diffusers and stable-diffusion-videos
Diffusers is a foundational framework that provides the core diffusion model implementations and pipelines, while Stable Diffusion Videos builds on top of it to add specialized video generation capabilities through latent space interpolation between prompts.
About diffusers
huggingface/diffusers
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
Provides modular, composable building blocks—including interchangeable noise schedulers, pretrained models, and end-to-end pipelines—enabling both quick inference and custom system design via the Hugging Face Model Hub. Emphasizes transparency and customizability over abstraction, allowing developers to inspect and modify individual diffusion components rather than treating them as black boxes.
About stable-diffusion-videos
nateraw/stable-diffusion-videos
Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts
Implements spherical linear interpolation (SLERP) in the latent space between seed vectors to generate smooth frame sequences, with optional audio-sync capabilities that tempo-match interpolation steps to music beats. Built as a Hugging Face Diffusers pipeline wrapper supporting float16 inference on CUDA/MPS, it includes a Gradio web interface for interactive video generation with configurable guidance scales and diffusion steps.
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