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97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
from abc import abstractmethod, ABC
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import torch
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class SchedulerInterface(ABC):
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"""
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Base class for diffusion noise schedule.
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"""
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alphas_cumprod: torch.Tensor # [T], alphas for defining the noise schedule
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@abstractmethod
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def add_noise(
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self, clean_latent: torch.Tensor, noise: torch.Tensor, timestep: torch.Tensor
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):
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"""
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Diffusion forward corruption process.
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Input:
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- clean_latent: the clean latent with shape [B, C, H, W]
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- noise: the noise with shape [B, C, H, W]
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- timestep: the timestep with shape [B]
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Output: the corrupted latent with shape [B, C, H, W]
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"""
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pass
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def convert_x0_to_noise(
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self, x0: torch.Tensor, xt: torch.Tensor, timestep: torch.Tensor
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) -> torch.Tensor:
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"""
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Convert the diffusion network's x0 prediction to noise predidction.
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x0: the predicted clean data with shape [B, C, H, W]
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xt: the input noisy data with shape [B, C, H, W]
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timestep: the timestep with shape [B]
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noise = (xt-sqrt(alpha_t)*x0) / sqrt(beta_t) (eq 11 in https://arxiv.org/abs/2311.18828)
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"""
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# use higher precision for calculations
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original_dtype = x0.dtype
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x0, xt, alphas_cumprod = map(
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lambda x: x.double().to(x0.device), [x0, xt, self.alphas_cumprod]
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)
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alpha_prod_t = alphas_cumprod[timestep].reshape(-1, 1, 1, 1)
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beta_prod_t = 1 - alpha_prod_t
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noise_pred = (xt - alpha_prod_t ** (0.5) * x0) / beta_prod_t ** (0.5)
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return noise_pred.to(original_dtype)
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def convert_noise_to_x0(
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self, noise: torch.Tensor, xt: torch.Tensor, timestep: torch.Tensor
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) -> torch.Tensor:
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"""
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Convert the diffusion network's noise prediction to x0 predidction.
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noise: the predicted noise with shape [B, C, H, W]
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xt: the input noisy data with shape [B, C, H, W]
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timestep: the timestep with shape [B]
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x0 = (x_t - sqrt(beta_t) * noise) / sqrt(alpha_t) (eq 11 in https://arxiv.org/abs/2311.18828)
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"""
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# use higher precision for calculations
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original_dtype = noise.dtype
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noise, xt, alphas_cumprod = map(
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lambda x: x.double().to(noise.device), [noise, xt, self.alphas_cumprod]
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)
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alpha_prod_t = alphas_cumprod[timestep].reshape(-1, 1, 1, 1)
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beta_prod_t = 1 - alpha_prod_t
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x0_pred = (xt - beta_prod_t ** (0.5) * noise) / alpha_prod_t ** (0.5)
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return x0_pred.to(original_dtype)
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def convert_velocity_to_x0(
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self, velocity: torch.Tensor, xt: torch.Tensor, timestep: torch.Tensor
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) -> torch.Tensor:
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"""
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Convert the diffusion network's velocity prediction to x0 predidction.
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velocity: the predicted noise with shape [B, C, H, W]
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xt: the input noisy data with shape [B, C, H, W]
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timestep: the timestep with shape [B]
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v = sqrt(alpha_t) * noise - sqrt(beta_t) x0
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noise = (xt-sqrt(alpha_t)*x0) / sqrt(beta_t)
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given v, x_t, we have
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x0 = sqrt(alpha_t) * x_t - sqrt(beta_t) * v
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see derivations https://chatgpt.com/share/679fb6c8-3a30-8008-9b0e-d1ae892dac56
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"""
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# use higher precision for calculations
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original_dtype = velocity.dtype
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velocity, xt, alphas_cumprod = map(
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lambda x: x.double().to(velocity.device),
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[velocity, xt, self.alphas_cumprod],
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)
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alpha_prod_t = alphas_cumprod[timestep].reshape(-1, 1, 1, 1)
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beta_prod_t = 1 - alpha_prod_t
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x0_pred = (alpha_prod_t**0.5) * xt - (beta_prod_t**0.5) * velocity
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return x0_pred.to(original_dtype)
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