rename

app.py

@@ -25,6 +25,10 @@ def _load_pipeline_internal(
     """Internal function to load the Sci-Fi pipeline"""
     global pipe
 
+    # Return early if pipeline is already loaded
+    if pipe is not None:
+        return "Pipeline already loaded!"
+
     dtype = torch.float16 if dtype_str == "float16" else torch.bfloat16
 
     # Load models
@@ -76,16 +80,6 @@ def _load_pipeline_internal(
     return "Pipeline loaded successfully!"
 
 
-@spaces.GPU(duration=1000)
-def load_pipeline(
-    pretrained_model_path="THUDM/CogVideoX-5b",
-    ef_net_path="weights/EF_Net.pth",
-    dtype_str="bfloat16",
-):
-    """Load the Sci-Fi pipeline (Gradio wrapper)"""
-    return _load_pipeline_internal(pretrained_model_path, ef_net_path, dtype_str)
-
-
 @spaces.GPU(duration=1000)
 def generate_inbetweening(
     first_image: Image.Image,
@@ -102,8 +96,13 @@ def generate_inbetweening(
     """Generate frame inbetweening video"""
     global pipe
 
+    # Load pipeline on first use (lazy loading with GPU access)
     if pipe is None:
-        return None, "Please load the pipeline first!"
+        progress(0, desc="Loading pipeline (first run)...")
+        try:
+            _load_pipeline_internal()
+        except Exception as e:
+            return None, f"ERROR: Failed to load pipeline: {str(e)}"
 
     if first_image is None or last_image is None:
         return None, "Please upload both start and end frames!"
@@ -112,11 +111,11 @@ def generate_inbetweening(
         return None, "Please provide a text prompt!"
 
     try:
-        progress(0, desc="Starting generation...")
+        progress(0.2, desc="Starting generation...")
         start_time = time.time()
 
         # Generate video
-        progress(0.2, desc="Processing frames...")
+        progress(0.4, desc="Processing frames...")
         video_frames = pipe(
             first_image=first_image,
             last_image=last_image,
@@ -154,7 +153,7 @@ with gr.Blocks(title="Sci-Fi: Frame Inbetweening") as demo:
 
     Upload start and end frames to generate smooth inbetweening video.
 
-    **Note:** Make sure to load the pipeline first before generating videos.
+    **Note:** Pipeline loads on first generation (this may take 1-2 minutes).
     """
     )
 
@@ -231,44 +230,6 @@ with gr.Blocks(title="Sci-Fi: Frame Inbetweening") as demo:
             outputs=[output_video, status_text],
         )
 
-    with gr.Tab("Setup"):
-        gr.Markdown(
-            """
-        ## Load Pipeline
-
-        Configure and load the model before generating videos.
-
-        **Default paths:**
-        - Model: `THUDM/CogVideoX-5b` (or your downloaded path)
-        - EF-Net: `weights/EF_Net.pth`
-        """
-        )
-
-        with gr.Row():
-            model_path = gr.Textbox(
-                label="Pretrained Model Path",
-                value="THUDM/CogVideoX-5b",
-                placeholder="Path to CogVideoX model",
-            )
-            ef_net_path = gr.Textbox(
-                label="EF-Net Checkpoint Path",
-                value="weights/EF_Net.pth",
-                placeholder="Path to EF-Net weights",
-            )
-
-        dtype_choice = gr.Radio(
-            choices=["bfloat16", "float16"], value="bfloat16", label="Data Type"
-        )
-
-        load_btn = gr.Button("Load Pipeline", variant="primary")
-        load_status = gr.Textbox(label="Load Status", interactive=False)
-
-        load_btn.click(
-            fn=load_pipeline,
-            inputs=[model_path, ef_net_path, dtype_choice],
-            outputs=load_status,
-        )
-
     with gr.Tab("Examples"):
         gr.Markdown(
             """
@@ -295,13 +256,5 @@ with gr.Blocks(title="Sci-Fi: Frame Inbetweening") as demo:
         )
 
 if __name__ == "__main__":
-    # Automatically load pipeline on startup
-    print("Loading pipeline automatically on startup...")
-    try:
-        _load_pipeline_internal()
-        print("Pipeline loaded successfully!")
-    except Exception as e:
-        print(f"Failed to load pipeline on startup: {e}")
-        print("You can manually load it from the Setup tab.")
-
+    print("App starting - pipeline will load on first generation request")
     demo.launch()