200 lines
7.1 KiB
Python
200 lines
7.1 KiB
Python
import comfy.samplers
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import comfy_extras.nodes_perpneg
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import torch
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cos = torch.nn.CosineSimilarity(dim=1)
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class Guider_AdaptiveGuidance(comfy.samplers.CFGGuider):
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threshold_timestep = 0
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uz_scale = 0.0
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def set_cfg(self, cfg):
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self.cfg = cfg
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def set_threshold(self, threshold):
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self.threshold = threshold
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def set_uncond_zero_scale(self, scale):
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self.uz_scale = scale
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def zero_cond(self, args):
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cond = args["cond_denoised"]
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x = args["input"]
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x -= x.mean()
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cond -= cond.mean()
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return x - (cond / cond.std() ** 0.5) * self.uz_scale
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def predict_noise(self, x, timestep, model_options={}, seed=None):
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cond = self.conds.get("positive")
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uncond = self.conds.get("negative")
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ts = timestep[0].item()
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if self.threshold_timestep > ts or self.cfg == 1.0:
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if self.uz_scale > 0.0:
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model_options = model_options.copy()
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model_options["sampler_cfg_function"] = self.zero_cond
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return comfy.samplers.sampling_function(
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self.inner_model, x, timestep, uncond, cond, 1.0, model_options=model_options, seed=seed
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)
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self.threshold_timestep = 0
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uncond_pred, cond_pred = comfy.samplers.calc_cond_batch(
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self.inner_model, [uncond, cond], x, timestep, model_options
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)
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if not self.threshold >= 1.0:
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# Is this reshape correct? It at least gives a scalar value...
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sim = cos(cond_pred.reshape(1, -1), uncond_pred.reshape(1, -1)).item()
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if sim >= self.threshold:
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print(f"\nAdaptiveGuidance: Cosine similarity {sim:.4f} exceeds threshold, setting CFG to 1.0")
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self.threshold_timestep = ts
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return comfy.samplers.cfg_function(
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self.inner_model,
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cond_pred,
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uncond_pred,
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self.cfg,
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x,
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timestep,
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model_options=model_options,
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cond=cond,
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uncond=uncond,
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)
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class AdaptiveGuidanceGuider:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL",),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"threshold": ("FLOAT", {"default": 0.990, "min": 0.90, "max": 1.0, "step": 0.001, "round": 0.001}),
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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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},
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"optional": {"uncond_zero_scale": ("FLOAT", {"default": 0.0, "max": 2.0, "step": 0.01})},
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}
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RETURN_TYPES = ("GUIDER",)
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FUNCTION = "get_guider"
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CATEGORY = "sampling/custom_sampling/guiders"
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def get_guider(self, model, positive, negative, threshold, cfg, uncond_zero_scale=0.0):
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g = Guider_AdaptiveGuidance(model)
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g.set_conds(positive, negative)
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g.set_threshold(threshold)
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g.set_uncond_zero_scale(uncond_zero_scale)
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g.set_cfg(cfg)
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return (g,)
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class Guider_PerpNegAG(comfy_extras.nodes_perpneg.Guider_PerpNeg):
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threshold_timestep = 0
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uz_scale = 0.0
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def set_threshold(self, threshold):
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self.threshold = threshold
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def set_uncond_zero_scale(self, scale):
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self.uz_scale = scale
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def zero_cond(self, args):
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cond = args["cond_denoised"]
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x = args["input"]
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x -= x.mean()
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cond -= cond.mean()
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return x - (cond / cond.std() ** 0.5) * self.uz_scale
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def predict_noise(self, x, timestep, model_options={}, seed=None):
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cond = self.conds.get("positive")
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uncond = self.conds.get("negative")
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ts = timestep[0].item()
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if self.threshold_timestep > ts or self.cfg == 1.0:
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if self.uz_scale > 0.0:
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model_options = model_options.copy()
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model_options["sampler_cfg_function"] = self.zero_cond
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return comfy.samplers.sampling_function(
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self.inner_model, x, timestep, uncond, cond, 1.0, model_options=model_options, seed=seed
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)
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self.threshold_timestep = 0
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# From comfy_extras.nodes_perpneg - Guider_PerpNeg
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# No need for calculating perp-neg when skipping negative
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empty_cond = self.conds.get("empty_negative_prompt")
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(cond_pred, uncond_pred, empty_cond_pred) = comfy.samplers.calc_cond_batch(
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self.inner_model, [cond, uncond, empty_cond], x, timestep, model_options
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)
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cfg_result = comfy_extras.nodes_perpneg.perp_neg(
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x, cond_pred, uncond_pred, empty_cond_pred, self.neg_scale, self.cfg
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)
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if not self.threshold >= 1.0:
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sim = cos(cond_pred.reshape(1, -1), uncond_pred.reshape(1, -1)).item()
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if sim >= self.threshold:
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print(f"\nPerpNegAG: Cosine similarity {sim:.4f} exceeds threshold, setting CFG to 1.0")
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self.threshold_timestep = ts
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# From comfy_extras.nodes_perpneg - Guider_PerpNeg
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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": cond,
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"uncond": uncond,
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"model": self.inner_model,
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"uncond_denoised": uncond_pred,
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"cond_denoised": cond_pred,
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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": empty_cond_pred,
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}
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cfg_result = fn(args)
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return cfg_result
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class PerpNegAGGuider:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"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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"threshold": ("FLOAT", {"default": 0.990, "min": 0.90, "max": 1.0, "step": 0.001, "round": 0.001}),
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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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"optional": {"uncond_zero_scale": ("FLOAT", {"default": 0.0, "max": 2.0, "step": 0.01})},
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}
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RETURN_TYPES = ("GUIDER",)
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FUNCTION = "get_guider"
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CATEGORY = "sampling/custom_sampling/guiders"
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def get_guider(
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self, model, positive, negative, empty_conditioning, threshold, cfg, neg_scale, uncond_zero_scale=0.0
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):
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g = Guider_PerpNegAG(model)
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g.set_conds(positive, negative, empty_conditioning)
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g.set_threshold(threshold)
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g.set_uncond_zero_scale(uncond_zero_scale)
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g.set_cfg(cfg, neg_scale)
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return (g,)
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NODE_CLASS_MAPPINGS = {
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"AdaptiveGuidance": AdaptiveGuidanceGuider,
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"PerpNegAdaptiveGuidanceGuider": PerpNegAGGuider,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AdaptiveGuidance": "AdaptiveGuider",
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"PerpNegAdaptiveGuidanceGuider": "PerpNegAdaptiveGuider",
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}
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