File size: 9,080 Bytes
4bc9607 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
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
from cldm.model import load_state_dict
import cv2
def extract_frames(video_path):
# εη»γγ‘γ€γ«γθͺγΏθΎΌγ
cap = cv2.VideoCapture(video_path)
frame_list = []
frame_num = 0
while True:
# γγ¬γΌγ γθͺγΏθΎΌγ
ret, frame = cap.read()
if not ret:
break
# γγ¬γΌγ γγͺγΉγγ«θΏ½ε
frame_list.append(frame)
frame_num += 1
# εη»γγ‘γ€γ«γιγγ
cap.release()
return frame_list
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)
#ToonCrafterModel
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
#ControlModel
cn_ckpt_path = "control_models/sketch_encoder.ckpt"
cn_config_file = 'configs/cldm_v21.yaml'
cn_config = OmegaConf.load(cn_config_file)
cn_model_config = cn_config.pop("control_stage_config", OmegaConf.create())
model_list = []
for gpu_id in range(gpu_num):
model = instantiate_from_config(model_config)
cn_model = instantiate_from_config(cn_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()
cn_model.load_state_dict(load_state_dict(cn_ckpt_path, location='cuda'))
cn_model.eval()
model.control_model = cn_model
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, frame_guides=None,control_scale=0.6):
control_frames = extract_frames(frame_guides)
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])
#control cond
if frame_guides is not None:
cn_videos = []
for frame in control_frames:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = cv2.bitwise_not(frame)
cn_tensor = torch.from_numpy(frame).unsqueeze(2).permute(2, 0, 1).float().to(model.device)
#cn_tensor = (cn_tensor / 255. - 0.5) * 2
cn_tensor = ( cn_tensor/255.0 )
cn_tensor_resized = transform(cn_tensor) #3,h,w
cn_video = cn_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw
cn_videos.append(cn_video)
cn_videos = torch.cat(cn_videos, dim=2)
model_list = []
for model in self.model_list:
model.control_scale = control_scale
model_list.append(model)
self.model_list = model_list
else:
cn_videos = None
# 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], "control_cond": cn_videos}
## 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) |