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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Exploring Civitai: Models, LoRA, and Creative Possibilities

mounish12439
mounish12439 04 Sep, 2024
7 min read

Introduction

How cool would it be if there was a platform where your creative visions come to life with just a few clicks—a world where you can fine-tune cutting-edge AI models to craft stunning images uniquely yours? Welcome to Civitai, a vibrant platform dedicated to empowering creators by providing access to a vast and ever-growing library of Stable Diffusion models. Whether you’re an artist, designer, or AI enthusiast, Civitai offers you the tools to explore, experiment, and collaborate with a global community. Ready to dive deeper into what makes Civitai the go-to hub for creative innovation? Let’s explore!

CivitAI

Overview

  1. Civitai is a creative platform where users can fine-tune AI models to generate unique, stunning images with ease.
  2. Civitai empowers creators by providing a collaborative space for sharing and discovering custom AI models.
  3. The platform offers diverse AI-generated models, enabling users to craft distinct digital content effortlessly.
  4. Civitai supports LoRA models, allowing efficient fine-tuning of large AI models for specific tasks and styles.
  5. Civitai is an innovative hub for AI enthusiasts, simplifying the process of generating captivating AI-driven media.

Table of contents

What is Civitai?

Civitai is a platform where fine-tuning enthusiasts can effortlessly share their creations and connect with other creators. It is an innovative platform designed to enhance the creation and exploration of AI-generated media. It provides an environment where users can upload, share, and discover custom models trained on various datasets. These models, supported by AI media software, serve as unique tools for generating original content. It gives access to various checkpoint-trained models.

Basic Terminology

  1. Model Checkpointing: Model checkpointing refers to saving the state of a machine learning model at certain points during training. A checkpoint includes the model’s weights, biases, and other parameters that define how it processes data. You can pause and resume training later by saving checkpoints or use these saved states (checkpoints) for inference. In platforms like Civitai, users can download these checkpoints to generate content or continue fine-tuning models.
  2. Base Models: Base models are pre-trained machine learning models that are the starting point for further fine-tuning. These models have been trained on large datasets and can perform specific tasks like image generation. When fine-tuning a base model, additional training is conducted to adapt it to more specific tasks or datasets, often leading to improved performance.
  3. LoRA (Low-Rank Adaptation): LoRA is a technique used to fine-tune large models more efficiently by introducing additional trainable parameters in the form of low-rank matrices. LoRA only adjusts a small subset of parameters during fine-tuning, making the process faster and less resource-intensive. LoRA is particularly useful when working with large pre-trained models like Stable Diffusion, as it allows users to adapt the model to new tasks or styles with less computational cost.

Also read: Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLoRA

Civitai Models

Civitai model refers to a machine-learning algorithm trained to produce art or media in a certain style. To create a media-generating model, a dataset representing the desired style or subject is assembled and used for training. The model then generates new, original media by identifying patterns and features from its training data. The output is not an exact copy of the training examples but a unique creation inspired by the dataset. Models can be trained to produce anything from photorealistic images and abstract patterns to diverse audio arrangements, short stories, or even videos, enabling the creation of content that would be challenging and time-consuming to produce manually.

Let’s look at a few models on Civitai:

1. Flux

A very popular model on Civitai, this checkpointed trained model can be downloaded and used locally or, optionally, directly on the platform. Let’s try generating an image using this stable diffusion model. 

I generated a 1024×1024 image with the prompt “A person sitting on a tree with a balloon.” You can set the number of images to 1 if you want to spend fewer credits, or Buzz, as they call it. After signing up, you’ll be offered 100 Buzz.

Note: 

The ‘create’ option on Civitai allows users to generate images using AI models, and can have credit limitations. However, if you download the model and use it locally or on a service that supports it, you shouldn’t encounter any restrictions from Civitai.

2. Photon_v1

Let’s look at another stable diffusion checkpoint model on Civitai. Let’s download the model and use it this time. We’ll need a GPU for this, so I’ll be using Google Colab for the demo.

We’ll be using the stable diffusion webui using Automatic 1111: Link

Note that Colab has limited support for Automatic1111 on the free version, so you may find it difficult as the notebook gets disconnected after 5 minutes of use, and you’ll get a warning like “you may be executing code that is disallowed.” If you have a good GPU on your computer, then I’ll suggest you do it locally.

Let’s look at the code.

Please make sure to change the runtime type and select GPU:

  1. Step 1: Clone the Automatic1111 repository.
!git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
  1. Step 2: Install the requirements from the text file.
!pip install -r /content/stable-diffusion-webui/requirements.txt
  1. Step 3: Download the model and upload the model to your Google Drive, and then mount the drive.

Let’s download the Photon (v1) model from Civitai: Civitai Photon

Note: You can click on the mount drive option on the top left of your notebook

  1. Step-4: Let’s move the file from drive this directory:

“/content/stable-diffusion-webui/models/Stable-diffusion”

!mv /content/drive/MyDrive/photon_v1.safetensors /content/stable-diffusion-webui/models/Stable-diffusion/photon_v1.safetensors
  1. Step-5: Now we can run launch.py, use –share to get a publicly accessible link to the web ui.
!python /content/stable-diffusion-webui/launch.py --share
Output

Select photon_v1 in the stable diffusion checkpoint and start prompting in the text2img section. As you can see, I prompted “A man carrying an umbrella” and clicked Generate at the top right. Feel free to tweak the settings before you generate.

You can download the image by right-clicking it; here’s the 512×512 image I generated.

3. Lora models in Civitai

Let’s look at how to use Civitai’s Lora models:

We need to download a Lora model and its base model, or else we won’t be able to use it. I’ll demonstrate this using: Vixon’s Anime/Manga Styles Lora model.

The base model for this Lora model is the Pony model; let’s download both models from Civitai.

Pony Diffusion V6 XL: Click Here

You can perform the steps we performed in the previous section for using the Photon_v1 model. Let’s continue with the remaining steps:

  1. Step 1: Download both the base model and Lora model and upload them to your Google Drive and then mount the drive.
  2. Step 2: Make a new directory for the Lora model. Now, move the Lora model to the Lora folder and the base model to the stable diffusion folder.
!mv /content/drive/MyDrive/ponyDiffusionV6XL_v6StartWithThisOne.safetensors /content/stable-diffusion-webui/models/Stable-diffusion/ponyDiffusionV6XL_v6StartWithThisOne.safetensors
!mkdir /content/stable-diffusion-webui/models/Lora
!mv /content/drive/MyDrive/DragonBallZXLP.safetensors /content/stable-diffusion-webui/models/Lora/DragonBallZXLP.safetensors
  1. Step 3: Launch the webui by running this line
!python /content/stable-diffusion-webui/launch.py --share

Click on the public URL, and you’ll be greeted with this UI

Now click on the stable diffusion checkpoint dropdown and select Pony, let’s now click on the Lora section and select Vixon’s Anime/Manga Styles and we’re good to prompt.

I prompted “<lora:DragonBallZXLP:1> Goku, green hair, fight, high quality, Dragon Ball Z, watercolor \(medium\), traditional media,” and clicked generate (I used the trigger words for the model, which helps give better results, you can find the trigger words on the model’s page on Civitai – Click Here)

This is the image I generated using Vixon’s Anime/Manga Styles Lora model.

Conclusion

Civitai serves as an innovative hub for AI enthusiasts and creators, offering a platform for refining and distributing models like Stable Diffusion. Whether it’s crafting digital art or testing out LoRA techniques, Civitai provides a wide range of tools and resources to inspire creativity. The ease of sharing and discovering models fosters a collaborative community for those passionate about AI-generated media. By utilizing Civitai, creators can explore new possibilities in AI-driven content, simplifying the process of generating distinct and captivating media.

Frequently Asked Questions

Q1. Why does my model disappear from Google Drive after I use the mv command?

Ans. The model vanishes from Google Drive because the mv command moves the file, removing it from its original spot. To prevent this, use the cp command instead, which duplicates the file to the target directory without deleting the original in Google Drive.

Q2. How can I set up and operate a Stable Diffusion model, such as Photon_v1, on Google Colab?

Ans. To set up a Stable Diffusion model like Photon_v1 on Google Colab, clone the Automatic1111 repository, install the required dependencies, download the model, place it in the appropriate directory, and then launch the web UI to start generating images.

Q3. What does a LoRA model represent, and how is it applied in Civitai?

Ans. A LoRA (Low-Rank Adaptation) model is a fine-tuning technique that adds extra parameters to a base model, making the fine-tuning process more efficient. On Civitai, you can download and pair LoRA models with their base models to produce specific media styles.

Q4. Is it possible to run Civitai models on my computer, or must I use the platform?

Ans. You can indeed run Civitai models locally on your computer. After downloading, you can use them with tools like the Stable Diffusion web UI (Automatic1111) on your local machine, as long as you have the necessary hardware, such as a GPU.

AIAI toolscivitaifine tuningfine-tune cutting-edge AI modelsimageimagesrefining modelsstable diffusion
mounish12439
mounish12439 04 Sep, 2024

I'm a tech enthusiast, graduated from Vellore Institute of Technology. I'm working as a Data Science Trainee right now. I am very much interested in Deep Learning and Generative AI.

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