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53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
import torch, math
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class ContinuousODEScheduler():
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def __init__(self, num_inference_steps=100, sigma_max=700.0, sigma_min=0.002, rho=7.0):
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self.sigma_max = sigma_max
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self.sigma_min = sigma_min
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self.rho = rho
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self.init_noise_sigma = math.sqrt(sigma_max*sigma_max + 1)
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self.set_timesteps(num_inference_steps)
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def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0):
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ramp = torch.linspace(0, denoising_strength, num_inference_steps)
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min_inv_rho = torch.pow(torch.tensor((self.sigma_min,)), (1 / self.rho))
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max_inv_rho = torch.pow(torch.tensor((self.sigma_max,)), (1 / self.rho))
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self.sigmas = torch.pow(max_inv_rho + ramp * (min_inv_rho - max_inv_rho), self.rho)
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self.timesteps = torch.log(self.sigmas) * 0.25
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def step(self, model_output, timestep, sample, to_final=False):
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timestep_id = torch.argmin((self.timesteps - timestep).abs())
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sigma = self.sigmas[timestep_id]
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estimated_sample = -sigma / (sigma*sigma + 1).sqrt() * model_output + 1 / (sigma*sigma + 1) * sample
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if to_final or timestep_id + 1 >= len(self.timesteps):
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prev_sample = estimated_sample
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else:
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dt = self.sigmas[timestep_id + 1] - sigma
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derivative = 1 / sigma * (sample - estimated_sample)
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prev_sample = sample + derivative * dt
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return prev_sample
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def scale_model_input(self, sample, timestep):
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timestep_id = torch.argmin((self.timesteps - timestep).abs())
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sigma = self.sigmas[timestep_id]
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sample = sample / (sigma*sigma + 1).sqrt()
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return sample
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def return_to_timestep(self, timestep, sample, sample_stablized):
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# This scheduler doesn't support this function.
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pass
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def add_noise(self, original_samples, noise, timestep):
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timestep_id = torch.argmin((self.timesteps - timestep).abs())
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sigma = self.sigmas[timestep_id]
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sample = original_samples + noise * sigma
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return sample
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