comfyui_longcat_image

ComfyUI LongCat-Image Integration

This custom node integrates the LongCat-Image pipeline into ComfyUI, enabling text-to-image generation and image editing with the LongCat-Image models.

Features

• Text-to-Image Generation: Generate high-quality images from text prompts using LongCat-Image models
• Image Editing: Edit existing images with instruction-based prompts using LongCat-Image-Edit models
• Chinese Text Support: Excellent Chinese text rendering capabilities
• Efficient: Only 6B parameters with competitive performance

Installation

1. Install Node

Search for comfyui_longcat_image in comfyui custom nodes manager / install missing node directly at the workflow provided

Or you could install manually with this command

cd custom_nodes/comfyui_longcat_image
pip install -r requirements.txt

2. (Optional) Install SageAttention for Speed Boost

For ~2x faster inference, install SageAttention and enable sage at attention_backend option in model loader node:

pip install sageattention

Requirements: CUDA-capable NVIDIA GPU with PyTorch CUDA support.

3. Download Models

Download the models using huggingface-cli:

pip install -U huggingface_hub

# For text-to-image
hf download meituan-longcat/LongCat-Image --local-dir models/diffusion_models/LongCat-Image

# For image editing
hf download meituan-longcat/LongCat-Image-Edit --local-dir models/diffusion_models/LongCat-Image-Edit

# For fine-tuning (optional)
hf download meituan-longcat/LongCat-Image-Dev --local-dir models/diffusion_models/LongCat-Image-Dev

Available Nodes

LongCat-Image Model Loader

Loads a LongCat-Image model for use with other nodes.

Inputs:

• model_path: Path to the model directory (e.g., "LongCat-Image" or "LongCat-Image-Edit")
• dtype: Data type for model weights (bfloat16, float16, float32)
• enable_cpu_offload: Enable CPU offload to save VRAM (false/true, default: true)
• attention_backend: Choose attention backend - "default" or "sage" (default: default)

Outputs:

• LONGCAT_PIPE: Pipeline object for use with generation nodes

Low VRAM Support

The model loader supports low VRAM mode via the enable_cpu_offload option:

• Disabled: All models loaded to GPU at once

  • Faster inference
  • Requires more VRAM (typically ~24GB+)

• Enabled (default): Models offloaded to CPU when not in use

  • Slower inference (due to model transfers)
  • Requires only ~17-19GB VRAM
  • Prevents Out-of-Memory errors on lower-end GPUs

When to use CPU offload:

• GPUs with less than 24GB VRAM
• When experiencing OOM errors
• When running multiple models simultaneously

SageAttention Backend

The model loader supports an optional SageAttention backend for improved inference speed:

• default: Uses PyTorch's standard scaled dot product attention

  • Works on all systems (CPU/GPU)
  • Standard performance

• sage: Uses SageAttention for accelerated attention computation

  • ~2x faster inference speed compared to default attention
  • Requires CUDA-capable GPU
  • Requires the sageattention package (see installation section above)
  • Automatically falls back to default attention for unsupported operations

To use SageAttention:

1. Install the sageattention package:

pip install sageattention

2. Set attention_backend to "sage" in the Model Loader node

Requirements:

• CUDA-capable NVIDIA GPU
• PyTorch with CUDA support
• The sageattention package installed

LongCat-Image Text to Image

Generates images from text prompts.

Inputs:

• LONGCAT_PIPE: Pipeline from the model loader
• prompt: Text description of the image to generate
• negative_prompt: Things to avoid in the generated image
• width: Image width (default: 1344)
• height: Image height (default: 768)
• steps: Number of inference steps (default: 50)
• guidance_scale: CFG scale (default: 4.5)
• seed: Random seed
• enable_cfg_renorm: Enable CFG renormalization (true/false)
• enable_prompt_rewrite: Enable built-in prompt rewriting (true/false)

Outputs:

• IMAGE: Generated image

LongCat-Image Edit

Edits images based on instruction prompts.

Inputs:

• LONGCAT_PIPE: Pipeline from the model loader (must be an edit model)
• image: Input image to edit
• prompt: Edit instruction
• negative_prompt: Things to avoid in the edited image
• steps: Number of inference steps (default: 50)
• guidance_scale: CFG scale (default: 4.5)
• seed: Random seed

Outputs:

• IMAGE: Edited image

Example Workflows

Example workflow JSON files are provided in this directory:

• example_workflow_t2i.json - Text-to-image generation workflow
• example_workflow_edit.json - Image editing workflow

You can load these workflows in ComfyUI by dragging and dropping the JSON file onto the canvas.

Text-to-Image

1. Add a LongCat-Image Model Loader node

   • Set model_path to "LongCat-Image"

2. Add a LongCat-Image Text to Image node

   • Connect the loader output to the pipeline input
   • Enter your prompt
   • Adjust settings as needed

3. Add a Save Image node to save the output

Image Editing

1. Add a LongCat-Image Model Loader node

   • Set model_path to "LongCat-Image-Edit"

2. Add a Load Image node to load your input image

3. Add a LongCat-Image Edit node

   • Connect the loader output to the pipeline input
   • Connect the image to edit
   • Enter your edit instruction (e.g., "将猫变成狗" - "change the cat to a dog")

4. Add a Save Image node to save the output

Model Information

| Model | Type | Description | |-------|------|-------------| | LongCat-Image | Text-to-Image | Final release model for out-of-the-box inference | | LongCat-Image-Dev | Text-to-Image | Mid-training checkpoint, suitable for fine-tuning | | LongCat-Image-Edit | Image Editing | Specialized model for image editing |

Performance

• Parameters: 6B (highly efficient)
• Supported Resolutions: 768x1344 and variations
• Chinese Text Support: Industry-leading Chinese dictionary coverage
• Quality: Competitive with much larger models

Attention Backend Performance

| Backend | Speed | Requirements | When to Use | |---------|-------|--------------|-------------| | default | 1x (baseline) | Any system | General use, CPU inference | | sage | ~2x faster | CUDA GPU + sageattention package | Maximum speed on NVIDIA GPUs |

Note: SageAttention provides approximately 2x speed improvement for attention operations on CUDA GPUs while maintaining output quality.

VRAM Requirements

| Mode | VRAM Required | Speed | When to Use | |------|---------------|-------|-------------| | Standard (CPU offload disabled) | ~24GB+ | Faster | High-end GPUs (e.g., RTX 3090, 4090, A100) | | Low VRAM (CPU offload enabled) | ~17-19GB | Slower | Mid-range GPUs (e.g., RTX 3080, 4080) |

Note: The Low VRAM mode uses CPU offloading to transfer models between CPU and GPU as needed, reducing VRAM usage at the cost of slower inference speed.

Tips

• For better results, use a strong LLM for prompt engineering
• The model has excellent Chinese text rendering capabilities
• Enable prompt rewriting for enhanced generation quality
• Default guidance scale of 4.5 works well for most cases

License

LongCat-Image is licensed under Apache 2.0. See the LongCat-Image repository for more information.

References

• LongCat-Image GitHub
• LongCat-Image on Hugging Face
• Technical Report