| | |
| | import numpy as np |
| | import torch |
| |
|
| | 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: |
| | @classmethod |
| | def INPUT_TYPES(s): |
| | return {"required": |
| | {"model_type": (["SD1", "SDXL", "SVD"], ), |
| | "steps": ("INT", {"default": 10, "min": 1, "max": 10000}), |
| | "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), |
| | } |
| | } |
| | RETURN_TYPES = ("SIGMAS",) |
| | CATEGORY = "sampling/custom_sampling/schedulers" |
| |
|
| | FUNCTION = "get_sigmas" |
| |
|
| | def get_sigmas(self, model_type, steps, denoise): |
| | total_steps = steps |
| | if denoise < 1.0: |
| | if denoise <= 0.0: |
| | return (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 (torch.FloatTensor(sigmas), ) |
| |
|
| | NODE_CLASS_MAPPINGS = { |
| | "AlignYourStepsScheduler": AlignYourStepsScheduler, |
| | } |
| |
|
