description: <p>SDXL1.0正式版训练，训练分辨率为1024*1024，原大小1.7g压缩为17m，使用压缩方法<span style="color:rgb(250, 82, 82)">sv_fro，</span><span style="color:rgb(255, 255, 255)">注意当前lora仅支持SDXL</span></p><p><span style="color:rgb(193, 194, 197)">AI净土交流群电报：</span><a target="_blank" rel="ugc" href="https://t.me/+soU-Ws67aBlkY2I1">https://t.me/+soU-Ws67aBlkY2I1</a></p><p>QQ群：895633778</p><p>WebUI推荐参数：</p><p>vae：sdxl-vae</p><p>采样：Euler a</p><p>步数：20-30</p><p>高分辨率修复 (Hires. fix)：<strong><s>高清修复一定要开！！</s><span style="color:#fa5252">不一定开！</span></strong></p><p><strong>高清迭代步数:15-20</strong></p><p>放大算法：R-ESRGAN 4x+</p><p>重绘幅度：0.25-0.4</p><p>放大倍数：2或者2以上</p><p>cfg：7（可自由尝试）</p><p></p><p><strong><span style="color:rgb(255, 255, 255)">压缩算法简介：</span></strong></p><p><span style="color:rgb(255, 255, 255)">sv_fro方法是一种对矩阵进行深度压缩的方法，主要用于减少矩阵的存储空间和计算复杂度。</span></p><p><span style="color:rgb(255, 255, 255)">sv_fro方法的基本思想是利用奇异值分解（Singular Value Decomposition，SVD）将矩阵分解为三个矩阵的乘积：A = U </span><em><span style="color:rgb(255, 255, 255)">S </span></em><span style="color:rgb(255, 255, 255)">V^T，其中U和V是正交矩阵，S是奇异值矩阵。奇异值矩阵S是一个对角矩阵，其对角线上的元素称为奇异值。</span></p><p></p><p><span style="color:rgb(255, 255, 255)">在sv_fro方法中，只保留矩阵A中奇异值较大的部分，将较小的奇异值设为0，从而实现对矩阵的深度压缩。具体步骤如下：</span></p><p><span style="color:rgb(255, 255, 255)">1. 对矩阵A进行奇异值分解，得到U、S和V。</span></p><p><span style="color:rgb(255, 255, 255)">2. 根据预设的阈值，将S中小于阈值的奇异值设为0。</span></p><p><span style="color:rgb(255, 255, 255)">3. 使用压缩后的奇异值矩阵S以及U和V重构原始矩阵A。</span></p><p></p><p><span style="color:rgb(255, 255, 255)">通过sv_fro方法进行深度压缩后，可大幅减少矩阵的存储空间，同时还能在一定程度上降低计算复杂度。这对于需要处理大规模矩阵的任务来说，具有重要的实际应用意义。</span></p>
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