39 lines
1.7 KiB
Python
39 lines
1.7 KiB
Python
import torch
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class simpleDynamicCFG:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "model_patches"
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def patch(self, model):
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top_k = 0.25
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reference_cfg = 8
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def linear_cfg(args):
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cond = args["cond"]
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cond_scale = args["cond_scale"]
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uncond = args["uncond"]
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input_x = args["input"]
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target_intensity = 0.8/reference_cfg*cond_scale
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denoised_tmp = input_x-(uncond + reference_cfg*(cond - uncond))
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for b in range(len(denoised_tmp)):
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for c in range(len(denoised_tmp[b])):
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channel = denoised_tmp[b][c]
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max_values = torch.topk(channel, k=int(len(channel)*top_k), largest=True).values
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min_values = torch.topk(channel, k=int(len(channel)*top_k), largest=False).values
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max_val = torch.mean(max_values).item()
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min_val = torch.mean(min_values).item()
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denoised_range = (max_val+abs(min_val))/2
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tmp_scale = reference_cfg*target_intensity/denoised_range
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denoised_tmp[b][c] = uncond[b][c] + tmp_scale * (cond[b][c] - uncond[b][c])
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return denoised_tmp
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m = model.clone()
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m.set_model_sampler_cfg_function(linear_cfg, disable_cfg1_optimization=True)
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return (m, )
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#It would have made more sense to make the min_val absolute before the mean but I've tried that way and since it's an arbitrary goal I'll leave it like that.
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