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| 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.") | |
| # 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 (add this before model initialization) | |
| 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}") | |
| if uploaded_file and prompt: | |
| try: | |
| # Debug: File and prompt received | |
| st.write(f"Uploaded file: {uploaded_file.name}") | |
| st.write(f"Prompt: {prompt}") | |
| # 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.") | |
| # Load the image | |
| st.write("Loading image...") | |
| image = load_image("uploaded_image.jpg") | |
| st.write("Image loaded successfully.") | |
| # Initialize the CogVideoX pipeline | |
| st.write("Initializing the pipeline...") | |
| 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() | |
| # 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, | |
| 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...") | |
| video_path = "output.mp4" | |
| export_to_video(video_frames, video_path, fps=8) | |
| st.write("Video exported successfully.") | |
| # 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.") | |