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