162 lines
4.2 KiB
Bash
Executable File
162 lines
4.2 KiB
Bash
Executable File
#!/bin/sh -e
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# Requirements: Python ~ 3.11, curl, unzip, git-lfs, awk
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#
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# This script downloads a bunch of models into the models/ directory,
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# after any necessary transformations to run them using the deeptagger binary.
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#
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# Once it succeeds, feel free to remove everything but *.{model,tags,onnx}
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git lfs install
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mkdir -p models
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cd models
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# Create a virtual environment for model conversion.
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#
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# If any of the Python stuff fails,
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# retry from within a Conda environment with a different version of Python.
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export VIRTUAL_ENV=$(pwd)/venv
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export TF_ENABLE_ONEDNN_OPTS=0
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if ! [ -f "$VIRTUAL_ENV/ready" ]
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then
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python3 -m venv "$VIRTUAL_ENV"
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#"$VIRTUAL_ENV/bin/pip3" install tensorflow[and-cuda]
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"$VIRTUAL_ENV/bin/pip3" install tf2onnx 'deepdanbooru[tensorflow]'
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touch "$VIRTUAL_ENV/ready"
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fi
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status() {
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echo "$(tput bold)-- $*$(tput sgr0)"
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}
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# Using the deepdanbooru package makes it possible to use other models
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# trained with the project.
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deepdanbooru() {
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local name=$1 url=$2
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status "$name"
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local basename=$(basename "$url")
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if ! [ -e "$basename" ]
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then curl -LO "$url"
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fi
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local modelname=${basename%%.*}
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if ! [ -d "$modelname" ]
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then unzip -d "$modelname" "$basename"
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fi
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if ! [ -e "$modelname.tags" ]
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then ln "$modelname/tags.txt" "$modelname.tags"
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fi
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if ! [ -d "$modelname.saved" ]
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then "$VIRTUAL_ENV/bin/python3" - "$modelname" "$modelname.saved" <<-'END'
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import sys
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import deepdanbooru.project as ddp
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model = ddp.load_model_from_project(
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project_path=sys.argv[1], compile_model=False)
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model.export(sys.argv[2])
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END
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fi
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if ! [ -e "$modelname.onnx" ]
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then "$VIRTUAL_ENV/bin/python3" -m tf2onnx.convert \
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--saved-model "$modelname.saved" --output "$modelname.onnx"
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fi
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cat > "$modelname.model" <<-END
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name=$name
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shape=nhwc
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channels=rgb
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normalize=true
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pad=edge
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END
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}
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# ONNX preconversions don't have a symbolic first dimension, thus doing our own.
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wd14() {
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local name=$1 repository=$2
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status "$name"
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local modelname=$(basename "$repository")
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if ! [ -d "$modelname" ]
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then git clone "https://huggingface.co/$repository"
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fi
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# Though link the original export as well.
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if ! [ -e "$modelname.onnx" ]
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then ln "$modelname/model.onnx" "$modelname.onnx"
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fi
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if ! [ -e "$modelname.tags" ]
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then awk -F, 'NR > 1 { print $2 }' "$modelname/selected_tags.csv" \
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> "$modelname.tags"
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fi
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cat > "$modelname.model" <<-END
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name=$name
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shape=nhwc
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channels=bgr
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normalize=false
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pad=white
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END
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if ! [ -e "batch-$modelname.onnx" ]
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then "$VIRTUAL_ENV/bin/python3" -m tf2onnx.convert \
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--saved-model "$modelname" --output "batch-$modelname.onnx"
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fi
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if ! [ -e "batch-$modelname.tags" ]
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then ln "$modelname.tags" "batch-$modelname.tags"
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fi
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if ! [ -e "batch-$modelname.model" ]
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then ln "$modelname.model" "batch-$modelname.model"
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fi
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}
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# These models are an undocumented mess, thus using ONNX preconversions.
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mldanbooru() {
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local name=$1 basename=$2
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status "$name"
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if ! [ -d ml-danbooru-onnx ]
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then git clone https://huggingface.co/deepghs/ml-danbooru-onnx
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fi
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local modelname=${basename%%.*}
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if ! [ -e "$basename" ]
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then ln "ml-danbooru-onnx/$basename"
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fi
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if ! [ -e "$modelname.tags" ]
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then awk -F, 'NR > 1 { print $1 }' ml-danbooru-onnx/tags.csv \
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> "$modelname.tags"
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fi
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cat > "$modelname.model" <<-END
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name=$name
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shape=nchw
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channels=rgb
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normalize=true
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pad=stretch
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size=640
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interpret=sigmoid
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END
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}
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status "Downloading models, beware that git-lfs doesn't indicate progress"
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deepdanbooru DeepDanbooru \
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'https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip'
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#wd14 'WD v1.4 ViT v1' 'SmilingWolf/wd-v1-4-vit-tagger'
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wd14 'WD v1.4 ViT v2' 'SmilingWolf/wd-v1-4-vit-tagger-v2'
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#wd14 'WD v1.4 ConvNeXT v1' 'SmilingWolf/wd-v1-4-convnext-tagger'
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wd14 'WD v1.4 ConvNeXT v2' 'SmilingWolf/wd-v1-4-convnext-tagger-v2'
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wd14 'WD v1.4 ConvNeXTV2 v2' 'SmilingWolf/wd-v1-4-convnextv2-tagger-v2'
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wd14 'WD v1.4 SwinV2 v2' 'SmilingWolf/wd-v1-4-swinv2-tagger-v2'
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wd14 'WD v1.4 MOAT v2' 'SmilingWolf/wd-v1-4-moat-tagger-v2'
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# As suggested by author https://github.com/IrisRainbowNeko/ML-Danbooru-webui
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mldanbooru 'ML-Danbooru Caformer dec-5-97527' 'ml_caformer_m36_dec-5-97527.onnx'
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mldanbooru 'ML-Danbooru TResNet-D 6-30000' 'TResnet-D-FLq_ema_6-30000.onnx'
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