Learn from pre-computed teacher denoising trajectories.
File: KD.py | Reference: Luhman & Luhman, 2021
MSE loss between student prediction and teacher output: L = ||f(x_t, t) - x_0^teacher||²
Data Requirements:
- Single-step:
{"real": clean, "noise": noise, "condition": cond} - Multi-step:
{"real": clean, "path": [B, steps, C, H, W], "condition": cond}
Key Parameters:
student_sample_steps: Number of student stepssample_t_cfg.t_list: Timesteps (must align with path)
Configs: WanT2V/config_kd.py, SDXL/config_kd.py, CogVideoX/config_kd.py
File: KD.py | Reference: Yin et al., 2024
KD for causal video models with inhomogeneous timesteps and autoregressive generation.
Data Requirements:
{"real": [B,C,T,H,W], "path": [B,steps,C,T,H,W], "condition": cond}
Key Parameters:
context_noise: Noise for cached context- See also key parameters of KD above
Configs: WanT2V/config_kd_path.py