This is a curated collection of relevant links and information. Outdated information is put into one of the collections in Archives for archival or sorting purposes.

This collection is currently hosted on the SD Goldmine rentry, the SD Updates rentry (3), and Github

All rentry links are ended with a '.org' here and can be changed to a '.co'. Also, use incognito/private browsing when opening google links, else you lose your anonymity / someone may dox you


If you have information/files not on this list, have questions, or want to help, please contact me with details

Trip: questianon !!YbTGdICxQOw
Discord: malt#6065
Reddit: u/questianon

How to use this resource

The goldmine is a general repository of links that might be helpful. If you are a newcomer to Stable Diffusion, it's highly recommended to use start from the beginning.

If something is missing from here that was here before, try checking


Items on this list with a πŸ₯’ next to them represent my top pick for the category. This rating is entirely opinionated and represents what I have personally used and recommend, not what is necessarily "the best".


  1. Ckpts/hypernetworks/embeddings and things downloaded from here are not interently safe as of right now. They can be pickled/contain malicious code. Use your common sense and protect yourself as you would with any random download link you would see on the internet.
  2. Monitor your GPU temps and increase cooling and/or undervolt them if you need to. There have been claims of GPU issues due to high temps.


Don't forget to git pull to get a lot of new optimizations + updates. If SD breaks, go backward in commits until it starts working again


  • If on Windows:
    1. navigate to the webui directory through command prompt or git bash
      a. Git bash: right click > git bash here
      b. Command prompt: click the spot in the "url" between the folder and the down arrow and type "command prompt".
      c. If you don't know how to do this, open command prompt, type "cd [path to stable-diffusion-webui]" (you can get this by right clicking the folder in the "url" or holding shift + right clicking the stable-diffusion-webui folder)
    2. git pull
    3. pip install -r requirements.txt
  • If on Linux:
    1. go to the webui directory
    2. source ./venv/bin/activate
      a. if this doesn't work, run python -m venv venv beforehandww
    3. git pull
    4. pip install -r requirements.txt





Getting Started


AMD isn't as easy to setup as NVIDIA.


Honestly I don't know what goes here. I'll add a guide if I remember


CPU is less documented.

Apple Silicon


Why are my outputs black? (Any card)

Add " --no-half-vae " (remove the quotations) to your commandline args in webui-user.bat

Why are my outputs black? (16xx card)

Add " --precision full --no-half " (remove the quotations) to your commandline args in webui-user.bat


These are repositories containing general AI knowledge





These are documents containing general prompting knowledge





Prompt Database




Tag Rankings

Tag Comparisons





Other Comparisons


Extensions are searchable through AUTOMATIC1111's extension browser



Text Files

Plugins for External Apps

I didn't check the safety of these plugins, but you can check the open-source ones yourself





everything past here is UNSORTED

Prompt word/phrase collection:
Japanese prompt generator:
Build your prompt (chinese):
NAI Prompts:
Prompt similarity tester:

Multilingual study:

Aesthetic value (imgs used to train SD):
Clip retrieval (text to CLIP to search):

Aesthetic scorer python script:
Another scorer:
Supposedly another one?:
Another Aesthetic Scorer:

Prompt editing parts of image but without using img2img/inpaint/prompt editing guide by anon:

Tip Dump:
Info dump of tips:
Tip for more photorealism:

  • TLDR: add noise to your img before img2img

NAI prompt tips:
NAI tips 2:

Masterpiece vs no masterpiece:

DPM-Solver Github:
Deep Danbooru:

Embedding tester:

Collection of Aesthetic Gradients:

Seed hunting:

  • By nai speedrun asuka imgur anon:
    >made something that might help the highres seed/prompt hunters out there. this mimics the "0x0" firstpass calculation and suggests lowres dimensions based on target higheres size. it also shows data about firstpass cropping as well. it's a single file so you can download and use offline. picrel.
    >view code and download from
    >for example you can run "firstpass" lowres batches for seed/prompt hunting, then use them in firstpass size to preserve composition when making highres.

Script for tagging (like in NAI) in AUTOMATIC's webui:
Danbooru Tag Exporter:
Tags (latest vers):
Basic gelbooru scraper:
Scrape danbooru images and tags like for e621 for tagging datasets:

Python script of generating random NSFW prompts:
Prompt randomizer:
Prompt generator:

  • apparently UMI uses these?

script that pulls prompt from and based on search terms:
randomize generation params for txt2img, works with other extensions:

Collection + Info:
Deforum (video animation):


Wildcard script + collection of wildcards:
Symmetric image script (latent mirroring):

macOS Finder right-click menu extension:
Search danbooru for tags directly in AUTOMATIC1111's webui extension:

  • Supports post IDs and all the normal Danbooru search syntax

Clip interrogator:
2 (apparently better than AUTO webui's interrogate):,

  • AUTOMATIC1111 webui modification that "compensates for the natural heavy-headedness of sd by adding a line from 0 sqrt(2) over the 0 74 token range (anon)" (evens out the token weights with a linear model, helps with the weight reset at 75 tokens (?))


Tutorial + how to use on ALL models (applies for the NAI vae too):

Booru tag scraping:

Creating fake animes:

Models, Embeddings, and Hypernetworks

Downloads listed as "sus" or "might be pickled" generally mean there were 0 replies and not enough "information" (like training info). or, the replies indicated they were suspicious. I don't think any of the embeds/hypernets have had their code checked so they could all be malicious, but as far as I know no one has gotten pickled yet

All files in this section (ckpt, vae, pt, hypernetwork, embedding, etc) can be malicious:, Make sure to check them for pickles using a tool like or


Model pruner:

πŸ₯’ CivitAI, an art-focused model repo alternative to HF:
πŸ₯’ HuggingFace, the standard model repo:
Collection of potentially dangerous models:

EveryDream Trainer

All files in this section (ckpt, vae, pt, hypernetwork, embedding, etc) can be malicious:, Make sure to check them for pickles using a tool like or

Download + info + prompt templates:

Dreambooth Models:

All files in this section (ckpt, vae, pt, hypernetwork, embedding, etc) can be malicious:, Make sure to check them for pickles using a tool like or



Use a download manager to download these. It saves a lot of time + good download managers will tell you if you have already downloaded one

All files in this section (ckpt, vae, pt, hypernetwork, embedding, etc) can be malicious:, Make sure to check them for pickles using a tool like or

You can check .pts here for their training info using a text editor


If a hypernetwork is <80mb, I mislabeled it and it's an embedding

Use a download manager to download these. It saves a lot of time + good download managers will tell you if you have already downloaded one

All files in this section (ckpt, vae, pt, hypernetwork, embedding, etc) can be malicious:, Make sure to check them for pickles using a tool like or
Senri Gan:
Big dumpy of a lot of hypernets (has slime too):
Collection of asanuggy + maybe some more:

Chinese telegram (uploaded by telegram anon): magnet:?xt=urn:btih:8cea1f404acfa11b5996d1f1a4af9e3ef2946be0&dn=ChatExport%5F2022-10-30&

I've made a full export of the Chinese Telegram channel.

It's 37 GB (~160 hypernetworks and a bunch of full models).
If you don't want all that, I would recommend downloading everything but the 'files' folder first (like 26 MB), then opening the html file to decide what you want.

Mogubro + constant updates (dead):


Train stable diffusion model with Diffusers, Hivemind and Pytorch Lightning:


Image tagger helper:

Euler vs. Euler A:

anything.ckpt comparisons
Old final-pruned: (embed)
v3-pruned-fp16: (embed)
v3-pruned-fp32: (embed)
v3 full or whatever: (embed)



I want to run this, but my computer is too bad. Is there any other way?
Check out one of these (I did not use most of these, so I can't attest to their safety):


Check out and for other questions

What's all the new stuff?

Check here to see if your question is answered:

What's the "Hello Asuka" test?

It's a flawed test to see if you're able to get a 1:1 recreation with NAI and have everything set up properly. Coined after asuka anon and his efforts to recreate 1:1 NAI before all the updates. Deviations arise with certain systems.

Refer to

What is pickling/getting pickled?

ckpt files and python files can execute code. Getting pickled is when these files execute malicious code that infect your computer with malware. It's a memey way of saying you got hacked.

How do I directly check AUTOMATIC1111's webui updates?

For a complete list of updates, go here:

What do I do if a new updates bricks/breaks my AUTOMATIC1111 webui installation?

Go to
See when the change happened that broke your install
Get the blue number on the right before the change
Open a command line/git bash to where you usually git pull (the root of your install)
'git checkout <blue number without these angled brackets>'
to reset your install, use 'git checkout master'

git checkout . will clean any changes you do

Another Guide:

What is...? (by anon)

What is a VAE?

Variational autoencoder, basically a "compressor" that can turn images into a smaller representation and then "decompress" them back to their original size. This is needed so you don't need tons of VRAM and processing power since the "diffusion" part is done in the smaller representation (I think). The newer SD 1.5 VAEs have been trained more and they can recreate some smaller details better.

What is pruning?

Removing unnecessary data (anything that isn't needed for image generation) from the model so that it takes less disk space and fits more easily into your VRAM

What is a pickle, not referring to the python file format? What is the meme surrounding this?

When the NAI model leaked people were scared that it might contain malicious code that could be executed when the model is loaded. People started making pickle memes because of the file format.

Why is some stuff tagged as being 'dangerous', and why does the StableDiffusion WebUI have a 'safe-unpickle' flag? -- I'm stuck on pytorch 1.11 so I have to disable this

Safe unpickling checks the pickle's code library imports against an approved list. If it tried to import something that isn't on the list it won't load it. This doesn't necessarily mean it's dangerous but you should be cautious. Some stuff might be able to slip through and execute arbitrary code on your computer.

Is the rentry stuff all written by one person or many?

There are many people maintaining different rentries.

What's the difference between embeds, hypernetworks, and dreambooths? What should I train?

I've tested a lot of the model modifications and here are my thoughts on them:
embeds: these are tiny files which find the best representation of whatever you're training them on in the base model. By far the most flexible option and will have very good results if the goal is to group or emphasize things the model already understands
hypernetworks: there are like instructions that slightly modify the result of the base model after each sampling step. They are quite powerful and work decently for everything I've tried (subjects, styles, compositions). The cons are they can't be easily combined like embeds. They are also harder to train because good parameters seem to vary wildly so a lot of experimentation is needed each time
dreambooth: modifies part of the model itself and is the only method which actually teaches it something new. Fast and accurate results but the weights for generating adjacent stuff will get trashed. These are gigantic and have the same cons as embeds




SDupdates 1 for v1 of sdupdates
SDupdates 2 for v2 of sdupdates
SDump 1 for stuff that's unsorted and/or I have no idea where to sort them
Soutdated 1 for stuff that's outdated

Pub: 07 Nov 2022 17:40 UTC
Edit: 04 May 2023 18:00 UTC
Views: 284516