59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
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import torch
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import comfy.model_management
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import comfy.sample
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import comfy.samplers
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import comfy.utils
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class PerpNeg:
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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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"clip": ("CLIP", ),
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"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "_for_testing"
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def patch(self, model, clip, neg_scale):
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m = model.clone()
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tokens = clip.tokenize("")
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nocond, nocond_pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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nocond = [[nocond, {"pooled_output": nocond_pooled}]]
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nocond = comfy.sample.convert_cond(nocond)
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def cfg_function(args):
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model = args["model"]
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noise_pred_pos = args["cond_denoised"]
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noise_pred_neg = args["uncond_denoised"]
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cond_scale = args["cond_scale"]
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x = args["input"]
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sigma = args["sigma"]
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model_options = args["model_options"]
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(noise_pred_nocond, _) = comfy.samplers.calc_cond_uncond_batch(model, nocond, None, x, sigma, model_options)
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pos = noise_pred_pos - noise_pred_nocond
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neg = noise_pred_neg - noise_pred_nocond
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perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg
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perp_neg = perp * neg_scale
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cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)
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cfg_result = x - cfg_result
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return cfg_result
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m.set_model_sampler_cfg_function(cfg_function)
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return (m, )
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NODE_CLASS_MAPPINGS = {
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"PerpNeg": PerpNeg,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"PerpNeg": "Perp-Neg",
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}
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