mirror of
https://github.com/comfyanonymous/ComfyUI.git
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70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
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import numpy as np
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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 loglinear_interp(t_steps, num_steps):
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"""
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Performs log-linear interpolation of a given array of decreasing numbers.
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"""
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xs = np.linspace(0, 1, len(t_steps))
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ys = np.log(t_steps[::-1])
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new_xs = np.linspace(0, 1, num_steps)
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new_ys = np.interp(new_xs, xs, ys)
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interped_ys = np.exp(new_ys)[::-1].copy()
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return interped_ys
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NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582],
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"SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582],
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"SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}
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class AlignYourStepsScheduler(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="AlignYourStepsScheduler",
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category="sampling/custom_sampling/schedulers",
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inputs=[
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io.Combo.Input("model_type", options=["SD1", "SDXL", "SVD"]),
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io.Int.Input("steps", default=10, min=1, max=10000),
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io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
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],
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outputs=[io.Sigmas.Output()],
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)
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def get_sigmas(self, model_type, steps, denoise):
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# Deprecated: use the V3 schema's `execute` method instead of this.
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return AlignYourStepsScheduler().execute(model_type, steps, denoise).result
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@classmethod
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def execute(cls, model_type, steps, denoise) -> io.NodeOutput:
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total_steps = steps
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if denoise < 1.0:
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if denoise <= 0.0:
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return io.NodeOutput(torch.FloatTensor([]))
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total_steps = round(steps * denoise)
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sigmas = NOISE_LEVELS[model_type][:]
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if (steps + 1) != len(sigmas):
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sigmas = loglinear_interp(sigmas, steps + 1)
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sigmas = sigmas[-(total_steps + 1):]
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sigmas[-1] = 0
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return io.NodeOutput(torch.FloatTensor(sigmas))
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class AlignYourStepsExtension(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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AlignYourStepsScheduler,
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]
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async def comfy_entrypoint() -> AlignYourStepsExtension:
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return AlignYourStepsExtension()
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