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

2. Install the required dependencies:

pip install -r requirements.txt

3. 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

See example_workflows.

1-Frame / LoRA @tori29umai	1-Frame / LoRA @kohya-ss	Kisekaeichi / LoRA @tori29umai

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

• FramePack: @lllyasviel's original implementation.
• ComfyUI-FramePackWrapper: @kijai's original implementation.
• ComfyUI-FramePackWrapper_Plus: @ShmuelRonen's F1-supported fork.
• ComfyUI-FramePackWrapper_PlusOne: @tori29umai0123's 1-frame inference-supported fork.
• musubi-tuner: @kohya-ss's high-quality FramePack training and inference library