import torch import os from tqdm import tqdm from PIL import Image, ImageDraw ,ImageFont from matplotlib import pyplot as plt import torchvision.transforms as T import os import yaml import numpy as np import gradio as gr # This file was copied from the DDPM inversion Repo - https://github.com/inbarhub/DDPM_inversion # def load_512(image_path, left=0, right=0, top=0, bottom=0, device=None): if type(image_path) is str: image = np.array(Image.open(image_path).convert('RGB'))[:, :, :3] else: image = image_path h, w, c = image.shape left = min(left, w-1) right = min(right, w - left - 1) top = min(top, h - left - 1) bottom = min(bottom, h - top - 1) image = image[top:h-bottom, left:w-right] h, w, c = image.shape if h < w: offset = (w - h) // 2 image = image[:, offset:offset + h] elif w < h: offset = (h - w) // 2 image = image[offset:offset + w] image = np.array(Image.fromarray(image).resize((512, 512))) image = torch.from_numpy(image).float() / 127.5 - 1 image = image.permute(2, 0, 1).unsqueeze(0).to(device) return image def load_real_image(folder = "data/", img_name = None, idx = 0, img_size=512, device='cuda'): from PIL import Image from glob import glob if img_name is not None: path = os.path.join(folder, img_name) else: path = glob(folder + "*")[idx] img = Image.open(path).resize((img_size, img_size)) img = pil_to_tensor(img).to(device) if img.shape[1]== 4: img = img[:,:3,:,:] return img def mu_tilde(model, xt,x0, timestep): "mu_tilde(x_t, x_0) DDPM paper eq. 7" prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod alpha_t = model.scheduler.alphas[timestep] beta_t = 1 - alpha_t alpha_bar = model.scheduler.alphas_cumprod[timestep] return ((alpha_prod_t_prev ** 0.5 * beta_t) / (1-alpha_bar)) * x0 + ((alpha_t**0.5 *(1-alpha_prod_t_prev)) / (1- alpha_bar))*xt def sample_xts_from_x0(model, x0, num_inference_steps=50): """ Samples from P(x_1:T|x_0) """ # torch.manual_seed(43256465436) alpha_bar = model.scheduler.alphas_cumprod sqrt_one_minus_alpha_bar = (1-alpha_bar) ** 0.5 alphas = model.scheduler.alphas betas = 1 - alphas variance_noise_shape = ( num_inference_steps, model.unet.in_channels, model.unet.sample_size, model.unet.sample_size) timesteps = model.scheduler.timesteps.to(model.device) t_to_idx = {int(v):k for k,v in enumerate(timesteps)} xts = torch.zeros(variance_noise_shape).to(x0.device) for t in reversed(timesteps): idx = t_to_idx[int(t)] xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0) * sqrt_one_minus_alpha_bar[t] xts = torch.cat([xts, x0 ],dim = 0) return xts def encode_text(model, prompts): text_input = model.tokenizer( prompts, padding="max_length", max_length=model.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) with torch.no_grad(): text_encoding = model.text_encoder(text_input.input_ids.to(model.device))[0] return text_encoding def forward_step(model, model_output, timestep, sample): next_timestep = min(model.scheduler.config.num_train_timesteps - 2, timestep + model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps) # 2. compute alphas, betas alpha_prod_t = model.scheduler.alphas_cumprod[timestep] # alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep] if next_ltimestep >= 0 else self.scheduler.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) # 5. TODO: simple noising implementatiom next_sample = model.scheduler.add_noise(pred_original_sample, model_output, torch.LongTensor([next_timestep])) return next_sample def get_variance(model, timestep): #, prev_timestep): prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps alpha_prod_t = model.scheduler.alphas_cumprod[timestep] alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) return variance def inversion_forward_process(model, x0, etas = None, prog_bar = False, prompt = "", cfg_scale = 3.5, num_inference_steps=50, eps = None ): if not prompt=="": text_embeddings = encode_text(model, prompt) uncond_embedding = encode_text(model, "") timesteps = model.scheduler.timesteps.to(model.device) variance_noise_shape = ( num_inference_steps, model.unet.in_channels, model.unet.sample_size, model.unet.sample_size) if etas is None or (type(etas) in [int, float] and etas == 0): eta_is_zero = True zs = None else: eta_is_zero = False if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps xts = sample_xts_from_x0(model, x0, num_inference_steps=num_inference_steps) alpha_bar = model.scheduler.alphas_cumprod zs = torch.zeros(size=variance_noise_shape, device=model.device) t_to_idx = {int(v):k for k,v in enumerate(timesteps)} xt = x0 op = tqdm(reversed(timesteps)) if prog_bar else reversed(timesteps) for t in op: idx = t_to_idx[int(t)] # 1. predict noise residual if not eta_is_zero: xt = xts[idx][None] with torch.no_grad(): out = model.unet.forward(xt, timestep = t, encoder_hidden_states = uncond_embedding) if not prompt=="": cond_out = model.unet.forward(xt, timestep=t, encoder_hidden_states = text_embeddings) if not prompt=="": ## classifier free guidance noise_pred = out.sample + cfg_scale * (cond_out.sample - out.sample) else: noise_pred = out.sample if eta_is_zero: # 2. compute more noisy image and set x_t -> x_t+1 xt = forward_step(model, noise_pred, t, xt) else: xtm1 = xts[idx+1][None] # pred of x0 pred_original_sample = (xt - (1-alpha_bar[t]) ** 0.5 * noise_pred ) / alpha_bar[t] ** 0.5 # direction to xt prev_timestep = t - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod variance = get_variance(model, t) pred_sample_direction = (1 - alpha_prod_t_prev - etas[idx] * variance ) ** (0.5) * noise_pred mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction z = (xtm1 - mu_xt ) / ( etas[idx] * variance ** 0.5 ) zs[idx] = z # correction to avoid error accumulation xtm1 = mu_xt + ( etas[idx] * variance ** 0.5 )*z xts[idx+1] = xtm1 if not zs is None: zs[-1] = torch.zeros_like(zs[-1]) return xt, zs, xts def reverse_step(model, model_output, timestep, sample, eta = 0, variance_noise=None): # 1. get previous step value (=t-1) prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps # 2. compute alphas, betas alpha_prod_t = model.scheduler.alphas_cumprod[timestep] alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) # 5. compute variance: "sigma_t(η)" -> see formula (16) # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) # variance = self.scheduler._get_variance(timestep, prev_timestep) variance = get_variance(model, timestep) #, prev_timestep) std_dev_t = eta * variance ** (0.5) # Take care of asymetric reverse process (asyrp) model_output_direction = model_output # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf # pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * model_output_direction # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction # 8. Add noice if eta > 0 if eta > 0: if variance_noise is None: variance_noise = torch.randn(model_output.shape, device=model.device) sigma_z = eta * variance ** (0.5) * variance_noise prev_sample = prev_sample + sigma_z return prev_sample def inversion_reverse_process(model, xT, etas = 0, prompts = "", cfg_scales = None, prog_bar = False, zs = None, controller=None, asyrp = False ): batch_size = len(prompts) cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(model.device) text_embeddings = encode_text(model, prompts) uncond_embedding = encode_text(model, [""] * batch_size) if etas is None: etas = 0 if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps assert len(etas) == model.scheduler.num_inference_steps timesteps = model.scheduler.timesteps.to(model.device) xt = xT.expand(batch_size, -1, -1, -1) op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:] t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])} for t in op: idx = t_to_idx[int(t)] ## Unconditional embedding with torch.no_grad(): uncond_out = model.unet.forward(xt, timestep = t, encoder_hidden_states = uncond_embedding) ## Conditional embedding if prompts: with torch.no_grad(): cond_out = model.unet.forward(xt, timestep = t, encoder_hidden_states = text_embeddings) z = zs[idx] if not zs is None else None z = z.expand(batch_size, -1, -1, -1) if prompts: ## classifier free guidance noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample) else: noise_pred = uncond_out.sample # 2. compute less noisy image and set x_t -> x_t-1 xt = reverse_step(model, noise_pred, t, xt, eta = etas[idx], variance_noise = z) if controller is not None: xt = controller.step_callback(xt) return xt, zs