ComfyUI-FramePackWrapper_Plus

ComfyUI-FramePackWrapper_Plus
★ 114

视频生成F1采样LoRA兼容时间戳提示
为ComfyUI提供基于FramePack的视频生成扩展，支持F1采样、HunyuanVideo LoRA、时间戳提示、自动分辨率与平滑过渡。
💡 用于基于时间戳与LoRA的高质量视频生成工作流。
🍴 8 Forks💻 Python🔄 2025-05-19
📦
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https://pan.quark.cn/s/8f9eee5e2cdb
📦 requirements.txt
accelerate>=1.6.0
diffusers>=0.33.1
transformers>=4.46.2
einops
safetensors
peft
image
📄 README

ComfyUI-FramePackWrapper_Plus

An improved wrapper for the FramePack project that allows the creation of videos of any length based on reference images and LoRAs with F1 sampler.

Features

  • F1 Sampler Support: Uses the improved F1 video generation method for higher quality and better temporal coherence
  • LoRA Integration: Full support for HunyuanVideo LoRAs with proper weight handling and fusion options
  • Timestamped Prompts: Create dynamic videos with changing prompts at specific timestamps
  • Flexible Input Options: Works with both reference images and empty latents for complete creative control
  • Resolution Control: Automatic bucket finding for optimal video dimensions
  • Blend Control: Smooth transitions between different prompts at timestamps

    • Installation

  • Clone this repository into your ComfyUI custom_nodes folder:
    • cd ComfyUI/custom_nodes
git clone https://github.com/ShmuelRonen/ComfyUI-FramePackWrapper_Plus

  • Install the required dependencies:
    • pip install -r requirements.txt

  • Download the necessary model files and place them in your models folder:
  • FramePackI2V_HY: HuggingFace Link
  • FramePack_F1_I2V_HY: HuggingFace Link

    • Model Files

    Main Model Options

  • FramePackI2V_HY_fp8_e4m3fn.safetensors – Optimized fp8 version (smaller file size)
  • FramePackI2V_HY_bf16.safetensors – BF16 version (better quality)

    • Required Components

  • CLIP Vision: sigclip_vision_384
  • Text Encoder and VAE: HunyuanVideo_repackaged

    • Usage

    Basic Workflow

  • Load the HunyuanVideo model with your preferred settings
  • (Optional) Add LoRAs with the FramePackLoraSelect node
  • Prepare your input image or empty latent
  • Set up CLIP Vision encoding for image embeddings
  • Create timestamped prompts with FramePackTimestampedTextEncode
  • Generate your video with FramePackSampler (F1)

    • Example Workflow

    Timestamped Prompts

    Use the following format for timestamped prompts:

    [0s: A beautiful landscape, mountains in the background]
[5s-10s: Camera pans to reveal a lake, reflections of clouds]
[10s: A boat appears on the horizon, sailing slowly]

  • [Xs: prompt]: Starts at X seconds and continues until the next timestamp
  • [Xs-Ys: prompt]: Active from X seconds to Y seconds

    • LoRA Usage

  • Place your HunyuanVideo LoRAs in the ComfyUI/models/loras folder
  • Use the FramePackLoraSelect node to add them to your workflow
  • Adjust strength as desired (typically 0.5-1.2)
  • Set fuse_lora to false for flexibility or true for performance

    • Node Reference

    FramePackSampler (F1)

    The main generation node using the F1 sampling technique.

    Inputs:

  • model: The loaded FramePack model
  • positive_timed_data: Timestamped positive prompts
  • negative: Negative prompt conditioning
  • start_latent: Initial latent for generation
  • start_image_embeds: CLIP Vision embeddings for start image
  • end_latent: (Optional) End latent for transitions
  • end_image_embeds: (Optional) CLIP Vision embeddings for end image
  • initial_samples: (Optional) For video-to-video generation
  • Various sampling parameters (steps, CFG, guidance scale, etc.)

    • FramePackTimestampedTextEncode

    Encodes text prompts with timestamps for timed conditioning.

    Inputs:

  • clip: CLIP text model
  • text: Text prompt with timestamps
  • negative_text: Negative prompt
  • total_second_length: Video duration in seconds
  • latent_window_size: Processing window size
  • prompt_blend_sections: Number of sections to blend prompts

    • FramePackLoraSelect

    Selects and configures LoRA models.

    Inputs:

  • lora: LoRA model selection
  • strength: LoRA strength (0.0-2.0)
  • fuse_lora: Whether to fuse the LoRA weights into the base model
  • prev_lora: (Optional) For chaining multiple LoRAs

    • LoadFramePackModel / DownloadAndLoadFramePackModel

    Loads the FramePack model with various precision options.

    Advanced Tips

  • Resolution Control: Use the FramePackFindNearestBucket node to optimize dimensions
  • Memory Management: Adjust gpu_memory_preservation for large models
  • Blending Prompts: Set prompt_blend_sections > 0 for smooth transitions
  • Multiple LoRAs: Chain several LoRAs together for combined effects
  • Empty Latents: Use an Empty Latent Image node when starting from scratch

    • Troubleshooting

  • CUDA Out of Memory: Reduce resolution, decrease latent_window_size, or increase gpu_memory_preservation
  • LoRA Loading Issues: Ensure LoRAs are in the correct format (safetensors)
  • Video Artifacts: Try increasing steps or adjusting CFG/guidance_scale

    • Acknowledgements

  • Based on the original ComfyUI-FramePackWrapper by kijai
  • Uses models from FramePack by lllyasviel
  • Special thanks to the ComfyUI and Stable Diffusion communities

    • License

    MIT License

    Credits

    This project is an extension of the original ComfyUI-FramePackWrapper created by kijai. The original wrapper provided the foundation for working with FramePack models in ComfyUI.

    ComfyUI-FramePackWrapper_Plus builds upon that foundation by adding support for:

  • F1 sampler for improved temporal coherence
  • LoRA integration for customized generation
  • Timestamped prompts for dynamic video creation
  • Additional workflow improvements and optimizations

    • Special thanks to kijai for the original implementation that made this extension possible.