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52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
import torch
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class FlowMatchScheduler():
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def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003/1.002):
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self.num_train_timesteps = num_train_timesteps
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self.shift = shift
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self.sigma_max = sigma_max
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self.sigma_min = sigma_min
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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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sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength
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self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps)
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self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas)
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self.timesteps = self.sigmas * self.num_train_timesteps
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def step(self, model_output, timestep, sample, to_final=False):
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if isinstance(timestep, torch.Tensor):
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timestep = timestep.cpu()
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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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if to_final or timestep_id + 1 >= len(self.timesteps):
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sigma_ = 0
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else:
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sigma_ = self.sigmas[timestep_id + 1]
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prev_sample = sample + model_output * (sigma_ - sigma)
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return prev_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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if isinstance(timestep, torch.Tensor):
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timestep = timestep.cpu()
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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 = (1 - sigma) * original_samples + sigma * noise
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return sample
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def training_target(self, sample, noise, timestep):
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target = noise - sample
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return target
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