LLavaTagger/README.md

@@ -1,21 +0,0 @@
-# LLavaTagger
-
-LLavaTagger is a python script that tags images based on a given prompt using the [LLaVA](https://llava-vl.github.io/) multi modal llm. LLavaTagger supports using any number of gpus in ddp parralel for this task.
-
-## How to use
-
-first create a python venv and install the required packages into it:
-
-	$ python -m venv venv
-	$ source venv/bin/activate
-	$ pip install -r requirements.txt
-
-Then run LLavaTagger for instance like so:
-
-	$ python LLavaTagger.py --common_description "a image of a cat, " --prompt "describe the cat in 10 to 20 words" --batch 8 --quantize --image_dir ~/cat_images
-
-By default LLavaTagger will run in parallel on all available gpus, if this is undesriable please use the ROCR_VISIBLE_DEVICES= or CUDA_VISIBLE_DEVICES= environment variable to hide unwanted gpus
-
-LLavaTagger will then create a meta.jsonl in the image directory sutable to be used by the scripts of [diffusers](https://github.com/huggingface/diffusers) to train stable diffusion (xl) if other formats are desired ../utils contains scripts to transform the metadata into other formats for instace for the use with [kohya](https://github.com/bmaltais/kohya_ss)
-
-If editing the created tags is desired, [QImageTagger](https://uvos.xyz/git/uvos/QImageTagger) can be used for this purpose

LLavaTagger/requirements.txt

@@ -5,7 +5,7 @@ ninja==1.11.1.1
 safetensors==0.4.2
 tokenizers==0.15.2
 transformers
-torch
+pytorch
 opencv-python
 numpy
 tqdm

PersonDatasetAssembler/README.md

@@ -1,20 +0,0 @@
-### PersonDatasetAssembler
-
-PersonDatasetAssembler is a python script that finds images of a spcific person, specified by a referance image in a directory of images or in a video file. PersonDatasetAssembler supports also raw images.
-
-## How to use
-
-first create a python venv and install the required packages into it:
-
-	$ python -m venv venv
-	$ source venv/bin/activate
-	$ pip install -r requirements.txt
-
-Then run PersonDatasetAssembler for instance like so:
-
-	$ python PersonDatasetAssembler.py --referance someperson.jpg --match_model ../Weights/face_recognition_sface_2021dec.onnx --detect_model ../Weights/face_detection_yunet_2023mar.onnx --input ~/Photos --out imagesOfSomePerson
-
-Or to extract images from a video:
-
-	$ python PersonDatasetAssembler.py --referance someperson.jpg --match_model ../Weights/face_recognition_sface_2021dec.onnx --detect_model ../Weights/face_detection_yunet_2023mar.onnx -i ~/SomeVideo.mkv --out imagesOfSomePerson
-

PersonDatasetAssembler/requirements.txt

@@ -1,4 +0,0 @@
-numpy==1.26.4
-opencv-python==4.10.0.82
-tqdm==4.66.4
-Wand==0.6.13

README.md

@@ -1,35 +0,0 @@
-# SDImagePreprocess
-
-This repo contains a collection of high performance tools intended to ease the createion of datasets for image generation AI training like stable diffusion.
-
-## Included tools
-
-This repo contains the following tools:
-
-### SmartCrop
-
-SmartCrop is an application that uses content aware croping using, [seam carving](https://en.wikipedia.org/wiki/Seam_carving) and resizeing to bring a directory of images into the deisred size and aspect ratio for training. SmartCrop ist configurable to prioritize specific items or specifc persons in the images provided.
-
-#### Content detected in image:
-
-![Content found in image](SmartCrop/images/IMGP3692.jpg)
-
-#### Cropped image based on content:
-![Cropped image](SmartCrop/images/IMGP3692C.jpg)
-
-### PersonDatasetAssembler
-
-PersonDatasetAssembler is a python script that finds images of a spcific person, specified by a referance image in a directory of images or in a video file. PersonDatasetAssembler supports also raw images.
-
-### LLavaTagger
-
-LLavaTagger is a python script that tags images based on a given prompt using the [LLaVA](https://llava-vl.github.io/) multi modal llm. LLavaTagger supports using any number of gpus in ddp parralel for this task.
-
-### DanbooruTagger
-
-DanbooruTagger is a python script of dubious utility that tags images based using the [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) convolutional network.
-
-
-## License
-
-All files in this repo are litcenced GPL V3, see LICENSE

SmartCrop/README.md

@@ -1,50 +0,0 @@
-# SmartCrop
-
-SmartCrop is an application that uses content aware croping using, [seam carving](https://en.wikipedia.org/wiki/Seam_carving) and resizeing to bring a directory of images into the deisred size and aspect ratio for training. SmartCrop ist configurable to prioritize specific items or specifc persons in the images provided.
-
-## Requirements
-
-* [cmake](https://cmake.org/) 3.6 or later
-* [opencv](https://opencv.org/) 4.8 or later
-* A c++17 capable compiler and standard lib like gcc or llvm/clang
-* git is required to get the source
-
-## Building
-
-The steps to build this application are:
-
-	$ git clone https://uvos.xyz/git/uvos/SDImagePreprocess.git
-	$ cd SDImagePreprocess
-	$ mkdir build
-	$ cmake ..
-	$ make
-
-The binary can then be found in build/SmartCrop and can optionaly be installed with:
-
-	$ sudo make install
-
-## Basic usage
-
-To process all images in the directory ~/images and output the images into ~/proceesedImages:
-
-	$ smartcrop --out processedImages ~/images/*
-
-To also focus on the person in the image ~/person.jpg
-
-	$ smartcrop --out processedImages --focus-person ~/person.jpg ~/images/*
-
-To also enable seam carving
-
-	$ smartcrop --out processedImages --focus-person ~/person.jpg --seam-carving ~/images/*
-
-see smartcrop --help for more
-
-## Example
-
-#### Content detected in image:
-![Content found in image](images/IMGP3692.jpg)
-
-#### Cropped image based on content:
-![Cropped image](images/IMGP3692C.jpg)
-
-