KohakuBlueleaf/KohakuMossTrainer

选用指南

AI 根据 README 整理 · 2026/10/19 10:26:48

使用方式

通过命令行使用，包括环境检查、配置生成、数据缓存、训练和采样等步骤。

查看 README 依据

```bash # 1. check the environment kohakumoss-doctor # 2. weights (~26 GB) hf download OpenMOSS-Team/MOSS-Image-Base --local-dir models/MOSS-Image-Base # 3. a config for this GPU and your dataset (kohya-style folders; --source for others) kohakumoss-config new train --weights models/MOSS-Image-Base --data path/to/dataset --name my-lora # 4. data: check the plan, then cache text embeddings and latents once kogine run scripts/data/inspect_data.py --config configs/user/my-lora_data.py kogine run scripts/data/cache_text.py --config configs/user/my-lora_data.py

kogine run scripts/data/cache_latents.py --config configs/user/my-lora_data.py # 5. train (any knob on the command line: --set LR=2e-4 --set STEPS=2000) kogine run scripts/train/moss.py --config configs/user/my-lora.py # 6. sample with the exported adapter kohakumoss-config new sample --weights models/MOSS-Image-Base --adapter runs/moss/my-lora/export/my-lora-comfy.safetensors --name my-lora-sample kogine run scripts/sample/sample.py --config configs/user/my-lora-sample.py --set PROMPT="a lighthouse on a rocky coast at sunset"

部署要求

需要 Python 3.10+，NVIDIA 驱动支持 CUDA 12.6+ 或 Intel Arc 驱动；Windows 上需要 Visual Studio Build Tools，sm70/sm75 需要 CUDA 工具包。

查看 README 依据

then runs `kohakumoss-doctor`, which lists anything missing with the command that fixes it. Requirements: Python 3.10+ (the helpers make a 3.12 venv), an NVIDIA driver for CUDA 12.6+ or the Intel Arc driver; on Windows the Visual Studio Build Tools ("Desktop development with C++"); for sm70 / sm75 the CUDA toolkit (`nvcc`). Windows (PowerShell): ```powershell git clone https://github.com/KohakuBlueleaf/KohakuMossTrainer; cd KohakuMossTrainer powershell -ExecutionPolicy Bypass -File scripts\install\install.ps1

已知限制

多 GPU 训练仅支持 Linux；sm70 架构的最终测试套件未在发布时重新运行。

查看 README 依据

(1) Tested during this release's development (training runs, DDP, the low-VRAM layers, the sm70 kernel suite); the final release tree's test suite was not re-run on sm70 (test host offline at release time). - **Windows:** sm75, sm86, sm89, sm120 and Intel Arc run every single-GPU feature; multi-GPU training is Linux-only. - **Per-card choice:** each op runs its kernel or `torch.compile`, whichever is measured faster on that card; the eager path is the reference both are checked against ([docs/performance/](docs/performance/README.md)). ## Memory profiles and VRAM