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 Respuesta
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Respuesta aceptada

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
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