# SDMatte_plus-fp16-and-bf16

This repository provides optimized, inference-only versions of the original [SDMatte model by LongfeiHuang](https://huggingface.co/LongfeiHuang/SDMatte).

The models here have been specifically processed to be lightweight and efficient for deployment and use in applications like ComfyUI, without compromising the quality of the matting results.

### What is this?

This repository contains inference-only weights for the **SDMatte** model. The original checkpoint file (`.pth`) was a full training checkpoint, which included not only the model weights but also ~6.5 GB of trainer states (like optimizer states). These trainer states are crucial for resuming training but are unnecessary for performing inference (i.e., actually using the model for matting).

### Optimizations Performed

1.  **Removal of Trainer States**: The largest optimization was stripping the `trainer` key from the original checkpoint. This removes all unnecessary data related to the training process, significantly reducing the file size without affecting the model's output.

2.  **16-bit Precision Quantization**: The model weights have been converted from their original 32-bit floating-point precision (FP32) to 16-bit precision. We provide two popular formats:
    * **FP16 (half-precision)**: Offers a great balance of speed, reduced memory usage, and high quality. It is supported by most modern NVIDIA GPUs (10-series and newer).
    * **BF16 (bfloat16)**: Offers a dynamic range identical to FP32, making it more resilient to overflow/underflow issues. It provides the best performance on newer NVIDIA GPUs (RTX 30-series and newer).

### DIY Quantization with `convert_precision.py`

This repository also includes the Python script, `convert_precision.py`, which was used to create these fp16 and bf16 models. You can use this script to convert the original FP32 checkpoint yourself.

1.  Place the original FP32 `SDMatte_plus.pth` file in the same folder as the script.
2.  Open the `convert_precision.py` file with a text editor.
3.  Modify the `TARGET_PRECISION` variable at the top to either `'fp16'` or `'bf16'`.
4.  Run the script from your terminal: `python convert_precision.py`.

### Acknowledgements

Huge thanks to **LongfeiHuang** for creating and open-sourcing the original SDMatte model. This repository is merely an optimized packaging of their incredible work. Please visit the original repository for more details on the model's architecture and training.

* **Original Model**: [https://huggingface.co/LongfeiHuang/SDMatte](https://huggingface.co/LongfeiHuang/SDMatte)

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## 中文版本

### 这是什么？

本仓库提供原始 [SDMatte 模型（作者 LongfeiHuang）](https://huggingface.co/LongfeiHuang/SDMatte) 的优化版，仅用于推理。

这里的模型都经过了专门处理，旨在使其轻量化且高效，以方便在 ComfyUI 等应用中部署和使用，同时不影响抠图效果的质量。

### 执行的优化

1.  **移除 Trainer 状态**：最大的优化是剥离了原始检查点中的 `trainer` 键。这移除了所有与训练过程相关的非必要数据（如优化器状态），在完全不影响模型输出质量的前提下，极大地减小了文件体积。原始文件中约 6.5 GB 的数据都属于此类。

2.  **16 位精度量化**：模型权重已从原始的 32 位浮点精度 (FP32) 转换为 16 位精度。我们提供了两种主流格式：
    * **FP16 (半精度)**：在速度、显存占用和高质量之间取得了绝佳的平衡。它被大多数现代 NVIDIA GPU（10 系及更新）所支持。
    * **BF16 (bfloat16)**：拥有与 FP32 相同的动态范围，使其在处理数据溢出/下溢问题时更具弹性。它在较新的 NVIDIA GPU（RTX 30 系及更新）上能提供最佳性能。

### 使用 `convert_precision.py` 自行量化

本仓库同样包含了用于创建这些 fp16 和 bf16 模型的 Python 脚本 `convert_precision.py`。您可以使用此脚本，自行将原始的 FP32 检查点进行转换。

1.  将原始的 FP32 `SDMatte_plus.pth` 文件放置于脚本所在的同一个文件夹内。
2.  使用文本编辑器打开 `convert_precision.py` 文件。
3.  修改文件顶部的 `TARGET_PRECISION` 变量，将其设置为 `'fp16'` 或 `'bf16'`。
4.  在您的终端中运行脚本：`python convert_precision.py`。

### 致谢

非常感谢 **LongfeiHuang** 创建并开源了卓越的 SDMatte 模型。本仓库的工作仅仅是对其出色成果的优化和打包。有关模型架构和训练的更多详细信息，请访问原始仓库。

* **原始模型仓库**：[https://huggingface.co/LongfeiHuang/SDMatte](https://huggingface.co/LongfeiHuang/SDMatte)