README.md

SVDQuant ComfyUI Node

Installation

1. Install nunchaku following README.md.
2. Set up the dependencies for ComfyUI with the following commands:

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt

Usage

1. Set Up ComfyUI and SVDQuant:

• Navigate to the root directory of ComfyUI and link (or copy) the nunchaku/comfyui folder to custom_nodes/svdquant.

• Place the SVDQuant workflow configurations from workflows into user/default/workflows.

• For example

  # 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
  
  # Copy workflow configurations
  mkdir -p user/default/workflows
  cp ../nunchaku/comfyui/workflows/* user/default/workflows/
  
  # Add SVDQuant nodes
  cd custom_nodes
  ln -s ../../nunchaku/comfyui svdquant

2. Download Required Models: Follow this tutorial and download the required models into the appropriate directories using the commands below:

   huggingface-cli download comfyanonymous/flux_text_encoders clip_l.safetensors --local-dir models/clip
   huggingface-cli download comfyanonymous/flux_text_encoders t5xxl_fp16.safetensors --local-dir models/clip
   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:

   python main.py

4. Select the SVDQuant Workflow: Choose one of the SVDQuant workflows (flux.1-dev-svdquant.json or flux.1-schnell-svdquant.json) to get started.

SVDQuant Nodes

• SVDQuant Flux DiT Loader: A node for loading the FLUX diffusion model.

  • model_path: Specifies the model location. If set to mit-han-lab/svdq-int4-flux.1-schnell or mit-han-lab/svdq-int4-flux.1-dev, the model will be automatically downloaded from our Hugging Face repository. Alternatively, you can manually download the model directory by running the following command:

    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.

  • device_id: Indicates the GPU ID for running the model.

• SVDQuant LoRA Loader: A node for loading LoRA modules for SVDQuant diffusion models.

  • Place your LoRA checkpoints in the models/loras directory. These will appear as selectable options under lora_name. **Ensure your LoRA checkpoints conform to the SVDQuant format. **A LoRA conversion script will be released soon. Meanwhile, example LoRAs are included and will automatically download from our Hugging Face repository when used.
  • 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 when using it:

    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:

    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.