ComfyUI NormalCrafter

This is a ComfyUI custom node implementation for NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors by Yanrui Bin, Wenbo Hu, Haoyuan Wang, Xinya Chen, and Bing Wang.

It allows you to generate temporally consistent normal map sequences from input video frames.

Installation

1. Clone this repository into your ComfyUI/custom_nodes/ directory:

   cd ComfyUI/custom_nodes/
   git clone https://github.com/AIWarper/ComfyUI-NormalCrafterWrapper.git

2. Install the required dependencies. Navigate to the ComfyUI-NormalCrafter directory and install using the requirements.txt:

   ACTIVATE YOUR VENV FIRST
   ComfyUI-NormalCrafterWrapper
   pip install -r requirements.txt

   (See the requirements.txt for notes on the diffusers dependency, which ComfyUI often manages.)
3. Restart ComfyUI.

Node: NormalCrafter (Process Video)

This node takes a sequence of images (video frames) and processes them to output a corresponding sequence of normal map images.

Parameters

• images (Input Socket): The input image sequence (video frames).
• pipe_override (Input Socket, Optional): Allows providing a pre-loaded NormalCrafter pipeline instance. If unconnected, the node loads its own.
• seed: (Integer, Default: 42) Controls the randomness for reproducible results.
• control_after_generate: (Fixed, Increment, Decrement, Randomize) Standard ComfyUI widget for seed behavior on subsequent runs. Note: The underlying pipeline uses the seed for each full video processing.
• max_res_dimension: (Integer, Default: 1024) The maximum dimension (height or width) to which input frames are resized while maintaining aspect ratio.
• window_size: (Integer, Default: 14) The number of consecutive frames processed together in a sliding window. Affects temporal consistency.
• time_step_size: (Integer, Default: 10) How many frames the sliding window moves forward after processing a chunk. If less than window_size, frames will overlap, potentially improving smoothness.
• decode_chunk_size: (Integer, Default: 4) Number of latent frames decoded by the VAE at once. Primarily a VRAM management setting.
• fps_for_time_ids: (Integer, Default: 7) Conditions the model on an intended Frames Per Second, influencing motion characteristics in the generated normals. Note: In testing, this parameter showed minimal to no visible effect on the output for this specific model and task. As such I hard coded the value
• motion_bucket_id: (Integer, Default: 127) Conditions the model on an expected amount of motion. Note: In testing, this parameter showed minimal to no visible effect on the output for this specific model and task. As such I hard coded the value
• noise_aug_strength: (Float, Default: 0.0) Strength of noise augmentation applied to conditioning information. Note: In testing, this parameter showed minimal to no visible effect on the output for this specific model and task. As such I hard coded the value

Troubleshooting Flicker / Improving Temporal Consistency

If you are experiencing flickering or temporal inconsistencies in your output:

• Increase window_size: A larger window allows the model to see more temporal context, which can significantly improve consistency between frames.
• Adjust time_step_size: Using a time_step_size smaller than window_size creates an overlap between processed windows. This overlap is merged, which can smooth transitions. For example, if window_size is 20, try a time_step_size of 10 or 15.

You may be able to increase window_size and time_step_size substantially (e.g., to their maximum values) without encountering Out Of Memory (OOM) issues, depending on your hardware. Experiment to find the best balance for your needs.

Dependencies

• mediapy
• decord
• diffusers (and its dependencies like transformers, huggingface_hub) - ComfyUI usually manages its own diffusers version. Install manually if you encounter specific import errors related to it.
• torch, numpy, Pillow (standard Python ML/Image libraries)

Refer to requirements.txt for more details.