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A universal Stable-Diffusion toolbox
| Date | Stars |
|---|---|
| 2026-07-31 | 909 |
| 2026-08-01 | 909 |
| 2026-08-06 | 909 |
Today
— stars today
This week
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Momentum
0.0
growth rate 0.00%/day
# HCP-Diffusion V2
[](https://pypi.org/project/hcpdiff/)
[](https://github.com/7eu7d7/HCP-Diffusion/stargazers)
[](https://github.com/7eu7d7/HCP-Diffusion/blob/master/LICENSE)
[](https://codecov.io/gh/7eu7d7/HCP-Diffusion)
[](https://github.com/7eu7d7/HCP-Diffusion/issues)
[📘中文说明](./README_cn.md)
[📘English document](https://hcpdiff.readthedocs.io/en/latest/)
[📘中文文档](https://hcpdiff.readthedocs.io/zh_CN/latest/)
Old HCP-Diffusion V1 at [main branch](https://github.com/IrisRainbowNeko/HCP-Diffusion/tree/main)
## Introduction
**HCP-Diffusion** is a Diffusion model toolbox built on top of the [🐱 RainbowNeko Engine](https://github.com/IrisRainbowNeko/RainbowNekoEngine).
It features a clean code structure and a flexible **Python-based configuration file**, making it easier to conduct and manage complex experiments. It includes a wide variety of training components, and compared to existing frameworks, it's more extensible, flexible, and user-friendly.
HCP-Diffusion allows you to use a single `.py` config file to unify training workflows across popular methods and model architectures, including Prompt-tuning (Textual Inversion), DreamArtist, Fine-tuning, DreamBooth, LoRA, ControlNet, ....
Different techniques can also be freely combined.
This framework also implements **DreamArtist++**, an upgraded version of DreamArtist based on LoRA. It enables high generalization and controllability with just a single image for training.
Compared to the original DreamArtist, it offers better stability, image quality, controllability, and faster training.
---
## Installation
Install [pytorch](https://pytorch.org/)
Install via pip:
```bash
pip install hcpdiff
# Initialize configuration
hcpinit
```
Install from source:
```bash
git clone https://github.com/7eu7d7/HCP-Diffusion.git
cd HCP-Diffusion
pip install -e .
# Initialize configuration
hcpinit
```
Use xFormers to reduce memory usage and accelerate training:
```bash
# Choose the appropriate xformers version for your PyTorch version
pip install xformers==?
```
## 🚀 Python Configuration Files
RainbowNeko Engine supports configuration files written in a Python-like syntax. This allows users to call functions and classes directly within the configuration file, with function parameters inheritable from parent configuration files. The framework automatically handles the formatting of these configuration files.
For example, consider the following configuration file:
```python
dict(
layer=Linear(in_features=4, out_features=4)
)
```
During parsing, this will be automatically compiled into:
```python
dict(
layer=dict(_target_=Linear, in_features=4, out_features=4)
)
```
After parsing, the framework will instantiate the components accordingly. This means users can write configuration files using familiar Python syntax.
---
## ✨ Features
<details>
<summary>Features</summary>
### 📦 Model Support
| Model Name | Status |
|--------------------------|-------------|
| Stable Diffusion 1.5 | ✅ Supported |
| Stable Diffusion XL (SDXL)| ✅ Supported |
| PixArt | ✅ Supported |
| FLUX | ✅ Supported |
| Stable Diffusion 3 (SD3) | 🚧 In Development |
---
### 🧠 Fine-Tuning Capabilities
| Feature | Description/Support |
|----------------------------------|---------------------|
| LoRA Layer-wise Configuration | ✅ Supported (including Conv2d) |
| Layer-wise Fine-Tuning | ✅ Supported |
| Multi-token Prompt-Tuning | ✅ Supported |
| Layer-wise Model Merging | ✅ Supported |
| Custom Optimizers | ✅ Supported (LioExcerpt of 8,959 characters
Read on GitHub
RainbowNeko
658
21
11
3
Wang Yabin · HiT
2
2
1
1
Kohaku-Blueleaf · NTHU · Taiwan
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f54fffd5c2b7fbee, desc:stable diffusion