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| #Imports | |
| import gradio as gr | |
| from diffusers import DiffusionPipeline | |
| from diffusers.schedulers import DPMSolverMultistepScheduler | |
| import torch | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from diffusers.utils import export_to_video | |
| from base64 import b64encode | |
| import torch | |
| device = "cpu" # Force CPU usage | |
| # Load pipeline (outside the function for efficiency) | |
| pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16") | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| # pipe.enable_model_cpu_offload() | |
| pipe.enable_vae_slicing() | |
| def Generate_video(prompt, video_duration_seconds): | |
| num_frames = video_duration_seconds * 10 | |
| video_frames = pipe(prompt=prompt, negative_prompt="low quality", | |
| num_inference_steps=25, num_frames=num_frames).frames | |
| video_path = export_to_video(video_frames) # Assuming you have this function defined | |
| return video_path | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=Generate_video, | |
| inputs=[ | |
| gr.Textbox(lines=5, label="Prompt"), | |
| gr.Number(label="Video Duration (seconds)", value=3), | |
| ], | |
| outputs=gr.Video(label="Generated Video"), | |
| ) | |
| # Launch the app | |
| iface.launch(debug=True) | |