56 lines
2.3 KiB
Python
56 lines
2.3 KiB
Python
import torch
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class uncondZeroNode:
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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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"scale": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step": 0.01, "round": 0.01}),
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"method":(["uncond_zero","rescale_cfg"],),
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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, scale, method):
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sigmin = model.model.model_sampling.sigma(model.model.model_sampling.timestep(model.model.model_sampling.sigma_min)).item()
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sigmax = model.model.model_sampling.sigma(model.model.model_sampling.timestep(model.model.model_sampling.sigma_max)).item()
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def uncond_zero(args):
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cond = args["cond_denoised"]
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x_orig = args["input"]
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x_orig -= x_orig.mean()
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cond -= cond.mean()
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return x_orig - (cond / cond.std() ** .5) * scale
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new_scale = 1 / (model.model.latent_format.scale_factor * 8)
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#Taken and adapted from comfy_extras/nodes_model_advanced
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def rescale_cfg(args):
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x_orig = args["input"]
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x_orig -= x_orig.mean()
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cond = args["cond_denoised"]
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cond -= cond.mean()
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cond = x_orig - (cond / cond.std() ** .5) * scale
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sigma = args["sigma"]
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sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
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#rescale cfg has to be done on v-pred model output
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x = x_orig / (sigma * sigma + 1.0)
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uncond = x / sigma
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cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
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#rescalecfg
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x_cfg = uncond + new_scale * (cond - uncond)
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ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
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ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
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x_rescaled = x_cfg * (ro_pos / ro_cfg)
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return x_orig - (x - x_rescaled * sigma / (sigma * sigma + 1.0) ** 0.5)
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m = model.clone()
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m.set_model_sampler_cfg_function({"uncond_zero":uncond_zero,"rescale_cfg":rescale_cfg}[method])
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return (m, ) |