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| from transformers import CLIPTextModel, CLIPTokenizer, logging | |
| from diffusers import ( | |
| AutoencoderKL, | |
| UNet2DConditionModel, | |
| DDIMScheduler, | |
| StableDiffusionPipeline, | |
| ) | |
| import torchvision.transforms.functional as TF | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import sys | |
| sys.path.append('./') | |
| from zero123 import Zero123Pipeline | |
| class Zero123(nn.Module): | |
| def __init__(self, device, fp16=True, t_range=[0.02, 0.98]): | |
| super().__init__() | |
| self.device = device | |
| self.fp16 = fp16 | |
| self.dtype = torch.float16 if fp16 else torch.float32 | |
| self.pipe = Zero123Pipeline.from_pretrained( | |
| # "bennyguo/zero123-diffusers", | |
| "bennyguo/zero123-xl-diffusers", | |
| # './model_cache/zero123_xl', | |
| variant="fp16_ema" if self.fp16 else None, | |
| torch_dtype=self.dtype, | |
| ).to(self.device) | |
| # for param in self.pipe.parameters(): | |
| # param.requires_grad = False | |
| self.pipe.image_encoder.eval() | |
| self.pipe.vae.eval() | |
| self.pipe.unet.eval() | |
| self.pipe.clip_camera_projection.eval() | |
| self.vae = self.pipe.vae | |
| self.unet = self.pipe.unet | |
| self.pipe.set_progress_bar_config(disable=True) | |
| self.scheduler = DDIMScheduler.from_config(self.pipe.scheduler.config) | |
| self.num_train_timesteps = self.scheduler.config.num_train_timesteps | |
| self.min_step = int(self.num_train_timesteps * t_range[0]) | |
| self.max_step = int(self.num_train_timesteps * t_range[1]) | |
| self.alphas = self.scheduler.alphas_cumprod.to(self.device) # for convenience | |
| self.embeddings = None | |
| def get_img_embeds(self, x): | |
| # x: image tensor in [0, 1] | |
| x = F.interpolate(x, (256, 256), mode='bilinear', align_corners=False) | |
| x_pil = [TF.to_pil_image(image) for image in x] | |
| x_clip = self.pipe.feature_extractor(images=x_pil, return_tensors="pt").pixel_values.to(device=self.device, dtype=self.dtype) | |
| c = self.pipe.image_encoder(x_clip).image_embeds | |
| v = self.encode_imgs(x.to(self.dtype)) / self.vae.config.scaling_factor | |
| self.embeddings = [c, v] | |
| def refine(self, pred_rgb, polar, azimuth, radius, | |
| guidance_scale=5, steps=50, strength=0.8, | |
| ): | |
| batch_size = pred_rgb.shape[0] | |
| self.scheduler.set_timesteps(steps) | |
| if strength == 0: | |
| init_step = 0 | |
| latents = torch.randn((1, 4, 32, 32), device=self.device, dtype=self.dtype) | |
| else: | |
| init_step = int(steps * strength) | |
| pred_rgb_256 = F.interpolate(pred_rgb, (256, 256), mode='bilinear', align_corners=False) | |
| latents = self.encode_imgs(pred_rgb_256.to(self.dtype)) | |
| latents = self.scheduler.add_noise(latents, torch.randn_like(latents), self.scheduler.timesteps[init_step]) | |
| T = np.stack([np.deg2rad(polar), np.sin(np.deg2rad(azimuth)), np.cos(np.deg2rad(azimuth)), radius], axis=-1) | |
| T = torch.from_numpy(T).unsqueeze(1).to(self.dtype).to(self.device) # [8, 1, 4] | |
| cc_emb = torch.cat([self.embeddings[0].repeat(batch_size, 1, 1), T], dim=-1) | |
| cc_emb = self.pipe.clip_camera_projection(cc_emb) | |
| cc_emb = torch.cat([cc_emb, torch.zeros_like(cc_emb)], dim=0) | |
| vae_emb = self.embeddings[1].repeat(batch_size, 1, 1, 1) | |
| vae_emb = torch.cat([vae_emb, torch.zeros_like(vae_emb)], dim=0) | |
| for i, t in enumerate(self.scheduler.timesteps[init_step:]): | |
| x_in = torch.cat([latents] * 2) | |
| t_in = torch.cat([t.view(1)] * 2).to(self.device) | |
| noise_pred = self.unet( | |
| torch.cat([x_in, vae_emb], dim=1), | |
| t_in.to(self.unet.dtype), | |
| encoder_hidden_states=cc_emb, | |
| ).sample | |
| noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) | |
| latents = self.scheduler.step(noise_pred, t, latents).prev_sample | |
| imgs = self.decode_latents(latents) # [1, 3, 256, 256] | |
| return imgs | |
| def train_step(self, pred_rgb, polar, azimuth, radius, step_ratio=None, guidance_scale=5, as_latent=False): | |
| # pred_rgb: tensor [1, 3, H, W] in [0, 1] | |
| batch_size = pred_rgb.shape[0] | |
| if as_latent: | |
| latents = F.interpolate(pred_rgb, (32, 32), mode='bilinear', align_corners=False) * 2 - 1 | |
| else: | |
| pred_rgb_256 = F.interpolate(pred_rgb, (256, 256), mode='bilinear', align_corners=False) | |
| latents = self.encode_imgs(pred_rgb_256.to(self.dtype)) | |
| if step_ratio is not None: | |
| # dreamtime-like | |
| # t = self.max_step - (self.max_step - self.min_step) * np.sqrt(step_ratio) | |
| t = np.round((1 - step_ratio) * self.num_train_timesteps).clip(self.min_step, self.max_step) | |
| t = torch.full((batch_size,), t, dtype=torch.long, device=self.device) | |
| else: | |
| t = torch.randint(self.min_step, self.max_step + 1, (batch_size,), dtype=torch.long, device=self.device) | |
| w = (1 - self.alphas[t]).view(batch_size, 1, 1, 1) | |
| with torch.no_grad(): | |
| noise = torch.randn_like(latents) | |
| latents_noisy = self.scheduler.add_noise(latents, noise, t) | |
| x_in = torch.cat([latents_noisy] * 2) | |
| t_in = torch.cat([t] * 2) | |
| T = np.stack([np.deg2rad(polar), np.sin(np.deg2rad(azimuth)), np.cos(np.deg2rad(azimuth)), radius], axis=-1) | |
| T = torch.from_numpy(T).unsqueeze(1).to(self.dtype).to(self.device) # [8, 1, 4] | |
| cc_emb = torch.cat([self.embeddings[0].repeat(batch_size, 1, 1), T], dim=-1) | |
| cc_emb = self.pipe.clip_camera_projection(cc_emb) | |
| cc_emb = torch.cat([cc_emb, torch.zeros_like(cc_emb)], dim=0) | |
| vae_emb = self.embeddings[1].repeat(batch_size, 1, 1, 1) | |
| vae_emb = torch.cat([vae_emb, torch.zeros_like(vae_emb)], dim=0) | |
| noise_pred = self.unet( | |
| torch.cat([x_in, vae_emb], dim=1), | |
| t_in.to(self.unet.dtype), | |
| encoder_hidden_states=cc_emb, | |
| ).sample | |
| noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) | |
| grad = w * (noise_pred - noise) | |
| grad = torch.nan_to_num(grad) | |
| target = (latents - grad).detach() | |
| loss = 0.5 * F.mse_loss(latents.float(), target, reduction='sum') | |
| return loss | |
| def decode_latents(self, latents): | |
| latents = 1 / self.vae.config.scaling_factor * latents | |
| imgs = self.vae.decode(latents).sample | |
| imgs = (imgs / 2 + 0.5).clamp(0, 1) | |
| return imgs | |
| def encode_imgs(self, imgs, mode=False): | |
| # imgs: [B, 3, H, W] | |
| imgs = 2 * imgs - 1 | |
| posterior = self.vae.encode(imgs).latent_dist | |
| if mode: | |
| latents = posterior.mode() | |
| else: | |
| latents = posterior.sample() | |
| latents = latents * self.vae.config.scaling_factor | |
| return latents | |
| if __name__ == '__main__': | |
| import cv2 | |
| import argparse | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('input', type=str) | |
| parser.add_argument('--polar', type=float, default=0, help='delta polar angle in [-90, 90]') | |
| parser.add_argument('--azimuth', type=float, default=0, help='delta azimuth angle in [-180, 180]') | |
| parser.add_argument('--radius', type=float, default=0, help='delta camera radius multiplier in [-0.5, 0.5]') | |
| opt = parser.parse_args() | |
| device = torch.device('cuda') | |
| print(f'[INFO] loading image from {opt.input} ...') | |
| image = cv2.imread(opt.input, cv2.IMREAD_UNCHANGED) | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| image = cv2.resize(image, (256, 256), interpolation=cv2.INTER_AREA) | |
| image = image.astype(np.float32) / 255.0 | |
| image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).contiguous().to(device) | |
| print(f'[INFO] loading model ...') | |
| zero123 = Zero123(device) | |
| print(f'[INFO] running model ...') | |
| zero123.get_img_embeds(image) | |
| while True: | |
| outputs = zero123.refine(image, polar=[opt.polar], azimuth=[opt.azimuth], radius=[opt.radius], strength=0) | |
| plt.imshow(outputs.float().cpu().numpy().transpose(0, 2, 3, 1)[0]) | |
| plt.show() |