Create app.py

app.py

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+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")