With proper training, an SD‑1.5 model can rival SDXL or other, more recent checkpoints.在正确的训练下，sd1.5的模型也可以有不弱于SDXL或者其它更先进的模型的能力High-definition, high-fidelity, photorealistic, with complex and richly detailed visuals.高清晰度，高拟真度，高写实度，照片感，丰富复杂的细节Generate beautiful Asian girl images生成美丽的东亚女性图片<h3 id="recommended-parameters-ife5lvahx">Recommended Parameters</h3>Negative prompt <code>(worst quality:2), (low quality:2), (normal quality:2), lowres</code>Samplers<ul><li>DPM++ 2M</li><li>Euler</li></ul>Steps 30 – 50 (or more)Image size 512 × 768Turn ADetailer on to clean up small or distant faces.I still think SD‑1.5 is a fantastic base even today: it’s simple, direct, fast, and it can produce images with bold style, rich details, and real charm. More than once, while experimenting with SDXL or other models, I’ve caught myself thinking, “Hmm… the older SD‑1.5 result actually looked nicer.” For most use‑cases, the SD‑1.5 parameter count may already be enough—so I set out to push that model further and see if I could break its limits, making it generate images that are even more striking, beautiful, and lifelike.我想即使在今天，sd1.5也是一个很好的模型，它简单、直接、快速，生成的图片也可以风格强烈、细节饱满、令人喜欢。我经常在使用SDXL模型或者其他某些模型时偶尔想到：这看起来还不如之前sd1.5模型的好看。我想，满足我们的需求，sd1.5的参数量或许就能做到。因此我来尝试改进已有的sd1.5模型，看能不能突破它能力的边界，让它生成的图片更加绝妙、美丽、真实The model is fine‑tuned from majicMIX realistic v7 using a dataset similar to Evalisennia SDXL v3. Compared with the original majicMIX realistic v7, it should deliver higher realism, stronger capabilities, broader knowledge, and a more versatile style range.模型基于majicMIX realistic v7使用类似Evalisennia SDXL v3的数据集进行训练，相较于majicMIX realistic v7应该进一步提升了真实度、模型能力、知识量、风格。Hope you enjoy it—happy creating!希望你们喜欢，使用愉快！

Files with SHA256: fd076cef784821204d757d8806ffab31496a06555257b1ec8825593d0f948d9a