mirror of
https://github.com/comfyanonymous/ComfyUI.git
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113 lines
4.4 KiB
Python
113 lines
4.4 KiB
Python
from __future__ import annotations
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import math
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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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from comfy_api.v3 import io
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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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return noise_pred_nocond + cond_scale*(pos - perp_neg)
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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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if not model_options.get("disable_cfg1_optimization", False):
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if math.isclose(self.neg_scale, 0.0):
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negative_cond = None
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if math.isclose(self.cfg, 1.0):
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empty_cond = None
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conds = [positive_cond, negative_cond, empty_cond]
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out = comfy.samplers.calc_cond_batch(self.inner_model, conds, x, timestep, model_options)
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# Apply pre_cfg_functions since sampling_function() is skipped
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for fn in model_options.get("sampler_pre_cfg_function", []):
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args = {"conds":conds, "conds_out": out, "cond_scale": self.cfg, "timestep": timestep,
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"input": x, "sigma": timestep, "model": self.inner_model, "model_options": model_options}
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out = fn(args)
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noise_pred_pos, noise_pred_neg, noise_pred_empty = out
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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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"cond_scale": self.cfg,
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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(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="PerpNegGuider_V3",
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category="_for_testing",
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inputs=[
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io.Model.Input(id="model"),
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io.Conditioning.Input(id="positive"),
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io.Conditioning.Input(id="negative"),
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io.Conditioning.Input(id="empty_conditioning"),
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io.Float.Input(id="cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
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io.Float.Input(id="neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
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],
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outputs=[
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io.Guider.Output(),
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],
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is_experimental=True,
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)
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@classmethod
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def execute(cls, 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 io.NodeOutput(guider)
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NODES_LIST = [PerpNegGuider]
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