Update README

Add example powershell code

README.md

@@ -55,11 +55,11 @@ accelerate config:
 Refer to the note to understand how to create the folde structure. In short it should look like:
 
 ```
-<wathever top folder name>
-|- reg_<class>
-    |- <repeat count>_<prompt>
-|- train_<class>
-   |- <repeat count>_<prompt>
+<arbitrary folder name>
+|- <arbitrary class folder name>
+    |- <repeat count>_<class>
+|- <arbitrary training folder name>
+   |- <repeat count>_<token> <class>
 ```
 
 Example for `sks dog`

examples/caption.ps1

@@ -0,0 +1,10 @@
+# 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
+
+$folder = "D:\dreambooth\train_sylvia_ritter\raw_data\all-images\"
+$file_pattern="*.*"
+$text_fir_file="a digital painting of xxx, by silvery trait"
+
+$files = Get-ChildItem $folder$file_pattern
+foreach ($file in $files) {New-Item -ItemType file -Path $folder -Name "$($file.BaseName).txt" -Value $text_fir_file}

examples/kohya.ps1

@@ -0,0 +1,154 @@
+# 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
+
+# Sylvia Ritter
+# variable values
+$pretrained_model_name_or_path = "D:\models\v1-5-pruned-mse-vae.ckpt"
+$train_dir = "D:\dreambooth\train_sylvia_ritter\raw_data"
+
+$landscape_image_num = 4
+$portrait_image_num = 25
+$square_image_num = 2
+
+$learning_rate = 1e-6
+$dataset_repeats = 120
+$train_batch_size = 4
+$epoch = 1
+$save_every_n_epochs=1
+$mixed_precision="fp16"
+$num_cpu_threads_per_process=6
+
+$landscape_folder_name = "landscape-pp"
+$landscape_resolution = "832,512"
+$portrait_folder_name = "portrait-pp"
+$portrait_resolution = "448,896"
+$square_folder_name = "square-pp"
+$square_resolution = "512,512"
+
+# You should not have to change values past this point
+
+$landscape_data_dir = $train_dir + "\" + $landscape_folder_name
+$portrait_data_dir = $train_dir + "\" + $portrait_folder_name
+$square_data_dir = $train_dir + "\" + $square_folder_name
+$landscape_output_dir = $train_dir + "\model-l"
+$portrait_output_dir = $train_dir + "\model-lp"
+$square_output_dir = $train_dir + "\model-lps"
+
+$landscape_repeats = $landscape_image_num * $dataset_repeats
+$portrait_repeats = $portrait_image_num * $dataset_repeats
+$square_repeats = $square_image_num * $dataset_repeats
+
+$landscape_mts = [Math]::Ceiling($landscape_repeats / $train_batch_size * $epoch)
+$portrait_mts = [Math]::Ceiling($portrait_repeats / $train_batch_size * $epoch)
+$square_mts = [Math]::Ceiling($square_repeats / $train_batch_size * $epoch)
+
+# Write-Output $landscape_repeats
+
+.\venv\Scripts\activate
+
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed-ber.py `
+    --pretrained_model_name_or_path=$pretrained_model_name_or_path `
+    --train_data_dir=$landscape_data_dir `
+    --output_dir=$landscape_output_dir `
+    --resolution=$landscape_resolution `
+    --train_batch_size=$train_batch_size `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$landscape_mts `
+    --use_8bit_adam `
+    --xformers `
+    --mixed_precision=$mixed_precision `
+    --cache_latents `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --fine_tuning `
+    --dataset_repeats=$dataset_repeats `
+    --save_half
+    
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed-ber.py `
+    --pretrained_model_name_or_path=$landscape_output_dir"\last.ckpt" `
+    --train_data_dir=$portrait_data_dir `
+    --output_dir=$portrait_output_dir `
+    --resolution=$portrait_resolution `
+    --train_batch_size=$train_batch_size `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$portrait_mts `
+    --use_8bit_adam `
+    --xformers `
+    --mixed_precision=$mixed_precision `
+    --cache_latents `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --fine_tuning `
+    --dataset_repeats=$dataset_repeats `
+    --save_half
+    
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed-ber.py `
+    --pretrained_model_name_or_path=$portrait_output_dir"\last.ckpt" `
+    --train_data_dir=$square_data_dir `
+    --output_dir=$square_output_dir `
+    --resolution=$square_resolution `
+    --train_batch_size=$train_batch_size `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$square_mts `
+    --use_8bit_adam `
+    --xformers `
+    --mixed_precision=$mixed_precision `
+    --cache_latents `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --fine_tuning `
+    --dataset_repeats=$dataset_repeats `
+    --save_half
+    
+# 2nd pass at half the dataset repeat value
+
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed-ber.py `
+    --pretrained_model_name_or_path=$square_output_dir"\last.ckpt" `
+    --train_data_dir=$landscape_data_dir `
+    --output_dir=$landscape_output_dir"2" `
+    --resolution=$landscape_resolution `
+    --train_batch_size=$train_batch_size `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$([Math]::Ceiling($landscape_mts/2)) `
+    --use_8bit_adam `
+    --xformers `
+    --mixed_precision=$mixed_precision `
+    --cache_latents `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --fine_tuning `
+    --dataset_repeats=$([Math]::Ceiling($dataset_repeats/2)) `
+    --save_half
+    
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed-ber.py `
+    --pretrained_model_name_or_path=$landscape_output_dir"2\last.ckpt" `
+    --train_data_dir=$portrait_data_dir `
+    --output_dir=$portrait_output_dir"2" `
+    --resolution=$portrait_resolution `
+    --train_batch_size=$train_batch_size `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$([Math]::Ceiling($portrait_mts/2)) `
+    --use_8bit_adam `
+    --xformers `
+    --mixed_precision=$mixed_precision `
+    --cache_latents `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --fine_tuning `
+    --dataset_repeats=$([Math]::Ceiling($dataset_repeats/2)) `
+    --save_half
+    
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed-ber.py `
+    --pretrained_model_name_or_path=$portrait_output_dir"2\last.ckpt" `
+    --train_data_dir=$square_data_dir `
+    --output_dir=$square_output_dir"2" `
+    --resolution=$square_resolution `
+    --train_batch_size=$train_batch_size `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$([Math]::Ceiling($square_mts/2)) `
+    --use_8bit_adam `
+    --xformers `
+    --mixed_precision=$mixed_precision `
+    --cache_latents `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --fine_tuning `
+    --dataset_repeats=$([Math]::Ceiling($dataset_repeats/2)) `
+    --save_half
+    

examples/kohya_diffuser.ps1

@@ -0,0 +1,72 @@
+# This powershell script will create a model using the new "diffusers_fine_tuning" code
+
+# Sylvia Ritter. AKA: by silvery trait
+
+# variable values
+$pretrained_model_name_or_path = "D:\models\v1-5-pruned-mse-vae.ckpt"
+$train_dir = "D:\dreambooth\train_sylvia_ritter\raw_data"
+$training_folder = "all-images"
+
+$image_num = 117
+
+$learning_rate = 5e-6
+$dataset_repeats = 40
+$train_batch_size = 6
+$epoch = 3
+$save_every_n_epochs=1
+$mixed_precision="bf16"
+$num_cpu_threads_per_process=6
+
+$max_resolution = "768,576"
+
+# You should not have to change values past this point
+
+$ErrorActionPreference = "Stop"
+
+$repeats = $image_num * $dataset_repeats
+
+$max_train_set = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
+
+# Write-Output $landscape_repeats
+
+# new method
+
+.\venv\Scripts\activate
+
+python D:\kohya_ss\diffusers_fine_tuning\merge_captions_to_metadata.py `
+--caption_extention ".txt" $train_dir"\"$training_folder $train_dir"\meta_cap.json"
+
+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
+
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process D:\kohya_ss\diffusers_fine_tuning\fine_tune_v1-ber.py `
+    --pretrained_model_name_or_path=$pretrained_model_name_or_path `
+    --in_json $train_dir"\meta_lat.json" `
+    --train_data_dir=$train_dir"\"$training_folder `
+    --output_dir=$train_dir"\fine_tuned" `
+    --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 `
+    --save_half
+
+accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process D:\kohya_ss\diffusers_fine_tuning\fine_tune_v1-ber.py `
+    --pretrained_model_name_or_path=$train_dir"\fine_tuned\last.ckpt" `
+    --in_json $train_dir"\meta_lat.json" `
+    --train_data_dir=$train_dir"\"$training_folder `
+    --output_dir=$train_dir"\fine_tuned2" `
+    --train_batch_size=$train_batch_size `
+    --dataset_repeats=$([Math]::Ceiling($dataset_repeats / 2)) `
+    --learning_rate=$learning_rate `
+    --max_train_steps=$([Math]::Ceiling($max_train_set / 2)) `
+    --use_8bit_adam --xformers `
+    --mixed_precision=$mixed_precision `
+    --save_every_n_epochs=$save_every_n_epochs `
+    --save_half