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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."):
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]
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