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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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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_tranform_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.FloatTensor` 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.FloatTensor |
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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.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: |
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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.FloatTensor`): |
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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.FloatTensor`: |
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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.FloatTensor, |
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timestep: Union[float, torch.FloatTensor], |
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sample: torch.FloatTensor, |
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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.FloatTensor`): |
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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.FloatTensor`): |
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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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