Update app.py

app.py

@@ -1,89 +1,96 @@
 import os
 import streamlit as st
-from diffusers import CogVideoXImageToVideoPipeline
-from diffusers.utils import export_to_video, load_image
 import torch
 
-# Debug: App started
-st.write("App started.")
+# Attempt to import the required pipeline
+try:
+    from diffusers import CogVideoXImageToVideoPipeline
+    pipeline_available = True
+    st.write("CogVideoXImageToVideoPipeline successfully imported.")
+except ImportError as e:
+    pipeline_available = False
+    st.error("Failed to import CogVideoXImageToVideoPipeline. Please check your diffusers version.")
+    st.write(f"Debug info: {e}")
 
 # 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.")
-
-# Cache migration step
-st.write("Migrating the cache for model files...")
-try:
-    from transformers.utils import move_cache
-    move_cache()
-    st.write("Cache migration completed successfully.")
-except Exception as e:
-    st.error(f"Cache migration failed: {e}")
-    st.write("Proceeding without cache migration...")
+# Check if the pipeline is available before proceeding
+if not pipeline_available:
+    st.error("The required pipeline is unavailable. Please ensure you have the correct version of the diffusers library.")
+else:
+    # 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:
+    # Cache migration step
+    st.write("Migrating the cache for model files...")
     try:
-        st.write(f"Uploaded file: {uploaded_file.name}")
-        st.write(f"Prompt: {prompt}")
+        from transformers.utils import move_cache
+        move_cache()
+        st.write("Cache migration completed successfully.")
+    except Exception as e:
+        st.error(f"Cache migration failed: {e}")
+        st.write("Proceeding without cache migration...")
 
-        # Save uploaded file
-        st.write("Saving uploaded image...")
-        with open("uploaded_image.jpg", "wb") as f:
-            f.write(uploaded_file.read())
-        st.write("Uploaded image saved successfully.")
+    if uploaded_file and prompt:
+        try:
+            st.write(f"Uploaded file: {uploaded_file.name}")
+            st.write(f"Prompt: {prompt}")
 
-        # Load the image
-        st.write("Loading image...")
-        image = load_image("uploaded_image.jpg")
-        st.write("Image loaded successfully.")
+            # Save uploaded file
+            st.write("Saving uploaded image...")
+            with open("uploaded_image.jpg", "wb") as f:
+                f.write(uploaded_file.read())
+            st.write("Uploaded image saved successfully.")
 
-        # Initialize the pipeline
-        st.write("Initializing the pipeline...")
-        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.")
+            # Load the image
+            from diffusers.utils import load_image
+            st.write("Loading image...")
+            image = load_image("uploaded_image.jpg")
+            st.write("Image loaded successfully.")
 
-        # Enable optimizations
-        pipe.enable_sequential_cpu_offload()
-        pipe.vae.enable_tiling()
-        pipe.vae.enable_slicing()
+            # Initialize the pipeline
+            st.write("Initializing the pipeline...")
+            pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+                "THUDM/CogVideoX1.5-5B-I2V",
+                torch_dtype=torch.bfloat16,
+                cache_dir="./huggingface_cache",
+                force_download=True  # Ensure fresh download
+            )
+            st.write("Pipeline initialized successfully.")
 
-        # Generate video
-        st.write("Generating video... This may take a while.")
-        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.")
+            # Enable optimizations
+            pipe.enable_sequential_cpu_offload()
+            pipe.vae.enable_tiling()
+            pipe.vae.enable_slicing()
 
-        # Export video
-        st.write("Exporting video...")
-        video_path = "output.mp4"
-        export_to_video(video_frames, video_path, fps=8)
-        st.write("Video exported successfully.")
+            # Generate video
+            st.write("Generating video... This may take a while.")
+            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.")
 
-        # Display video
-        st.video(video_path)
+            # Export video
+            st.write("Exporting video...")
+            from diffusers.utils import export_to_video
+            video_path = "output.mp4"
+            export_to_video(video_frames, video_path, fps=8)
+            st.write("Video exported successfully.")
 
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
-        st.write(f"Debug info: {e}")
-else:
-    st.write("Please upload an image and provide a prompt to get started.")
+            # Display video
+            st.video(video_path)
 
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+            st.write(f"Debug info: {e}")
+    else:
+        st.write("Please upload an image and provide a prompt to get started.")