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| """SAMPLING ONLY.""" | |
| import pdb | |
| import random | |
| import numpy as np | |
| import torch | |
| from model_lib.ControlNet.ldm.modules.diffusionmodules.util import ( | |
| extract_into_tensor, make_ddim_sampling_parameters, make_ddim_timesteps, | |
| noise_like) | |
| from model_lib.ControlNet.ldm.util import default | |
| 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 | |
| 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, | |
| 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, | |
| x_T=None, | |
| log_every_t=100, | |
| unconditional_guidance_scale=1., | |
| unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... | |
| dynamic_threshold=None, | |
| ucg_schedule=None, | |
| inpaint=None, | |
| **kwargs | |
| ): | |
| if conditioning is not None: | |
| if isinstance(conditioning, dict): | |
| ctmp = conditioning[list(conditioning.keys())[0]] | |
| while isinstance(ctmp, list): ctmp = ctmp[0] | |
| cbs = ctmp.shape[0] | |
| if cbs != batch_size: | |
| print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") | |
| elif isinstance(conditioning, list): | |
| for ctmp in conditioning: | |
| if ctmp.shape[0] != 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_eta=eta, verbose=verbose) | |
| # sampling | |
| C, H, W = shape | |
| size = (batch_size, C, H, W) | |
| print(f'Data shape for DDIM sampling is {C, H, W}') | |
| samples, 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, | |
| dynamic_threshold=dynamic_threshold, | |
| ucg_schedule=ucg_schedule, | |
| inpaint=inpaint | |
| ) | |
| return samples, intermediates | |
| 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, dynamic_threshold=None, | |
| ucg_schedule=None,inpaint=None): | |
| device = self.model.betas.device | |
| b = shape[0] | |
| if x_T is None: | |
| img = torch.randn(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.model.q_sample(x0, ts) # TODO: deterministic forward pass? | |
| img = img_orig * mask + (1. - mask) * img | |
| if ucg_schedule is not None: | |
| assert len(ucg_schedule) == len(time_range) | |
| unconditional_guidance_scale = ucg_schedule[i] | |
| model_output = self.p_sample_ddim(img, cond, ts, | |
| unconditional_guidance_scale=unconditional_guidance_scale, | |
| unconditional_conditioning=unconditional_conditioning, | |
| inpaint=inpaint) | |
| outs = self.pred_x_prev_from_eps(img, cond, ts, model_output, 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, | |
| 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 | |
| def p_sample_ddim(self, x, c, t, unconditional_guidance_scale=1., unconditional_conditioning=None, inpaint=None): | |
| if inpaint is None: | |
| x_In = x | |
| else: | |
| x_In = torch.cat([x,inpaint],dim=1) | |
| if unconditional_conditioning is None or unconditional_guidance_scale == 1.: | |
| model_output = self.model.apply_model(x_In, t, c) | |
| else: | |
| x_in = torch.cat([x_In] * 2) | |
| t_in = torch.cat([t] * 2) | |
| if isinstance(c, dict): | |
| assert isinstance(unconditional_conditioning, dict) | |
| c_in = dict() | |
| for k in c: | |
| if isinstance(c[k], list): | |
| c_in[k] = [torch.cat([ | |
| unconditional_conditioning[k][i], | |
| c[k][i]]) for i in range(len(c[k]))] | |
| else: | |
| c_in[k] = torch.cat([ | |
| unconditional_conditioning[k], | |
| c[k]]) | |
| elif isinstance(c, list): | |
| c_in = list() | |
| assert isinstance(unconditional_conditioning, list) | |
| for i in range(len(c)): | |
| c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) | |
| else: | |
| c_in = torch.cat([unconditional_conditioning, c]) | |
| model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) # , reference_image_noisy | |
| model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond) | |
| return model_output | |
| def pred_x_prev_from_eps(self, x, c, t, model_output, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, | |
| temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, | |
| dynamic_threshold=None): | |
| b, *_, device = *x.shape, x.device | |
| 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.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.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 quantize_denoised: | |
| pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) | |
| if dynamic_threshold is not None: | |
| raise NotImplementedError() | |
| # direction pointing to x_t | |
| 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 | |
| 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.model.device, dtype=torch.long) | |
| if unconditional_guidance_scale == 1.: | |
| noise_pred = self.model.apply_model(x_next, t, c) | |
| else: | |
| assert unconditional_conditioning is not None | |
| e_t_uncond, noise_pred = torch.chunk( | |
| self.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 | |
| 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) | |
| def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, | |
| use_original_steps=False, callback=None, inpaint=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) | |
| model_output = self.p_sample_ddim(x_dec, cond, ts, | |
| unconditional_guidance_scale=unconditional_guidance_scale, | |
| unconditional_conditioning=unconditional_conditioning, inpaint=inpaint) | |
| x_dec, _ = self.pred_x_prev_from_eps(x_dec, cond, ts, model_output, index=index, use_original_steps=use_original_steps) | |
| if callback: callback(i) | |
| return x_dec | |
| class DDIMSampler_ReferenceOnly(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, | |
| 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, | |
| x_T=None, | |
| log_every_t=100, | |
| unconditional_guidance_scale=1., | |
| unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... | |
| dynamic_threshold=None, | |
| ucg_schedule=None, | |
| inpaint=None, | |
| num_overlap=0, | |
| **kwargs | |
| ): | |
| if conditioning is not None: | |
| if isinstance(conditioning, dict): | |
| ctmp = conditioning[list(conditioning.keys())[0]] | |
| while isinstance(ctmp, list): ctmp = ctmp[0] | |
| cbs = ctmp.shape[0] | |
| if cbs != batch_size: | |
| print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") | |
| elif isinstance(conditioning, list): | |
| for ctmp in conditioning: | |
| if ctmp.shape[0] != 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_eta=eta, verbose=verbose) | |
| # sampling | |
| C, H, W = shape | |
| size = (batch_size, C, H, W) | |
| print(f'Data shape for DDIM sampling is {C, H, W}') | |
| samples, 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, | |
| dynamic_threshold=dynamic_threshold, | |
| ucg_schedule=ucg_schedule, | |
| inpaint=inpaint, | |
| num_overlap=num_overlap | |
| ) | |
| return samples, intermediates | |
| 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, dynamic_threshold=None, | |
| ucg_schedule=None,inpaint=None,num_overlap=0): | |
| device = self.model.betas.device | |
| b = shape[0] | |
| if x_T is None: | |
| img = torch.randn(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] | |
| iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) | |
| num_frames = img.shape[0] | |
| 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.model.q_sample(x0, ts) # TODO: deterministic forward pass? | |
| img = img_orig * mask + (1. - mask) * img | |
| if ucg_schedule is not None: | |
| assert len(ucg_schedule) == len(time_range) | |
| unconditional_guidance_scale = ucg_schedule[i] | |
| if num_overlap == 0: | |
| model_output = self.p_sample_ddim(img, cond, ts, unconditional_guidance_scale=unconditional_guidance_scale, | |
| unconditional_conditioning=unconditional_conditioning, inpaint=inpaint) | |
| else: | |
| model_output_all = torch.zeros_like(img) | |
| counts = torch.zeros(num_frames).cuda() | |
| offset = random.randint(0, num_frames-1) | |
| skip = b - num_overlap | |
| for start_idx in range(offset, offset+num_frames-num_overlap, skip): | |
| indices = torch.arange(start_idx, start_idx + b) % num_frames | |
| sel_cond = {} | |
| for k, v in cond.items(): | |
| if isinstance(v, list) and k != 'more_image_control': | |
| sel_cond[k] = [c[indices] for c in v] | |
| elif k == 'more_image_control': | |
| num_more_refs = len(v) | |
| sel_cond[k] = [] | |
| for i in range(num_more_refs): | |
| sel_cond[k].append([c[indices] for c in v[i]]) | |
| else: | |
| sel_cond[k] = v | |
| sel_uncond = {} | |
| for k, v in unconditional_conditioning.items(): | |
| if isinstance(v, list) and k != 'more_image_control': | |
| sel_uncond[k] = [c[indices] for c in v] | |
| elif k == 'more_image_control': | |
| num_more_refs = len(v) | |
| sel_uncond[k] = [] | |
| for i in range(num_more_refs): | |
| sel_uncond[k].append([c[indices] for c in v[i]]) | |
| else: | |
| sel_uncond[k] = v | |
| model_output = self.p_sample_ddim(img[indices], sel_cond, ts, unconditional_guidance_scale=unconditional_guidance_scale, | |
| unconditional_conditioning=sel_uncond, inpaint=inpaint) | |
| model_output_all[indices] += model_output | |
| counts[indices] += 1 | |
| model_output = model_output_all / counts.reshape(-1, 1, 1, 1) | |
| outs = self.pred_x_prev_from_eps(img, cond, ts, model_output, index=index, temperature=temperature, | |
| noise_dropout=noise_dropout, score_corrector=score_corrector, | |
| corrector_kwargs=corrector_kwargs, 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 | |
| def p_sample_ddim(self, x, c, t, unconditional_guidance_scale=1., unconditional_conditioning=None, inpaint=None): | |
| if inpaint is None: | |
| x_In = x | |
| else: | |
| x_In = torch.cat([x,inpaint],dim=1) | |
| if 'image_control' in c and c['image_control'] is not None: | |
| cond_image_start = torch.cat(c['image_control'], 1) | |
| if c['wonoise']: | |
| reference_image_noisy = cond_image_start | |
| else: | |
| reference_image_noisy = self.model.q_sample(cond_image_start,t) | |
| more_reference_image_noisy = [] | |
| if 'more_image_control' in c and c['more_image_control'] is not None: | |
| num_additional_ref_imgs = len(c['more_image_control']) | |
| for i in range(num_additional_ref_imgs): | |
| m_ref_img_noisy = torch.cat(c['more_image_control'][i], 1) | |
| if not c['wonoise']: | |
| m_ref_img_noisy = self.model.q_sample(m_ref_img_noisy, t) | |
| more_reference_image_noisy.append(m_ref_img_noisy) | |
| if unconditional_conditioning is None or unconditional_guidance_scale == 1.: | |
| model_output = self.model.apply_model(x_In, t, c) | |
| else: | |
| if 'image_control' in unconditional_conditioning and unconditional_conditioning['image_control'] is not None: | |
| x_in = torch.cat([x_In] * 2) | |
| t_in = torch.cat([t] * 2) | |
| reference_image_noisy_in = torch.cat([reference_image_noisy] * 2) | |
| more_reference_image_noisy = [torch.cat([m_ref_img] * 2) for m_ref_img in more_reference_image_noisy] | |
| if isinstance(c, dict): | |
| assert isinstance(unconditional_conditioning, dict) | |
| c_in = dict() | |
| for k in c: | |
| if isinstance(c[k], list): | |
| c_in[k] = [torch.cat([ | |
| unconditional_conditioning[k][i], | |
| c[k][i]]) for i in range(len(c[k]))] | |
| else: | |
| try: | |
| c_in[k] = torch.cat([ | |
| unconditional_conditioning[k], | |
| c[k]]) | |
| except: | |
| c_in[k] = unconditional_conditioning[k] | |
| elif isinstance(c, list): | |
| c_in = list() | |
| assert isinstance(unconditional_conditioning, list) | |
| for i in range(len(c)): | |
| c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) | |
| else: | |
| c_in = torch.cat([unconditional_conditioning, c]) | |
| # pdb.set_trace() | |
| model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in, reference_image_noisy_in, more_reference_image_noisy = more_reference_image_noisy).chunk(2) # , reference_image_noisy | |
| else: | |
| x_in = x_In | |
| t_in = t | |
| c_in = c | |
| reference_image_noisy_in = reference_image_noisy | |
| model_t = self.model.apply_model(x_in, t_in, c_in, reference_image_noisy_in, more_reference_image_noisy = more_reference_image_noisy) | |
| model_uncond = self.model.apply_model(x_in, t_in, unconditional_conditioning, None,uc=True) | |
| # pdb.set_trace() | |
| model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond) | |
| return model_output | |
| def pred_x_prev_from_eps(self, x, c, t, model_output, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, | |
| temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, | |
| dynamic_threshold=None): | |
| b, *_, device = *x.shape, x.device | |
| 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.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) | |
| # print ('sigma_t: {}'.format(sigma_t[0, 0, 0, 0])) | |
| sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) | |
| # current prediction for x_0 | |
| 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 quantize_denoised: | |
| pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) | |
| if dynamic_threshold is not None: | |
| raise NotImplementedError() | |
| # direction pointing to x_t | |
| 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 | |
| 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.model.device, dtype=torch.long) | |
| if unconditional_guidance_scale == 1.: | |
| noise_pred = self.model.apply_model(x_next, t, c) | |
| else: | |
| assert unconditional_conditioning is not None | |
| e_t_uncond, noise_pred = torch.chunk( | |
| self.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 | |
| 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) | |
| def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, | |
| use_original_steps=False, callback=None, inpaint=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) | |
| model_output = self.p_sample_ddim(x_dec, cond, ts, | |
| unconditional_guidance_scale=unconditional_guidance_scale, | |
| unconditional_conditioning=unconditional_conditioning, inpaint=inpaint) | |
| x_dec, _ = self.pred_x_prev_from_eps(x_dec, cond, ts, model_output, index) | |
| if callback: callback(i) | |
| return x_dec |