# Mretsis Style LoRA — 半厚涂画风 LoRA&gt; 🇨🇳 中文 ↓ | 🇬🇧 English ↓↓---## 中文介绍这是我训练的第一个 LoRA，还有很多不足之处，敬请谅解。### 初衷我的本意是制作一个用于改善工作流画风的 Style LoRA——追求一种**有轻微厚涂质感、但没有油腻 AI 感**的渲染风格，同时让画面形成**人物与背景主次分明、详略得当**的层次关系。简而言之：清爽、有笔触、不塑料。### 版本说明v1 — 风格-角色纠缠版v1 在训练过程中出现了「风格-角色纠缠」(style-character entanglement) 的问题：由于训练数据集中角色多样性不足（原作 CG 女主基本只有一个人），模型将画风和角色外观绑定在了一起，导致在非训练角色上泛化能力很差——换一个角色，画风就跑偏了。不过，如果你恰好需要的话，也可以把 v1 当作一个 Mretsis 游戏女主角色 LoRA 来使用。输入相应的角色提示词（白/灰发、蓝瞳、军装等），应该能较好地还原。v2 — 泛化改进版v2 通过以下策略显著改善了泛化能力：- 数据集精选（删除冗余构图，保留姿势/光线/场景多样性）- Caption 增强（随机替换角色外观描述，打破视觉-文本关联）- 提高 Caption Dropout Rate（迫使模型学习视觉模式而非文本关联）- 详尽的角色外观描述（让文本编码器承担角色信息，减轻 UNet 负担）v2 在未训练角色上能保持一致的画风表现。### 使用建议- 推荐权重: 0.8–1.2（建议从 0.8 开始调整，游戏中的角色特征0.8，否则推荐1.2）- 触发词: <code>Mretsis</code>- 建议**配合其他画师串和 LoRA** 使用，形成一种画风 cocktail，而非单独依赖本 LoRA- 如果在特定角色上画风偏弱，可以尝试： - 提高权重到 0.9–1.2---## English IntroductionThis is my first LoRA — there's still a lot of room for improvement, and I appreciate your understanding.### PurposeI built this as a style LoRA to refine my generation workflow. The goal: a rendering style with subtle impasto texture without the greasy AI look, combined with a clear visual hierarchy between subject and background — clean, brushy, not plastic.### Version Notesv1 — Style-Character Entanglementv1 suffers from style-character entanglement: the training dataset lacked character diversity (the source material features essentially one protagonist), so the model fused the rendering style with the character's appearance. Generalization to non-training characters is poor — switch the character and the style drifts.That said, v1 can still serve as a character LoRA for the Mretsis game protagonist. Use the appropriate character tags (white/grey hair, blue eyes, military attire) for best results.v2 — Improved Generalizationv2 significantly improves generalization through:- Dataset curation (removed redundant compositions, preserved pose/lighting/scene diversity)- Caption augmentation (randomly swapped character appearance descriptors to break visual-text associations)- Increased caption dropout rate (forces the model to learn visual patterns rather than text associations)- Exhaustive character captioning (offloads character identity to the text encoder, reducing UNet entanglement)v2 maintains consistent style across unseen characters.### Usage Recommendations- Recommended weight: 0.8–1.2 (start at 0.8, if not character in Mretsis's game, try 1.2)- Trigger word: <code>Mretsis</code>- Best used as part of a style cocktail with other artist tags and LoRAs, rather than standalone- If style is weak on a particular character, try: - Increasing weight to 0.9–1.2---## Technical Details### Base Model| Item | Value ||------|-------|| Checkpoint | chenkinNoobXLCKXL v02 (NoobAI-XL / Illustrious-XL lineage) || VAE | sdxl-vae-fp16-fix |### Training Parameters| Parameter | v1 | v2 ||-----------|----|----|| Dataset size | ~70 images | ~45 images (curated) || Network type | LoRA (Standard) | LoRA (Standard) || Network dim (rank) | 32 | 32 || Network alpha | 16 | 16 || Learning rate | 1e-4 | 1e-4 || UNet LR | 1e-4 | 1e-4 || Text Encoder LR | 5e-5 | 5e-5 || Optimizer | AdamW8bit | AdamW8bit || LR scheduler | cosine_with_restarts | cosine_with_restarts || Scheduler cycles | 1 | 3 || Warmup steps | 0 | ~10% of total steps || Epochs | 20 | 14 || Total steps | ~1,400 | ~2,400 || Batch size | 1 | 1 || Resolution | 1024 | 1024 || Buckets | ON (512–2048, step 64) | ON (512–2048, step 64) || Noise offset | 0.05 | 0.1 || Min SNR gamma | 5 | 5 || Caption dropout | 0 | 0.1 || Shuffle caption | ON | ON || Keep tokens | 1 | 1 || Clip skip | 2 | 2 || Mixed precision | fp16 | fp16 || Max token length | 225 | 225 |### Key Differences v1 → v2- Dataset curated from ~70 → ~45 images (removed redundant compositions)- Caption augmentation: 30% random swap on character-specific tags (hair color, eye color, body type)- Caption dropout rate: 0 → 0.1- LR warmup: 0 → 10% of total steps- Cosine restart cycles: 1 → 3- Noise offset: 0.05 → 0.1- Exhaustive character descriptions in captions to reduce style-character entanglement### Training Tools- Training framework: Kohya SS (sd-scripts)- Hardware: RunPod cloud GPU- Captioning: Manual review + WD14 tagger baseline---First LoRA, lessons learned. Thanks for trying it out. 🌲

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