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# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import torch
import ldm_patched.modules.model_management
import ldm_patched.modules.sample
import ldm_patched.modules.samplers
import ldm_patched.modules.utils
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}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
def patch(self, model, empty_conditioning, neg_scale):
m = model.clone()
nocond = ldm_patched.modules.sample.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 = ldm_patched.modules.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative")
(noise_pred_nocond, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options)
pos = noise_pred_pos - noise_pred_nocond
neg = noise_pred_neg - noise_pred_nocond
perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg
perp_neg = perp * neg_scale
cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)
cfg_result = x - cfg_result
return cfg_result
m.set_model_sampler_cfg_function(cfg_function)
return (m, )
NODE_CLASS_MAPPINGS = {
"PerpNeg": PerpNeg,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PerpNeg": "Perp-Neg",
}
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