGORY = "api node/video/Luma"
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="LumaConceptsNode",
display_name="Luma Concepts",
category="api node/video/Luma",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.Combo.Input(
"concept1",
options=get_luma_concepts(include_none=True),
),
comfy_io.Combo.Input(
"concept2",
options=get_luma_concepts(include_none=True),
),
comfy_io.Combo.Input(
"concept3",
options=get_luma_concepts(include_none=True),
),
comfy_io.Combo.Input(
"concept4",
options=get_luma_concepts(include_none=True),
),
comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Input(
"luma_concepts",
tooltip="Optional Camera Concepts to add to the ones chosen here.",
optional=True,
),
],
outputs=[comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Output(display_name="luma_concepts")],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
comfy_io.Hidden.unique_id,
],
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"concept1": (get_luma_concepts(include_none=True),),
"concept2": (get_luma_concepts(include_none=True),),
"concept3": (get_luma_concepts(include_none=True),),
"concept4": (get_luma_concepts(include_none=True),),
},
"optional": {
"luma_concepts": (
LumaIO.LUMA_CONCEPTS,
{
"tooltip": "Optional Camera Concepts to add to the ones chosen here."
},
),
},
}
def create_concepts(
self,
def execute(
cls,
concept1: str,
concept2: str,
concept3: str,
concept4: str,
luma_concepts: LumaConceptChain = None,
):
) -> comfy_io.NodeOutput:
chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4])
if luma_concepts is not None:
chain = luma_concepts.clone_and_merge(chain)
return (chain,)
return comfy_io.NodeOutput(chain)
class LumaImageGenerationNode(ComfyNodeABC):
class LumaImageGenerationNode(comfy_io.ComfyNode):
"""
Generates images synchronously based on prompt and aspect ratio.
"""
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/image/Luma"
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="LumaImageNode",
display_name="Luma Text to Image",
category="api node/image/Luma",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the image generation",
),
comfy_io.Combo.Input(
"model",
options=[model.value for model in LumaImageModel],
),
comfy_io.Combo.Input(
"aspect_ratio",
options=[ratio.value for ratio in LumaAspectRatio],
default=LumaAspectRatio.ratio_16_9,
),
comfy_io.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
),
comfy_io.Float.Input(
"style_image_weight",
default=1.0,
min=0.0,
max=1.0,
step=0.01,
tooltip="Weight of style image. Ignored if no style_image provided.",
),
comfy_io.Custom(LumaIO.LUMA_REF).Input(
"image_luma_ref",
tooltip="Luma Reference node connection to influence generation with input images; up to 4 images can be considered.",
optional=True,
),
comfy_io.Image.Input(
"style_image",
tooltip="Style reference image; only 1 image will be used.",
optional=True,
),
comfy_io.Image.Input(
"character_image",
tooltip="Character reference images; can be a batch of multiple, up to 4 images can be considered.",
optional=True,
),
],
outputs=[comfy_io.Image.Output()],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
comfy_io.Hidden.unique_id,
],
is_api_node=True,
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Prompt for the image generation",
},
),
"model": ([model.value for model in LumaImageModel],),
"aspect_ratio": (
[ratio.value for ratio in LumaAspectRatio],
{
"default": LumaAspectRatio.ratio_16_9,
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 0xFFFFFFFFFFFFFFFF,
"control_after_generate": True,
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
},
),
"style_image_weight": (
IO.FLOAT,
{
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.01,
"tooltip": "Weight of style image. Ignored if no style_image provided.",
},
),
},
"optional": {
"image_luma_ref": (
LumaIO.LUMA_REF,
{
"tooltip": "Luma Reference node connection to influence generation with input images; up to 4 images can be considered."
},
),
"style_image": (
IO.IMAGE,
{"tooltip": "Style reference image; only 1 image will be used."},
),
"character_image": (
IO.IMAGE,
{
"tooltip": "Character reference images; can be a batch of multiple, up to 4 images can be considered."
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
"unique_id": "UNIQUE_ID",
},
}
async def api_call(
self,
async def execute(
cls,
prompt: str,
model: str,
aspect_ratio: str,
@ -227,27 +240,29 @@ class LumaImageGenerationNode(ComfyNodeABC):
image_luma_ref: LumaReferenceChain = None,
style_image: torch.Tensor = None,
character_image: torch.Tensor = None,
unique_id: str = None,
**kwargs,
):
) -> comfy_io.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=3)
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
# handle image_luma_ref
api_image_ref = None
if image_luma_ref is not None:
api_image_ref = await self._convert_luma_refs(
image_luma_ref, max_refs=4, auth_kwargs=kwargs,
api_image_ref = await cls._convert_luma_refs(
image_luma_ref, max_refs=4, auth_kwargs=auth_kwargs,
)
# handle style_luma_ref
api_style_ref = None
if style_image is not None:
api_style_ref = await self._convert_style_image(
style_image, weight=style_image_weight, auth_kwargs=kwargs,
api_style_ref = await cls._convert_style_image(
style_image, weight=style_image_weight, auth_kwargs=auth_kwargs,
)
# handle character_ref images
character_ref = None
if character_image is not None:
download_urls = await upload_images_to_comfyapi(
character_image, max_images=4, auth_kwargs=kwargs,
character_image, max_images=4, auth_kwargs=auth_kwargs,
)
character_ref = LumaCharacterRef(
identity0=LumaImageIdentity(images=download_urls)
@ -268,7 +283,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
style_ref=api_style_ref,
character_ref=character_ref,
),
auth_kwargs=kwargs,
auth_kwargs=auth_kwargs,
)
response_api: LumaGeneration = await operation.execute()
@ -283,18 +298,19 @@ class LumaImageGenerationNode(ComfyNodeABC):
failed_statuses=[LumaState.failed],
status_extractor=lambda x: x.state,
result_url_extractor=image_result_url_extractor,
node_id=unique_id,
auth_kwargs=kwargs,
node_id=cls.hidden.unique_id,
auth_kwargs=auth_kwargs,
)
response_poll = await operation.execute()
async with aiohttp.ClientSession() as session:
async with session.get(response_poll.assets.image) as img_response:
img = process_image_response(await img_response.content.read())
return (img,)
return comfy_io.NodeOutput(img)
@classmethod
async def _convert_luma_refs(
self, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None
cls, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None
):
luma_urls = []
ref_count = 0
@ -308,82 +324,84 @@ class LumaImageGenerationNode(ComfyNodeABC):
break
return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs)
@classmethod
async def _convert_style_image(
self, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None
cls, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None
):
chain = LumaReferenceChain(
first_ref=LumaReference(image=style_image, weight=weight)
)
return await self._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs)
return await cls._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs)
class LumaImageModifyNode(ComfyNodeABC):
class LumaImageModifyNode(comfy_io.ComfyNode):
"""
Modifies images synchronously based on prompt and aspect ratio.
"""
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/image/Luma"
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="LumaImageModifyNode",
display_name="Luma Image to Image",
category="api node/image/Luma",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.Image.Input(
"image",
),
comfy_io.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the image generation",
),
comfy_io.Float.Input(
"image_weight",
default=0.1,
min=0.0,
max=0.98,
step=0.01,
tooltip="Weight of the image; the closer to 1.0, the less the image will be modified.",
),
comfy_io.Combo.Input(
"model",
options=[model.value for model in LumaImageModel],
),
comfy_io.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
),
],
outputs=[comfy_io.Image.Output()],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
comfy_io.Hidden.unique_id,
],
is_api_node=True,
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": (IO.IMAGE,),
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Prompt for the image generation",
},
),
"image_weight": (
IO.FLOAT,
{
"default": 0.1,
"min": 0.0,
"max": 0.98,
"step": 0.01,
"tooltip": "Weight of the image; the closer to 1.0, the less the image will be modified.",
},
),
"model": ([model.value for model in LumaImageModel],),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 0xFFFFFFFFFFFFFFFF,
"control_after_generate": True,
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
},
),
},
"optional": {},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
"unique_id": "UNIQUE_ID",
},
}
async def api_call(
self,
async def execute(
cls,
prompt: str,
model: str,
image: torch.Tensor,
image_weight: float,
seed,
unique_id: str = None,
**kwargs,
):
) -> comfy_io.NodeOutput:
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
# first, upload image
download_urls = await upload_images_to_comfyapi(
image, max_images=1, auth_kwargs=kwargs,
image, max_images=1, auth_kwargs=auth_kwargs,
)
image_url = download_urls[0]
# next, make Luma call with download url provided
@ -401,7 +419,7 @@ class LumaImageModifyNode(ComfyNodeABC):
url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2)
),
),
auth_kwargs=kwargs,
auth_kwargs=auth_kwargs,
)
response_api: LumaGeneration = await operation.execute()
@ -416,88 +434,84 @@ class LumaImageModifyNode(ComfyNodeABC):
failed_statuses=[LumaState.failed],
status_extractor=lambda x: x.state,
result_url_extractor=image_result_url_extractor,
node_id=unique_id,
auth_kwargs=kwargs,
node_id=cls.hidden.unique_id,
auth_kwargs=auth_kwargs,
)
response_poll = await operation.execute()
async with aiohttp.ClientSession() as session:
async with session.get(response_poll.assets.image) as img_response:
img = process_image_response(await img_response.content.read())
return (img,)
return comfy_io.NodeOutput(img)
class LumaTextToVideoGenerationNode(ComfyNodeABC):
class LumaTextToVideoGenerationNode(comfy_io.ComfyNode):
"""
Generates videos synchronously based on prompt and output_size.
"""
RETURN_TYPES = (IO.VIDEO,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/video/Luma"
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="LumaVideoNode",
display_name="Luma Text to Video",
category="api node/video/Luma",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the video generation",
),
comfy_io.Combo.Input(
"model",
options=[model.value for model in LumaVideoModel],
),
comfy_io.Combo.Input(
"aspect_ratio",
options=[ratio.value for ratio in LumaAspectRatio],
default=LumaAspectRatio.ratio_16_9,
),
comfy_io.Combo.Input(
"resolution",
options=[resolution.value for resolution in LumaVideoOutputResolution],
default=LumaVideoOutputResolution.res_540p,
),
comfy_io.Combo.Input(
"duration",
options=[dur.value for dur in LumaVideoModelOutputDuration],
),
comfy_io.Boolean.Input(
"loop",
default=False,
),
comfy_io.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
),
comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Input(
"luma_concepts",
tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
optional=True,
)
],
outputs=[comfy_io.Video.Output()],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
comfy_io.Hidden.unique_id,
],
is_api_node=True,
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Prompt for the video generation",
},
),
"model": ([model.value for model in LumaVideoModel],),
"aspect_ratio": (
[ratio.value for ratio in LumaAspectRatio],
{
"default": LumaAspectRatio.ratio_16_9,
},
),
"resolution": (
[resolution.value for resolution in LumaVideoOutputResolution],
{
"default": LumaVideoOutputResolution.res_540p,
},
),
"duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
"loop": (
IO.BOOLEAN,
{
"default": False,
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 0xFFFFFFFFFFFFFFFF,
"control_after_generate": True,
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
},
),
},
"optional": {
"luma_concepts": (
LumaIO.LUMA_CONCEPTS,
{
"tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
"unique_id": "UNIQUE_ID",
},
}
async def api_call(
self,
async def execute(
cls,
prompt: str,
model: str,
aspect_ratio: str,
@ -506,13 +520,15 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
loop: bool,
seed,
luma_concepts: LumaConceptChain = None,
unique_id: str = None,
**kwargs,
):
) -> comfy_io.NodeOutput:
validate_string(prompt, strip_whitespace=False, min_length=3)
duration = duration if model != LumaVideoModel.ray_1_6 else None
resolution = resolution if model != LumaVideoModel.ray_1_6 else None
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/luma/generations",
@ -529,12 +545,12 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
loop=loop,
concepts=luma_concepts.create_api_model() if luma_concepts else None,
),
auth_kwargs=kwargs,
auth_kwargs=auth_kwargs,
)
response_api: LumaGeneration = await operation.execute()
if unique_id:
PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
if cls.hidden.unique_id:
PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", cls.hidden.unique_id)
operation = PollingOperation(
poll_endpoint=ApiEndpoint(
@ -547,90 +563,94 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
failed_statuses=[LumaState.failed],
status_extractor=lambda x: x.state,
result_url_extractor=video_result_url_extractor,
node_id=unique_id,
node_id=cls.hidden.unique_id,
estimated_duration=LUMA_T2V_AVERAGE_DURATION,
auth_kwargs=kwargs,
auth_kwargs=auth_kwargs,
)
response_poll = await operation.execute()
async with aiohttp.ClientSession() as session:
async with session.get(response_poll.assets.video) as vid_response:
return (VideoFromFile(BytesIO(await vid_response.content.read())),)
return comfy_io.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))
class LumaImageToVideoGenerationNode(ComfyNodeABC):
class LumaImageToVideoGenerationNode(comfy_io.ComfyNode):
"""
Generates videos synchronously based on prompt, input images, and output_size.
"""
RETURN_TYPES = (IO.VIDEO,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/video/Luma"
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="LumaImageToVideoNode",
display_name="Luma Image to Video",
category="api node/video/Luma",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the video generation",
),
comfy_io.Combo.Input(
"model",
options=[model.value for model in LumaVideoModel],
),
# comfy_io.Combo.Input(
# "aspect_ratio",
# options=[ratio.value for ratio in LumaAspectRatio],
# default=LumaAspectRatio.ratio_16_9,
# ),
comfy_io.Combo.Input(
"resolution",
options=[resolution.value for resolution in LumaVideoOutputResolution],
default=LumaVideoOutputResolution.res_540p,
),
comfy_io.Combo.Input(
"duration",
options=[dur.value for dur in LumaVideoModelOutputDuration],
),
comfy_io.Boolean.Input(
"loop",
default=False,
),
comfy_io.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
),
comfy_io.Image.Input(
"first_image",
tooltip="First frame of generated video.",
optional=True,
),
comfy_io.Image.Input(
"last_image",
tooltip="Last frame of generated video.",
optional=True,
),
comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Input(
"luma_concepts",
tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
optional=True,
)
],
outputs=[comfy_io.Video.Output()],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
comfy_io.Hidden.unique_id,
],
is_api_node=True,
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Prompt for the video generation",
},
),
"model": ([model.value for model in LumaVideoModel],),
# "aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], {
# "default": LumaAspectRatio.ratio_16_9,
# }),
"resolution": (
[resolution.value for resolution in LumaVideoOutputResolution],
{
"default": LumaVideoOutputResolution.res_540p,
},
),
"duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
"loop": (
IO.BOOLEAN,
{
"default": False,
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 0xFFFFFFFFFFFFFFFF,
"control_after_generate": True,
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
},
),
},
"optional": {
"first_image": (
IO.IMAGE,
{"tooltip": "First frame of generated video."},
),
"last_image": (IO.IMAGE, {"tooltip": "Last frame of generated video."}),
"luma_concepts": (
LumaIO.LUMA_CONCEPTS,
{
"tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
"unique_id": "UNIQUE_ID",
},
}
async def api_call(
self,
async def execute(
cls,
prompt: str,
model: str,
resolution: str,
@ -640,14 +660,16 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
first_image: torch.Tensor = None,
last_image: torch.Tensor = None,
luma_concepts: LumaConceptChain = None,
unique_id: str = None,
**kwargs,
):
) -> comfy_io.NodeOutput:
if first_image is None and last_image is None:
raise Exception(
"At least one of first_image and last_image requires an input."
)
keyframes = await self._convert_to_keyframes(first_image, last_image, auth_kwargs=kwargs)
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
keyframes = await cls._convert_to_keyframes(first_image, last_image, auth_kwargs=auth_kwargs)
duration = duration if model != LumaVideoModel.ray_1_6 else None
resolution = resolution if model != LumaVideoModel.ray_1_6 else None
@ -668,12 +690,12 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
keyframes=keyframes,
concepts=luma_concepts.create_api_model() if luma_concepts else None,
),
auth_kwargs=kwargs,
auth_kwargs=auth_kwargs,
)
response_api: LumaGeneration = await operation.execute()
if unique_id:
PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
if cls.hidden.unique_id:
PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", cls.hidden.unique_id)
operation = PollingOperation(
poll_endpoint=ApiEndpoint(
@ -686,18 +708,19 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
failed_statuses=[LumaState.failed],
status_extractor=lambda x: x.state,
result_url_extractor=video_result_url_extractor,
node_id=unique_id,
node_id=cls.hidden.unique_id,
estimated_duration=LUMA_I2V_AVERAGE_DURATION,
auth_kwargs=kwargs,
auth_kwargs=auth_kwargs,
)
response_poll = await operation.execute()
async with aiohttp.ClientSession() as session:
async with session.get(response_poll.assets.video) as vid_response:
return (VideoFromFile(BytesIO(await vid_response.content.read())),)
return comfy_io.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))
@classmethod
async def _convert_to_keyframes(
self,
cls,
first_image: torch.Tensor = None,
last_image: torch.Tensor = None,
auth_kwargs: Optional[dict[str,str]] = None,
@ -719,23 +742,18 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
return LumaKeyframes(frame0=frame0, frame1=frame1)
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"LumaImageNode": LumaImageGenerationNode,
"LumaImageModifyNode": LumaImageModifyNode,
"LumaVideoNode": LumaTextToVideoGenerationNode,
"LumaImageToVideoNode": LumaImageToVideoGenerationNode,
"LumaReferenceNode": LumaReferenceNode,
"LumaConceptsNode": LumaConceptsNode,
}
class LumaExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
return [
LumaImageGenerationNode,
LumaImageModifyNode,
LumaTextToVideoGenerationNode,
LumaImageToVideoGenerationNode,
LumaReferenceNode,
LumaConceptsNode,
]
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"LumaImageNode": "Luma Text to Image",
"LumaImageModifyNode": "Luma Image to Image",
"LumaVideoNode": "Luma Text to Video",
"LumaImageToVideoNode": "Luma Image to Video",
"LumaReferenceNode": "Luma Reference",
"LumaConceptsNode": "Luma Concepts",
}
async def comfy_entrypoint() -> LumaExtension:
return LumaExtension()

153
comfy_api_nodes/nodes_rodin.py
View File

@ -11,7 +11,6 @@ from comfy.comfy_types.node_typing import IO
import folder_paths as comfy_paths
import aiohttp
import os
import datetime
import asyncio
import io
import logging
@ -121,10 +120,10 @@ class Rodin3DAPI:
else:
return "Generating"
async def create_generate_task(self, images=None, seed=1, material="PBR", quality="medium", tier="Regular", mesh_mode="Quad", **kwargs):
async def create_generate_task(self, images=None, seed=1, material="PBR", quality_override=18000, tier="Regular", mesh_mode="Quad", TAPose = False, **kwargs):
if images is None:
raise Exception("Rodin 3D generate requires at least 1 image.")
if len(images) >= 5:
if len(images) > 5:
raise Exception("Rodin 3D generate requires up to 5 image.")
path = "/proxy/rodin/api/v2/rodin"
@ -139,8 +138,9 @@ class Rodin3DAPI:
seed=seed,
tier=tier,
material=material,
quality=quality,
mesh_mode=mesh_mode
quality_override=quality_override,
mesh_mode=mesh_mode,
TAPose=TAPose,
),
files=[
(
@ -211,26 +211,39 @@ class Rodin3DAPI:
return await operation.execute()
def get_quality_mode(self, poly_count):
if poly_count == "200K-Triangle":
polycount = poly_count.split("-")
poly = polycount[1]
count = polycount[0]
if poly == "Triangle":
mesh_mode = "Raw"
quality = "medium"
elif poly == "Quad":
mesh_mode = "Quad"
else:
mesh_mode = "Quad"
if poly_count == "4K-Quad":
quality = "extra-low"
elif poly_count == "8K-Quad":
quality = "low"
elif poly_count == "18K-Quad":
quality = "medium"
elif poly_count == "50K-Quad":
quality = "high"
else:
quality = "medium"
return mesh_mode, quality
if count == "4K":
quality_override = 4000
elif count == "8K":
quality_override = 8000
elif count == "18K":
quality_override = 18000
elif count == "50K":
quality_override = 50000
elif count == "2K":
quality_override = 2000
elif count == "20K":
quality_override = 20000
elif count == "150K":
quality_override = 150000
elif count == "500K":
quality_override = 500000
else:
quality_override = 18000
async def download_files(self, url_list):
save_path = os.path.join(comfy_paths.get_output_directory(), "Rodin3D", datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
return mesh_mode, quality_override
async def download_files(self, url_list, task_uuid):
save_path = os.path.join(comfy_paths.get_output_directory(), f"Rodin3D_{task_uuid}")
os.makedirs(save_path, exist_ok=True)
model_file_path = None
async with aiohttp.ClientSession() as session:
@ -300,13 +313,13 @@ class Rodin3D_Regular(Rodin3DAPI):
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality = self.get_quality_mode(Polygon_count)
mesh_mode, quality_override = self.get_quality_mode(Polygon_count)
task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
quality=quality, tier=tier, mesh_mode=mesh_mode,
quality_override=quality_override, tier=tier, mesh_mode=mesh_mode,
**kwargs)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list)
model = await self.download_files(download_list, task_uuid)
return (model,)
@ -346,13 +359,13 @@ class Rodin3D_Detail(Rodin3DAPI):
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality = self.get_quality_mode(Polygon_count)
mesh_mode, quality_override = self.get_quality_mode(Polygon_count)
task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
quality=quality, tier=tier, mesh_mode=mesh_mode,
quality_override=quality_override, tier=tier, mesh_mode=mesh_mode,
**kwargs)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list)
model = await self.download_files(download_list, task_uuid)
return (model,)
@ -392,13 +405,13 @@ class Rodin3D_Smooth(Rodin3DAPI):
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality = self.get_quality_mode(Polygon_count)
mesh_mode, quality_override = self.get_quality_mode(Polygon_count)
task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
quality=quality, tier=tier, mesh_mode=mesh_mode,
quality_override=quality_override, tier=tier, mesh_mode=mesh_mode,
**kwargs)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list)
model = await self.download_files(download_list, task_uuid)
return (model,)
@ -446,14 +459,88 @@ class Rodin3D_Sketch(Rodin3DAPI):
for i in range(num_images):
m_images.append(Images[i])
material_type = "PBR"
quality = "medium"
quality_override = 18000
mesh_mode = "Quad"
task_uuid, subscription_key = await self.create_generate_task(
images=m_images, seed=Seed, material=material_type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs
images=m_images, seed=Seed, material=material_type, quality_override=quality_override, tier=tier, mesh_mode=mesh_mode, **kwargs
)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list)
model = await self.download_files(download_list, task_uuid)
return (model,)
class Rodin3D_Gen2(Rodin3DAPI):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"Images":
(
IO.IMAGE,
{
"forceInput":True,
}
)
},
"optional": {
"Seed": (
IO.INT,
{
"default":0,
"min":0,
"max":65535,
"display":"number"
}
),
"Material_Type": (
IO.COMBO,
{
"options": ["PBR", "Shaded"],
"default": "PBR"
}
),
"Polygon_count": (
IO.COMBO,
{
"options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "2K-Triangle", "20K-Triangle", "150K-Triangle", "500K-Triangle"],
"default": "500K-Triangle"
}
),
"TAPose": (
IO.BOOLEAN,
{
"default": False,
}
)
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
async def api_call(
self,
Images,
Seed,
Material_Type,
Polygon_count,
TAPose,
**kwargs
):
tier = "Gen-2"
num_images = Images.shape[0]
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = self.get_quality_mode(Polygon_count)
task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
quality_override=quality_override, tier=tier, mesh_mode=mesh_mode, TAPose=TAPose,
**kwargs)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list, task_uuid)
return (model,)
@ -464,6 +551,7 @@ NODE_CLASS_MAPPINGS = {
"Rodin3D_Detail": Rodin3D_Detail,
"Rodin3D_Smooth": Rodin3D_Smooth,
"Rodin3D_Sketch": Rodin3D_Sketch,
"Rodin3D_Gen2": Rodin3D_Gen2,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
@ -472,4 +560,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"Rodin3D_Detail": "Rodin 3D Generate - Detail Generate",
"Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate",
"Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate",
"Rodin3D_Gen2": "Rodin 3D Generate - Gen-2 Generate",
}

149
comfy_api_nodes/nodes_wan.py
View File

@ -28,6 +28,12 @@ class Text2ImageInputField(BaseModel):
negative_prompt: Optional[str] = Field(None)
class Image2ImageInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
images: list[str] = Field(..., min_length=1, max_length=2)
class Text2VideoInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
@ -49,6 +55,13 @@ class Txt2ImageParametersField(BaseModel):
watermark: bool = Field(True)
class Image2ImageParametersField(BaseModel):
size: Optional[str] = Field(None)
n: int = Field(1, description="Number of images to generate.") # we support only value=1
seed: int = Field(..., ge=0, le=2147483647)
watermark: bool = Field(True)
class Text2VideoParametersField(BaseModel):
size: str = Field(...)
seed: int = Field(..., ge=0, le=2147483647)
@ -73,6 +86,12 @@ class Text2ImageTaskCreationRequest(BaseModel):
parameters: Txt2ImageParametersField = Field(...)
class Image2ImageTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Image2ImageInputField = Field(...)
parameters: Image2ImageParametersField = Field(...)
class Text2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Text2VideoInputField = Field(...)
@ -135,7 +154,12 @@ async def process_task(
url: str,
request_model: Type[T],
response_model: Type[R],
payload: Union[Text2ImageTaskCreationRequest, Text2VideoTaskCreationRequest, Image2VideoTaskCreationRequest],
payload: Union[
Text2ImageTaskCreationRequest,
Image2ImageTaskCreationRequest,
Text2VideoTaskCreationRequest,
Image2VideoTaskCreationRequest,
],
node_id: str,
estimated_duration: int,
poll_interval: int,
@ -288,6 +312,128 @@ class WanTextToImageApi(comfy_io.ComfyNode):
return comfy_io.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url)))
class WanImageToImageApi(comfy_io.ComfyNode):
@classmethod
def define_schema(cls):
return comfy_io.Schema(
node_id="WanImageToImageApi",
display_name="Wan Image to Image",
category="api node/image/Wan",
description="Generates an image from one or two input images and a text prompt. "
"The output image is currently fixed at 1.6 MP; its aspect ratio matches the input image(s).",
inputs=[
comfy_io.Combo.Input(
"model",
options=["wan2.5-i2i-preview"],
default="wan2.5-i2i-preview",
tooltip="Model to use.",
),
comfy_io.Image.Input(
"image",
tooltip="Single-image editing or multi-image fusion, maximum 2 images.",
),
comfy_io.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
),
comfy_io.String.Input(
"negative_prompt",
multiline=True,
default="",
tooltip="Negative text prompt to guide what to avoid.",
optional=True,
),
# redo this later as an optional combo of recommended resolutions
# comfy_io.Int.Input(
# "width",
# default=1280,
# min=384,
# max=1440,
# step=16,
# optional=True,
# ),
# comfy_io.Int.Input(
# "height",
# default=1280,
# min=384,
# max=1440,
# step=16,
# optional=True,
# ),
comfy_io.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=comfy_io.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed to use for generation.",
optional=True,
),
comfy_io.Boolean.Input(
"watermark",
default=True,
tooltip="Whether to add an \"AI generated\" watermark to the result.",
optional=True,
),
],
outputs=[
comfy_io.Image.Output(),
],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
comfy_io.Hidden.unique_id,
],
is_api_node=True,
)
@classmethod
async def execute(
cls,
model: str,
image: torch.Tensor,
prompt: str,
negative_prompt: str = "",
# width: int = 1024,
# height: int = 1024,
seed: int = 0,
watermark: bool = True,
):
n_images = get_number_of_images(image)
if n_images not in (1, 2):
raise ValueError(f"Expected 1 or 2 input images, got {n_images}.")
images = []
for i in image:
images.append("data:image/png;base64," + tensor_to_base64_string(i, total_pixels=4096*4096))
payload = Image2ImageTaskCreationRequest(
model=model,
input=Image2ImageInputField(prompt=prompt, negative_prompt=negative_prompt, images=images),
parameters=Image2ImageParametersField(
# size=f"{width}*{height}",
seed=seed,
watermark=watermark,
),
)
response = await process_task(
{
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
},
"/proxy/wan/api/v1/services/aigc/image2image/image-synthesis",
request_model=Image2ImageTaskCreationRequest,
response_model=ImageTaskStatusResponse,
payload=payload,
node_id=cls.hidden.unique_id,
estimated_duration=42,
poll_interval=3,
)
return comfy_io.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url)))
class WanTextToVideoApi(comfy_io.ComfyNode):
@classmethod
def define_schema(cls):
@ -593,6 +739,7 @@ class WanApiExtension(ComfyExtension):
async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
return [
WanTextToImageApi,
WanImageToImageApi,
WanTextToVideoApi,
WanImageToVideoApi,
]

223
comfy_extras/nodes_audio.py
View File

@ -11,6 +11,7 @@ import json
import random
import hashlib
import node_helpers
import logging
from comfy.cli_args import args
from comfy.comfy_types import FileLocator
@ -364,6 +365,216 @@ class RecordAudio:
return (audio, )
class TrimAudioDuration:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"audio": ("AUDIO",),
"start_index": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Start time in seconds, can be negative to count from the end (supports sub-seconds)."}),
"duration": ("FLOAT", {"default": 60.0, "min": 0.0, "step": 0.01, "tooltip": "Duration in seconds"}),
},
}
FUNCTION = "trim"
RETURN_TYPES = ("AUDIO",)
CATEGORY = "audio"
DESCRIPTION = "Trim audio tensor into chosen time range."
def trim(self, audio, start_index, duration):
waveform = audio["waveform"]
sample_rate = audio["sample_rate"]
audio_length = waveform.shape[-1]
if start_index < 0:
start_frame = audio_length + int(round(start_index * sample_rate))
else:
start_frame = int(round(start_index * sample_rate))
start_frame = max(0, min(start_frame, audio_length - 1))
end_frame = start_frame + int(round(duration * sample_rate))
end_frame = max(0, min(end_frame, audio_length))
if start_frame >= end_frame:
raise ValueError("AudioTrim: Start time must be less than end time and be within the audio length.")
return ({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate},)
class SplitAudioChannels:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"audio": ("AUDIO",),
}}
RETURN_TYPES = ("AUDIO", "AUDIO")
RETURN_NAMES = ("left", "right")
FUNCTION = "separate"
CATEGORY = "audio"
DESCRIPTION = "Separates the audio into left and right channels."
def separate(self, audio):
waveform = audio["waveform"]
sample_rate = audio["sample_rate"]
if waveform.shape[1] != 2:
raise ValueError("AudioSplit: Input audio has only one channel.")
left_channel = waveform[..., 0:1, :]
right_channel = waveform[..., 1:2, :]
return ({"waveform": left_channel, "sample_rate": sample_rate}, {"waveform": right_channel, "sample_rate": sample_rate})
def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2):
if sample_rate_1 != sample_rate_2:
if sample_rate_1 > sample_rate_2:
waveform_2 = torchaudio.functional.resample(waveform_2, sample_rate_2, sample_rate_1)
output_sample_rate = sample_rate_1
logging.info(f"Resampling audio2 from {sample_rate_2}Hz to {sample_rate_1}Hz for merging.")
else:
waveform_1 = torchaudio.functional.resample(waveform_1, sample_rate_1, sample_rate_2)
output_sample_rate = sample_rate_2
logging.info(f"Resampling audio1 from {sample_rate_1}Hz to {sample_rate_2}Hz for merging.")
else:
output_sample_rate = sample_rate_1
return waveform_1, waveform_2, output_sample_rate
class AudioConcat:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"audio1": ("AUDIO",),
"audio2": ("AUDIO",),
"direction": (['after', 'before'], {"default": 'after', "tooltip": "Whether to append audio2 after or before audio1."}),
}}
RETURN_TYPES = ("AUDIO",)
FUNCTION = "concat"
CATEGORY = "audio"
DESCRIPTION = "Concatenates the audio1 to audio2 in the specified direction."
def concat(self, audio1, audio2, direction):
waveform_1 = audio1["waveform"]
waveform_2 = audio2["waveform"]
sample_rate_1 = audio1["sample_rate"]
sample_rate_2 = audio2["sample_rate"]
if waveform_1.shape[1] == 1:
waveform_1 = waveform_1.repeat(1, 2, 1)
logging.info("AudioConcat: Converted mono audio1 to stereo by duplicating the channel.")
if waveform_2.shape[1] == 1:
waveform_2 = waveform_2.repeat(1, 2, 1)
logging.info("AudioConcat: Converted mono audio2 to stereo by duplicating the channel.")
waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2)
if direction == 'after':
concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2)
elif direction == 'before':
concatenated_audio = torch.cat((waveform_2, waveform_1), dim=2)
return ({"waveform": concatenated_audio, "sample_rate": output_sample_rate},)
class AudioMerge:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"audio1": ("AUDIO",),
"audio2": ("AUDIO",),
"merge_method": (["add", "mean", "subtract", "multiply"], {"tooltip": "The method used to combine the audio waveforms."}),
},
}
FUNCTION = "merge"
RETURN_TYPES = ("AUDIO",)
CATEGORY = "audio"
DESCRIPTION = "Combine two audio tracks by overlaying their waveforms."
def merge(self, audio1, audio2, merge_method):
waveform_1 = audio1["waveform"]
waveform_2 = audio2["waveform"]
sample_rate_1 = audio1["sample_rate"]
sample_rate_2 = audio2["sample_rate"]
waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2)
length_1 = waveform_1.shape[-1]
length_2 = waveform_2.shape[-1]
if length_2 > length_1:
logging.info(f"AudioMerge: Trimming audio2 from {length_2} to {length_1} samples to match audio1 length.")
waveform_2 = waveform_2[..., :length_1]
elif length_2 < length_1:
logging.info(f"AudioMerge: Padding audio2 from {length_2} to {length_1} samples to match audio1 length.")
pad_shape = list(waveform_2.shape)
pad_shape[-1] = length_1 - length_2
pad_tensor = torch.zeros(pad_shape, dtype=waveform_2.dtype, device=waveform_2.device)
waveform_2 = torch.cat((waveform_2, pad_tensor), dim=-1)
if merge_method == "add":
waveform = waveform_1 + waveform_2
elif merge_method == "subtract":
waveform = waveform_1 - waveform_2
elif merge_method == "multiply":
waveform = waveform_1 * waveform_2
elif merge_method == "mean":
waveform = (waveform_1 + waveform_2) / 2
max_val = waveform.abs().max()
if max_val > 1.0:
waveform = waveform / max_val
return ({"waveform": waveform, "sample_rate": output_sample_rate},)
class AudioAdjustVolume:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"audio": ("AUDIO",),
"volume": ("INT", {"default": 1.0, "min": -100, "max": 100, "tooltip": "Volume adjustment in decibels (dB). 0 = no change, +6 = double, -6 = half, etc"}),
}}
RETURN_TYPES = ("AUDIO",)
FUNCTION = "adjust_volume"
CATEGORY = "audio"
def adjust_volume(self, audio, volume):
if volume == 0:
return (audio,)
waveform = audio["waveform"]
sample_rate = audio["sample_rate"]
gain = 10 ** (volume / 20)
waveform = waveform * gain
return ({"waveform": waveform, "sample_rate": sample_rate},)
class EmptyAudio:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"duration": ("FLOAT", {"default": 60.0, "min": 0.0, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Duration of the empty audio clip in seconds"}),
"sample_rate": ("INT", {"default": 44100, "tooltip": "Sample rate of the empty audio clip."}),
"channels": ("INT", {"default": 2, "min": 1, "max": 2, "tooltip": "Number of audio channels (1 for mono, 2 for stereo)."}),
}}
RETURN_TYPES = ("AUDIO",)
FUNCTION = "create_empty_audio"
CATEGORY = "audio"
def create_empty_audio(self, duration, sample_rate, channels):
num_samples = int(round(duration * sample_rate))
waveform = torch.zeros((1, channels, num_samples), dtype=torch.float32)
return ({"waveform": waveform, "sample_rate": sample_rate},)
NODE_CLASS_MAPPINGS = {
"EmptyLatentAudio": EmptyLatentAudio,
"VAEEncodeAudio": VAEEncodeAudio,
@ -375,6 +586,12 @@ NODE_CLASS_MAPPINGS = {
"PreviewAudio": PreviewAudio,
"ConditioningStableAudio": ConditioningStableAudio,
"RecordAudio": RecordAudio,
"TrimAudioDuration": TrimAudioDuration,
"SplitAudioChannels": SplitAudioChannels,
"AudioConcat": AudioConcat,
"AudioMerge": AudioMerge,
"AudioAdjustVolume": AudioAdjustVolume,
"EmptyAudio": EmptyAudio,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@ -387,4 +604,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"SaveAudioMP3": "Save Audio (MP3)",
"SaveAudioOpus": "Save Audio (Opus)",
"RecordAudio": "Record Audio",
"TrimAudioDuration": "Trim Audio Duration",
"SplitAudioChannels": "Split Audio Channels",
"AudioConcat": "Audio Concat",
"AudioMerge": "Audio Merge",
"AudioAdjustVolume": "Audio Adjust Volume",
"EmptyAudio": "Empty Audio",
}

93
comfy_extras/nodes_clip_sdxl.py
View File

@ -1,43 +1,52 @@
from nodes import MAX_RESOLUTION
from typing_extensions import override
class CLIPTextEncodeSDXLRefiner:
import nodes
from comfy_api.latest import ComfyExtension, io
class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
def define_schema(cls):
return io.Schema(
node_id="CLIPTextEncodeSDXLRefiner",
category="advanced/conditioning",
inputs=[
io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.String.Input("text", multiline=True, dynamic_prompts=True),
io.Clip.Input("clip"),
],
outputs=[io.Conditioning.Output()],
)
CATEGORY = "advanced/conditioning"
def encode(self, clip, ascore, width, height, text):
@classmethod
def execute(cls, clip, ascore, width, height, text) -> io.NodeOutput:
tokens = clip.tokenize(text)
return (clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}), )
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}))
class CLIPTextEncodeSDXL:
class CLIPTextEncodeSDXL(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
"crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
"target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
def define_schema(cls):
return io.Schema(
node_id="CLIPTextEncodeSDXL",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.String.Input("text_g", multiline=True, dynamic_prompts=True),
io.String.Input("text_l", multiline=True, dynamic_prompts=True),
],
outputs=[io.Conditioning.Output()],
)
CATEGORY = "advanced/conditioning"
def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l):
@classmethod
def execute(cls, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l) -> io.NodeOutput:
tokens = clip.tokenize(text_g)
tokens["l"] = clip.tokenize(text_l)["l"]
if len(tokens["l"]) != len(tokens["g"]):
@ -46,9 +55,17 @@ class CLIPTextEncodeSDXL:
tokens["l"] += empty["l"]
while len(tokens["l"]) > len(tokens["g"]):
tokens["g"] += empty["g"]
return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}), )
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}))
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner,
"CLIPTextEncodeSDXL": CLIPTextEncodeSDXL,
}
class ClipSdxlExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
CLIPTextEncodeSDXLRefiner,
CLIPTextEncodeSDXL,
]
async def comfy_entrypoint() -> ClipSdxlExtension:
return ClipSdxlExtension()

61
comfy_extras/nodes_fresca.py
View File

@ -1,6 +1,8 @@
# Code based on https://github.com/WikiChao/FreSca (MIT License)
import torch
import torch.fft as fft
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
@ -51,25 +53,31 @@ def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
return x_filtered
class FreSca:
class FreSca(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"scale_low": ("FLOAT", {"default": 1.0, "min": 0, "max": 10, "step": 0.01,
"tooltip": "Scaling factor for low-frequency components"}),
"scale_high": ("FLOAT", {"default": 1.25, "min": 0, "max": 10, "step": 0.01,
"tooltip": "Scaling factor for high-frequency components"}),
"freq_cutoff": ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1,
"tooltip": "Number of frequency indices around center to consider as low-frequency"}),
}
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
DESCRIPTION = "Applies frequency-dependent scaling to the guidance"
def patch(self, model, scale_low, scale_high, freq_cutoff):
def define_schema(cls):
return io.Schema(
node_id="FreSca",
display_name="FreSca",
category="_for_testing",
description="Applies frequency-dependent scaling to the guidance",
inputs=[
io.Model.Input("model"),
io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
tooltip="Scaling factor for low-frequency components"),
io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
tooltip="Scaling factor for high-frequency components"),
io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
tooltip="Number of frequency indices around center to consider as low-frequency"),
],
outputs=[
io.Model.Output(),
],
is_experimental=True,
)
@classmethod
def execute(cls, model, scale_low, scale_high, freq_cutoff):
def custom_cfg_function(args):
conds_out = args["conds_out"]
if len(conds_out) <= 1 or None in args["conds"][:2]:
@ -91,13 +99,16 @@ class FreSca:
m = model.clone()
m.set_model_sampler_pre_cfg_function(custom_cfg_function)
return (m,)
return io.NodeOutput(m)
NODE_CLASS_MAPPINGS = {
"FreSca": FreSca,
}
class FreScaExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
FreSca,
]
NODE_DISPLAY_NAME_MAPPINGS = {
"FreSca": "FreSca",
}
async def comfy_entrypoint() -> FreScaExtension:
return FreScaExtension()

88
comfy_extras/nodes_hidream.py
View File

@ -1,55 +1,73 @@
from typing_extensions import override
import folder_paths
import comfy.sd
import comfy.model_management
from comfy_api.latest import ComfyExtension, io
class QuadrupleCLIPLoader:
class QuadrupleCLIPLoader(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
"clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
"clip_name3": (folder_paths.get_filename_list("text_encoders"), ),
"clip_name4": (folder_paths.get_filename_list("text_encoders"), )
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "load_clip"
def define_schema(cls):
return io.Schema(
node_id="QuadrupleCLIPLoader",
category="advanced/loaders",
description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
inputs=[
io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
io.Combo.Input("clip_name3", options=folder_paths.get_filename_list("text_encoders")),
io.Combo.Input("clip_name4", options=folder_paths.get_filename_list("text_encoders")),
],
outputs=[
io.Clip.Output(),
]
)
CATEGORY = "advanced/loaders"
DESCRIPTION = "[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct"
def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4):
@classmethod
def execute(cls, clip_name1, clip_name2, clip_name3, clip_name4):
clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3)
clip_path4 = folder_paths.get_full_path_or_raise("text_encoders", clip_name4)
clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3, clip_path4], embedding_directory=folder_paths.get_folder_paths("embeddings"))
return (clip,)
return io.NodeOutput(clip)
class CLIPTextEncodeHiDream:
class CLIPTextEncodeHiDream(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"llama": ("STRING", {"multiline": True, "dynamicPrompts": True})
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, clip_l, clip_g, t5xxl, llama):
def define_schema(cls):
return io.Schema(
node_id="CLIPTextEncodeHiDream",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
io.String.Input("llama", multiline=True, dynamic_prompts=True),
],
outputs=[
io.Conditioning.Output(),
]
)
@classmethod
def execute(cls, clip, clip_l, clip_g, t5xxl, llama):
tokens = clip.tokenize(clip_g)
tokens["l"] = clip.tokenize(clip_l)["l"]
tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
tokens["llama"] = clip.tokenize(llama)["llama"]
return (clip.encode_from_tokens_scheduled(tokens), )
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
NODE_CLASS_MAPPINGS = {
"QuadrupleCLIPLoader": QuadrupleCLIPLoader,
"CLIPTextEncodeHiDream": CLIPTextEncodeHiDream,
}
class HiDreamExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
QuadrupleCLIPLoader,
CLIPTextEncodeHiDream,
]
async def comfy_entrypoint() -> HiDreamExtension:
return HiDreamExtension()

59
comfy_extras/nodes_hypertile.py
View File

@ -1,9 +1,11 @@
#Taken from: https://github.com/tfernd/HyperTile/
import math
from typing_extensions import override
from einops import rearrange
# Use torch rng for consistency across generations
from torch import randint
from comfy_api.latest import ComfyExtension, io
def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
min_value = min(min_value, value)
@ -20,25 +22,31 @@ def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
return ns[idx]
class HyperTile:
class HyperTile(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}),
"swap_size": ("INT", {"default": 2, "min": 1, "max": 128}),
"max_depth": ("INT", {"default": 0, "min": 0, "max": 10}),
"scale_depth": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
def define_schema(cls):
return io.Schema(
node_id="HyperTile",
category="model_patches/unet",
inputs=[
io.Model.Input("model"),
io.Int.Input("tile_size", default=256, min=1, max=2048),
io.Int.Input("swap_size", default=2, min=1, max=128),
io.Int.Input("max_depth", default=0, min=0, max=10),
io.Boolean.Input("scale_depth", default=False),
],
outputs=[
io.Model.Output(),
],
)
CATEGORY = "model_patches/unet"
def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
@classmethod
def execute(cls, model, tile_size, swap_size, max_depth, scale_depth) -> io.NodeOutput:
latent_tile_size = max(32, tile_size) // 8
self.temp = None
temp = None
def hypertile_in(q, k, v, extra_options):
nonlocal temp
model_chans = q.shape[-2]
orig_shape = extra_options['original_shape']
apply_to = []
@ -58,14 +66,15 @@ class HyperTile:
if nh * nw > 1:
q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
self.temp = (nh, nw, h, w)
temp = (nh, nw, h, w)
return q, k, v
return q, k, v
def hypertile_out(out, extra_options):
if self.temp is not None:
nh, nw, h, w = self.temp
self.temp = None
nonlocal temp
if temp is not None:
nh, nw, h, w = temp
temp = None
out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
return out
@ -76,6 +85,14 @@ class HyperTile:
m.set_model_attn1_output_patch(hypertile_out)
return (m, )
NODE_CLASS_MAPPINGS = {
"HyperTile": HyperTile,
}
class HyperTileExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
HyperTile,
]
async def comfy_entrypoint() -> HyperTileExtension:
return HyperTileExtension()

42
comfy_extras/nodes_lotus.py
View File

@ -1,20 +1,22 @@
from typing_extensions import override
import torch
import comfy.model_management as mm
from comfy_api.latest import ComfyExtension, io
class LotusConditioning:
class LotusConditioning(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
},
}
def define_schema(cls):
return io.Schema(
node_id="LotusConditioning",
category="conditioning/lotus",
inputs=[],
outputs=[io.Conditioning.Output(display_name="conditioning")],
)
RETURN_TYPES = ("CONDITIONING",)
RETURN_NAMES = ("conditioning",)
FUNCTION = "conditioning"
CATEGORY = "conditioning/lotus"
def conditioning(self):
@classmethod
def execute(cls) -> io.NodeOutput:
device = mm.get_torch_device()
#lotus uses a frozen encoder and null conditioning, i'm just inlining the results of that operation since it doesn't change
#and getting parity with the reference implementation would otherwise require inference and 800mb of tensors
@ -22,8 +24,16 @@ class LotusConditioning:
cond = [[prompt_embeds, {}]]
return (cond,)
return io.NodeOutput(cond)
NODE_CLASS_MAPPINGS = {
"LotusConditioning" : LotusConditioning,
}
class LotusExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
LotusConditioning,
]
async def comfy_entrypoint() -> LotusExtension:
return LotusExtension()

99
comfy_extras/nodes_lumina2.py
View File

@ -1,20 +1,27 @@
from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
from typing_extensions import override
import torch
from comfy_api.latest import ComfyExtension, io
class RenormCFG:
class RenormCFG(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"cfg_trunc": ("FLOAT", {"default": 100, "min": 0.0, "max": 100.0, "step": 0.01}),
"renorm_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
def define_schema(cls):
return io.Schema(
node_id="RenormCFG",
category="advanced/model",
inputs=[
io.Model.Input("model"),
io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01),
io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01),
],
outputs=[
io.Model.Output(),
],
)
CATEGORY = "advanced/model"
def patch(self, model, cfg_trunc, renorm_cfg):
@classmethod
def execute(cls, model, cfg_trunc, renorm_cfg) -> io.NodeOutput:
def renorm_cfg_func(args):
cond_denoised = args["cond_denoised"]
uncond_denoised = args["uncond_denoised"]
@ -53,10 +60,10 @@ class RenormCFG:
m = model.clone()
m.set_model_sampler_cfg_function(renorm_cfg_func)
return (m, )
return io.NodeOutput(m)
class CLIPTextEncodeLumina2(ComfyNodeABC):
class CLIPTextEncodeLumina2(io.ComfyNode):
SYSTEM_PROMPT = {
"superior": "You are an assistant designed to generate superior images with the superior "\
"degree of image-text alignment based on textual prompts or user prompts.",
@ -69,36 +76,52 @@ class CLIPTextEncodeLumina2(ComfyNodeABC):
"Alignment: You are an assistant designed to generate high-quality images with the highest "\
"degree of image-text alignment based on textual prompts."
@classmethod
def INPUT_TYPES(s) -> InputTypeDict:
return {
"required": {
"system_prompt": (list(CLIPTextEncodeLumina2.SYSTEM_PROMPT.keys()), {"tooltip": CLIPTextEncodeLumina2.SYSTEM_PROMPT_TIP}),
"user_prompt": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
"clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."})
}
}
RETURN_TYPES = (IO.CONDITIONING,)
OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",)
FUNCTION = "encode"
def define_schema(cls):
return io.Schema(
node_id="CLIPTextEncodeLumina2",
display_name="CLIP Text Encode for Lumina2",
category="conditioning",
description="Encodes a system prompt and a user prompt using a CLIP model into an embedding "
"that can be used to guide the diffusion model towards generating specific images.",
inputs=[
io.Combo.Input(
"system_prompt",
options=list(cls.SYSTEM_PROMPT.keys()),
tooltip=cls.SYSTEM_PROMPT_TIP,
),
io.String.Input(
"user_prompt",
multiline=True,
dynamic_prompts=True,
tooltip="The text to be encoded.",
),
io.Clip.Input("clip", tooltip="The CLIP model used for encoding the text."),
],
outputs=[
io.Conditioning.Output(
tooltip="A conditioning containing the embedded text used to guide the diffusion model.",
),
],
)
CATEGORY = "conditioning"
DESCRIPTION = "Encodes a system prompt and a user prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
def encode(self, clip, user_prompt, system_prompt):
@classmethod
def execute(cls, clip, user_prompt, system_prompt) -> io.NodeOutput:
if clip is None:
raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
system_prompt = CLIPTextEncodeLumina2.SYSTEM_PROMPT[system_prompt]
system_prompt = cls.SYSTEM_PROMPT[system_prompt]
prompt = f'{system_prompt} <Prompt Start> {user_prompt}'
tokens = clip.tokenize(prompt)
return (clip.encode_from_tokens_scheduled(tokens), )
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeLumina2": CLIPTextEncodeLumina2,
"RenormCFG": RenormCFG
}
class Lumina2Extension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
CLIPTextEncodeLumina2,
RenormCFG,
]
NODE_DISPLAY_NAME_MAPPINGS = {
"CLIPTextEncodeLumina2": "CLIP Text Encode for Lumina2",
}
async def comfy_entrypoint() -> Lumina2Extension:
return Lumina2Extension()

50
comfy_extras/nodes_mahiro.py
View File

@ -1,17 +1,29 @@
from typing_extensions import override
import torch
import torch.nn.functional as F
class Mahiro:
from comfy_api.latest import ComfyExtension, io
class Mahiro(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL",),
}}
RETURN_TYPES = ("MODEL",)
RETURN_NAMES = ("patched_model",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
DESCRIPTION = "Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt."
def patch(self, model):
def define_schema(cls):
return io.Schema(
node_id="Mahiro",
display_name="Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)",
category="_for_testing",
description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
inputs=[
io.Model.Input("model"),
],
outputs=[
io.Model.Output(display_name="patched_model"),
],
is_experimental=True,
)
@classmethod
def execute(cls, model) -> io.NodeOutput:
m = model.clone()
def mahiro_normd(args):
scale: float = args['cond_scale']
@ -30,12 +42,16 @@ class Mahiro:
wm = (simsc*cfg + (4-simsc)*leap) / 4
return wm
m.set_model_sampler_post_cfg_function(mahiro_normd)
return (m, )
return io.NodeOutput(m)
NODE_CLASS_MAPPINGS = {
"Mahiro": Mahiro
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Mahiro": "Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)",
}
class MahiroExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
Mahiro,
]
async def comfy_entrypoint() -> MahiroExtension:
return MahiroExtension()

21
comfy_extras/nodes_mask.py
View File

@ -12,35 +12,38 @@ from nodes import MAX_RESOLUTION
def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
source = source.to(destination.device)
if resize_source:
source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
source = torch.nn.functional.interpolate(source, size=(destination.shape[-2], destination.shape[-1]), mode="bilinear")
source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
x = max(-source.shape[-1] * multiplier, min(x, destination.shape[-1] * multiplier))
y = max(-source.shape[-2] * multiplier, min(y, destination.shape[-2] * multiplier))
left, top = (x // multiplier, y // multiplier)
right, bottom = (left + source.shape[3], top + source.shape[2],)
right, bottom = (left + source.shape[-1], top + source.shape[-2],)
if mask is None:
mask = torch.ones_like(source)
else:
mask = mask.to(destination.device, copy=True)
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[-2], source.shape[-1]), mode="bilinear")
mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
# calculate the bounds of the source that will be overlapping the destination
# this prevents the source trying to overwrite latent pixels that are out of bounds
# of the destination
visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
visible_width, visible_height = (destination.shape[-1] - left + min(0, x), destination.shape[-2] - top + min(0, y),)
mask = mask[:, :, :visible_height, :visible_width]
if mask.ndim < source.ndim:
mask = mask.unsqueeze(1)
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[:, :, :visible_height, :visible_width]
destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
source_portion = mask * source[..., :visible_height, :visible_width]
destination_portion = inverse_mask * destination[..., top:bottom, left:right]
destination[:, :, top:bottom, left:right] = source_portion + destination_portion
destination[..., top:bottom, left:right] = source_portion + destination_portion
return destination
class LatentCompositeMasked:

49
comfy_extras/nodes_mochi.py
View File

@ -1,23 +1,40 @@
import nodes
from typing_extensions import override
import torch
import comfy.model_management
import nodes
from comfy_api.latest import ComfyExtension, io
class EmptyMochiLatentVideo:
class EmptyMochiLatentVideo(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"length": ("INT", {"default": 25, "min": 7, "max": nodes.MAX_RESOLUTION, "step": 6}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
def define_schema(cls):
return io.Schema(
node_id="EmptyMochiLatentVideo",
category="latent/video",
inputs=[
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("length", default=25, min=7, max=nodes.MAX_RESOLUTION, step=6),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
io.Latent.Output(),
],
)
CATEGORY = "latent/video"
def generate(self, width, height, length, batch_size=1):
@classmethod
def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
return ({"samples":latent}, )
return io.NodeOutput({"samples": latent})
NODE_CLASS_MAPPINGS = {
"EmptyMochiLatentVideo": EmptyMochiLatentVideo,
}
class MochiExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
EmptyMochiLatentVideo,
]
async def comfy_entrypoint() -> MochiExtension:
return MochiExtension()

93
comfy_extras/nodes_perpneg.py
View File

@ -5,6 +5,9 @@ import comfy.samplers
import comfy.utils
import node_helpers
import math
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale):
pos = noise_pred_pos - noise_pred_nocond
@ -16,20 +19,27 @@ def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, co
return cfg_result
#TODO: This node should be removed, it has been replaced with PerpNegGuider
class PerpNeg:
class PerpNeg(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL", ),
"empty_conditioning": ("CONDITIONING", ),
"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
def define_schema(cls):
return io.Schema(
node_id="PerpNeg",
display_name="Perp-Neg (DEPRECATED by PerpNegGuider)",
category="_for_testing",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("empty_conditioning"),
io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
],
outputs=[
io.Model.Output(),
],
is_experimental=True,
is_deprecated=True,
)
CATEGORY = "_for_testing"
DEPRECATED = True
def patch(self, model, empty_conditioning, neg_scale):
@classmethod
def execute(cls, model, empty_conditioning, neg_scale) -> io.NodeOutput:
m = model.clone()
nocond = comfy.sampler_helpers.convert_cond(empty_conditioning)
@ -50,7 +60,7 @@ class PerpNeg:
m.set_model_sampler_cfg_function(cfg_function)
return (m, )
return io.NodeOutput(m)
class Guider_PerpNeg(comfy.samplers.CFGGuider):
@ -112,35 +122,42 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider):
return cfg_result
class PerpNegGuider:
class PerpNegGuider(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"empty_conditioning": ("CONDITIONING", ),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
}
}
def define_schema(cls):
return io.Schema(
node_id="PerpNegGuider",
category="_for_testing",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Conditioning.Input("empty_conditioning"),
io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
],
outputs=[
io.Guider.Output(),
],
is_experimental=True,
)
RETURN_TYPES = ("GUIDER",)
FUNCTION = "get_guider"
CATEGORY = "_for_testing"
def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale):
@classmethod
def execute(cls, model, positive, negative, empty_conditioning, cfg, neg_scale) -> io.NodeOutput:
guider = Guider_PerpNeg(model)
guider.set_conds(positive, negative, empty_conditioning)
guider.set_cfg(cfg, neg_scale)
return (guider,)
return io.NodeOutput(guider)
NODE_CLASS_MAPPINGS = {
"PerpNeg": PerpNeg,
"PerpNegGuider": PerpNegGuider,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)",
}
class PerpNegExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
PerpNeg,
PerpNegGuider,
]
async def comfy_entrypoint() -> PerpNegExtension:
return PerpNegExtension()

74
comfy_extras/nodes_photomaker.py
View File

@ -4,6 +4,8 @@ import folder_paths
import comfy.clip_model
import comfy.clip_vision
import comfy.ops
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0
VISION_CONFIG_DICT = {
@ -116,41 +118,52 @@ class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection):
return updated_prompt_embeds
class PhotoMakerLoader:
class PhotoMakerLoader(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}}
def define_schema(cls):
return io.Schema(
node_id="PhotoMakerLoader",
category="_for_testing/photomaker",
inputs=[
io.Combo.Input("photomaker_model_name", options=folder_paths.get_filename_list("photomaker")),
],
outputs=[
io.Photomaker.Output(),
],
is_experimental=True,
)
RETURN_TYPES = ("PHOTOMAKER",)
FUNCTION = "load_photomaker_model"
CATEGORY = "_for_testing/photomaker"
def load_photomaker_model(self, photomaker_model_name):
@classmethod
def execute(cls, photomaker_model_name):
photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name)
photomaker_model = PhotoMakerIDEncoder()
data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True)
if "id_encoder" in data:
data = data["id_encoder"]
photomaker_model.load_state_dict(data)
return (photomaker_model,)
return io.NodeOutput(photomaker_model)
class PhotoMakerEncode:
class PhotoMakerEncode(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "photomaker": ("PHOTOMAKER",),
"image": ("IMAGE",),
"clip": ("CLIP", ),
"text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}),
}}
def define_schema(cls):
return io.Schema(
node_id="PhotoMakerEncode",
category="_for_testing/photomaker",
inputs=[
io.Photomaker.Input("photomaker"),
io.Image.Input("image"),
io.Clip.Input("clip"),
io.String.Input("text", multiline=True, dynamic_prompts=True, default="photograph of photomaker"),
],
outputs=[
io.Conditioning.Output(),
],
is_experimental=True,
)
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "apply_photomaker"
CATEGORY = "_for_testing/photomaker"
def apply_photomaker(self, photomaker, image, clip, text):
@classmethod
def execute(cls, photomaker, image, clip, text):
special_token = "photomaker"
pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()
try:
@ -178,11 +191,16 @@ class PhotoMakerEncode:
else:
out = cond
return ([[out, {"pooled_output": pooled}]], )
return io.NodeOutput([[out, {"pooled_output": pooled}]])
NODE_CLASS_MAPPINGS = {
"PhotoMakerLoader": PhotoMakerLoader,
"PhotoMakerEncode": PhotoMakerEncode,
}
class PhotomakerExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
PhotoMakerLoader,
PhotoMakerEncode,
]
async def comfy_entrypoint() -> PhotomakerExtension:
return PhotomakerExtension()

52
comfy_extras/nodes_pixart.py
View File

@ -1,24 +1,38 @@
from nodes import MAX_RESOLUTION
from typing_extensions import override
import nodes
from comfy_api.latest import ComfyExtension, io
class CLIPTextEncodePixArtAlpha:
class CLIPTextEncodePixArtAlpha(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
# "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
}}
def define_schema(cls):
return io.Schema(
node_id="CLIPTextEncodePixArtAlpha",
category="advanced/conditioning",
description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.",
inputs=[
io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
# "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
io.String.Input("text", multiline=True, dynamic_prompts=True),
io.Clip.Input("clip"),
],
outputs=[
io.Conditioning.Output(),
],
)
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
DESCRIPTION = "Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma."
def encode(self, clip, width, height, text):
@classmethod
def execute(cls, clip, width, height, text):
tokens = clip.tokenize(text)
return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}),)
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}))
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodePixArtAlpha": CLIPTextEncodePixArtAlpha,
}
class PixArtExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
CLIPTextEncodePixArtAlpha,
]
async def comfy_entrypoint() -> PixArtExtension:
return PixArtExtension()

249
comfy_extras/nodes_post_processing.py
View File

@ -1,3 +1,4 @@
from typing_extensions import override
import numpy as np
import torch
import torch.nn.functional as F
@ -7,33 +8,27 @@ import math
import comfy.utils
import comfy.model_management
import node_helpers
from comfy_api.latest import ComfyExtension, io
class Blend:
def __init__(self):
pass
class Blend(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="ImageBlend",
category="image/postprocessing",
inputs=[
io.Image.Input("image1"),
io.Image.Input("image2"),
io.Float.Input("blend_factor", default=0.5, min=0.0, max=1.0, step=0.01),
io.Combo.Input("blend_mode", options=["normal", "multiply", "screen", "overlay", "soft_light", "difference"]),
],
outputs=[
io.Image.Output(),
],
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image1": ("IMAGE",),
"image2": ("IMAGE",),
"blend_factor": ("FLOAT", {
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}),
"blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "blend_images"
CATEGORY = "image/postprocessing"
def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
def execute(cls, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str) -> io.NodeOutput:
image1, image2 = node_helpers.image_alpha_fix(image1, image2)
image2 = image2.to(image1.device)
if image1.shape != image2.shape:
@ -41,12 +36,13 @@ class Blend:
image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
image2 = image2.permute(0, 2, 3, 1)
blended_image = self.blend_mode(image1, image2, blend_mode)
blended_image = cls.blend_mode(image1, image2, blend_mode)
blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
blended_image = torch.clamp(blended_image, 0, 1)
return (blended_image,)
return io.NodeOutput(blended_image)
def blend_mode(self, img1, img2, mode):
@classmethod
def blend_mode(cls, img1, img2, mode):
if mode == "normal":
return img2
elif mode == "multiply":
@ -56,13 +52,13 @@ class Blend:
elif mode == "overlay":
return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
elif mode == "soft_light":
return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (cls.g(img1) - img1))
elif mode == "difference":
return img1 - img2
else:
raise ValueError(f"Unsupported blend mode: {mode}")
raise ValueError(f"Unsupported blend mode: {mode}")
def g(self, x):
@classmethod
def g(cls, x):
return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
def gaussian_kernel(kernel_size: int, sigma: float, device=None):
@ -71,38 +67,26 @@ def gaussian_kernel(kernel_size: int, sigma: float, device=None):
g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
return g / g.sum()
class Blur:
def __init__(self):
pass
class Blur(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="ImageBlur",
category="image/postprocessing",
inputs=[
io.Image.Input("image"),
io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),
io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1),
],
outputs=[
io.Image.Output(),
],
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"blur_radius": ("INT", {
"default": 1,
"min": 1,
"max": 31,
"step": 1
}),
"sigma": ("FLOAT", {
"default": 1.0,
"min": 0.1,
"max": 10.0,
"step": 0.1
}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "blur"
CATEGORY = "image/postprocessing"
def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
def execute(cls, image: torch.Tensor, blur_radius: int, sigma: float) -> io.NodeOutput:
if blur_radius == 0:
return (image,)
return io.NodeOutput(image)
image = image.to(comfy.model_management.get_torch_device())
batch_size, height, width, channels = image.shape
@ -115,31 +99,24 @@ class Blur:
blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]
blurred = blurred.permute(0, 2, 3, 1)
return (blurred.to(comfy.model_management.intermediate_device()),)
return io.NodeOutput(blurred.to(comfy.model_management.intermediate_device()))
class Quantize:
def __init__(self):
pass
class Quantize(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"colors": ("INT", {
"default": 256,
"min": 1,
"max": 256,
"step": 1
}),
"dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "quantize"
CATEGORY = "image/postprocessing"
def define_schema(cls):
return io.Schema(
node_id="ImageQuantize",
category="image/postprocessing",
inputs=[
io.Image.Input("image"),
io.Int.Input("colors", default=256, min=1, max=256, step=1),
io.Combo.Input("dither", options=["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"]),
],
outputs=[
io.Image.Output(),
],
)
@staticmethod
def bayer(im, pal_im, order):
@ -167,7 +144,8 @@ class Quantize:
im = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
return im
def quantize(self, image: torch.Tensor, colors: int, dither: str):
@classmethod
def execute(cls, image: torch.Tensor, colors: int, dither: str) -> io.NodeOutput:
batch_size, height, width, _ = image.shape
result = torch.zeros_like(image)
@ -187,46 +165,29 @@ class Quantize:
quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
result[b] = quantized_array
return (result,)
return io.NodeOutput(result)
class Sharpen:
def __init__(self):
pass
class Sharpen(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="ImageSharpen",
category="image/postprocessing",
inputs=[
io.Image.Input("image"),
io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1),
io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01),
io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01),
],
outputs=[
io.Image.Output(),
],
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"sharpen_radius": ("INT", {
"default": 1,
"min": 1,
"max": 31,
"step": 1
}),
"sigma": ("FLOAT", {
"default": 1.0,
"min": 0.1,
"max": 10.0,
"step": 0.01
}),
"alpha": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 5.0,
"step": 0.01
}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "sharpen"
CATEGORY = "image/postprocessing"
def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float):
def execute(cls, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float) -> io.NodeOutput:
if sharpen_radius == 0:
return (image,)
return io.NodeOutput(image)
batch_size, height, width, channels = image.shape
image = image.to(comfy.model_management.get_torch_device())
@ -245,23 +206,29 @@ class Sharpen:
result = torch.clamp(sharpened, 0, 1)
return (result.to(comfy.model_management.intermediate_device()),)
return io.NodeOutput(result.to(comfy.model_management.intermediate_device()))
class ImageScaleToTotalPixels:
class ImageScaleToTotalPixels(io.ComfyNode):
upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
crop_methods = ["disabled", "center"]
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
"megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
def define_schema(cls):
return io.Schema(
node_id="ImageScaleToTotalPixels",
category="image/upscaling",
inputs=[
io.Image.Input("image"),
io.Combo.Input("upscale_method", options=cls.upscale_methods),
io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
],
outputs=[
io.Image.Output(),
],
)
CATEGORY = "image/upscaling"
def upscale(self, image, upscale_method, megapixels):
@classmethod
def execute(cls, image, upscale_method, megapixels) -> io.NodeOutput:
samples = image.movedim(-1,1)
total = int(megapixels * 1024 * 1024)
@ -271,12 +238,18 @@ class ImageScaleToTotalPixels:
s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
s = s.movedim(1,-1)
return (s,)
return io.NodeOutput(s)
NODE_CLASS_MAPPINGS = {
"ImageBlend": Blend,
"ImageBlur": Blur,
"ImageQuantize": Quantize,
"ImageSharpen": Sharpen,
"ImageScaleToTotalPixels": ImageScaleToTotalPixels,
}
class PostProcessingExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
Blend,
Blur,
Quantize,
Sharpen,
ImageScaleToTotalPixels,
]
async def comfy_entrypoint() -> PostProcessingExtension:
return PostProcessingExtension()

88
comfy_extras/nodes_qwen.py
View File

@ -1,24 +1,29 @@
import node_helpers
import comfy.utils
import math
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
class TextEncodeQwenImageEdit:
class TextEncodeQwenImageEdit(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
},
"optional": {"vae": ("VAE", ),
"image": ("IMAGE", ),}}
def define_schema(cls):
return io.Schema(
node_id="TextEncodeQwenImageEdit",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
io.Vae.Input("vae", optional=True),
io.Image.Input("image", optional=True),
],
outputs=[
io.Conditioning.Output(),
],
)
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, prompt, vae=None, image=None):
@classmethod
def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput:
ref_latent = None
if image is None:
images = []
@ -40,28 +45,30 @@ class TextEncodeQwenImageEdit:
conditioning = clip.encode_from_tokens_scheduled(tokens)
if ref_latent is not None:
conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
return (conditioning, )
return io.NodeOutput(conditioning)
class TextEncodeQwenImageEditPlus:
class TextEncodeQwenImageEditPlus(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
},
"optional": {"vae": ("VAE", ),
"image1": ("IMAGE", ),
"image2": ("IMAGE", ),
"image3": ("IMAGE", ),
}}
def define_schema(cls):
return io.Schema(
node_id="TextEncodeQwenImageEditPlus",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
io.Vae.Input("vae", optional=True),
io.Image.Input("image1", optional=True),
io.Image.Input("image2", optional=True),
io.Image.Input("image3", optional=True),
],
outputs=[
io.Conditioning.Output(),
],
)
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, prompt, vae=None, image1=None, image2=None, image3=None):
@classmethod
def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None) -> io.NodeOutput:
ref_latents = []
images = [image1, image2, image3]
images_vl = []
@ -94,10 +101,17 @@ class TextEncodeQwenImageEditPlus:
conditioning = clip.encode_from_tokens_scheduled(tokens)
if len(ref_latents) > 0:
conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True)
return (conditioning, )
return io.NodeOutput(conditioning)
NODE_CLASS_MAPPINGS = {
"TextEncodeQwenImageEdit": TextEncodeQwenImageEdit,
"TextEncodeQwenImageEditPlus": TextEncodeQwenImageEditPlus,
}
class QwenExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
TextEncodeQwenImageEdit,
TextEncodeQwenImageEditPlus,
]
async def comfy_entrypoint() -> QwenExtension:
return QwenExtension()

97
comfy_extras/nodes_rebatch.py
View File

@ -1,18 +1,25 @@
from typing_extensions import override
import torch
class LatentRebatch:
from comfy_api.latest import ComfyExtension, io
class LatentRebatch(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "latents": ("LATENT",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
}}
RETURN_TYPES = ("LATENT",)
INPUT_IS_LIST = True
OUTPUT_IS_LIST = (True, )
FUNCTION = "rebatch"
CATEGORY = "latent/batch"
def define_schema(cls):
return io.Schema(
node_id="RebatchLatents",
display_name="Rebatch Latents",
category="latent/batch",
is_input_list=True,
inputs=[
io.Latent.Input("latents"),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
io.Latent.Output(is_output_list=True),
],
)
@staticmethod
def get_batch(latents, list_ind, offset):
@ -53,7 +60,8 @@ class LatentRebatch:
result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)]
return result
def rebatch(self, latents, batch_size):
@classmethod
def execute(cls, latents, batch_size):
batch_size = batch_size[0]
output_list = []
@ -63,24 +71,24 @@ class LatentRebatch:
for i in range(len(latents)):
# fetch new entry of list
#samples, masks, indices = self.get_batch(latents, i)
next_batch = self.get_batch(latents, i, processed)
next_batch = cls.get_batch(latents, i, processed)
processed += len(next_batch[2])
# set to current if current is None
if current_batch[0] is None:
current_batch = next_batch
# add previous to list if dimensions do not match
elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]:
sliced, _ = self.slice_batch(current_batch, 1, batch_size)
sliced, _ = cls.slice_batch(current_batch, 1, batch_size)
output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
current_batch = next_batch
# cat if everything checks out
else:
current_batch = self.cat_batch(current_batch, next_batch)
current_batch = cls.cat_batch(current_batch, next_batch)
# add to list if dimensions gone above target batch size
if current_batch[0].shape[0] > batch_size:
num = current_batch[0].shape[0] // batch_size
sliced, remainder = self.slice_batch(current_batch, num, batch_size)
sliced, remainder = cls.slice_batch(current_batch, num, batch_size)
for i in range(num):
output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})
@ -89,7 +97,7 @@ class LatentRebatch:
#add remainder
if current_batch[0] is not None:
sliced, _ = self.slice_batch(current_batch, 1, batch_size)
sliced, _ = cls.slice_batch(current_batch, 1, batch_size)
output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
#get rid of empty masks
@ -97,23 +105,27 @@ class LatentRebatch:
if s['noise_mask'].mean() == 1.0:
del s['noise_mask']
return (output_list,)
return io.NodeOutput(output_list)
class ImageRebatch:
class ImageRebatch(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "images": ("IMAGE",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
}}
RETURN_TYPES = ("IMAGE",)
INPUT_IS_LIST = True
OUTPUT_IS_LIST = (True, )
def define_schema(cls):
return io.Schema(
node_id="RebatchImages",
display_name="Rebatch Images",
category="image/batch",
is_input_list=True,
inputs=[
io.Image.Input("images"),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
io.Image.Output(is_output_list=True),
],
)
FUNCTION = "rebatch"
CATEGORY = "image/batch"
def rebatch(self, images, batch_size):
@classmethod
def execute(cls, images, batch_size):
batch_size = batch_size[0]
output_list = []
@ -125,14 +137,17 @@ class ImageRebatch:
for i in range(0, len(all_images), batch_size):
output_list.append(torch.cat(all_images[i:i+batch_size], dim=0))
return (output_list,)
return io.NodeOutput(output_list)
NODE_CLASS_MAPPINGS = {
"RebatchLatents": LatentRebatch,
"RebatchImages": ImageRebatch,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"RebatchLatents": "Rebatch Latents",
"RebatchImages": "Rebatch Images",
}
class RebatchExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
LatentRebatch,
ImageRebatch,
]
async def comfy_entrypoint() -> RebatchExtension:
return RebatchExtension()

50
comfy_extras/nodes_sag.py
View File

@ -2,10 +2,13 @@ import torch
from torch import einsum
import torch.nn.functional as F
import math
from typing_extensions import override
from einops import rearrange, repeat
from comfy.ldm.modules.attention import optimized_attention
import comfy.samplers
from comfy_api.latest import ComfyExtension, io
# from comfy/ldm/modules/attention.py
# but modified to return attention scores as well as output
@ -104,19 +107,26 @@ def gaussian_blur_2d(img, kernel_size, sigma):
img = F.conv2d(img, kernel2d, groups=img.shape[-3])
return img
class SelfAttentionGuidance:
class SelfAttentionGuidance(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.01}),
"blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
def define_schema(cls):
return io.Schema(
node_id="SelfAttentionGuidance",
display_name="Self-Attention Guidance",
category="_for_testing",
inputs=[
io.Model.Input("model"),
io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01),
io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1),
],
outputs=[
io.Model.Output(),
],
is_experimental=True,
)
CATEGORY = "_for_testing"
def patch(self, model, scale, blur_sigma):
@classmethod
def execute(cls, model, scale, blur_sigma):
m = model.clone()
attn_scores = None
@ -170,12 +180,16 @@ class SelfAttentionGuidance:
# unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch
m.set_model_attn1_replace(attn_and_record, "middle", 0, 0)
return (m, )
return io.NodeOutput(m)
NODE_CLASS_MAPPINGS = {
"SelfAttentionGuidance": SelfAttentionGuidance,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SelfAttentionGuidance": "Self-Attention Guidance",
}
class SagExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
SelfAttentionGuidance,
]
async def comfy_entrypoint() -> SagExtension:
return SagExtension()

54
comfy_extras/nodes_sdupscale.py
View File

@ -1,23 +1,31 @@
from typing_extensions import override
import torch
import comfy.utils
from comfy_api.latest import ComfyExtension, io
class SD_4XUpscale_Conditioning:
class SD_4XUpscale_Conditioning(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "images": ("IMAGE",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
def define_schema(cls):
return io.Schema(
node_id="SD_4XUpscale_Conditioning",
category="conditioning/upscale_diffusion",
inputs=[
io.Image.Input("images"),
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
FUNCTION = "encode"
CATEGORY = "conditioning/upscale_diffusion"
def encode(self, images, positive, negative, scale_ratio, noise_augmentation):
@classmethod
def execute(cls, images, positive, negative, scale_ratio, noise_augmentation):
width = max(1, round(images.shape[-2] * scale_ratio))
height = max(1, round(images.shape[-3] * scale_ratio))
@ -39,8 +47,16 @@ class SD_4XUpscale_Conditioning:
out_cn.append(n)
latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
return (out_cp, out_cn, {"samples":latent})
return io.NodeOutput(out_cp, out_cn, {"samples":latent})
NODE_CLASS_MAPPINGS = {
"SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning,
}
class SdUpscaleExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
SD_4XUpscale_Conditioning,
]
async def comfy_entrypoint() -> SdUpscaleExtension:
return SdUpscaleExtension()

51
comfy_extras/nodes_tcfg.py
View File

@ -1,8 +1,9 @@
# TCFG: Tangential Damping Classifier-free Guidance - (arXiv: https://arxiv.org/abs/2503.18137)
from typing_extensions import override
import torch
from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
from comfy_api.latest import ComfyExtension, io
def score_tangential_damping(cond_score: torch.Tensor, uncond_score: torch.Tensor) -> torch.Tensor:
@ -26,23 +27,24 @@ def score_tangential_damping(cond_score: torch.Tensor, uncond_score: torch.Tenso
return uncond_score_td.reshape_as(uncond_score).to(uncond_score.dtype)
class TCFG(ComfyNodeABC):
class TCFG(io.ComfyNode):
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
return {
"required": {
"model": (IO.MODEL, {}),
}
}
def define_schema(cls):
return io.Schema(
node_id="TCFG",
display_name="Tangential Damping CFG",
category="advanced/guidance",
description="TCFG – Tangential Damping CFG (2503.18137)\n\nRefine the uncond (negative) to align with the cond (positive) for improving quality.",
inputs=[
io.Model.Input("model"),
],
outputs=[
io.Model.Output(display_name="patched_model"),
],
)
RETURN_TYPES = (IO.MODEL,)
RETURN_NAMES = ("patched_model",)
FUNCTION = "patch"
CATEGORY = "advanced/guidance"
DESCRIPTION = "TCFG – Tangential Damping CFG (2503.18137)\n\nRefine the uncond (negative) to align with the cond (positive) for improving quality."
def patch(self, model):
@classmethod
def execute(cls, model):
m = model.clone()
def tangential_damping_cfg(args):
@ -59,13 +61,16 @@ class TCFG(ComfyNodeABC):
return [cond_pred, uncond_pred_td] + conds_out[2:]
m.set_model_sampler_pre_cfg_function(tangential_damping_cfg)
return (m,)
return io.NodeOutput(m)
NODE_CLASS_MAPPINGS = {
"TCFG": TCFG,
}
class TcfgExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
TCFG,
]
NODE_DISPLAY_NAME_MAPPINGS = {
"TCFG": "Tangential Damping CFG",
}
async def comfy_entrypoint() -> TcfgExtension:
return TcfgExtension()

2
comfyui_version.py
View File

@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.3.60"
__version__ = "0.3.62"

1
main.py
View File

@ -127,6 +127,7 @@ if __name__ == "__main__":
if args.cuda_device is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
os.environ['HIP_VISIBLE_DEVICES'] = str(args.cuda_device)
os.environ["ASCEND_RT_VISIBLE_DEVICES"] = str(args.cuda_device)
logging.info("Set cuda device to: {}".format(args.cuda_device))
if args.oneapi_device_selector is not None:

11
middleware/cache_middleware.py
View File

@ -26,11 +26,12 @@ async def cache_control(
"""Cache control middleware that sets appropriate cache headers based on file type and response status"""
response: web.Response = await handler(request)
if (
request.path.endswith(".js")
or request.path.endswith(".css")
or request.path.endswith("index.json")
):
path_filename = request.path.rsplit("/", 1)[-1]
is_entry_point = path_filename.startswith("index") and path_filename.endswith(
".json"
)
if request.path.endswith(".js") or request.path.endswith(".css") or is_entry_point:
response.headers.setdefault("Cache-Control", "no-cache")
return response

2
pyproject.toml
View File

@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.3.60"
version = "0.3.62"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.9"

2
requirements.txt
View File

@ -1,5 +1,5 @@
comfyui-frontend-package==1.26.13
comfyui-workflow-templates==0.1.86
comfyui-workflow-templates==0.1.91
comfyui-embedded-docs==0.2.6
torch
torchsde

4
server.py
View File

@ -550,6 +550,8 @@ class PromptServer():
vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True)
vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True)
required_frontend_version = FrontendManager.get_required_frontend_version()
installed_templates_version = FrontendManager.get_installed_templates_version()
required_templates_version = FrontendManager.get_required_templates_version()
system_stats = {
"system": {
@ -558,6 +560,8 @@ class PromptServer():
"ram_free": ram_free,
"comfyui_version": __version__,
"required_frontend_version": required_frontend_version,
"installed_templates_version": installed_templates_version,
"required_templates_version": required_templates_version,
"python_version": sys.version,
"pytorch_version": comfy.model_management.torch_version,
"embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded",

71
tests-unit/app_test/frontend_manager_test.py
View File

@ -205,3 +205,74 @@ numpy"""
# Assert
assert version is None
def test_get_templates_version():
# Arrange
expected_version = "0.1.41"
mock_requirements_content = """torch
torchsde
comfyui-frontend-package==1.25.0
comfyui-workflow-templates==0.1.41
other-package==1.0.0
numpy"""
# Act
with patch("builtins.open", mock_open(read_data=mock_requirements_content)):
version = FrontendManager.get_required_templates_version()
# Assert
assert version == expected_version
def test_get_templates_version_not_found():
# Arrange
mock_requirements_content = """torch
torchsde
comfyui-frontend-package==1.25.0
other-package==1.0.0
numpy"""
# Act
with patch("builtins.open", mock_open(read_data=mock_requirements_content)):
version = FrontendManager.get_required_templates_version()
# Assert
assert version is None
def test_get_templates_version_invalid_semver():
# Arrange
mock_requirements_content = """torch
torchsde
comfyui-workflow-templates==1.0.0.beta
other-package==1.0.0
numpy"""
# Act
with patch("builtins.open", mock_open(read_data=mock_requirements_content)):
version = FrontendManager.get_required_templates_version()
# Assert
assert version is None
def test_get_installed_templates_version():
# Arrange
expected_version = "0.1.40"
# Act
with patch("app.frontend_management.version", return_value=expected_version):
version = FrontendManager.get_installed_templates_version()
# Assert
assert version == expected_version
def test_get_installed_templates_version_not_installed():
# Act
with patch("app.frontend_management.version", side_effect=Exception("Package not found")):
version = FrontendManager.get_installed_templates_version()
# Assert
assert version is None

7
tests-unit/server_test/test_cache_control.py
View File

@ -48,6 +48,13 @@ CACHE_SCENARIOS = [
"expected_cache": "no-cache",
"should_have_header": True,
},
{
"name": "localized_index_json_no_cache",
"path": "/templates/index.zh.json",
"status": 200,
"expected_cache": "no-cache",
"should_have_header": True,
},
# Non-matching files
{
"name": "html_no_header",