import numpy as np import torch import copy from unifolm_wma.utils.diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg from unifolm_wma.utils.common import noise_like from unifolm_wma.utils.common import extract_into_tensor from tqdm import tqdm 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 self.counter = 0 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): device = self.model.betas.device cache_key = (ddim_num_steps, ddim_discretize, float(ddim_eta), str(device)) if getattr(self, "_schedule_cache", None) == cache_key: return 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) if self.model.use_dynamic_rescale: self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps] self.ddim_scale_arr_prev = torch.cat( [self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]]) 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) ddim_sigmas = torch.as_tensor(ddim_sigmas, device=self.model.device, dtype=torch.float32) ddim_alphas = torch.as_tensor(ddim_alphas, device=self.model.device, dtype=torch.float32) ddim_alphas_prev = torch.as_tensor(ddim_alphas_prev, device=self.model.device, dtype=torch.float32) 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', torch.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) self._schedule_cache = cache_key @torch.no_grad() def sample( self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, verbose=True, schedule_verbose=False, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, precision=None, fs=None, timestep_spacing='uniform', #uniform_trailing for starting from last timestep guidance_rescale=0.0, **kwargs): # Check condition bs if conditioning is not None: if isinstance(conditioning, dict): try: cbs = conditioning[list(conditioning.keys())[0]].shape[0] except: cbs = conditioning[list( conditioning.keys())[0]][0].shape[0] if cbs != batch_size: print( f"Warning: Got {cbs} conditionings but batch-size is {batch_size}" ) else: if conditioning.shape[0] != batch_size: print( f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}" ) self.make_schedule(ddim_num_steps=S, ddim_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose) # Make shape if len(shape) == 3: C, H, W = shape size = (batch_size, C, H, W) elif len(shape) == 4: C, T, H, W = shape size = (batch_size, C, T, H, W) samples, actions, states, intermediates = self.ddim_sampling( conditioning, size, callback=callback, img_callback=img_callback, quantize_denoised=quantize_x0, mask=mask, x0=x0, ddim_use_original_steps=False, noise_dropout=noise_dropout, temperature=temperature, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, verbose=verbose, precision=precision, fs=fs, guidance_rescale=guidance_rescale, **kwargs) return samples, actions, states, intermediates @torch.no_grad() def ddim_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True, precision=None, fs=None, guidance_rescale=0.0, **kwargs): device = self.model.betas.device dp_ddim_scheduler_action = self.model.dp_noise_scheduler_action dp_ddim_scheduler_state = self.model.dp_noise_scheduler_state b = shape[0] if x_T is None: img = torch.randn(shape, device=device) action = torch.randn((b, 16, self.model.agent_action_dim), device=device) state = torch.randn((b, 16, self.model.agent_state_dim), device=device) else: img = x_T action = torch.randn((b, 16, self.model.agent_action_dim), device=device) state = torch.randn((b, 16, self.model.agent_state_dim), device=device) if precision is not None: if precision == 16: img = img.to(dtype=torch.float16) action = action.to(dtype=torch.float16) state = state.to(dtype=torch.float16) if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int( min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] intermediates = { 'x_inter': [img], 'pred_x0': [img], 'x_inter_action': [action], 'pred_x0_action': [action], 'x_inter_state': [state], 'pred_x0_state': [state], } if ddim_use_original_steps: time_range = np.arange(timesteps - 1, -1, -1) else: time_range = np.flip(timesteps) time_range = np.ascontiguousarray(time_range) total_steps = int(time_range.shape[0]) t_seq = torch.as_tensor(time_range, device=device, dtype=torch.long) ts_batch = t_seq.unsqueeze(1).expand(total_steps, b).contiguous() if verbose: iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) else: iterator = time_range clean_cond = kwargs.pop("clean_cond", False) dp_ddim_scheduler_action.set_timesteps(len(timesteps)) dp_ddim_scheduler_state.set_timesteps(len(timesteps)) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = ts_batch[i] # Use mask to blend noised original latent (img_orig) & new sampled latent (img) if mask is not None: assert x0 is not None if clean_cond: img_orig = x0 else: img_orig = self.model.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img outs = self.p_sample_ddim( img, action, state, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, mask=mask, x0=x0, fs=fs, guidance_rescale=guidance_rescale, **kwargs) img, pred_x0, model_output_action, model_output_state = outs action = dp_ddim_scheduler_action.step( model_output_action, step, action, generator=None, ).prev_sample state = dp_ddim_scheduler_state.step( model_output_state, step, state, generator=None, ).prev_sample if callback: callback(i) if img_callback: img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) intermediates['x_inter_action'].append(action) intermediates['x_inter_state'].append(state) return img, action, state, intermediates @torch.no_grad() def p_sample_ddim(self, x, x_action, x_state, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, uc_type=None, conditional_guidance_scale_temporal=None, mask=None, x0=None, guidance_rescale=0.0, **kwargs): b, *_, device = *x.shape, x.device if x.dim() == 5: is_video = True else: is_video = False if unconditional_conditioning is None or unconditional_guidance_scale == 1.: model_output, model_output_action, model_output_state = self.model.apply_model( x, x_action, x_state, t, c, **kwargs) # unet denoiser else: # do_classifier_free_guidance if isinstance(c, torch.Tensor) or isinstance(c, dict): e_t_cond, e_t_cond_action, e_t_cond_state = self.model.apply_model( x, x_action, x_state, t, c, **kwargs) e_t_uncond, e_t_uncond_action, e_t_uncond_state = self.model.apply_model( x, x_action, x_state, t, unconditional_conditioning, **kwargs) else: raise NotImplementedError model_output = e_t_uncond + unconditional_guidance_scale * ( e_t_cond - e_t_uncond) model_output_action = e_t_uncond_action + unconditional_guidance_scale * ( e_t_cond_action - e_t_uncond_action) model_output_state = e_t_uncond_state + unconditional_guidance_scale * ( e_t_cond_state - e_t_uncond_state) if guidance_rescale > 0.0: model_output = rescale_noise_cfg( model_output, e_t_cond, guidance_rescale=guidance_rescale) model_output_action = rescale_noise_cfg( model_output_action, e_t_cond_action, guidance_rescale=guidance_rescale) model_output_state = rescale_noise_cfg( model_output_state, e_t_cond_state, guidance_rescale=guidance_rescale) if self.model.parameterization == "v": e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) else: e_t = model_output if score_corrector is not None: assert self.model.parameterization == "eps", 'not implemented' e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) 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.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas if is_video: size = (1, 1, 1, 1, 1) else: size = (1, 1, 1, 1) a_t = alphas[index].view(size) a_prev = alphas_prev[index].view(size) sigma_t = sigmas[index].view(size) sqrt_one_minus_at = sqrt_one_minus_alphas[index].view(size) if self.model.parameterization != "v": pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() else: pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) if self.model.use_dynamic_rescale: scale_t = self.ddim_scale_arr[index].view(size) prev_scale_t = self.ddim_scale_arr_prev[index].view(size) rescale = (prev_scale_t / scale_t) pred_x0 *= rescale if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, 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, model_output_action, model_output_state @torch.no_grad() def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, use_original_steps=False, callback=None): timesteps = np.arange(self.ddpm_num_timesteps ) if use_original_steps else self.ddim_timesteps timesteps = timesteps[:t_start] time_range = np.flip(timesteps) total_steps = timesteps.shape[0] print(f"Running DDIM Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc='Decoding image', total=total_steps) x_dec = x_latent for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((x_latent.shape[0], ), step, device=x_latent.device, dtype=torch.long) x_dec, _ = self.p_sample_ddim( x_dec, cond, ts, index=index, use_original_steps=use_original_steps, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning) if callback: callback(i) return x_dec @torch.no_grad() def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): # fast, but does not allow for exact reconstruction if use_original_steps: sqrt_alphas_cumprod = self.sqrt_alphas_cumprod sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod else: sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas if noise is None: noise = torch.randn_like(x0) return ( extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)