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import streamlit as st
import replicate
import requests
from PIL import Image
from io import BytesIO
# Set the replicate API token
import os
os.environ["REPLICATE_API_TOKEN"] = "r8_JSR8xlRoCk6cmq3qEOOThVTn3dAgdPq1bWXdj"
def run_replicate_model(input_params):
output = replicate.run(
"stability-ai/stable-video-diffusion:3f0457e4619daac51203dedb472816fd4af51f3149fa7a9e0b5ffcf1b8172438",
input=input_params
)
return output
st.title("Stable Video Diffusion")
st.write("Fill in the input parameters and click on the button to run the Stable Video Diffusion model.")
cond_aug = st.number_input("Conditioning Augmentation", value=0.02)
decoding_t = st.number_input("Decoding Time", value=7)
input_image = st.text_input("Input Image URL", "https://example.com/s-widua-icjKmXvsO7U-unsplash.jpg")
video_length = st.selectbox("Video Length", ["14_frames_with_svd", "25_frames_with_svd_xt"])
sizing_strategy = st.selectbox("Sizing Strategy", ["maintain_aspect_ratio", "other_options"])
motion_bucket_id = st.number_input("Motion Bucket ID", value=127)
frames_per_second = st.number_input("Frames Per Second", value=6)
input_params = {
"cond_aug": cond_aug,
"decoding_t": decoding_t,
"input_image": input_image,
"video_length": video_length,
"sizing_strategy": sizing_strategy,
"motion_bucket_id": motion_bucket_id,
"frames_per_second": frames_per_second
}
if st.button("Run Model"):
output_result = run_replicate_model(input_params)
st.write("Output Video:")
try:
response = requests.get(output_result)
video_bytes = BytesIO(response.content)
st.video(video_bytes)
except Exception as e:
st.write("Unable to display the video.")
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