Models, sometimes called checkpoint files, are pre-trained Stable Diffusion weights intended for generating general or a particular genre of images.
What images a model can generate depends on the data used to train them. A model won’t be able to generate a cat’s image if there’s never a cat in the training data. Likewise, if you only train a model with cat images, it will only generate cats.
We will go introduce what models are, some common ones (v1.4, v1.5, F222, Anything V3, Open Journey v4), and how to install, use and merge them.
This is part 4 of the beginner’s guide series.
Read part 1: Absolute beginner’s guide.
Read part 2: Prompt building.
Read part 3: Inpainting.
Contents
Fine-tuned models
What is fine-tuning?
Fine-tuning is a common technique in machine learning. It takes a model that is trained on a wide dataset and trains a bit more on a narrow dataset.
A fine-tuned model will be biased toward generating images similar to your dataset while maintaining the versatility of the original model.
Why do people make them?
Stable diffusion is great but is not good at everything. For example, it can and will generate anime-style images with the keyword “anime” in the prompt. But it could be difficult to generate images of a sub-genre of anime. Instead of tinkering with the prompt, you can fine-tune the model with images of that sub-genre.
How are they made?
Two main fine-tuning methods are (1) Additional training and (2) Dreambooth. They both start with a base model like Stable Diffusion v1.4 or v1.5.
Additional training is achieved by training a base model with an additional dataset you are interested in. For example, you can train Stable Diffusion v1.5 with an additional dataset of vintage cars to bias the aesthetic of cars towards the sub-genre.
Dreambooth, initially developed by Google, is a technique to inject custom subjects into text-to-image models. It works with as few as 3-5 custom images. You can take a few pictures of yourself and use Dreambooth to put yourself into the model. A model trained with Dreambooth requires a special keyword to condition the model.
There’s another less popular fine-tuning technique called textual inversion (sometimes called embedding). The goal is similar to Dreambooth: Inject a custom subject into the model with only a few examples. A new keyword is created specifically for the new object. Only the text embedding network is fine-tuned while keeping the rest of the model unchanged. In layman’s terms, it’s like using existing words to describe a new concept.
Models
There are two groups of models: v1 and v2. I will cover the v1 models in this section and the v2 models in the next section.
There are thousands of fine-tuned Stable Diffusion models. The number is increasing every day. Below is a list of models that can be used for general purposes.
Stable diffusion v1.4
Released in August 2022 by Stability AI, v1.4 model is considered to be the first publicly available Stable Diffusion model.
You can treat v1.4 as a general-purpose model. Most of the time, it is enough to use it as is unless you are really picky about certain styles.
Stable diffusion v1.5
v1.5 is released in Oct 2022 by Runway ML, a partner of Stability AI. The model is based on v1.2 with further training.
The model page does not mention what the improvement is. It produces slightly different results compared to v1.4 but it is unclear if they are better.
Like v1.4, you can treat v1.5 as a general-purpose model.
In my experience, v1.5 is a fine choice as the initial model and can be used interchangeably with v1.4.
F222
F222 is trained originally for generating nudes, but people found it helpful in generating beautiful female portraits with correct body part relations. Interestingly, contrary to what you might think, it’s quite good at generating aesthetically pleasing clothing.
F222 is good for portraits. It has a high tendency to generate nudes. Include wardrobe terms like “dress” and “jeans” in the prompt.
Anything V3
Anything V3 is a special-purpose model trained to produce high-quality anime-style images. You can use danbooru tags (like 1girl, white hair) in the text prompt.
It’s useful for casting celebrities to amine style, which can then be blended seamlessly with illustrative elements.
One drawback (at least to me) is that it produces females with disproportional body shapes. I like to tone it down with F222.
Open Journey
Open Journey is a model fine-tuned with images generated by Mid Journey v4. It has a different aesthetic and is a good general-purpose model.
Triggering keyword: mdjrny-v4 style
Model comparison
Here’s a comparison of these models with the same prompt and seed. All but Anything v3 generate realistic images but with different aesthetics.
Other models
There are hundreds of Stable Diffusion models available. Many of them are special-purpose models designed to generate a particular style. Some notable ones are:
DreamShaper
Dreamshaper model is fine-tuned for a portrait illustration style that sits between photorealistic and computer graphics. It’s easy to use and you will like it if you like this style.
ChilloutMix
ChilloutMix is a special model for generating photo-quality Asian females. It is like the Asian counterpart of F222. Use with Korean embedding ulzzang-6500-v1 to generate girls like k-pop.
Like F222, it generates nudes sometimes. Suppress with wardrobe terms like “dress” and “jeans” in the prompt, and “nude” in the negative prompt.
Waifu-diffusion
Waifu Diffusion is a Japanese anime style.
Robo Diffusion
Robot Diffusion is an interesting robot-style model that will turn your every subject into a robot!
Mo-di-diffusion
This model is for you if you want to generate some Pixar-like style.
Use keywords: modern disney style
Inkpunk Diffusion
Inkpunk Diffusion is a Dreambooth-trained model with a very distinct illustration style.
Use keyword: nvinkpunk
Finding more models
You can find more models in Huggingface.
Civitai is another great resource to search for models.
v2 models
Stability AI released a new series of models version 2. So far 2.0 and 2.1 models are released. The main change in v2 models are
- In addition to 512×512 pixels, a higher resolution version 768×768 pixels is available.
- You can no longer generate explicit content because pornographic materials were removed from training.
You may assume that everyone has moved on to using the v2 models. However, the Stable Diffusion community found that the images looked worse in the 2.0 model. People also have difficulty in using power keywords like celebrity names and artist names.
The 2.1 model has partially addressed these issues. The images look better out of the box. It’s easier to generate artistic style.
As of now, most people have not completely moved on to the 2.1 model. Many use them occasionally but spend most of the time with v1 models.
If you decided to try out v2 models, be sure to check out these tips to avoid some common frustrations.
How to install and use a model
These instructions are only applicable to v1 models. See the instructions for v2.0 and v2.1.
To install a model in AUTOMATIC1111 GUI, download and place the checkpoint (.ckpt) file in the following folder
stable-diffusion-webui/models/Stable-diffusion/Press reload button next to the checkpoint drop box
You should see the checkpoint file you just put in available for selection. Select the new checkpoint file to use the model.
Alternatively, you can press the “iPod” button under Generate.
The model panel will appear. Select the Checkpoints tab and choose a model.
If you are new to AUTOMATIC1111 GUI, some models are preloaded in the Colab notebook included in the Quick Start Guide.
Merging two models
To merge two models using AUTOMATIC1111 GUI, go to the Checkpoint Merger tab and select the two models you want to merge in Primary model (A) and Secondary model (B).
Adjust the multiplier (M) to adjust the relative weight of the two models. Setting it to 0.5 would merge the two models with equal importance.
After pressing Run, the new merged model will be available for use.
Example of a merged model
Here are sample images from merging F222 and Anything V3 with equal weight (0.5):
The merged model sits between the realistic F222 and the anime Anything V3 styles. It is a very good model for generating illustration art with human figures.
Other model types
Four main types of files can be called “models”. Let’s clarify them, so you know what people are talking about.
- Checkpoint models: These are the real Stable Diffusion models. They contain all you need to generate an image. No additional files are required. They are large, typically 2 – 7 GB. They are the subject of this article.
- Textual inversions: Also called embeddings. They are small files defining new keywords to generate new objects or styles. They are small, typically 10 – 100 KB. You must use them with a checkpoint model.
- LoRA models: They are small patch files to checkpoint models for modifying styles. They are typically 10-200 MB. You must use them with a checkpoint model.
- Hypernetworks: They are additional network modules added to checkpoint models. They are typically 5 – 300 MB. You must use them with a checkpoint model.
Summary
In this article, I have introduced what Stable Diffusion models are, how they are made, a few common ones, and how to merge them. Using models can make your life easier when you have a specific style in mind.
This is part 4 of the beginner’s guide series.
Read part 1: Absolute beginner’s guide.
Read part 2: Prompt building.
Read part 3: Inpainting.
AWWW- I found it: https://stable-diffusion-art.com/embedding/#AUTOMATIC1111
-Thanks! You have the best, easiest tutorials I’ve ever seen!!
Do you have to merge the files? (it all starts getting really big) So say I have small niche LORA file do I have to merge it with a big checkpoint file to use it? Or can I say something in a prompt? Other methods?
Thanks loads for all your content, really informative and a huge help to get me started with AI content 😊
You are welcome. Don’t forget to subscribe! 🙂
Seems like Anything URL is broken
Fixed. Thanks!
When I try to merge models it says: Error merging checkpoints: unhashable type: ‘list’
am I doing something wrong? I’m trying to merge a dreambooth face I trained, with a model I downloaded online called “midjourneyPapercut_v1.ckpt”
I’m not familiar with the papercut model. Perhaps you can systematically figure out whether it is a issue with setup or models.
1. Merge v1.4 and anythingv3 model with your setup. It should work.
2. Merge dreambooth model with v1.4. If it doesn’t work, the issue is with dreambooth.
3. Do the same for papercut.
Have you tried Dreambooth on a non SD base model (not 1.4 or 1.5) but rather using f222 as base? My finding is that you can’t get more than 5% of your likeness into one of these already finetuned models, I would like to hear from other’s experience.
No I haven’t tried it. I think models fine tuned with additional training is less stable since the fine tuning samples lack diversity. Dreambooth and text inversion were designed to solve this issue.