lora inspector
0.2 Safetensors less than 0.4.0 Version
检查Lora文件以获取元信息和洛拉权重的定量分析。
safetensors （要支持所有lora文件）请注意，这是一项正在进行的工作，并不是为了生产使用。笔记
考虑使用新的Web界面LORA检查员进行GUI表示。
克隆此回购或下载Python脚本文件。
需要依赖性：
torch
safetensors
tqdm
可以安装它们之一：
venv ）。受到推崇的pip install safetensors tqdm （有关如何安装Pytorch的说明，请参见“请参见开始”） $ python lora-inspector.py --help
usage: lora-inspector.py [-h] [-s] [-w] [-t] [-d] lora_file_or_dir
positional arguments:
lora_file_or_dir Directory containing the lora files
options:
-h, --help show this help message and exit
-s, --save_meta Should we save the metadata to a file ?
-w, --weights Show the average magnitude and strength of the weights
-t, --tags Show the most common tags in the training set
-d, --dataset Show the dataset metadata including directory names and number of images您可以添加目录或文件：
$ python lora-inspector.py /mnt/900/training/sets/landscape-2023-11-06-200718-e4d7120b -w
/mnt/900/training/sets/landscape-2023-11-06-200718-e4d7120b/landscape-2023-11-06-200718-e4d7120b-000015.safetensors
Date: 2023-11-06T20:16:34 Title: landscape
License: CreativeML Open RAIL-M Author: rockerBOO
Description: High quality landscape photos
Resolution: 512x512 Architecture: stable-diffusion-v1/lora
Network Dim/Rank: 16.0 Alpha: 8.0 Dropout: 0.3 dtype: torch.float32
Module: networks.lora : { ' block_dims ' : ' 4,4,4,4,4,4,4,4,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8 ' , ' block_alphas ' : ' 16,16,16,16,16,16,16,16,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32 ' , ' block_dropout ' : ' 0.01, 0.010620912260804992, 0.01248099020159499, 0.015572268683063176, 0.01988151037617019, 0.02539026244641935, 0.032074935571726845, 0.03990690495552037, 0.04885263290251277, 0.058873812432261884, 0.0699275313155418, 0.08196645583109653, 0.09493903345590124, 0.10878971362098, 0.12345918558747097, 0.13888463242431537, 0.155, 0.17173627983648962, 0.18902180461412393, 0.20678255506208312, 0.22494247692026895, 0.2434238066153228, 0.26214740425618505, 0.2810330925232585 ' , ' dropout ' : 0.3}
Learning Rate (LR): 2e-06 UNet LR: 1.0 TE LR: 1.0
Optimizer: prodigyopt.prodigy.Prodigy(weight_decay=0.1,betas=(0.9, 0.9999),d_coef=1.5,use_bias_correction=True)
Scheduler: cosine Warmup steps: 0
Epoch: 15 Batches per epoch: 57 Gradient accumulation steps: 24
Train images: 57 Regularization images: 0
Noise offset: 0.05 Adaptive noise scale: 0.01 IP noise gamma: 0.1 Multires noise discount: 0.3
Min SNR gamma: 5.0 Zero terminal SNR: True Debiased Estimation: True
UNet weight average magnitude: 0.7865518983141094
UNet weight average strength: 0.00995593195090544
No Text Encoder found in this LoRA
----------------------
/mnt/900/training/sets/landscape-2023-11-06-200718-e4d7120b/landscape-2023-11-06-200718-e4d7120b.safetensors
Date: 2023-11-06T20:27:12 Title: landscape
License: CreativeML Open RAIL-M Author: rockerBOO
Description: High quality landscape photos
Resolution: 512x512 Architecture: stable-diffusion-v1/lora
Network Dim/Rank: 16.0 Alpha: 8.0 Dropout: 0.3 dtype: torch.float32
Module: networks.lora : { ' block_dims ' : ' 4,4,4,4,4,4,4,4,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8 ' , ' block_alphas ' : ' 16,16,16,16,16,16,16,16,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32 ' , ' block_dropout ' : ' 0.01, 0.010620912260804992, 0.01248099020159499, 0.015572268683063176, 0.01988151037617019, 0.02539026244641935, 0.032074935571726845, 0.03990690495552037, 0.04885263290251277, 0.058873812432261884, 0.0699275313155418, 0.08196645583109653, 0.09493903345590124, 0.10878971362098, 0.12345918558747097, 0.13888463242431537, 0.155, 0.17173627983648962, 0.18902180461412393, 0.20678255506208312, 0.22494247692026895, 0.2434238066153228, 0.26214740425618505, 0.2810330925232585 ' , ' dropout ' : 0.3}
Learning Rate (LR): 2e-06 UNet LR: 1.0 TE LR: 1.0
Optimizer: prodigyopt.prodigy.Prodigy(weight_decay=0.1,betas=(0.9, 0.9999),d_coef=1.5,use_bias_correction=True)
Scheduler: cosine Warmup steps: 0
Epoch: 30 Batches per epoch: 57 Gradient accumulation steps: 24
Train images: 57 Regularization images: 0
Noise offset: 0.05 Adaptive noise scale: 0.01 IP noise gamma: 0.1 Multires noise discount: 0.3
Min SNR gamma: 5.0 Zero terminal SNR: True Debiased Estimation: True
UNet weight average magnitude: 0.8033398082829257
UNet weight average strength: 0.010114916750103732
No Text Encoder found in this LoRA
----------------------$ python lora-inspector.py /mnt/900/lora/testing/landscape-2023-11-06-200718-e4d7120b.safetensors
/mnt/900/lora/testing/landscape-2023-11-06-200718-e4d7120b.safetensors
Date: 2023-11-06T20:27:12 Title: landscape
License: CreativeML Open RAIL-M Author: rockerBOO
Description: High quality landscape photos
Resolution: 512x512 Architecture: stable-diffusion-v1/lora
Network Dim/Rank: 16.0 Alpha: 8.0 Dropout: 0.3 dtype: torch.float32
Module: networks.lora : { ' block_dims ' : ' 4,4,4,4,4,4,4,4,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8 ' , ' block_alphas ' : ' 16,16,16,16,16,16,16,16,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32 ' , ' block_dropout ' : ' 0.01, 0.010620912260804992, 0.01248099020159499, 0.015572268683063176, 0.01988151037617019, 0.02539026244641935, 0.032074935571726845, 0.03990690495552037, 0.04885263290251277, 0.058873812432261884, 0.0699275313155418, 0.08196645583109653, 0.09493903345590124, 0.10878971362098, 0.12345918558747097, 0.13888463242431537, 0.155, 0.17173627983648962, 0.18902180461412393, 0.20678255506208312, 0.22494247692026895, 0.2434238066153228, 0.26214740425618505, 0.2810330925232585 ' , ' dropout ' : 0.3}
Learning Rate (LR): 2e-06 UNet LR: 1.0 TE LR: 1.0
Optimizer: prodigyopt.prodigy.Prodigy(weight_decay=0.1,betas=(0.9, 0.9999),d_coef=1.5,use_bias_correction=True)
Scheduler: cosine Warmup steps: 0
Epoch: 30 Batches per epoch: 57 Gradient accumulation steps: 24
Train images: 57 Regularization images: 0
Noise offset: 0.05 Adaptive noise scale: 0.01 IP noise gamma: 0.1 Multires noise discount: 0.3
Min SNR gamma: 5.0 Zero terminal SNR: True Debiased Estimation: True
UNet weight average magnitude: 0.8033398082829257
UNet weight average strength: 0.010114916750103732
No Text Encoder found in this LoRA
----------------------我们还支持保存从字符串中提取和转换的元数据。然后，我们可以将它们保存到JSON文件中。这些将将元数据保存到当前工作目录中的meta/alorafile.safetensors-{session_id}.json 。
$ python lora-inspector.py ~ /loras/alorafile.safetensors --save_meta$ python lora-inspector.py /mnt/900/training/cyberpunk-anime-21-min-snr/unet-1.15-te-1.15-noise-0.1-steps--linear-DAdaptation-networks.lora/last.safetensors --save_meta
/mnt/900/training/cyberpunk-anime-21-min-snr/unet-1.15-te-1.15-noise-0.1-steps--linear-DAdaptation-networks.lora/last.safetensors
train images: 1005 regularization images: 32000
learning rate: 1.15 unet: 1.15 text encoder: 1.15
epoch: 1 batches: 2025
optimizer: dadaptation.dadapt_adam.DAdaptAdam lr scheduler: linear
network dim/rank: 8.0 alpha: 4.0 module: networks.lora
----------------------找到体重的平均大小和平均强度。将它们与其他洛拉斯进行比较，以了解您的权重有多么强大。注明的权重对良好的价值不是结论性。它们是最初的例子。
$ python lora-inspector.py /mnt/900/lora/studioGhibliStyle_offset.safetensors -w
UNet weight average magnitude: 4.299801171795097
UNet weight average strength: 0.01127891692482733
Text Encoder weight average magnitude: 3.128134997225176
Text Encoder weight average strength: 0.00769676965767913显示标签的频率（单词被逗号分隔）。触发单词通常是最常见的，因为它们会在整个培训数据集中使用该单词。
$ python lora-inspector.py -t /mnt/900/lora/booscapes.safetensors
...
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Tags
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
4k photo” 23
spectacular mountains 17
award winning nature photo 16
ryan dyar 14
image credit nasa nat geo 11
sunset in a valley 11
garden 10
british columbia 10
dramatic autumn landscape 10
autumn mountains 10
an amazing landscape image 10
austria 9
nature scenery 9
pristine water 9
boreal forest 9
scenic view of river 9
alpes 9
mythical floral hills 8
misty environment 8
a photo of a lake on a sunny day 8
majestic beautiful world 8
breathtaking stars 8
lush valley 7
dramatic scenery 7
solar storm 7
siberia 7
cosmic skies 7
dolomites 7
oregon 6
landscape photography 4k 6
very long spires 6
beautiful forests and trees 6
wildscapes 6
mountain behind meadow 6
colorful wildflowers 6
photo of green river 6
beautiful night sky 6
switzerland 6
natural dynamic range color 6
middle earth 6
jessica rossier color scheme 6
arizona 6
enchanting and otherworldly 6
具有目录和图像数量的数据集的一个非常基本的视图。
$ python lora-inspector.py -d /mnt/900/lora/booscapes.safetensors
Dataset dirs: 2
[source] 50 images
[p7] 4 images
时代：一个时代一次看到整个数据集
每个时期批次：每个时期多少批次（不包括梯度积累步骤）
梯度积累步骤：梯度积累步骤
火车图像：您拥有的培训图像数量
正则化图像：正则化图像的数量
调度程序：学习率调度程序（cosine，cosine_with_restart，linear，constand，…）
优化器：优化器（Adam，Prodigy，Dadaptation，Lion，…）
网络DIM/等级：LORA网络的等级
alpha：alpha to lora网络等级
模块：创建网络的Python模块
噪声偏移：噪声偏移选项
自适应噪音量表：自适应噪音量表
IP噪声伽玛：输入扰动噪声伽马输入扰动减少了扩散模型中的暴露偏差
…我们提出了一个非常简单但有效的训练正规化，包括扰动地面真相样本以模拟推论时间预测错误。
多次噪声折扣：多频噪声折扣（请参阅扩散模型训练的多分辨率噪声）
多种噪音量表：多频噪声量表
平均幅度：每个重量平方，加起来，获得平方根
平均强度：每次体重，加起来，获得平均
辩护估计损失：DEBIAS扩散模型的训练
简单脚本以更新您的元数据值。对于使用此值为其设置好名称的应用程序，可更改ss_output_name 。
要查看当前的元数据值，请使用lora-inspector.py --save_meta ...并检查JSON文件。
$ python update_metadata.py --help
usage: update_metadata.py [-h] [--key KEY] [--value VALUE] safetensors_file
positional arguments:
safetensors_file
options:
-h, --help show this help message and exit
--key KEY Key to change in the metadata
--value VALUE Value to set to the metadata
$ python update_metadata.py /mnt/900/lora/testing/armored-core-2023-08-02-173642-ddb4785e.safetensors --key ss_output_name --value mechBOO_v2
Updated ss_output_name with mechBOO_v2
Saved to /mnt/900/lora/testing/armored-core-2023-08-02-173642-ddb4785e.safetensors
--weights ，使您可以看到Lora UNET和文本编码器重量的平均幅度和强度。 用black格式化。
您还想看到什么？提出问题或公关。
用例/想法可以扩展到：
