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| import os | |
| import time | |
| from omegaconf import OmegaConf | |
| import torch | |
| from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z | |
| from utils.utils import instantiate_from_config | |
| from huggingface_hub import hf_hub_download | |
| from einops import repeat | |
| import torchvision.transforms as transforms | |
| from pytorch_lightning import seed_everything | |
| from einops import rearrange | |
| class Image2Video(): | |
| def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256') -> None: | |
| self.resolution = (int(resolution.split('_')[0]), int(resolution.split('_')[1])) #hw | |
| self.download_model() | |
| self.result_dir = result_dir | |
| if not os.path.exists(self.result_dir): | |
| os.mkdir(self.result_dir) | |
| ckpt_path='checkpoints/tooncrafter_'+resolution.split('_')[1]+'_interp_v1/model.ckpt' | |
| config_file='configs/inference_'+resolution.split('_')[1]+'_v1.0.yaml' | |
| config = OmegaConf.load(config_file) | |
| model_config = config.pop("model", OmegaConf.create()) | |
| model_config['params']['unet_config']['params']['use_checkpoint']=False | |
| model_list = [] | |
| for gpu_id in range(gpu_num): | |
| model = instantiate_from_config(model_config) | |
| # model = model.cuda(gpu_id) | |
| print(ckpt_path) | |
| assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" | |
| model = load_model_checkpoint(model, ckpt_path) | |
| model.eval() | |
| model_list.append(model) | |
| self.model_list = model_list | |
| self.save_fps = 8 | |
| def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, image2=None): | |
| seed_everything(seed) | |
| transform = transforms.Compose([ | |
| transforms.Resize(min(self.resolution)), | |
| transforms.CenterCrop(self.resolution), | |
| ]) | |
| torch.cuda.empty_cache() | |
| print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) | |
| start = time.time() | |
| gpu_id=0 | |
| if steps > 60: | |
| steps = 60 | |
| model = self.model_list[gpu_id] | |
| model = model.cuda() | |
| batch_size=1 | |
| channels = model.model.diffusion_model.out_channels | |
| frames = model.temporal_length | |
| h, w = self.resolution[0] // 8, self.resolution[1] // 8 | |
| noise_shape = [batch_size, channels, frames, h, w] | |
| # text cond | |
| with torch.no_grad(), torch.cuda.amp.autocast(): | |
| text_emb = model.get_learned_conditioning([prompt]) | |
| # img cond | |
| img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) | |
| img_tensor = (img_tensor / 255. - 0.5) * 2 | |
| image_tensor_resized = transform(img_tensor) #3,h,w | |
| videos = image_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw | |
| # z = get_latent_z(model, videos) #bc,1,hw | |
| videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2) | |
| img_tensor2 = torch.from_numpy(image2).permute(2, 0, 1).float().to(model.device) | |
| img_tensor2 = (img_tensor2 / 255. - 0.5) * 2 | |
| image_tensor_resized2 = transform(img_tensor2) #3,h,w | |
| videos2 = image_tensor_resized2.unsqueeze(0).unsqueeze(2) # bchw | |
| videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames//2) | |
| videos = torch.cat([videos, videos2], dim=2) | |
| z, hs = self.get_latent_z_with_hidden_states(model, videos) | |
| img_tensor_repeat = torch.zeros_like(z) | |
| img_tensor_repeat[:,:,:1,:,:] = z[:,:,:1,:,:] | |
| img_tensor_repeat[:,:,-1:,:,:] = z[:,:,-1:,:,:] | |
| cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc | |
| img_emb = model.image_proj_model(cond_images) | |
| imtext_cond = torch.cat([text_emb, img_emb], dim=1) | |
| fs = torch.tensor([fs], dtype=torch.long, device=model.device) | |
| cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} | |
| ## inference | |
| batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, hs=hs) | |
| ## remove the last frame | |
| if image2 is None: | |
| batch_samples = batch_samples[:,:,:,:-1,...] | |
| ## b,samples,c,t,h,w | |
| prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt | |
| prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str | |
| prompt_str=prompt_str[:40] | |
| if len(prompt_str) == 0: | |
| prompt_str = 'empty_prompt' | |
| save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) | |
| print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds") | |
| model = model.cpu() | |
| return os.path.join(self.result_dir, f"{prompt_str}.mp4") | |
| def download_model(self): | |
| REPO_ID = 'Doubiiu/ToonCrafter' | |
| filename_list = ['model.ckpt'] | |
| if not os.path.exists('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/'): | |
| os.makedirs('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/') | |
| for filename in filename_list: | |
| local_file = os.path.join('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/', filename) | |
| if not os.path.exists(local_file): | |
| hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/', local_dir_use_symlinks=False) | |
| def get_latent_z_with_hidden_states(self, model, videos): | |
| b, c, t, h, w = videos.shape | |
| x = rearrange(videos, 'b c t h w -> (b t) c h w') | |
| encoder_posterior, hidden_states = model.first_stage_model.encode(x, return_hidden_states=True) | |
| hidden_states_first_last = [] | |
| ### use only the first and last hidden states | |
| for hid in hidden_states: | |
| hid = rearrange(hid, '(b t) c h w -> b c t h w', t=t) | |
| hid_new = torch.cat([hid[:, :, 0:1], hid[:, :, -1:]], dim=2) | |
| hidden_states_first_last.append(hid_new) | |
| z = model.get_first_stage_encoding(encoder_posterior).detach() | |
| z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) | |
| return z, hidden_states_first_last | |
| if __name__ == '__main__': | |
| i2v = Image2Video() | |
| video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset') | |
| print('done', video_path) |