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

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

All nodes are classified under the vid2vid category.

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 can improve the time consistency of the output image sequence.

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.

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

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.

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

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.

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 that will be used to fine-tune the incoming model.

Outputs:

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

Parameters:

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

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

• UNet3DCoditionModel has high demand for GPU memory. If you encounter out of memory error, try to reduce n_sample_frames. If your computer has less than 6GB of GPU memory, n_sample_frames should not exceed 3, but this may reduce the quality of the generated results

• 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