LanPaint-Diffusers

Training-free diffusion inpainting and outpainting with LanPaint, built on Hugging Face Diffusers.

Features

• Diffusers-native: Uses the diffusers library only. No ComfyUI or other graph-based UI dependency.
• Multi-model: One CLI and one pipeline API for all supported backends. Currently supported:
  • Flux2 Klein (flux-klein)
  • Z-Image Turbo (z-image)
  • Stable Diffusion 3 (sd3)
• Extensible: More LanPaint-supported models will be added over time; new backends are integrated via the adapter registry.

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Usage

Installation

• Python 3.10+ (3.12 tested), NVIDIA GPU and CUDA-compatible PyTorch recommended.
• LanPaint and diffusers are installed from GitHub (see requirements.txt);

cd LanPaint-diffusers   # or your clone path
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install --upgrade pip
pip install -r requirements.txt

If you need a different PyTorch/CUDA build, install PyTorch first, then:

pip install -r requirements.txt --no-deps

Verify:

python -c "import torch, diffusers, LanPaint; print(torch.__version__, diffusers.__version__)"
python run_lanpaint.py --list-models

Commands

We recommend using the example commands in run_lanpaint.sh. Uncomment the block you need (or run it as-is if one is already uncommented), then:

./run_lanpaint.sh
# or: bash run_lanpaint.sh

The script includes ready-to-run examples for:

• List models: python run_lanpaint.py --list-models
• Flux2 Klein (inpaint, URL or local image + mask)
• SD3 (inpaint with example prompt and URLs)
• Z-Image Turbo (inpaint and outpaint, with --outpaint-pad)

Quick reference (same CLI, custom args):

• Inpaint: --model <name> --prompt "..." --image <path-or-URL> --mask <path-or-URL>
• Outpaint: --model <name> --prompt "..." --image <path> --outpaint-pad l200r200t200b200 (no --mask; do not use --height/--width with --outpaint-pad)

Useful options: --guidance-scale, --num-steps, --seed, --output <path>, --model-id <hf-or-local-path>, --save-preprocess-dir <dir>, --local-files-only

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Results Showcase

Below are visual examples for flux-klein and z-image.

Flux2 Klein (Example 24)

Original	Masked	Diffusers Version	ComfyUI Version

Z-Image Turbo (Example 21)

Original	Masked	Diffusers Version	ComfyUI Version

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Library Structure

LanPaint-diffusers/
├── README.md
├── requirements.txt
├── run_lanpaint.py      # Unified CLI
├── run_lanpaint.sh      # Example commands
└── lanpaint_pipeline/
    ├── __init__.py
    ├── model_adapter.py   # Abstract adapter interface
    ├── pipeline.py        # LanPaintInpaintPipeline (orchestrator)
    ├── registry.py        # Model registry (flux-klein, sd3, z-image)
    ├── utils.py           # Blend, time helpers, image loading
    └── adapters/
        ├── __init__.py
        ├── flux_klein.py  # Flux2KleinAdapter
        ├── sd3.py         # SD3Adapter
        └── z_image.py     # ZImageAdapter

• LanPaintInpaintPipeline (pipeline.py): model-agnostic flow (preprocess → encode → LanPaint Langevin + scheduler loop → decode → blend).
• ModelAdapter (model_adapter.py): abstract interface (encode_prompt, encode_and_prepare, mask_to_latent_space, prepare_timesteps, predict_x0, decode_latents). Each backend implements one adapter in adapters/.
• Registry (registry.py): maps model keys to pipeline class, adapter class, and default config; adding a model is done by registering one entry.

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Citation

If you use this repository or LanPaint in your work, please cite:

@article{
zheng2025lanpaint,
title={LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling},
author={Candi Zheng and Yuan Lan and Yang Wang},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=JPC8JyOUSW},
note={}
}