Spaces:
Build error
Build error
File size: 2,445 Bytes
8a800d5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import streamlit as st
from diffusers import StableDiffusionImg2ImgPipeline
from moviepy.editor import ImageSequenceClip
from PIL import Image
import torch
import os
# Title and instructions
st.title("Image-to-Video Conversion")
st.write("Upload an image, provide a prompt, and generate a video using AI.")
# Sidebar for user input
st.sidebar.title("Settings")
num_frames = st.sidebar.slider("Number of Frames", 5, 50, 10)
fps = st.sidebar.slider("Frames Per Second (FPS)", 1, 30, 12)
guidance_scale = st.sidebar.slider("Guidance Scale", 5.0, 15.0, 7.5)
strength_base = st.sidebar.slider("Base Strength (Image Influence)", 0.1, 1.0, 0.5)
# File uploader
uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
prompt = st.text_input("Enter a Prompt", value="A cinematic animation of a sunset over mountains")
# Load the pre-trained model
@st.cache_resource
def load_model():
return StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
revision="fp16"
).to("cuda")
pipe = load_model()
# Process the uploaded image and generate video frames
if uploaded_image and st.button("Generate Video"):
# Load the input image
input_image = Image.open(uploaded_image).convert("RGB")
st.image(input_image, caption="Uploaded Image", use_column_width=True)
st.write("Generating video frames... This might take a few minutes.")
progress = st.progress(0)
frames = []
for i in range(num_frames):
progress.progress((i + 1) / num_frames)
result = pipe(
prompt=prompt,
image=input_image,
strength=strength_base + (i * 0.05), # Incremental strength
guidance_scale=guidance_scale
)
frames.append(result.images[0])
# Save video frames as a video file
video_path = "./output_video.mp4"
video_clip = ImageSequenceClip([frame for frame in frames], fps=fps)
video_clip.write_videofile(video_path, codec="libx264")
st.success("Video generated successfully!")
# Display video
st.video(video_path)
# Download link for the video
with open(video_path, "rb") as file:
btn = st.download_button(
label="Download Video",
data=file,
file_name="output_video.mp4",
mime="video/mp4"
)
# Footer
st.write("Powered by Hugging Face Diffusers and Streamlit")
|