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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) | |