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Fine-Tuning Methods

Standard diffusion training with denoising score matching (DSM) / flow matching.

SFT (Supervised Fine-Tuning)

File: sft.py | References: Ho et al., 2020, Song et al., 2020, Lipman et al., 2022, Albergo et al., 2023

Standard DSM / flow matching training: L = ||f(x_t, t) - target||²

Key Parameters:

  • cond_dropout_prob: Condition dropout probability
  • guidance_scale: CFG scale for inference
  • sample_t_cfg: Config of the distribution for sampling t

Configs: Flux/config_sft.py, QwenImage/config_sft.py, WanT2V/config_sft.py, CosmosPredict2/config_sft.py, SD15/config_sft.py, SDXL/config_sft.py, EDM/config_sft_edm_cifar10.py, EDM/config_sft_edm_in64.py, EDM2/config_sft_s.py, EDM2/config_sft_xl.py, DiT/config_sft_dit_xl.py

CausalSFT

File: sft.py | Reference: Chen et al., 2024

SFT for causal video models with inhomogeneous timesteps per frame chunk.

Key Parameters:

  • context_noise: Noise level for context frames
  • Inherits also the key parameters of SFT above

Configs: WanT2V/config_sft_causal.py, WanI2V/config_sft_causal_14b.py, WanI2V/config_sft_causal_wan22_5b.py, WanV2V/config_sft_causal.py