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feat: Add Epsilon Scaling node for exposure bias correction (#10132)
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comfy_extras/nodes_eps.py
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60
comfy_extras/nodes_eps.py
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class EpsilonScaling:
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"""
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Implements the Epsilon Scaling method from 'Elucidating the Exposure Bias in Diffusion Models'
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(https://arxiv.org/abs/2308.15321v6).
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This method mitigates exposure bias by scaling the predicted noise during sampling,
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which can significantly improve sample quality. This implementation uses the "uniform schedule"
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recommended by the paper for its practicality and effectiveness.
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL",),
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"scaling_factor": ("FLOAT", {
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"default": 1.005,
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"min": 0.5,
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"max": 1.5,
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"step": 0.001,
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"display": "number"
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}),
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}
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}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "model_patches/unet"
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def patch(self, model, scaling_factor):
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# Prevent division by zero, though the UI's min value should prevent this.
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if scaling_factor == 0:
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scaling_factor = 1e-9
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def epsilon_scaling_function(args):
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"""
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This function is applied after the CFG guidance has been calculated.
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It recalculates the denoised latent by scaling the predicted noise.
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"""
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denoised = args["denoised"]
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x = args["input"]
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noise_pred = x - denoised
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scaled_noise_pred = noise_pred / scaling_factor
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new_denoised = x - scaled_noise_pred
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return new_denoised
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# Clone the model patcher to avoid modifying the original model in place
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model_clone = model.clone()
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model_clone.set_model_sampler_post_cfg_function(epsilon_scaling_function)
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return (model_clone,)
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NODE_CLASS_MAPPINGS = {
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"Epsilon Scaling": EpsilonScaling
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}
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