Overview

Next

LightDiffusion-Next is a refactored and performance-first Stable Diffusion stack that bundles a modern Streamlit UI, an optional Gradio web app, a batched FastAPI backend and highly tuned inference primitives such as Stable-Fast, SageAttention and WaveSpeed caching.

Why pick LightDiffusion-Next

LightDiffusion-Next is built to handle day-to-day generation workloads on consumer GPUs while still scaling up to multi-user servers.

  • Fast by default. Stable-Fast compilation, SageAttention, SpargeAttn and WaveSpeed caching are wired in so you can hit top-tier it/s without manual patching.
  • Multiple front-doors. Choose between the Streamlit control room, a Gradio web UI (great for Spaces) or the programmable FastAPI queue for integrations.
  • Feature complete. Txt2Img, Img2Img, Flux pipelines, AutoHDR, TAESD previews, prompt enhancement through Ollama, multi-scale diffusion with presets, LoRA mixing and automatic detailing are all available out of the box.
  • Operations friendly. Docker images, GPU-aware batched serving, model caching controls and observability endpoints make it easy to deploy and monitor.

What ships in the box

  • 🚀 Streamlined UI with live previews, history, presets, interrupt/resume controls and automatic metadata tagging.
  • 🧠 Prompt toolkit including reusable negative embeddings, multi-concept weighting, prompt enhancement and prompt history.
  • 🧩 Modular pipeline that routes SD1.5, SDXL-inspired workflows and quantized Flux models through a single code path with per-sample overrides for HiresFix, ADetailer or Img2Img.
  • 🛠️ Production API powered by FastAPI with smart request coalescing, telemetry endpoints and base64 image responses ready for bots or creative tooling.
  • 📦 Deployment artifacts such as Dockerfiles, docker-compose, run scripts for Windows, configurable GPU architecture flags and optional Ollama/Stable-Fast builds.

Quick pathways

  • Installation — pick Docker, Windows batch or manual Python setup.
  • First run & UI tour — learn the Streamlit layout, generation controls and history tools.
  • Workflow playbook — step through Txt2Img, Flux, Img2Img and API recipes.
  • Performance optimizations — understand SageAttention, Stable-Fast, WaveSpeed caching and the new AYS scheduler for 2-5x speedup.
  • Align Your Steps — learn about AYS scheduler and prompt caching for additional speedup.
  • Prompt Caching — deep dive into prompt attention caching mechanics and tuning.
  • Performance tuning — squeeze out extra throughput or reduce VRAM usage.
  • Architecture — understand how the UI, pipeline and server cooperate.
  • REST & automation — integrate Discord bots, automations or other clients.

Supported environments at a glance

  • NVIDIA GPUs with CUDA 12.x drivers. SageAttention and SpargeAttn availability is detected at runtime and depends on installed kernels, drivers and GPU compute capability; some kernels may be disabled on newer CUDA runtimes (for example CUDA 12+). RTX 50xx and newer cards may use SageAttention + Stable-Fast where supported.
  • Windows 10/11, Ubuntu 22.04+ and containerized deployments via Docker with NVIDIA Container Toolkit.
  • Optional CPU-only mode for experimentation (no Stable-Fast/SageAttention speed-ups).

Where to head next

  • Start with Installation to get your environment ready.
  • Drop into the Streamlit UI guide for a tour of generation features and presets.
  • Explore Architecture when you are ready to customize or embed LightDiffusion-Next in larger systems.
Installation