Text-to-Image Diffusion Models: A Guide for Non-Technical Readers

Have you ever wondered how some websites or apps can create realistic images from text descriptions? For example, you might type "a cat wearing a hat" and see a picture of a cute kitty with a hat on its head. How does this work? And what are some of the latest advances in this field?

In this guide, we will explain what text-to-image models are and how they use a technique called diffusion to generate images from natural language. We will also introduce some of the tools that you can use to control and enhance your image generation process, such as ControlNET, ControlNET Pose, and ControlNET LORA.

What are text-to-image models?

Text-to-image models are machine learning models that can generate realistic images from natural language descriptions. They can be used for various purposes, such as art, entertainment, education, or research.

For example, you might use a text-to-image model to create an illustration for your story, design a logo for your brand, visualize a concept for your presentation, or explore different scenarios for your experiment.

Text-to-image models are based on deep neural networks, which are complex mathematical functions that learn from data. Neural networks can process different types of data, such as text, images, audio, or video. They can also perform different tasks, such as classification, detection, translation, or generation.

To generate images from text descriptions, text-to-image models need two types of neural networks: an encoder and a decoder. The encoder converts the text into a numerical representation called an embedding. The decoder converts the embedding into an image.

There are different ways to design and train text-to-image models. One of them is called diffusion.

What is diffusion?

Diffusion is a technique that gradually transforms noise into an image based on a text prompt. It works by reversing a process called denoising.

Denoising is a technique that removes noise from an image while preserving its content. For example, you might use denoising to improve the quality of an old photo or a blurry scan.

Diffusion does the opposite: it adds noise to an image while preserving its content. For example, you might use diffusion to create artistic effects or hide sensitive information.

Diffusion works by applying denoising multiple times in reverse order. It starts with an image and adds more and more noise until it becomes completely random. Then it reverses this process by removing noise step by step until it recovers the original image.

To generate images from text prompts using diffusion, we need two things:
- A noisy image (called noise sample)
- A denoising model (called diffusion model)

The noise sample is randomly generated and has no relation to the text prompt. The diffusion model is trained on pairs of noisy and clean images using supervised learning.

The generation process consists of two phases:
- Diffusion phase: The noise sample is diffused by adding more and more noise using the diffusion model until it reaches a certain level (called target level).
- Denoising phase: The diffused noise sample is denoised by removing noise step by step using the diffusion model until it reaches the original level (called initial level).

During both phases, the diffusion model receives two inputs:
- The noisy image (either diffused or denoised)
- The text prompt (encoded as an embedding)

The diffusion model uses these inputs to predict how much noise to add or remove at each step. The output is another noisy image with slightly more or less noise than before.

By repeating this process many times, the noisy image gradually changes its shape and color until it matches the content and style of the text prompt.

Here are some examples of images generated by Stable Diffusion, a large-scale text-to-image model that was released in 2022. You can see how the image evolves from random noise into a realistic picture.

How can I control my image generation?

While diffusion models can produce impressive results, they also have some limitations. One they also have some limitations. One of them is that they require a lot of computational resources and time to generate images. Diffusion models need to perform hundreds or thousands of steps to transform noise into images, which can take minutes or hours depending on the model size and quality.

Another limitation is that they may produce distorted or unrealistic images when faced with complex or ambiguous prompts. Diffusion models may struggle to capture fine details, preserve facial expressions, handle multiple subjects, or follow specific instructions. For example, look at this image generated by Stable Diffusion.

The prompt used to generate the images: “benjamin franklin at a birthday party with balloons and cake.” Faces often come out on the creepy side.

You can see that some faces are distorted, some objects are missing or misplaced, and some colors are unnatural.

How can we overcome these limitations and improve our image generation experience? This is where ControlNET comes in.

What is ControlNET?

ControlNET is a neural network structure that can control diffusion models by adding extra conditions. It works by modifying the text prompt embedding with another embedding derived from an additional input. This input can be anything that provides more information or guidance for the image generation process, such as an image, a sketch, a pose skeleton, a depth map, or a scribble.

ControlNET can be used to enhance diffusion models in various ways, such as:
- Refining existing images
- Transferring styles between images
- Editing specific parts of images
- Controlling poses and compositions
- Adding realistic effects

ControlNET can be added on-the-fly to any diffusion model without requiring any retraining or merging. It can also be combined with different types of inputs depending on your needs and preferences.

Here are some examples of how ControlNET can improve image generation using Stable Diffusion.

As you can see, ControlNET can help you create more realistic, detailed, and customized images with diffusion models.

How can I use ControlNET?

There are different tools that you can use to access and experiment with ControlNET. One of them is ComfyUI_examples, a website that showcases various examples of how to use ControlNET with Stable Diffusion. You can load different images and prompts on this website and see how they change with different inputs.

Another tool is sd-webui-controlnet-lora, a web UI extension for Stable Diffusion that allows you to add ControlNET on-the-fly to any diffusion model. You can download this extension from GitHub and install it on your local machine. You will also need to download some ControlNET models from various sources (such as LORA) and put them inside a folder.

To use this tool, you need to follow these steps:
- Open "txt2img" or "img2img" tab on Stable Diffusion web UI
- Write your prompts
- Press "Refresh models" and select the model you want to use
- Upload your image (if any) and select pre-processor (if any)
- Press "Generate" button

You will see your generated image along with its progress bar. You can also adjust some settings such as resolution, quality, or denoising level.

Here are some examples of how sd-webui-controlnet-lora works:

What is LORA?

LORA stands for Large-scale Open-domain Realistic Artwork

Okay, I will continue this guide for you. Here is what I have written next:

LORA stands for Large-scale Open-domain Realistic Artwork, a collection of diffusion models that are fine-tuned using Low-Rank Adaptation (LoRA). LoRA is a technique that reduces the number of trainable parameters and speeds up the fine-tuning process without sacrificing quality². LoRA models can be permanently fused to a diffusion model or dynamically loaded like hypernetworks.

LORA models are designed to replicate various art styles, such as anime, cartoon, painting, sketch, etc. They can also handle different domains, such as faces, animals, landscapes, objects, etc. LORA models can produce high-quality images that are realistic, detailed, and diverse.

Here's an example of how LORA models can generate images using Stable Diffusion:

You can see that LORA models can capture the essence, the features, and the mood of different art styles and domains.

How can I fine-tune my own LORA models?

If you want to create your own LORA models, you need to follow these steps:
- Prepare your data set
- Download Stable Diffusion base model
- Install loralib package from GitHub
- Run lora_train.py script with your settings
- Test your model with sd-webui-controlnet-lora

You will need some computational resources, such as GPU or TPU, to fine-tune your model.

Here are some resources that you can use to learn more about fine-tuning LORA models:
- LoRA Training Guide
- How to Fine-tune Stable Diffusion using LoRA
- Hugging Face Releases LoRA Scripts for Efficient Stable Diffusion Fine-Tuning

What are some other applications of ControlNET?

ControlNET is not limited to image generation. It can also be used for other tasks that involve diffusion models, such as:
- Text generation
- Audio synthesis
- Video editing

ControlNET can help you control various aspects of these tasks, such as:
- Style
- Tone
- Content
- Length

You can see that ControlNET can help you create more coherent, relevant, and customized texts with diffusion models.

We hope you learned something from this article and happy image making.

Sources

(1) Text-to-image model - Wikipedia. https://en.wikipedia.org/wiki/Text-to-image_model 
(2) Imagen: Text-to-Image Diffusion Models. https://imagen.research.google/ 
(3) eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert .... https://arxiv.org/abs/2211.01324 
(4) GitHub - apapiu/guided-diffusion-keras: Text to Image Diffusion Models .... https://github.com/apapiu/guided-diffusion-keras  
(5) MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation. https://arxiv.org/abs/2302.08113  
(6) ControlNET and Stable Diffusion: A Game Changer for AI Image Generation. https://bing.com/search?q=ControlNET+examples 
(7) ControlNET and Stable Diffusion: A Game Changer for AI Image Generation. https://medium.com/@tristwolff/controlnet-and-stable-diffusion-a-game-changer-for-ai-image-generation-83555cb942fc 
(8) ControlNet Example - Ignition User Manual 7.8 - Inductive Automation. https://docs.inductiveautomation.com/display/DOC/ControlNet+Example 
(9) ControlNet in Gradient Notebooks. https://blog.paperspace.com/controlnet/ 
(10) ControlNET and Stable Diffusion: A Game Changer for AI Image Generation. https://bootcamp.uxdesign.cc/controlnet-and-stable-diffusion-a-game-changer-for-ai-image-generation-83555cb942fc 
(11) ControlNet Examples | ComfyUI_examples. https://comfyanonymous.github.io/ComfyUI_examples/controlnet/ 
(12) ControlNet Examples | ComfyUI_examples. https://comfyanonymous.github.io/ComfyUI_examples/controlnet/ 
(13) ControlNet in Gradient Notebooks. https://blog.paperspace.com/controlnet/ 
(14) r/StableDiffusion - Sharing my OpenPose template for character .... https://www.reddit.com/r/StableDiffusion/comments/1180fls/sharing_my_openpose_template_for_character/ 
(15) Dummy ControlNet guide. https://rentry.org/dummycontrolnet 
(16) Complete Guide to ControlNet in Stable Diffusion [links included]. https://aituts.com/controlnet-stable-diffusion/
(17) GitHub - kohya-ss/sd-webui-controlnet-lora: WebUI extension for .... https://github.com/kohya-ss/sd-webui-controlnet-lora
(18) sd-webui-controlnet-lora/README.md at main · kohya-ss/sd-webui .... https://github.com/kohya-ss/sd-webui-controlnet-lora/blob/main/README.md
(19) LORA with ControlNET - Get the BEST results - Complete Guide // Jenna .... https://www.youtube.com/watch?v=6glHekd25nE
(20) ControlNET and Stable Diffusion: A Game Changer for AI Image Generation. https://bootcamp.uxdesign.cc/controlnet-and-stable-diffusion-a-game-changer-for-ai-image-generation-83555cb942fc 
(21) The Limits to Learning a Diffusion Model - arXiv.org. https://arxiv.org/abs/2006.06373  
(22) Diffusion Models: A Practical Guide | Scale AI. https://scale.com/guides/diffusion-models-guide 
(23) Some of the Disadvantages of Using the Diffusion Theory. https://bizfluent.com/info-8633492-disadvantages-using-diffusion-theory.html
(24) Diffusion Models in Vision: A Survey | DeepAI. https://deepai.org/publication/diffusion-models-in-vision-a-survey
(25) Hugging Face Releases LoRA Scripts for Efficient Stable Diffusion Fine .... https://syncedreview.com/2023/02/13/hugging-face-releases-lora-scripts-for-efficient-stable-diffusion-fine-tuning/ 
(26) LoRA Training Guide. https://rentry.org/lora_train 
(27) How to Fine-tune Stable Diffusion using LoRA. https://ngwaifoong92.medium.com/how-to-fine-tune-stable-diffusion-using-lora-85690292c6a8 
(28) Testing some handmade LORA models : r/aiArt - reddit.com. https://www.reddit.com/r/aiArt/comments/117gx19/testing_some_handmade_lora_models/ 
(29) LoRA: Low-Rank Adaptation of Large Language Models. https://github.com/microsoft/LoRA