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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
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/dynamicrafter_256_v1/model.ckpt'
config_file='configs/inference_256_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)
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):
seed_everything(seed)
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
])
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 = 256 // 8, 256 // 8
noise_shape = [batch_size, channels, frames, h, w]
# text cond
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) #3,256,256
videos = image_tensor_resized.unsqueeze(0) # bchw
z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
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)
## 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]
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/DynamiCrafter'
filename_list = ['model.ckpt']
if not os.path.exists('./checkpoints/dynamicrafter_256_v1/'):
os.makedirs('./dynamicrafter_256_v1/')
for filename in filename_list:
local_file = os.path.join('./checkpoints/dynamicrafter_256_v1/', filename)
if not os.path.exists(local_file):
hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_256_v1/', local_dir_use_symlinks=False)
if __name__ == '__main__':
i2v = Image2Video()
video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset')
print('done', video_path) |