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A10G
Running
on
A10G
| 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 | |
| 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, dtype =torch.float16) | |
| return image | |
| 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, dtype =torch.float16) | |
| for t in reversed(timesteps): | |
| idx = t_to_idx[int(t)] | |
| xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0, dtype =torch.float16) * 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, dtype =torch.float16) | |
| t_to_idx = {int(v):k for k,v in enumerate(timesteps)} | |
| xt = x0 | |
| op = tqdm(reversed(timesteps), desc= "Inverting...") 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, dtype =torch.float16) | |
| 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, dtype=torch.float16) | |
| 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 | |