fix(docs): describe how to install inpainting model
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README.md
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README.md
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@ -33,6 +33,9 @@ This is still an early project and the instructions are a little rough, but it w
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- txt2img mode
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- outputs are saved to file
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- show image history
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- img2img mode
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- mask painting
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- source to mask conversion tools
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## Contents
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@ -249,7 +252,11 @@ Download the conversion script from the `huggingface/diffusers` repository to th
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Run the conversion script with your desired model(s):
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```shell
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# on linux:
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> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-v1-5" --output_path="./models/stable-diffusion-onnx-v1-5"
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# on windows:
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> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-v1-5" --output_path=".\models\stable-diffusion-onnx-v1-5"
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```
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This will take a little while to convert each model. Stable diffusion v1.4 is about 6GB, v1.5 is at least 10GB or so.
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@ -257,6 +264,16 @@ This will take a little while to convert each model. Stable diffusion v1.4 is ab
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You should verify that all of the steps up to this point have worked correctly by attempting to run the
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`api/test-setup.py` script, which is a slight variation on the original txt2img script.
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If you want to use inpainting, you will need a second model trained for that purpose:
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```shell
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# on linux:
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> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-inpainting" --output_path="./models/stable-diffusion-inpainting"
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# on windows:
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> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-inpainting" --output_path=".\models\stable-diffusion-inpainting"
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```
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## Usage
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### Configuring and running the server
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