ComfyUI Extension: ComfyUI-FramePackWrapper_PlusOne

Authored by xhiroga

Created

Updated

28 stars

ComfyUI custom node for FramePack, supporting 1-frame inferences.

Custom Nodes (0)

    README

    ComfyUI-FramePackWrapper_PlusOne

    日本語

    ComfyUI-FramePackWrapper_PlusOne is a fork derived from ComfyUI-FramePackWrapper and ComfyUI-FramePackWrapper_Plus, containing FramePack's single-frame inference node (with kisekaeichi support).

    This repository was forked for public release at the request of @tori29umai0123 as requested here.

    Features

    • 1-Frame Inference: Supports basic single frame inference and the kisekaeichi method. For technical details, please refer to the musubi-tuner documentation.
    • 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

    Not yet supported

    • 1-Frame Inference: f-mc (one frame multi-control) is not supported yet.

    Installation

    1. Clone this repository into your ComfyUI custom_nodes folder:
    cd ComfyUI/custom_nodes
git clone https://github.com/xhiroga/ComfyUI-FramePackWrapper_PlusOne.git
    1. Install the required dependencies:
    pip install -r requirements.txt
    1. Download the necessary model files and place them in your models folder:

    Model Files

    Main Model Options

    Required Components

    Usage

    See example_workflows.

    | 1-Frame / LoRA @tori29umai | 1-Frame / LoRA @kohya-ss | Kisekaeichi / LoRA @tori29umai | | --- | --- | --- | | kisekaeichi | chibi | body2img |

    License

    MIT License

    Changelog

    v2.0.0 - Full musubi-tuner Compatibility (2025-08-08)

    Achieved complete compatibility with musubi-tuner specifications to improve inference result consistency when using multiple reference images.

    破壊的変更

    The denoise_strength of workflows created up to v0.0.2 may be reset to 0. After updating the node, please manually reset it to 1.0.

    Major Changes

    1. Improved Embedding Integration Method

    • ❌ Previous: Weighted average integration (70% input image, 30% reference images)
    • ✅ New: musubi-tuner compatible processing (using first reference image embedding)

    2. Unified Latent Combination Structure

    • ❌ Previous: Separate management of input and reference images before combination
    • ✅ New: Direct control_latents combination following musubi-tuner specification 
      control_latents = [input_image, reference_image1, reference_image2, ..., zero_latent]
clean_latents = torch.cat(control_latents, dim=2)

    3. Optimized Mask Application Timing

    • ❌ Previous: Individual application before latent combination
    • ✅ New: Mask application after clean_latents generation (musubi-tuner specification)

    4. Dynamic Index Setting Processing

    • ❌ Previous: Fixed clean_latent_indices configuration
    • ✅ New: Dynamic application of control_indices parameters 
      # control_index="0;7;8;9;10" → clean_latent_indices = [0, 7, 8, 9, 10]
while i < len(control_indices_list) and i < clean_latent_indices.shape[1]:
    clean_latent_indices[:, i] = control_indices_list[i]

    5. Improved latent_indices Initialization

    • ❌ Previous: ComfyUI-specific initialization method
    • ✅ New: musubi-tuner specification initialization 
      latent_indices = torch.zeros((1, 1), dtype=torch.int64)
latent_indices[:, 0] = latent_window_size  # default value
latent_indices[:, 0] = target_index        # parameter application

    Expected Benefits

    • Improved Inference Consistency: Generate identical results to musubi-tuner with same reference images and parameters
    • Stabilized Multi-Reference Processing: More stable quality through accurate index management
    • Parameter Compatibility: Correct functionality of musubi-tuner's control_index and target_index parameters

    Technical Details

    This update ensures the following processing flow matches musubi-tuner completely:

    1. Control Image Processing: Sequential processing of multiple images specified by --control_image_path
    2. Index Management: Dynamic application of --one_frame_inference="control_index=0;7;8;9;10,target_index=5"
    3. Embedding Processing: Implementation simulating section-wise individual processing
    4. Mask Application: Unified mask processing after clean_latents construction

    Credits