Will update this post with links of stuff I have from the “# cool-finds” on the fusion gen discord: https://discord.gg/exBKyyrbtG

I use that space to yeet links that might be useful. I will try to organize the links here a few items at a time.

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Cover image prompt "[ #FTSA# : "These are real in long pleated skirt and bangs standing in ruined city Monegasque by ilya kushinova they are all parc. Pretty cute , huh? (leigh cartoon dari courtney-anime wrath art style :0.3) green crowded mountains and roots unique visual effect intricate futuristic hair behind ear hyper realistic5 angry evil : 0.1] "

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Prompt syntax

Perchance prompt syntax: https://perchance.org/prompt-guide

A111 wiki : https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features

Prompt parser.py : https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/prompt_parser.py

Image Interrogators

Converts an image to a prompt

Pharmapsychotic (most popular one) : https://huggingface.co/spaces/pharmapsychotic/CLIP-Interrogator

Danbooru tags : https://huggingface.co/spaces/hysts/DeepDanbooru

How Stable Diffusion prompts works

Just good, technical source material on the “prompt text” => image output works.

https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted_prompts

https://arxiv.org/abs/2406.02965

This video explains cross-attention : https://youtu.be/sFztPP9qPRc?si=jhoupp4rPfJshj8V

Sampler guide: https://stablediffusionweb.com/blog/stable-diffusion-samplers:-a-comprehensive-guide

AI chat

Audio SFX/ voice lines :

https://www.sounds-resource.com/

https://youtube.com/@soundmefreelyyt?si=yjUPqUVJA7JmUXQC

Lorebooks : https://www.chub.ai/

Online Tokenizer

https://sd-tokenizer.rocker.boo/

The Civitai prompt set

In a separate category because of how useful it is.

The best/largest set of prompts for SD that can be found online , assuming you can find a way to filter out all the “garbage” prompts. Has a lot if NSFW items.

https://huggingface.co/datasets/AdamCodd/Civitai-8m-prompts

The set is massive so I advice using Google colab to avoid filling up your entire harddrive with the .txt documents

I’ve split a part of the set into more managable 500MB chunks for text processing : https://huggingface.co/codeShare/JupyterNotebooks/tree/main

Prompt Styles

People who have crammed different artists / styles into SD 1.5 and/or SDXL and made a list of what “sticks” , and writtend the results in lists.

https://lightroom.adobe.com/shares/e02b386129f444a7ab420cb28798c6b6 https://cheatsheet.strea.ly/

https://github.com/proximasan/sdxl_artist_styles_studies

https://huggingface.co/spaces/terrariyum/SDXL-artists-browser

https://docs.google.com/spreadsheets/d/1_jgQ9SyvUaBNP1mHHEzZ6HhL_Es1KwBKQtnpnmWW82I/htmlview#gid=1637207356

https://weirdwonderfulai.art/resources/stable-diffusion-xl-sdxl-art-medium/

https://rikkar69.github.io/SDXL-artist-study/

https://medium.com/@soapsudtycoon/stable-diffusion-trending-on-art-station-and-other-myths-c09b09084e33

https://docs.google.com/spreadsheets/u/0/d/1SRqJ7F_6yHVSOeCi3U82aA448TqEGrUlRrLLZ51abLg/htmlview

https://stable-diffusion-art.com/illustrated-guide/

https://rentry.org/artists_sd-v1-4

https://aiartes.com/

https://stablediffusion.fr/artists

https://proximacentaurib.notion.site/e28a4f8d97724f14a784a538b8589e7d?v=42948fd8f45c4d47a0edfc4b78937474

https://sdxl.parrotzone.art/

https://www.shruggingface.com/blog/blending-artist-styles-together-with-stable-diffusion-and-lora

3 Rules of prompting

There is no correct way to prompt.
Stable diffusion reads your prompt left to right, one token at a time, finding association from the previous token to the current token and to the image generated thus far (Cross Attention Rule)
Stable Diffusion is an optimization problem that seeks to maximize similarity to prompt and minimize similarity to negatives (Optimization Rule)

The SD pipeline

For every step (20 in total by default) :

Prompt text => (tokenizer)
=> Nx768 token vectors =>(CLIP model) =>
1x768 encoding => ( the SD model / Unet ) =>
=> Desired image per Rule 3 => ( sampler)
=> Paint a section of the image => (image)

Latent space properties

Weights for token A = assigns magnitude value to be multiplied with the 1x768 token vector A. By default 1.

Direction of token A = The theta angle between tokens A and B is equivalent to similarity between A and B. Calculated as the normalized dot product between A and B (cosine similarity).

CLIP properties (used in SD 1.5 , SDXL and FLUX)

The vocab.json = a list of 47K tokens of fixed value which corresponds to english words , or fragments of english words.

ID of token A = the lower the ID , the more “fungible” A is in the prompt.

The higher the ID , the more “niche” the training data for token A will be

Perchance sub-generators (text-to-image)

The following generators contain prompt items which you may use for your own T2i projects. These ones are recently updated to allow you to download their contents as a JSON file. I’m writing these here to keep track of generator that are updated vs. non-updated.

For the full list of available datasets , scroll through the code on the fusion gen :

https://perchance.org/fusion-ai-image-generator

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https://perchance.org/fusion-t2i-prompt-features-1

https://perchance.org/fusion-t2i-prompt-features-2

https://perchance.org/fusion-t2i-prompt-features-3

https://perchance.org/fusion-t2i-prompt-features-4

https://perchance.org/fusion-t2i-prompt-features-5

https://perchance.org/fusion-t2i-prompt-features-6

https://perchance.org/fusion-t2i-prompt-features-7

https://perchance.org/fusion-t2i-prompt-features-8

https://perchance.org/fusion-t2i-prompt-features-9