Principle:Zai org CogVideo SAT Model Loading for Inference

Attribute	Value
Principle Name	SAT Model Loading for Inference
Workflow	SAT Video Generation
Step	2 of 5
Type	Model Initialization
Repository	zai-org/CogVideo
Paper	CogVideoX
Last Updated	2026-02-10 00:00 GMT

Overview

Technique for loading a pre-trained SAT video diffusion model and its checkpoint weights for inference. SAT model loading constructs the model architecture from configuration and then loads pretrained weights from a checkpoint file, preparing the model for generation by disabling gradients and setting evaluation mode.

Description

SAT model loading operates in two distinct phases:

1. Architecture construction: The get_model function instantiates the SATVideoDiffusionEngine class using parameters from the parsed configuration. This includes the transformer backbone, VAE, text encoder, conditioner, and sampler components.
2. Weight loading: The load_checkpoint function loads pretrained weights from the checkpoint path specified in args.load. Weights are mapped to the constructed architecture, handling any distributed sharding if applicable.

After loading, the model is set to evaluation mode (model.eval()) and all parameter gradients are disabled to reduce memory usage and prevent accidental parameter updates during inference.

Usage

Use SAT Model Loading for Inference after configuration parsing and before prompt input or sampling. This step is required exactly once per inference session. The loaded model object is then passed to the sampling and decoding stages.

Theoretical Basis

Separating model construction from weight loading allows the same architecture code to be used for both training and inference. This design pattern provides several benefits:

• Architecture-weight independence: The model class defines the computation graph, while the checkpoint provides learned parameters. Different checkpoints (e.g., different fine-tuned versions) can be loaded into the same architecture.
• Eval mode semantics: Setting model.eval() changes the behavior of layers like dropout and batch normalization. For diffusion models, this primarily affects dropout layers in the transformer blocks.
• Memory efficiency: Disabling gradient computation (torch.no_grad()) avoids storing intermediate activations needed for backpropagation, significantly reducing GPU memory requirements during inference.

Related Pages

• Implementation:Zai_org_CogVideo_SAT_Get_Model_Load_Checkpoint -- Implementation of model loading and checkpoint restoration
• Zai_org_CogVideo_SAT_Inference_Configuration -- Previous step: configuration parsing that provides model parameters
• Zai_org_CogVideo_SAT_Prompt_Input -- Next step: reading prompts for generation
• Zai_org_CogVideo_Diffusion_Sampling -- Sampling step that uses the loaded model