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Copy file name to clipboardExpand all lines: unit3/README.md
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@@ -9,7 +9,7 @@ Here are the steps for this unit:
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- Make sure you've [signed up for this course](https://huggingface.us17.list-manage.com/subscribe?u=7f57e683fa28b51bfc493d048&id=ef963b4162) so that you can be notified when new material is released
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- Read through the material below for an overview of the key ideas of this unit
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- Check out the _**Stable Diffusion Introduction**_ notebook to see SD applied in practice to some common use-cases
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- Use the _**Annotated Dreambooth**_ notebook to fine-tune your own custom Stable Diffusion model and share it with the community for a chance to win some prizes and swag
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- Use the _**Dreambooth**_ notebook in the **hackathon** folder to fine-tune your own custom Stable Diffusion model and share it with the community for a chance to win some prizes and swag
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- (Optional) Check out the [_**Stable Diffusion Deep Dive video**_](https://www.youtube.com/watch?app=desktop&v=0_BBRNYInx8) and the accompanying [_**notebook**_](https://github.com/fastai/diffusion-nbs/blob/master/Stable%20Diffusion%20Deep%20Dive.ipynb) for a deeper exploration of the different componets and how they can be adapted for different effects. This material was created for the new FastAI course, ['Stable Diffusion from the Foundations'](https://www.fast.ai/posts/part2-2022.html) - the first few lessons are already available and the rest will be released in the next few months, making this a great supplement to this class for anyone curious about building these kinds of models completely from scratch.
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_Image from the [dreambooth project page](https://dreambooth.github.io/) based on the Imagen model_
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Dreambooth is a technique for fine-tuning a text-to-image model to 'teach' it a new concept, such as a specific object or style. The technique was originally developed for Google's Imagen model but was quickly adapted to [work for stable diffusion](https://huggingface.co/docs/diffusers/training/dreambooth). Results can be extremely impressive (if you've seen anyone with an AI profile piture on social media recently the odds are high it came from a dreambooth-based service) but the technique is also sensitive to the settings used, so check out our notebook and [this great investigation into the different training parameters](https://huggingface.co/blog/dreambooth) for some tips on getting it working as well as possible.
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DreamBooth is a technique for fine-tuning a text-to-image model to 'teach' it a new concept, such as a specific object or style. The technique was originally developed for Google's Imagen model but was quickly adapted to [work for stable diffusion](https://huggingface.co/docs/diffusers/training/dreambooth). Results can be extremely impressive (if you've seen anyone with an AI profile piture on social media recently the odds are high it came from a dreambooth-based service) but the technique is also sensitive to the settings used, so check out our notebook and [this great investigation into the different training parameters](https://huggingface.co/blog/dreambooth) for some tips on getting it working as well as possible.
| Stable Diffusion Introduction |[](https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/unit3/01_stable_diffusion_introduction.ipynb)|[](https://kaggle.com/kernels/welcome?src=https://github.com/huggingface/diffusion-models-class/blob/main/unit3/01_stable_diffusion_introduction.ipynb)|[](https://console.paperspace.com/github/huggingface/diffusion-models-class/blob/main/unit3/01_stable_diffusion_introduction.ipynb)|[](https://studiolab.sagemaker.aws/import/github/huggingface/diffusion-models-class/blob/main/unit3/01_stable_diffusion_introduction.ipynb)|
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|Annotated Dreambooth |[](https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/unit2/01_finetuning_and_guidance.ipynb)|[](https://kaggle.com/kernels/welcome?src=https://github.com/huggingface/diffusion-models-class/blob/main/unit2/01_finetuning_and_guidance.ipynb)|[](https://console.paperspace.com/github/huggingface/diffusion-models-class/blob/main/unit2/01_finetuning_and_guidance.ipynb)|[](https://studiolab.sagemaker.aws/import/github/huggingface/diffusion-models-class/blob/main/unit2/01_finetuning_and_guidance.ipynb)|
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|DreamBooth Hackathon Notebook|[](https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/hackathon/dreambooth.ipynb)|[](https://kaggle.com/kernels/welcome?src=https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/dreambooth.ipynb)|[](https://console.paperspace.com/github/huggingface/diffusion-models-class/blob/main/hackathon/dreambooth.ipynb)|[](https://studiolab.sagemaker.aws/import/github/huggingface/diffusion-models-class/blob/main/hackathon/dreambooth.ipynb)|
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| Stable Diffusion Deep Dive |[](https://colab.research.google.com/github/fastai/diffusion-nbs/blob/master/Stable%20Diffusion%20Deep%20Dive.ipynb)|[](https://kaggle.com/kernels/welcome?src=https://github.com/fastai/diffusion-nbs/blob/master/Stable%20Diffusion%20Deep%20Dive.ipynb)|[](https://console.paperspace.com/github/fastai/diffusion-nbs/blob/master/Stable%20Diffusion%20Deep%20Dive.ipynb)|[](https://studiolab.sagemaker.aws/import/github/fastai/diffusion-nbs/blob/master/Stable%20Diffusion%20Deep%20Dive.ipynb)|
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TODO links to dreamboot nb
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At this point, you know enough to get started with the accompanying notebooks! Open them in your platform of choice using the links above. Dreambooth requires quite a lot of compute power, so if you're using Kaggle or Google Colab make sure you set the runtime type to 'GPU' for best results.
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The 'Stable Diffusion Introduction' notebook is a short introduction to stable diffusion with the 🤗 Diffusers library, stepping through some basic usage examples using pipelines to generate and modify images.
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In 'Annotated Dreambooth' we show how you can fine-tune SD on your own images to create a custom version of the model covering a new style or concept.
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In the DreamBooth Hackathon Notebook (in the [hackathon folder](https://github.com/huggingface/diffusion-models-class/tree/main/hackathon)) we show how you can fine-tune SD on your own images to create a custom version of the model covering a new style or concept.
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Finally, the 'Stable Diffusion Deep Dive' notebook and video break down every step in a typical generation pipeline, suggesting some novel ways to modify each stage for additional creative control.
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## Project Time
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Follow the instructions in the **Annotated Dreambooth** notebook to train your own model for one of the specified categories. Make sure you include the example outputs in your submission so that we can choose the best models in each category! Prizes will be awarded [TODO details]. If you're short of GPU power, [TODO info on colab credits]
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Follow the instructions in the **DreamBooth** notebook to train your own model for one of the specified categories. Make sure you include the example outputs in your submission so that we can choose the best models in each category! See the [hackathon info](https://github.com/huggingface/diffusion-models-class/tree/main/hackathon) for details on prizes, GPU credits and more.
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