Update app.py

app.py

@@ -4,16 +4,23 @@ from diffusers import CogVideoXImageToVideoPipeline
 from diffusers.utils import export_to_video, load_image
 import torch
 
+# Debug: App started
 st.write("App started.")
 
 # Streamlit interface
 st.title("Image to Video with Hugging Face")
+st.write("Upload an image and provide a prompt to generate a video.")
+
+# Debug: Waiting for user inputs
+st.write("Waiting for image upload and prompt input...")
+
+# File uploader for the input image
 uploaded_file = st.file_uploader("Upload an image (JPG or PNG):", type=["jpg", "jpeg", "png"])
 prompt = st.text_input("Enter your prompt:", "A little girl is riding a bicycle at high speed. Focused, detailed, realistic.")
 
 if uploaded_file and prompt:
     try:
-        # Debugging
+        # Debug: File and prompt received
         st.write(f"Uploaded file: {uploaded_file.name}")
         st.write(f"Prompt: {prompt}")
 
@@ -28,18 +35,28 @@ if uploaded_file and prompt:
         image = load_image("uploaded_image.jpg")
         st.write("Image loaded successfully.")
 
-        # Initialize pipeline
+        # Initialize the CogVideoX pipeline
         st.write("Initializing the pipeline...")
-        pipe = CogVideoXImageToVideoPipeline.from_pretrained(
-            "THUDM/CogVideoX1.5-5B-I2V",
-            torch_dtype=torch.bfloat16
-        )
+        try:
+            pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+                "THUDM/CogVideoX1.5-5B-I2V",
+                torch_dtype=torch.bfloat16
+            )
+            st.write("Pipeline initialized successfully.")
+        except Exception as e:
+            st.error("Error during pipeline initialization.")
+            st.write(f"Debug info: {e}")
+            raise e
+
+        # Enable optimizations for large models
         pipe.enable_sequential_cpu_offload()
         pipe.vae.enable_tiling()
         pipe.vae.enable_slicing()
-        st.write("Pipeline initialized successfully.")
 
-        # Generate video
+        # Debug: Ready to generate video
+        st.write("Pipeline setup complete. Ready to generate video.")
+
+        # Generate the video
         st.write("Generating video... This may take a while.")
         video_frames = pipe(
             prompt=prompt,
@@ -58,12 +75,14 @@ if uploaded_file and prompt:
         export_to_video(video_frames, video_path, fps=8)
         st.write("Video exported successfully.")
 
-        # Display video
+        # Display video in Streamlit
         st.video(video_path)
 
     except Exception as e:
         st.error(f"An error occurred: {e}")
         st.write(f"Debug info: {e}")
 else:
+    # Debug: Waiting for inputs
     st.write("Please upload an image and provide a prompt to get started.")
 
+