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| import os | |
| import streamlit as st | |
| import torch | |
| from diffusers.utils import load_image | |
| try: | |
| from diffusers import CogVideoXImageToVideoPipeline | |
| pipeline_available = True | |
| except ImportError: | |
| pipeline_available = False | |
| st.error("Failed to import `CogVideoXImageToVideoPipeline`. Please run `pip install diffusers`.") | |
| st.title("Image to Video with Hugging Face") | |
| st.write("Upload an image and provide a prompt to generate a video.") | |
| if pipeline_available: | |
| 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: | |
| # Save uploaded file | |
| import uuid | |
| file_name = f"{uuid.uuid4()}_uploaded_image.jpg" | |
| with open(file_name, "wb") as f: | |
| f.write(uploaded_file.read()) | |
| st.write("Uploaded image saved successfully.") | |
| # Load the image | |
| image = load_image(file_name) | |
| # Initialize pipeline | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = CogVideoXImageToVideoPipeline.from_pretrained( | |
| "THUDM/CogVideoX1.5-5B-I2V", | |
| torch_dtype=torch.bfloat16, | |
| cache_dir="./huggingface_cache", | |
| ) | |
| pipe.enable_sequential_cpu_offload() | |
| pipe.vae.enable_tiling() | |
| pipe.vae.enable_slicing() | |
| # Generate video | |
| with st.spinner("Generating video... This may take a while."): | |
| try: | |
| # Attempt to generate the video | |
| 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=device).manual_seed(42), | |
| ).frames[0] | |
| except Exception as e: | |
| # Handle errors gracefully | |
| st.error(f"An error occurred during video generation: {e}") | |