import os import numpy as np import torch from loguru import logger from libs.model.base import InpaintModel from libs.schema import Config torch.manual_seed(42) import torch.nn as nn from tqdm import tqdm from libs.helper import download_model, norm_img, get_cache_path_by_url from libs.model.utils import make_beta_schedule, make_ddim_timesteps, make_ddim_sampling_parameters, noise_like, \ timestep_embedding LDM_ENCODE_MODEL_URL = os.environ.get( "LDM_ENCODE_MODEL_URL", "https://ae-rt.oss-cn-beijing.aliyuncs.com/matting_module/inpaint/cond_stage_model_encode.pt" #"https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_encode.pt", ) LDM_DECODE_MODEL_URL = os.environ.get( "LDM_DECODE_MODEL_URL", "https://ae-rt.oss-cn-beijing.aliyuncs.com/matting_module/inpaint/cond_stage_model_decode.pt" #https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_decode.pt", ) LDM_DIFFUSION_MODEL_URL = os.environ.get( "LDM_DIFFUSION_MODEL_URL", "https://ae-rt.oss-cn-beijing.aliyuncs.com/matting_module/inpaint/diffusion.pt" #"https://github.com/Sanster/models/releases/download/add_ldm/diffusion.pt", ) class DDPM(nn.Module): # classic DDPM with Gaussian diffusion, in image space def __init__(self, device, timesteps=1000, beta_schedule="linear", linear_start=0.0015, linear_end=0.0205, cosine_s=0.008, original_elbo_weight=0., v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta l_simple_weight=1., parameterization="eps", # all assuming fixed variance schedules use_positional_encodings=False): super().__init__() self.device = device self.parameterization = parameterization self.use_positional_encodings = use_positional_encodings self.v_posterior = v_posterior self.original_elbo_weight = original_elbo_weight self.l_simple_weight = l_simple_weight self.register_schedule(beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): betas = make_beta_schedule(self.device, beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = lambda x: torch.tensor(x, dtype=torch.float32).to(self.device) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(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))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( 1. - alphas_cumprod) + self.v_posterior * betas # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_torch(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) if self.parameterization == "eps": lvlb_weights = self.betas ** 2 / ( 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) elif self.parameterization == "x0": lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) else: raise NotImplementedError("mu not supported") # TODO how to choose this term lvlb_weights[0] = lvlb_weights[1] self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) assert not torch.isnan(self.lvlb_weights).all() class LatentDiffusion(DDPM): def __init__(self, diffusion_model, device, cond_stage_key="image", cond_stage_trainable=False, concat_mode=True, scale_factor=1.0, scale_by_std=False, *args, **kwargs): self.num_timesteps_cond = 1 self.scale_by_std = scale_by_std super().__init__(device, *args, **kwargs) self.diffusion_model = diffusion_model self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key self.num_downs = 2 self.scale_factor = scale_factor def make_cond_schedule(self, ): self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() self.cond_ids[:self.num_timesteps_cond] = ids def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) self.shorten_cond_schedule = self.num_timesteps_cond > 1 if self.shorten_cond_schedule: self.make_cond_schedule() def apply_model(self, x_noisy, t, cond): # x_recon = self.model(x_noisy, t, cond['c_concat'][0]) # cond['c_concat'][0].shape 1,4,128,128 t_emb = timestep_embedding(x_noisy.device, t, 256, repeat_only=False) x_recon = self.diffusion_model(x_noisy, t_emb, cond) return x_recon class DDIMSampler(object): def __init__(self, model, schedule="linear"): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def register_buffer(self, name, attr): 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, # array([1]) num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose) alphas_cumprod = self.model.alphas_cumprod # torch.Size([1000]) 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) @torch.no_grad() def sample(self, steps, conditioning, batch_size, shape): self.make_schedule(ddim_num_steps=steps, ddim_eta=0, verbose=False) # sampling C, H, W = shape size = (batch_size, C, H, W) # samples: 1,3,128,128 return self.ddim_sampling(conditioning, size, quantize_denoised=False, ddim_use_original_steps=False, noise_dropout=0, temperature=1., ) @torch.no_grad() def ddim_sampling(self, cond, shape, ddim_use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0.): device = self.model.betas.device b = shape[0] img = torch.randn(shape, device=device) # 用了 timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps # 用了 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] logger.info(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) outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout) img, _ = outs return img @torch.no_grad() def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0.): b, *_, device = *x.shape, x.device e_t = self.model.apply_model(x, t, c) 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 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: # 没用 pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) # 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.: # 没用 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 load_jit_model(url, device): model_path = download_model(url) logger.info(f"Load LDM model from: {model_path}") model = torch.jit.load(model_path).to(device) model.eval() return model class LDM(InpaintModel): pad_mod = 32 def __init__(self, device): super().__init__(device) self.device = device def init_model(self, device): self.diffusion_model = load_jit_model(LDM_DIFFUSION_MODEL_URL, device) self.cond_stage_model_decode = load_jit_model(LDM_DECODE_MODEL_URL, device) self.cond_stage_model_encode = load_jit_model(LDM_ENCODE_MODEL_URL, device) model = LatentDiffusion(self.diffusion_model, device) self.sampler = DDIMSampler(model) @staticmethod def is_downloaded() -> bool: model_paths = [ get_cache_path_by_url(LDM_DIFFUSION_MODEL_URL), get_cache_path_by_url(LDM_DECODE_MODEL_URL), get_cache_path_by_url(LDM_ENCODE_MODEL_URL), ] return all([os.path.exists(it) for it in model_paths]) def forward(self, image, mask, config: Config): """ image: [H, W, C] RGB mask: [H, W, 1] return: BGR IMAGE """ # image [1,3,512,512] float32 # mask: [1,1,512,512] float32 # masked_image: [1,3,512,512] float32 steps = config.ldm_steps image = norm_img(image) mask = norm_img(mask) mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 image = torch.from_numpy(image).unsqueeze(0).to(self.device) mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) masked_image = (1 - mask) * image image = self._norm(image) mask = self._norm(mask) masked_image = self._norm(masked_image) c = self.cond_stage_model_encode(masked_image) cc = torch.nn.functional.interpolate(mask, size=c.shape[-2:]) # 1,1,128,128 c = torch.cat((c, cc), dim=1) # 1,4,128,128 shape = (c.shape[1] - 1,) + c.shape[2:] samples_ddim = self.sampler.sample(steps=steps, conditioning=c, batch_size=c.shape[0], shape=shape) x_samples_ddim = self.cond_stage_model_decode(samples_ddim) # samples_ddim: 1, 3, 128, 128 float32 # image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) # mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0) inpainted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) # inpainted = (1 - mask) * image + mask * predicted_image inpainted_image = inpainted_image.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255 inpainted_image = inpainted_image.astype(np.uint8)[:, :, ::-1] return inpainted_image def _norm(self, tensor): return tensor * 2.0 - 1.0