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README_CN.md

@@ -13,50 +13,51 @@ base_model_relation: quantized
 license: apache-2.0
 ---
 
-# 模型说明（SVDQuant）
+# Model Card (SVDQuant)
 
-> **文档语言**：中文｜[English](README.md)
+> **Language**: English | [中文](README_CN.md)
 
 ![Chroma1-HD](chroma1-hd.png)
 
 
-## 模型名称
+## Model name
 
-- **模型仓库**：`tonera/Chroma1-HD-SVDQ`
-- **Base（Diffusers 权重路径）**：`tonera/Chroma1-HD-SVDQ`（本仓库根目录）
-- **量化 Transformer 权重**：`tonera/Chroma1-HD-SVDQ/svdq-<precision>_r32-Chroma1-HD.safetensors`
+- **Model repo**: `tonera/Chroma1-HD-SVDQ`
+- **Base (Diffusers weights path)**: `tonera/Chroma1-HD-SVDQ` (repo root)
+- **Quantized Transformer weights**: `tonera/Chroma1-HD-SVDQ/svdq-<precision>_r32-Chroma1-HD.safetensors`
 
-## 量化 / 推理技术
+## Quantization / inference tech
 
-- **推理引擎**：Nunchaku（`https://github.com/nunchaku-ai/nunchaku`）
+- **Inference engine**: Nunchaku (`https://github.com/nunchaku-ai/nunchaku`)
 
-Nunchaku 是一个面向 **4-bit（FP4/INT4）低比特神经网络**的高性能推理引擎，核心目标是在尽量保持生成质量的同时显著降低显存占用并提升推理速度。它实现并工程化了 **SVDQuant** 等后训练量化方案，并通过算子/内核融合等优化减少低秩分支带来的额外开销。
+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.
 
-本模型仓库中的 Chroma1-HD 量化权重（例如 `svdq-*_r32-*.safetensors`）用于配合 Nunchaku，在支持的 GPU 上进行高效推理。
+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.
 
+## You must install Nunchaku before use
 
-## 使用前必须安装 Nunchaku
+- **Official installation docs** (recommended source of truth): `https://nunchaku.tech/docs/nunchaku/installation/installation.html`
 
-- **官方安装文档**（建议以此为准）：`https://nunchaku.tech/docs/nunchaku/installation/installation.html`
+### (Recommended) Install the official prebuilt wheel
 
-### （推荐）方式：安装官方预编译 Wheel
-
-- **前置条件**：安装 `PyTorch >= 2.5`（实际以对应 wheel 的要求为准）
-- **安装 nunchaku wheel**：从 GitHub Releases / HuggingFace / ModelScope 选择与你环境匹配的 wheel（注意 `cp311` 表示 Python 3.11）：
+- **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`
 
 ```bash
-# 示例（请按你的 torch/cuda/python 版本选择正确的 wheel URL）
+# 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
 ```
 
-- **提示（50 系 GPU）**：通常建议 `CUDA >= 12.8`，并优先使用 FP4 模型以获得更好的兼容性与性能（以官方文档为准）。
-
-## 使用示例（Diffusers + Nunchaku Transformer）
-- 注意：我正在推动Nunchaku官方合并此PR:https://github.com/nunchaku-ai/nunchaku/pull/916
-在此之前，如果您想体验，可将仓库中transformer_chroma.py
-拷贝到nunchaku/models/transformers/transformer_chroma.py中。这是一个相对低性能版本，高性能版本仍然要等PR通过。
+- **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).
 
+## 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
+```
 
 ```python
 import torch
@@ -65,7 +66,7 @@ from diffusers import ChromaPipeline
 from nunchaku import NunchakuChromaTransformer2dModel
 from nunchaku.utils import get_precision
 
-MODEL = "Chroma1-HD-SVDQ" 
+MODEL = "Chroma1-HD-SVDQ"
 REPO_ID = f"tonera/{MODEL}"
 
 if __name__ == "__main__":