43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import torch
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from tqdm import trange
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class SamplerInversedEulerNode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {}}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "sampling/custom_sampling/samplers"
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FUNCTION = "get_sampler"
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def get_sampler(self):
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@torch.no_grad()
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def sample_inversed_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
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"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
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extra_args = {} if extra_args is None else extra_args
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s_in = x.new_ones([x.shape[0]])
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for i in trange(1, len(sigmas), disable=disable):
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sigma_in = sigmas[i-1]
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if i == 1:
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sigma_t = sigmas[i]
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else:
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sigma_t = sigma_in
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denoised = model(x, sigma_t * s_in, **extra_args)
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if i == 1:
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d = (x - denoised) / (2 * sigmas[i])
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else:
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d = (x - denoised) / sigmas[i-1]
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dt = sigmas[i] - sigmas[i-1]
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x = x + d * dt
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if callback is not None:
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callback(
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{'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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return x
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return (sample_inversed_euler, )
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