mirror of
https://github.com/comfyanonymous/ComfyUI.git
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107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
import torch
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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def project(v0, v1):
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v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
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v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
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v0_orthogonal = v0 - v0_parallel
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return v0_parallel, v0_orthogonal
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class APG(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="APG",
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display_name="Adaptive Projected Guidance",
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category="sampling/custom_sampling",
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inputs=[
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io.Model.Input("model"),
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io.Float.Input(
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"eta",
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default=1.0,
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min=-10.0,
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max=10.0,
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step=0.01,
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tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.",
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),
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io.Float.Input(
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"norm_threshold",
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default=5.0,
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min=0.0,
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max=50.0,
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step=0.1,
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tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.",
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),
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io.Float.Input(
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"momentum",
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default=0.0,
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min=-5.0,
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max=1.0,
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step=0.01,
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tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.",
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),
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],
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outputs=[io.Model.Output()],
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)
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@classmethod
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def execute(cls, model, eta, norm_threshold, momentum) -> io.NodeOutput:
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running_avg = 0
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prev_sigma = None
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def pre_cfg_function(args):
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nonlocal running_avg, prev_sigma
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if len(args["conds_out"]) == 1: return args["conds_out"]
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cond = args["conds_out"][0]
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uncond = args["conds_out"][1]
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sigma = args["sigma"][0]
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cond_scale = args["cond_scale"]
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if prev_sigma is not None and sigma > prev_sigma:
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running_avg = 0
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prev_sigma = sigma
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guidance = cond - uncond
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if momentum != 0:
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if not torch.is_tensor(running_avg):
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running_avg = guidance
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else:
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running_avg = momentum * running_avg + guidance
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guidance = running_avg
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if norm_threshold > 0:
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guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True)
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scale = torch.minimum(
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torch.ones_like(guidance_norm),
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norm_threshold / guidance_norm
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)
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guidance = guidance * scale
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guidance_parallel, guidance_orthogonal = project(guidance, cond)
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modified_guidance = guidance_orthogonal + eta * guidance_parallel
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modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale
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return [modified_cond, uncond] + args["conds_out"][2:]
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m = model.clone()
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m.set_model_sampler_pre_cfg_function(pre_cfg_function)
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return io.NodeOutput(m)
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class ApgExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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APG,
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]
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async def comfy_entrypoint() -> ApgExtension:
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return ApgExtension()
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