Q: Where do I put my checkpoints?
A: Put them in include/checkpoints (create the folder if missing). The UI and src/FileManaging/Loader will detect and list them.
Q: Why is GPU memory insufficient?
A: Try reducing width/height, turning off keep models loaded, or enable quantized Flux/GGUF models. See Performance & Troubleshooting.
Q: Can I run headless on a server?
A: Yes — use the FastAPI backend with docker-compose or run server.py directly. Disable Streamlit if you don’t need the web UI.
Q: How do I contribute models or LoRAs?
A: Place LoRA files in include/loras and embeddings in include/embeddings. See Contributing for guidelines.
Which diffusion models are supported out of the box?
LightDiffusion-Next ships with Stable Diffusion 1.5-friendly defaults and includes helpers for SDXL-inspired checkpoints, Flux (via the include/Flux assets) and quantized Stable-Fast backends. Drop your .safetensors or .ckpt files into include/checkpoints, LoRAs into include/loras, embeddings into include/embeddings, and Flux weights into include/Flux. The loader auto-detects formats and will prompt for missing companions (VAE, CLIP) at startup.
What GPU and driver versions do I need?
NVIDIA GPUs with CUDA 12.1+ drivers are recommended. Availability of Stable-Fast, SageAttention and SpargeAttn depends on your installed kernels, drivers and GPU compute capability — the runtime detects and enables compatible backends automatically. For Docker, install the NVIDIA Container Toolkit and verify nvidia-smi works inside the container.
Can I run LightDiffusion-Next without a GPU?
Yes, but performance will be limited. Install CPU wheels of PyTorch or rely on the bundled Intel oneAPI runtime (Linux only). Disable Stable-Fast/SageAttention in settings, reduce resolution (≤384×384), lower steps (<20) and turn off AutoDetailer/HiResFix to avoid minute-long renders.
Where do generated images and metadata live?
Outputs are grouped by workflow under output/. For example, standard Txt2Img lands in output/classic, HiresFix into output/HiresFix, Flux into output/Flux, Img2Img upscales into output/Img2Img, etc. Each PNG embeds prompt metadata; accompanying JSON manifests are saved when enabled in settings.
How do I switch between Streamlit, Gradio and the API?
Use the launch scripts:
streamlit run streamlit_app.py(default UI)python app.py(Gradio app for Spaces/remote hosting)uvicorn server:app --host 0.0.0.0 --port 7861(FastAPI)
All three share the same pipeline and config. Streamlit/Gradio speak directly to the pipeline, while the API feeds the batching queue in server.py.
How do I enable Stable-Fast or SageAttention?
In Streamlit, toggle Stable-Fast under Performance. The app will compile kernels the first time and reuse them afterwards (cache in ~/.cache/torch_extensions). SageAttention is enabled automatically on supported GPUs; you can force-disable it by setting LD_DISABLE_SAGE_ATTENTION=1 before launching. Docker images already ship with the patched kernels compiled.
What if the app says a model is missing?
The downloader checks include/ on startup and whenever a feature needs a new asset (YOLO, Flux, TAESD). Provide URLs or Hugging Face tokens when prompted, or pre-populate the folders manually. For offline environments, copy the files into the correct directories and ensure filenames match the expected suffixes (e.g., anything-v4.5-pruned.safetensors).
Can I enhance prompts automatically with Ollama?
Yes. Install Ollama locally, download a language model (ollama run mistral), then enable Prompt Enhancer in the UI or set enhance_prompt=true in the REST payload. Set OLLAMA_BASE_URL if Ollama is not on http://localhost:11434.
How do I reset persistent settings or history?
Delete webui_settings.json in the project root to reset saved toggles and defaults. Remove individual history directories under ui/history/ to clear the UI gallery without touching generated images.
Need more help?
Check the Troubleshooting guide or open an issue with logs, hardware specs and steps to reproduce.