from typing import Literal, Optional from tqdm import tqdm import numpy as np import torch from TorchJaekwon.Util.UtilTorch import UtilTorch from TorchJaekwon.Model.Diffusion.DDPM.DDPM import DDPM from TorchJaekwon.Model.Diffusion.DDPM.DiffusionUtil import DiffusionUtil class DDIM(object): def __init__(self, ddpm_model:DDPM): self.ddpm_model:DDPM = ddpm_model self.ddpm_num_timesteps:int = ddpm_model.timesteps self.device:torch.device = UtilTorch.get_model_device(self.ddpm_model) def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != self.device: is_mps = self.device == "mps" or self.device == torch.device("mps") if is_mps and attr.dtype == torch.float64: attr = attr.to(self.device, dtype=torch.float32) else: attr = attr.to(self.device) setattr(self, name, attr) def make_schedule( self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True ): self.ddim_timesteps = DDIM.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.ddpm_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.device) self.register_buffer("betas", to_torch(self.ddpm_model.betas)) self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) self.register_buffer( "alphas_cumprod_prev", to_torch(self.ddpm_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 = DDIM.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 ) @staticmethod 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 * 0.8), 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 @staticmethod 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 @torch.no_grad() def infer( self, x_shape:tuple = None, cond:Optional[dict] = None, is_cond_unpack:bool = False, num_steps:int = 50, batch_size:int = None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=1.0, 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, dynamic_threshold=None, ucg_schedule=None, ): _, cond, additional_data_dict = self.ddpm_model.preprocess(x_start = None, cond=cond) if x_shape is None: x_shape = self.ddpm_model.get_x_shape(cond=cond) if batch_size is not None: x_shape[0] = batch_size self.make_schedule(ddim_num_steps=num_steps, ddim_eta=eta, verbose=verbose) samples, intermediates = self.ddim_sampling( x_shape, cond, is_cond_unpack, 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, dynamic_threshold=dynamic_threshold, ucg_schedule=ucg_schedule, ) return self.ddpm_model.postprocess(samples, additional_data_dict) @torch.no_grad() def ddim_sampling( self, x_shape:tuple = None, cond:Optional[dict] = None, is_cond_unpack:bool = False, 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=1.0, noise_dropout=0.0, score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None, dynamic_threshold=None, ucg_schedule=None, ): device = self.ddpm_model.betas.device b = x_shape[0] if x_T is None: img = torch.randn(x_shape, device=device) else: img = x_T 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]} time_range = ( reversed(range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps) ) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] print(f"Running DDIM Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) if mask is not None: assert x0 is not None img_orig = self.ddpm_model.q_sample( x0, ts ) # TODO: deterministic forward pass? img = img_orig * mask + (1.0 - mask) * img if ucg_schedule is not None: assert len(ucg_schedule) == len(time_range) unconditional_guidance_scale = ucg_schedule[i] outs = self.p_sample_ddim( img, ts, index=index, cond = cond, is_cond_unpack = is_cond_unpack, 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, dynamic_threshold=dynamic_threshold, ) img, pred_x0 = outs 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) return img, intermediates @torch.no_grad() def p_sample_ddim( self, x, t, index, cond:Optional[dict] = None, is_cond_unpack:bool = False, 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, dynamic_threshold=None, ): b, *_, device = *x.shape, x.device model_output = self.ddpm_model.apply_model(x, t, cond, is_cond_unpack, cfg_scale = self.ddpm_model.cfg_scale) if self.ddpm_model.model_output_type == "v_prediction": e_t = self.ddpm_model.predict_noise_from_v(x, t, model_output) else: e_t = model_output if score_corrector is not None: assert self.ddpm_model.parameterization == "eps", "not implemented" e_t = score_corrector.modify_score( self.ddpm_model, e_t, x, t, c, **corrector_kwargs ) alphas = self.ddpm_model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = ( self.ddpm_model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev ) sqrt_one_minus_alphas = ( self.ddpm_model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas ) sigmas = ( self.ddpm_model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas ) # select parameters corresponding to the currently considered timestep a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) sqrt_one_minus_at = torch.full( (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device ) # current prediction for x_0 if self.ddpm_model.model_output_type != "v_prediction": pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() else: pred_x0 = self.ddpm_model.predict_x_start_from_v(x, t, model_output) if quantize_denoised: pred_x0, _, *_ = self.ddpm_model.first_stage_model.quantize(pred_x0) if dynamic_threshold is not None: raise NotImplementedError() # direction pointing to x_t dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * DiffusionUtil.noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.0: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 @torch.no_grad() def encode( self, x0, c, t_enc, use_original_steps=False, return_intermediates=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None, ): num_reference_steps = ( self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0] ) assert t_enc <= num_reference_steps num_steps = t_enc if use_original_steps: alphas_next = self.alphas_cumprod[:num_steps] alphas = self.alphas_cumprod_prev[:num_steps] else: alphas_next = self.ddim_alphas[:num_steps] alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) x_next = x0 intermediates = [] inter_steps = [] for i in tqdm(range(num_steps), desc="Encoding Image"): t = torch.full( (x0.shape[0],), i, device=self.ddpm_model.device, dtype=torch.long ) if unconditional_guidance_scale == 1.0: noise_pred = self.ddpm_model.apply_model(x_next, t, c) else: assert unconditional_conditioning is not None e_t_uncond, noise_pred = torch.chunk( self.ddpm_model.apply_model( torch.cat((x_next, x_next)), torch.cat((t, t)), torch.cat((unconditional_conditioning, c)), ), 2, ) noise_pred = e_t_uncond + unconditional_guidance_scale * ( noise_pred - e_t_uncond ) xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next weighted_noise_pred = ( alphas_next[i].sqrt() * ((1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred ) x_next = xt_weighted + weighted_noise_pred if ( return_intermediates and i % (num_steps // return_intermediates) == 0 and i < num_steps - 1 ): intermediates.append(x_next) inter_steps.append(i) elif return_intermediates and i >= num_steps - 2: intermediates.append(x_next) inter_steps.append(i) if callback: callback(i) out = {"x_encoded": x_next, "intermediate_steps": inter_steps} if return_intermediates: out.update({"intermediates": intermediates}) return x_next, out @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 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