130 lines
5.2 KiB
Python
130 lines
5.2 KiB
Python
import torch
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import comfy.model_management
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import comfy.sampler_helpers
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import comfy.samplers
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import comfy.utils
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import node_helpers
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def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale):
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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 = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos
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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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return cfg_result
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#TODO: This node should be removed, it has been replaced with PerpNegGuider
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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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"empty_conditioning": ("CONDITIONING", ),
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"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
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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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DEPRECATED = True
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def patch(self, model, empty_conditioning, neg_scale):
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m = model.clone()
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nocond = comfy.sampler_helpers.convert_cond(empty_conditioning)
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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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nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative")
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(noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options)
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cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale)
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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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class Guider_PerpNeg(comfy.samplers.CFGGuider):
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def set_conds(self, positive, negative, empty_negative_prompt):
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empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"})
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self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative})
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def set_cfg(self, cfg, neg_scale):
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self.cfg = cfg
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self.neg_scale = neg_scale
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def predict_noise(self, x, timestep, model_options={}, seed=None):
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# in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg
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# but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly
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positive_cond = self.conds.get("positive", None)
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negative_cond = self.conds.get("negative", None)
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empty_cond = self.conds.get("empty_negative_prompt", None)
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(noise_pred_pos, noise_pred_neg, noise_pred_empty) = \
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comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options)
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cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg)
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# normally this would be done in cfg_function, but we skipped
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# that for efficiency: we can compute the noise predictions in
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# a single call to calc_cond_batch() (rather than two)
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# so we replicate the hook here
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for fn in model_options.get("sampler_post_cfg_function", []):
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args = {
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"denoised": cfg_result,
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"cond": positive_cond,
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"uncond": negative_cond,
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"model": self.inner_model,
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"uncond_denoised": noise_pred_neg,
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"cond_denoised": noise_pred_pos,
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"sigma": timestep,
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"model_options": model_options,
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"input": x,
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# not in the original call in samplers.py:cfg_function, but made available for future hooks
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"empty_cond": empty_cond,
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"empty_cond_denoised": noise_pred_empty,}
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cfg_result = fn(args)
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return cfg_result
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class PerpNegGuider:
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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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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"empty_conditioning": ("CONDITIONING", ),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
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"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("GUIDER",)
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FUNCTION = "get_guider"
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CATEGORY = "_for_testing"
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def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale):
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guider = Guider_PerpNeg(model)
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guider.set_conds(positive, negative, empty_conditioning)
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guider.set_cfg(cfg, neg_scale)
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return (guider,)
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NODE_CLASS_MAPPINGS = {
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"PerpNeg": PerpNeg,
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"PerpNegGuider": PerpNegGuider,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)",
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}
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