This repository provides tools for generating videos by interpolating between text prompts using Stable Diffusion. It's designed for researchers and artists interested in exploring latent space and creating dynamic visual content, offering a novel approach to smooth transitions between generated images.

How It Works

The core functionality is built around the StableDiffusionWalkPipeline, which generates a sequence of images by interpolating through the latent space of a Stable Diffusion model. This allows for smooth visual morphing between different text prompts and seeds. The library also supports synchronizing video generation with audio, using audio cues to control the interpolation speed and create music-driven visuals.

Quick Start & Requirements

• Install via pip: pip install stable_diffusion_videos
• Requires PyTorch with CUDA support (torch_dtype=torch.float16 recommended for NVIDIA GPUs).
• Apple M1 users should use torch.float32 due to MPS limitations.
• Example usage and scripts are available in the examples folder.

Highlighted Details

• Morphing between text prompts and seeds for smooth visual transitions.
• Optional audio synchronization for music videos, controlling interpolation rate by beat.
• Includes a Gradio-based UI for interactive generation.
• Built upon foundational work from @karpathy.

Maintenance & Community

• Open to issues and feature requests via GitHub.
• No explicit community channels (Discord/Slack) or roadmap are mentioned.

Licensing & Compatibility

• The repository does not explicitly state a license.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is presented as a personal project without explicit versioning or formal support. The lack of a specified license may pose restrictions for commercial applications. Apple M1 users need to be aware of the float32 requirement for MPS.