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ComfyUI-FLATTEN

ComfyUI nodes to use FLATTEN.

Original research repo: FLATTEN

ComfyUI-FLATTEN.mp4

Table of Contents

Installation

How to Install

Clone or download this repo into your ComfyUI/custom_nodes/ directory or use the ComfyUI-Manager to automatically install the nodes. No additional Python packages outside of ComfyUI requirements should be necessary.

Nodes

flatten_nodes_screenshot

  • Node: Load Checkpoint with FLATTEN model

    • Loads any given SD1.5 checkpoint with the FLATTEN optical flow model. Use the sdxl branch of this repo to load SDXL models
    • The loaded model only works with the Flatten KSampler and a standard ComfyUI checkpoint loader is required for other KSamplers

  • Node: Sample Trajectories

    • Takes the input images and samples their optical flow into trajectories. Trajectories are created for the dimensions of the input image and must match the latent size Flatten processes.
    • Context Length and Overlap for Batching with AnimateDiff-Evolved
      • Context Length defines the window size Flatten processes at a time. Flatten is not limitted to a certain frame count, but this can be used to reduce VRAM usage at a single time
      • Context Overlap is the overlap between windows
      • Can only use Standard Static from AnimateDiff-Evolved and these values must match the values given to AnimateDiff's Evolved Sampling context
      • Currently does not support Views

  • Node: Unsampler (Flatten)

    • Unsamples the input latent and creates the needed injections required for sampling
    • Only use Euler or ddpm2m as the sampling method since this process creates noise from the input images

  • Node: KSampler (Flatten)

    • Samples the unsampled latents and uses the injections from the Unsampler
    • Can use any sampling method, but use Euler or ddpm2m for editing pieces of the video or another sampling method to get drastic changes in the video

  • Node: Apply Flatten Attention (SD1.5 Only)

    • Use Flatten's Optical Flow attention mechanism without the rest of Flatten's model -- can be used to combine with other models
    • Warning: Flatten's attention requires "Flow Noise" so it does not always work with methods that add normal noise

  • Node: Create Flow Noise

    • Creates flow noise given a latent and trajectories
    • Can be used to add initial noise to a latent instead of using normal noise from a traditional KSampler

Accompanying Node Repos

Examples

For working ComfyUI example workflows see the example_workflows/ directory.

Video Editing

FLATTEN excels at editing videos with temporal consistency. The recommended settings for this are to use an Unsampler and KSampler with old_qk = 0. The Unsampler should use the euler sampler and the KSampler should use the dpmpp_2m sampler. Users may experiment with old_qk depending on their use case, but it is not recommended to use other samplers or add_noise for video editing. Style transfer nodes such as IP-Adapter may have difficulty making quality edits without the additional noise and will require fine tuning.

Scene Editing (Experimental)

Inspired by the optical flow use in FLATTEN, these nodes can utilize noise that is driven by optical flow. The current implementation is experimental and allows the user to create highly altered scenes, however it can lose some of the consistency and does not work well with high motion scenes.

To use this, it is recommended to use LCM on the KSampler (not the Unsampler) alongside setting old_qk = 1 on the KSampler. Ancestral sampling methods also work well. Users may experiment with toggling the add_noise setting on the KSampler when using a sampling method that injects noise (e.g. anything besides Euler and dpmpp2). Using IPAdapter can help guide these generations towards a specific look.

wolf_noise_example.mp4
runner_noise_example.mp4
ComfyUI-FLATTEN.mp4
trucks_noise_example.mp4

ComfyUI Support

The ComfyUI-FLATTEN implementation can support most ComfyUI nodes, including ControlNets, IP-Adapter, LCM, InstanceDiffusion/GLIGEN, and many more.

Batching

Currently batching for large amount of frames results in a loss in consistency and a possible solution is under consideration.

The current batching mechanism utilizes the AnimateDiff-Evolved batching nodes and is required to batch. See the example workflow for a working example.

SDXL Support

Experiments for supporting SDXL were made and resulted in generating somewhat consistent videos, but not up-to-par with the SD1.5 implementation. Feel free to check out the sdxl branch, but there will be no further development in this direction.

Unsupported

Currently the known unsupported custom ComfyUI features are:

  • Scheduled Prompting
  • Context Views for advanced batching

Acknowledgements