


Description
# YOMI Style — MOSS-Image LoRA
A style LoRA for MOSS-Image, trained on the SFT checkpoint (the
checkpoint most people actually generate with), so there is no
train/inference distribution gap.
## Trigger word
> *yomi**
## Recommended prompt
yomi, bold painterly brushstrokes, vibrant high-contrast colors,
chunky color blocking, dynamic composition, iridescent light leaks,
glowing rim light, 1girl, white background, looking at viewer
The style follows a modern theatrical-anime look: strong linework
dissolved into colour blocks, high-contrast saturated palettes, and
specular highlights with iridescent light leaks.
## Recommended settings
| Setting | Value |
|---|---|
| Base model | MOSS-Image SFT (bf16) |
| LoRA strength | 0.8 – 1.0 |
| Sampler | Euler |
| Scheduler | simple |
| Steps | 50 |
| CFG | 3.0 |
| Shift | 5.0 |
| Negative-branch skip | layer 22 |
| Resolution | 1024×1024 or higher |
Negative prompt (only active above CFG 1):
worst quality, bad anatomy, watermark, jpeg artifact
Tip: with the 8-step DMD speed LoRA loaded at 0.7 and CFG 1,
the LoRA still works but the style is noticeably softer. Use CFG 3
when style fidelity matters.
## Training details
| Item | Value |
|---|---|
| Base | MOSS-Image SFT bf16 |
| Trainer | Fizgig v8.5.0 (open source) |
| Network | LoRA, attn + MLP |
| Rank / Alpha | 4 / 4 |
| Learning rate | 3e-4, constant with 20-step warmup |
| Optimizer | AdamW |
| Epochs / Steps | 20 / 1320 |
| Batch size | 1 |
| Resolution | 768 (0.59 MP) |
| Precision | bf16 |
| EMA | 0.98 |
| Dataset | 66 images, single style |
| Captions | WD14 content tags + fixed style description |
| Text encoder | frozen (DiT-only LoRA) |
| Output | 18.2 MB safetensors, 246 modules |
Notes:
- Trained on SFT, not Base — it patches the model you are actually
loading, so strength behaves predictably.
- DiT-only adapter: no CLIP/text-encoder weights are included.
- Trained at 0.59 MP; it generalises upward but 1024+ gives the best
line quality.
- Works with the regular ComfyUI LoRA loader for MOSS-Image.



