Update app.py

app.py

@@ -18,7 +18,7 @@ st.write("Waiting for image upload and prompt input...")
 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.")
 
-# Cache migration step (add this before model initialization)
+# Cache migration step
 st.write("Migrating the cache for model files...")
 try:
     from transformers.utils import move_cache
@@ -30,7 +30,6 @@ except Exception as e:
 
 if uploaded_file and prompt:
     try:
-        # Debug: File and prompt received
         st.write(f"Uploaded file: {uploaded_file.name}")
         st.write(f"Prompt: {prompt}")
 
@@ -42,73 +41,49 @@ if uploaded_file and prompt:
 
         # Load the image
         st.write("Loading image...")
-        try:
-            image = load_image("uploaded_image.jpg")
-            st.write("Image loaded successfully.")
-        except Exception as e:
-            st.error("Error loading the image.")
-            st.write(f"Debug info: {e}")
-            raise e
+        image = load_image("uploaded_image.jpg")
+        st.write("Image loaded successfully.")
 
-        # Initialize the CogVideoX pipeline
+        # Initialize the pipeline
         st.write("Initializing the pipeline...")
-        try:
-            pipe = CogVideoXImageToVideoPipeline.from_pretrained(
-                "THUDM/CogVideoX1.5-5B-I2V",
-                torch_dtype=torch.bfloat16,
-                cache_dir="./huggingface_cache"  # Adjust path if necessary
-            )
-            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 = CogVideoXImageToVideoPipeline.from_pretrained(
+            "THUDM/CogVideoX1.5-5B-I2V",
+            torch_dtype=torch.bfloat16,
+            cache_dir="./huggingface_cache",
+            force_download=True
+        )
+        st.write("Pipeline initialized successfully.")
+
+        # Enable optimizations
         pipe.enable_sequential_cpu_offload()
         pipe.vae.enable_tiling()
         pipe.vae.enable_slicing()
 
-        # Debug: Ready to generate video
-        st.write("Pipeline setup complete. Ready to generate video.")
-
-        # Generate the video
+        # Generate video
         st.write("Generating video... This may take a while.")
-        try:
-            video_frames = pipe(
-                prompt=prompt,
-                image=image,
-                num_videos_per_prompt=1,
-                num_inference_steps=50,
-                num_frames=81,
-                guidance_scale=6,
-                generator=torch.Generator(device="cuda").manual_seed(42),
-            ).frames[0]
-            st.write("Video generated successfully.")
-        except Exception as e:
-            st.error("Error during video generation.")
-            st.write(f"Debug info: {e}")
-            raise e
+        video_frames = pipe(
+            prompt=prompt,
+            image=image,
+            num_videos_per_prompt=1,
+            num_inference_steps=50,
+            num_frames=81,
+            guidance_scale=6,
+            generator=torch.Generator(device="cuda").manual_seed(42),
+        ).frames[0]
+        st.write("Video generated successfully.")
 
         # Export video
         st.write("Exporting video...")
-        try:
-            video_path = "output.mp4"
-            export_to_video(video_frames, video_path, fps=8)
-            st.write("Video exported successfully.")
-        except Exception as e:
-            st.error("Error exporting video.")
-            st.write(f"Debug info: {e}")
-            raise e
+        video_path = "output.mp4"
+        export_to_video(video_frames, video_path, fps=8)
+        st.write("Video exported successfully.")
 
-        # Display video in Streamlit
+        # Display video
         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.")
 
-