Stable Diffusion General Guide

Introduction

This and the attached rentrys will majorly cover topics related to using Stable Diffusion as a local installation on your computer. It will provide a general overview of the technology, its requirements and different UIs and tools to generate images offline. As such you must own a computer up to those requirements to be able to use these tools to their full extend.
There are online generation services for those that are currently unable to use a local installation, they are covered in their own section.

QUICKSTART

This section is for those who want to start as soon as possible and features detailed step-by-step instructions without in-depth explanations. The next section explains the various Stable Diffusion related options in more detail. This section assumes you have a current NVidia GPU with at least 8GB VRAM, a current CPU and at least 16GB of system RAM.

Step 1: Install reForge

Step 2: Download a model

There are a ton of models available, often with marginal differences, and new ones appear every week. Here is a quick selection of three sdg favorites with metadata, but also check this page for more suggestions.

Browse to the civitai page and click the "Download" button on the top right of the page. Put the downloaded safetensor file into <your SD base dir>\models\stable-diffusion and click the refresh button besides the "Stable Diffusion checkpoint" selection (if your UI is still open).

Step 3: Basic options

After a fresh install the default options of reForge are not suitable for current IllustriousXL/noobaiXL based models. Change your settings to the following new defaults as a starting point. These are not the end-all only settings that will work but a proven baseline from which you can experiment. Expand the "Hires. fix" to see the corresponding settings

Switch from the "txt2img" to the "img2img" tab, then open the "Inpaint" subtab

Change the following settings

Go to the "Settings" tab, scroll down until you reach the "Other" section on the left menu bar. Click on "Defaults", then scroll up again until you see the "View changes" and "Apply" button. Click "View changes" to review your settings, then click "Apply" to save them as defaults.

Step 4: Basic prompt

With this basic setup you can write a prompt and start generating.

This guide contains a lot of additional information and prompting tips and advanced topics you should take a look at when you have familiarized yourself with the UI a bit.

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Fundamentals

This section describes the base components that are currently relevant to get started in more detail. As the software landscape is always changing it may not cover any cutting edge developments but will be maintained to keep a good general overview.

Hardware requirements

In general, Stable Diffusion image generation is heavly optimized for NVidia graphics cards (GPUs) at this time. It is possible to utilize AMD or Intel ARC based graphic cards, but there are some things to consider and set up.
While by far the most important hardware part is your GPU, your system should have at least a 10th generation Intel quad core (eight cores recommended) or comparable AMD CPU, and 16-32GB system RAM is highly recommended.
Stable Diffusion can -with restrictions- work even on old 4GB VAM GPUs, but you will have very slow generation speeds and may not be able to use all of the features available. Nowadays it is recommended to have at least 8GB of VRAM, and 12GB will give you some buffer for advanced topics/tools like training loras. While you can use an old HDD drive to save your gens and for backup, the drive that your Stable Diffusion install and data resides on should be a fast SSD drive.

Software requirements

These guides will majorly cover installing and using Stable Diffusion based tools on a Microsoft Windows operatig system. Some links to get started on Linux will be linked below.

User Interface (UI)

There are quite a few different programs available for offline image generation that each have their own strength and weaknesses, some concerned about being very stable, some at the experimental cutting edge, part provide a standardized user interface, others give you maximum customizability.
This list covers some of the most popular ones

Automatic1111 / Forge / reForge (WebUI forks)

Automatic1111 was one of the first popular UIs but today development has largely halted and it is not recommended anymore. A fork called "Forge" was created that kept the same look and feel and compatiliby to A1111 plugins, but for a while development on Forge halted as well, leading to another fork called "reForge", also keeping the same look and feel and compatibility. In the meantime, development on Forge has resumed.

So which one should you use? As both Forge and reForge are under constant development, the specific details will shift, but as a general statement the developer of Forge tries to incorporate more cutting edge/experimental features sooner, which sometimes can lead to instabilities or incompatibilites with plugins, while the reForge developer tries to keep the UI in a more stable state with incorporating new features only after extensive testing.

Currently both UIs are considered stable in their production release for everyday use, and it comes mostly down to preference. reForge does come with a few useful plugins builtin that would need to be added to Forge manually. Both UIs have nearly the same look and feel and the same baseline features.

A comprehensive install guide is over here: https://rentry.org/sdginstall

ComfyUI

ComfyUI is an entirly different beast. It boasts a very high customizability, an extensive library of custom extensions, very good performance and a stable experience. All of this comes at the price of having a non-standardized and depending on use case very complex user interface that at first glance makes tasks that are simple buttonclicks in the WebUIs a daunting challenge.

If you want to customize your UI to your own preference in the most detailed way, there is nothing else like comfyUI.

A comprehensive install guide is over here: https://rentry.org/sdginstall
Repository of official workflow examples: https://github.com/comfyanonymous/ComfyUI_examples
Another anon has collected tips and workflows here: https://rentry.org/comfyui4a1111

InvokeAI

Targeting traditional artists, InvokeUI is very easy to setup and maintain, and provides a paid version with support besides the free full-featured community edition. Boasting a native UI instead of a web interface, it handles very smoothly.

Its look and feel and workflow is very different to WebUI and comfyUI ... tbc.

SD.next

SD.next is another WebUI fork that changed the look and feel a bit. It is listed here as it provides (native?) support for AMD and Intel hardware via special libraries. No practical experience yet, hopefully more to come.

Stability Matrix

A package manager for other UIs, also has its own UI
https://lykos.ai/

Krita plugin

https://github.com/Acly/krita-ai-diffusion

Photoshop plugin

https://github.com/AbdullahAlfaraj/Auto-Photoshop-StableDiffusion-Plugin

Model types

While Stable Diffusion provides the math and the User Interface is what you use to control it, a model contains all the information that is required to actually turn your prompt into an image. It is a large file that is the "memory" of all information that was fed into it during its training. As such, each model can have vastly different capabilites, knowledge and style, e.g. there might be models that concentrate on creating highly (photo)realistic images, others may be specialized in creating cartoons or highly stylized abstract art. Some models may not work well on their own (see loras) while others may be not very flexible in the output they can produce.

In general there are three important type of models

Base Models (SDXL1.0, SD1.5, Flux)

These models were trained with immense effort in datacenters by large corporations on millions of images. They form the base of bascially any model available today for private users and offline generation. Creating such a base model is not feasibly for anyone.

Finetunes (PonyDiffusionXL, Illustrious XL, NoobAI-XL)

A finetune takes a base model and trains/adds a lot of additional information to it. As the data added is a much smaller amount than a base model but still much larger than a merge model, it is still unfeasible for a single person/small group to create, but well funded teams are capable of doing so.
PonyDiffusionXL and IllustriousXL are two popular examples of finetunes of the SDXL base model, adding respectively furry and anime training data to the SDXL model.
A finetune usually has the implication that while new information is added to an existing model, the result will likely lose some of the original models capabilites as well, as the overall "capacity" is limited.

The currently popular noobAI-XL is special case as it is not a finetune of a base model but a finetune of the Illustrious-XL, which is a finetune itself. High-level, it added the furry training data to Illustrious.

Merge models

Merge models (sometimes calles style merges) are models that are not created by adding more original training data directly, but by merging two or more pre-existing models, or by merging an existing model and an existing Lora into a new model. This method, as it does not require any training, requires little resources and can be done by everyone. This process, while technically simple, is very complex to understand (see sep. guide).
These models are usually created to enforce a specific output style or to add a specific knowledge to an existing model. A popular example are the novaFurry XL models which provide a strong builtin art style but dont add any other knowledge.

Prompts

The prompt (sometimes called positive prompt) and the negative prompt is human-readable text that is provided by the user to steer the image creation. Without any prompt (called unconditional generation), the model will create a random image from its knowledge.
There are two common approaches for prompting, tag based and Natural Language Prompting (NLP). Which one can be used depends on the model. As a general guideline the majority of models based on SDXL are trained for tag-based prompts, while Flux is trained for NLP. SDXL models may understand some very basic NLP, but usually they still just filter the tags from it.

Tag based prompt example:

outside, day, sun
calico cat, sitting, stone wall, looking_at_viewer

Natural language prompt example:

A calico cat sitting on a stone wall outside on a bright summer day looking at the viewer

General prompting tips

Some general tips

• Do not over-prompt! The webUIs show you a token counter on the top right of the prompt and negative prompt box. Try to stay below 150 or even better below 75 overall tokens. Do not copy crazy long prompts from old metadata or civitai!
• The model will try to fit everything you tag in the image. Don't prompt for "pink pawpads" if you dont want to see some broken feet. Don't prompt for breasts if you want to have your character shown from behind, etc.
• Some tags imply other tags. If you prompt for hair_bun the model will very likely generate a character with hair, etc.
• If a tag contains parentheses (), then you must escape them with backslash \ in your prompt, otherwise they will get interpreted as weighing instructions, e.g. "watercolor (style)" must be written as "watercolor \(style\)".
• use tag weighing sparingly.
• Don't use BREAK with curent models.

Quality Tags

IllustriousXL and noobaiXL based models support/require a variety of quality tags which were used during training to sort images into certain quality categories. As such these supposedly help to steer the overall quality of the generated images towards better quality as well.

They have been the subject of constant experimentation and discussion as it is never 100% clear how much and what kind of influence these quality tags have. Sometimes using them can overwrite other (artist) styles, sometimes they can restrict the models flexibility, but overall they seem to indeed help increase the quality (if not necessarily the coherence) of the generated images. One common issue is that these quality tags are trained on a lot of anime data from danbooru, so their usage in general can steer the look in that direction. If you are using a specialized style merge model this effect is usualy reduced as the model style again overwrites the base style influence at least partially.

the noobaiXL creators have a high level overview of the tags they used documented here: https://huggingface.co/Laxhar/noobai-XL-Vpred-0.5?not-for-all-audiences=true#quality-tags

As a commonly accepted baseline you can start your prompt with

masterpiece, best quality, newest

and this negative prompt

worst quality, worst aesthetic

Testing showed that it does not matter at which place in the prompt the quality tags are placed.

An example grid of these tags, though changing anything on the prompt will always trigger a bit of randomness so to be really sure a larger amount of test cases need to be made. slight mistake on the grid, uncond in pos prompt means its just without the quality tags. uncond in neg means its completely empty

Artist Tags

All IllustriousXL based models know the artist tags of danbooru and all noobaiXL based models additionally know the artist tags of e621.

See here for some choice artist tag galleries:
https://rentry.org/jinkies-doomp
https://rentry.org/stablemondai-doomp

Artist style tags should be used like any other tag in the prompt

• iamtheartisttag,

without any additional words, so no "by " or "artist:" is needed.

• by iamtheartisttag,
• artist:iamtheartistag,

Some artist tags contain additional word(s) in parentheses, make sure to escape them properly or they will not be recognized well!

• iamtheartisttag \(artist\),

Style Tags

Besides the general quality tags and the styles that come via artist tags there are also some general style tags that can greatly influence the visual style of the image. How well these work depends on the model used, some of them e.g. can produce better realistic images cause they have been specifically trained to do so, while others may be better at toon style. Experiment or look at demo generations on e.g. civitai.

Some example style tags
1980s \(style\), 1990s \(style\), 2000s \(style\), 3D, anime screencap, art deco, color pencil, game cg, impressionism, kemono, manhwa, minimalism, oil painting, oekaki, official art, photorealistic, pixel art, pop art, realism, retro artstyle, sketch, tegaki, toon, traditional media, watercolor

Here is an example style grid using the StableMondAI model:

Sampler/Scheduler/Steps

This topic is ... pretty complicated and there are a lot of different opinions. There is no easy answer what sampler, scheduler and number of steps one should use in any given circumstance as it's dependent on different factors and personal preference. Usually a model comes with a recommendation on what combination to use, and that is always a good starting point to experiment.

On a high level, the sampler and scheduler control how much noise is removed in a specific way from the image during each of the steps. As such, the combination of these options must fit together, as some samplers need a higher or lower amount of steps than others, some samplers work well only with specific schedulers, and then again specific models (e.g. lightning) need a specific amount of steps to generate good images.

If there is no information available from the model creator, you can use the x/y/z plot scripts to easily test multiple combinations. For some of the currently popular models these grids are available here:
https://rentry.org/truegrid-models

Resolution (width, height)

While the UIs will allow you to choose any arbitrary resolution for your images not all of them work well. The models are trained on training data in specific resolutions, or rather megapixels, and if your rawgen resolution differs too much from that it will result in lower quality or incoherent images.
Nearly all SDXL based models (PonyXL, IllustriousXL, noobaiXL) are trained on images of about 1 megapixel resolution, so using one of the following settings is recommended:

Seed

The seed is used to create the random noise from which the scheduler and sampler create the final image during the generation process. seed is a random positive number, and by default a new one is used for each new generation process. This is indicated by a "-1" in the seed field:
Random seed:

Sometimes it might be appropriate to reuse the last seed for the next generation process, or to reproduce an earlier image. You can click the green "Recycle" button next to the seed field or enter it manually:
Fixed seed:

To discard the fixed seed and use a random one for the next generation, click the dice button or write "-1" into the field.

Using a fixed seed can be useful to "prompt tweak" if you want to make limited changes to your generation by changing some tags.

NOTE: The value if the seed is just one parameter that influences the generation process, changing the prompt, CFG, sampler or even just the resolution might have a large impact on the final image even if the seed is unchanged. (link to variation seed)

CFG

The CFG scale (classifier-free guidance scale) is another mystery topic. While the high-level description is "The Classifier-Free Guidance (CFG) scale controls how closely a prompt should be followed", you would think to crank it up all the way to eleven, because of course you want your prompt to be followed? Wrong.

Usually, again, the model will come with a recommended CFG range to use, and while there are always edge cases for exceeding these recommendations, it is usually best to follow them. Some samplers (e.g. the CFG++) samplers have specific requirements for the CFG scale setting as well.

If you are unsure or no data is available from the model creator, you can start with a value between 4 and 7 which usually works ok with most models, create a x/y/z plot or for some of the currently popular models these grids are available here:
https://rentry.org/truegrid-models

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Advanced Tools

Hiresfix

Upscaler

Does it matter? For hires.fix and img2img upscales, the answer is likely "no". The upscaler is responsible for increasing the resolution of the image, but if you denoise the image afterwards the negligible differences of modern upscalers are irrelevant. Whatever small differences appear are more likely to be caused by AI RNG during the denoise step than the upscaler.

If you try to upscale by a very large factor (>1.75) or use the "Extras" tab to do pure upscaling without denoising the image then the choice of upscaler may become relevant again. See here for more information.

ADetailer

LoRa (& LyCoris & LoHa)

(While there are technical differences between the three, for the sake of this guide we will call them all LoRa)
If the model is the encyclopedia of all the knowledge you can leverage, a LoRa is a small addendum that adds a specific part to the whole. This can be used to add information about a character, a concept or an overall style to the existing model at runtime (during the generaton process) without the need to modify the model itself. As such, the use of LoRas is highly flexible, and you can even combine multiple of them.

There is an immense amount of LoRas out there, and as such the usual caveat applies, as in that many are not of very good quality. Additionally, a LoRa must fit the model you are using. For the purpose of this guide one main to consider is that LoRas created for the older SD1.5 based models CANNOT be used with SDXL1.0 based models (pony, IllustriousXL, noobaiXL) and vice versa.

As pony, IllustriousXL, noobaiXL are all SDXL1.0 based LoRas trained on a different chain can be used, but their effectivness varies greately, .... tbc

All current LoRas should come as a .safetensor file at usually between 50mb and 500mb size. Put them in this folder stable-diffusion-webui-reForge\models\Lora. After that you can load them by clicking on them in the "Lora" tab. Clicking on it again will remove it from the prompt, which you can also achieve by just deleting the string added.

By default the LoRa is loaded with a weight of ":1". Usually the creator of the LoRa gives a recommendation on how much weight to use, it can be beneficial to reduce LoRa weight to decrease the influence it applies during the generation process. Also read the section on LoraCTL for advanced control over how the LoRa is applied.

Variation Seed

Advanced Prompt syntax

The webUI forks (A1111,Forge,reForge) support advanced prompt syntax which is described here. ComfyUI and InvokeAI support other/different syntax, separate guides or links will follow.

Weighting (Attention/emphasis)

https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#attentionemphasis
Using () in the prompt increases the model's attention to enclosed words, and [] decreases it. You can combine multiple modifiers:
(word) - increase attention to word by a factor of 1.1
((word)) - increase attention to word by a factor of 1.21 (= 1.1 * 1.1)
[word] - decrease attention to word by a factor of 1.1
(word:1.5) - increase attention to word by a factor of 1.5
(word:0.25) - decrease attention to word by a factor of 4 (= 1 / 0.25)

If you want to use parentheses in your prompt, e.g. if they belong to a tag, you must escape them with \(word\). This is relevant if the tag itself contains them, e.g. for artist tags or character tags.

• loona (helluva boss) - this can be interpreted as having a tag "loona" and a tag "helluva boss" with putting weight on the helluva boss
• loona \(helluva boss\) - this will be interpreted as the tag "loona (helluva boss)"

Note that both may work depending on how the specific tag was trained, how much training data went into the model with or without the words in parentheses etc. It is recommended to use the tags as they appear on danbooru/e621, so if the tag contains parentheses over there you need to take care to escape them in your prompt properly.

Editing / Scheduling

https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#prompt-editing
Prompt Editing allows you to change parts of your prompt during the generation.
[TAG1:TAG2:when] - replaces TAG1 with TAG2 at number of steps (when)
[TAG:when] - adds TAG to the prompt after a number of steps (when)
[TAG::when] - removes TAG from from the prompt after a number of steps (when)

The exact time (step) when the change occurs (when) can either be set either as

• positive integer - make the change at exactly this step (regardless of total number of steps)
• decimal number - make the change at the percentage of steps, 0.3 for example after 30%

Alternating tags

https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alternating-words
[cow|horse|pig|cat]
Using this syntax the tag is swapped every step to the next one separated by |, meaning on step 1 the prompt will contain "cow", on the second step "horse", etc. The fifth step will then revert to "cow".

A common use case for this feature is to create hybrid characters.

Dynamic Prompts

https://github.com/adieyal/sd-dynamic-prompts
Dynamic Prompts is an extension that allows for more possible ways of varying and randomizing prompts. It has far too much to cover here, so check out the link above if you want to know more. I will only mention how to use wildcards in the following.

Wildcards are simple text files that, if called in the prompt field, pick a random line of the file and replace the wildcard's name in the prompt with whatever is listed in the selected line. This allows for random changes of a prompt that vary from seed to seed. Wildcards need to be placed in stable-diffusion-webui\extensions\sd-dynamic-prompts\wildcards.
One example could be a list of artists taken from the fluffyrock.csv, which you can use to randomly select artists for artist mixing.

Installing Dynamic Prompts adds a Wildcards Manager tab to the WebUI. From here, you can download collections of wildcards from the GitHub repo to your local install. These collections contain various wildcards for artists, styles, and much more which can give you a solid base to work with.

Wildcards are used by writing their name, surrounded by two underscores. A wildcard named "e621artists" containing three line-seperated artist names would be called by writing __e621artists__ in the prompt field; one of the artists would be picked for each image you make.

Regional Prompting

Forge Couple

Some guides another anon created until I adapt them, click for large

Regional Prompter

LoRa Scheduling (LoraCTL)

This extension allows to schedule the application and strength of a lora during the generation process. A common use case is to apply a style lora only after a certain percentage of the steps are done to ensure the flexibility of the base model is not constrained during the critical first steps.
When you enable this extension, you can still use the normal LoRa weighting which will load it from step 1 with a static weight, or the advanced syntax which gives you fine grained control. To achieve that, you replace the static weight with a comma separated list of "(weight)@(step),(weight)@(step),(weight)@(step),....".
The weight is the same weighting you normally use, while step can be defined as

• positive integer - make the change at exactly this step (regardless of total number of steps)
• decimal number - make the change at the percentage of steps, 0.3 for example after 30%

<lora:tool-noob-Mega_Fluffy_for_Illustrious V2:0@0,0.8@0.3,1@0.9>
In this example the LoRa is not loaded at the start of the generation process (weight 0 @ step 0), instead it is loaded at 30% of the generation steps and applied with 0.8 weight (0.8 @ 0.3) and then the weight is increased to 1 at 90% of the steps (1 @ 0.9)

img2img

Sketch img2img

Inpainting

https://rentry.org/fluffscaler-inpaint

Inpaint tab

Image editor

Controlnet

https://rentry.org/IcyIbis-Quick-ControlNet-Guide

IPAdapter

https://civitai.com/models/1233692/style-ipadapter-for-noobai-xl
https://civitai.com/models/1000401/noob-ipa-mark1

VectorScopeCC

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FAQ

Resources

Various resources, tools and links

Catbox

Catbox is a free image and file sharing service
URL: https://catbox.moe/
Uploading images to 4chan or imgbox deletes the generation metadata from the image. Uploading it to catbox allows sharing this information with other people. Filesize limit is 200mb, if you have a larger file to share you can use https://litterbox.catbox.moe/ to upload a file up to 2gb in size which is stored for up to three days (you MUST set the expiration limit BEFORE starting the upload).

Catboxanon maintains a 4chanX extension script that allows to directly upload images to catbox and 4chan simultanously, as well as viewing metadata of these catboxed images in the browser
https://gist.github.com/catboxanon/ca46eb79ce55e3216aecab49d5c7a3fb
Follow the instructions in the GitHub to install it.

Online generation services

Civitai.com

• Largest repository of models, lora and other resources
• Online generation of t2i, i2i, t2v and i2v with models that are otherwise only available via (paid) API access (e.g. Flux Ultra)
• Freemium model with (confusing) dual premium currency buzz. Can collect free daily buzz for generation services
  https://www.civitai.com

Tensor.art

• Lots of models and loras, but can be set to exclusive mode by uploader so only available for onsite generation
• Online generation of t2i and i2i with advanced editing capabilities (inpaint)
• Freemium model with onsite currency, free amounts for (daily) activities
  https://tensor.art

Perchance.org

• Free, but very limited
  https://perchance.org/ai-furry-generator

frosting.ai

• Free generation only possible with old (SD1.5?) based models in low quality
• Subscription service for newer SDXL based models
• New experimental video generation service
  https://frosting.ai

NovelAI

• Subscription only (?) with free monthly premium currency
• Provides own models which are supposed to be pretty good
  https://novelai.net/

LoRa collects

Most of the sdg LoRa masters upload their creations to civitai so there is no more big collects lora rentry necessary.
Some civitai links (tbc)
https://civitai.com/user/cloud9999
https://civitai.com/user/homer_26 alternate: https://mega.nz/folder/lnZkTKQQ#gLPWq0TQ6-yyMjzSexa7BQ
https://civitai.com/user/KumquatMcGee
https://civitai.com/user/gokro_biggo
Professor Harkness
https://www.mediafire.com/folder/30caesntlcq6u/LoRA's#myfiles
A collection of LoRas that were only shared via Catbox links
https://huggingface.co/datasets/Xeno443/sdg_Lora_Collect/tree/main

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Useful links

Original trashfaq:
https://rentry.org/trashfaq
https://rentry.org/trashcollects
https://rentry.org/trashcollects_loras

Comparison grids:
https://rentry.org/truegrid

Automatic1111 feature list (still relevant for Forge/reForge)
https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features

RedRocket Joint Tagger Project (autotagger):
https://huggingface.co/RedRocket
https://rentry.org/basic-autotagger-guide

Awesome ComfyUI Custom Nodes Megalist:
https://github.com/ComfyUI-Workflow/awesome-comfyui?tab=readme-ov-file#new-workflows

Lama-Cleaner:
https://huggingface.co/spaces/Sanster/Lama-Cleaner-lama
https://github.com/light-and-ray/sd-webui-lama-cleaner-masked-content

Prompt Squirrel:
https://huggingface.co/spaces/FoodDesert/Prompt_Squirrel

Lora Metadata Viewer:
https://xypher7.github.io/lora-metadata-viewer/

AI Image Metadata Editor
https://xypher7.github.io/ai-image-metadata-editor/

Standalone SD Prompt Reader:
https://github.com/receyuki/stable-diffusion-prompt-reader

Backup of thread OP:
https://rentry.org/sdg-op

NAI dynamic prompts wildcard lists:
https://rentry.org/NAIwildcards

/g/ /sdg/ large link collection:
https://rentry.org/sdg-link

PonyXL info rentry:
https://rentry.org/ponyxl_loras_n_stuff

Nomos8kSCHAT-L:
https://openmodeldb.info/models/4x-Nomos8kSCHAT-L

For later:
https://civitai.com/models/1003088/mega-scaly-for-illustrious-xl
https://civitai.com/models/890007/mega-fluffy-for-illustrious-xl
https://civitai.com/models/1073409/mega-feathery-for-illustrious-xl

A quick visual guide to what's actually happening when you generate an image with Stable Diffusion:
https://www.reddit.com/r/StableDiffusion/comments/13belgg/a_quick_visual_guide_to_whats_actually_happening/

Steve Mould randomly explains the inner workings of Stable Diffusion
https://www.youtube.com/watch?v=FMRi6pNAoag

Wildcard lists:
Species https://files.catbox.moe/06i981.txt
Outfits https://files.catbox.moe/97y4r8.txt
Art Styles https://files.catbox.moe/k9l1c6.txt, https://files.catbox.moe/zr30th.txt
Anime Chars https://files.catbox.moe/2kcyyc.txt
Animal Crossing Chars https://files.catbox.moe/tcswhx.txt
Pokemons (?) https://files.catbox.moe/dma3tl.txt
Assorted danbooru artists https://files.catbox.moe/tfm6gg.txt
Waifu Generator (Wildcards) https://civitai.com/models/147399/waifu-generator-wildcards
e621 above 50 posts https://files.catbox.moe/y352dr.txt
e621 50-500 posts https://files.catbox.moe/p64u2f.txt
Danbooru above 50 posts https://files.catbox.moe/t4s0za.txt
Danbooru 50-500 posts https://files.catbox.moe/4m5c3p.txt

Illustrious XL v0.1 Visual Dictionary
https://rentry.org/w4dzqdri

Master Corneo's Danbooru Tagging Visualization for PonyXL/AutismMix
https://rentry.org/corneo_visual_dictionary

Using Blender to create consistent backgrounds:
https://blog.io7m.com/2024/01/07/consistent-environments-stable-diffusion.xhtml

List of colors
https://en.wikipedia.org/wiki/List_of_colors_(alphabetical)

BIGUS'S GUIDE ON HOW TO TRAIN LORAS
https://files.catbox.moe/0euyoe.txt

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