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Enable cfg1 optimization for DualModelGuider with CFGGuider (#14290)
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* Enable cfg1 optimization for DualModelGuider * Fix CFG Override tooltip
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@ -933,9 +933,10 @@ class Guider_DualModel(comfy.samplers.CFGGuider):
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def predict_noise(self, x, timestep, model_options={}, seed=None):
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def predict_noise(self, x, timestep, model_options={}, seed=None):
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positive = self.conds.get("positive", None)
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positive = self.conds.get("positive", None)
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if self.uncond_inner is None: # cfg == 1 or no negative -> single model, cond only
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return comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
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cond = comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
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cond = comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
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# uncond model not loaded (base cfg==1/no negative), or cfg driven to 1.0 this step -> single model, cond only
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if self.uncond_inner is None or (math.isclose(self.cfg, 1.0) and not model_options.get("disable_cfg1_optimization", False)):
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return cond
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uncond_model_options = model_options
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uncond_model_options = model_options
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if "multigpu_clones" in model_options: # TODO: support multigpu instead of just running uncond on a single GPU
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if "multigpu_clones" in model_options: # TODO: support multigpu instead of just running uncond on a single GPU
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@ -1140,7 +1141,7 @@ class CFGOverride(io.ComfyNode):
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return io.Schema(
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return io.Schema(
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node_id="CFGOverride",
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node_id="CFGOverride",
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display_name="CFG Override",
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display_name="CFG Override",
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description="Override cfg to a fixed value over a [start, end] percent slice of the steps. "
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description="Override cfg to a fixed value over a [start, end] percent (sigma) range. "
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"With multiple overrides, the one nearest the sampler wins on overlap.",
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"With multiple overrides, the one nearest the sampler wins on overlap.",
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category="sampling/custom_sampling",
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category="sampling/custom_sampling",
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inputs=[
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inputs=[
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