Principle:Zai org CogVideo Training Configuration

Principle Metadata
Name	Training_Configuration
Category	Configuration_Management
Domains	Fine_Tuning, Diffusion_Models
Knowledge Sources	CogVideo Repository, CogVideoX Paper
Last Updated	2026-02-10 00:00 GMT

Overview

Training Configuration is the principle of centralized, validated configuration management for deep learning training pipelines.

Description

Training configuration involves gathering hyperparameters, model paths, data paths, LoRA settings, and validation options into a single validated schema. Using schema-validated configuration (Pydantic) catches errors before training begins, ensuring resolution constraints are met, required fields are present, and incompatible options are flagged.

A well-structured configuration system for video diffusion fine-tuning must handle:

• Model specification: Model path, model name (e.g., cogvideox-5b), model type (t2v or i2v), training type (lora or sft).
• Data specification: Data root, caption and video column paths, resolution (frames, height, width).
• LoRA hyperparameters: Rank, alpha, target modules.
• Training hyperparameters: Learning rate, batch size, epochs/steps, gradient accumulation, mixed precision.
• Infrastructure settings: Output directory, checkpointing frequency, validation configuration, logging.

Usage

Use at the start of any Diffusers-based CogVideoX fine-tuning run to parse and validate all training parameters. Configuration validation should be the first step in the training pipeline, executed before any model loading or data preparation.

Theoretical Basis

Configuration validation prevents silent failures in long training runs. Key constraints that must be enforced include:

• Frame count constraint: (frames - 1) % 8 == 0 to satisfy CogVideoX's temporal compression requirements.
• Resolution constraints: CogVideoX-5B requires 480x720 resolution; CogVideoX-2B supports flexible resolutions.
• Precision constraints: fp16 is only numerically stable for the 2B model; the 5B model requires bf16.
• LoRA constraints: Target modules must exist in the transformer architecture; rank must be a positive integer.

By encoding these constraints as Pydantic validators, the system provides immediate, actionable error messages rather than cryptic runtime failures hours into training.

Related Pages

• Implementation:Zai_org_CogVideo_Args_Parse_Args
• Principle:Zai_org_CogVideo_Dataset_Preparation
• Principle:Zai_org_CogVideo_Model_Loading_and_LoRA_Injection
• Heuristic:Zai_org_CogVideo_BF16_FP16_Precision_Selection
• Heuristic:Zai_org_CogVideo_Frame_Count_and_Resolution_Constraints
• Heuristic:Zai_org_CogVideo_Training_Hyperparameter_Defaults