30 lines
1.1 KiB
Markdown
30 lines
1.1 KiB
Markdown
# Training Guide
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The training is based on [Kohya-SS](https://github.com/kohya-ss/sd-scripts). Kohya-SS is a Python library for finetuning stable diffusion model which is friendly for consumer-grade GPU and compatible with the Stable Diffusion WebUI. The solution can do LoRA training both on SDXL and SD 1.5.
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## Training User Guide
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### Prepare Foundation Model
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Upload your local SD model to S3 bucket by following commands
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```
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# Configure credentials
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aws configure
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# Copy local SD model to S3 bucket
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aws s3 cp *safetensors s3://<bucket_path>/<model_path>
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```
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### Prepare Dataset
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Execute AWS CLI command to copy the dataset to S3 bucket
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```
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aws s3 sync local_folder_name s3://<bucket_name>/<folder_name>
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```
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The folder name should be started with a number and underline, eg. 100_demo. Each image should be paired with a txt file with the same name, eg. demo1.png, demo1.txt, the demo1.txt contains the captions of demo1.png.
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### Invoke Training API
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Refer to [API document](https://awslabs.github.io/stable-diffusion-aws-extension/en/developer-guide/api/1.5.0/) to invoke training API.
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