| import torch |
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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): |
| self.sigma_max = sigma_max |
| self.sigma_min = sigma_min |
| self.rho = rho |
| self.set_timesteps(num_inference_steps) |
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| def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, **kwargs): |
| ramp = torch.linspace(1-denoising_strength, 1, num_inference_steps) |
| min_inv_rho = torch.pow(torch.tensor((self.sigma_min,)), (1 / self.rho)) |
| max_inv_rho = torch.pow(torch.tensor((self.sigma_max,)), (1 / self.rho)) |
| self.sigmas = torch.pow(max_inv_rho + ramp * (min_inv_rho - max_inv_rho), self.rho) |
| self.timesteps = torch.log(self.sigmas) * 0.25 |
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| def step(self, model_output, timestep, sample, to_final=False): |
| timestep_id = torch.argmin((self.timesteps - timestep).abs()) |
| sigma = self.sigmas[timestep_id] |
| sample *= (sigma*sigma + 1).sqrt() |
| estimated_sample = -sigma / (sigma*sigma + 1).sqrt() * model_output + 1 / (sigma*sigma + 1) * sample |
| if to_final or timestep_id + 1 >= len(self.timesteps): |
| prev_sample = estimated_sample |
| else: |
| sigma_ = self.sigmas[timestep_id + 1] |
| derivative = 1 / sigma * (sample - estimated_sample) |
| prev_sample = sample + derivative * (sigma_ - sigma) |
| prev_sample /= (sigma_*sigma_ + 1).sqrt() |
| return prev_sample |
| |
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| def return_to_timestep(self, timestep, sample, sample_stablized): |
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| pass |
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| |
| def add_noise(self, original_samples, noise, timestep): |
| timestep_id = torch.argmin((self.timesteps - timestep).abs()) |
| sigma = self.sigmas[timestep_id] |
| sample = (original_samples + noise * sigma) / (sigma*sigma + 1).sqrt() |
| return sample |
| |
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| def training_target(self, sample, noise, timestep): |
| timestep_id = torch.argmin((self.timesteps - timestep).abs()) |
| sigma = self.sigmas[timestep_id] |
| target = (-(sigma*sigma + 1).sqrt() / sigma + 1 / (sigma*sigma + 1).sqrt() / sigma) * sample + 1 / (sigma*sigma + 1).sqrt() * noise |
| return target |
| |
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| def training_weight(self, timestep): |
| timestep_id = torch.argmin((self.timesteps - timestep).abs()) |
| sigma = self.sigmas[timestep_id] |
| weight = (1 + sigma*sigma).sqrt() / sigma |
| return weight |
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|
