/sdupdates

A mega collection of all resources and news related to Stable Diffusion. Focused around AUTOMATIC1111's webui (https://github.com/AUTOMATIC1111/stable-diffusion-webui)

The UnlicenseUnlicense

SD Updates (3)

->Only news here<- ->Find downloads and links here: https://rentry.org/sdgoldmine<- ->Old stuff here https://rentry.org/sdupdates2 and here https://rentry.org/sdupdates<-

!!! danger Warnings:

1. Ckpts/hypernetworks/embeddings and scripts 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.

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

Contact

If you have information/files (e.g. embed) not on this list, have questions, or want to help, please contact me with details

Socials: Trip: questianon !!YbTGdICxQOw Discord: malt#6065 Reddit: u/questianon Github: https://github.com/questianon Twitter: https://twitter.com/questianon)

!!! note 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 Instructions: * 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 beforehand 3. git pull 4. pip install -r requirements.txt

!!! info

**Notable upcoming events:**


**Waifu Diffusion v1.4 is coming out on December 26th**
* WD 1.4 information provided to me:
	* New Deepdanbooru for better tagging (prerelease right now)
	* much better hands - look at 'Cafe Unofficial Instagram TEST Model Release' for a sample of what it can do in an unfinished model
	* Trained off SD 1.5
	* Creator: "In terms of general flexibility of being able to prompt a wide range of things, wd1.4 should be better than everything" (planned to supercede all current models, including NAI and anything.ckpt, to the point where you don't need to merge)
	* Creator: "we may create our own version of hypernetworks and create fine tunes for anime and realistic styles"
	* Creator: the instagram model training includes improvements such as:
		1. dynamic image aspect training (as in we trained images with ZERO cropping, the entire image is fed into SD all at once, even if it's landscape or portrait)
		2. unconditional training such that the model can somewhat self improve
		3. higher resolutions during training (640x640 max)
		4. much faster training code (6-8x performance increase)
		5. better training hyperparameters
		6. automated blip captioning of all images
	* Dataset and associated tags will be public
	* Haru and Cafe came up with a temporary plan that may be able to drastically improve the performance of clip without having to retrain clip from scratch, though it'll have to happen after wd1.4
	* to prevent bleed from the images, each source will have a tag associated with it in the caption data when fed into SD

11/26 to 12/12

11/25+11/26

11/24

  • SD Training Labs is going to conduct the first global public distributed training on November 27th
    • Distributed training information provided to me:
      • Attempted combination of the compute power of over 40+ peers worldwide to train a finetune of Stable Diffusion with Hivemind
      • This is an experimental test that is not guaranteed to work
      • This is a peer-to-peer network.
        • You can use a VPN to connect
        • Run inside an isolated container if possible
        • Developer will try to add code to prevent malicious scripting, but nothing is guaranteed
      • Current concerns with training like this:
        • Concern 1 - Poisoning: A node can connect and use a malicious dataset hence affecting the averaged gradients. Similar to a blockchain network, this will only have a small effect on the averaged weights. The larger the amount of malicious nodes connected, the more power they will have on the averaged weights. At the moment we are implementing super basic (and vague) discord account verification.
        • Concern 2 - RCE: Pickle exploits should not be possible but haven't been tested.
        • Concern 3 - IP leak & firewall issues: Due to the structure of hivemind, IPs will be seen by other peers. You can avoid this by seting client-only mode, but you will limit the network reach. IPFS should be possible to be used to avoid firewall and NAT issues but doesn't work at the moment
  • Unstable Diffusion launching Kickstarter on December 9th to fund the research and development of AI models fine-tuned and trained on extremely large datasets specifically curated on NSFW
  • Current implementations (WIP or not) of getting SD V2 on AUTOMATIC1111's webuiL

Rest of 11/22 + 11/23

11/19 (continued) + 11/20 + 11/21 + some of 11/22

11/19

11/14+11/15+11/16+11/17+11/18 (sdg + hdg done)

11/13+11/14

11/11+11/12

11/10

11/9+11/8

11/8+11/7

11/7

11/5 continued+11/6

11/5

11/4

11/3

11/2

11/1

10/31

train multiple denoisers, use one for the starting few steps to form rough shapes, use one for the last few steps to finalize detail while training, use a image classifier to mark regions corresponding to subjects in the text descriptor. If text descriptor doesn't exist, add it to the prompt modify attention function to increase the attention weight between subjects found by the classifier modify loss function to give regions marked by the classifier more weight

>2. unloads vae from VRAM during training. This is done in hypernetworks, and idk why it wasn't in the code for TI. It doesn't break anything and doesn't make anything worse.
>This saves around .2 GB VRAM
>
>After you apply this, turn on Move VAE and CLIP to RAM and Use cross attention optimizations while training

10/30

10/29

10/28

10/27

10/26

10/21 - 10/25 (big news bolded, big thanks to asuka-test-imgur-anon-who-also-made-the-speedrun-tutorial for some info)

10/20

10/19

10/18

  • Clarification on censoring SD's next model by the question asker

10/17

10/16

10/15

  • Embeddings now shareable via images
  • Stability AI update pipeline (https://www.reddit.com/r/StableDiffusion/comments/y2x51n/the_stability_ai_pipeline_summarized_including/)
    • This week:
      • Updates to CLIP (not sure about the specifics, I assume the output will be closer to the prompt)
      • Clip-guidance comes out open source (supposedly)
    • Next week:
      • DNA Diffusion (applying generative diffusion models to genetics)
      • A diffusion based upscaler ("quite snazzy")
      • A new decoding architecture for better human faces ("and other elements")
      • Dreamstudio credit pricing adjustment (cheaper, that is more options with credits)
      • Discord bot open sourcing
    • Before the end of the year:
      • Text to Video ("better" than Meta's recent work)
      • LibreFold (most advanced protein folding prediction in the world, better than Alphafold, with Havard and UCL teams)
      • "A ton" of partnerships to be announced for "converting closed source AI companies into open source AI companies"
      • (Potentially) CodeCARP, Code generation model from Stability umbrella team Carper AI (currently training)
      • (Potentially) Gyarados (Refined user preference prediction for generated content by Carper AI, currently training)
      • (Potentially) CHEESE (some sort of platform for user preference prediction for generated content)
      • (Potentially) Dance Diffusion, generative audio architecture from Stability umbrella project HarmonAI (there is already a colab for it and some training going on i think)
  • Animation Stable Diffusion:
  • Stable Diffusion in Blender
  • DreamStudio will now use CLIP guidance
  • Stable Diffusion running on iPhone
  • Cycle Diffusion: https://github.com/ChenWu98/cycle-diffusion
    • txt2img > img2img editors, look at github to see examples
  • Information about difference merging added to FAQ
  • Distributed model training planned
    • SD Training Labs server
  • Gradio updated
    • Optimized, increased speeds
    • Git pulling should be safe

10/14

10/13

10/12

10/11

10/10

  • New unpickler for new ckpts: https://rentry.org/safeunpickle2
  • HENTAI DIFFUSION MIGHT HAVE A VIRUS confirmed to be safe by some kind people
    • github taken down because of nude preview images, hf files taken down because of complaints, windows defender false positive, some kind anons scanned the files with a pickle scanner and and it came back safe
    • automatic's repo has security checks for pickles
    • anon scanned with a "straced-container", safe
  • NAI's euler A is now implemented in AUTOMATIC1111's build
    • git pull to access
  • New open-source (?) generation method revealed making good images in 4 steps
    • Supposedly only 64x64, might be wrong
  • Discovered that hypernetworks were meant to create anime using the default SD model

10/9

SD RESOURCE GOLDMINE

Preamble

This is a curated collection of up to date links and information. Everything else 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

Contact

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

Socials: Trip: questianon !!YbTGdICxQOw Discord: malt#6065 Reddit: u/questianon Github: https://github.com/questianon Twitter: https://twitter.com/questianon)

How to use this resource

The goldmine is ordered from surface-level content to deep level content. If you are a newcomer to Stable Diffusion, it's highly recommended to use start from the beginning.

To prevent redundancies, all items on this list are listed only once. To make sure you find what you're looking for, please use 'Ctrl + F' ('Cmd + F' on macOS).

Emoji

Items on this list with a 🥒 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".

Warnings

  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.

Updates

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

Instructions:

  • 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

Localizations

French:

Contents

Tutorial

Hypertextbook: https://rentry.org/sdhypertextbook This is a tutorial/commentary to guide a newcomer how to setup and use Stable Diffusion to its fullest. It's meant to be a supplementary to SD Goldmine: https://rentry.org/sdgoldmine, but can be used without it.

Getting Started

AMD

AMD isn't as easy to setup as NVIDIA. I don't have an AMD so I don't know if these guides are good

Linux

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

CPU

CPU is even less documented. I don't use my CPU for SD, so I don't know if these guides are good

Apple Silicon

Even less documented

Troubleshooting

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

Repositories

These are repositories containing general AI knowledge

English:

Korean:

Prompting

Documents

These are documents containing general prompting knowledge

English:

Chinese:

Japanese:

Korean:

Prompt Database

Tips

Negatives

Tags

Tag Rankings

Tag Comparisons

Comparisons:

Artists

Images:

Sites:

Other Comparisons

Extensions

Some extensions I came across that are probably in the webui extension browser

Wildcards

Collections:

Dump:

Plugins for External Apps

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

Photoshop:

Krita:

GIMP:

Blender:

Unsorted but update was pushed

Prompt word/phrase collection: https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion/raw/main/ideas.txt

  • Anon says that "8k, 4k, (highres:1.1), best quality, (masterpiece:1.3)" leads to nice details

According to an anon, the vae seems to be provide saturation/contrast and some line thickness (vae-ft-ema-56000-ema-pruned, https://huggingface.co/stabilityai/sd-vae-ft-ema-original/blob/main/vae-ft-ema-560000-ema-pruned.ckpt). Example (left with 56k, right with anything vae): https://i.4cdn.org/h/1669086238979897s.jpg

Japanese prompt generator: https://magic-generator.herokuapp.com/ Build your prompt (chinese): https://tags.novelai.dev/ NAI Prompts: https://seesaawiki.jp/nai_ch/d/%c8%c7%b8%a2%a5%ad%a5%e3%a5%e9%ba%c6%b8%bd/%a5%a2%a5%cb%a5%e1%b7%cf Prompt similarity tester: https://gitlab.com/azamshato/simula

Multilingual study: https://jalonso.notion.site/Stable-Diffusion-Language-Comprehension-5209abc77a4f4f999ec6c9b4a48a9ca2

Aesthetic value (imgs used to train SD): https://laion-aesthetic.datasette.io/laion-aesthetic-6pls Clip retrieval (text to CLIP to search): https://rom1504.github.io/clip-retrieval/

Aesthetic scorer python script: https://github.com/grexzen/SD-Chad Another scorer: https://github.com/christophschuhmann/improved-aesthetic-predictor Supposedly another one?: https://developer.huawei.com/consumer/en/hiai/engine/aesthetic-score Another Aesthetic Scorer: https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer

NAI to webui translator (not 100% accurate): https://seesaawiki.jp/nai_ch/d/%a5%d7%a5%ed%a5%f3%a5%d7%a5%c8%ca%d1%b4%b9

Prompt editing parts of image but without using img2img/inpaint/prompt editing guide by anon: https://files.catbox.moe/fglywg.JPG

Tip Dump: https://rentry.org/robs-novel-ai-tips Tips: https://github.com/TravelingRobot/NAI_Community_Research/wiki/NAI-Diffusion:-Various-Tips-&-Tricks Info dump of tips: https://rentry.org/Learnings Outdated guide: https://rentry.co/8vaaa Tip for more photorealism: https://www.reddit.com/r/StableDiffusion/comments/yhn6xx/comment/iuf1uxl/

  • TLDR: add noise to your img before img2img

NAI prompt tips: https://docs.novelai.net/image/promptmixing.html NAI tips 2: https://docs.novelai.net/image/uifunctionalities.html

Masterpiece vs no masterpiece: https://desuarchive.org/g/thread/89714899#89715160

DPM-Solver Github: https://github.com/LuChengTHU/dpm-solver

Prompt: 1girl, pointy ears, white hair, medium hair, ahoge, hair between eyes, green eyes, medium:small breasts, cyberpunk, hair strand, dynamic angle, cute, wide hips, blush, sharp eyes, ear piercing, happy, hair highlights, multicoloured hair, cybersuit, cyber gas mask, spaceship computers, ai core, spaceship interior Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, animal ears, panties

Original image: Steps: 50, Sampler: DDIM, CFG scale: 11, Seed: 3563250880, Size: 1024x1024, Model hash: cc024d46, Denoising strength: 0.57, Clip skip: 2, ENSD: 31337, First pass size: 512x512 NAI/SD mix at 0.25

Deep Danbooru: https://github.com/KichangKim/DeepDanbooru Demo: https://huggingface.co/spaces/hysts/DeepDanbooru

Embedding tester: https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer

Collection of Aesthetic Gradients: https://github.com/vicgalle/stable-diffusion-aesthetic-gradients/tree/main/aesthetic_embeddings

Euler vs. Euler A: AUTOMATIC1111/stable-diffusion-webui#2017 (comment)

According to anon: DPM++ should converge to result much much faster than Euler does. It should still converge to the same result though.

(info by anon) According to https://arxiv.org/pdf/2211.01095.pdf, the M samplers are better than the S samplers

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. https://preyx.github.io/sd-scale-calc/ view code and download from https://files.catbox.moe/8ml5et.html 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: https://github.com/DominikDoom/a1111-sd-webui-tagcomplete Danbooru Tag Exporter: https://sleazyfork.org/en/scripts/452976-danbooru-tags-select-to-export Another: https://sleazyfork.org/en/scripts/453380-danbooru-tags-select-to-export-edited Tags (latest vers): https://sleazyfork.org/en/scripts/453304-get-booru-tags-edited Basic gelbooru scraper: https://pastebin.com/0yB9s338 Scrape danbooru images and tags like fetch.py for e621 for tagging datasets: https://github.com/JetBoom/boorutagparser UMI AI: https://www.patreon.com/klokinator

Random Prompts: https://rentry.org/randomprompts Python script of generating random NSFW prompts: https://rentry.org/nsfw-random-prompt-gen Prompt randomizer: https://github.com/adieyal/sd-dynamic-prompting Prompt generator: https://github.com/h-a-te/prompt_generator

  • apparently UMI uses these?

http://dalle2-prompt-generator.s3-website-us-west-2.amazonaws.com/ https://randomwordgenerator.com/ funny prompt gen that surprisingly works: https://www.grc.com/passwords.htm Unprompted extension released: https://github.com/ThereforeGames/unprompted

  • HAS ADS

StylePile: https://github.com/some9000/StylePile script that pulls prompt from Krea.ai and Lexica.art based on search terms: https://github.com/Vetchems/sd-lexikrea randomize generation params for txt2img, works with other extensions: https://github.com/stysmmaker/stable-diffusion-webui-randomize

Ideas for when you have none: https://pentoprint.org/first-line-generator/ Colors: http://colorcode.is/search?q=pantone

External masking for inpainting (no more brush or WIN magnifier): https://github.com/dfaker/stable-diffusion-webui-cv2-external-masking-script anon: theres a commanda rg for adding basic painting, its '--gradio-img2img-tool'

Script collection: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts Prompt matrix tutorial: https://gigazine.net/gsc_news/en/20220909-automatic1111-stable-diffusion-webui-prompt-matrix/ Animation Script: https://github.com/amotile/stable-diffusion-studio Animation script 2: https://github.com/Animator-Anon/Animator Video Script: https://github.com/memes-forever/Stable-diffusion-webui-video Masking Script: https://github.com/dfaker/stable-diffusion-webui-cv2-external-masking-script XYZ Grid Script: https://github.com/xrpgame/xyz_plot_script Vector Graphics: https://github.com/GeorgLegato/Txt2Vectorgraphics/blob/main/txt2vectorgfx.py Txt2mask: https://github.com/ThereforeGames/txt2mask Prompt changing scripts:

Interpolation script (img2img + txt2img mix): https://github.com/DiceOwl/StableDiffusionStuff

img2tiles script: https://github.com/arcanite24/img2tiles Script for outpainting: https://github.com/TKoestlerx/sdexperiments Img2img animation script: https://github.com/Animator-Anon/Animator/blob/main/animation_v6.py

Google's interpolation script: https://github.com/google-research/frame-interpolation

Deforum guide: https://docs.google.com/document/d/1RrQv7FntzOuLg4ohjRZPVL7iptIyBhwwbcEYEW2OfcI/edit Animation Guide: https://rentry.org/AnimAnon#introduction Rotoscope guide: https://rentry.org/AnimAnon-Rotoscope Chroma key after SD (fully prompted?): https://files.catbox.moe/d27xdl.gif

Prompt travel: https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel

More animation guide: https://www.reddit.com/r/StableDiffusion/comments/ymwk53/better_frame_consistency/ Animation guide + example for face: https://www.reddit.com/r/StableDiffusion/comments/ys434h/animating_generated_face_test/ Something for aninmation: https://github.com/nicolai256/Few-Shot-Patch-Based-Training

Animating faces by anon:

workflow looks like this:
>generate square portrait (i use 1024 for this example)
>create or find driving video
>crop driving video to square with ffmpeg, making sure to match the general distance from camera and face position(it does not do well with panning/zooming video or too much head movement)
>run thin-plate-spline-motion-model
>take result.mp4 and put it into Video2x (Waifu2x Caffe)
>put into flowframes for 60fps and webm

>if you don't care about upscaling it makes 256x256 pretty easily
>an extension for webui could probably be made by someone smarter than me, its a bit tedious right now with so many terminals

here is a pastebin of useful commands for my workflow
https://pastebin.com/6Y6ZK8PN

Another person who used it: https://www.reddit.com/r/StableDiffusion/comments/ynejta/stable_diffusion_animated_with_thinplate_spline/

Img2img megalist + implementations: AUTOMATIC1111/stable-diffusion-webui#2940

Runway inpaint model: https://huggingface.co/runwayml/stable-diffusion-inpainting

Inpainting Tips: https://www.pixiv.net/en/artworks/102083584 Rentry version: https://rentry.org/inpainting-guide-SD

Extensions: Artist inspiration: https://github.com/yfszzx/stable-diffusion-webui-inspiration

History: https://github.com/yfszzx/stable-diffusion-webui-images-browser Collection + Info: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Extensions Deforum (video animation): https://github.com/deforum-art/deforum-for-automatic1111-webui

Auto-SD-Krita: https://github.com/Interpause/auto-sd-paint-ext

ddetailer (object detection and auto-mask, helpful in fixing faces without manually masking): https://github.com/dustysys/ddetailer Aesthetic Gradients: https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients Autocomplete Tags: https://github.com/DominikDoom/a1111-sd-webui-tagcomplete Prompt Randomizer: https://github.com/adieyal/sd-dynamic-prompting Wildcards: https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards/ Wildcard script + collection of wildcards: https://app.radicle.xyz/seeds/pine.radicle.garden/rad:git:hnrkcfpnw9hd5jb45b6qsqbr97eqcffjm7sby Symmetric image script (latent mirroring): https://github.com/dfaker/SD-latent-mirroring

macOS Finder right-click menu extension: https://github.com/anastasiuspernat/UnderPillow Search danbooru for tags directly in AUTOMATIC1111's webui extension: https://github.com/stysmmaker/stable-diffusion-webui-booru-prompt

  • Supports post IDs and all the normal Danbooru search syntax

Clip interrogator: https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb 2 (apparently better than AUTO webui's interrogate): https://huggingface.co/spaces/pharma/CLIP-Interrogator, https://github.com/pharmapsychotic/clip-interrogator

Enchancement Workflow with SD Upscale and inpainting by anon: https://pastebin.com/8WVyDxt9

Upscaling + detail with SD Upscale: https://www.reddit.com/r/StableDiffusion/comments/xkjjf9/upscale_to_huge_sizes_and_add_detail_with_sd/?context=3

Inpainting a face by anon:

send the picture to inpaint modify the prompt to remove anything related to the background add (face) to the prompt slap a masking blob over the whole face mask blur 10-16 (may have to adjust after), masked content: original, inpaint at full resolution checked, full resolution padding 0, sampling steps ~40-50, sampling method DDIM, width and height set to your original picture's full res denoising strength .4-.5 if you want minor adjustments, .6-.7 if you want to really regenerate the entire masked area let it rip

  • 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 (?))

VAEs

Tutorial + how to use on ALL models (applies for the NAI vae too): https://www.reddit.com/r/StableDiffusion/comments/yaknek/you_can_use_the_new_vae_on_old_models_as_well_for/

Booru tag scraping:

Creating fake animes:

Some observations by anon:

  1. Removing the spaces after the commas changed nothing
  2. Using "best_quality" instead of "best_quality" did change the image. masterpiece,best_quality,akai haato but she is a spider,blonde hair,blue eyes
  3. Changing all of the spaces into underscores changed the image somewhat substantially.
  4. Replacing those commas with spaces changed the image again.

Reduce bias of dreambooth models: https://www.reddit.com/r/StableDiffusion/comments/ygyq2j/a_simple_method_explained_in_the_comments_to/?utm_source=share&utm_medium=web2x&context=3

Landscape tutorial: https://www.reddit.com/r/StableDiffusion/comments/yivokx/landscape_matte_painting_with_stable_diffusion/

Anon's process:

  • Start with a prompt to get the general scenario you have in mind, here I was just looking to seggs the rrat so I used the embed here >>36743515 and described some of her character features to help steer the AI (in this case hair details, sharp teeth, her mouse ears and tail) as well as making her be naked and having vaginal sex
  • Generate images at a default resolution size (512 by X pixels) at a relative standard number of steps (30 in this case) and keep going until I find an image thats in a position I like (in this case seed 1920052602 gave me a very nice one to work with, as you can see here https://files.catbox.moe/8z2mua.png (embed))
  • Copy the seed of the image and paste it into the Seed field on the Web UI, which will maintain the composition of the image. I then double the resolution I was working with (so here I went from 512 by 768 to 1024 by 1536) and checkmark the "Hires fix option" underneath the width and height sliders. Hires fix is the secret sauce on the Web UI that helps maintain the detail of the image when you are upscaling the resolution of the image, and combined with that Upscale latent space option I mentioned earlier it really enhances the detail. With that done you can generate the upscaled image.
  • Play around with the weights of the prompt tags and add things to the negatives to fix little things like hair being too red, tummy too chubby, etc. You have to be careful with adding new tags because that can drastically change the image

Anon's booba process: >you can generate a perfect barbie doll anatomy but more accurate chuba in curated >then switch to full, img2img it on the same seed after blotching nipples on it like a caveman, and hit generate

Boooba v2:

  1. Generate whatever NSFW proompt you were thinking of using the CURATED model, yes, I know that sounds ridiculous https://files.catbox.moe/b6k6i4.png (embed)
  2. Inpaint the naughty bits back in. You REALLY don't have to do a good job of this: https://files.catbox.moe/yegjrw.png (embed)
  3. Switch to Full after clicking "Save", set Strength to 0.69, Noise to 0.17, and make sure you copy/paste the same seed # back in. Hit Generate: https://files.catbox.moe/8dag88.png (embed) Compare that with what you'd get trying to generate the same exact proompt using the Full model purely txt2img on the same seed: https://files.catbox.moe/ytfdv3.png (embed)

Img2img rotoscoping tutorial by anon:

1. extract image sequence from video
2. testing prompt by using the 1st photo from the batch
3. find the suitable prompt that you want, the pose/sexual acts should be the same as the original to prevent weirdness
4. CFG Scale and Denoising Strength is very important
> Low CFG Scale will make your image less follow your prompt and make it more blurry and messy (i use 9-13)
> Denoising Strength determines the mix between your prompt and your image: 0 = Original input 1 = Only Prompt, nothing resemble of the input except the colors.
the interesting thing that i've noticed from Denoising strength is not linear, its behave more exponential ( my speculation is 0-0.6 = still reminds of the original 0.61-0.76 = starting to change 0.77-1 = change a lot )
5. sampler:
> Euler-a is quite nice, but lack of consistency between the step, adding/lower 1 step can change the entire photo
> Euler is better than euler-a in terms of consistency but requires more steps = longer generation time between each image
> DPM++ 2S a Karras is the best in quality (for me) but it is very slow, good for generate single image
> DDIM is the fastest and very useful for this case, 20-30 steps can produces a nice quality anime image.
6. test prompting into a batch of 4-6 to choosing a seed
7. Batch img2img
8. Assembling the generated images into video, i don't want to use eveyframes so i rendered into 2 frame steps and half the frame rate
9. Use Flowframes to interpolate the inbetween frame to match the original video frame rate.

Ex: https://files.catbox.moe/e30szo.mp4

File2prompt (I think it's multiple generations in a row?): https://rentry.org/file2prompt

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: https://docs.python.org/3/library/pickle.html, https://huggingface.co/docs/hub/security-pickle. Make sure to check them for pickles using a tool like https://github.com/zxix/stable-diffusion-pickle-scanner or https://github.com/lopho/pickle_inspector

Models*

Model pruner: https://github.com/harubaru/waifu-diffusion/blob/bc626e8/scripts/prune.py

Collection of potentially dangerous models: https://bt4g.org/search/.ckpt/1 Collection?: https://civitai.com/ Huggingface collection: https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads

potential magnet that someone gave me

magnet:?xt=urn:btih:689c0fe075ab4c7b6c08a6f1e633491d41186860&dn=Anything-V3.0.ckpt&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337%2fannounce&tr=udp%3a%2f%2f9.rarbg.com%3a2810%2fannounce&tr=udp%3a%2f%2ftracker.openbittorrent.com%3a6969%2fannounce&tr=udp%3a%2f%2fopentracker.i2p.rocks%3a6969%2fannounce&tr=https%3a%2f%2fopentracker.i2p.rocks%3a443%2fannounce&tr=udp%3a%2f%2ftracker.torrent.eu.org%3a451%2fannounce&tr=udp%3a%2f%2fopen.stealth.si%3a80%2fannounce&tr=http%3a%2f%2ftracker.openbittorrent.com%3a80%2fannounce&tr=udp%3a%2f%2fvibe.sleepyinternetfun.xyz%3a1738%2fannounce&tr=udp%3a%2f%2ftracker1.bt.moack.co.kr%3a80%2fannounce&tr=udp%3a%2f%2ftracker.zerobytes.xyz%3a1337%2fannounce&tr=udp%3a%2f%2ftracker.tiny-vps.com%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.theoks.net%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.swateam.org.uk%3a2710%2fannounce&tr=udp%3a%2f%2ftracker.publictracker.xyz%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.monitorit4.me%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.moeking.me%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.encrypted-data.xyz%3a1337%2fannounce&tr=udp%3a%2f%2ftracker.dler.org%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.army%3a6969%2fannounce&tr=http%3a%2f%2ftracker.bt4g.com%3a2095%2fannounce

Mag2

Little update, here's the link with all including VAE (second one)
magnet:?xt=urn:btih:689C0FE075AB4C7B6C08A6F1E633491D41186860&dn=Anything-V3.0.ckpt&tr=udp%3a%2f%2ftracker.openbittorrent.com%3a80%2fannounce&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337%2fannounce

magnet:?xt=urn:btih:E87B1537A4B5B5F2E23236C55F2F2F0A0BB6EA4A&dn=NAI-Anything&tr=udp%3a%2f%2ftracker.openbittorrent.com%3a80%2fannounce&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337%2fannounce

Mag3

magnet:?xt=urn:btih:689c0fe075ab4c7b6c08a6f1e633491d41186860&dn=Anything-V3.0.ckpt&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=udp%3A%2F%2F9.rarbg.com%3A2810%2Fannounce&tr=udp%3A%2F%2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=https%3A%2F%2Fopentracker.i2p.rocks%3A443%2Fannounce&tr=udp%3A%2F%2Ftracker.torrent.eu.org%3A451%2Fannounce&tr=udp%3A%2F%2Fopen.stealth.si%3A80%2Fannounce&tr=http%3A%2F%2Ftracker.openbittorrent.com%3A80%2Fannounce&tr=udp%3A%2F%2Fvibe.sleepyinternetfun.xyz%3A1738%2Fannounce&tr=udp%3A%2F%2Ftracker1.bt.moack.co.kr%3A80%2Fannounce&tr=udp%3A%2F%2Ftracker.zerobytes.xyz%3A1337%2Fannounce&tr=udp%3A%2F%2Ftracker.tiny-vps.com%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.theoks.net%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.swateam.org.uk%3A2710%2Fannounce&tr=udp%3A%2F%2Ftracker.publictracker.xyz%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.monitorit4.me%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.moeking.me%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.encrypted-data.xyz%3A1337%2Fannounce&tr=udp%3A%2F%2Ftracker.dler.org%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.army%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.altrosky.nl%3A6969%2Fannounce&tr=http%3A%2F%2Ftracker.bt4g.com%3A2095%2Fannounce

from: https://bt4g.org/magnet/689c0fe075ab4c7b6c08a6f1e633491d41186860

another magnet on https://rentry.org/sdmodels from the author

*Hrrzg style 768px: https://huggingface.co/TheLastBen/hrrzg-style-768px

MODEL MIXES

Raspberry mix download by anon (not sure if safe): https://pixeldrain.com/u/F2mkQEYp Strawberry Mix (anon, safety caution): https://pixeldrain.com/u/z5vNbVYc

magnet:?xt=urn:btih:eb085b3e22310a338e6ea00172cb887c10c54cbc&dn=cafe-instagram-unofficial-test-epoch-9-140k-images-fp32.ckpt&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopentor.org%3A2710&tr=udp%3A%2F%2Ftracker.ccc.de%3A80&tr=udp%3A%2F%2Ftracker.blackunicorn.xyz%3A6969&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Ftracker.leechers-paradise.org%3A6969

ThisModel:

  1. (Weighted Sum 0.05) Anything3 + SD1.5 = Temp1
  2. (Add Difference 1.0) Temp1 + F222 + SD1.5 = Temp2
  3. (Weighted Sum 0.2) Temp2 + TrinArt2_115000 = ThisModel

Anon's model for vampires(?):

My steps

Step 1:
>A : Anything-V3.0
>B : trinart2_step115000.ckpt [f1c7e952]
>C : stable-diffusion-v-1-4-original

A from https://huggingface.co/Linaqruf/anything-v3.0/blob/main/Anything-V3.0-pruned.ckpt
B from https://rentry.org/sdmodels#trinart2_step115000ckpt-f1c7e952
C from https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt

and I "Add Difference" at 0.45, and name as part1.ckpt

Step 2:
>A : part1.ckpt (What I made in Step 1)
>B: Cafe Unofficial Instagram TEST Model [50b987ae]

B is from https://rentry.org/sdmodels#cafe-unofficial-instagram-test-model-50b987ae

and I "Weighted Sum" at 0.5, and name it TrinArtMix.ckpt

Antler's Mix (didn't check for pickles) https://mega.nz/file/nZtz0LZL#ExSHp7icsZedxOH_yRUOKAliPGfKRsWiOYHqULZy9Yo

Alternate mix, apparently? (didn't check for pickles)

((anything_0.95 + sd-1.5_0.05) + f222 - sd-1.5)_0.75 + trinart2_115000_0.25

RandoMix2 (didn't check for pickles) magnet:?xt=urn:btih:AB6A6C3F6AA0858030B9B85D28B243A4FF9F5935&dn=RandoMix2.zip&tr=udp%3A%2F%2Ftracker.torrent.eu.org%3A451%2Fannounce&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce

RaptorBerry (didn't check for pickles) magnet:?xt=urn:btih:166c9caf38801ba4e10912b5c91ccaaec585534c&dn=RaptorBerry%20Final%20Mix.ckpt&tr=http%3a%2f%2ftracker.opentrackr.org%3a1337%2fannounce&tr=http%3a%2f%2ftracker.openbittorrent.com%3a80%2fannounce&tr=udp%3a%2f%2fopentracker.i2p.rocks%3a6969%2fannounce&tr=udp%3a%2f%2fopen.stealth.si%3a80%2fannounce&tr=udp%3a%2f%2ftracker.torrent.eu.org%3a451%2fannounce

NAI+SD+Trinart characters+Trinart+F222 (weighted sum, values less than 0.3): https://mega.nz/file/JblSFKia#n8JNfYWXaMeeQEstB-1A1Ju5u3m9I-u-n3WcmVpz2lo

"Ben Dover Mix"©®™ is my mix
if you're interested
follow this guide https://rentry.org/lftbl#berrymix
The mix is done exactly the same way as berrymix
but with anythingv3 instead of nai
f222 instead of f111
and sd v1.5 instead of sd v1.4

AloeVera mix: https://mega.nz/file/4bEzxB6Q#j3QwgNxHiYOmT8Y4OgHP9mlzvFbCkEK1DUepMoIBI50

Nutmeg mix:

0.05 NAI + SD1.5
0.05 mix + f222
0.05 mix + r34
0.05 mix + SF
0.3 Anything + mix

Hyper-versatile SD model: https://huggingface.co/BuniRemo/Redshift-WD12-SD14-NAI-FMD_Checkpoint_Merger_-_Hyper-Versatile_Stable_Diffusion_Model

  • Made from Redshift Diffusion, Waifu Diffusion 1.2, Stable Diffusion 1.4, Novel AI, Yiffy, and Zack3D_Kinky-v1; capable of rendering humans, furries, landscapes, backgrounds, buildings, Disney style, painterly styles, and more

Hassan (has a few mixes, not sure if the dls are safe): https://rentry.org/sdhassan

Anonmix:

Weighted Sum @ 0.05 to make tempmodel1

A: Anything.V3, B: SD1.5, C: null

Add Difference @ 1.0 to make tempmodel2

A: tempmodel1, B: Zeipher F222, C: SD1.5

Weighted Sum @ 0.25 to make tempmodel3

A: tempmodel2, B: r34_e4, C: Null

Weighted Sum @ 0.20 to make FINAL MODEL

A: tempmodel3, B: NAI

Big collection of berry mixes: https://rentry.org/dbhhk (https://archived.moe/h/thread/6984678/#q6985842)

Super duper mixing cookbook from hdg (most updated): https://rentry.org/hdgrecipes

EveryDream Trainer

!!! All files in this section (ckpt, vae, pt, hypernetwork, embedding, etc) can be malicious: https://docs.python.org/3/library/pickle.html, https://huggingface.co/docs/hub/security-pickle. Make sure to check them for pickles using a tool like https://github.com/zxix/stable-diffusion-pickle-scanner or https://github.com/lopho/pickle_inspector

Download + info + prompt templates: https://github.com/victorchall/EveryDream-trainer

Dreambooth Models:

!!! All files in this section (ckpt, vae, pt, hypernetwork, embedding, etc) can be malicious: https://docs.python.org/3/library/pickle.html, https://huggingface.co/docs/hub/security-pickle. Make sure to check them for pickles using a tool like https://github.com/zxix/stable-diffusion-pickle-scanner or https://github.com/lopho/pickle_inspector

Links:

Embeddings

!!! info If an embedding is >80mb, I mislabeled it and it's a hypernetwork

!!! info 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: https://docs.python.org/3/library/pickle.html, https://huggingface.co/docs/hub/security-pickle. Make sure to check them for pickles using a tool like https://github.com/zxix/stable-diffusion-pickle-scanner or https://github.com/lopho/pickle_inspector

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

Found on 4chan:

Hypernetworks:

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

!!! info 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: https://docs.python.org/3/library/pickle.html, https://huggingface.co/docs/hub/security-pickle. Make sure to check them for pickles using a tool like https://github.com/zxix/stable-diffusion-pickle-scanner or https://github.com/lopho/pickle_inspector

Chinese telegram (uploaded by telegram anon): magnet:?xt=urn:btih:8cea1f404acfa11b5996d1f1a4af9e3ef2946be0&dn=ChatExport%5F2022-10-30&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce

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.

Found on 4chan:

Found on Discord:

Colored eyes:

>Hey everyone , this hypernetwork was released by me (IWillRemember) (IWillRemember#1912 on discord) if you have any questions you can find me on discord!
>
>Did the Hn as a commission for a friend 😄
>
>I'm releasing an Hn to do better animation like glowing eyes, and a more slender face/upper body.
>
>The tags are : 
>detailed eyes, 
>(color) eyes  = ex: white eyes, blue eyes, etc etc
>collarbone
>
>Trained for 12k steps on a 80 ish images dataset
>
>You can use the Hn with a str of 1 without any problem.
>
>Happy prompting!
>
>Example: https://media.discordapp.net/attachments/1023082871822503966/1038115846222008392/00162-3940698197-masterpiece_highest_quality_digital_art_1girl_on_back_detailed_eyes_perfect_face_detailed_face_breasts_white_hair_yell.png?width=648&height=702
>
>https://mega.nz/file/dHFwmaxS#NQhMPjT4TElPXX_YAZhTsFrQ36PDJhpWFm9BcHU_BO4

Aesthetic Gradients

Collection of Aesthetic Gradients: https://github.com/vicgalle/stable-diffusion-aesthetic-gradients/tree/main/aesthetic_embeddings

Polar Resources

DEAD/MISSING

If you have one of these, please get it to me

Apparently there's a Google drive collection of downloads? (might be the korean site but mistyped)

Dreambooth:

Embed:

Hypernetworks:

Datasets:

Training

Use pics where:

  • Character doesn't blend with background and isn't overlapped by random stuff
  • Character is in different poses, angles, and backgrounds
  • Resolution is 512x512 (crop if it's not)

Train stable diffusion model with Diffusers, Hivemind and Pytorch Lightning: https://github.com/Mikubill/naifu-diffusion

Official pytoch implementation of one shot text to image generation via contrastive prompt-tuning AKA 1 image embedding training: https://github.com/7eu7d7/DreamArtist-stable-diffusion Extension: https://github.com/7eu7d7/DreamArtist-sd-webui-extension DreamArtist extension changes ui.py code in the modules directory, which might not be safe

Dreambooth colab with custom model (old, so might be outdated): https://desuarchive.org/g/thread/89140837/#89140895

Dreambooth thing in Japanese: https://note.com/kohya_ss/n/nee3ed1649fb6

  • "Has aspect ratio bucketing, saving in fp16, etc."

GPU seems to determine training results (--low/med vram arg too)

Extension: https://github.com/d8ahazard/sd_dreambooth_extension

Image tagger helper: https://github.com/nub2927/image_tagger/

anything.ckpt comparisons Old final-pruned: https://files.catbox.moe/i2zu0b.png (embed) v3-pruned-fp16: https://files.catbox.moe/k1tvgy.png (embed) v3-pruned-fp32: https://files.catbox.moe/cfmpu3.png (embed) v3 full or whatever: https://files.catbox.moe/t9jn7y.png (embed)

for key in tqdm(theta_0.keys(), desc="Stage 1/2"):
    if "model" in key and key in theta_1:
        # sigmoid
        alpha = alpha * alpha * (3 - (2 * alpha))
        theta_0[key] = theta_0[key] + ((theta_1[key] - theta_0[key]) * alpha)

        # inverse sigmoid
        #alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
        #theta_0[key] = theta_0[key] + ((theta_1[key] - theta_0[key]) * alpha)

        # Weighted sum
        #theta_0[key] = ((1 - alpha) * theta_0[key]) + (alpha * theta_1[key])

Supposedly how to append model data without merging by anon:

x = (Final Dreambooth Model) - (Original Model) filter x for x >= (Some Threshold) out = (Model You Want To Merge It With) * (1 - M) + x * M

Model merging method that preserves weights: https://github.com/samuela/git-re-basin

Alternate model merging using https://github.com/bmaltais/dehydrate by anon:

Dehydrate a model Hydrate it back into a dreambooth Merge with other stuff run python ckpt_subtract.py dreamboothmodel.ckpt basemode.ckpt --output dreambooth_only to dehydrate run 'python ckpt_add.py dreambooth_only target_model.ckpt --output output_model.ckpt' to hydrate it into another model.

3rd party git re basin: https://github.com/ogkalu2/Merge-Stable-Diffusion-models-without-distortion

Git rebasin pytorch: https://github.com/themrzmaster/git-re-basin-pytorch

>2. unloads vae from VRAM during training. This is done in hypernetworks, and idk why it wasn't in the code for TI. It doesn't break anything and doesn't make anything worse.
>This saves around .2 GB VRAM
>
>After you apply this, turn on Move VAE and CLIP to RAM and Use cross attention optimizations while training
  • By anon:

No idea if someone else will have a use for this but I needed to make it for myself since I can't get a hypernetwork trained regardless of what I do.

https://mega.nz/file/LDwi1bab#xrGkqJ9m-IsqsTQNixVkeWrGw2HvmAr_fx9FxNhrrbY

That link above is a spreadsheet where you paste the hypernetwork_loss.csv data into A1 cell (A2 is where numbers should start). Then you can use M1 to set how many epochs of the most recent data you want to use for the red trendline (green is the same length but starting before red). Outlayer % is if you want to filter out extreme points 100% means all points are considered for trendline 95% filters out top and bottom 5 etc. Basically you can use this to see where the training started fucking up.

  • Anon's best:

Creation: 1,2,1 Normalized Layers Dropout Enabled Swish XavierNormal (Not sure yet on this one. Normal or XavierUniform might be better)

Training:

Rate: 5e-5:1000, 5e-6:5000, 5e-7:20000, 5e-8:100000 Max Steps: 100,000

Vector guide by anon: https://rentry.org/dah4f

  • Another training guide: https://www.reddit.com/r/stablediffusion/comments/y91luo

  • Super simple embed guide by anon: Grab the high quality images, run them through the processor. Create an embedding called art by {artist}. Then train that same embedding with your processed images and set the learning rate to the following: 0.1:500,0.05:1000,0.025:1500,0.001:2000,1e-5` Run it for 10k steps and you'll be good. No need for an entire hypernetwork.

  • Has training info and a tutorial for Asagi Igawa, Edjit, and Rouge the Bat embeds (RealYiffingFar#4510): https://mega.nz/folder/5nIAnJaA#YMClwO8r7tR1zdJJeTfegA

  • Anon's dreambooth guide: for a character, steps ~1500-2000 checkpoint every 500 if you have the VRAM for it, else 99999 (ie: at the end), previews are shit don't even bother, 99999 learning rate: 0.000001-0.000005, I don't have a reason for it, default is probably fine. instance prompt: [filewords], class prompt: 1girl, 20x regularisation images than training images, style matters, if you want anime get anime regularisation stuff. advanced: auto-adjust, batch size: 2, 8bit adam, fp16, don't cache latents (noticeable speedup if you do cache), train text, train EMA, gradient checkpointing, 2 gradient accumulation

none of this is concrete stuff I do every time, I just roll whatever works. the single most important stuff is to ensure you never tag anything that isn't in an image after cropping. reduce the tags as much as humanly possible, ie:

legwear, black thighhighs, long socks, long thighhighs, pantyhose, stockings, etc.

to just:

thighhighs

try add images that both do and do not use all of your tags. if you have a pic with thighhighs, include at least one without, otherwise the tag is meaningless if your training cannot establish a positive and negative for each tag it's gonna struggle to recall those features have makima with yellow eyes? include some girl with similar features but red or blue eyes, or just an entirely different girl that's been accurately tagged with the negatives you need in this way you can distinguish between features and emphasise stuff.

Datasets:

Training dataset with aesthetic ratings: https://github.com/JD-P/simulacra-aesthetic-captions

FAQ

Check out https://rentry.org/sdupdates and https://rentry.org/sdupdates2 for other questions https://rentry.org/sdg_FAQ

What's all the new stuff?

Check here to see if your question is answered:

How do I set this up?

Refer to https://rentry.org/nai-speedrun (has the "Asuka test") Paperspace: https://rentry.org/865dy

What's the "Hello Asuka" test?

It's a basic 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.

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/funny way of saying you got hacked.

I want to run this, but my computer is too bad. Is there any other way? Check out one of these (I did not used most of these, so they might be unsafe to use):

How do I directly check AUTOMATIC1111's webui updates?

For a complete list of updates, go here: https://github.com/AUTOMATIC1111/stable-diffusion-webui/commits/master

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

Go to https://github.com/AUTOMATIC1111/stable-diffusion-webui/commits/master 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 ' to reset your install, use 'git checkout master'

git checkout . will clean any changes you do

Another Guide: https://rentry.org/git_retard

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? Anon:

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

Misc

Archives

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