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Running
on
L4
| """SAMPLING ONLY.""" | |
| import torch | |
| import numpy as np | |
| from tqdm import tqdm | |
| from functools import partial | |
| from .diffusion_utils import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like | |
| class DDIMSampler(object): | |
| def __init__(self, model, schedule="linear", **kwargs): | |
| super().__init__() | |
| self.model = model | |
| self.ddpm_num_timesteps = model.num_timesteps | |
| self.schedule = schedule | |
| def register_buffer(self, name, attr): | |
| if type(attr) == torch.Tensor: | |
| if attr.device != torch.device("cuda"): | |
| attr = attr.to(torch.device("cuda")) | |
| setattr(self, name, attr) | |
| def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): | |
| self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, | |
| num_ddim_timesteps=ddim_num_steps, | |
| num_ddpm_timesteps=self.ddpm_num_timesteps, | |
| verbose=verbose) | |
| alphas_cumprod = self.model.alphas_cumprod | |
| assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' | |
| to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) | |
| self.register_buffer('betas', to_torch(self.model.betas)) | |
| self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) | |
| self.register_buffer('alphas_cumprod_prev', to_torch(self.model.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.cpu()))) | |
| self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) | |
| self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) | |
| self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) | |
| self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) | |
| # ddim sampling parameters | |
| ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( | |
| alphacums=alphas_cumprod.cpu(), | |
| ddim_timesteps=self.ddim_timesteps, | |
| eta=ddim_eta,verbose=verbose) | |
| self.register_buffer('ddim_sigmas', ddim_sigmas) | |
| self.register_buffer('ddim_alphas', ddim_alphas) | |
| self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) | |
| self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) | |
| sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( | |
| (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( | |
| 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) | |
| self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) | |
| def sample(self, | |
| steps, | |
| shape, | |
| x_info, | |
| c_info, | |
| eta=0., | |
| temperature=1., | |
| noise_dropout=0., | |
| verbose=True, | |
| log_every_t=100,): | |
| self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose) | |
| print(f'Data shape for DDIM sampling is {shape}, eta {eta}') | |
| samples, intermediates = self.ddim_sampling( | |
| shape, | |
| x_info=x_info, | |
| c_info=c_info, | |
| noise_dropout=noise_dropout, | |
| temperature=temperature, | |
| log_every_t=log_every_t,) | |
| return samples, intermediates | |
| def ddim_sampling(self, | |
| shape, | |
| x_info, | |
| c_info, | |
| noise_dropout=0., | |
| temperature=1., | |
| log_every_t=100,): | |
| device = self.model.device | |
| dtype = c_info['conditioning'].dtype | |
| bs = shape[0] | |
| timesteps = self.ddim_timesteps | |
| if ('xt' in x_info) and (x_info['xt'] is not None): | |
| xt = x_info['xt'].astype(dtype).to(device) | |
| x_info['x'] = xt | |
| elif ('x0' in x_info) and (x_info['x0'] is not None): | |
| x0 = x_info['x0'].type(dtype).to(device) | |
| ts = timesteps[x_info['x0_forward_timesteps']].repeat(bs) | |
| ts = torch.Tensor(ts).long().to(device) | |
| timesteps = timesteps[:x_info['x0_forward_timesteps']] | |
| x0_nz = self.model.q_sample(x0, ts) | |
| x_info['x'] = x0_nz | |
| else: | |
| x_info['x'] = torch.randn(shape, device=device, dtype=dtype) | |
| intermediates = {'pred_xt': [], 'pred_x0': []} | |
| time_range = np.flip(timesteps) | |
| total_steps = timesteps.shape[0] | |
| iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) | |
| for i, step in enumerate(iterator): | |
| index = total_steps - i - 1 | |
| ts = torch.full((bs,), step, device=device, dtype=torch.long) | |
| outs = self.p_sample_ddim( | |
| x_info, c_info, ts, index, | |
| noise_dropout=noise_dropout, | |
| temperature=temperature,) | |
| pred_xt, pred_x0 = outs | |
| x_info['x'] = pred_xt | |
| if index % log_every_t == 0 or index == total_steps - 1: | |
| intermediates['pred_xt'].append(pred_xt) | |
| intermediates['pred_x0'].append(pred_x0) | |
| return pred_xt, intermediates | |
| def p_sample_ddim(self, x_info, c_info, t, index, | |
| repeat_noise=False, | |
| use_original_steps=False, | |
| noise_dropout=0., | |
| temperature=1.,): | |
| x = x_info['x'] | |
| unconditional_guidance_scale = c_info['unconditional_guidance_scale'] | |
| b, *_, device = *x.shape, x.device | |
| if unconditional_guidance_scale == 1.: | |
| c_info['c'] = c_info['conditioning'] | |
| e_t = self.model.apply_model(x_info, t, c_info) | |
| else: | |
| x_in = torch.cat([x] * 2) | |
| t_in = torch.cat([t] * 2) | |
| c_in = torch.cat([c_info['unconditional_conditioning'], c_info['conditioning']]) | |
| x_info['x'] = x_in | |
| c_info['c'] = c_in | |
| e_t_uncond, e_t = self.model.apply_model(x_info, t_in, c_info).chunk(2) | |
| e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) | |
| alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas | |
| alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev | |
| sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas | |
| sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas | |
| # select parameters corresponding to the currently considered timestep | |
| extended_shape = [b] + [1]*(len(e_t.shape)-1) | |
| a_t = torch.full(extended_shape, alphas[index], device=device, dtype=x.dtype) | |
| a_prev = torch.full(extended_shape, alphas_prev[index], device=device, dtype=x.dtype) | |
| sigma_t = torch.full(extended_shape, sigmas[index], device=device, dtype=x.dtype) | |
| sqrt_one_minus_at = torch.full(extended_shape, sqrt_one_minus_alphas[index], device=device, dtype=x.dtype) | |
| # current prediction for x_0 | |
| pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() | |
| dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t | |
| noise = sigma_t * noise_like(x, repeat_noise) * temperature | |
| if noise_dropout > 0.: | |
| noise = torch.nn.functional.dropout(noise, p=noise_dropout) | |
| x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise | |
| return x_prev, pred_x0 | |
| def sample_multicontext(self, | |
| steps, | |
| shape, | |
| x_info, | |
| c_info_list, | |
| eta=0., | |
| temperature=1., | |
| noise_dropout=0., | |
| verbose=True, | |
| log_every_t=100,): | |
| self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose) | |
| print(f'Data shape for DDIM sampling is {shape}, eta {eta}') | |
| samples, intermediates = self.ddim_sampling_multicontext( | |
| shape, | |
| x_info=x_info, | |
| c_info_list=c_info_list, | |
| noise_dropout=noise_dropout, | |
| temperature=temperature, | |
| log_every_t=log_every_t,) | |
| return samples, intermediates | |
| def ddim_sampling_multicontext(self, | |
| shape, | |
| x_info, | |
| c_info_list, | |
| noise_dropout=0., | |
| temperature=1., | |
| log_every_t=100,): | |
| device = self.model.device | |
| dtype = c_info_list[0]['conditioning'].dtype | |
| bs = shape[0] | |
| timesteps = self.ddim_timesteps | |
| if ('xt' in x_info) and (x_info['xt'] is not None): | |
| xt = x_info['xt'].astype(dtype).to(device) | |
| x_info['x'] = xt | |
| elif ('x0' in x_info) and (x_info['x0'] is not None): | |
| x0 = x_info['x0'].type(dtype).to(device) | |
| ts = timesteps[x_info['x0_forward_timesteps']].repeat(bs) | |
| ts = torch.Tensor(ts).long().to(device) | |
| timesteps = timesteps[:x_info['x0_forward_timesteps']] | |
| x0_nz = self.model.q_sample(x0, ts) | |
| x_info['x'] = x0_nz | |
| else: | |
| x_info['x'] = torch.randn(shape, device=device, dtype=dtype) | |
| intermediates = {'pred_xt': [], 'pred_x0': []} | |
| time_range = np.flip(timesteps) | |
| total_steps = timesteps.shape[0] | |
| iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) | |
| for i, step in enumerate(iterator): | |
| index = total_steps - i - 1 | |
| ts = torch.full((bs,), step, device=device, dtype=torch.long) | |
| outs = self.p_sample_ddim_multicontext( | |
| x_info, c_info_list, ts, index, | |
| noise_dropout=noise_dropout, | |
| temperature=temperature,) | |
| pred_xt, pred_x0 = outs | |
| x_info['x'] = pred_xt | |
| if index % log_every_t == 0 or index == total_steps - 1: | |
| intermediates['pred_xt'].append(pred_xt) | |
| intermediates['pred_x0'].append(pred_x0) | |
| return pred_xt, intermediates | |
| def p_sample_ddim_multicontext( | |
| self, x_info, c_info_list, t, index, | |
| repeat_noise=False, | |
| use_original_steps=False, | |
| noise_dropout=0., | |
| temperature=1.,): | |
| x = x_info['x'] | |
| b, *_, device = *x.shape, x.device | |
| unconditional_guidance_scale = None | |
| for c_info in c_info_list: | |
| if unconditional_guidance_scale is None: | |
| unconditional_guidance_scale = c_info['unconditional_guidance_scale'] | |
| else: | |
| assert unconditional_guidance_scale==c_info['unconditional_guidance_scale'], \ | |
| "A different unconditional guidance scale between different context is not allowed!" | |
| if unconditional_guidance_scale == 1.: | |
| c_info['c'] = c_info['conditioning'] | |
| else: | |
| c_in = torch.cat([c_info['unconditional_conditioning'], c_info['conditioning']]) | |
| c_info['c'] = c_in | |
| if unconditional_guidance_scale == 1.: | |
| e_t = self.model.apply_model_multicontext(x_info, t, c_info_list) | |
| else: | |
| x_in = torch.cat([x] * 2) | |
| t_in = torch.cat([t] * 2) | |
| x_info['x'] = x_in | |
| e_t_uncond, e_t = self.model.apply_model_multicontext(x_info, t_in, c_info_list).chunk(2) | |
| e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) | |
| alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas | |
| alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev | |
| sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas | |
| sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas | |
| # select parameters corresponding to the currently considered timestep | |
| extended_shape = [b] + [1]*(len(e_t.shape)-1) | |
| a_t = torch.full(extended_shape, alphas[index], device=device, dtype=x.dtype) | |
| a_prev = torch.full(extended_shape, alphas_prev[index], device=device, dtype=x.dtype) | |
| sigma_t = torch.full(extended_shape, sigmas[index], device=device, dtype=x.dtype) | |
| sqrt_one_minus_at = torch.full(extended_shape, sqrt_one_minus_alphas[index], device=device, dtype=x.dtype) | |
| # current prediction for x_0 | |
| pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() | |
| dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t | |
| noise = sigma_t * noise_like(x, repeat_noise) * temperature | |
| if noise_dropout > 0.: | |
| noise = torch.nn.functional.dropout(noise, p=noise_dropout) | |
| x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise | |
| return x_prev, pred_x0 | |