Principle:Zai org CogVideo Image Conditioning Preparation
Metadata
| Field | Value |
|---|---|
| Page Type | Principle |
| Knowledge Sources | Repo (CogVideo), Paper (CogVideoX) |
| Domains | Video_Generation, Diffusion_Models, Image_Conditioning |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
Technique for loading and preparing a reference image as conditioning input for image-to-video generation.
Description
Image conditioning preparation loads a source image from file or URL and converts it into the format expected by the I2V pipeline. The image serves as the first frame or visual anchor for the generated video, ensuring spatial consistency between the reference and generated content.
Image Loading
The conditioning image can be sourced from:
- Local file path: A path to an image file on disk (e.g., JPEG, PNG).
- URL: A remote URL pointing to an image resource, which is downloaded and decoded automatically.
The loaded image is returned as a PIL Image object, which is the standard format consumed by the diffusers pipeline.
Image Requirements
The conditioning image does not need to match the exact output resolution. The pipeline internally resizes and encodes the image to match the target video dimensions. However, using an image with an aspect ratio close to the target resolution (e.g., 480x720 for CogVideoX-5b-I2V) produces the best results, as it avoids significant distortion during resizing.
Usage
Use before calling the I2V pipeline. The image should represent the desired starting visual content for the video. Typical workflow:
- Load the image from a file path or URL.
- Pass the resulting PIL Image to the I2V pipeline's
imageparameter.
The image preparation step is required for any I2V generation -- without a conditioning image, the I2V pipeline cannot generate video.
Theoretical Basis
Latent Space Encoding
The conditioning image is encoded by the VAE into latent space, producing a spatial latent representation z_img that captures the image's visual content at a compressed resolution. This latent is then concatenated with the noise latents along the channel dimension before being processed by the transformer.
Channel Concatenation
During the denoising process, the image latent z_img is concatenated with the noisy video latent z_t along the channel dimension:
input = concat(z_t, z_img, dim=channels)
The transformer then learns to generate temporally consistent frames that extend from this initial visual information. The image latent provides a strong spatial prior that guides the denoising process toward maintaining structural and appearance consistency with the reference image across all generated frames.
Visual Anchor Effect
The conditioning image acts as a visual anchor. The generated video frames are constrained to share low-level features (color palette, object positions, scene layout) with the reference image. This is achieved through the channel concatenation mechanism, which allows the model to attend to image features at every denoising step rather than just at initialization.