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https://github.com/FilippoMB/Diffusion_models_tutorial
https://github.com/FilippoMB/Diffusion_models_tutorial
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/FilippoMB/Diffusion_models_tutorial
- Owner: FilippoMB
- Created: 2022-08-28T16:33:41.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-17T15:21:41.000Z (5 months ago)
- Last Synced: 2024-07-04T05:33:46.334Z (3 months ago)
- Language: Jupyter Notebook
- Size: 71.2 MB
- Stars: 62
- Watchers: 1
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Diffusion-Models - [Github
README
# Diffusion models tutorial
A while ago [Stable Diffusion Public Release](https://stability.ai/blog/stable-diffusion-public-release) made available to everyone one of the most powerful deep learning models for image generation.
This repository contains:
- Tutorials on the theory behind diffusion models and on the software frameworks used to implement them.
- A collection of scripts and notebooks that can be used to generate images with Stable Diffusion.
- A basic guide to prompt engineering.
- A list of resources to dig deeper into the world of diffusion models.
## 1. 🚀 Quick start
### 📃 Read up
Check these [slides](https://docs.google.com/presentation/d/1jUO9jZLtUGoK7kgg0kurBgDwDsNOLybrYKU-O2y98xM/edit?usp=sharing) for a short introduction about the idea behind diffusion models and stable diffusion.
### 💻 Play with notebooks
To try out **Stable Diffusion** by running run one of the Colab notebooks below.
- Text to image [](https://colab.research.google.com/drive/1MyESLsR8D5l_EBqumwxL0eMzNmd3uqs6?usp=sharing)
- Image to image [](https://colab.research.google.com/drive/1zjr9n60q3G8Qd87WG3ZnjDgI-D1YBCbG?usp=sharing)
To try out **Stable Diffusion 2**, you can run one of the Colab notebooks below.
- Text to image [](https://colab.research.google.com/drive/1e8MV75aD66It0WXo5-ddUEjfvhVG8URv?usp=sharing)
- Impainting [](https://colab.research.google.com/drive/1JuTzAY0NzojvSU-rB4BNI9F8z3tcv9iX?usp=sharing)
- Super-resolution [](https://colab.research.google.com/drive/1qnKCWs4IB-n1xfCjeNgbaSu-mFwp7bLv?usp=sharing)
- Depth-to-image [](https://colab.research.google.com/drive/1wG2L3viHBb3vyPy1-l29bwTGePEA4gLJ?usp=sharing)
### ⚒ Understand the theory and learn to build pipelines
- Understand the theory behind stable diffusion models and learn how to code a simple diffusion model from scratch in [this notebook](https://nbviewer.org/github/FilippoMB/Diffusion_models_tutorial/blob/main/diffusion_from_scratch.ipynb).
- Become familiar with the stable diffusion pipeline and the diffusers 🧨 library in [this notebook](https://nbviewer.org/github/FilippoMB/Diffusion_models_tutorial/blob/main/Diffusers_library.ipynb).
To run the notebooks you need to have several libraries installed. You can do that by installing Anaconda (or [Miniconda](https://docs.anaconda.com/free/miniconda/miniconda-install/)) and then create the environment using the provided env files.
First, try to create the environment using [environment.yml](https://github.com/FilippoMB/Diffusion_models_tutorial/blob/main/environment.yml):
````bash
conda env create -f environment.yml
````
If it doesn't work out, try with [env_flex.yml](https://github.com/FilippoMB/Diffusion_models_tutorial/blob/main/env_flex.yml) that allows for a more flexible installation.
````bash
conda env create -f env_flex.yml
````
The risk here is that it will install more recent versions of the software packages and the notebooks might give some errors.
You might need to this more flexible install also if you are on Windows.
## 2. 💡 Prompt engineering guide
Let's say you want to draw an image of ``lion``. The raw promt, ``lion`` will give you images that are usually a bit chaotic or worse quality.
To obtain better results, the prompt should be egineered. A basic recipe is the following:
``raw prompt`` + ``style`` + ``artist`` + ``details``
- Examples of ``style`` are: *Portrait*, *Realistic*, *Oil painting*, *Pencil drawing*, *Concept art*
- Examples of ``artist`` are: Jan van Eyck (when ``style`` = *Portrait*), *Vincent Van Gogh* (when ``style`` = *Oil painting*), *Leonardo Da Vinci* (when ``style`` = *Pencil drawing*), and so on. Note that you can also mix artists, to get original results.
- Examples of ``details`` are *Unreal Engine* if you want to add realistic lightining, *8 k* if you want to add more details, *artstation* if you want to make your image more artistic, and so on.
Example of elaborated prompts:
*"Professional photograph of a lion with a black mane, high quality, highly detailed, award-winning, hd, 8k, awe-inspirin"*
*"retrofuturistic portrait of a lion in astro suit, space graphics art in background, close up, wlop, dan mumford, artgerm, liam brazier, peter mohrbacher, raw, featured in artstation, octane render, cinematic, elegant, intricate, 8 k"*
To see more examples of prompts and get inspirations, check [here](https://lexica.art/). To find a prompt for a specific image, you can use [this](https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb?authuser=0&pli=1#scrollTo=rbDEMDGJrJEo) image classifier notebook.
## 3. 📚 Resources
**Repositories**
- A web-interface with tons of advanced features that runs locally - [WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
- A WebUI extension to generate videos - [Deforum WebUI](https://github.com/deforum-art/sd-webui-deforum)
**Colab notebooks (demo)**
- text2img and img2img with advanced features [](https://colab.research.google.com/drive/1AfAmwLMd_Vx33O9IwY2TmO9wKZ8ABRRa)
- Generate video animations [](https://colab.research.google.com/github/deforum/stable-diffusion/blob/main/Deforum_Stable_Diffusion.ipynb) (you need to download the weights from [here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) and upload them to your Google Drive)
- Find prompts with the interrogator [](https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb?authuser=0&pli=1#scrollTo=rbDEMDGJrJEo)
- Stable Diffusion in Tensorflow/Keras [](https://colab.research.google.com/drive/1zVTa4mLeM_w44WaFwl7utTaa6JcaH1zK)
- Image2Image pipeline for Stable Diffusion [](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/image_2_image_using_diffusers.ipynb)
**Colab notebooks (tutorials)**
- Introduction to diffusers 🧨, the Hugging Face 🤗 library for diffusion models [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
- Introduction to Stable Diffusion with diffusers 🧨 [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb)
- Training a diffusion model with diffusers 🧨 [](https://colab.research.google.com/gist/anton-l/f3a8206dae4125b93f05b1f5f703191d/diffusers_training_example.ipynb#scrollTo=smJeP67bF0yj)
- Denoising Diffusion Implicit Models in Tensorflow/Keras [](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/generative/ipynb/ddim.ipynb)
**Blogs**
- [What are Diffusion Models?](https://lilianweng.github.io/posts/2021-07-11-diffusion-models/#nice) introduction to Diffusion models and mathematical derivations.
- [The Annotated Diffusion Model](https://huggingface.co/blog/annotated-diffusion) step-by-step tutorial for building a Diffusion model from scratch in Pytorch
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://yang-song.net/blog/2021/score/) Introduction to score-based generative models.
**Papers**
- [[1](https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf)] Rombach, Robin, et al. "High-resolution image synthesis with latent diffusion models." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
- [[2](https://arxiv.org/abs/2006.11239)] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. "Denoising diffusion probabilistic models." Advances in Neural Information Processing Systems, 2020.
- [[3](https://arxiv.org/abs/1907.05600)] Song, Yang, and Stefano Ermon. "Generative modeling by estimating gradients of the data distribution." Advances in Neural Information Processing Systems, 2019.