Spaces:
Runtime error
Runtime error
File size: 2,947 Bytes
42d94eb |
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 |
import os
import time
from pathlib import Path
from loguru import logger
from datetime import datetime
from hyvideo.utils.file_utils import save_videos_grid
from hyvideo.config import parse_args
from hyvideo.inference import HunyuanVideoSampler
def main():
args = parse_args()
print(args)
models_root_path = Path(args.model_base)
if not models_root_path.exists():
raise ValueError(f"`models_root` not exists: {models_root_path}")
# Create save folder to save the samples
save_path = args.save_path if args.save_path_suffix=="" else f'{args.save_path}_{args.save_path_suffix}'
if not os.path.exists(args.save_path):
os.makedirs(save_path, exist_ok=True)
models_root_path = "ckpts/hunyuan-video-t2v-720p/transformers/hunyuan_video_720_bf16.safetensors"
text_encoder_filename = "ckpts/hunyuan-video-t2v-720p/text_encoder/llava-llama-3-8b_fp16.safetensors"
import json
with open("./ckpts/hunyuan-video-t2v-720p/vae/config.json", "r", encoding="utf-8") as reader:
text = reader.read()
vae_config= json.loads(text)
# reduce time window used by the VAE for temporal splitting (former time windows is too large for 24 GB)
if vae_config["sample_tsize"] == 64:
vae_config["sample_tsize"] = 32
with open("./ckpts/hunyuan-video-t2v-720p/vae/config.json", "w", encoding="utf-8") as writer:
writer.write(json.dumps(vae_config))
# Load models
hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(models_root_path,text_encoder_filename, args=args)
from mmgp import offload, profile_type
pipe = hunyuan_video_sampler.pipeline
offload.profile(pipe, profile_no= profile_type.HighRAM_LowVRAM_Fast)
# Get the updated args
args = hunyuan_video_sampler.args
# Start sampling
# TODO: batch inference check
outputs = hunyuan_video_sampler.predict(
prompt=args.prompt,
height=args.video_size[0],
width=args.video_size[1],
video_length=args.video_length,
seed=args.seed,
negative_prompt=args.neg_prompt,
infer_steps=args.infer_steps,
guidance_scale=args.cfg_scale,
num_videos_per_prompt=args.num_videos,
flow_shift=args.flow_shift,
batch_size=args.batch_size,
embedded_guidance_scale=args.embedded_cfg_scale
)
samples = outputs['samples']
# Save samples
if 'LOCAL_RANK' not in os.environ or int(os.environ['LOCAL_RANK']) == 0:
for i, sample in enumerate(samples):
sample = samples[i].unsqueeze(0)
time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%H:%M:%S")
save_path = f"{save_path}/{time_flag}_seed{outputs['seeds'][i]}_{outputs['prompts'][i][:100].replace('/','')}.mp4"
save_videos_grid(sample, save_path, fps=24)
logger.info(f'Sample save to: {save_path}')
if __name__ == "__main__":
main()
|