print(f"已生成：{name}")
import torch
from diffusers import HunyuanVideoPipeline
from diffusers.utils import export_to_video
import tempfile
pipe = HunyuanVideoPipeline.from_pretrained("tencent/HunyuanVideo", torch_dtype=torch.float16)
pipe.to("cuda")
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
def generate(prompt, negative_prompt, num_frames, steps, guidance, seed):
import gradio as gr
video_frames = pipe(
os.makedirs("./variations", exist_ok=True)
negative_prompt=negative_prompt,
num_frames=num_frames,
def relight_image(image, prompt, steps, seed):
guidance_scale=guidance,
generator = torch.Generator("cuda").manual_seed(seed) if seed > 0 else None
).frames[0]
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as f:
export_to_video(video_frames, f.name, fps=15)
return f.name
demo = gr.Interface(
fn=generate,
inputs=[
gr.Textbox(label="提示词", lines=3),
gr.Textbox(label="反向提示词", value="模糊、低质量"),
gr.Slider(16, 60, value=45, step=1, label="帧数"),
gr.Slider(20, 100, value=50, step=5, label="步数"),
gr.Slider(3, 12, value=7, step=0.5, label="引导强度"),
gr.Number(value=-1, label="随机种子")
],
outputs=gr.Video(label="生成的视频"),
title="混元视频 - CLORE.AI 上的文本到视频"
)
demo.launch(server_name="0.0.0.0", server_port=7860)
