Personalized Image Generation (using Dreambooth) explained!

DeepFindr · Beginner ·🎨 Image & Video AI ·3y ago
▬▬ Papers / Resources ▬▬▬ Colab Notebook: https://colab.research.google.com/drive/1QUjLK6oUB_F4FsIDYusaHx-Yl7mL-Lae?usp=sharing Stable Diffusion Tutorial: https://jalammar.github.io/illustrated-stable-diffusion/ Stable Diffusion Paper: https://arxiv.org/abs/2112.10752 Hypernet Blogpost: https://blog.novelai.net/novelai-improvements-on-stable-diffusion-e10d38db82ac Dreambooth Paper: https://arxiv.org/abs/2208.12242 LoRa Paper: https://arxiv.org/abs/2106.09685 Textual Inversion Paper: https://arxiv.org/abs/2208.01618 Ideas for further quality improvement: https://www.youtube.com/watch?v=YF3MtNuX_F0&ab_channel=SamsonVowles-DelightfulDesign ▬▬ Support me if you like 🌟 ►Link to this channel: https://bit.ly/3zEqL1W ►Support me on Patreon: https://bit.ly/2Wed242 ►Buy me a coffee on Ko-Fi: https://bit.ly/3kJYEdl ►E-Mail: deepfindr@gmail.com ▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬ Music from #Uppbeat (free for Creators!): https://uppbeat.io/t/kidcut/jazz-and-hop License code: 5BVWL3POSHDLOBH8 ▬▬ Used Icons ▬▬▬▬▬▬▬▬▬▬ All Icons are from flaticon: https://www.flaticon.com/authors/freepik ▬▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬ 00:00 Introduction 00:47 Stable Diffusion 02:13 Things you can do with it 02:43 Issues with fine-tuning SD 03:45 Techniques for personalized Generation 06:17 Dreambooth explained 08:00 Coding set-up 09:11 [CODE] 14:51 My generation process 16:45 Prompt Engineering 17:03 Final Results ▬▬ My equipment 💻 - Microphone: https://amzn.to/3DVqB8H - Microphone mount: https://amzn.to/3BWUcOJ - Monitors: https://amzn.to/3G2Jjgr - Monitor mount: https://amzn.to/3AWGIAY - Height-adjustable table: https://amzn.to/3aUysXC - Ergonomic chair: https://amzn.to/3phQg7r - PC case: https://amzn.to/3jdlI2Y - GPU: https://amzn.to/3AWyzwy - Keyboard: https://amzn.to/2XskWHP - Bluelight filter glasses: https://amzn.to/3pj0fK2
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1 Understanding Graph Neural Networks | Part 1/3 - Introduction
Understanding Graph Neural Networks | Part 1/3 - Introduction
DeepFindr
 2 Understanding Graph Neural Networks | Part 2/3 - GNNs and it's Variants
Understanding Graph Neural Networks | Part 2/3 - GNNs and it's Variants
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 3 Understanding Graph Neural Networks | Part 3/3 - Pytorch Geometric and Molecule Data using RDKit
Understanding Graph Neural Networks | Part 3/3 - Pytorch Geometric and Molecule Data using RDKit
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 4 Node Classification on Knowledge Graphs using PyTorch Geometric
Node Classification on Knowledge Graphs using PyTorch Geometric
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 5 Understanding Convolutional Neural Networks | Part 1 / 3 - The Basics
Understanding Convolutional Neural Networks | Part 1 / 3 - The Basics
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 6 Understanding Convolutional Neural Networks | Part 2 / 3 - Wonders of the world CNN with PyTorch
Understanding Convolutional Neural Networks | Part 2 / 3 - Wonders of the world CNN with PyTorch
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 7 Understanding Convolutional Neural Networks | Part 3 / 3 - Transfer Learning and Explainable AI
Understanding Convolutional Neural Networks | Part 3 / 3 - Transfer Learning and Explainable AI
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 8 How to use edge features in Graph Neural Networks (and PyTorch Geometric)
How to use edge features in Graph Neural Networks (and PyTorch Geometric)
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 9 Explainable AI explained! | #1 Introduction
Explainable AI explained! | #1 Introduction
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 10 Explainable AI explained! | #2 By-design interpretable models with Microsofts InterpretML
Explainable AI explained! | #2 By-design interpretable models with Microsofts InterpretML
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 11 Explainable AI explained! | #3 LIME
Explainable AI explained! | #3 LIME
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 12 Explainable AI explained! | #4 SHAP
Explainable AI explained! | #4 SHAP
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 13 Explainable AI explained! | #5 Counterfactual explanations and adversarial attacks
Explainable AI explained! | #5 Counterfactual explanations and adversarial attacks
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 14 Explainable AI explained! | #6 Layerwise Relevance Propagation with MRI data
Explainable AI explained! | #6 Layerwise Relevance Propagation with MRI data
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 15 Understanding Graph Attention Networks
Understanding Graph Attention Networks
DeepFindr
 16 GNN Project #1 - Introduction to HIV dataset
GNN Project #1 - Introduction to HIV dataset
DeepFindr
 17 GNN Project #2 - Creating a Custom Dataset in Pytorch Geometric
GNN Project #2 - Creating a Custom Dataset in Pytorch Geometric
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 18 GNN Project #3.2 - Graph Transformer
GNN Project #3.2 - Graph Transformer
DeepFindr
 19 GNN Project #4.1 - Graph Variational Autoencoders
GNN Project #4.1 - Graph Variational Autoencoders
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 20 GNN Project #4.2 - GVAE Training and Adjacency reconstruction
GNN Project #4.2 - GVAE Training and Adjacency reconstruction
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 21 GNN Project #4.3 - One-shot molecule generation - Part 1
GNN Project #4.3 - One-shot molecule generation - Part 1
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 22 GNN Project #4.3 - Code explanation
GNN Project #4.3 - Code explanation
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 23 Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 1/2
Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 1/2
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 24 Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 2/2
Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 2/2
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 25 How to explain Graph Neural Networks (with XAI)
How to explain Graph Neural Networks (with XAI)
DeepFindr
 26 Explaining Twitch Predictions with GNNExplainer
Explaining Twitch Predictions with GNNExplainer
DeepFindr
 27 Python Graph Neural Network Libraries (an Overview)
Python Graph Neural Network Libraries (an Overview)
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 28 Friendly Introduction to Temporal Graph Neural Networks (and some Traffic Forecasting)
Friendly Introduction to Temporal Graph Neural Networks (and some Traffic Forecasting)
DeepFindr
 29 Traffic Forecasting with Pytorch Geometric Temporal
Traffic Forecasting with Pytorch Geometric Temporal
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 30 Fraud Detection with Graph Neural Networks
Fraud Detection with Graph Neural Networks
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 31 Fake News Detection using Graphs with Pytorch Geometric
Fake News Detection using Graphs with Pytorch Geometric
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 32 Recommender Systems using Graph Neural Networks
Recommender Systems using Graph Neural Networks
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 33 How to handle Uncertainty in Deep Learning #1.1
How to handle Uncertainty in Deep Learning #1.1
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 34 How to handle Uncertainty in Deep Learning #1.2
How to handle Uncertainty in Deep Learning #1.2
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 35 How to handle Uncertainty in Deep Learning #2.1
How to handle Uncertainty in Deep Learning #2.1
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 36 How to handle Uncertainty in Deep Learning #2.2
How to handle Uncertainty in Deep Learning #2.2
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 37 Converting a Tabular Dataset to a Graph Dataset for GNNs
Converting a Tabular Dataset to a Graph Dataset for GNNs
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 38 Converting a Tabular Dataset to a Temporal Graph Dataset for GNNs
Converting a Tabular Dataset to a Temporal Graph Dataset for GNNs
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 39 How to get started with Data Science (Career tracks and advice)
How to get started with Data Science (Career tracks and advice)
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 40 Causality and (Graph) Neural Networks
Causality and (Graph) Neural Networks
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 41 Diffusion models from scratch in PyTorch
Diffusion models from scratch in PyTorch
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 42 Self-/Unsupervised GNN Training
Self-/Unsupervised GNN Training
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 43 Contrastive Learning in PyTorch - Part 1: Introduction
Contrastive Learning in PyTorch - Part 1: Introduction
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 44 Contrastive Learning in PyTorch - Part 2: CL on Point Clouds
Contrastive Learning in PyTorch - Part 2: CL on Point Clouds
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 45 State of AI 2022 - My Highlights
State of AI 2022 - My Highlights
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 46 Equivariant Neural Networks | Part 1/3 - Introduction
Equivariant Neural Networks | Part 1/3 - Introduction
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 47 Equivariant Neural Networks | Part 2/3 - Generalized CNNs
Equivariant Neural Networks | Part 2/3 - Generalized CNNs
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 48 Equivariant Neural Networks | Part 3/3 - Transformers and GNNs
Equivariant Neural Networks | Part 3/3 - Transformers and GNNs
DeepFindr
 ▶ Personalized Image Generation (using Dreambooth) explained!
Personalized Image Generation (using Dreambooth) explained!
DeepFindr
 50 Vision Transformer Quick Guide - Theory and Code in (almost) 15 min
Vision Transformer Quick Guide - Theory and Code in (almost) 15 min
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 51 LoRA explained (and a bit about precision and quantization)
LoRA explained (and a bit about precision and quantization)
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 52 Dimensionality Reduction Techniques | Introduction and Manifold Learning (1/5)
Dimensionality Reduction Techniques | Introduction and Manifold Learning (1/5)
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 53 Principal Component Analysis (PCA) | Dimensionality Reduction Techniques (2/5)
Principal Component Analysis (PCA) | Dimensionality Reduction Techniques (2/5)
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 54 Multidimensional Scaling (MDS) | Dimensionality Reduction Techniques (3/5)
Multidimensional Scaling (MDS) | Dimensionality Reduction Techniques (3/5)
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 55 t-distributed Stochastic Neighbor Embedding (t-SNE) | Dimensionality Reduction Techniques (4/5)
t-distributed Stochastic Neighbor Embedding (t-SNE) | Dimensionality Reduction Techniques (4/5)
DeepFindr
 56 Uniform Manifold Approximation and Projection (UMAP) | Dimensionality Reduction Techniques (5/5)
Uniform Manifold Approximation and Projection (UMAP) | Dimensionality Reduction Techniques (5/5)
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Chapters (11)

Introduction
0:47 Stable Diffusion
2:13 Things you can do with it
2:43 Issues with fine-tuning SD
3:45 Techniques for personalized Generation
6:17 Dreambooth explained
8:00 Coding set-up
9:11 [CODE]
14:51 My generation process
16:45 Prompt Engineering
17:03 Final Results
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