DaSiWa TrainFlow

Portable Go wrapper around the sd-scripts and Musubi Python training stacks — train LoRAs and Textual Inversions for Anima, SDXL/Pony/Illustrious, LTX 2.3/Wan 2.2 video, and Krea 2 image models from a single embedded UI on Linux and Windows.

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Quick Start

Linux (with Git)

git clone --depth 1 https://github.com/darksidewalker/DaSiWa-TrainFlow.git && cd DaSiWa-TrainFlow
chmod +x TrainFlow TrainFlow_Runtime_Tool && ./TrainFlow_Runtime_Tool

Linux (without Git)

curl -L -o TrainFlow.zip https://github.com/darksidewalker/DaSiWa-TrainFlow/archive/refs/heads/main.zip
unzip TrainFlow.zip && cd DaSiWa-TrainFlow-main
chmod +x TrainFlow TrainFlow_Runtime_Tool && ./TrainFlow_Runtime_Tool

Windows PowerShell (with Git)

git clone --depth 1 https://github.com/darksidewalker/DaSiWa-TrainFlow.git; cd DaSiWa-TrainFlow
.\TrainFlow_Runtime_Tool.exe

Windows PowerShell (without Git)

Invoke-WebRequest -Uri https://github.com/darksidewalker/DaSiWa-TrainFlow/archive/refs/heads/main.zip -OutFile TrainFlow.zip
Expand-Archive TrainFlow.zip -Force; cd DaSiWa-TrainFlow-main
.\TrainFlow_Runtime_Tool.exe

The runtime tool opens at http://127.0.0.1:7870. Click Verify Runtime, then Install Requirements. Once ready, launch the trainer (./TrainFlow or .\TrainFlow.exe) — the UI opens at http://127.0.0.1:7860.

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How To Use

1. Set up the runtime — run the Runtime Tool, verify it, install requirements
2. Pick a profile — choose your model family in the main UI
3. Point to your data — select model files and dataset folders using the Browse buttons
4. Configure training — set trigger word, rank, optimizer, steps — or click Auto Calc for smart defaults based on your dataset size and GPU VRAM
5. Start training — watch the live log, monitor hardware, browse preview samples
6. Finish — click Quit when done; your outputs are in the project folder

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Support Matrix

| | Linux | Windows | |------------------------|:-----:|:-------:| | CUDA (NVIDIA GPU) | ✅ | ✅ | | ROCm (AMD GPU, Linux) | ✅ | 🟥 | | AMD GPU monitoring | ✅ | ✅ | | Portable Python runtime| ✅ | ✅ | | uv / pip installs | ✅ | ✅ | | Hardware overlay | ✅ | ✅ |

| Hardware Monitor | NVIDIA (CUDA) | AMD (Linux sysfs) | AMD (Windows WMI) | |-------------------------|:-------------:|:-----------------:|:-----------------:| | GPU utilization | ✅ | ✅ | 🟥 | | VRAM used / total | ✅ | ✅ | ✅ | | Temperature | ✅ | ✅ | 🟥 | | Power draw / limit | ✅ | ✅ | 🟥 | | CPU usage | ✅ | ✅ | ✅ | | CPU temperature | ✅ | ✅ | ✅ | | RAM usage | ✅ | ✅ | ✅ |

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Feature Matrix

| Feature | Anima | SDXL / Pony / Illustrious | LTX 2.3 Video | Wan 2.2 Video | Krea 2 Image | |----------------------------------|:-----:|:-------------------------:|:-------------:|:-------------:|:------------:| | LoRA training | ✅ | ✅ | ✅ | ✅ | ✅ | | Textual Inversion | ✅ | ✅ | 🟥 | 🟥 | 🟥 | | Auto Calc (profile-aware) | ✅ | ✅ | ✅ | ✅ | ✅ | | Training previews (enabled by default) | ✅ | ✅ | ✅ | ✅ | ✅ | | Dynamic multi-prompt sampling | ✅ | ✅ | ✅ | ✅ | ✅ | | Resume from state | ✅ | ✅ | ✅ | ✅ | ✅ | | Prodigy optimizer | ✅ | ✅ | ✅ | ✅ | ✅ | | AdamW / AdamW8bit | ✅ | ✅ | ✅ | ✅ | ✅ | | Flash Attention | ✅ | 🟥 | 🟥 | 🟥 | 🟥 | | torch.compile | ✅ | 🟥 | 🟥 | 🟥 | 🟥 | | FP8 base / scaled | 🟥 | 🟥 | ✅ | ✅ | ✅ | | Native FP8 checkpoint detection | 🟥 | 🟥 | ✅ | ✅ | ✅ | | Block swap + pinned memory | 🟥 | 🟥 | ✅ | ✅ | ✅ | | H2D-only LoRA block swap | 🟥 | 🟥 | ✅ | ✅ | ✅ | | VRAM-based batch sizing | ✅ | ✅ | ✅ | ✅ | ✅ | | Metadata (author + tags) | ✅ | ✅ | ✅ | ✅ | ✅ | | Text/latent caching | 🟥 | 🟥 | ✅ | ✅ | ✅ | | Video normalization | 🟥 | 🟥 | ✅ | ✅ | 🟥 | | Managed model download | ✅ | 🟥 | 🟥 | 🟥 | ✅ |

✅ Supported — 🟥 Not applicable or not supported

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Training Profiles

| Profile | Model Files | Network Module | Bucket Step | Pipeline | |---------|-------------|----------------|-------------|----------| | Anima | DiT + Qwen3 + VAE | networks.lora_anima | 64px | sd-scripts | | SDXL / Pony / Illustrious | checkpoint (+ optional VAE) | networks.lora | 32px | sd-scripts | | LTX 2.3 | checkpoint + Gemma encoder | networks.lora_ltx2 | 16px | Musubi video | | Wan 2.2 | DiT + T5 + VAE | networks.lora_wan | 16px | Musubi video | | Krea 2 | RAW DiT + Qwen3-VL + Qwen-Image VAE | networks.lora_krea2 | 32px | Musubi image |

Auto Calc reads your profile and dataset count, then picks rank, learning rates, batch size, gradient accumulation, steps, and save/sample intervals — all tuned to your available VRAM. It preserves your chosen optimizer (Prodigy stays at lr=1.0 constant; AdamW/AdamW8bit stay at 1e-4 cosine).

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Features At A Glance

Embedded Training GUI

Single portable binary, no separate web build step. Everything runs from one download:

• Colored profile switcher for all five model families
• Local file browser for datasets, models, and resume paths
• Settings autosaved to training/settings.json
• Optimizer-aware defaults (Prodigy, AdamW8bit, AdamW)
• Full training controls: rank, alpha, learning rates, batch, grad accum, steps, intervals
• SDXL-specific UNet/text-encoder LR fields and UNet-only toggle
• Optional Flash Attention and torch.compile (Anima)
• Multi-prompt sample generation with color-coded prompts
• Training preview toggle (on by default, configurable per session)
• Resume panel with automatic latest-state discovery
• Live training log streamed in real time
• Preview gallery with image overlay
• Output button to open the project output folder

Dataset Preparation

• WD EVA02 ONNX tagging/captioning
• Combined Tag + Resize workflow with configurable thresholds
• Resize-copy helper (training/prepared/<project>)
• Video normalization pipeline: resolution, FPS, duration, codec, quality, parallel workers, speed control, skip frames
• Automatic Musubi dataset TOML generation with text/latent cache rebuild triggers

Runtime & Model Management

• Companion Runtime Tool at http://127.0.0.1:7870
• Verify, update, and install Python runtime dependencies (uv-first with pip fallback)
• Download Anima base models and Krea 2 runtime models directly from the UI
• Download prep assets (WD tagger, U2Net)
• PyTorch backend selector: CUDA 12.4 default, experimental ROCm 6.4, or existing user-managed install
• GPU auto-detection with vendor badge in the header
• Vendor-colored backend panel (green NVIDIA/CUDA, orange AMD/ROCm) with mismatch warnings
• Platform-specific portable Python (no system Python required)

Hardware Monitoring Overlay

Compact real-time display inside the sampler panel:

• CPU usage, RAM usage, CPU tem