mirror of
https://github.com/comfyanonymous/ComfyUI.git
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d71cc1c8f2
* Move detection category under image category * Add missing categories * Move detection nodes to detection category * Move save nodes to image root catefory * Rename postprocessors * Move mask category under image * Move guiders category to parent level at root of sampling category * Move custom_sampling category to parent level at the root of sampling category * Modify description of LoRA loaders * Fix node id SolidMask * Move VOID Quadmask under image/mask * Group compositing nodes under image/compositing * Move load image as mask to image category for consistency with other load image nodes * Align display name with Load Checkpoint * Move dataset category under training category * Rename Number Convert to Conver Number (verb first) * Rename Canny node * Revert wanBlockSwap + description * Add description to RemoveBackground node * Revert category update of dataset
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
# from https://github.com/bebebe666/OptimalSteps
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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 = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001],
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"Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001],
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"Chroma": [0.992, 0.99, 0.988, 0.985, 0.982, 0.978, 0.973, 0.968, 0.961, 0.953, 0.943, 0.931, 0.917, 0.9, 0.881, 0.858, 0.832, 0.802, 0.769, 0.731, 0.69, 0.646, 0.599, 0.55, 0.501, 0.451, 0.402, 0.355, 0.311, 0.27, 0.232, 0.199, 0.169, 0.143, 0.12, 0.101, 0.084, 0.07, 0.058, 0.048, 0.001],
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}
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class OptimalStepsScheduler(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="OptimalStepsScheduler",
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category="sampling/schedulers",
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inputs=[
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io.Combo.Input("model_type", options=["FLUX", "Wan", "Chroma"]),
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io.Int.Input("steps", default=20, min=3, max=1000),
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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=[
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io.Sigmas.Output(),
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],
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)
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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 OptimalStepsExtension(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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OptimalStepsScheduler,
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]
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async def comfy_entrypoint() -> OptimalStepsExtension:
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return OptimalStepsExtension()
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