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90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
from __future__ import annotations
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import torch
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from comfy_api.latest import io
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# https://github.com/WeichenFan/CFG-Zero-star
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def optimized_scale(positive, negative):
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positive_flat = positive.reshape(positive.shape[0], -1)
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negative_flat = negative.reshape(negative.shape[0], -1)
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# Calculate dot production
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dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
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# Squared norm of uncondition
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squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
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# st_star = v_cond^T * v_uncond / ||v_uncond||^2
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st_star = dot_product / squared_norm
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return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1))
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class CFGZeroStar(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="CFGZeroStar_V3",
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category="advanced/guidance",
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inputs=[
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io.Model.Input("model"),
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],
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outputs=[io.Model.Output(display_name="patched_model")],
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)
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@classmethod
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def execute(cls, model) -> io.NodeOutput:
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m = model.clone()
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def cfg_zero_star(args):
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guidance_scale = args['cond_scale']
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x = args['input']
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cond_p = args['cond_denoised']
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uncond_p = args['uncond_denoised']
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out = args["denoised"]
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alpha = optimized_scale(x - cond_p, x - uncond_p)
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return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha)
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m.set_model_sampler_post_cfg_function(cfg_zero_star)
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return io.NodeOutput(m)
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class CFGNorm(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="CFGNorm_V3",
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category="advanced/guidance",
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inputs=[
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io.Model.Input("model"),
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io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01),
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],
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outputs=[io.Model.Output(display_name="patched_model")],
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is_experimental=True,
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)
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@classmethod
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def execute(cls, model, strength) -> io.NodeOutput:
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m = model.clone()
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def cfg_norm(args):
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cond_p = args['cond_denoised']
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pred_text_ = args["denoised"]
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norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True)
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norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True)
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scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0)
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return pred_text_ * scale * strength
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m.set_model_sampler_post_cfg_function(cfg_norm)
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return io.NodeOutput(m)
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NODES_LIST: list[type[io.ComfyNode]] = [
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CFGNorm,
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CFGZeroStar,
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]
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