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46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
import torch
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# https://github.com/WeichenFan/CFG-Zero-star
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def optimized_scale(positive, negative):
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positive_flat = positive.reshape(positive.shape[0], -1)
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negative_flat = negative.reshape(negative.shape[0], -1)
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# Calculate dot production
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dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
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# Squared norm of uncondition
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squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
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# st_star = v_cond^T * v_uncond / ||v_uncond||^2
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st_star = dot_product / squared_norm
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return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1))
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class CFGZeroStar:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"model": ("MODEL",),
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}}
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RETURN_TYPES = ("MODEL",)
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RETURN_NAMES = ("patched_model",)
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FUNCTION = "patch"
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CATEGORY = "advanced/guidance"
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def patch(self, model):
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m = model.clone()
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def cfg_zero_star(args):
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guidance_scale = args['cond_scale']
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x = args['input']
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cond_p = args['cond_denoised']
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uncond_p = args['uncond_denoised']
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out = args["denoised"]
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alpha = optimized_scale(x - cond_p, x - uncond_p)
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return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha)
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m.set_model_sampler_post_cfg_function(cfg_zero_star)
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return (m, )
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NODE_CLASS_MAPPINGS = {
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"CFGZeroStar": CFGZeroStar
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}
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