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Commit aece8f87 authored by Muyang Li's avatar Muyang Li Committed by Zhekai Zhang
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Fix the docker image; remove comfyui folder;

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.github/workflows/lint.yaml
View file @ aece8f87
...@@ -20,4 +20,4 @@ jobs: ...@@ -20,4 +20,4 @@ jobs:
- name: Install dependencies - name: Install dependencies
run: pip install ruff run: pip install ruff
- name: Run ruff check - name: Run ruff check
run: ruff check nunchaku comfyui examples tests --output-format github run: ruff check nunchaku examples tests --output-format github
Dockerfile
View file @ aece8f87
# Use an NVIDIA base image with CUDA support # Use an NVIDIA base image with CUDA support
FROM nvidia/cuda:12.8.1-devel-ubuntu24.04
ARG CUDA_IMAGE="12.8.1-devel-ubuntu24.04"
FROM nvidia/cuda:${CUDA_IMAGE}
ENV DEBIAN_FRONTEND=noninteractive ENV DEBIAN_FRONTEND=noninteractive
ARG PYTHON_VERSION=3.11
ARG TORCH_VERSION=2.6
ARG TORCHVISION_VERSION=0.21
ARG TORCHAUDIO_VERSION=2.6
ARG CUDA_SHORT_VERSION=12.8
# Set working directory # Set working directory
WORKDIR / WORKDIR /
RUN echo PYTHON_VERSION=${PYTHON_VERSION} \
&& echo CUDA_SHORT_VERSION=${CUDA_SHORT_VERSION} \
&& echo TORCH_VERSION=${TORCH_VERSION} \
&& echo TORCHVISION_VERSION=${TORCHVISION_VERSION} \
&& echo TORCHAUDIO_VERSION=${TORCHAUDIO_VERSION}
# Setup timezone and install system dependencies # Setup timezone and install system dependencies
RUN 'tzdata tzdata/Areas select America' | debconf-set-selections \ RUN 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select New_York' | debconf-set-selections \ && echo 'tzdata tzdata/Zones/America select New_York' | debconf-set-selections \
&& apt update -y \ && apt update -y \
&& apt install software-properties-common -y \ && apt install software-properties-common -y \
&& add-apt-repository ppa:deadsnakes/ppa -y && apt update \ && add-apt-repository ppa:deadsnakes/ppa -y \
&& apt install python3.11 python3.11-dev g++-11 gcc-11 -y \ && apt update
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1 \
&& update-alternatives --set python3 /usr/bin/python3.11 && apt install python3.11-distutils -y \ RUN apt install python${PYTHON_VERSION} python${PYTHON_VERSION}-dev g++-11 gcc-11 -y \
&& apt install curl git sudo libibverbs-dev -y \ && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} && apt install python${PYTHON_VERSION}-distutils -y \
&& update-alternatives --install /usr/bin/python python /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python /usr/bin/python${PYTHON_VERSION}
RUN apt install curl git sudo libibverbs-dev -y \
&& apt install -y rdma-core infiniband-diags openssh-server perftest ibverbs-providers libibumad3 libibverbs1 libnl-3-200 libnl-route-3-200 librdmacm1 \ && apt install -y rdma-core infiniband-diags openssh-server perftest ibverbs-providers libibumad3 libibverbs1 libnl-3-200 libnl-route-3-200 librdmacm1 \
&& curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py && python3 get-pip.py \ && curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py && python3 get-pip.py \
&& python3 --version \ && python3 --version \
&& python3 -m pip --version \ && python3 -m pip --version \
&& rm -rf /var/lib/apt/lists/* \ && rm -rf /var/lib/apt/lists/* \
&& update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1 && update-alternatives --set python /usr/bin/python3.11 \
&& update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 1 && update-alternatives --set gcc /usr/bin/gcc-11 \ && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 1 && update-alternatives --set gcc /usr/bin/gcc-11 \
&& update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-11 1 && update-alternatives --set g++ /usr/bin/g++-11 \ && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-11 1 && update-alternatives --set g++ /usr/bin/g++-11 \
&& apt clean && apt clean
# Install building dependencies # Install building dependencies
RUN pip install torch torchvision torchaudio \ RUN pip install torch==${TORCH_VERSION} torchvision==${TORCHVISION_VERSION} torchaudio==${TORCHAUDIO_VERSION} --index-url https://download.pytorch.org/whl/cu${CUDA_SHORT_VERSION}
&& pip install ninja wheel diffusers transformers accelerate sentencepiece protobuf huggingface_hub comfy-cli RUN pip install ninja wheel diffusers transformers accelerate sentencepiece protobuf huggingface_hub comfy-cli
# Start building # Start building
RUN git clone https://github.com/mit-han-lab/nunchaku.git \ RUN git clone https://github.com/mit-han-lab/nunchaku.git \
&& cd nunchaku \ && cd nunchaku \
&& git submodule init \ && git submodule init \
&& git submodule update \ && git submodule update \
&& NUNCHAKU_INSTALL_MODE=ALL pip install -e . --no-build-isolation && NUNCHAKU_INSTALL_MODE=ALL python setup.py develop
RUN cd .. && git clone https://github.com/comfyanonymous/ComfyUI \ RUN cd .. && git clone https://github.com/comfyanonymous/ComfyUI \
&& cd ComfyUI/custom_nodes && git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager \ && cd ComfyUI/custom_nodes && git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager \
&& git clone https://github.com/mit-han-lab/ComfyUI-nunchaku.git nunchaku_nodes \ && git clone https://github.com/mit-han-lab/ComfyUI-nunchaku.git nunchaku_nodes \
&& cd .. && mkdir -p user/default/workflows/ && cp custom_nodes/nunchaku_nodes/workflows/* user/default/workflows/ && cd .. && mkdir -p user/default/workflows/ && cp custom_nodes/nunchaku_nodes/workflows/* user/default/workflows/
## Set default command
#CMD ["python3", "app.py"]
comfyui/LICENCE.txt deleted 100644 → 0
View file @ cdf5a19b
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comfyui/README.md deleted 100644 → 0
View file @ cdf5a19b
# SVDQuant ComfyUI Node
**Note**: This node is **deprecated**! Please check **[ComfyUI-nunchaku/](https://github.com/mit-han-lab/ComfyUI-nunchaku/)** for the latest version.
![comfyui](../assets/comfyui.jpg)
## Installation
Please first install `nunchaku` following the instructions in [README.md](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation). Then just install `image_gen_aux` with
```shell
pip install git+https://github.com/asomoza/image_gen_aux.git
```
### ComfyUI-CLI
```shell
pip install comfy-cli # install the comfyui-cli
comfy node registry-install svdquant
```
### ComfyUI-Manager (Experimental)
1. Install [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) with the following commands then restart ComfyUI:
```shell
cd ComfyUI/custom_nodes
git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
```
2. Open the Manager, search `svdquant` in the Custom Nodes Manager and then install it.
### Manual Installation
1. Install dependencies needed to run custom ComfyUI nodes:
```shell
pip install git+https://github.com/asomoza/image_gen_aux.git
```
2. Set up the dependencies for [ComfyUI](https://github.com/comfyanonymous/ComfyUI/tree/master) with the following commands:
```shell
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt
```
3. Navigate to the root directory of ComfyUI and link (or copy) the [`nunchaku/comfyui`](./) folder to `custom_nodes/svdquant`. For example:
```shell
# Clone repositories (skip if already cloned)
git clone https://github.com/comfyanonymous/ComfyUI.git
git clone https://github.com/mit-han-lab/nunchaku.git
cd ComfyUI
# Add SVDQuant nodes
cd custom_nodes
ln -s ../../nunchaku/comfyui svdquant
```
## Usage
1. **Set Up ComfyUI and SVDQuant**:
* SVDQuant workflows can be found at [`workflows`](./workflows). You can place them in `user/default/workflows` in ComfyUI root directory to load them. For example:
```shell
cd ComfyUI
# Copy workflow configurations
mkdir -p user/default/workflows
cp ../nunchaku/comfyui/workflows/* user/default/workflows/
```
* Install missing nodes (e.g., comfyui-inpainteasy) following [this tutorial](https://github.com/ltdrdata/ComfyUI-Manager?tab=readme-ov-file#support-of-missing-nodes-installation).
2. **Download Required Models**: Follow [this tutorial](https://comfyanonymous.github.io/ComfyUI_examples/flux/) and download the required models into the appropriate directories using the commands below:
```shell
huggingface-cli download comfyanonymous/flux_text_encoders clip_l.safetensors --local-dir models/text_encoders
huggingface-cli download comfyanonymous/flux_text_encoders t5xxl_fp16.safetensors --local-dir models/text_encoders
huggingface-cli download black-forest-labs/FLUX.1-schnell ae.safetensors --local-dir models/vae
```
3. **Run ComfyUI**: From ComfyUI’s root directory, execute the following command to start the application:
```shell
python main.py
```
4. **Select the SVDQuant Workflow**: Choose one of the SVDQuant workflows (workflows that start with `svdq-`) to get started. For the FLUX.1-Fill workflow, you can use the built-in MaskEditor tool to add mask on top of an image.
## SVDQuant Nodes
* **SVDQuant Flux DiT Loader**: A node for loading the FLUX diffusion model.
* `model_path`: Specifies the model location. If set to the folder starting with `mit-han-lab`, the model will be automatically downloaded from our Hugging Face repository. Alternatively, you can manually download the model directory by running the following command example:
```shell
huggingface-cli download mit-han-lab/svdq-int4-flux.1-dev --local-dir models/diffusion_models/svdq-int4-flux.1-dev
```
After downloading, specify the corresponding folder name as the `model_path`.
* `cpu_offload`: Enables CPU offloading for the transformer model. While this may reduce GPU memory usage, it can slow down inference. Memory usage will be further optimized in node v0.1.6.
* `device_id`: Indicates the GPU ID for running the model.
* **SVDQuant FLUX LoRA Loader**: A node for loading LoRA modules for SVDQuant FLUX models.
* Place your LoRA checkpoints in the `models/loras` directory. These will appear as selectable options under `lora_name`. Meanwhile, the [example Ghibsky LoRA](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration) is included and will automatically download from our Hugging Face repository when used.
* `lora_format` specifies the LoRA format. Supported formats include:
* `auto`: Automatically detects the appropriate LoRA format.
* `diffusers` (e.g., [aleksa-codes/flux-ghibsky-illustration](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration))
* `comfyui` (e.g., [Shakker-Labs/FLUX.1-dev-LoRA-Children-Simple-Sketch](https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-Children-Simple-Sketch))
* `xlab` (e.g., [XLabs-AI/flux-RealismLora](https://huggingface.co/XLabs-AI/flux-RealismLora))
* `svdquant` (e.g., [mit-han-lab/svdquant-lora-collection](https://huggingface.co/mit-han-lab/svdquant-lora-collection)).
* `base_model_name` specifies the path to the quantized base model. If `lora_format` is already set to `svdquant`, this option has no use. You can set it to the same value as `model_path` in the above **SVDQuant Flux DiT Loader**.
* **Note**: Currently, **only one LoRA** can be loaded at a time.
* **SVDQuant Text Encoder Loader**: A node for loading the text encoders.
* For FLUX, use the following files:
- `text_encoder1`: `t5xxl_fp16.safetensors`
- `text_encoder2`: `clip_l.safetensors`
* `t5_min_length`: Sets the minimum sequence length for T5 text embeddings. The default in `DualCLIPLoader` is hardcoded to 256, but for better image quality in SVDQuant, use 512 here.
* `t5_precision`: Specifies the precision of the T5 text encoder. Choose `INT4` to use the INT4 text encoder, which reduces GPU memory usage by approximately 15GB. Please install [`deepcompressor`](https://github.com/mit-han-lab/deepcompressor) when using it:
```shell
git clone https://github.com/mit-han-lab/deepcompressor
cd deepcompressor
pip install poetry
poetry install
```
* `int4_model`: Specifies the INT4 model location. This option is only used when `t5_precision` is set to `INT4`. By default, the path is `mit-han-lab/svdq-flux.1-t5`, and the model will automatically download from our Hugging Face repository. Alternatively, you can manually download the model directory by running the following command:
```shell
huggingface-cli download mit-han-lab/svdq-flux.1-t5 --local-dir models/text_encoders/svdq-flux.1-t5
```
After downloading, specify the corresponding folder name as the `int4_model`.
* **FLUX.1 Depth Preprocessor**: A node for loading the depth estimation model and output the depth map. `model_path` specifies the model location. If set to [`LiheYoung/depth-anything-large-hf`](https://huggingface.co/LiheYoung/depth-anything-large-hf), the model will be automatically downloaded from the Hugging Face repository. Alternatively, you can manually download the repository at `models/checkpoints` by running the following command example:
```shell
huggingface-cli download LiheYoung/depth-anything-large-hf --local-dir models/checkpoints/depth-anything-large-hf
```
comfyui/__init__.py deleted 100644 → 0
View file @ cdf5a19b
# only import if running as a custom node
from .nodes.lora import SVDQuantFluxLoraLoader
from .nodes.models import SVDQuantFluxDiTLoader, SVDQuantTextEncoderLoader
from .nodes.preprocessors import FluxDepthPreprocessor
NODE_CLASS_MAPPINGS = {
"SVDQuantFluxDiTLoader": SVDQuantFluxDiTLoader,
"SVDQuantTextEncoderLoader": SVDQuantTextEncoderLoader,
"SVDQuantFluxLoraLoader": SVDQuantFluxLoraLoader,
"SVDQuantDepthPreprocessor": FluxDepthPreprocessor,
}
NODE_DISPLAY_NAME_MAPPINGS = {k: v.TITLE for k, v in NODE_CLASS_MAPPINGS.items()}
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
comfyui/nodes/lora/__init__.py deleted 100644 → 0
View file @ cdf5a19b
from .flux import SVDQuantFluxLoraLoader
comfyui/nodes/lora/flux.py deleted 100644 → 0
View file @ cdf5a19b
import logging
import os
import folder_paths
from safetensors.torch import save_file
from nunchaku.lora.flux import comfyui2diffusers, convert_to_nunchaku_flux_lowrank_dict, detect_format, xlab2diffusers
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("SVDQuantFluxLoraLoader")
class SVDQuantFluxLoraLoader:
def __init__(self):
self.cur_lora_name = "None"
@classmethod
def INPUT_TYPES(s):
lora_name_list = ["None", *folder_paths.get_filename_list("loras")]
prefixes = folder_paths.folder_names_and_paths["diffusion_models"][0]
base_model_paths = set()
for prefix in prefixes:
if os.path.exists(prefix) and os.path.isdir(prefix):
base_model_paths_ = os.listdir(prefix)
base_model_paths_ = [
folder
for folder in base_model_paths_
if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
]
base_model_paths.update(base_model_paths_)
base_model_paths = sorted(list(base_model_paths))
return {
"required": {
"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
"lora_name": (lora_name_list, {"tooltip": "The name of the LoRA."}),
"lora_format": (
["auto", "comfyui", "diffusers", "svdquant", "xlab"],
{"tooltip": "The format of the LoRA."},
),
"base_model_name": (
base_model_paths,
{
"tooltip": "If the lora format is SVDQuant, this field has no use. Otherwise, the base model's state dictionary is required for converting the LoRA weights to SVDQuant." # noqa: E501
},
),
"lora_strength": (
"FLOAT",
{
"default": 1.0,
"min": -100.0,
"max": 100.0,
"step": 0.01,
"tooltip": "How strongly to modify the diffusion model. This value can be negative.",
},
),
"save_converted_lora": (
["disable", "enable"],
{
"tooltip": "If enabled, the converted LoRA will be saved as a .safetensors file in the save directory of your LoRA file." # noqa: E501
},
),
}
}
RETURN_TYPES = ("MODEL",)
OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
FUNCTION = "load_lora"
TITLE = "SVDQuant FLUX.1 LoRA Loader"
CATEGORY = "SVDQuant"
DESCRIPTION = (
"LoRAs are used to modify the diffusion model, "
"altering the way in which latents are denoised such as applying styles. "
"Currently, only one LoRA nodes can be applied."
)
def load_lora(
self,
model,
lora_name: str,
lora_format: str,
base_model_name: str,
lora_strength: float,
save_converted_lora: str,
):
if self.cur_lora_name == lora_name:
if self.cur_lora_name == "None":
pass # Do nothing since the lora is None
else:
model.model.diffusion_model.model.set_lora_strength(lora_strength)
else:
if lora_name == "None":
model.model.diffusion_model.model.set_lora_strength(0)
else:
try:
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
except FileNotFoundError:
lora_path = lora_name
if lora_format == "auto":
lora_format = detect_format(lora_path)
if lora_format != "svdquant":
if lora_format == "comfyui":
input_lora = comfyui2diffusers(lora_path)
elif lora_format == "xlab":
input_lora = xlab2diffusers(lora_path)
elif lora_format == "diffusers":
input_lora = lora_path
else:
raise ValueError(f"Invalid LoRA format {lora_format}.")
prefix = os.path.join(folder_paths.models_dir, "diffusion_models")
base_model_path = os.path.join(prefix, base_model_name, "transformer_blocks.safetensors")
if not os.path.exists(base_model_path):
# download from huggingface
base_model_path = os.path.join(base_model_name, "transformer_blocks.safetensors")
state_dict = convert_to_nunchaku_flux_lowrank_dict(base_model_path, input_lora)
if save_converted_lora == "enable" and lora_format != "svdquant":
dirname = os.path.dirname(lora_path)
basename = os.path.basename(lora_path)
if "int4" in base_model_path:
precision = "int4"
else:
assert "fp4" in base_model_path
precision = "fp4"
converted_name = f"svdq-{precision}-{basename}"
lora_converted_path = os.path.join(dirname, converted_name)
if not os.path.exists(lora_converted_path):
save_file(state_dict, lora_converted_path)
logger.info(f"Saved converted LoRA to: {lora_converted_path}")
else:
logger.info(f"Converted LoRA already exists at: {lora_converted_path}")
model.model.diffusion_model.model.update_lora_params(state_dict)
else:
model.model.diffusion_model.model.update_lora_params(lora_path)
model.model.diffusion_model.model.set_lora_strength(lora_strength)
self.cur_lora_name = lora_name
return (model,)
comfyui/nodes/models/__init__.py deleted 100644 → 0
View file @ cdf5a19b
from .flux import SVDQuantFluxDiTLoader
from .text_encoder import SVDQuantTextEncoderLoader
comfyui/nodes/models/flux.py deleted 100644 → 0
View file @ cdf5a19b
import os
import comfy.model_patcher
import folder_paths
import torch
from comfy.ldm.common_dit import pad_to_patch_size
from comfy.supported_models import Flux, FluxSchnell
from diffusers import FluxTransformer2DModel
from einops import rearrange, repeat
from torch import nn
from nunchaku import NunchakuFluxTransformer2dModel
class ComfyUIFluxForwardWrapper(nn.Module):
def __init__(self, model: NunchakuFluxTransformer2dModel, config):
super(ComfyUIFluxForwardWrapper, self).__init__()
self.model = model
self.dtype = next(model.parameters()).dtype
self.config = config
def forward(
self,
x,
timestep,
context,
y,
guidance,
control=None,
transformer_options={},
**kwargs,
):
assert control is None # for now
bs, c, h, w = x.shape
patch_size = self.config["patch_size"]
x = pad_to_patch_size(x, (patch_size, patch_size))
img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
h_len = (h + (patch_size // 2)) // patch_size
w_len = (w + (patch_size // 2)) // patch_size
img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(
0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype
).unsqueeze(1)
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(
0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype
).unsqueeze(0)
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
out = self.model(
hidden_states=img,
encoder_hidden_states=context,
pooled_projections=y,
timestep=timestep,
img_ids=img_ids,
txt_ids=txt_ids,
guidance=guidance if self.config["guidance_embed"] else None,
).sample
out = rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:, :, :h, :w]
return out
class SVDQuantFluxDiTLoader:
@classmethod
def INPUT_TYPES(s):
prefixes = folder_paths.folder_names_and_paths["diffusion_models"][0]
local_folders = set()
for prefix in prefixes:
if os.path.exists(prefix) and os.path.isdir(prefix):
local_folders_ = os.listdir(prefix)
local_folders_ = [
folder
for folder in local_folders_
if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
]
local_folders.update(local_folders_)
model_paths = sorted(list(local_folders))
ngpus = torch.cuda.device_count()
return {
"required": {
"model_path": (
model_paths,
{"tooltip": "The SVDQuant quantized FLUX.1 models. It can be a huggingface path or a local path."},
),
"cpu_offload": (
["auto", "enable", "disable"],
{
"default": "auto",
"tooltip": "Whether to enable CPU offload for the transformer model. 'auto' will enable it if the GPU memory is less than 14G.", # noqa: E501
},
),
"device_id": (
"INT",
{
"default": 0,
"min": 0,
"max": ngpus - 1,
"step": 1,
"display": "number",
"lazy": True,
"tooltip": "The GPU device ID to use for the model.",
},
),
}
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "load_model"
CATEGORY = "SVDQuant"
TITLE = "SVDQuant Flux DiT Loader"
def load_model(self, model_path: str, cpu_offload: str, device_id: int, **kwargs) -> tuple[FluxTransformer2DModel]:
device = f"cuda:{device_id}"
prefixes = folder_paths.folder_names_and_paths["diffusion_models"][0]
for prefix in prefixes:
if os.path.exists(os.path.join(prefix, model_path)):
model_path = os.path.join(prefix, model_path)
break
# Check if the device_id is valid
if device_id >= torch.cuda.device_count():
raise ValueError(f"Invalid device_id: {device_id}. Only {torch.cuda.device_count()} GPUs available.")
# Get the GPU properties
gpu_properties = torch.cuda.get_device_properties(device_id)
gpu_memory = gpu_properties.total_memory / (1024**2) # Convert to MB
gpu_name = gpu_properties.name
print(f"GPU {device_id} ({gpu_name}) Memory: {gpu_memory} MB")
# Check if CPU offload needs to be enabled
if cpu_offload == "auto":
if gpu_memory < 14336: # 14GB threshold
cpu_offload_enabled = True
print("VRAM < 14GiB，enable CPU offload")
else:
cpu_offload_enabled = False
print("VRAM > 14GiB，disable CPU offload")
elif cpu_offload == "enable":
cpu_offload_enabled = True
print("Enable CPU offload")
else:
cpu_offload_enabled = False
print("Disable CPU offload")
capability = torch.cuda.get_device_capability(0)
sm = f"{capability[0]}{capability[1]}"
precision = "fp4" if sm == "120" else "int4"
transformer = NunchakuFluxTransformer2dModel.from_pretrained(
model_path, precision=precision, offload=cpu_offload_enabled
)
transformer = transformer.to(device)
dit_config = {
"image_model": "flux",
"patch_size": 2,
"out_channels": 16,
"vec_in_dim": 768,
"context_in_dim": 4096,
"hidden_size": 3072,
"mlp_ratio": 4.0,
"num_heads": 24,
"depth": 19,
"depth_single_blocks": 38,
"axes_dim": [16, 56, 56],
"theta": 10000,
"qkv_bias": True,
"guidance_embed": True,
"disable_unet_model_creation": True,
}
if "schnell" in model_path:
dit_config["guidance_embed"] = False
dit_config["in_channels"] = 16
model_config = FluxSchnell(dit_config)
elif "canny" in model_path or "depth" in model_path:
dit_config["in_channels"] = 32
model_config = Flux(dit_config)
elif "fill" in model_path:
dit_config["in_channels"] = 64
model_config = Flux(dit_config)
else:
dit_config["in_channels"] = 16
model_config = Flux(dit_config)
model_config.set_inference_dtype(torch.bfloat16, None)
model_config.custom_operations = None
model = model_config.get_model({})
model.diffusion_model = ComfyUIFluxForwardWrapper(transformer, config=dit_config)
model = comfy.model_patcher.ModelPatcher(model, device, device_id)
return (model,)
comfyui/nodes/models/text_encoder.py deleted 100644 → 0
View file @ cdf5a19b
import os
import types
import comfy.sd
import folder_paths
import torch
from torch import nn
from transformers import T5EncoderModel
from nunchaku import NunchakuT5EncoderModel
def svdquant_t5_forward(
self: T5EncoderModel,
input_ids: torch.LongTensor,
attention_mask,
embeds=None,
intermediate_output=None,
final_layer_norm_intermediate=True,
dtype: str | torch.dtype = torch.bfloat16,
**kwargs,
):
assert attention_mask is None
assert intermediate_output is None
assert final_layer_norm_intermediate
outputs = self.encoder(input_ids=input_ids, inputs_embeds=embeds, attention_mask=attention_mask)
hidden_states = outputs["last_hidden_state"]
hidden_states = hidden_states.to(dtype=dtype)
return hidden_states, None
class WrappedEmbedding(nn.Module):
def __init__(self, embedding: nn.Embedding):
super().__init__()
self.embedding = embedding
def forward(self, input: torch.Tensor, out_dtype: torch.dtype | None = None):
return self.embedding(input)
@property
def weight(self):
return self.embedding.weight
class SVDQuantTextEncoderLoader:
@classmethod
def INPUT_TYPES(s):
prefixes = folder_paths.folder_names_and_paths["text_encoders"][0]
local_folders = set()
for prefix in prefixes:
if os.path.exists(prefix) and os.path.isdir(prefix):
local_folders_ = os.listdir(prefix)
local_folders_ = [
folder
for folder in local_folders_
if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
]
local_folders.update(local_folders_)
model_paths = sorted(list(local_folders))
return {
"required": {
"model_type": (["flux"],),
"text_encoder1": (folder_paths.get_filename_list("text_encoders"),),
"text_encoder2": (folder_paths.get_filename_list("text_encoders"),),
"t5_min_length": (
"INT",
{"default": 512, "min": 256, "max": 1024, "step": 128, "display": "number", "lazy": True},
),
"use_4bit_t5": (["disable", "enable"],),
"int4_model": (model_paths, {"tooltip": "The name of the 4-bit T5 model."}),
}
}
RETURN_TYPES = ("CLIP",)
FUNCTION = "load_text_encoder"
CATEGORY = "SVDQuant"
TITLE = "SVDQuant Text Encoder Loader"
def load_text_encoder(
self,
model_type: str,
text_encoder1: str,
text_encoder2: str,
t5_min_length: int,
use_4bit_t5: str,
int4_model: str,
):
text_encoder_path1 = folder_paths.get_full_path_or_raise("text_encoders", text_encoder1)
text_encoder_path2 = folder_paths.get_full_path_or_raise("text_encoders", text_encoder2)
if model_type == "flux":
clip_type = comfy.sd.CLIPType.FLUX
else:
raise ValueError(f"Unknown type {model_type}")
clip = comfy.sd.load_clip(
ckpt_paths=[text_encoder_path1, text_encoder_path2],
embedding_directory=folder_paths.get_folder_paths("embeddings"),
clip_type=clip_type,
)
if model_type == "flux":
clip.tokenizer.t5xxl.min_length = t5_min_length
if use_4bit_t5 == "enable":
transformer = clip.cond_stage_model.t5xxl.transformer
param = next(transformer.parameters())
dtype = param.dtype
device = param.device
prefixes = folder_paths.folder_names_and_paths["text_encoders"][0]
model_path = None
for prefix in prefixes:
if os.path.exists(os.path.join(prefix, int4_model)):
model_path = os.path.join(prefix, int4_model)
break
if model_path is None:
model_path = int4_model
transformer = NunchakuT5EncoderModel.from_pretrained(model_path)
transformer.forward = types.MethodType(svdquant_t5_forward, transformer)
transformer.shared = WrappedEmbedding(transformer.shared)
clip.cond_stage_model.t5xxl.transformer = (
transformer.to(device=device, dtype=dtype) if device.type == "cuda" else transformer
)
return (clip,)
comfyui/nodes/preprocessors/__init__.py deleted 100644 → 0
View file @ cdf5a19b
from .depth import FluxDepthPreprocessor
comfyui/nodes/preprocessors/depth.py deleted 100644 → 0
View file @ cdf5a19b
import os
import folder_paths
import numpy as np
import torch
class FluxDepthPreprocessor:
@classmethod
def INPUT_TYPES(s):
model_paths = []
prefix = os.path.join(folder_paths.models_dir, "checkpoints")
local_folders = os.listdir(prefix)
local_folders = sorted(
[
folder
for folder in local_folders
if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
]
)
model_paths = local_folders + model_paths
return {
"required": {
"image": ("IMAGE", {}),
"model_path": (
model_paths,
{"tooltip": "Name of the depth preprocessor model."},
),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "depth_preprocess"
CATEGORY = "SVDQuant"
TITLE = "FLUX.1 Depth Preprocessor"
def depth_preprocess(self, image, model_path):
prefixes = folder_paths.folder_names_and_paths["checkpoints"][0]
for prefix in prefixes:
if os.path.exists(os.path.join(prefix, model_path)):
model_path = os.path.join(prefix, model_path)
break
from image_gen_aux import DepthPreprocessor
processor = DepthPreprocessor.from_pretrained(model_path)
np_image = np.asarray(image)
np_result = np.array(processor(np_image)[0].convert("RGB"))
out_tensor = torch.from_numpy(np_result.astype(np.float32) / 255.0).unsqueeze(0)
return (out_tensor,)
comfyui/pyproject.toml deleted 100644 → 0
View file @ cdf5a19b
[project]
name = "svdquant"
description = "SVDQuant ComfyUI Node. SVDQuant is a new post-training training quantization paradigm for diffusion models, which quantize both the weights and activations of FLUX.1 to 4 bits, achieving 3.5× memory and 8.7× latency reduction on a 16GB laptop 4090 GPU. GitHub: https://github.com/mit-han-lab/nunchaku"
version = "0.1.5"
license = { file = "LICENSE.txt" }
dependencies = []
requires-python = ">=3.10, <3.13"
#[project.urls]
#Repository = "https://github.com/mit-han-lab/nunchaku"
# Used by Comfy Registry https://comfyregistry.org
[tool.comfy]
PublisherId = "lmxyy1999"
DisplayName = "svdquant"
Icon = "https://raw.githubusercontent.com/mit-han-lab/nunchaku/bfd9aa3ae84f51a26414f1600d725a0098472820/assets/logo.svg"
comfyui/requirements.txt deleted 100644 → 0
View file @ cdf5a19b
GPUtil
diffusers>=0.32.2
accelerate
sentencepiece
protobuf
huggingface_hub
comfyui/workflows/svdq-flux.1-canny.json deleted 100644 → 0
View file @ cdf5a19b
{
"last_node_id": 38,
"last_link_id": 76,
"nodes": [
{
"id": 3,
"type": "KSampler",
"pos": [
1290,
40
],
"size": [
315,
262
],
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "model",
"localized_name": "model",
"label": "model",
"type": "MODEL",
"link": 71
},
{
"name": "positive",
"localized_name": "positive",
"label": "positive",
"type": "CONDITIONING",
"link": 64
},
{
"name": "negative",
"localized_name": "negative",
"label": "negative",
"type": "CONDITIONING",
"link": 65
},
{
"name": "latent_image",
"localized_name": "latent_image",
"label": "latent_image",
"type": "LATENT",
"link": 66
}
],
"outputs": [
{
"name": "LATENT",
"localized_name": "LATENT",
"label": "LATENT",
"type": "LATENT",
"links": [
7
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KSampler"
},
"widgets_values": [
875054580097021,
"randomize",
20,
1,
"euler",
"normal",
1
]
},
{
"id": 35,
"type": "InstructPixToPixConditioning",
"pos": [
1040,
50
],
"size": [
235.1999969482422,
86
],
"flags": {},
"order": 10,
"mode": 0,
"inputs": [
{
"name": "positive",
"localized_name": "positive",
"label": "positive",
"type": "CONDITIONING",
"link": 67
},
{
"name": "negative",
"localized_name": "negative",
"label": "negative",
"type": "CONDITIONING",
"link": 68
},
{
"name": "vae",
"localized_name": "vae",
"label": "vae",
"type": "VAE",
"link": 69
},
{
"name": "pixels",
"localized_name": "pixels",
"label": "pixels",
"type": "IMAGE",
"link": 70
}
],
"outputs": [
{
"name": "positive",
"localized_name": "positive",
"label": "positive",
"type": "CONDITIONING",
"links": [
64
],
"slot_index": 0
},
{
"name": "negative",
"localized_name": "negative",
"label": "negative",
"type": "CONDITIONING",
"links": [
65
],
"slot_index": 1
},
{
"name": "latent",
"localized_name": "latent",
"label": "latent",
"type": "LATENT",
"links": [
66
],
"slot_index": 2
}
],
"properties": {
"Node name for S&R": "InstructPixToPixConditioning"
},
"widgets_values": []
},
{
"id": 8,
"type": "VAEDecode",
"pos": [
1620,
40
],
"size": [
210,
46
],
"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{
"name": "samples",
"localized_name": "samples",
"label": "samples",
"type": "LATENT",
"link": 7
},
{
"name": "vae",
"localized_name": "vae",
"label": "vae",
"type": "VAE",
"link": 60
}
],
"outputs": [
{
"name": "IMAGE",
"localized_name": "IMAGE",
"label": "IMAGE",
"type": "IMAGE",
"links": [
9
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAEDecode"
},
"widgets_values": []
},
{
"id": 9,
"type": "SaveImage",
"pos": [
1850,
40
],
"size": [
828.9535522460938,
893.8475341796875
],
"flags": {},
"order": 13,
"mode": 0,
"inputs": [
{
"name": "images",
"localized_name": "images",
"label": "images",
"type": "IMAGE",
"link": 9
}
],
"outputs": [],
"properties": {},
"widgets_values": [
"ComfyUI"
]
},
{
"id": 32,
"type": "VAELoader",
"pos": [
1290,
350
],
"size": [
315,
58
],
"flags": {},
"order": 0,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "VAE",
"localized_name": "VAE",
"label": "VAE",
"type": "VAE",
"links": [
60,
69
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAELoader"
},
"widgets_values": [
"ae.safetensors"
]
},
{
"id": 26,
"type": "FluxGuidance",
"pos": [
700,
50
],
"size": [
317.4000244140625,
58
],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "conditioning",
"localized_name": "conditioning",
"label": "conditioning",
"type": "CONDITIONING",
"link": 41
}
],
"outputs": [
{
"name": "CONDITIONING",
"localized_name": "CONDITIONING",
"label": "CONDITIONING",
"type": "CONDITIONING",
"shape": 3,
"links": [
67
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "FluxGuidance"
},
"widgets_values": [
30
]
},
{
"id": 34,
"type": "DualCLIPLoader",
"pos": [
-80,
110
],
"size": [
315,
122
],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "CLIP",
"localized_name": "CLIP",
"label": "CLIP",
"type": "CLIP",
"links": [
62,
63
]
}
],
"properties": {
"Node name for S&R": "DualCLIPLoader"
},
"widgets_values": [
"clip_l.safetensors",
"t5xxl_fp16.safetensors",
"flux",
"default"
]
},
{
"id": 23,
"type": "CLIPTextEncode",
"pos": [
260,
50
],
"size": [
422.84503173828125,
164.31304931640625
],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "clip",
"localized_name": "clip",
"label": "clip",
"type": "CLIP",
"link": 62
}
],
"outputs": [
{
"name": "CONDITIONING",
"localized_name": "CONDITIONING",
"label": "CONDITIONING",
"type": "CONDITIONING",
"links": [
41
],
"slot_index": 0
}
],
"title": "CLIP Text Encode (Positive Prompt)",
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
],
"color": "#232",
"bgcolor": "#353"
},
{
"id": 19,
"type": "PreviewImage",
"pos": [
1127.9403076171875,
554.3356323242188
],
"size": [
571.5869140625,
625.5296020507812
],
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "images",
"localized_name": "images",
"label": "images",
"type": "IMAGE",
"link": 26
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 18,
"type": "Canny",
"pos": [
744.2684936523438,
566.853515625
],
"size": [
315,
82
],
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "image",
"localized_name": "image",
"label": "image",
"type": "IMAGE",
"link": 76
}
],
"outputs": [
{
"name": "IMAGE",
"localized_name": "IMAGE",
"label": "IMAGE",
"type": "IMAGE",
"shape": 3,
"links": [
26,
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comfyui/workflows/svdq-flux.1-dev.json deleted 100644 → 0
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comfyui/workflows/svdq-flux.1-fill.json deleted 100644 → 0
View file @ cdf5a19b
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comfyui/workflows/svdq-flux.1-schnell.json deleted 100644 → 0
View file @ cdf5a19b
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