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| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from functools import partial | |
| from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule | |
| from ldm.util import default | |
| class AbstractLowScaleModel(nn.Module): | |
| # for concatenating a downsampled image to the latent representation | |
| def __init__(self, noise_schedule_config=None): | |
| super(AbstractLowScaleModel, self).__init__() | |
| if noise_schedule_config is not None: | |
| self.register_schedule(**noise_schedule_config) | |
| def register_schedule(self, beta_schedule="linear", timesteps=1000, | |
| linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): | |
| betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, | |
| cosine_s=cosine_s) | |
| alphas = 1. - betas | |
| alphas_cumprod = np.cumprod(alphas, axis=0) | |
| alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) | |
| timesteps, = betas.shape | |
| self.num_timesteps = int(timesteps) | |
| self.linear_start = linear_start | |
| self.linear_end = linear_end | |
| assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' | |
| to_torch = partial(torch.tensor, dtype=torch.float32) | |
| self.register_buffer('betas', to_torch(betas)) | |
| self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) | |
| self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) | |
| # calculations for diffusion q(x_t | x_{t-1}) and others | |
| self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) | |
| self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) | |
| self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) | |
| self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) | |
| self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) | |
| def q_sample(self, x_start, t, noise=None): | |
| noise = default(noise, lambda: torch.randn_like(x_start)) | |
| return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + | |
| extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) | |
| def forward(self, x): | |
| return x, None | |
| def decode(self, x): | |
| return x | |
| class SimpleImageConcat(AbstractLowScaleModel): | |
| # no noise level conditioning | |
| def __init__(self): | |
| super(SimpleImageConcat, self).__init__(noise_schedule_config=None) | |
| self.max_noise_level = 0 | |
| def forward(self, x): | |
| # fix to constant noise level | |
| return x, torch.zeros(x.shape[0], device=x.device).long() | |
| class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): | |
| def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): | |
| super().__init__(noise_schedule_config=noise_schedule_config) | |
| self.max_noise_level = max_noise_level | |
| def forward(self, x, noise_level=None): | |
| if noise_level is None: | |
| noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() | |
| else: | |
| assert isinstance(noise_level, torch.Tensor) | |
| z = self.q_sample(x, noise_level) | |
| return z, noise_level | |