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 @torch.no_grad() 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] @torch.no_grad() 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()