1st commit

README.md

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+---
+license: unknown
+---
+# Cables Retake finetuned style Model
+
+Produced from publicly available pictures in 576x704 format.
+
+Two models available. One trained using kohya_ss train_db script and the other one using diffusers_fine_tuning sctipt.
+
+## Using the model
+
+* common subject prompt tokens: `cables retake artstyle <wathever>`
+
+## Example prompts
+
+`cables retake artstyle smiling woman`:

cables_retake_artstyle-kohys_ss-diffusers.ckpt

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+size 2132856686

cables_retake_artstyle-kohys_ss-train_db.ckpt

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+size 2132856686

caption.ps1

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+# This powershell script will create a text file for each files in the folder
+#
+# Usefull to create base caption that will be augmented on a per image basis
+
+$folder = "D:\models\dariusz_zawadzki\subset_bucket\"
+$file_pattern="*.*"
+$caption_text="radius kawazaki artstyle"
+
+$files = Get-ChildItem $folder$file_pattern -Include *.png, *.jpg, *.webp -File
+foreach ($file in $files) {
+    if (-not(Test-Path -Path $folder\"$($file.BaseName).txt" -PathType Leaf)) {
+        New-Item -ItemType file -Path $folder -Name "$($file.BaseName).txt" -Value $caption_text
+    }
+}

kohya_diffusers_fine_tuning.ps1

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+# Sylvia Ritter. AKA: by silvery trait
+
+# variable values
+$pretrained_model_name_or_path = "D:\models\v1-5-pruned-mse-vae.ckpt"
+$train_dir = "D:\models\blakecreates"
+$output_dir = "D:\models\blakecreates\diffusers_fine_tuned_model_bucket"
+$training_folder = "dataset\192_cables retake artstyle"
+
+$learning_rate = 1e-6
+$dataset_repeats = 192
+$train_batch_size = 4
+$epoch = 1
+$save_every_n_epochs=1
+$mixed_precision="fp16"
+$num_cpu_threads_per_process=6
+$lr_scheduler="constant"
+
+$max_resolution = "576,704"
+
+# You should not have to change values past this point
+$data_dir = $train_dir + "\" + $training_folder
+# Get smalest image resolution to prevent upscaling to that
+$shellApp = New-Object -ComObject 'shell.application'
+$folderNamespace = $shellApp.Namespace($data_dir)
+$get_smalest_resolution = Get-ChildItem $data_dir -Recurse -File -Include *.png, *.jpg, *.webp |
+ForEach-Object {
+    $image = $folderNamespace.ParseName($_.Name)
+
+    if ($folderNamespace.GetDetailsOf($image, 31) -match '(?<width>\d+) x (?<height>\d+)') {
+        [PsCustomObject]@{
+            Image           = $_.FullName
+            Width           = $Matches.width
+            Height          = $Matches.height
+            PixelResolution = [int]($Matches.width) * [int]($Matches.height)
+            # IsPortrait = $([int]$Matches.height -gt [int]$Matches.width)
+        }
+    }
+} | Sort-Object -Property PixelResolution | Select-Object -first 1
+
+$width, $height = $max_resolution -split ","
+
+if (([int]$width * [int]$height) -gt $get_smalest_resolution.PixelResolution) {
+
+    $smalest_resolution = $get_smalest_resolution.Width + "," + $get_smalest_resolution.Height
+    Write-Output "Smallest resolution of images data found is " $get_smalest_resolution.Image "at $smalest_resolution. Replace image with a larger one, or change resolution to prevent this resolution change."
+
+    $max_resolution = $smalest_resolution
+}
+
+# stop script on error
+$ErrorActionPreference = "Stop"
+
+# activate venv
+cd D:\kohya_ss
+.\venv\Scripts\activate
+
+# create caption json file
+python D:\kohya_ss\diffusers_fine_tuning\merge_captions_to_metadata.py `
+--caption_extention ".caption" $train_dir"\"$training_folder $train_dir"\meta_cap.json"
+
+# create images buckets
+python D:\kohya_ss\diffusers_fine_tuning\prepare_buckets_latents.py `
+    $train_dir"\"$training_folder `
+    $train_dir"\meta_cap.json" `
+    $train_dir"\meta_lat.json" `
+    $pretrained_model_name_or_path `
+    --batch_size 4 --max_resolution $max_resolution --mixed_precision fp16
+
+# Get number of valid images
+$image_num = Get-ChildItem "$train_dir\$training_folder" -Recurse -File -Include *.npz | Measure-Object | %{$_.Count}
+$repeats = $image_num * $dataset_repeats
+
+# calculate max_train_set
+$max_train_set = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
+
+
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process D:\kohya_ss\diffusers_fine_tuning\fine_tune.py `
+    --pretrained_model_name_or_path=$pretrained_model_name_or_path `
+    --in_json $train_dir"\meta_lat.json" `
+    --train_data_dir=$train_dir"\"$training_folder `
+    --output_dir=$output_dir `
+    --train_batch_size=$train_batch_size `
+    --dataset_repeats=$dataset_repeats `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$max_train_set `
+    --use_8bit_adam --xformers `
+    --mixed_precision=$mixed_precision `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --lr_scheduler=$lr_scheduler `
+    --seed=494481440 `
+    --train_text_encoder `
+    --save_precision="fp16"

kohya_train_db_fixed_reg.ps1

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+# This powershell script will create a model using the fine tuning dreambooth method. It will require landscape,
+# portrait and square images.
+#
+# Adjust the script to your own needs
+
+# variable values
+$pretrained_model_name_or_path = "D:\models\v1-5-pruned-mse-vae.ckpt"
+$data_dir = "D:\models\blakecreates\dataset"
+$reg_data_dir = "D:\models\blakecreates\reg"
+$logging_dir = "D:\models\blakecreates\logs"
+$output_dir = "D:\models\blakecreates\train_db_fixed_model_reg"
+$resolution = "576,704"
+$lr_scheduler="constant"
+
+$image_num = Get-ChildItem $data_dir -Recurse -File -Include *.png, *.jpg, *.webp | Measure-Object | %{$_.Count}
+
+Write-Output "image_num: $image_num"
+
+$dataset_repeats = 64
+$learning_rate = 1e-6
+$train_batch_size = 4
+$epoch = 1
+$save_every_n_epochs=1
+$mixed_precision="fp16"
+$num_cpu_threads_per_process=6
+
+# You should not have to change values past this point
+
+$repeats = $image_num * $dataset_repeats
+$mts = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
+
+Write-Output "Repeats: $repeats"
+Write-Output "max_train_steps: $mts"
+
+cd D:\kohya_ss
+.\venv\Scripts\activate
+
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed.py `
+    --pretrained_model_name_or_path=$pretrained_model_name_or_path `
+    --train_data_dir=$data_dir `
+    --output_dir=$output_dir `
+    --resolution=$resolution `
+    --train_batch_size=$train_batch_size `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$mts `
+    --use_8bit_adam `
+    --xformers `
+    --mixed_precision=$mixed_precision `
+    --cache_latents `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --logging_dir=$logging_dir `
+    --save_precision="fp16" `
+    --reg_data_dir=$reg_data_dir `
+    --lr_scheduler=$lr_scheduler