| |
| |
|
|
| from typing import TYPE_CHECKING |
|
|
| if TYPE_CHECKING: |
| from backend.patcher.unet import UnetPatcher |
|
|
|
|
| class EpsilonScaling: |
| """ |
| Implements the Epsilon Scaling method from 'Elucidating the Exposure Bias in Diffusion Models' |
| |
| This method mitigates exposure bias by scaling the predicted noise during sampling, |
| which can significantly improve sample quality. This implementation uses the "uniform schedule" |
| recommended by the paper for its practicality and effectiveness. |
| """ |
|
|
| @staticmethod |
| def patch(model: "UnetPatcher", scaling_factor: float): |
|
|
| def epsilon_scaling_function(args): |
| """ |
| This function is applied after the CFG guidance has been calculated. |
| It recalculates the denoised latent by scaling the predicted noise. |
| """ |
| denoised = args["denoised"] |
| x = args["input"] |
|
|
| noise_pred = x - denoised |
|
|
| scaled_noise_pred = noise_pred / scaling_factor |
|
|
| new_denoised = x - scaled_noise_pred |
|
|
| return new_denoised |
|
|
| m = model.clone() |
| m.set_model_sampler_post_cfg_function(epsilon_scaling_function) |
| return m |
|
|
