1st commit

README.md

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+---
+license: unknown
+---
+# Soda Stream finetuned style Model
+
+Produced from publicly available pictures in landscape, portrait and square format.
+
+## Model info
+
+The models included was trained on "multi-resolution" images.
+
+## Using the model
+
+* common subject prompt tokens: `<wathever> by soda stream`
+
+## Example prompts
+
+`woman near a fountain by soda stream`:
+
+<img src="https://huggingface.co/cyburn/lego_set/resolve/main/1.png" alt="Picture." width="500"/>
+
+`woman in taxi by soda stream`:
+
+<img src="https://huggingface.co/cyburn/lego_set/resolve/main/2.png" alt="Picture." width="500"/>
+
+`woman portrait by soda stream`:
+
+<img src="https://huggingface.co/cyburn/lego_set/resolve/main/3.png" alt="Picture." width="500"/>

by_soda_stream.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eed7686d1d6e7c78fdc0e58cf510abac386be37d43326762728c5d72193c9cf9
+size 2132856686

kohya_diffusers_train.ps1

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+# by soda stream
+
+# variable values
+$pretrained_model_name_or_path = "D:\models\v1-5-pruned-mse-vae.ckpt"
+$train_dir = "D:\dreambooth\train_sandosart"
+$training_folder = "raw"
+
+$learning_rate = 1e-6
+$dataset_repeats = 40
+$train_batch_size = 8
+$epoch = 4
+$save_every_n_epochs=1
+$mixed_precision="bf16"
+$num_cpu_threads_per_process=6
+
+$max_resolution = "576,576"
+
+# You should not have to change values past this point
+
+# stop script on error
+$ErrorActionPreference = "Stop"
+
+# activate venv
+.\venv\Scripts\activate
+
+# Usefull to create base caption that will be augmented on a per image basis
+
+$caption="by soda stream"
+
+$files = Get-ChildItem $train_dir\$training_folder\"*.*" -Include *.png,*.jpg,*.webp
+foreach ($file in $files) {New-Item -ItemType file -Path $train_dir\$training_folder -Name "$($file.BaseName).txt" -Value $caption}
+
+
+# create caption json file
+python D:\kohya_ss\diffusers_fine_tuning\merge_captions_to_metadata-ber.py `
+--caption_extention ".txt" $train_dir"\"$training_folder $train_dir"\meta_cap.json"
+
+# create images buckets
+python D:\kohya_ss\diffusers_fine_tuning\prepare_buckets_latents-ber.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"\"$training_folder `
+    --output_dir=$train_dir"\fine_tuned2" `
+    --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 `
+    --train_text_encoder `
+    --save_precision="fp16"
+
+# accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process D:\kohya_ss\diffusers_fine_tuning\fine_tune_v1-ber.py `
+#     --pretrained_model_name_or_path=$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