Pure Vision v0.9 is a high-fidelity photorealistic model built on Stable Diffusion 1.5. It was designed to generate realistic, prompt-accurate, and clean images — both SFW and NSFW — without relying on trigger words or strong negative prompts.This model provides a neutral, natural rendering style suitable for portraits, full-body compositions, and concept-driven prompts. It is highly stable at high resolutions and ideal for building semantic LoRA datasets, thanks to its consistent anatomy, skin tone, and body proportions.<hr /><h3 id="merge-composition">🧱 Merge Composition</h3><ul><li>SD 1.5 pruned (ema-only) – core stability</li><li>Realistic Vision v5.1 – photorealistic balance</li><li>CyberRealistic v9.0 – anatomy and body fidelity</li><li>Dreamlike Photoreal 2.0 – soft natural rendering</li><li>StL Detail LoRA (baked-in) – skin, lighting &amp; facial refinement</li><li></li></ul><hr /><h3 id="generation-settings-(recommended)">⚙️ Generation Settings (Recommended)</h3><ul><li>Steps: 35</li><li>CFG Scale: 7.5</li><li>Sampler: DPM++ 2M Karras</li><li>Resolution: 512×768 or use HiRes Fix (preferred)</li><li>No need for negative prompt (optional: standard SD 1.5 anti-glitch)</li></ul><hr /><h3 id="strengths">✨ Strengths</h3><ul><li>True photorealism without harsh stylization</li><li>No need for trigger tokens or manual conditioning</li><li>Perfect for full-body images, portraits, and LoRA training datasets</li><li>Compatible with ControlNet, AnimateDiff, LoRA</li></ul>

Pure Vision

🆕 Version 0.9 – Initial ReleaseThis is the first official release of Pure Vision, a fully merged photorealistic checkpoint based on Stable Diffusion 1.5.<ul><li>Includes 4 carefully balanced base models</li><li>Bakes in 1 high-quality LoRA (<code>StL Detail</code>) for facial refinement</li><li>Optimized for high-resolution output without needing prompt tricks</li><li>Built to serve as a foundation for semantic LoRA training (body shape, age, expressions, etc.)</li></ul>⚠️ No style LoRA included. This model is meant to be visually neutral and prompt-driven. Recommended for users seeking full-body realism and dataset consistency.

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