| import torch | |
| import gradio as gr | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from diffusers.utils import export_to_video | |
| # Отключение CUDA (GPU) | |
| torch.device('cuda') | |
| def generate_video(prompt): | |
| # load pipeline | |
| pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16").device | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config).device | |
| # optimize for GPU memory | |
| pipe.enable_model_cpu_offload() | |
| pipe.enable_vae_slicing() | |
| # generate | |
| video_frames = pipe(prompt, num_inference_steps=25, num_frames=200).frames | |
| # convert to video | |
| video_path = export_to_video(video_frames) | |
| return video_path | |
| demo = gr.Interface(fn=generate_video, inputs="text", outputs="file") | |
| demo.launch(share=True) |