Update main.py

main.py

@@ -15,23 +15,33 @@ client = InferenceClient("stabilityai/stable-video-diffusion-img2vid-xt-1-1-tens
 
 @app.post("/generate_video/")
 async def generate_video_api(
-        file: UploadFile = File(...),
-        num_frames: int = Form(14),
-        fps: int = Form(7)
+    file: UploadFile = File(...),
+    num_frames: int = Form(14),
+    fps: int = Form(7),
+    motion_bucket_id: int = Form(127),
+    cond_aug: float = Form(0.02),
+    seed: int = Form(0)
 ):
     try:
         # Read the uploaded image file
         image_content = await file.read()
         image = Image.open(io.BytesIO(image_content))
 
+        # Convert image to bytes
+        img_byte_arr = io.BytesIO()
+        image.save(img_byte_arr, format='PNG')
+        img_byte_arr = img_byte_arr.getvalue()
+
         # Generate video frames using the stable-video-diffusion model
         video_frames = client.post(
-            json={
-                "inputs": image,
-                "parameters": {
-                    "num_inference_steps": 25,
-                    "num_frames": num_frames,
-                }
+            "video-generation",
+            files=[("image", img_byte_arr)],
+            data={
+                "num_inference_steps": 25,
+                "num_frames": num_frames,
+                "motion_bucket_id": motion_bucket_id,
+                "cond_aug": cond_aug,
+                "seed": seed
             }
         )
 
@@ -65,7 +75,7 @@ async def generate_video_api(
 
     except Exception as err:
         # Handle any errors
-        raise HTTPException(status_code=500, detail=f"An error occurred: {err}")
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(err)}")
 
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)