| import os |
| import time |
| from omegaconf import OmegaConf |
| import torch |
| from scripts.evaluation.funcs import load_model_checkpoint, load_image_batch, save_videos, batch_ddim_sampling |
| from utils.utils import instantiate_from_config |
| from huggingface_hub import hf_hub_download |
|
|
| class Image2Video(): |
| def __init__(self,result_dir='./tmp/',gpu_num=1) -> None: |
| self.download_model() |
| self.result_dir = result_dir |
| if not os.path.exists(self.result_dir): |
| os.mkdir(self.result_dir) |
| ckpt_path='checkpoints/i2v_512_v1/model.ckpt' |
| config_file='configs/inference_i2v_512_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) |
| |
| 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=12.0, eta=1.0, fps=16): |
| 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.in_channels |
| frames = model.temporal_length |
| h, w = 320 // 8, 512 // 8 |
| noise_shape = [batch_size, channels, frames, h, w] |
|
|
| |
| text_emb = model.get_learned_conditioning([prompt]) |
|
|
| |
| img_tensor = torch.from_numpy(image).permute(2, 0, 1).float() |
| img_tensor = (img_tensor / 255. - 0.5) * 2 |
| img_tensor = img_tensor.unsqueeze(0) |
| cond_images = img_tensor.to(model.device) |
| img_emb = model.get_image_embeds(cond_images) |
| imtext_cond = torch.cat([text_emb, img_emb], dim=1) |
| cond = {"c_crossattn": [imtext_cond], "fps": fps} |
| |
| |
| batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale) |
| |
| 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[:30] |
|
|
| 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 = 'VideoCrafter/Image2Video-512' |
| filename_list = ['model.ckpt'] |
| if not os.path.exists('./checkpoints/i2v_512_v1/'): |
| os.makedirs('./checkpoints/i2v_512_v1/') |
| for filename in filename_list: |
| local_file = os.path.join('./checkpoints/i2v_512_v1/', filename) |
| if not os.path.exists(local_file): |
| hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/i2v_512_v1/', local_dir_use_symlinks=False) |
| |
| if __name__ == '__main__': |
| i2v = Image2Video() |
| video_path = i2v.get_image('prompts/i2v_prompts/horse.png','horses are walking on the grassland') |
| print('done', video_path) |
