ComfyUI > Nodes > ComfyUI-PainterI2V > PainterI2V (Wan2.2 Slow-Motion Fix)

ComfyUI Node: PainterI2V (Wan2.2 Slow-Motion Fix)

Class Name

PainterI2V

Category
conditioning/video_models
Author
princepainter (Account age: 1050days)
Extension
ComfyUI-PainterI2V
Latest Updated
2025-11-24
Github Stars
0.49K

How to Install ComfyUI-PainterI2V

Install this extension via the ComfyUI Manager by searching for ComfyUI-PainterI2V
  • 1. Click the Manager button in the main menu
  • 2. Select Custom Nodes Manager button
  • 3. Enter ComfyUI-PainterI2V in the search bar
After installation, click the Restart button to restart ComfyUI. Then, manually refresh your browser to clear the cache and access the updated list of nodes.

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PainterI2V (Wan2.2 Slow-Motion Fix) Description

PainterI2V enhances image-to-video conversion by eliminating slow-motion artifacts and maintaining visual quality.

PainterI2V (Wan2.2 Slow-Motion Fix):

PainterI2V is an advanced node designed to enhance the process of converting images to video, specifically addressing the slow-motion issue encountered in 4-step LoRAs, such as lightx2v. This node is part of the ComfyUI suite and is tailored to maintain brightness stability while enhancing motion amplitude, making it ideal for creating smooth and visually appealing video sequences from static images. By leveraging the enhanced Wan2.2 Image-to-Video model, PainterI2V ensures that the generated videos are not only free from slow-motion artifacts but also retain the desired visual quality and consistency. This node is particularly beneficial for artists and creators looking to produce high-quality video content from image inputs, offering a seamless and efficient workflow.

PainterI2V (Wan2.2 Slow-Motion Fix) Input Parameters:

positive

This parameter represents the positive conditioning input, which is used to guide the video generation process. It influences the characteristics and features that should be emphasized in the output video. The positive input is crucial for setting the desired tone and style of the video.

negative

The negative conditioning input serves as a counterbalance to the positive input, helping to suppress unwanted features or characteristics in the video output. By adjusting the negative input, you can refine the video generation process to avoid certain styles or elements.

vae

The VAE (Variational Autoencoder) input is used to encode and decode images during the video generation process. It plays a vital role in transforming the input images into a latent space representation, which is then used to create the video frames.

width

This parameter specifies the width of the output video frames. It accepts integer values ranging from 16 to 4096, with a default value of 832. Adjusting the width allows you to control the resolution and aspect ratio of the video.

height

Similar to the width parameter, the height determines the vertical resolution of the video frames. It also accepts values between 16 and 4096, with a default of 480. Modifying the height can help achieve the desired video dimensions.

length

The length parameter defines the number of frames in the output video. It ranges from 1 to 4096, with a default value of 81. This parameter is crucial for setting the duration of the video, with longer lengths resulting in more extended sequences.

batch_size

This parameter controls the number of video sequences processed simultaneously. It accepts values from 1 to 4096, with a default of 1. Increasing the batch size can improve processing efficiency but may require more computational resources.

motion_amplitude

Motion amplitude influences the degree of motion enhancement applied to the video frames. It ranges from 1.0 to 2.0, with a default of 1.15. Adjusting this parameter can enhance or reduce the perceived motion in the video, allowing for more dynamic or subtle effects.

clip_vision_output

An optional input that provides additional vision-based conditioning to the video generation process. When used, it can enhance the visual coherence and style of the output video based on pre-trained vision models.

start_image

This optional parameter allows you to specify a starting image for the video sequence. The start image serves as the initial frame, providing a reference point for the subsequent frames in the video.

PainterI2V (Wan2.2 Slow-Motion Fix) Output Parameters:

positive

The positive output represents the conditioned video sequence that aligns with the desired features and characteristics specified by the positive input. It reflects the successful application of the positive conditioning during video generation.

negative

The negative output is the counterpart to the positive output, showcasing the video sequence with suppressed features as dictated by the negative input. It helps ensure that unwanted elements are minimized in the final video.

latent

The latent output provides the latent space representation of the video frames, which is crucial for understanding the underlying structure and features of the generated video. This output can be used for further analysis or refinement of the video content.

PainterI2V (Wan2.2 Slow-Motion Fix) Usage Tips:

  • To achieve smoother motion in your videos, experiment with the motion_amplitude parameter. Higher values can create more dynamic effects, while lower values can result in subtler motion.
  • Utilize the start_image parameter to set a specific starting point for your video, ensuring that the initial frame aligns with your creative vision.
  • Adjust the width and height parameters to match the desired resolution and aspect ratio of your final video output, keeping in mind the computational resources required for higher resolutions.

PainterI2V (Wan2.2 Slow-Motion Fix) Common Errors and Solutions:

"Invalid input dimensions"

  • Explanation: This error occurs when the specified width, height, or length parameters are outside the acceptable range or not divisible by the required step size.
  • Solution: Ensure that the width and height are within the range of 16 to 4096 and divisible by 16, and the length is within 1 to 4096 and divisible by 4.

"VAE encoding failed"

  • Explanation: This error indicates an issue with the VAE input, possibly due to an incompatible or missing VAE model.
  • Solution: Verify that a compatible VAE model is loaded and properly configured before running the node.

"Insufficient resources for batch size"

  • Explanation: The specified batch size exceeds the available computational resources, leading to processing failures.
  • Solution: Reduce the batch size to a level that your system can handle, or consider upgrading your hardware for larger batch processing.

PainterI2V (Wan2.2 Slow-Motion Fix) Related Nodes

Go back to the extension to check out more related nodes.
ComfyUI-PainterI2V
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