Vid2vid Node Suite for ComfyUI

A node suite for ComfyUI that allows you to load image sequence and generate new image sequence with different styles or content.

Original repo: https://github.com/sylym/stable-diffusion-vid2vid

Install

Firstly, install comfyui

Then run:

cd ComfyUI/custom_nodes
git clone https://github.com/sylym/comfy_vid2vid
cd comfy_vid2vid

Next, download dependencies:

python -m pip install -r requirements.txt

For ComfyUI portable standalone build:

#You may need to replace "..\..\..\python_embeded\python.exe" depends your python_embeded location
..\..\..\python_embeded\python.exe -m pip install -r requirements.txt

Usage

All nodes are classified under the vid2vid category. For some workflow examples you can check out:

vid2vid workflow examples

Nodes

LoadImageSequence

Load image sequence from a folder.

Inputs:

• None

Outputs:

• IMAGE

  • Image sequence

• MASK_SEQUENCE

  • The alpha channel of the image sequence is the channel we will use as a mask.

Parameters:

• image_sequence_folder

  • Select the folder that contains a sequence of images. Node only uses folders in the input folder.
  • The folder should only contain images with the same size.

• sample_start_idx

  • The start index of the image sequence. The image sequence will be sorted by image names.

• sample_frame_rate

  • The frame rate of the image sequence. If the frame rate is 2, the node will sample every 2 images.

• n_sample_frames

  • The number of images in the sequence. The number of images in image_sequence_folder must be greater than or equal to sample_start_idx - 1 + n_sample_frames * sample_frame_rate.
  • If you want to use the node CheckpointLoaderSimpleSequence to generate a sequence of pictures, set n_sample_frames >= 3.

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LoadImageMaskSequence

Load mask sequence from a folder.

Inputs:

• None

Outputs:

• MASK_SEQUENCE
  • Image mask sequence

Parameters:

• image_sequence_folder

  • Select the folder that contains a sequence of images. Node only uses folders in the input folder.
  • The folder should only contain images with the same size.

• channel

  • The channel of the image sequence that will be used as a mask.

• sample_start_idx

  • The start index of the image sequence. The image sequence will be sorted by image names.

• sample_frame_rate

  • The frame rate of the image sequence. If the frame rate is 2, the node will sample every 2 images.

• n_sample_frames

  • The number of images in the sequence. The number of images in image_sequence_folder must be greater than or equal to sample_start_idx - 1 + n_sample_frames * sample_frame_rate.

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VAEEncodeForInpaintSequence

Encode the input image sequence into a latent vector using a Variational Autoencoder (VAE) model. Also add image mask sequence to latent vector.

Inputs:

• pixels: IMAGE

  • Image sequence that will be encoded.

• vae: VAE

  • VAE model that will be used to encode the image sequence.

• mask_sequence: MASK_SEQUENCE

  • Image mask sequence that will be added to the latent vector. The number of images and masks must be the same.

Outputs:

• LATENT
  • The latent vector with image mask sequence. The image mask sequence in the latent vector will only take effect when using the node KSamplerSequence.

Parameters:

• None

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DdimInversionSequence

Generate a specific noise vector by inverting the input latent vector using the Ddim model. Usually used to improve the time consistency of the output image sequence.

Inputs:

• samples: LATENT

  • The latent vector that will be inverted.

• model: MODEL

  • Full model that will be used to invert the latent vector.

• clip: CLIP

  • Clip model that will be used to invert the latent vector.

Outputs:

• NOISE
  • The noise vector that will be used to generate the image sequence.

Parameters:

• steps
  • The number of steps to invert the latent vector.

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SetLatentNoiseSequence

Add noise vector to latent vector.

Inputs:

• samples: LATENT

  • The latent vector that will be added noise.

• noise: NOISE

  • The noise vector that will be added to the latent vector.

Outputs:

• LATENT
  • The latent vector with noise. The noise vector in the latent vector will only take effect when using the node KSamplerSequence.

Parameters:

• None

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CheckpointLoaderSimpleSequence

Load the checkpoint model into UNet3DConditionModel. Usually used to generate a sequence of pictures with time continuity.

Inputs:

• None

Outputs:

• ORIGINAL_MODEL

  • Model for fine-tuning, not for inference

• CLIP

  • The clip model

• VAE

  • The VAE model

Parameters:

• ckpt_name
  • The name of the checkpoint model. The model should be in the models/checkpoints folder.

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LoraLoaderSequence

Same function as LoraLoader node, but acts on UNet3DConditionModel. Used after the CheckpointLoaderSimpleSequence node and before the TrainUnetSequence node. The input and output of the model are both of ORIGINAL_MODEL type.

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TrainUnetSequence

Fine-tune the incoming model using latent vector and context, and convert the model to inference mode.

Inputs:

• samples: LATENT

  • The latent vector that will be used to fine-tune the incoming model.

• model: ORIGINAL_MODEL

  • The model that will be fine-tuned.

• context: CONDITIONING

  • The context used for fine-tuning the input model, typically consists of words or sentences describing the subject of the action in the latent vector and its behavior.

Outputs:

• MODEL
  • The fine-tuned model. This model is ready for inference.

Parameters:

• seed
  • The seed used in model fine-tuning.
• steps
  • The number of steps to fine-tune the model. If the steps is 0, the model will not be fine-tuned.

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KSamplerSequence

Same function as KSampler node, but added support for noise vector and image mask sequence.

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Limits

• UNet3DCoditionModel has high demand for GPU memory. If you encounter out of memory error, try to reduce n_sample_frames. However, n_sample_frames must be greater than or equal to 3.

• Some custom nodes do not support processing image sequences. The nodes listed below have been tested and are working properly:

  • Official node
  • comfy_controlnet_preprocessors