180 lines
6.7 KiB
Python
180 lines
6.7 KiB
Python
import math
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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import torch
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from ..configuration_utils import ConfigMixin, register_to_config
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from ..utils import BaseOutput
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from ..utils.torch_utils import randn_tensor
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from .scheduling_utils import SchedulerMixin
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# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
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def betas_for_alpha_bar(
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num_diffusion_timesteps,
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max_beta=0.999,
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alpha_transform_type="cosine",
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):
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"""
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Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
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(1-beta) over time from t = [0,1].
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Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
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to that part of the diffusion process.
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Args:
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num_diffusion_timesteps (`int`): the number of betas to produce.
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max_beta (`float`): the maximum beta to use; use values lower than 1 to
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prevent singularities.
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alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
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Choose from `cosine` or `exp`
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Returns:
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betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
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"""
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if alpha_transform_type == "cosine":
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def alpha_bar_fn(t):
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return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
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elif alpha_transform_type == "exp":
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def alpha_bar_fn(t):
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return math.exp(t * -12.0)
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else:
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raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}")
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betas = []
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for i in range(num_diffusion_timesteps):
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t1 = i / num_diffusion_timesteps
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t2 = (i + 1) / num_diffusion_timesteps
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betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
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return torch.tensor(betas, dtype=torch.float32)
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@dataclass
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class ConsistencyDecoderSchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's `step` function.
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Args:
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prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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"""
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prev_sample: torch.Tensor
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class ConsistencyDecoderScheduler(SchedulerMixin, ConfigMixin):
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order = 1
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@register_to_config
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def __init__(
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self,
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num_train_timesteps: int = 1024,
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sigma_data: float = 0.5,
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):
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betas = betas_for_alpha_bar(num_train_timesteps)
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alphas = 1.0 - betas
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alphas_cumprod = torch.cumprod(alphas, dim=0)
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self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
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self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
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sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)
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sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
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self.c_skip = sqrt_recip_alphas_cumprod * sigma_data**2 / (sigmas**2 + sigma_data**2)
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self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5
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self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5
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def set_timesteps(
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self,
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num_inference_steps: Optional[int] = None,
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device: Union[str, torch.device] = None,
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):
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if num_inference_steps != 2:
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raise ValueError("Currently more than 2 inference steps are not supported.")
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self.timesteps = torch.tensor([1008, 512], dtype=torch.long, device=device)
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self.sqrt_alphas_cumprod = self.sqrt_alphas_cumprod.to(device)
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self.sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod.to(device)
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self.c_skip = self.c_skip.to(device)
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self.c_out = self.c_out.to(device)
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self.c_in = self.c_in.to(device)
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@property
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def init_noise_sigma(self):
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return self.sqrt_one_minus_alphas_cumprod[self.timesteps[0]]
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def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
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"""
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Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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current timestep.
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Args:
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sample (`torch.Tensor`):
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The input sample.
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timestep (`int`, *optional*):
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The current timestep in the diffusion chain.
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Returns:
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`torch.Tensor`:
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A scaled input sample.
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"""
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return sample * self.c_in[timestep]
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def step(
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self,
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model_output: torch.Tensor,
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timestep: Union[float, torch.Tensor],
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sample: torch.Tensor,
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generator: Optional[torch.Generator] = None,
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return_dict: bool = True,
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) -> Union[ConsistencyDecoderSchedulerOutput, Tuple]:
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"""
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Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
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process from the learned model outputs (most often the predicted noise).
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Args:
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model_output (`torch.Tensor`):
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The direct output from the learned diffusion model.
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timestep (`float`):
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The current timestep in the diffusion chain.
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sample (`torch.Tensor`):
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A current instance of a sample created by the diffusion process.
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generator (`torch.Generator`, *optional*):
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A random number generator.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a
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[`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`.
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Returns:
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[`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`:
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If return_dict is `True`,
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[`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] is returned, otherwise
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a tuple is returned where the first element is the sample tensor.
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"""
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x_0 = self.c_out[timestep] * model_output + self.c_skip[timestep] * sample
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timestep_idx = torch.where(self.timesteps == timestep)[0]
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if timestep_idx == len(self.timesteps) - 1:
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prev_sample = x_0
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else:
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noise = randn_tensor(x_0.shape, generator=generator, dtype=x_0.dtype, device=x_0.device)
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prev_sample = (
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self.sqrt_alphas_cumprod[self.timesteps[timestep_idx + 1]].to(x_0.dtype) * x_0
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+ self.sqrt_one_minus_alphas_cumprod[self.timesteps[timestep_idx + 1]].to(x_0.dtype) * noise
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)
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if not return_dict:
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return (prev_sample,)
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return ConsistencyDecoderSchedulerOutput(prev_sample=prev_sample)
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