Spaces:
Runtime error
Runtime error
| # adopted from | |
| # https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py | |
| # and | |
| # https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py | |
| # and | |
| # https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py | |
| # | |
| # thanks! | |
| import os | |
| import math | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from einops import repeat | |
| from util import instantiate_from_config | |
| def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): | |
| if schedule == "linear": | |
| betas = ( | |
| torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 | |
| ) | |
| elif schedule == "cosine": | |
| timesteps = ( | |
| torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s | |
| ) | |
| alphas = timesteps / (1 + cosine_s) * np.pi / 2 | |
| alphas = torch.cos(alphas).pow(2) | |
| alphas = alphas / alphas[0] | |
| betas = 1 - alphas[1:] / alphas[:-1] | |
| betas = np.clip(betas, a_min=0, a_max=0.999) | |
| elif schedule == "sqrt_linear": | |
| betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) | |
| elif schedule == "sqrt": | |
| betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 | |
| else: | |
| raise ValueError(f"schedule '{schedule}' unknown.") | |
| return betas.numpy() | |
| def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): | |
| if ddim_discr_method == 'uniform': | |
| c = num_ddpm_timesteps // num_ddim_timesteps | |
| ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) | |
| elif ddim_discr_method == 'quad': | |
| ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) | |
| else: | |
| raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') | |
| # assert ddim_timesteps.shape[0] == num_ddim_timesteps | |
| # add one to get the final alpha values right (the ones from first scale to data during sampling) | |
| steps_out = ddim_timesteps + 1 | |
| if verbose: | |
| print(f'Selected timesteps for ddim sampler: {steps_out}') | |
| return steps_out | |
| def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): | |
| # select alphas for computing the variance schedule | |
| alphas = alphacums[ddim_timesteps] | |
| alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) | |
| # according the the formula provided in https://arxiv.org/abs/2010.02502 | |
| sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) | |
| if verbose: | |
| print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') | |
| print(f'For the chosen value of eta, which is {eta}, ' | |
| f'this results in the following sigma_t schedule for ddim sampler {sigmas}') | |
| return sigmas, alphas, alphas_prev | |
| def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): | |
| """ | |
| Create a beta schedule that discretizes the given alpha_t_bar function, | |
| which defines the cumulative product of (1-beta) over time from t = [0,1]. | |
| :param num_diffusion_timesteps: the number of betas to produce. | |
| :param alpha_bar: a lambda that takes an argument t from 0 to 1 and | |
| produces the cumulative product of (1-beta) up to that | |
| part of the diffusion process. | |
| :param max_beta: the maximum beta to use; use values lower than 1 to | |
| prevent singularities. | |
| """ | |
| betas = [] | |
| for i in range(num_diffusion_timesteps): | |
| t1 = i / num_diffusion_timesteps | |
| t2 = (i + 1) / num_diffusion_timesteps | |
| betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) | |
| return np.array(betas) | |
| def extract_into_tensor(a, t, x_shape): | |
| b, *_ = t.shape | |
| out = a.gather(-1, t) | |
| return out.reshape(b, *((1,) * (len(x_shape) - 1))) | |
| def checkpoint(func, inputs, params, flag): | |
| """ | |
| Evaluate a function without caching intermediate activations, allowing for | |
| reduced memory at the expense of extra compute in the backward pass. | |
| :param func: the function to evaluate. | |
| :param inputs: the argument sequence to pass to `func`. | |
| :param params: a sequence of parameters `func` depends on but does not | |
| explicitly take as arguments. | |
| :param flag: if False, disable gradient checkpointing. | |
| """ | |
| if flag: | |
| args = tuple(inputs) + tuple(params) | |
| return CheckpointFunction.apply(func, len(inputs), *args) | |
| else: | |
| return func(*inputs) | |
| class CheckpointFunction(torch.autograd.Function): | |
| def forward(ctx, run_function, length, *args): | |
| ctx.run_function = run_function | |
| ctx.input_tensors = list(args[:length]) | |
| ctx.input_params = list(args[length:]) | |
| with torch.no_grad(): | |
| output_tensors = ctx.run_function(*ctx.input_tensors) | |
| return output_tensors | |
| def backward(ctx, *output_grads): | |
| ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] | |
| with torch.enable_grad(): | |
| # Fixes a bug where the first op in run_function modifies the | |
| # Tensor storage in place, which is not allowed for detach()'d | |
| # Tensors. | |
| shallow_copies = [x.view_as(x) for x in ctx.input_tensors] | |
| output_tensors = ctx.run_function(*shallow_copies) | |
| input_grads = torch.autograd.grad( | |
| output_tensors, | |
| ctx.input_tensors + ctx.input_params, | |
| output_grads, | |
| allow_unused=True, | |
| ) | |
| del ctx.input_tensors | |
| del ctx.input_params | |
| del output_tensors | |
| return (None, None) + input_grads | |
| def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): | |
| """ | |
| Create sinusoidal timestep embeddings. | |
| :param timesteps: a 1-D Tensor of N indices, one per batch element. | |
| These may be fractional. | |
| :param dim: the dimension of the output. | |
| :param max_period: controls the minimum frequency of the embeddings. | |
| :return: an [N x dim] Tensor of positional embeddings. | |
| """ | |
| if not repeat_only: | |
| half = dim // 2 | |
| freqs = torch.exp( | |
| -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half | |
| ).to(device=timesteps.device) | |
| args = timesteps[:, None].float() * freqs[None] | |
| embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) | |
| if dim % 2: | |
| embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) | |
| else: | |
| embedding = repeat(timesteps, 'b -> b d', d=dim) | |
| return embedding | |
| def zero_module(module): | |
| """ | |
| Zero out the parameters of a module and return it. | |
| """ | |
| for p in module.parameters(): | |
| p.detach().zero_() | |
| return module | |
| def scale_module(module, scale): | |
| """ | |
| Scale the parameters of a module and return it. | |
| """ | |
| for p in module.parameters(): | |
| p.detach().mul_(scale) | |
| return module | |
| def mean_flat(tensor): | |
| """ | |
| Take the mean over all non-batch dimensions. | |
| """ | |
| return tensor.mean(dim=list(range(1, len(tensor.shape)))) | |
| def normalization(channels): | |
| """ | |
| Make a standard normalization layer. | |
| :param channels: number of input channels. | |
| :return: an nn.Module for normalization. | |
| """ | |
| return GroupNorm32(32, channels) | |
| # PyTorch 1.7 has SiLU, but we support PyTorch 1.5. | |
| class SiLU(nn.Module): | |
| def forward(self, x): | |
| return x * torch.sigmoid(x) | |
| class GroupNorm32(nn.GroupNorm): | |
| def forward(self, x): | |
| return super().forward(x.float()).type(x.dtype) | |
| def conv_nd(dims, *args, **kwargs): | |
| """ | |
| Create a 1D, 2D, or 3D convolution module. | |
| """ | |
| if dims == 1: | |
| return nn.Conv1d(*args, **kwargs) | |
| elif dims == 2: | |
| return nn.Conv2d(*args, **kwargs) | |
| elif dims == 3: | |
| return nn.Conv3d(*args, **kwargs) | |
| raise ValueError(f"unsupported dimensions: {dims}") | |
| def linear(*args, **kwargs): | |
| """ | |
| Create a linear module. | |
| """ | |
| return nn.Linear(*args, **kwargs) | |
| def avg_pool_nd(dims, *args, **kwargs): | |
| """ | |
| Create a 1D, 2D, or 3D average pooling module. | |
| """ | |
| if dims == 1: | |
| return nn.AvgPool1d(*args, **kwargs) | |
| elif dims == 2: | |
| return nn.AvgPool2d(*args, **kwargs) | |
| elif dims == 3: | |
| return nn.AvgPool3d(*args, **kwargs) | |
| raise ValueError(f"unsupported dimensions: {dims}") | |
| class HybridConditioner(nn.Module): | |
| def __init__(self, c_concat_config, c_crossattn_config): | |
| super().__init__() | |
| self.concat_conditioner = instantiate_from_config(c_concat_config) | |
| self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) | |
| def forward(self, c_concat, c_crossattn): | |
| c_concat = self.concat_conditioner(c_concat) | |
| c_crossattn = self.crossattn_conditioner(c_crossattn) | |
| return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} | |
| def noise_like(shape, device, repeat=False): | |
| repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) | |
| noise = lambda: torch.randn(shape, device=device) | |
| return repeat_noise() if repeat else noise() |