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)