''' ldm.models.diffusion.sampler Base class for ldm.models.diffusion.ddim, ldm.models.diffusion.ksampler, etc ''' import torch import numpy as np from tqdm import tqdm from functools import partial from ldm.invoke.devices import choose_torch_device from ldm.modules.diffusionmodules.util import ( make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor, ) class Sampler(object): def __init__(self, model, schedule='linear', steps=None, device=None, **kwargs): self.model = model self.ddim_timesteps = None self.ddpm_num_timesteps = steps self.schedule = schedule self.device = device or choose_torch_device() def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != torch.device(self.device): attr = attr.to(torch.float32).to(torch.device(self.device)) setattr(self, name, attr) # This method was copied over from ddim.py and probably does stuff that is # ddim-specific. Disentangle at some point. def make_schedule( self, ddim_num_steps, ddim_discretize='uniform', ddim_eta=0.0, verbose=False, ): self.total_steps = ddim_num_steps 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(1.0 - alphas_cumprod.cpu())), ) self.register_buffer( 'log_one_minus_alphas_cumprod', to_torch(np.log(1.0 - alphas_cumprod.cpu())), ) self.register_buffer( 'sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())), ) self.register_buffer( 'sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1.0 / 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(1.0 - 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, ) @torch.no_grad() def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): # fast, but does not allow for exact reconstruction # t serves as an index to gather the correct alphas 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 ) @torch.no_grad() def sample( self, S, # S is steps batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0.0, mask=None, x0=None, temperature=1.0, noise_dropout=0.0, score_corrector=None, corrector_kwargs=None, verbose=False, x_T=None, log_every_t=100, unconditional_guidance_scale=1.0, unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **kwargs, ): # check to see if make_schedule() has run, and if not, run it if self.ddim_timesteps is None: self.make_schedule( ddim_num_steps=S, ddim_eta = eta, verbose = False, ) ts = self.get_timesteps(S) # sampling C, H, W = shape shape = (batch_size, C, H, W) samples, intermediates = self.do_sampling( conditioning, shape, timesteps=ts, 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, steps=S, ) return samples, intermediates #torch.no_grad() def do_sampling( self, cond, shape, timesteps=None, x_T=None, ddim_use_original_steps=False, callback=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1.0, noise_dropout=0.0, score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None, steps=None, ): b = shape[0] time_range = ( list(reversed(range(0, timesteps))) if ddim_use_original_steps else np.flip(timesteps) ) total_steps=steps iterator = tqdm( time_range, desc=f'{self.__class__.__name__}', total=total_steps, dynamic_ncols=True, ) old_eps = [] self.prepare_to_sample(t_enc=total_steps) img = self.get_initial_image(x_T,shape,total_steps) # probably don't need this at all intermediates = {'x_inter': [img], 'pred_x0': [img]} for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full( (b,), step, device=self.device, dtype=torch.long ) ts_next = torch.full( (b,), time_range[min(i + 1, len(time_range) - 1)], device=self.device, dtype=torch.long, ) if mask is not None: assert x0 is not None img_orig = self.model.q_sample( x0, ts ) # TODO: deterministic forward pass? img = img_orig * mask + (1.0 - mask) * img outs = self.p_sample( img, 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, old_eps=old_eps, t_next=ts_next, ) img, pred_x0, e_t = outs old_eps.append(e_t) if len(old_eps) >= 4: old_eps.pop(0) if callback: callback(i) if img_callback: img_callback(img,i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates # NOTE that decode() and sample() are almost the same code, and do the same thing. # The variable names are changed in order to be confusing. @torch.no_grad() def decode( self, x_latent, cond, t_start, img_callback=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None, use_original_steps=False, init_latent = None, mask = 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 {self.__class__.__name__} sampling starting at step {self.total_steps - t_start} of {self.total_steps} ({total_steps} new sampling steps)') iterator = tqdm(time_range, desc='Decoding image', total=total_steps) x_dec = x_latent x0 = init_latent self.prepare_to_sample(t_enc=total_steps) 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, ) ts_next = torch.full( (x_latent.shape[0],), time_range[min(i + 1, len(time_range) - 1)], device=self.device, dtype=torch.long, ) if mask is not None: assert x0 is not None xdec_orig = self.q_sample(x0, ts) # TODO: deterministic forward pass? x_dec = xdec_orig * mask + (1.0 - mask) * x_dec outs = self.p_sample( x_dec, cond, ts, index=index, use_original_steps=use_original_steps, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, t_next = ts_next, ) x_dec, pred_x0, e_t = outs if img_callback: img_callback(x_dec,i) return x_dec def get_initial_image(self,x_T,shape,timesteps=None): if x_T is None: return torch.randn(shape, device=self.device) else: return x_T def p_sample( self, img, cond, ts, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1.0, noise_dropout=0.0, score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None, old_eps=None, t_next=None, steps=None, ): raise NotImplementedError("p_sample() must be implemented in a descendent class") def prepare_to_sample(self,t_enc,**kwargs): ''' Hook that will be called right before the very first invocation of p_sample() to allow subclass to do additional initialization. t_enc corresponds to the actual number of steps that will be run, and may be less than total steps if img2img is active. ''' pass def get_timesteps(self,ddim_steps): ''' The ddim and plms samplers work on timesteps. This method is called after ddim_timesteps are created in make_schedule(), and selects the portion of timesteps that will be used for sampling, depending on the t_enc in img2img. ''' return self.ddim_timesteps[:ddim_steps] def q_sample(self,x0,ts): ''' Returns self.model.q_sample(x0,ts). Is overridden in the k* samplers to return self.model.inner_model.q_sample(x0,ts) ''' return self.model.q_sample(x0,ts)