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LanPaint-diffusers
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LanPaint diffusers version code
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LanPaint-diffusers
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# LanPaint-Diffusers Training-free diffusion inpainting and outpainting with [LanPaint](https://github.com/scraed/LanPaint), built on [Hugging Face Diffusers](https://github.com/huggingface/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`) - **Krea 2** (`krea2`) - **Stable Diffusion 3** (`sd3`) - **Qwen Image** (`qwen`) — thanks [@spartanz51](https://github.com/spartanz51) - **Extensible**: More LanPaint-supported models will be added over time; new backends are integrated via the adapter registry. --- ## 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`); ```bash 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: ```bash pip install -r requirements.txt --no-deps ``` Verify: ```bash 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: ```bash ./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`) - **Krea 2 Turbo** (inpaint) - **Qwen Image** (inpaint ) **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` ### Mask convention (`--mask`) The pipeline supports both standalone mask images and RGBA masks: - **Binary/grayscale mask image** (`L` mode or RGB converted to grayscale): - **0** (black, or normalized **0.0**) = region to be edited (inpaint area) - **255** (white, or normalized **1.0**) = region to keep (non-editable) - **RGBA mask image** (mask stored in alpha channel): - **alpha = 0** = region to be edited (inpaint area) - **alpha = 255** = region to keep (non-editable) --- ## Results Showcase Below are visual examples for `flux-klein`, `z-image`, `krea2`, and `qwen`. ### Flux2 Klein (Example 24) | Original | Masked | Diffusers Version | ComfyUI Version | |:--------:|:------:|:-----------------:|:---------------:| |  |  |  |  | ### Z-Image Turbo (Example 21) | Original | Masked | Diffusers Version | ComfyUI Version | |:--------:|:------:|:-----------------:|:---------------:| |  |  |  |  | ### Krea 2 Turbo (Example 28) | Original | Masked | Diffusers Version | ComfyUI Version | |:--------:|:------:|:-----------------:|:---------------:| |  |  |  |  | ### Qwen Image Edit (Example 14) | Original | Masked | Diffusers Version | ComfyUI Version | |:--------:|:------:|:-----------------:|:---------------:| |  |  |  |  | --- ## 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, krea2, qwen) ├── utils.py # Blend, time helpers, image loading └── adapters/ ├── __init__.py ├── flux_klein.py # Flux2KleinAdapter ├── krea2.py # Krea2Adapter ├── qwen.py # QwenAdapter ├── 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. --- ## To-Do - [ ] **Edit-model reference image switch**: Add an option to control whether edit-style models (e.g. Flux Klein, Qwen) use the reference image as input at inference. --- ## Citation If you use this repository or LanPaint in your work, please cite: ```bibtex @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={} } ```
