xdecoder/Instruct-X-Decoder

1"""SAMPLING ONLY."""2 3import torch4import numpy as np5from tqdm import tqdm6from functools import partial7 8from .util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like9 10 11class DDIMSampler(object):12    def __init__(self, model, schedule="linear", **kwargs):13        super().__init__()14        self.model = model15        self.ddpm_num_timesteps = model.num_timesteps16        self.schedule = schedule17 18    def register_buffer(self, name, attr):19        if type(attr) == torch.Tensor:20            if attr.device != torch.device("cuda"):21                attr = attr.to(torch.device("cuda"))22        setattr(self, name, attr)23 24    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):25        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,26                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)27        alphas_cumprod = self.model.alphas_cumprod28        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'29        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)30 31        self.register_buffer('betas', to_torch(self.model.betas))32        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))33        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))34 35        # calculations for diffusion q(x_t | x_{t-1}) and others36        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))37        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))38        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))39        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))40        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))41 42        # ddim sampling parameters43        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),44                                                                                   ddim_timesteps=self.ddim_timesteps,45                                                                                   eta=ddim_eta,verbose=verbose)46        self.register_buffer('ddim_sigmas', ddim_sigmas)47        self.register_buffer('ddim_alphas', ddim_alphas)48        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)49        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))50        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(51            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (52                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))53        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)54 55    @torch.no_grad()56    def sample(self,57               S,58               batch_size,59               shape,60               conditioning=None,61               callback=None,62               normals_sequence=None,63               img_callback=None,64               quantize_x0=False,65               eta=0.,66               mask=None,67               x0=None,68               temperature=1.,69               noise_dropout=0.,70               score_corrector=None,71               corrector_kwargs=None,72               verbose=True,73               x_T=None,74               log_every_t=100,75               unconditional_guidance_scale=1.,76               unconditional_conditioning=None,77               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...78               **kwargs79               ):80        if conditioning is not None:81            if isinstance(conditioning, dict):82                cbs = conditioning[list(conditioning.keys())[0]].shape[0]83                if cbs != batch_size:84                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")85            else:86                if conditioning.shape[0] != batch_size:87                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")88 89        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)90        # sampling91        C, H, W = shape92        size = (batch_size, C, H, W)93        print(f'Data shape for DDIM sampling is {size}, eta {eta}')94 95        samples, intermediates = self.ddim_sampling(conditioning, size,96                                                    callback=callback,97                                                    img_callback=img_callback,98                                                    quantize_denoised=quantize_x0,99                                                    mask=mask, x0=x0,100                                                    ddim_use_original_steps=False,101                                                    noise_dropout=noise_dropout,102                                                    temperature=temperature,103                                                    score_corrector=score_corrector,104                                                    corrector_kwargs=corrector_kwargs,105                                                    x_T=x_T,106                                                    log_every_t=log_every_t,107                                                    unconditional_guidance_scale=unconditional_guidance_scale,108                                                    unconditional_conditioning=unconditional_conditioning,109                                                    )110        return samples, intermediates111 112    @torch.no_grad()113    def ddim_sampling(self, cond, shape,114                      x_T=None, ddim_use_original_steps=False,115                      callback=None, timesteps=None, quantize_denoised=False,116                      mask=None, x0=None, img_callback=None, log_every_t=100,117                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,118                      unconditional_guidance_scale=1., unconditional_conditioning=None,):119        device = self.model.betas.device120        b = shape[0]121        if x_T is None:122            img = torch.randn(shape, device=device)123        else:124            img = x_T125 126        if timesteps is None:127            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps128        elif timesteps is not None and not ddim_use_original_steps:129            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1130            timesteps = self.ddim_timesteps[:subset_end]131 132        intermediates = {'x_inter': [img], 'pred_x0': [img]}133        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)134        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]135        print(f"Running DDIM Sampling with {total_steps} timesteps")136 137        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)138 139        for i, step in enumerate(iterator):140            index = total_steps - i - 1141            ts = torch.full((b,), step, device=device, dtype=torch.long)142 143            if mask is not None:144                assert x0 is not None145                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?146                img = img_orig * mask + (1. - mask) * img147 148            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,149                                      quantize_denoised=quantize_denoised, temperature=temperature,150                                      noise_dropout=noise_dropout, score_corrector=score_corrector,151                                      corrector_kwargs=corrector_kwargs,152                                      unconditional_guidance_scale=unconditional_guidance_scale,153                                      unconditional_conditioning=unconditional_conditioning)154            img, pred_x0 = outs155            if callback: callback(i)156            if img_callback: img_callback(pred_x0, i)157 158            if index % log_every_t == 0 or index == total_steps - 1:159                intermediates['x_inter'].append(img)160                intermediates['pred_x0'].append(pred_x0)161 162        return img, intermediates163 164    @torch.no_grad()165    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,166                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,167                      unconditional_guidance_scale=1., unconditional_conditioning=None):168        b, *_, device = *x.shape, x.device169 170        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:171            e_t = self.model.apply_model(x, t, c)172        else:173            x_in = torch.cat([x] * 2)174            t_in = torch.cat([t] * 2)175            c_in = torch.cat([unconditional_conditioning, c])176            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)177            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)178 179        if score_corrector is not None:180            assert self.model.parameterization == "eps"181            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)182 183        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas184        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev185        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas186        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas187        # select parameters corresponding to the currently considered timestep188        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)189        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)190        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)191        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)192 193        # current prediction for x_0194        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()195        if quantize_denoised:196            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)197        # direction pointing to x_t198        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t199        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature200        if noise_dropout > 0.:201            noise = torch.nn.functional.dropout(noise, p=noise_dropout)202        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise203        return x_prev, pred_x0204