Sign In

SDXL Dark Angel

111
1.2k
26
Updated: Oct 5, 2024
stylewomanangelstyelsdxl
Verified:
SafeTensor
Type
LoRA
Stats
1,150
0
Reviews
Published
Aug 13, 2023
Base Model
SDXL 1.0
Trigger Words
angel
Hash
AutoV2
66E8A46C0A
default creator card background decoration
ArienTOP's Avatar
ArienTOP

AI Pure Land traffic group telegrams: https://t.me/+soU-Ws67aBlkY2I1

Kaiden AI private model custom consultation telegram: https://t.me/+Ql4sngcJlbhmOTU1

Contact information: wechat ID: ArienTOP, Email: [email protected]

Model introduction

The original size of the lora model is 1.7g, in order to save hard disk space and video memory space, through sv_fro method for matrix compression, reduced to the original 1/100 size, and the loss of accuracy control within 5%, if you need the full version of 1.7g can join our QQ group: 895633778

Introduction to compression algorithm:

sv_fro method is a method of deep compression of matrix, which is mainly used to reduce the storage space and computational complexity of matrix.

The basic idea of the sv_fro method is to decompose A matrix into the product of three matrices using Singular Value Decomposition (SVD) : A = U SV ^T, where U and V are orthogonal matrices and S is a singular value matrix. A singular value matrix S is a diagonal matrix whose diagonal elements are called singular values.

In sv_fro method, only the part with large singular value in matrix A is kept, and the smaller singular value is set to 0, so as to realize the deep compression of matrix. The specific steps are as follows:

1. Perform singular value decomposition on matrix A to obtain U, S and V.

2. According to the preset threshold, set the singular value in S that is less than the threshold to 0.

3. Reconstruct the original matrix A using the compressed singular value matrix S, U and V.

After deep compression by sv_fro method, the storage space of matrix can be greatly reduced, and the computational complexity can be reduced to a certain extent. This has important practical significance for the tasks that need to deal with large-scale matrices.

模型简介

本lora模型原大小为1.7g,为了节省硬盘空间和显存空间,通过sv_fro方法进行矩阵压缩,缩小到原来的1/100大小,而损失精度控制在百分之5以内,如果你需要完整版1.7g的可以加入我们的QQ群:895633778

压缩算法简介:

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。

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 S 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。

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:

1. 对矩阵A进行奇异值分解,得到U、S和V。

2. 根据预设的阈值,将S中小于阈值的奇异值设为0。

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。

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。

Alpha1

本版本的封面你可以非常清晰的看到XL与SD1.5之间的差距。

在原训练素材中,有衣服上带文字的图,如同封面你看到的一样,XL有个文字概念,而且非常的清晰!

这是对XL的第一次探索,希望大家能够多多返图!