Create app.py

app.py

@@ -0,0 +1,29 @@
+import torch
+import os
+import gradio as gr
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
+from IPython.display import HTML
+from base64 import b64encode
+
+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 model(txt, time):
+    prompt = txt
+    video_duration_seconds = time
+    num_frames = video_duration_seconds * 10
+    video_frames = pipe(prompt, negative_prompt="low quality",
+                        num_inference_steps=25, num_frames=num_frames).frames
+    video_path = export_to_video(video_frames)
+    return video_path
+    
+demo = gr.Interface(
+    fn=model,
+    inputs=["text", gr.Slider(1, 10, step=1)],
+    outputs=gr.Video(label="Out",output_width=400, output_height=300)
+)
+
+demo.launch(inline = False)