import os
import time
from pathlib import Path
from loguru import logger
from datetime import datetime
import gradio as gr
import random
import spaces
import torch
from hyvideo.utils.file_utils import save_videos_grid
from hyvideo.utils.preprocess_text_encoder_tokenizer_utils import preprocess_text_encoder_tokenizer
from hyvideo.config import parse_args
from hyvideo.inference import HunyuanVideoSampler
from hyvideo.constants import NEGATIVE_PROMPT
from huggingface_hub import snapshot_download
if torch.cuda.device_count() > 0:
snapshot_download(repo_id="tencent/HunyuanVideo", repo_type="model", local_dir="ckpts", force_download=True)
snapshot_download(repo_id="xtuner/llava-llama-3-8b-v1_1-transformers", repo_type="model", local_dir="ckpts/llava-llama-3-8b-v1_1-transformers", force_download=True)
preprocess_text_encoder_tokenizer(input_dir = "ckpts/llava-llama-3-8b-v1_1-transformers", output_dir = "ckpts/text_encoder")
snapshot_download(repo_id="openai/clip-vit-large-patch14", repo_type="model", local_dir="ckpts/text_encoder_2", force_download=True)
def initialize_model(model_path):
print('initialize_model: ' + model_path)
if torch.cuda.device_count() == 0:
return None
args = parse_args()
models_root_path = Path(model_path)
if not models_root_path.exists():
raise ValueError(f"`models_root` not exists: {models_root_path}")
print(f"`models_root` exists: {models_root_path}")
hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(models_root_path, args=args)
print('Model initialized: ' + model_path)
return hunyuan_video_sampler
@spaces.GPU(duration=120)
def generate_video(
model,
prompt,
resolution,
video_length,
seed,
num_inference_steps,
guidance_scale,
flow_shift,
embedded_guidance_scale
):
if torch.cuda.device_count() == 0:
gr.Warning('Set this space to GPU config to make it work.')
return None
seed = None if seed == -1 else seed
width, height = resolution.split("x")
width, height = int(width), int(height)
negative_prompt = "" # not applicable in the inference
outputs = model.predict(
prompt=prompt,
height=height,
width=width,
video_length=video_length,
seed=seed,
negative_prompt=negative_prompt,
infer_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_videos_per_prompt=1,
flow_shift=flow_shift,
batch_size=1,
embedded_guidance_scale=embedded_guidance_scale
)
samples = outputs['samples']
sample = samples[0].unsqueeze(0)
save_path = "./gradio_outputs"
os.makedirs(save_path, exist_ok=True)
time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%H:%M:%S")
video_path = f"{save_path}/{time_flag}_seed{outputs['seeds'][0]}_{outputs['prompts'][0][:100].replace('/','')}.mp4"
save_videos_grid(sample, video_path, fps=24)
logger.info(f'Sample saved to: {video_path}')
return video_path
def create_demo(model_path):
model = initialize_model(model_path)
with gr.Blocks() as demo:
if torch.cuda.device_count() == 0:
with gr.Row():
gr.HTML("""
<p style="background-color: red;"><big><big><big><b>⚠️To use <i>Hunyuan Video</i>, <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/HunyuanVideo?duplicate=true">duplicate this space</a> and set a GPU with 80 GB VRAM.</b>
You can't use <i>Hunyuan Video</i> directly here because this space runs on a CPU, which is not enough for <i>Hunyuan Video</i>. Please provide <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/HunyuanVideo/discussions/new">feedback</a> if you have issues.
</big></big></big></p>
""")
gr.Markdown("# Hunyuan Video Generation")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", value="A cat walks on the grass, realistic style.")
with gr.Row():
resolution = gr.Dropdown(
choices=[
# 720p
("1280x720 (16:9, 720p)", "1280x720"),
("720x1280 (9:16, 720p)", "720x1280"),
("1104x832 (4:3, 720p)", "1104x832"),
("832x1104 (3:4, 720p)", "832x1104"),
("960x960 (1:1, 720p)", "960x960"),
# 540p
("960x544 (16:9, 540p)", "960x544"),
("544x960 (9:16, 540p)", "544x960"),
("832x624 (4:3, 540p)", "832x624"),
("624x832 (3:4, 540p)", "624x832"),
("720x720 (1:1, 540p)", "720x720"),
],
value="832x624",
label="Resolution"
)
video_length = gr.Dropdown(
label="Video Length",
choices=[
("2s(65f)", 65),
("5s(129f)", 129),
],
value=65,
)
num_inference_steps = gr.Slider(1, 100, value=5, step=1, label="Number of Inference Steps")
with gr.Accordion("Advanced Options", open=False):
with gr.Column():
seed = gr.Slider(label="Seed (-1 for random)", value=-1, minimum=-1, maximum=2**63 - 1, step=1)
guidance_scale = gr.Slider(1.0, 20.0, value=1.0, step=0.5, label="Guidance Scale")
flow_shift = gr.Slider(0.0, 10.0, value=7.0, step=0.1, label="Flow Shift")
embedded_guidance_scale = gr.Slider(1.0, 20.0, value=6.0, step=0.5, label="Embedded Guidance Scale")
generate_btn = gr.Button(value = "🚀 Generate Video", variant = "primary")
with gr.Row():
output = gr.Video(label = "Generated Video", autoplay = True)
gr.Markdown("""
## **Alternatives**
If you can't use _Hunyuan Video_, you can use _[CogVideoX](https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space)_ or _[LTX Video Playground](https://huggingface.co/spaces/Lightricks/LTX-Video-Playground)_ instead.
""")
generate_btn.click(
fn=lambda *inputs: generate_video(model, *inputs),
inputs=[
prompt,
resolution,
video_length,
seed,
num_inference_steps,
guidance_scale,
flow_shift,
embedded_guidance_scale
],
outputs=output
)
return demo
if __name__ == "__main__":
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
demo = create_demo("ckpts")
demo.queue(10).launch()