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app.py

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+import streamlit as st
+import torch
+from diffusers import DiffusionPipeline
+import imageio
+from io import BytesIO
+from PIL import Image
+
+# Title of the app
+st.title("Text-to-Video Generator")
+
+# Load the model (only once when the app starts)
+@st.cache_resource
+def load_model():
+    pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
+    pipe.enable_model_cpu_offload()
+    pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
+    pipe.enable_vae_slicing()
+    return pipe
+
+pipe = load_model()
+
+# Text input for the prompt
+prompt = st.text_input("Enter a text prompt for the video")
+
+# If a prompt is entered, generate the video
+if prompt:
+    st.write("Generating video... please wait.")
+
+    # Generate video frames
+    video_frames = pipe(prompt, num_frames=24).frames[0]
+
+    # Save video to in-memory buffer
+    video_buffer = BytesIO()
+    with imageio.get_writer(video_buffer, fps=10, format="mp4") as writer:
+        for frame in video_frames:
+            writer.append_data(frame)
+
+    video_buffer.seek(0)
+
+    # Display the video
+    st.video(video_buffer, format="video/mp4")
+
+    # Optional: Allow users to download the video
+    st.download_button(label="Download Video", data=video_buffer, file_name="generated_video.mp4", mime="video/mp4")

requirements.txt

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+streamlit
+diffusers
+transformers
+accelerate
+torch
+imageio[ffmpeg]