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# Reference: https://github.com/comfyanonymous/ComfyUI/blob/master/comfy_extras/nodes_eps.py
# Credit: https://arxiv.org/abs/2308.15321v6
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
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