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