Create app.py

app.py

@@ -0,0 +1,80 @@
+import gradio as gr
+import spaces
+import torch
+import os
+from glob import glob
+from diffusers import StableVideoDiffusionPipeline
+from diffusers.utils import export_to_video
+from PIL import Image
+
+output_folder = "outputs"
+
+pipe = StableVideoDiffusionPipeline.from_pretrained(
+    "stabilityai/stable-video-diffusion-img2vid-xt", variant="fp16"
+).to("cuda")
+
+@spaces.GPU(duration=480)
+def sample(
+    image: Image,
+    width: int = 1024,
+    height: int = 576,
+    motion_bucket_id: int = 127,
+    fps_id: int = 30,
+):
+    width = int(width)
+    height = int(height)
+    img = image.resize((width, height))
+
+    os.makedirs(output_folder, exist_ok=True)
+    base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+    video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+
+    frames = pipe(
+        img,
+        decode_chunk_size=3,
+        generator=None,
+        motion_bucket_id=motion_bucket_id,
+        noise_aug_strength=0.1,
+        num_frames=25,
+    ).frames[0]
+
+    export_to_video(frames, video_path, fps=fps_id)
+    return video_path
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        image = gr.Image(label="Upload your image", type="pil")
+        video = gr.Video()
+
+    with gr.Column():
+        generate_btn = gr.Button("Generate")
+
+        with gr.Accordion("Advanced options", open=False):
+            width = gr.Number(label="Width", value=1024, minimum=1)
+            height = gr.Number(label="Height", value=576, minimum=1)
+            motion_bucket_id = gr.Slider(
+                label="Motion bucket id",
+                info="Controls how much motion to add/remove from the image",
+                value=60,
+                minimum=1,
+                maximum=255,
+            )
+            fps_id = gr.Slider(
+                label="Frames per second",
+                info="Video length will be 25 frames.",
+                value=30,
+                minimum=5,
+                maximum=60,
+            )
+
+    image.upload(fn=lambda img: img, inputs=image, outputs=image, queue=False)
+    generate_btn.click(
+        fn=sample,
+        inputs=[image, width, height, motion_bucket_id, fps_id],
+        outputs=[video],
+        api_name="video",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20, api_open=False)
+    demo.launch(show_api=False)