ic_thresholding_ratio": 0.995,
  "num_train_timesteps": 1000,
  "prediction_type": "epsilon",
  "sample_max_value": 1.0,
  "steps_offset": 0,
  "thresholding": false,
  "timestep_spacing": "leading",
  "trained_betas": null,
  "variance_type": "fixed_small"
} 💡 Unlike a model, a scheduler does not have trainable weights and is parameter-free! Some of the most important parameters are: num_train_timesteps: the length of the denoising process or,