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These two notebooks cover the process of fine-tuning StableDiffusion to a personal dataset.
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The Huggingface Diffusers documentation doesn't show how to fine-tune the model provided by Diffusers, only how to use a pre-written script file for fine-tuning.
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There are some good tutorials based on keras, but it's hard to find a tutorial based on pytorch that shows how to code and fine-tune from scratch.
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The train_text_to_image[_lora].py provided by Diffusers has too much code that is not necessary for a learner who just wants to quickly grasp the logic, so it is necessary to organize it into a sequential notebook file.
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For that reason, this notebook breaks down train_text_to_image[_lora].py and describes how to quickly full-fine-tune a stable diffusion with minimal code required.
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