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README.md

@@ -28,37 +28,39 @@ license: apache-2.0
 
 ## Quantization / inference tech
 
-- **Inference engine**: Nunchaku (`https://github.com/nunchaku-ai/nunchaku`)
+- **Inference engine**: [vitoom-nunchaku](https://huggingface.co/tonera/vitoom-nunchaku) — community-maintained Nunchaku build with **Chroma** support
 
-Nunchaku is a high-performance inference engine for **4-bit (FP4/INT4) low-bit neural networks**. Its goal is to significantly reduce VRAM usage and improve inference speed while preserving generation quality as much as possible. It implements and productionizes post-training quantization methods such as **SVDQuant**, and uses operator/kernel fusion and other optimizations to reduce the extra overhead introduced by low-rank branches.
+Nunchaku is a high-performance inference engine for **4-bit (FP4/INT4) low-bit neural networks**. It implements **SVDQuant** and related optimizations. The Chroma1-HD quantized weights in this repository are meant to be used with **vitoom-nunchaku** on supported GPUs.
 
-The Chroma1-HD quantized weights in this repository (e.g. `svdq-*_r32-*.safetensors`) are meant to be used with Nunchaku for efficient inference on supported GPUs.
+Upstream [Nunchaku](https://github.com/nunchaku-ai/nunchaku) has not merged Chroma support for a long time ([PR #928](https://github.com/nunchaku-ai/nunchaku/pull/928) still pending). **Do not copy `transformer_chroma.py` manually.**
 
-## You must install Nunchaku before use
+## Install vitoom-nunchaku (Option 1: manual Python environment)
 
-- **Official installation docs** (recommended source of truth): `https://nunchaku.tech/docs/nunchaku/installation/installation.html`
+Install the prebuilt wheel from **[tonera/vitoom-nunchaku](https://huggingface.co/tonera/vitoom-nunchaku)** that matches your platform, Python, and CUDA:
 
-### (Recommended) Install the official prebuilt wheel
+```bash
+pip install torch==2.11.* torchvision==0.26.* torchaudio==2.11.* \
+  --index-url https://download.pytorch.org/whl/cu130
 
-- **Prerequisite**: `PyTorch >= 2.5` (follow the wheel requirements as the source of truth)
-- **Install the nunchaku wheel**: pick the wheel matching your environment from GitHub Releases / HuggingFace / ModelScope (note `cp311` means Python 3.11):
-  - `https://github.com/nunchaku-ai/nunchaku/releases`
+hf download tonera/vitoom-nunchaku \
+  nunchaku-1.3.0.dev20260622+cu13.0torch2.11-cp311-cp311-linux_x86_64.whl \
+  --local-dir ./wheels
 
-```bash
-# Example (choose the correct wheel URL for your torch/cuda/python versions)
-pip install https://github.com/nunchaku-ai/nunchaku/releases/download/vX.Y.Z/nunchaku-X.Y.Z+torch2.9-cp311-cp311-linux_x86_64.whl
+pip install ./wheels/nunchaku-1.3.0.dev20260622+cu13.0torch2.11-cp311-cp311-linux_x86_64.whl
 ```
 
-- **Tip (RTX 50 series GPUs)**: usually `CUDA >= 12.8` is recommended, and FP4 models are preferred for better compatibility and performance (follow the official docs).
+For cu128, cp310, or ARM64 aarch64 wheels, see the [vitoom-nunchaku README](https://huggingface.co/tonera/vitoom-nunchaku).
 
-## Usage example (Diffusers + Nunchaku Transformer)
-Note: I am pushing for the official Nunchaku PR to be merged: https://github.com/nunchaku-ai/nunchaku/pull/928
-Until then, if you want to try it out, you can copy `transformer_chroma.py` from the repository to `nunchaku/models/transformers/transformer_chroma.py`.
-Usage like this:
-```
-from nunchaku.models.transformers.transformer_chroma import NunchakuChromaTransformer2dModel
+Verify:
+
+```bash
+python -c "import nunchaku; from nunchaku import NunchakuChromaTransformer2dModel; print(nunchaku.__version__)"
 ```
 
+## Usage example (Diffusers + Nunchaku Transformer)
+
+Assumes **vitoom-nunchaku** is installed:
+
 ```python
 import torch
 from diffusers import ChromaPipeline
@@ -86,3 +88,18 @@ if __name__ == "__main__":
     image.save("Chroma1.png")
 ```
 
+## Option 2 (recommended: vitoom)
+
+For a ready-to-use Web UI without manual wheel install, deploy [vitoom](https://github.com/tonera/vitoom). Its **visual** module includes **vitoom-nunchaku** with Chroma support. See [docker-usage-en.md](https://github.com/tonera/vitoom/blob/main/docker-usage-en.md).
+
+```bash
+git clone https://github.com/tonera/vitoom.git
+cd vitoom
+python scripts/setup_vitoom.py
+python scripts/load_vitoom_images.py --components backend,visual
+docker compose up -d backend
+docker compose -f docker-compose.inference.release.yml --profile visual up -d
+```
+
+In the Web UI: **Models** → download and activate **`tonera/Chroma1-HD-SVDQ`** → run in **Image** workspace.
+