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ai_diffusion_model_training's Introduction

mlex_tf_ddim

This is a cookbook for training a Denoising Diffusion Implicit Model (DDIM) from scratch and inferencing new images from the trained weights. The diffusion model code (ddim.py) is adapted from an open-source Keras example https://keras.io/examples/generative/ddim/.

Software requirements:

tensorflow 
matplotlib
jupyter
numpy

Install Tensorflow with GPU support on Apple M1/M2, follow https://github.com/deganza/Install-TensorFlow-on-Mac-M1-GPU/blob/main/Install-TensorFlow-on-Mac-M1-GPU.ipynb

New features:

  • added capability to diffuse noise (and denoise) to (from) an arbitrary level
  • support resuming training from the saved checkpoint
  • added a dataloader and preprocessing pipeline
  • added parameter schema and validations
  • support arbitrary image size ratio
  • added saving options for training history and the generated images

Instructions:
A notebook for training: train.ipynb
A notebook for inferencing: inference.ipynb

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