Multi Class Model Training - SageMaker Jumpstart Issue

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I am using SageMaker JumpStart to try and train a stable diffusion model. I have tried hypertuning the parameters to a very specific degree but cant seem to provide prompts/ output that shows my class data reliably. I have increased the epochs, steps and optimized the learning rate. My data is formatted as per the JumpStart documentation (here: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_to_image/Amazon_JumpStart_Text_To_Image.ipynb) where I have:

Input_directory |---instance_image_1.png |---instance_image_2.png |---instance_image_3.png |---dataset_info.json |--- classA_dir |---classA_image_1.png |---classA_image_2.png |---classA_image_3.png |---dataset_info.json

Appreciate any help - TIA!

1 Answer
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Accepted Answer

Hey there, looks like you are trying to train your stable diffusion model on a multi class training set. Unfortunately this is not a feature in SageMaker JumpStart just yet. There is a way to get around this however which follows the strategy of

  1. Fine-tune the model for Subject A.
  2. Fine-tune the resulting model from Step 1 for Subject B.
  3. Generate images of Subject A and Subject B using the output model from Step 2.

If you’re after more information / an example, check out this blog and scroll down to fine tuning considerations. It goes through the process of training a stable diffusion model with one class then runs through how it has trained it on multiple classes and the associated strengths and weakness of both approaches. Hope this helps!

AWS
answered 2 months ago
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