Stable Diffusion Models: a beginner’s guide

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stable diffusion model ghostmix

Stable Diffusion Models, or checkpoint models, are pre-trained Stable Diffusion weights for generating a particular style of images.

What kind of images a model generates depends on the training images. 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 introduce what models are, some popular ones, 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.

Fine-tuned models

What is fine-tuning?

Fine-tuning is a common technique in machine learning. It takes a model trained on a wide dataset and trains a bit more on a narrow dataset.

A fine-tuned model tends to generate images similar to those used in the training. For example, the Anything v3 model is trained with anime images. So, it generates anime-style images by default.

Why do people make Stable Diffusion models?

The Stable diffusion base model is great but is not good at everything. For example, it generates anime-style images with the keyword “anime” in the prompt. However, it could be difficult to generate images of a sub-genre of anime. Instead of tinkering with the prompt, you can use a custom model fine-tuned with images of that sub-genre.

Images below are generated using the same prompts and settings but with different models.

  • Realistic Vision: Realistic photo style.
  • Anything v3: Anime style.
  • Dreamshaper: Realistic painting style.

Using a model is an easy way to achieve a certain style.

How are models created?

Custom checkpoint models are made with (1) additional training and (2) Dreambooth. They both start with a base model like Stable Diffusion v1.5 or XL.

Additional training is achieved by training a base model with an additional dataset you are interested in. For example, you can train the Stable Diffusion v1.5 with an additional dataset of vintage cars to bias the aesthetic of cars towards the vintage sub-genre.

Dreambooth, 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.

The checkpoint model is not the only model type. We also have textual inversion (also called embedding), LoRA, LyCORIS, and hypernetwork. We will focus on the checkpoint model in this article.

Structured Stable Diffusion courses
Become a Stable Diffusion Pro step-by-step.

Popular Stable Diffusion Models

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

v1.4 image

Model Page

Download link

Released in August 2022 by Stability AI, the v1.4 model is the first publicly available Stable Diffusion model.

It is a general-purpose model, capable of producing various styles.

Most people has moved on to the v1.5 model since its release.

Stable diffusion v1.5

v1.5 image.

Model Page

Download link

v1.5 was 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.

Realistic Vision

Realistic Vision v2 is good for generating anything realistic, whether they are people, objects, or scenes.

Learn more about generating realistic people.

Model Download link


Dreamshaper model

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.

Model page

Download link

SDXL model

SDXL model is an upgrade to the celebrated v1.5 and the forgotten v2 models.

The benefits of using the SDXL model are

  • Higher native resolution – 1024 px compared to 512 px for v1.5
  • Higher image quality (compared to the v1.5 base model)
  • Capable of generating legible text
  • It is easy to generate darker images

Anything V3

Anything v3 model.

Model Page

Download Link

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.

Best Stable Diffusion Models

There are thousands of Stable Diffusion models available. Many of them are special-purpose models designed to generate a particular style. Where should you start?

In addition to the ones I just mentioned, here are some of the best models I keep going back to.

Deliberate v2

Deliberate v2 is another must-have model (so many!) that renders realistic illustrations. The results can be surprisingly good. Whenever you have a good prompt, switch to this model and see what you get!

Download link



Download link

F222 is trained originally for generating nudes, but people found it helpful in generating beautiful female portraits with correct body part relations. 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.

Find more realistic photo-style models in this post.


Model Page

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.

Protogen v2.2 (Anime)

Protogen v2.2 is classy. It generates illustration and anime-style images with good taste.

Protogen v2.2 model page

Download link


beautiful face, long hair, sci-fi girl, mechanical limbs, (machine made joints:1.2), impressionist, highly detailed, an extremely delicate and beautiful, side view, cinematic light,solo,full body,(blood vessels connected to tubes),(mechanical vertebra attaching to back),((mechanical cervial attaching to neck)),(sitting),expressionless,(wires and cables attaching to neck:1.2),(wires and cables on head:1.2)(character focus),science fiction,white background, extreme detailed,colorful,highest detailed

Negative Prompt:
NSFW,monochrome, zombie,overexposure, watermark,text,bad anatomy, distorted, oversized head, ugly, huge eyes, text, logo,(blurry:2.0), bad-artist, cartoon,Scribbles,Low quality,Low rated,Mediocre,3D rendering,Screenshot,Software,UI,watermark,signature

GhostMix is trained with Ghost in the Shell style, a classic anime in the 90s. You will find it useful for generating cyborgs and robots.

Download link


Download link

Waifu Diffusion is a Japanese anime style.

Inkpunk Diffusion

Inkpunk diffusion

Model Page

Download link

Inkpunk Diffusion is a Dreambooth-trained model with a very distinct illustration style.

Use keyword: nvinkpunk

Finding more models

Civitai is the go-to place for downloading models.

Huggingface is another good source though the interface is not designed for Stable Diffusion models.

Stable Diffusion v2 models

Sample 2.1 image.

Stable Diffusion v2 are two official Stable Diffusion models. The main change in v2 models are

  • In addition to 512×512 pixels, a higher resolution version of 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 from v1.5. However, the Stable Diffusion community found that the images looked worse in the 2.0 model. People also have difficulty using powerful keywords like celebrity 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.

Now, most people don’t use v2 models. We use v1 and SDXL. But if you want to try out v2 models, check out these tips to avoid common frustrations.

How to install and use a model

These instructions are for v1 and SDXL models.

To install a model in AUTOMATIC1111 GUI, download and place the checkpoint model file in the following folder


Press the reload button next to the checkpoint dropbox on top left.

refresh checkpoint dropdown menu

You should see the checkpoint file you just put in available for selection. Select the new checkpoint file to use the model.

Alternatively, select the Checkpoints tab on the txt2img or img2img page and choose a model.

checkpoint models tab

If you are new to AUTOMATIC1111 GUI, some models are preloaded in the Colab notebook included in the Quick Start Guide.

See the instructions for v2.0 and v2.1.

See the SDXL article for using the SDXL model.


Some models recommend a different Clip Skip setting. You should follow this setting to get the intended style.

What is CLIP Skip?

CLIP Skip is a feature that skips a number of last layers in the CLIP text embedding network during image generation in Stable Diffusion. CLIP is the language model used in Stable Diffusion v1.5 models. It converts text tokens in the prompt into embeddings. It is a deep neural network model that contains many layers. CLIP Skip refers to how many of the last layers to skip.

In AUTOMATIC1111 and many Stable Diffusion software, CLIP Skip of 1 does not skip any layers. CLIP Skip of 2 skips the last layer, and so on.

Why do you want to skip some CLIP layers? A neural network summarizes information as it passes through layers. The earlier the layer is, the richer the information it contains.

Skipping CLIP layers can have a dramatic effect on images. Many anime models are trained with CLIP Skip of 2. See the examples below with different CLIP skips but the same prompt and seed.

Setting CLIP Skip in AUTOMATIC1111

You can set the CLIP Skip on the Settings Page > Stable Diffusion > Clip Skip. Adjust the value and click Apply Settings.

But if you need to change CLIP Skip regularly, a better way is to add it to the Quick Settings. Go to the Settings page > User Interface > Quicksettings list. Add CLIP_stop_at_last_layer. Click Apply Settings and Reload UI.

The clip skip slider should appear at the top of AUTOMATIC1111.

Merging two models

Settings for 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):

Compare F222, Anything V3 and Merged (50% each)

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.

Stable Diffusions model file formats

On a model download page, you may see several model file formats.

  • Pruned
  • Full
  • EMA-only
  • FP16
  • FP32
  • .pt
  • .safetensor

This is confusing! Which one should you download?

Pruned vs Full vs EMA-only models

Some Stable Diffusion checkpoint models consist of two sets of weights: (1) The weights after the last training step and (2) the average weights over the last few training steps, called EMA (exponential moving average).

You can download the EMA-only model if you are only interested in using the model. These are the weights you use when you use the model. They are sometimes called pruned models.

You will only need the full model (i.e. A checkpoint file consisting of two sets of weights) if you want to fine-tune the model with additional training.

So, download the pruned or EMA-only model if you want to use it to generate images. This saves you some disk space. Trust me, your hard drive will fill up very soon!

Fp16 and fp32 models

FP stands for floating point. It is a computer’s way of storing decimal numbers. Here the decimal numbers are the model weights. FP16 takes 16 bits per number and is called half precision. FP32 takes 32 bits and is called full precision.

The training data for deep learning models (such as Stable Diffusion) is pretty noisy. You rarely need a full-precision model. The extra precision just stores noise!

So, download the FP16 models if available. They are about half as big. This saves you a few GB!

Safetensor models

The original pytorch model format is .pt. The downside of this format is that it is not secure. Someone can pack malicious codes in it. The code can run on your machine when you use the model.

Safetensors is an improved version of the PT model format. It does the same thing as storing the weights but will not execute any codes.

So, download the safetensors version whenever it is available. If not, download the PT files from a trustworthy source.

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 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) 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 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 are additional network modules added to checkpoint models. They are typically 5 – 300 MB. You must use them with a checkpoint model.


I have introduced Stable Diffusion models, 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.

Check out the Stable Diffusion Course for a step-by-step guided course.

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.


By Andrew

Andrew is an experienced engineer with a specialization in Machine Learning and Artificial Intelligence. He is passionate about programming, art, photography, and education. He has a Ph.D. in engineering.


  1. I am not a Stable Diffusion user yet. I am trying to rap my head around what is possible, first. As I read about it, obvious questions pop up. Search engines are not very helpful. Your articles have answered a lot of my questions. Thank you. A question I have not found an answer for is. Models are just fine tuning a base model. SD1.5 for example. Is there a way to train a model without the base model? I would like to get past any limitations caused by CompVis or Stability AI. Is it possible to make your own tiny SD1.5?

    1. It is possible to train a v1 model from scratch but requires significant skill and resources. The base model costed $600k to train for gpus alone.

      1. I’d like to have some information about the process, to have a better understanding of SD… not to actually train a new model. How is it done?

  2. I read the four “Absolute beginner” posts and I have a question that what is the good choice to read next tutorial?

  3. Of course it was not necessary. But it was appreciated. Don’t body shame. There might be women with large breasts reading this. They should feel as welcome and celebrated as anyone else.

    Please try to be more kind and considerate of others in the future. Thank you. Be well.

  4. For some models, when I click on the link, I don’t see a download button. How do I download these models? For example: inkpunk diffusion.

  5. Was it really necessary to use images of women with extensive cleavage to illustrate your post? Does this indicate you think your market is mostly men seeking images of women with exaggerated breasts?

    1. OP did not say anything about women with extensive cleavage in the article. Does this mean that you are assuming the gender of a person just by looking how big their breasts are?

    2. I will give you a free advice Marie. Next time, write your own instructions article and use images of your choice.
      I am thankful to the author for the hard work he put in to publish this very useful information. And frankly, i also liked his pictures, but that’s just my personal taste.

  6. hello i have problem

    C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui>git pull
    error: Your local changes to the following files would be overwritten by merge:
    Please commit your changes or stash them before you merge.
    Updating 5ab7f213..89f9faa6
    venv “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\venv\Scripts\Python.exe”
    Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
    Commit hash: 5ab7f213bec2f816f9c5644becb32eb72c8ffb89
    Installing requirements
    Launching Web UI with arguments:
    No module ‘xformers’. Proceeding without it.
    ControlNet v1.1.191
    ControlNet v1.1.191
    Error loading script:
    Traceback (most recent call last):
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\modules\”, line 256, in load_scripts
    script_module = script_loading.load_module(scriptfile.path)
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\modules\”, line 11, in load_module
    File “”, line 883, in exec_module
    File “”, line 241, in _call_with_frames_removed
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\scripts\”, line 16, in
    from imwatermark import WatermarkEncoder
    ModuleNotFoundError: No module named ‘imwatermark’

    Error loading script:
    Traceback (most recent call last):
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\modules\”, line 256, in load_scripts
    script_module = script_loading.load_module(scriptfile.path)
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\modules\”, line 11, in load_module
    File “”, line 883, in exec_module
    File “”, line 241, in _call_with_frames_removed
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\scripts\”, line 14, in
    from imwatermark import WatermarkEncoder
    ModuleNotFoundError: No module named ‘imwatermark’

    Loading weights [c0d1994c73] from C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\models\Stable-diffusion\realisticVisionV20_v20.safetensors
    Creating model from config: C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\configs\v1-inference.yaml
    LatentDiffusion: Running in eps-prediction mode
    DiffusionWrapper has 859.52 M params.
    Applying cross attention optimization (Doggettx).
    Textual inversion embeddings loaded(0):
    Model loaded in 8.0s (load weights from disk: 0.3s, create model: 0.5s, apply weights to model: 3.9s, apply half(): 0.7s, move model to device: 0.7s, load textual inversion embeddings: 1.9s).
    Traceback (most recent call last):
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\”, line 353, in
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\”, line 348, in start
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\”, line 302, in webui
    shared.demo = modules.ui.create_ui()
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\modules\”, line 461, in create_ui
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\modules\”, line 298, in initialize_scripts
    script = script_class()
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\scripts\”, line 209, in __init__
    self.preprocessor = global_state.cache_preprocessors(global_state.cn_preprocessor_modules)
    File “C:\stable-diffusion\stable-diffusion1\stable-diffusion-webui\scripts\”, line 22, in cache_preprocessors
    CACHE_SIZE = shared.cmd_opts.controlnet_preprocessor_cache_size
    AttributeError: ‘Namespace’ object has no attribute ‘controlnet_preprocessor_cache_size’
    Press any key to continue . . .

  7. Excuse me, sir. I want to add new model and VAE of like GhostMix. I already have inserted model and vae files into “stable-diffusion-webui/models/Stable-diffusion/” and “stable-diffusion-webui/models/VAE/” but it didn’t come out in the web UI.

    1. Try to refresh button? It should come up after restart.
      And check to see if you have downloaded the file correctly. The size of models should be at least 2GB.

  8. hwo do you know the trigger word for specific model, also how do you know who is in the model file.

  9. I downloaded F222 and it arrived as a zip file. When I unzip it, do I just use the file called ‘data’ and do I need to rename it?

  10. im currently stuck at this point its not downloading

    Already up to date.
    venv “C:\Users\User\stable-diffusion-webui\venv\Scripts\Python.exe”
    Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
    Commit hash: 22bcc7be428c94e9408f589966c2040187245d81
    Installing requirements for Web UI
    Launching Web UI with arguments: –xformers
    Loading weights [9e2c6ceff3] from C:\Users\User\stable-diffusion-webui\models\Stable-diffusion\f222.ckpt
    Creating model from config: C:\Users\User\stable-diffusion-webui\configs\v1-inference.yaml
    LatentDiffusion: Running in eps-prediction mode
    DiffusionWrapper has 859.52 M params.

  11. Thank you so much, by the way.

    Like a good little geek, I’ve looked at Reddit and such for help. But people throw all of these terms around, that I have no idea what means. Figured it would all make sense eventually, but finding someone to break it all down is a godsend.

  12. 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?

  13. 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”

    1. 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.

  14. 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.

    1. 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.

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