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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}")
st.write("Proceeding without cache migration...")
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...")
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
# Initialize the CogVideoX 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.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.")
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
# 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
# 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.")
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