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LiQUiDMD2 [XL / Illustrious / Pony / NAI]

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LiQUiDMD2 [XL / Illustrious / Pony / NAI] - AI Model cover image
업로드 날짜
2026. 2. 15. 오전 2:22
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17.3k
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  • 이미지 생성 및 공유 허용
  • 다른 사용자가 내 모델을 다운로드하도록 허용합니다
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설명

https://civitai.com/models/2279556/liquidmd2-xl-illustrious-pony-nai

(Civitaiより転載。制作ありがとうございます。もしご自分でアップロードされるようでしたらお知らせ下さい。 Thank you to the creators. If you prefer to post it yourself, please leave a comment, and I will remove it.)


LiQUiDMD2 is a modified and refined version of DMD2, designed to improve image quality, stability, and prompt fidelity during ultra-low-step SDXL, Illustrious, Pony & NoobAI generation.

Building on the strengths of prior DMD2 distillation LoRAs, this version introduces targeted adjustments to training and tuning that reduce common issues such as oversmoothing, color drift, structural collapse, and prompt inconsistency at low sampling steps. The result is more reliable outputs with cleaner details and better composition—without sacrificing the speed benefits of DMD2.

What’s Improved

  • 🧩 Better structural stability at very low step counts

  • 🎯 Improved prompt adherence and concept clarity

  • 🎨 Reduced color washout and contrast loss

  • 🧼 Cleaner textures and edges compared to earlier DMD2 LoRAs

  • ⚡ Maintains ultra-fast generation performance

Key Features

  • Optimized specifically for SDXL base models, including Illustrious & Pony

  • Designed for 4-10 step inference

  • Lightweight LoRA, easy to integrate into existing pipelines

  • More consistent results across a wider range of prompts

Recommended Settings

  • Steps: 6–8

  • CFG: 1–2 (lower CFG often works best)

  • Sampler: Euler A, LCM

  • Resolution: 1024×1024, 896x1152, 832x1216 or other SDXL-native sizes

Ideal Use Cases

  • Rapid prototyping and prompt exploration

  • Real-time or low-latency generation workflows

  • Batch generation with minimal compute

  • Users dissatisfied with quality or stability of earlier DMD2 LoRAs

This enhanced DMD2 LoRA aims to push low-step SDXL generation further, offering faster iteration with noticeably improved reliability over previous DMD2-based LoRAs.

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