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# Diffusion From Scratch
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* Rebuild the Stable Diffusion Model in a single python script.
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* Train your toy version of stable diffusion on classic datasets like CelebA
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Tutorial on Stable Diffusion Models at ML from Scratch seminar series at Harvard.
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![](https://scholar.harvard.edu/sites/scholar.harvard.edu/files/styles/os_files_large/public/binxuw/files/diffusion_proc1.gif)
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[Homepage](https://scholar.harvard.edu/binxuw/classes/machine-learning-scratch/materials/stable-diffusion-scratch)
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* [Homepage](https://scholar.harvard.edu/binxuw/classes/machine-learning-scratch/materials/stable-diffusion-scratch)
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* [Tutorial Slides](https://scholar.harvard.edu/files/binxuw/files/stable_diffusion_a_tutorial.pdf)
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This tiny self-contained code base allows you to
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* Rebuild the Stable Diffusion Model in a single Python script.
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* Train your toy version of stable diffusion on classic datasets like CelebA
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## Colab notebooks
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* Playing with Stable Diffusion and inspecting the internal architecture of the models. [Open in Colab](https://colab.research.google.com/drive/1TvOlX2_l4pCBOKjDI672JcMm4q68sKrA?usp=sharing)
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* Build your own Stable Diffusion UNet model from scratch in a notebook. (with < 300 lines of codes!) [Open in Colab](https://colab.research.google.com/drive/1mm67_irYu3qU3hnfzqK5yQC38Fd5UFam?usp=sharing)
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* [Self contained script](https://github.com/Animadversio/DiffusionFromScratch/blob/master/StableDiff_UNet_model.py)
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* [Unit tests](https://github.com/Animadversio/DiffusionFromScratch/blob/master/StableDiff_UNet_unittest.py)
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* Build a Diffusion model (with UNet + cross attention) and train it to generate MNIST images based on the "text prompt". [Open in Colab (exercise)](https://colab.research.google.com/drive/1Y5wr91g5jmpCDiX-RLfWL1eSBWoSuLqO?usp=sharing) [Open in Colab (answer)](https://colab.research.google.com/drive/1_MEFfBdOI06GAuANrs1b8L-BBLn3x-ZJ?usp=sharing)

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