Outdated stuff that is either easier to do now, has a better repo elsewhere, or was made on older versions of SD:
Random stuff/info from random places
Random Prompts: https://rentry.org/randomprompts
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
Extensions:
Artist inspiration: https://github.com/yfszzx/stable-diffusion-webui-inspiration - https://huggingface.co/datasets/yfszzx/inspiration
- delete the 0 bytes folders from their dataset zip or you might get an error extracting it
History: https://github.com/yfszzx/stable-diffusion-webui-images-browser 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/
Ideas for when you have none: https://pentoprint.org/first-line-generator/
Colors: http://colorcode.is/search?q=pantone
vae info that probably doesn't apply to many models: 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
not sure if people still use NAI:
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
Outdated guide: https://rentry.co/8vaaa
- Manually tagging the pictures allows for faster convergence than auto-tagging. More work is needed to see if deepdanbooru autotagging helps convergence
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/
Img2img rotoscoping tutorial by anon:
Ex: https://files.catbox.moe/e30szo.mp4
File2prompt (I think it's multiple generations in a row?): https://rentry.org/file2prompt
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
Animating faces by anon:
- https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model
- How to Animate faces from Stable Diffusion!
Another person who used it: https://www.reddit.com/r/StableDiffusion/comments/ynejta/stable_diffusion_animated_with_thinplate_spline/
Img2img megalist + implementations: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/2940
Runway inpaint model: https://huggingface.co/runwayml/stable-diffusion-inpainting
- Tutorial from their github: https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion
Inpainting Tips: https://www.pixiv.net/en/artworks/102083584
Rentry version: https://rentry.org/inpainting-guide-SD
- Image editor for SD for inpainting/outpainting/txt2img/img2img: https://github.com/BlinkDL/Hua
- https://www.painthua.com/ - New GUI focusing on Inpainting and Outpainting
- https://www.reddit.com/r/StableDiffusion/comments/ygp0iv/painthuacom_new_gui_focusing_on_inpainting_and/
- To use it with webui add this to webui-user.bat: --api --cors-allow-origins=https://www.painthua.com
- Vid: https://www.bilibili.com/video/BV16e4y1a7ne/
- CLIPSeg (text-based inpainting): https://huggingface.co/spaces/nielsr/text-based-inpainting
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'
Animation stuff
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:
- https://github.com/yownas/seed_travel
- https://github.com/feffy380/prompt-morph
- https://github.com/EugeoSynthesisThirtyTwo/prompt-interpolation-script-for-sd-webui
- https://github.com/some9000/StylePile
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
- Can use in txt2img mode and combine with https://film-net.github.io/ for content aware interpolation
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
Prompt travel: https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel
- Example (30 min, 5k steps, 124 images): https://i.4cdn.org/g/1668879797247188.webm
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
Models + mixes, you can find most on Civit and HF now
- V1 repo: https://github.com/CompVis/stable-diffusion
- V2 repo: https://github.com/Stability-AI/stablediffusion
- anything.ckpt (v3 6569e224; v2.1 619c23f0), a Chinese finetune/training continuation of NAI, is released: https://www.bilibili.com/read/cv19603218
- Huggingface: https://huggingface.co/Linaqruf/anything-v3.0/tree/main
- Torrent: https://rentry.org/sdmodels#anything-v30-38c1ebe3-1a7df6b8-6569e224
- Supposed ddl, I didn't check these for pickles: https://rentry.org/NAI-Anything_v3_0_n_v2_1
- instructions to download from Baidu from outside China and without SMS or an account and with speeds more than 100KBps:
>Download a download manager that allows for a custom user-agent (e.g. IDM)
>If you need IDM, contact me
>Go here: https://udown.vip/#/
>In the "在线解析" section, put 'https://pan.baidu.com/s/1gsk77KWljqPBYRYnuzVfvQ' into the first prompt box and 'hheg' in the second (remove the ')
>Click the first blue button
>In the bottom box area, click the folder icon next to NovelAI
>Open your dl manager and add 'netdisk;11.33.3;' into the user-agent section (remove the ')
>Click the paperclip icon next to the item you want to download in the bottom box and put it into your download manager
>
>To get anything v3 and v2.1: first box:https://pan.baidu.com/s/1r--2XuWV--MVoKKmTftM-g, second box:ANYN
* another link that has 1 letter changed that could mean it's pickled: https://pan.baidu.com/s/1r--2XuWV--MVoKKmTfyM-g - seems to be better (e.g. provide more detailed backgrounds and characters) than NAI, but can overfry some stuff. Try lowering the cfg if that happens
- Passes AUTOMATIC's pickle tester and https://github.com/zxix/stable-diffusion-pickle-scanner, but there's no guarantee on pickle safety, so it still might be ccp spyware
- Use the vae or else your outputs will have a grey filter
- Windows Defender might mark this as a virus, it should be a false positive
ThisModel:
- (Weighted Sum 0.05) Anything3 + SD1.5 = Temp1
- (Add Difference 1.0) Temp1 + F222 + SD1.5 = Temp2
- (Weighted Sum 0.2) Temp2 + TrinArt2_115000 = ThisModel
Anon's model for vampires(?):
- Samdoesbimbos (sandoesart dreambooth dehydrated from original model and hydrated into thepit bimbo dreambooth): https://mega.nz/file/xpECiaAI#_KeDMAvxAnyOkLlo82IP09BHc1KBZoUfT-0jFaDhF3c
- One recommended merge: [email protected] and [email protected]
- Another is F222 SD15 WD12 SxDv0.8 at low ratios (0.1-0.4)
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
AloeVera mix: https://mega.nz/file/4bEzxB6Q#j3QwgNxHiYOmT8Y4OgHP9mlzvFbCkEK1DUepMoIBI50
Nutmeg 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
- An anon recommended the Hassan1.3 if you do 3DPD
- 1.4 ex: https://imgur.com/a/TUWkJmh
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
SquidwardBlendv2: https://mega.nz/file/r9MzkQxb#QeiM9HJf0wAw68yg-q9RtrbGucB7h712yu-EpvFX3n0
- "generates consistent quality realistic painterly tasteful nudes"
- Ex: https://i.4cdn.org/g/1669218820252711.png
- https://www.reddit.com/r/sdnsfw/comments/z1ymul/squidwardblendv2/
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
Dreambooths
- Nami: https://mega.nz/file/VlQk0IzC#8MEhKER_IjoS8zj8POFDm3ZVLHddNG5woOcGdz4bNLc
- https://huggingface.co/IShallRiseAgain/StudioGhibli/tree/main
- Jinx: https://huggingface.co/jinofcoolnes/sksjinxmerge/tree/main
- Arcane Vi: https://huggingface.co/jinofcoolnes/VImodel/tree/main
- Lucy (Edgerunners): https://huggingface.co/jinofcoolnes/Lucymodel/tree/main
- Gundam (full ema, non pruned): https://huggingface.co/Gazoche/stable-diffusion-gundam
- Starsector Portraits: https://huggingface.co/Severian-Void/Starsector-Portraits
- Evangelion style: https://huggingface.co/crumb/eva-fusion-v2
- Robo Diffusion: https://huggingface.co/nousr/robo-diffusion/tree/main/models
- Arcane Diffusion: https://huggingface.co/nitrosocke/Arcane-Diffusion
- Archer: https://huggingface.co/nitrosocke/archer-diffusion
- Wikihow style: https://huggingface.co/jvkape/WikiHowSDModel
- 60 Images. 2500 Steps. Embedding Aesthetics + 40 Image Embedding options
- Their patreon: https://www.patreon.com/user?u=81570187
- Lain girl: https://mega.nz/file/VK0U0ALD#YDfGgOu8rquuR5FbFxmzKD5hzxO1iF0YQafN0ipw-Ck
- Wikiart: https://huggingface.co/valhalla/sd-wikiart-v2/tree/main/unet
- diffusion_pytorch_model.bin, just rename to whatever.ckpt
- Megaman zero: https://huggingface.co/jinofcoolnes/Zeromodel/tree/main
- Cyberware: https://huggingface.co/Eppinette/Cyberware/tree/main
- taffy (keyword: champi): https://drive.google.com/file/d/1ZKBf63fV1Zm5_-a0bZzYsvwhnO16N6j6/view?usp=sharing
- Disney (3d?): https://huggingface.co/nitrosocke/modern-disney-diffusion/
- El Risitas (KEK guy): https://huggingface.co/Fictiverse/ElRisitas
- Cyberpunk Anime Diffusion: https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion
- Kurzgesagt (called with "kurzgesagt! style"): https://drive.google.com/file/d/1-LRNSU-msR7W1HgjWf8g1UhgD_NfQjJ4/view?usp=sharing
- SHA-256: d47168677d75045ae1a3efb8ba911f87cfcde4fba38d5c601ef9e008ccc6086a
- Robodiffusion (good outputs for "meh" prompting): https://huggingface.co/nousr/robo-diffusion
- 2D Illustration style: https://huggingface.co/ogkalu/hollie-mengert-artstyle
- Rebecca (edgerunners, by booru anon, info is in link): https://huggingface.co/demibit/rebecca
- Kiwi (by booru anon): https://huggingface.co/demibit/kiwi
- Ranni (Elden Ring): https://huggingface.co/bitspirit3/SD-Ranni-dreambooth-finetune
- Cloud: https://huggingface.co/jinofcoolnes/cloud/tree/main
- Comics: https://huggingface.co/ogkalu/Comic-Diffusion
- Modern Disney style (modi, mo-di): https://huggingface.co/nitrosocke/mo-di-diffusion/
- Silco: https://huggingface.co/jinofcoolnes/silcomodel/tree/main
- Lara: https://huggingface.co/jinofcoolnes/Oglaramodel/tree/main
- theofficialpit bimbo (26 pics for 2600 steps, Use "thepit bimbo" in prompt for more effect): https://mega.nz/file/wSdigRxJ#WrF8cw85SDebO8EK35gIjYIl7HYAz6WqOxcA-pWJ_X8
- DCAU (Batman_the_animated_series): https://huggingface.co/IShallRiseAgain/DCAU/blob/main/DCAUV1.ckpt
- https://www.reddit.com/r/StableDiffusion/comments/yf2qz0/initial_version_of_dcau_model_im_making/
- hand captioning 782 screencap, 44,000 steps, training set for the regularization images
- NSFW: https://megaupload.nz/N7m7S4E7yf/Magnum_Opus_alpha_22500_steps_mini_version_ckpt
- Hardcore: https://pixeldrain.com/u/Stk98vyH
- Trained on 3498 images and around 250K steps
- porn, sex acts of all sorts: anal sex, anilingus, ass, ass fingering, ball sucking, blowjob, cumshot, cunnilingus, dick, dildo, double penetration, exposed pussy, female masturbation, fingering, full nelson, handjob, large ass, large tits, lesbian kissing, massive ass, massive tits, o-face, sixty-nine, spread pussy, tentacle sex (try also oral/anal tentacle sex and tentacle dp), tit fucking, tit sucking, underboob, vaginal sex, long tongue, tits
- Example grid from training (single shot batch): https://cdn.discordapp.com/attachments/1010982959525929010/1035236689850941440/samples_gs-995960_e-000046_b000000.png
- Trained on 3498 images and around 250K steps
- disney 2d (classic) animation style: https://huggingface.co/nitrosocke/classic-anim-diffusion
- Kim Jung Gi: https://drive.google.com/drive/folders/1uL-oUUhuHL-g97ydqpDpHRC1m3HVcqBt
- Pyro's Blowjob Model: https://rentry.org/pyros-sd-model
- Pixel Art Sprite Sheet (stardew valley): https://huggingface.co/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator
- 4 different angles
- Examples + Reddit post: https://www.reddit.com/r/StableDiffusion/comments/yj1kbi/ive_trained_a_new_model_to_output_pixel_art/
- WLOP: https://huggingface.co/SirVeggie/wlop
- corporate memphis A.I model (infographics): https://huggingface.co/jinofcoolnes/corporate_memphis/tree/main
- Tron: https://huggingface.co/dallinmackay/Tron-Legacy-diffusion
- Superhero: https://huggingface.co/ogkalu/Superhero-Diffusion
- Chicken (trained on images from r/chickens): https://huggingface.co/fake4325634/chkn
- 1.5 based model created from the Spede images (not too sure if this is Dreambooth): https://mega.nz/file/mdcVARhL#FUq5TL2xp7FuzzgMS4B20sOYYnPZsyPMw93sPMHeQ78
- Redshift Diffusion (High quality 3D renders): https://huggingface.co/nitrosocke/redshift-diffusion
- Cats: https://huggingface.co/dallinmackay/Cats-Musical-diffusion
- Van Gogh: https://huggingface.co/dallinmackay/Van-Gogh-diffusion
- Rouge the Bat (44 SFW images of Rouge the Bat for 1600 or 2400 steps, keyword: 'rkugasebz'): https://huggingface.co/ChanseyIsForeverAI/Rouge-the-bat-dreambooth
- Made in Abyss (MIA 1-6 V2): https://drive.google.com/drive/folders/1FxFitSdqMmR-fNrULmTpaQwKEefi4UGI?usp=sharing
- Uploader note: I was hesitant to share this one because I have been having a lot of problems with the new captioning format. With the new format essentially we have much better multiple character flexibility and outfits. You can generate 2 characters in completely separate outfits with a high percentage of no blending. However, my new captioning was causing everything to train significantly slower, so some side characters don't look as good as they did in the original 1-6 model. There is also a strict captioning format I used, so I also uploaded a prompt readme to the folder which contains all the information needed to best use this model
- Gyokai/onono imoko/@_himehajime: https://mega.nz/folder/HzYT1T7L#H9TWVVYowA0cX8Eh6x_H3g
- use term 'gyokai' under class '1girl' e.g 'illustration of gyokai 1girl' + optionally 'multicolored hair, halftone, polka dot'
- Img: https://i.4cdn.org/h/1667881224238388.jpg
- Amano: https://huggingface.co/RayHell/Amano-Diffusion
- Midjourney: https://huggingface.co/prompthero/midjourney-v4-diffusion
- Borderlands (training info in reddit):
https://www.reddit.com/r/StableDiffusion/comments/yong77/borderlands_model_works_for/ - Pixel art model: https://publicprompts.art/all-in-one-pixel-art-dreambooth-model/
- Satania (has two iterations of the model, 500 step has more flexibility but 1k can look nicer if you want base Satania, link will expire soon): https://i.mmaker.moe/sd/mmkr-greatmosu-satania.7z
- Pokemon: https://huggingface.co/justinpinkney/pokemon-stable-diffusion
- final fantasy tactics: https://huggingface.co/jinofcoolnes/FinalfantasyTactics/tree/main
- smthdssmth: https://huggingface.co/Marre-Barre/smthdssmth
- A model I found on /vt/, not too sure what it is of: https://drive.google.com/file/d/1iR9wVI1wm4M6ZTJgJR_i3TZPAQBDB0Bk/view?usp=share_link
- Anmi: https://drive.google.com/drive/folders/1YFzJKQNVhCRgu0EnkVYgSQ5v63i_LBa4
- Samdoesart (merged model using the original, chewtoy's model, and Chris(orginalcode)'s model): https://huggingface.co/jinofcoolnes/sammod/tree/main
- Uploader note: all training credit goes to the 3 model maker this merge made from, thank you to them!
- CopeSeetheMald (samdoesart) (Both were trained with the same dataset. 204 images @ 20.4k steps, 1e-6 learning rate. It's just the base model that differs):
- berry-based model: https://mega.nz/folder/1a1xkQQK#4atlB1cJqI35InXxlxyA7A
- blossom-based model: https://mega.nz/folder/ZG0UnRBJ#jykESWBUCr7hjOoNVTXwLw
- Comparison: https://i.4cdn.org/g/1668068841516679.png
- CopeSeetheMald v2 (10k CHINAI (anything.ckpt)): https://mega.nz/file/xT9jVToK#Sj1S76kl-PC-zCRwJ2FWen6DS0NHY0IXFFAkXhm03eo
- SOVLFUL original Xbox/PS2/2006 PC era (jaggy92500): https://mega.nz/file/0SER2YpC#_MRc6p_sG9cSWqihpt33jpOWyMR8bCZrUaVkh4z5kGE
- Midna (wip): https://mega.nz/folder/E18R2SwC#jHBFsK7zCSuVemOsU4UZ9Q
- dreambooth midna training config: https://pastebin.com/5EWnMJEz
- Tagging tool in "Datasets:" section
- Pepe (word: pepestyle): https://mega.nz/file/NbUShTDR#bZpcYFlv--VqpqUfgDnU95duQlr3wFhRZ4m26WK-Qts
- Pepe continued: https://huggingface.co/SpiteAnon/Pepestyle
- Gigachad: https://huggingface.co/SpiteAnon/gigachad-diffusion
- y2k (by JF#8026): https://mega.nz/file/hT0mgTqR#d8g133APl30UtDwsNmzV73_ZESi_kTa5pmQgJoxomn0
- ykgl.ckpt. It does cgi girls from the y2k era. Trained for 40k steps.
- You call on them with (ykgl cgi_girl), or (ykgl cgi_girls), or just (ykgl girl), and then maybe with , cgi_artstyle.
- dbmai (model by 火柴人之父L): https://rentry.org/3en6a
- Vulcan (from Star Trek): https://huggingface.co/mitchtech/vulcan-diffusion
- DND: https://huggingface.co/0xJustin/Dungeons-and-Diffusion
- Complex Lineart: https://huggingface.co/Conflictx/Complex-Lineart
- More Abmayo (has model and imgs): https://mega.nz/folder/l5NxwTKa#9fA_tn_OZxWm3kHjdA9TPg
- Yuzuki Yukari: https://mega.nz/folder/8hNEiSSC#fYPUNzazZQ04dSizcjmhcg
- Samdoesartv2: https://huggingface.co/kijaw/samdoesarts_v2
- Nadanainone (created and trained on their own art, 1076 images (including flipped copies), 10k steps, 1e-6 learning rate): https://huggingface.co/nadanainone/istolemyownart
- Pop n Music: https://huggingface.co/nadanainone/popnm
- Heaven burns red artstyle: https://gofile.io/d/3q5WO3
- use hbrs as a prompt
- highly recommand to use 1girl and portrait as those were trained on those the most
- Samus enjoying tentacles + Dataset: https://mega.nz/folder/ls10yJBK#WsnlUfHkcle4FEc_jXS6eA
- CModel: https://huggingface.co/jinofcoolnes/cmodel/tree/main
- https://www.patreon.com/posts/cmodel-74660500
- https://twitter.com/Rahmeljackson/status/1592400206733115393
- Reported to work with NAI hypernets well
- heavy paint style from the same author of dbmai: https://drive.google.com/drive/folders/1ssyBg5Fw8O80_T6nvTrzcnluXEx0YD0I
- use lastmodel
- source: https://tieba.baidu.com/p/8147386385
- Kurzgesagt (another?): https://huggingface.co/questcoast/SD-Kurzgesagt-style-finetune
- Rei and Liduke (not sure if safe): https://rentry.org/eeayv
- Mikasa + Dataset: https://mega.nz/folder/x9FRkAzC#zPs19Nhx7ASZQwauj0PiyA
- Note: You have to get a bit creative with the prompt since I didn't clean up the tags, you often need to combine a ton of redundant tags to get the effect you want. Example images included along with a list of all tags for experimentation. If you merge the checkpoint with something else the visuals improve dramatically. The artist tags in the list will guide it towards a particular aesthetic.
- Ranma (replicates late '80s early '90s anime, specifically the Ranma 1/2 anime): https://huggingface.co/tashachan28/ranma_diffusion
-
VRass (based on Anytthing V3, trained on free VRoid clothing, good for img2img, supports high denoising (0.6-0.8), token: vrass): https://huggingface.co/Abysz/Img2Ass_VRass1.1
Export the png of the VRoid asset (or use whatever you want)
Place it in img2img. Use the "vrass" token and the style of clothing you want.
Experiment!
Upscale 4x (Recommended)
Import and enjoy! - Hapu: https://mega.nz/file/xWdTAbzI#TVaq9Fgds2V43IWai09NdoLDSJHx6FMy_14UTWL1HEQ
- AISee (made from 3k artworks from some website, more info in the link): https://huggingface.co/grinman/AIsee
- BTD6 monkeys (not sure if dreambooth): https://huggingface.co/Junglerally/Stable-BTD6
- Kobo (kbknr): https://huggingface.co/cntfcknwrtvwls/kbknr
- Alternate download: https://mega.nz/folder/jUQ20ZwC#15bhNyCG9SjgQYe5X_E5JA
- Yoji Shinkawa's artwork (47 images, 4k): https://civitai.com/models/1051
- An experimental Dreambooth model trained on individual frames of looping 3D animations that were then laid out on a 4x4 grid. Generates sprite sheets that can create very interesting abstract animations: https://huggingface.co/Avrik/abstract-anim-spritesheets
- Use the token AbstrAnm spritesheet. Size must be set at 512x512 or your outputs may not work properly
Embeddings
Found on 4chan:
- Embeddings + Artists: https://rentry.org/anime_and_titties (https://mega.nz/folder/7k0R2arB#5_u6PYfdn-ZS7sRdoecD2A)
- Random embedding I found: https://ufile.io/c3s5xrel
- Embeddings: https://rentry.org/embeddings
- Anon's collection of embeddings: https://mega.nz/folder/7k0R2arB#5_u6PYfdn-ZS7sRdoecD2A
- Collection: https://gitgud.io/ZeroMun/stable-diffusion-tis/-/tree/master/embedding
- Collection: https://gitgud.io/sn33d/stable-diffusion-embeddings
- Collection from anon's "friend" (might be malicious): https://files.catbox.moe/ilej0r.7z
- Collection from anon: https://files.catbox.moe/22rncc.7z
- Collection: https://gitlab.com/rakurettocorp/stable-diffusion-embeddings/-/tree/main/
- Collection: https://gitlab.com/mwlp/sd
- Senri Gan: https://files.catbox.moe/8sqmeh.rar
- Collection: https://gitgud.io/viper1/stable-diffusion-embeddings
- Repo for some: https://git.evulid.cc/wasted-raincoat/Textual-Inversion-Embeds/src/branch/master/simonstalenhag
- automatic's secret embedding list: https://gitlab.com/16777216c/stable-diffusion-embeddings
- Collection of /vt/ embeds in 0-Embeds folder: https://mega.nz/folder/23oAxTLD#vNH9tPQkiP1KCp72d2qINQ
- Henreader embedding, all 311 imgs on gelbooru, trained on NAI: https://files.catbox.moe/gr3hu7.pt
- Henreader (a different one, made for SD 1.4 or WD 1.2 with a small dataset): https://mega.nz/folder/7k0R2arB#5_u6PYfdn-ZS7sRdoecD2A/folder/Go9CRRoC
- Kantoku (NAI, 12 vectors, WD 1.3): https://files.catbox.moe/j4acm4.pt
- Asanagi (NAI): https://files.catbox.moe/xks8j7.pt
- Asanagi trained on 135 images augmented to 502 for 150296 steps on NAI Anime Full Pruned with 16 vectors per token with init word as voluptuous
- training imgs: https://litter.catbox.moe/2flguc.7z
- DEAD LINK Asanagi (another one): https://litter.catbox.moe/g9nbpx.pt
- Imp midna (NAI, 80k steps): mega.nz/folder/QV9lERIY#Z9FXQIbtXXFX5SjGf1Ba1Q
- imp midna 2 (NAI_80K): mega.nz/file/1UkgWRrD#2-DMrwM0Ph3Ebg-M8Ceoam_YUWhlQWsyo1rcBtuKTcU
- inverted nipples: https://anonfiles.com/300areCby8/invertedNipples-13000_zip (reupload)
- Dead link: https://litter.catbox.moe/wh0tkl.pt
- Takeda Hiromitsu Embedding 130k steps: https://litter.catbox.moe/a2cpai.pt
- Takeda embedding at 120000 steps: https://filebin.net/caggim3ldjvu56vn
- Nenechi (momosuzu nene) embedding: https://mega.nz/folder/E0lmSCrb#Eaf3wr4ZdhI2oettRW4jtQ
- Touhou Fumo embedding (57 epochs): https://birchlabs.co.uk/share/textual-inversion/fumo.cpu.pt
- Abigail from Great Pretender (24k steps): https://workupload.com/file/z6dQQC8hWzr
- Naoki Ikushima (40k steps): https://files.catbox.moe/u88qu5.pt
- Abmayo: https://files.catbox.moe/rzep6d.pt
- Gigachad: https://easyupload.io/nlha2m
- Kusada Souta (95k steps): https://files.catbox.moe/k78y65.pt
- Yohan1754: https://files.catbox.moe/3vkg2o.pt
- Niro: https://take-me-to.space/WKRY9IE.pt
- Kaneko Kazuma (Kazuma Kaneko): https://litter.catbox.moe/6glsh1.pt
- Senran Kagura (850 CGs, deepdanbooru tags, 0.005 learning rate, 768x768, 3000 iterations): https://files.catbox.moe/jwiy8u.zip
- Abmayo (miku) (14.7k): https://www.mediafire.com/folder/trxo3wot10j41/abmono
- Aroma Sensei (86k, "aroma"): https://files.catbox.moe/wlylr6.pt
- Zun (75:25 weighted sum NAI full:WD): https://www.fluffyboys.moe/sd/zunstyle.pt
- Kurisu Mario (20k): https://files.catbox.moe/r7puqx.pt
- creator anon: "I suggest using him for the first 40% of steps so that the AI draws the body in his style, but it's up to you. Also, put speech_bubble in the negative prompt, since the training data had them"
- ATDAN (33k): https://files.catbox.moe/8qoag3.pt
- Valorant (25k): https://files.catbox.moe/n7i9lq.pt
- Takifumi (40k, 153 imgs, NAI): https://freeufopictures.com/ai/embeddings/takafumi/
- for competition swimsuit lovers
- 40hara (228 imgs, 70k, 421 after processing): https://freeufopictures.com/ai/embeddings/40hara/
- Tsurai (160k, NAI): https://mega.nz/file/bBYjjRoY#88o-WcBXOidEwp-QperGzEr1qb8J2UFLHbAAY7bkg4I
- jtveemo (150k): https://a.pomf.cat/kqeogh.pt
- Creator anon: "I didn't crop out any of the @jtveemo stuff so put twitter username in the negatives."
- 150k steps, 0.005 LR, art from exhentai collection and processed with mirror and autocrop, deepdanbooru
- Nahida (Genshin Impact): https://files.catbox.moe/nwqx5b.zip
- Arcane (SD 1.4): https://files.catbox.moe/z49k24.pt
- People say this triggered the pickle warning, so it might be pickled.
- Gothica: https://litter.catbox.moe/yzp91q.pt
- Mordred: https://a.pomf.cat/ytyrvk.pt
- 100k steps tenako (mugu77): https://www.mediafire.com/file/1afk5fm4f33uqoa/tenako-mugu77-100000.pt/file
- erere-26k (fuckass(?)): https://litter.catbox.moe/cxmll4.pt
- Great Mosu (44k): https://files.catbox.moe/6hca0u.pt
- no idea what this embedding is, apparently it's an artist?: https://files.catbox.moe/2733ce.pt
- Dohna Dohna, Rance remakes (305 images (all VN-style full-body standing character CGs). 12000 steps): https://files.catbox.moe/gv9col.pt
- trained only on dohna dohna's VN sprites
- Onono imoko
- Raita: https://files.catbox.moe/mhrvmk.pt
- Senri Gan: https://files.catbox.moe/8sqmeh.rar
- 2 hypernetworks and 5 TI
- Anon: "For the best results I think using hyper + TI is the way. I'm using TI-6000 and Hyper-8000. It was trained on CLIP 1 Vae off with those rates 5e-5:100, 5e-6:1500, 5e-7:10000, 5e-8:20000."
- om_(n2007): https://files.catbox.moe/gntkmf.zip
- Kenkou Cross: https://mega.nz/folder/ZYAx3ITR#pxjhWOEw0IF-hZjNA8SWoQ
- Baffu (~47500 steps): https://files.catbox.moe/l8hrip.pt
- Biased toward brown-haired OC girl (Hitoyo)
- Danganronpa: https://files.catbox.moe/3qh6jb.pt
- Hifumi Takimoto: https://files.catbox.moe/wiucep.png
- 18500 steps, prompt tag is takimoto_hifumi. Trained on NAI + Trinart2 80/20, but works fine using just NAI
- Power (WIP): https://files.catbox.moe/bzdnzw.7z
- shiki_(psychedelic_g2): https://files.catbox.moe/smeilx.rar
- Akari: https://files.catbox.moe/b7jdng.pt
- Embeddings using the old version of TI
- Takeda Hiromitsu reupload: https://www.mediafire.com/file/ljemvmmtz0dqy0y/takeda_hiromitsu.pt/file
- Takeda Hiromitsu (another reupload): https://a.pomf.cat/eabxqt.pt
- Pochi: https://files.catbox.moe/7vegvg.rar
- Author's notes: Smut version was trained on a lot of doujins and it looks more like her old style from the start of smut version of Ane doujin (compare to chapter 1 and you can see that it worked). 200k version is looking a bit more like her recent style but I can see it isn't going to work the way I hoped.
- By accident I started with 70 pics where half of them were doujins to give reference for smut. Complete data is 200 with again those same 35 doujins for smut. I realized that I used half smut instead of full set so I went back to around 40k steps and then gave it complete 200 picture set hoping it would course correct since non smut is more recent art style. Now it looks like it didn't course correct and will never do that. On the other hand recent iterations are less horny.
- Power (Chainsaw Man): https://files.catbox.moe/c1rf8w.pt
- ooyari:
- 70k (last training): https://litter.catbox.moe/gndvee.pt
- 20k (last stable loss trend): https://litter.catbox.moe/i7nh3x.pt
- 60k (lowest loss rate state in trending graph): https://litter.catbox.moe/8wot9a.pt
- Kunaboto (195 images. 16 vectors per token, default learning rate of 0.005): https://files.catbox.moe/uk964z.pt
- Erika (Shadowverse): https://files.catbox.moe/y9cgr0.pt
- Luna (Shadowverse): https://files.catbox.moe/zwq5jz.pt
- Fujisaka Lyric: https://files.catbox.moe/8j6ith.pt
- Hitoyo (maybe WIP?): https://files.catbox.moe/srg90p.pt
- Hitoyo (58k): https://files.catbox.moe/btjsfg.pt
- kunaboto v2 (Same dataset, just a different training rate of 0.005:25000,0.0005:75000,0.00005:-1, 70k): https://files.catbox.moe/v9j3bz.pt
- Hitoyo (another, final vers?) (100k steps, bonnie-esque): https://files.catbox.moe/l9j1f4.pt
- Fatamoru: https://litter.catbox.moe/pn9xep.pt
- Dead link: https://litter.catbox.moe/xd2ht9.pt
- Zip of Fatamoru, Morgana, and Kaneko Kazuma: https://litter.catbox.moe/9bf77l.zip
- Tekuho (NAI model, Clip Skip 2, VAE unloaded, Learning rate 0.002:2000, 0.0005:5000, 0.0001:9000): https://mega.nz/folder/VB5XyByY#HLvKyIJ6U5nMXx6i3M__VQ
- manually cropped about 150 images, making sure that all of them have a full body shot, a shot from torso and up, and if applicable a closeup on the face
- Images not from Danbooru
- Best results around 4000 steps
- Reine: https://litter.catbox.moe/saav38.zip
- Embed of a girl anon liked (2500 steps, keyword "jma"): https://files.catbox.moe/1qlhjf.pt
- Carpet Crawler: https://anonfiles.com/i3a2o0E5y0/carpetcrawlerv2-12500_pt
- Embedding trained on nai-final-pruned at 8 vectors up to 20k steps. Turned into ugly overtrained garbage over 125000 steps so this is the one I'm releasing. Not good for much other than eldritch abominations.
- https://www.deviantart.com/carpet-crawler/gallery
- recommend using it in combination with other horror artist embeddings for best results.
- nora higuma (Fuckass, 0.0038, 24k, 1000+ dataset, might be pickle): https://litter.catbox.moe/tkj61z.pt
- dead link: https://litter.catbox.moe/25n10h.pt
- mdf an (Bitchass train: 0.0038, steps: 48k, loss rate trend: 0.095, dataset: 500+, issue: nsfw majority, will darken sfw images): https://litter.catbox.moe/lxsnyi.pt
- dead link: https://litter.catbox.moe/4liook.p
- subachi (shitass, train: 0.0038, steps: 48k, loss rate trend: 0.118, dataset: 500+, issue: due to artist's style, it's on sigma male mode; respecting woman is not an option with this embedding): https://litter.catbox.moe/6nykny.pt
- dead link: https://litter.catbox.moe/idskrg.pt
- irys: https://files.catbox.moe/1iwmv1.pt
- DEAD LINK Omaru-polka: https://litter.catbox.moe/qfchu1.pt
- Embed for "veemo" (?), used to make this picture (https://s1.alice.al/vt/image/1665/54/1665544747543.png): https://files.catbox.moe/18bgla.pt
- Lui: https://files.catbox.moe/m54t0p.pt
- Reine:
- 39,5k steps, pretty high vectors per token: https://files.catbox.moe/s2s5qg.pt
- smaller clip skip and less steps, trained it to 13k: https://files.catbox.moe/nq126i.pt
- Big reine collection: https://files.catbox.moe/xe139m.zip
- Ilulu (64k steps with a learning rate of 0.001): https://files.catbox.moe/8acmvo.pt
- random embed from furry thread (6500 steps, 10 vectors, 1 placeholder_string, init_word "girl" these four images used): https://files.catbox.moe/4qiy0k.pt
- Cookie (from furry thread, apprently good with inpainting): https://files.catbox.moe/9iq7hh.pt
- Cutie (cyclops, from furry thread, 8k steps): https://files.catbox.moe/aqs3x3.pt
- Felino's artstyle (from furry thread, 7 images): https://files.catbox.moe/vp21w4.pt
- Yakov (from mlp thread): https://i.4cdn.org/mlp/1666224881260593.png
- Rebecca (by booru anon, info is in link): https://huggingface.co/demibit/rebecca
- eastern artists combinination: https://mega.nz/file/SlQVmRxR#nLBxMj7_Zstv4XqfuEcF-pgza3T1NPlejCm1KGBbw70
- Elana (Shadowverse): https://files.catbox.moe/vbpo7m.pt
-
Info by anon: I just grab all the good images I can find, tag with BLIP and Deepdanbooru in the preprocessing, and pick a number of vectors based on how many images I have (16 here since not a lot). Other than that, I trained 6500 steps at 1 batch size under the schedule:
0.02:200, 0.01:1000, 0.005:2000, 0.002:3000, 0.0005:4000, 0.00005
-
- Lina: https://files.catbox.moe/jnfo98.pt
- Power (60k): https://files.catbox.moe/72dfvc.pt
- Takeda, Mogudan Fourchanbal (?, from KR site): https://files.catbox.moe/430rus.pt
- Mikan (30 tokens, 36 images (before flipping/splitting), 5700steps, 5e-02:2000, 5e-03:4000): https://files.catbox.moe/xwdohx.pt
- creator: I've been getting best results with these tags: (orange hair and (hair tubes:1.2), (dog ears and dog tail and (huge ahoge:1.2):1.2)), green eyes
- apparently it's not very effective. a hypernetwork is WIP
- Fuurin Rei (6000, 5.5k most): https://files.catbox.moe/s19ub3.7z
- Mutsuki (Blue Archive) embedding (10k step,150 image, no clip skip [set the "stop at last layers of clip model" option at 1 to get good results], 0.02:300, 0.01:1000, 0.005:2000, 0.002:3000, 0.0005:4000, 0.0005, vae disabled by renaming): https://files.catbox.moe/6yklfl.pt
- Reine: https://files.catbox.moe/tv1zf4.pt
- as109 (trained with 1000+ dataset, 0.003 learning rate, 0.12 loss rate trend, 25k step snapshot): https://litter.catbox.moe/5iwbi5.pt
- sasamori tomoe (0.92 loss trend, 60k+ steps, 0.003 learning rate. 500+ dataset, pruned pre 2015 images. biased to doujin, weak to certain positions (mostly side)): https://litter.catbox.moe/mybrvu.pt
- egami(500+ dataset, 0.03 learning rate, 0.13 loss trend, 40k steps): https://litter.catbox.moe/dpqp1k.pt
- pink doragon (20k+ steps, 0.0031 learning rate, 0.113 loss trend, 800+ dataset): https://litter.catbox.moe/mml9b9.pt
- kind of failure: fancy recent artworks are ignored due to dataset bias - will train with 2018+ data.
- leaning to BIG ASS and BIG TIDDIES.
- Kiwi (by booru anon): https://huggingface.co/demibit/kiwi
- Labiata (8 vectors/token): https://files.catbox.moe/0kri2d.pt
- Akari (another, one I missed): https://files.catbox.moe/dghjhh.pt
- Arona from Blue Archive (I'm pretty sure): https://files.catbox.moe/4cp6rl.pt
- Emma (arcane, 50 vector embedding trained on ~250 pics for ~13500 steps): https://files.catbox.moe/2cd7s3.pt
- blade4649 embedding (10k steps, 352 images,16 vectors,learning rate at 0.005): https://files.catbox.moe/5evrpn.pt
- fechtbuch of Mair: https://files.catbox.moe/vcisig.pt
- Longsword (mainly for img2img): https://files.catbox.moe/r442ma.pt
- Le Malin (listless Lapin skin, 10k steps with 712 inputs): https://files.catbox.moe/3rhbvq.pt
- minakata hizuru (summertime girl): https://files.catbox.moe/9igh8t.pt
- Roon (Azur Lane) (NAI model, 10k steps but with 83 different inputs): https://files.catbox.moe/9b77mp.pt
- arcane-32500: https://files.catbox.moe/nxe9qr.pt
- mashu003 (https://mashu003.tumblr.com/) (all danbooru images used as dataset): https://files.catbox.moe/kk7v9w.pt
- Takimoto Hifumi (18500 steps, prompt tag is takimoto_hifumi. Trained on NAI + Trinart2 80/20, but works fine using just NAI): https://files.catbox.moe/wiucep.png
- momosuzu nene: https://mega.nz/folder/s8UXSJoZ#2Beh1O4aroLaRbjx2YuAPg
- Harada Takehito (disgaea artist) (78k steps with 150 images): https://files.catbox.moe/e2iatm.pt
- Mda (1700 images and trained for 20k): https://files.catbox.moe/tz37dj.pt
- Polka (NAI, 16 vectors, 5500 steps): https://files.catbox.moe/pmzyhi.png
- Enna: https://files.catbox.moe/7edtp0.pt
- Ghislaine Dedoldia ("dark-skinned female" init word, 12 vectors per token, 0.02:200, 0.01:1000, 0.005:2000, 0.002:3000, 0.0005:4000, 0.00005 LR, 10k steps 75 image crop data set) https://mega.nz/folder/JPVSVLbQ#SqGZb7OVKe_UNRvI0R8U8A
- Notes by uploader: Here is a terrible Ghislaine Dedoldia embedding I made while testing sd-tagging-helper and the new webui dataset tag editor extension. She has no tail because the crops were shit and I only trained on crops made using the helper. Sometimes the eye patch is on the wrong side because of two images where it was on the wrong eye. Stomach scar is sometimes there sort of but probably needed more time in the oven. She doesn't have dark skin because the AI is racist and probably because it was only tagged on half the images.
- Uploader: Use "dark-skinned female" in your prompt or she will be pale
- Mizuryu-Kei (Mizuryu Kei): https://files.catbox.moe/bcy7vx.pt
- kidmo: https://litter.catbox.moe/44e28e.pt
- dataset:kidmo
- dataset:no filter
- 10 tokens
- 26k steps
- 0.129 loss trend
- 90-ish dataset
- 0.0028 learning rate
- issue: generic as shit, irradiated with kpop, potato gaming (you'll know when you try to use this shit with i2i)
- asanugget-16: https://litter.catbox.moe/9r0ixj.pt
- dataset: asanagi
- dataset: no pre-2010 artworks
- 16 tokens
- 22k steps
- 0.114 loss trend
- 500+ dataset (w/ auto focalpoint)
- 0.0028 learning rate
- Ohisashimono (20k to 144k): https://www.mediafire.com/folder/eslki3wzlmesj/ohi
- Shadman: https://files.catbox.moe/fhwn7m.png
- ratatatat74: https://mega.nz/folder/PfhRUbST#6oXUaNjk_B6nhJzjc_M0UA
- Uploader: Because the source images were prominently lewd in some shape or form, it really likes to give half-naked people.
- In combination with Puuzaki Puuna, it certainly brings out some interesting humanoid Nanachis.
- WLOP: https://mega.nz/folder/PfhRUbST#6oXUaNjk_B6nhJzjc_M0UA/folder/KWJUSR7T
- This embedding has 24 vectors, has been trained by a rate of 0.00005 and was completed at the steps of around 35000.
- The embedding was trained on NovelAI (final-pruned.ckpt).
- Uploader's Note: This embedding has a HUGE problem in keeping the signature out - feel free to crop out the signature if you wish to redo the embedding. If you do find a way to remove it without recreating the whole embedding - feel free to post it in 4chan/g/ and I may stumble upon it.
- Note 2: Use inpainting on the face in img2img to create some beautiful faces if they come out distorted initially.
- Asutora style embedding (mainly reflected in coloring and shading, since his faces are very inconsistent): https://mega.nz/folder/nZoECZyI#vkuZJoQyBZN8p66n4DP62A
- uploader: satisfactory results over 20k steps
- Comparisons: https://i.4cdn.org/g/1667701438177228.jpg
- y'shtola: https://files.catbox.moe/5hefsb.pt
- Uploader: You may need to use square brackets to lower it's impact. Also it likes making cencored pics unless you add penis to the input prompt
- Selentoxx (nai, 16v, 10k): https://files.catbox.moe/0j7ugy.png
- Aki (Goodboy, nai, 16v, 10k): https://files.catbox.moe/1p14ra.png
- Sana (nai, 15v):
- 10k: https://files.catbox.moe/g112gm.png
- 100k: https://files.catbox.moe/3ndubu.png
- In case these aren't good, use:
- 10k: https://files.catbox.moe/r5ciho.pt
- 25k: https://files.catbox.moe/e6aurx.pt
- 50k: https://files.catbox.moe/lz016k.pt
- 75k: https://files.catbox.moe/jhdjc9.pt
- 100k: https://files.catbox.moe/2lvv2z.pt
- Uploader note: don't use more than 0.8 weighting or else it gets deep fried
- delutaya: https://files.catbox.moe/r6pylz.pt
- Delutaya (another unrelated, 16v, 10k, nai): https://files.catbox.moe/kv2hdd.png
- mano-aloe-v1q (nai, manoaloe,mano aloe): https://files.catbox.moe/0i5qfl.pt
- Fauna (16v, 10k, nai): https://files.catbox.moe/zizgrw.png
- wawa (15v, 10k, nai): https://files.catbox.moe/2vpyi2.png
- Wagashi: https://mega.nz/file/exM21aTT#eawWbqsmajzs-TUCWfrVHvsG2HBEZ3HcYR5cy1AxFPw
- Deadflow: https://mega.nz/file/y41WHIgC#pXtCly7bzjDNJ7RZl7685_Nj1LTliIif_f_1BWMhHSE
- Elira (16v, 3k, nai sfw): https://litter.catbox.moe/4ylbez.png
- Rratatat (NAI, 16v, 10k): https://files.catbox.moe/nrekhk.png
- Uploader: Works better with "red hair, multicolored hair, twintails"
- WLOP (reupload, retrained WITHOUT signatures - 24 vectors, 0.00005 learning rate, around 19000 steps: https://mega.nz/folder/PfhRUbST#6oXUaNjk_B6nhJzjc_M0UA
- ratatatat74 (reupload, retrained WITHOUT VAE - 24 vectors, 0.00005 learning rate, 13500 steps): https://mega.nz/folder/PfhRUbST#6oXUaNjk_B6nhJzjc_M0UA
- Wiwa embed steps pre deep frying: https://files.catbox.moe/6lu6od.zip
- Nilou (by anon, not sure if safe or of any training info, NOTE TO MYSELF SEARCH FOR THIS EMBED's DISCORD): https://cdn.discordapp.com/attachments/1019446913268973689/1039909937884713070/nilou.pt
- Makima (500s, 4v, NAI): https://i.4cdn.org/h/1668023713496532.png
- Fauna (updated, NAI, 8v, 10k): https://files.catbox.moe/dmu00i.png
- New rrat (8v, 10k, NAI): https://files.catbox.moe/fyqxjf.png
- Weine (8v, 10k, nai): https://files.catbox.moe/b9cn4z.png
- Moona (10k, 8v, nai): https://files.catbox.moe/tuh4nj.png
- Comparison with Moona 2: https://i.4cdn.org/vt/1668038525258037s.jpg
- Aki (another, Goodboy, nai, 8v, 10k, nai): https://files.catbox.moe/k2cgxj.png
- Delu (another, notaloe, 8v, 10k, nai): https://files.catbox.moe/cvykdm.png
- Moona 2 (another anon, nai, moonmoon, nai, 8v, 10k): https://files.catbox.moe/yh8ora.png
- Comparison with the Moona 4 links up: https://i.4cdn.org/vt/1668038525258037s.jpg
- Kobogaki (nai, 8v, 10k): https://files.catbox.moe/0r3a8o.png
- Yopi (nai, 8v, 10k): https://files.catbox.moe/hoh865.png
- FreeStyle/Yohan TI by andite#8484 (trained on ALL of his artwork, not only skin): https://cdn.discordapp.com/attachments/1019446913268973689/1038423463314075658/yohanstyle.pt
- Matchach TI by methane#3131: https://cdn.discordapp.com/attachments/1019446913268973689/1040271410217635920/matcha-20000.pt
- Might need to add cat ears to negative prompt because for some reason it appears
- Elira (8v, 10k, nai): https://files.catbox.moe/ldeg3v.png
- Linked comparison (Elira default-5500 16v 5500 steps, Wiwa 4v 10000 steps, Elira t8 8v 10000 steps): https://i.4cdn.org/vt/1668135849025419s.jpg
- Reine (35v, 39500s, nai90sd10): https://files.catbox.moe/m0he7i.png
- Kobo (kbknr, 10k, 16v, NAI): https://files.catbox.moe/kphjec.png
- Kaela (Kovalski) (NAI, 4500k, 8v): https://files.catbox.moe/nxp368.png
- Uploader: Try, eyewear on head, blonde hair, red eyes, fur trim, jacket, white dress, red ribbon behind hair,
- Dataset?: https://files.catbox.moe/sqci4d.PNG
- Luna (LunaHime, NAI, 8v, 10k): https://files.catbox.moe/45fe4m.png
- needs heterochromia to force the two eye colors
- Zeta (Zetanism, 8v, 10k, nai): https://files.catbox.moe/z1u5py.png
- Aki (Goodboy) (NAI, 8v, 10k): https://litter.catbox.moe/vfd9fw.png
- Kobo (KoboGaki) (NAI, 8v, 10k): https://litter.catbox.moe/7hssgl.png
- Delu(?) (NotAloe) (NAI, 8v, 10k): https://litter.catbox.moe/9gdr5t.png
- Yopi (NAI, 8v, 10k): https://litter.catbox.moe/bhd01v.png
- AChan (NAI, 8v, 10k): https://i.4cdn.org/vt/1668274461405432.png
- Wiwa's alt hair (Elira, NAI, 8v, 10k): https://files.catbox.moe/vxz1yo.png
- miata8674 (45k training steps (4 Colab rounds)): https://mega.nz/folder/nZoECZyI#vkuZJoQyBZN8p66n4DP62A
- Focuses on faces, the defining features of the style are general sketchiness and the eyes. Strengthened by the mention of eyelashes and eyeshadow
- Asagi Igawa, Edjit, and Rouge the Bat (RealYiffingFar#4510): https://mega.nz/folder/5nIAnJaA#YMClwO8r7tR1zdJJeTfegA
- Has training info and a tutorial
- NIXEU: https://mega.nz/folder/PfhRUbST#6oXUaNjk_B6nhJzjc_M0UA
- By uploader: 24 vectors, 0.00005 training rate, around 16500 steps and 48 reference images with NovelAI (final-prune.ckpt)
- From the testings that I have done, it is able to replicate the artstyle quite well with one exception - the primary problem being the eyes - they seem to be slightly overbaked. My suggestion is to use img2img to circumvent that problem.
- Regardless: I recommend a CFG of around 8.5 and prompts such as 'soft lighting' which would underline the style. Requires a bit of fine tuning regarding prompts seeing that it is rather delicate to the touch,
- frank franzetta: https://huggingface.co/sd-concepts-library/frank-frazetta
- meme50 (WIP, 0.004 LR, 20k): https://litter.catbox.moe/e9v33j.pt
- Anya (probably a reupload from a collection, v8, 8500s, NAI): https://files.catbox.moe/b8ghxx.png
- Amelia Watson (amedoko, 8v, 10k, NAI): https://files.catbox.moe/qc3qt2.png
- Produces yellow eyes, prompt for blue eyes
- Kiara (kiarer, 10k, 5v, NAI): https://files.catbox.moe/87hdj3.png
- op: tried to get a nice spread of quality images from different outfits and artists. It probably won't get any of her outfits right, but the girl in the output is very clearly a wawa
- NecoArc: https://mega.nz/folder/ToFEARJa#yvSV_Cb5c6KxjM3wXR2_ZA
- Another anon uploaded a mirror (not sure if safe): https://gofile.io/d/fvz1Tl
- Trixie Lulamoon (100k, 16v, anything 3.0 pruned fp16): https://files.catbox.moe/8ek5o0.png
- For blue/purple witches
- The embedding associated the right blue tone with "aqua", as well as the correct purple tone with "purple". It tends to add long eyelashes and eyeshadow, but those can be enhanced with prompts.
- The correct hairstyle comes from "hair over shoulder" and "asymmetrical hair", but "asymmetrical bangs" helps achieve it.
- It dislikes clothes and will try to cheat them right off your prompts.
- As far as I can tell it works decently well on any Nai-based model and at varying Clip Skip levels, but it was trained on Anything v3 with Clip Skip on 1. Going over 1.2 weight on clip skip 1 looks weird sometimes
- Reupload by anon (not sure if safe): anonfiles.com/1ev4m8Hey1/trixie_lulamoon_pt
- Not sure what this is: https://files.catbox.moe/9l2nrw.pt
- Blannie from Bleague of Blegends (Annie from LOL, 18 tokens large(?)): https://files.catbox.moe/un85e4.pt
- Ex pic with skin: https://i.4cdn.org/h/1668567424263709s.jpg
- Nahida v2: https://cdn.discordapp.com/attachments/1019446913268973689/1031321278713446540/nahida_v2.zip
- Nahida (50k, very experimental, not enough images): https://files.catbox.moe/2794ea.pt
- look at the 2nd and 3rd images: https://www.reddit.com/gallery/y4tmzo
- Negative embedding to be used in the negatives (supposedly a quality enhancer, supposedly fixes hands): https://huggingface.co/datasets/Nerfgun3/bad_prompt
- Elysia (Honkai Impact): https://mega.nz/file/qnxiDQxR#3g7_gI-8OD83gPEWu-XjcPCedHCsvbjnxzjxW4c8GAo
- Koyori (Koyoyo, NAI, 8v, 10k): https://files.catbox.moe/empi4b.png
- Might need to prompt for the ears and midriff
- Kronii (10k, 8v, NAI): https://files.catbox.moe/vltkov.png
- Choco (ChocoSen, NAI, 8v, 10k): https://files.catbox.moe/1jf119.png
- Calli (Yaboy, NAI, v8, 10k) (https://s1.alice.al/vt/image/1668/53/1668537070785.jpg): https://files.catbox.moe/ilcpnn.png
- Might need to prompt pink hair
- Ina (Hololive, tag: ina-nai-100, https://s1.alice.al/vt/image/1668/66/1668666743766.jpg): https://files.catbox.moe/lsaydm.pt
- Omega Alpha (NAI, 8v, 10k): https://files.catbox.moe/19tun3.png
- Elira (old): https://files.catbox.moe/6lu6od.zip
- Elira alt (NAI, v8, 10k): https://mega.nz/folder/23oAxTLD#vNH9tPQkiP1KCp72d2qINQ/file/jvJwzDhB
- Pomu (ImPomu, NAI, v8, 10k, https://s1.alice.al/vt/image/1668/69/1668692914583.jpg): https://files.catbox.moe/9zdw2f.png
- Dead: https://litter.catbox.moe/qp32ku.png
- Try: Blonde hair, fairy wings
- Might be a good idea to exclude ocean? Dont ask, literally 1 image in dataset out of like 40 I dont understand why.
- Feesh (NAI, v8, 10k, https://s1.alice.al/vt/image/1668/70/1668703156236.jpg): https://files.catbox.moe/3v7yhj.png
- Suisei (no training info, https://i.4cdn.org/vt/1668897948153665.png): https://files.catbox.moe/9z6ni5.pt
- Rosemi (RosemiSama, NAI, v8, 10k, https://i.4cdn.org/vt/1668897966152847.jpg): https://files.catbox.moe/2oj2qd.png
- Might be tough to remove her thornyness.
- SelenToxx (NAI, v8, 10k, https://i.4cdn.org/vt/1668897792400569.png): https://files.catbox.moe/obxl01.png
- Just beware of ember. I need to remove any trace of him from the dataset.
- Anya (from Anya-Petra thread, https://i.4cdn.org/vt/1668969732782744.png): https://files.catbox.moe/h3t0du.pt
- Reiumuwu (NAI, v8, 10k, https://i.4cdn.org/vt/1669075492548084.jpg): https://files.catbox.moe/woxu4q.png
- NinaMommy (NAI, v8, 10k): https://files.catbox.moe/875g3s.png
- Enna (EnnaBird, NAI, v8, 10k, https://i.4cdn.org/vt/1669164314343707.jpg): https://files.catbox.moe/dkwu0d.png
- Ethyria (MillieMilk, NAI, 8v, 7.5k, https://i.4cdn.org/vt/1669158802233887.jpg): https://files.catbox.moe/57vpza.png
- rd_rn99/roadie3 (no training info: https://mega.nz/file/gY80FTrC#BwV_FS1EqmVcvw6SkiOHFuiE3NpyF66gdJAyDMLN4bU
- Artist's twitter: https://twitter.com/RD_RN00
- pwcsponson (133 images, 12k steps, ~0.11 loss, default settings)
- https://files.catbox.moe/85e2k6.zip
- Apparently generates strange things
- "It's a pwcsponson model that I intended to use to create his characteristic breasts and genitals and unless I rein it in heavily in the prompt everything is made of uncut dicks, even the buildings"
- Bad artist (negative embedding): https://huggingface.co/nick-x-hacker/bad-artist
Hypernetworks
- Dead link: https://t.me/+H4EGgSS-WH8wYzBl
- Big collection: https://drive.google.com/drive/folders/1-itk7b_UTrxVdWJcp6D0h4ak6kFDKsce?usp=sharing
- NAIHypernetwork collection: https://rentry.org/naihypernetworks
Hypernetwork Dump: https://gitgud.io/necoma/sd-database
Collection: https://gitlab.com/mwlp/sd
Another collection: https://www.mediafire.com/folder/bu42ajptjgrsj/hn
Found on 4chan:
- bigrbear: https://files.catbox.moe/wbt30i.pt
- Senran Kagura v3 (850 images, 0.000005 learn rate, 20000 steps, 768x768): https://files.catbox.moe/m6jynp.pt
- CGs from the Senran Kagura mobile game (NAI model): https://files.catbox.moe/vyjmgw.pt
- Ran for 19,000 steps with a learning rate of 0.0000005. Source images were 768x576. It seems to only reproduce the art style well if you specify senran kagura, illustration, game cg, in your prompt.
- Old version (19k steps, learning rate of 0.0000005. Source images were 768x576. NAI model. 850 CGs): https://files.catbox.moe/di476p.pt
- Senran Kagura again (850, deepdanbooru, 0.000006, 768x576, 7k steps): https://files.catbox.moe/f40el4.pt
- CGs from the Senran Kagura mobile game (NAI model): https://files.catbox.moe/vyjmgw.pt
- Danganronpa: https://files.catbox.moe/9o5w64.pt
- Trained on 100 images, up to 12k with 0.000025 rate, then up to 18.5k with 0.000005
- Also seed 448840911 seems to be great quality for char showcase with just name + base NAI prompts.
- Alexi-trained hypernetwork (22000 steps): https://files.catbox.moe/ukzwlp.pt
- Reupload by anon: https://files.catbox.moe/slbk3m.pt
- works best with oppai loli tag
- https://files.catbox.moe/xgozyz.zip
- Etrian Odyssey Shading hypernetwork (20k steps, WIP, WD 1.3)
- colored drawings by Hass Chagaev (6k steps, NAI): https://files.catbox.moe/3jh1kk.pt
- Morgana: https://litter.catbox.moe/3holmx.pt
- EOa2Nai: https://files.catbox.moe/ex7yow.7z
- EO (WD 1.3): https://files.catbox.moe/h5phfo.7z
- Taran Jobu (oppai loli, WIP, apparently it's kobu not jobu)
- Higurashi (NAI:SD 50:50): https://litter.catbox.moe/lfg6ik.pt
- by op anon: "1girl, [your tags here], looking at viewer, solo, art by higurashi", cfg 7, steps about 40"
- Tatata (15 imgs, 10k steps): https://files.catbox.moe/7hp2es.pt
- Zankuro (0.75 NAI:WD, 51 imgs, 25k+ steps): https://files.catbox.moe/tlurbe.pt
- Training info + hypernetwork: https://files.catbox.moe/4do43z.zip
- Test Hypernetwork (350 imgs where half are flipped, danooru tags, 0.00001 learning rate for 3000 steps, 0.000004 until step 7500): https://files.catbox.moe/coux0u.pt
- Kyokucho (40k steps, good at 10-15k, NAI:WD1.2): https://workupload.com/file/TFRuGpdGZZn
- Final Ixy (more detail in discord section): https://mega.nz/folder/yspgEBhQ#GLo7mBc1EH7RK7tQbtC68A
- Old Ixy (more data, more increments): https://mega.nz/file/z8AyDYSS#zbZFo9YLeJHd8tWcvWiRlYwLz2n4QXTKk04-cKMmlrg
- Old Ixy (less increments, no training data): https://mega.nz/file/ixxzkR5T#cxxSNxPF1KmszJDqiP4K4Ou8tbl1SFKL6DdQC58k6zE
- Grandblue Fantasy character art (836 images, 5e-5:100, 5e-6:1500, 5e-7:10000, 5e-8:20000 learn rate, 20000 steps, 1024x1024): https://files.catbox.moe/2uiyd4.pt
- Bombergirl (Stats: 178 images, 5e-8 learn rate continuing from old Bombergirl, 20000 steps, 768x768): https://files.catbox.moe/9bgew0.pt
- Old Bombergirl (178 imgs, 0.000005 learning rate, 10k, 768x768): https://files.catbox.moe/4d3df4.pt
- Aki99 (200 images , 512x512, 0.00005, 19K steps, NAI): https://files.catbox.moe/bwff89.pt
- Aki99 (200 images , 512x512, 0.0000005, 112K steps, learning prompt: [filewords], NAI): https://www.mediafire.com/file/sud6u1vb0gvqswu/aki99-112000.7z/filehttps://files.catbox.moe/6hca0u.pt
- Great Mosu: https://files.catbox.moe/mc1l37.pt
- mda starou: https://a.pomf.cat/xcygvk.pt
- Mogudan (12 vectors per token, 221 image dataset, preprocessing: split oversize, flipped mirrors, deepdanbooru auto-tag, 0.00005 learning rate, 62,500 steps): https://mega.nz/file/UtAz1CZK#Y5OSHPkD38untOPSEkNttAVi2tdRLBFEsKVkYCFFaHo
- Onono Imoko: https://files.catbox.moe/amfy2x.pt
- Dataset: https://files.catbox.moe/dkn85w.zip
- Etrian Odyssey (training rate 5e-5:100, 5e-6:1500, 5e-7:10000, 5e-8:20000,20k steps, 512 x 512 pics): https://files.catbox.moe/94qm83.7z
- Jesterwii: https://files.catbox.moe/hlylo4.zip
- jtveemo (v1): https://mega.nz/folder/ctUXmYzR#_Kscs6m8ccIzYzgbCSupWA
- 35k max steps, 0.000005 learning rate, 180 images, ran through deepbooru and manually cleaned up the txt files for incorrect/redundant tags.
- Recommended the 13500.pt, or something near it
- Recommended: https://files.catbox.moe/zijpip.pt
- Artsyle based on Yuugen (HBR) (Stats: 103 images, 5e-5:100, 5e-6:1500, 5e-7:10000, 5e-8:20000 learn rate, 20000 steps, 1024x1024,Trained on NAI model): https://files.catbox.moe/bi2ts0.7z
- Alexi: https://files.catbox.moe/3yj2lz.pt (70000 steps)
- as usual, works best with oppai loli tag. chibi helps as well
- changes from original one i noticed during testing:
-hair shading is more subtle now
-nipple color transition is also more subtle
-eyelashes not as thick as before, probably because i used more pre-2022 pictures. actually bit sad about it, but w/e
-eyes in general look better, i recommend generating on 768×768 with highres fix
-blonde hair got a pink gradient for some reason
-tends to hide dicks between the breasts more often, but does it noticeably better
-likes to add backgrounds, i think i overcooked it a bit so those look more like artifacts, perhaps with other prompts it will look better
-less hags
-from my test prompts, it looked like it breaks anatomy less often now, but i mostly tested pov paizuri
-became kinda worse at non-paizuri pictures, less sharpness. because of that, i'm also including 60000 steps version, which is slightly better at that, but in the end, it's a matter of preference, whether to use newer version or not: https://files.catbox.moe/1zt65u.pt
- Ishikei: https://www.mediafire.com/folder/obbbwkkvt7uhk/ishikemono
- Curss style (slime girls): https://files.catbox.moe/0sixyq.pt
- WIP Collection of hypernets: https://litter.catbox.moe/xxys2d.7z
- DEAD LINK Mumumu's art: https://mega.nz/folder/tgpikL6C#Mj0sHUnr-O6u4MOMDRTiMQ
- Senri Gan: https://files.catbox.moe/8sqmeh.rar
- 2 hypernetworks and 5 TI
- Anon: "For the best results I think using hyper + TI is the way. I'm using TI-6000 and Hyper-8000. It was trained on CLIP 1 Vae off with those rates 5e-5:100, 5e-6:1500, 5e-7:10000, 5e-8:20000."
- Ulrich: https://files.catbox.moe/jhgsxw.zip
- akisora: https://files.catbox.moe/gfdidn.pt
- lilandy: https://files.catbox.moe/spzm60.pt
- shadman: https://files.catbox.moe/kc850y.pt
- anon: "if anyone else wants to try training, can recommend - 0.00005:2000, 0.000005:4000, 0.0000005:6000 learning rate setup (6k steps total with 250~1000 images in dataset)"
- not sure what this is, probably a style: https://files.catbox.moe/lnxwks.pt
- ndc hypernet, muscle milfs: https://files.catbox.moe/hsx4ml.pt
- Asanuggy: https://mega.nz/folder/Uf1jFTiT#TZe4d41knlvkO1yg4MYL2A
- Tomubobu: https://files.catbox.moe/bzotb7.pt
- Works best with jaggy lines, oekaki, and clothed sex tags.
- satanichia kurumizawa macdowell (around 552 pics in total with 44.5k steps, most of the datasets are fanarts but some of them are from the anime, tagged with deepdanbooru, flipped and manually cropped): https://files.catbox.moe/g519cu.pt
- Imazon v1: https://files.catbox.moe/0e43tq.pt
- Imazon v2: https://files.catbox.moe/86pkaq.pt
- WIP Baffu: https://gofile.io/d/4SNmm5
- Ilulu (74k steps at 0.0005 learning rate, full NAI, init word "art by Ilulu"): https://files.catbox.moe/18ad25.pt
- belko paizuri (86k swish + normalization): https://www.mediafire.com/folder/urirter91ect0/belkomono
- WIP: training/0.000005/swish/normalization
- Pinvise (Suzutsuki Kirara) (NAI-Full with 5e-6 for 8000 steps and 5e-7 until 12000 steps on 200 (400 with flipped) images): https://litter.catbox.moe/glk7ni.zip
- Bonnie: https://files.catbox.moe/sc50gl.pt
- Another batch of artists hypernetworks (some are with 1221 structure, so bigger size)
- https://files.catbox.moe/srhrn6.pt - diathorn
- https://files.catbox.moe/dytn06.pt - gozaru
- https://files.catbox.moe/69t1im.pt - Sunahara Wataru
- kunaboto (new swish activation function + dropout using a learning rate of 5e-6:12000, 5e-7:30000): https://files.catbox.moe/lynmxm.pt
- aesthetic: https://files.catbox.moe/qrka4m.pt
- Reine: https://litter.catbox.moe/1yjgjg.pt
- Om (nk2007):
- 250 images (augmented to 380), learning rate: 5e-5:380,5e-6:10000,5e-7:20000, template: [filewords]
- 10k step : https://files.catbox.moe/8kqb4c.pt
- 16k step : https://files.catbox.moe/7vtcgt.pt
- 20k step (omHyper): https://files.catbox.moe/f8xiz1.pt
- Spacezin: https://mega.nz/folder/Os5iBQDY#42xOYeZq08ZG0j8ds4uL2Q
- excels at his massive tits, covered nipples, body form, sharp eyes, all that nice stuff
- no cbt data
- using the new swish activation method +dropout, works very well, trained at 5e-6 to 14000
- data it was trained on and cfg test grid included in the folder
- Hypernetwork trained on 13 handpicked images from spacezin
- recommend using spacezin in the prompt, using 14000 step hypernetwork, lesser steps are included for testing
- Aesthetic gradient embedding included
- amagami artstyle (30k,5e-6:12000, 5e-7:30000,swish+dropout): https://files.catbox.moe/3a2cll.7z
- Ken Sugimori (pokemon gen1 and gen2) art: https://files.catbox.moe/uifwt7.pt
- mikozin: https://mega.nz/folder/a0wxgQrR#OnJ0dK_F6_7WZiWscfb5hg
- Trained a hypernetwork on mikozin's art, using nai full pruned, swish activation method+dropout
- placing mikozin in the prompt will make it have a stronger effect, as all the training prompts include the [name] at the end.
- has a number of influences on your output, but mostly gives a very soft, painted style to the output image
- Aesthetic gradient embedding also included, but not necessary
- check the training data rar to read the filewords to see if you want to call anything it was specifically trained on
- Found on Discord (copied from SD Training Labs discord, so grammar mistakes may be present):
- Pippa (trained on NAI 70%full-30%sfw): https://files.catbox.moe/uw1y8g.pt
- reine (WIP): https://files.catbox.moe/od4609.pt
- WiseSpeak/RubbishFox (updated): https://files.catbox.moe/pzix7f.pt
- Info: Uses 176 Fanbox images that were preprocessed with splitting, flipping and mild touchup to remove text in Paint on about 1/4th of the images. I removed images from the Preprocess folder that did not have discernable character traits. Most images are of Tamamo since that is his waifu. Total images after split, flip, and corrections was 636. Took 13 hours at 0.000005 Rate at 512x512. Seems maybe a bit more touchy than the 61.5K file, but I believe that when body horror isn't present you can match the RubbishFox's style better.
- Style from furry thread: https://files.catbox.moe/vgojsa.pt
- 2bofkatt (from furry thread): https://files.catbox.moe/cw30m8.pt
- Hypernetwork trained on all 126 cards from the first YGO set in North America, 'Legend Of The Blue Eyes White Dragon' released on 03/08/2002: https://mega.nz/folder/ILkwRZLb#UJ03LDIfcMiFTn6-pyNyXQ
- WiseSpeak (Rubbish Fox on Twitter): https://files.catbox.moe/kyllcc.pt
- Info: Uses 176 images that were preprocessed with splitting, flipping and mild touchup to remove text in Paint on about 1/4th of the images. I removed images from the Preprocess folder that did not have discernable character traits. Most images are of Tamamo since that is his waifu. Total images after split, flip, and corrections was 636. Took 8 hours at 0.000005 Rate at 512x512
- 93k, less overtrained: https://files.catbox.moe/fluegz.pt
- Large collection of stuff from korean megacollection: https://mega.nz/folder/sSACBAgC#kNiPVzRwnuzs8JClovS1Tw
- Crunchy: https://files.catbox.moe/tv1zf4.pt
- Obui styled hypernetwork (125k steps): https://files.catbox.moe/6huecu.pt
- KurosugatariAI (2 hypernets, 1 embed, embedding is light at 17 token weight. at 24 or higher creator anon thinks the effect would be better): https://mega.nz/folder/TAggRTYT#fbxf3Ru8PkXz_edIkD2Ttg
- Amagami (Layer structure 1, 1.5 1.5 1; mish; xaviernormal; No layer normalization; Dropout O (appling only at 2nd layer due to bug); LR 8e-06 fixed; 20k done): https://files.catbox.moe/ucziks.7z
- Reine (from VTuber dump, might be pickled): https://files.catbox.moe/uf09mp.pt
- Onono imoko: https://mega.nz/file/67AUDQ4K#8n4bzcxGGUgaAVy7wLXvVib0jhVjt2wPS-jsoCxcCus
- Info moved to discord section
- Sironora:
- minakata hizuru (summertime girl): https://files.catbox.moe/gmbnnr.pt
- a1 (4.5k): https://files.catbox.moe/x6zt6u.pt
- 焦茶 / cogecha hypernetwork, trained against NAI (DEAD LINK): https://mega.nz/folder/BLtkVIjC#RO6zQaAYCOIii8GnfT92dw
- 山北東 / northeast_mountain hypernetwork, trained against NAI (DEAD LINK): https://mega.nz/folder/RflGBS7R#88znRpu7YC1J1JYa9N-6_A
- emoting mokou (cursed): https://mega.nz/folder/oPUTQaoR#yAmxD_yqeGqyIGfOYCR4PQ
- Cutesexyrobutts and gram: https://files.catbox.moe/silh2p.7z
- Scott: https://files.catbox.moe/qgqbs7.7z
- zunart (NAI, steps from 20000 to 50000): https://mega.nz/file/T9RmlbCQ#_JPkZqY5f0aaNxVc8MnU3WQHW4bv_yCWzJqOwL8Uz1U
- HBRv3D aka Heaven Burns Red (yuugen) retrained on new dataset of 142 mixed images: https://files.catbox.moe/urjkbm.7z
- Setting was 1,2,1 relu ,Learning rate: 5e-6:12000, 5e-7:30000
- momosuzu nene: https://mega.nz/folder/s8UXSJoZ#2Beh1O4aroLaRbjx2YuAPg
- TATATA and Alkemanubis: https://mega.nz/folder/zYph3LgT#oP3QYKmwqurwc9ievrl9dQ
- Tatata: Contains dataset, hypernetworks for steps 10000-19000 with a 1000 steps step, as well as full res sfw and nsfw comparisons.
- It was created before layer structure option, so it parameters are 1, 2, 1 layer structure, linear activation function.
- Alkemanubis: Alkemanubis is with elu activation function and normalisation, Alkemanubis4 is with swish and dropout, Alkemanubis5 is with linear and dropout. All have 1, 2, 4, 2, 1 layer structure.
- dataset and more fullres preview grid are inside too.
- HKSW (wrong eye color because of dataset): https://files.catbox.moe/dykyab.pt
- Nanachi and Puuzaki Puuna (retrained, 4700 steps, sketches are good, VAE turned off): https://mega.nz/folder/PfhRUbST#6oXUaNjk_B6nhJzjc_M0UA
- HiRyS: https://mega.nz/file/Mk8jTZ4I#TdlF5Bxwz_gAuQeR0PWa_YUZotcQkA34d6m49I6eUMc
- Dead link, I think this is the same hypernetwork: https://litter.catbox.moe/rx8uv0.pt
- 4k, 3d, highres images: 4k, 3d, highres images: https://mega.nz/file/UAEHkbhK#R-zdpiIz6Ig2-laa-M9_Hmtq6xgLNJZ0ZwVOiXt3OSc
- It has a preference for 3d design, large breasts and curves. It also has a preference for applying backgrounds,if none suggested. usually parks, beaches, indoors or cityscapes
- Comparison: https://i.4cdn.org/h/1667278030582788.png
- Okegom (Funamusea / Deep Sea Prisoner): https://mega.nz/file/XYQF3YoZ#BAvBQduEx-tnUKvyJQ3mH-zOa_cKUKxpc58YpO8h2jc
- Crashed after 5.3k steps, continued training after when hypernetwork training resuming is broken. Apprently it got better
- Uploader: Alright, it's done. Maybe it's the small training data or the mediocre tagging but sometimes you get stuff that doesn't resemble their art style. Still releasing the three models I liked though, they work nicely with img2img.
- Funamusea/Okegom/Mogeko (12500 steps): https://mega.nz/file/SBg0zBIa#BU1KkBY1vMvLXpfkDci1RZYi5f8P0yN5oyQzGYXF8q0
- Notes by uploader:
Most results (at least with img2img) will have a chibi style regardless of your prompt.
30 steps recommended.
Does very well with white skin/pale characters, this is because the hypernetwork is trained mainly on white characters. Not because I wanted to but because it's what she tends to draw the most.
Hypernetwork has most of her NSFW art in its data including a fanart which looks like it was drawn by her, just so the AI has a reference. So, yes, it can generate nudity and porn in her style, although I'm not sure about penetration stuff because I haven't tried.
"outline" tag is recommended in prompt to have the same thick outlines she often uses in her artwork.
- Notes by uploader:
- Sakimichan: https://mega.nz/file/TBJwFDLI#H_bgih8qbWe-EN4ntL_7ur6Ylr2qbcxhDwlC2AfWpnc
- arnest (109 images, 12000 steps): https://mega.nz/file/HNIhlZ7B#o1hpR04PxBDWTEHDfxLfbRi_9K56HVJ58YgCwDUeRMw
- uploader: Hypernetwork trained on 109 total images dating from 2015 to 2022, including his deleted NSFW commissions and Fanbox content. Also trained on like two or three pre-2015 images just because why not. Should be able to do Touhou characters (especially Alice and Patchouli) extremely well.
- I recommend using the white pupils tag for the eyes to look like picrel.
- Zanamaoria (20k steps, 47 imgs, mostly dark-skinned elves, and paizuri/huge tits): https://files.catbox.moe/10iasp.pt
- 18500 steps: https://files.catbox.moe/xgf1ho.pt
- Pinvise (30k steps, 5e-6 for 8k steps and 5e-7 for the rest): https://files.catbox.moe/dec3h3.pt
- Black Souls II (V2 wasn't uploaded because it was "disappointing"):
- V1 (Image: 181 augmented to 362,Learning Rate 5e-5:362,5e-6:14000,5e-7:20000, steps: 10k): https://files.catbox.moe/fdoyt9.pt
- V3 (Image:164 augmented to 328, Learning Rate 5e-5:328,5e-6:14000,5e-7:20000, steps: 10k): https://files.catbox.moe/1r36tp.pt
- uploader: Unlike V1, I manually edited almost all tags generated with deepdanbooru with the dataset tag editor
- Uncensored X/Y plots:
- V3 (strength: 1) : https://files.catbox.moe/tse4kr.png, https://files.catbox.moe/8y91f0.png (no 'sketch')
- V1 (strength: 0.7): https://files.catbox.moe/pml06i.png ('sketch'), https://files.catbox.moe/18993y.png (without "sketch")
- Uploader: Those two hypernetwork seem to be more accurate if we put "sketch" in the prompt. V1 break if we set hypernetwork strength to 1 (or anything over 0.8) and 0.7 seem to be the sweet spot. V3 does not seem to have the same problem.
-
Hataraki Ari (30k, 50k, and 100k steps): https://mega.nz/folder/TZ5jXYrb#-NXJo8wlmanr8ebbJ5GBBQ
- Training Info:
Modules: 768, 320, 640, 1280
Hypernetwork layer structure: 1, 2, 1
Activation function: swish + dropout
Layer weights initialization / normalization: none
115 images, size 512x512, manually selected from patreon gallery on sadpanda
Watermarks + text manually removed or cropped out
Deepbooru used for captions
Hypernetwork learning rate: 5e-6:12000, 5e-7:30000, 2.5e-7:50000, 1e-7:100000 - Uploader note: Works best with huge or gigantic breasts. Occasionally has some problems with extra limbs or nipples. Tags like tall female, muscular female or abs may lead to small heads or weirdly proportioned bodies, so I recommend lowering the weighting on those.
- Training Info:
- IRyS (not sure if this is a reupload of a previous one): https://files.catbox.moe/qnery5.pt
- Nanachi (reupload, re-retrained WITHOUT sneaky VAE - 0.000005 learning rate, around 16000 steps, around 13000 steps): https://mega.nz/folder/PfhRUbST#6oXUaNjk_B6nhJzjc_M0UA
- Puuzaki Puuna (reupload, re-retrained WITHOUT sneaky VAE - 0.000005 learning rate): https://mega.nz/folder/PfhRUbST#6oXUaNjk_B6nhJzjc_M0UA
- Sayori (trained on mostly nsfw CGs (30 out of 40 images were nsfw) from nekopara, koikuma + fandisc, and tropical liquor, trained on NAI pruned): https://mega.nz/file/LegFzJxa#Q1Se9fByKcjuXA2DNWt0gCaV3rCP8U-voBKgFjOevF8
- Henreader: https://files.catbox.moe/q6t6vw.pt
- 104 imgs, mostly from Loli no Himo and some of his recent art, used a grabber to download with gelbooru tags
- Training settings:
- layer: 1, 2, 1
- activation function: linear
- initialization: Normal
- Images: 104 (208 with flipped images)
- dataset: https://files.catbox.moe/e0e3nk.7z (NSFW + loli)
- resolution: 512x512
- Learning rate: 5e-5:832,5e-6:14000,5e-7:2000
- steps: 10000
- Template: [filewords]
- Sakimichan (not sure if it's a reupload): https://cdn.discordapp.com/attachments/1041563266041794580/1041563947528093746/sakimichan.pt
- Dohna2 (apparently incorporates backgrounds better (for stuff like tentacles), anon reports underbaked, wip): https://mega.nz/file/y65DwBYQ#1BLmT4IyuUVeUrjYSIjE-oBpmMvkVp4ZSCXVV3jkOb8
- Password: https://rentry.org/f787o
- Original dead link: https://litter.catbox.moe/8m4ue9.7z
- Kinjo kuromomo: https://files.catbox.moe/8bgjto.pt
- Example pic (Prompt: https://rentry.org/e8mhs): https://i.4cdn.org/g/1668467554941924s.jpg
- ruan chen yue (artstyle, rcy3):
- Example: https://i.4cdn.org/g/1668780254347358s.jpg
- Based on Anythingv3 (op recommended the anything vae too), set to CFG 9 for best results
- Example prompt: masterpiece, highest quality, colorful, shiny hair, colored hair shine, ((by ruan chen yue)), rcy3, vibrant, soft face, lips, blush, sparkles, glitter, HDR
- Negative: nsfw, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, poorly drawn hands, poorly drawn limbs, bad anatomy, deformed, amateur drawing, odd, lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, multiple body parts, two-colored hair, multicolored hair
- chinanago (chinanago7010) (NAI, 10k, all drawings on gelbooru): https://files.catbox.moe/kk1rss.pt
- Dishwasher1910 (also known as dickwasher on /alg/) (NAI): https://files.catbox.moe/7fs8xu.pt
- Muk (monsieur): https://files.catbox.moe/q8jnsd.pt
- Olga Discordia (35k, old version): https://www.dropbox.com/s/fc8bg0ti7uy8qxz/olgadiscordiav6-35000.pt?dl=0
- make sure you have these prompt to activate her: yellow eyes, intricate eyes, (symmetrical face), (mature female:1.2), pointy ears, elf, earrings, hair over one eye, jewelry, dark elf, breasts, black hair, long hair, dark skin, parted lips, thighhighs, gloves, 1girl, solo,
- final-pruned model. mixberry gives good results too. clip set at 2. vae is optional
- More info by creator: can use final-pruned, berrymix and v3 without any problem. i do not recommend vae with it. clip set at 1 for higher detail. 2 for whatever. to get the prompt working, make sure to included dark elf, dark skinned female, and pointy ears.
- olga discordia (Kuroinu) (50k steps):
- https://www.dropbox.com/s/ivz7nr43b0xmbth/olgadiscordiav8-10000-7000%20armor%20-%20Copy.pt?dl=0
- to activate olga, please use this prompt: 1girl, olga discordia, amber eyes, dark elf and dark skinned female, hair covering one eye.
- for additional stuff, such as her outfit, you have to use purple armor, purple sleeves and purple thighhighs. for the top acccuracy, use disconnected corset purple armor. weight accordingly too.
- NOTE: was trained with censored images. make sure to have censored and mosaic censored in the negative. put 1 or 2 emphasis on it. up to you.
- works with every model too. works best with naileak and all berrymixes. clip at 1 or 2 depends on the model. enjoy
- Mogudan), Mumumu (Three Emu), Satou Shouji, Onomeshin, Bang-You, Aoi Nagisa, Honjou Raita, Amazon : https://mega.nz/folder/hlZAwara#wgLPMSb4lbo7TKyCI1TGvQ
- Changelog of new stuff: onomeshin (pic https://i.4cdn.org/h/1669000305326267.png), Bang-You (https://i.4cdn.org/h/1669000350687492.png), Aoi Nagisa (https://i.4cdn.org/h/1669000401417659.png), Honjou Raita (uses new options, comparison between current one and the one mogubro uploaded: https://i.4cdn.org/h/1669000583045601.png), Amazon (https://i.4cdn.org/h/1669114002712582.png):
- Has training info, dataset, comparison, and hypernetworks
- Amazon hypernet does the best on DPM++ 2M a Karras, has deformities on some other samplers
- Albino_Otokuyou (Otokuyou's Shinroi Ko character) (White eyelashes and true albinos): https://files.catbox.moe/k7ftww.rar
- Good when using with Nerfgun3's new Albino_style
- ex: https://i.4cdn.org/g/1668896107598228.jpg (Prompt: https://rentry.org/gacnc)
- Best results: Half-closed eyes at low weight may have some benefits. Using my Albino_Otokuyou Shiroi Ko Hypernet at just 0.2 strength helps with white eyelash cohesion. Use 'parted bangs' and 'swept bangs' and prompts that clear space above the eyes to help consistently generate white eyelashes, experiment as you like.
- Issues: My 30000step Hypernet has a slight problem squashing images, reducing the strength helps mitigate this quite abit, the AlbinoFix is slightly weaker but may be best used with Nerfgun3's embedding.
- Tatata + Dataset (supposedly trained on CHINAI + 0.45(Trinart115-SD-1.4)): https://mega.nz/folder/sTV0EI6b#hGotLRXpotvmYxqfTb_KMw/folder/8eMylBTK
- Eula Lawrence (old, seemed to be misplaced): https://mega.nz/file/l9tAHJBD#xdXMf7vulY4GJBigxegFVLSOULONnk4o86qKHYoBZmc
- Kincora (Azur Lane artist): https://files.catbox.moe/enzbw6.pt
- Training:
-
layer: 1, 2, 1
-
activation function: linear
-
initialization: Normal
-
Images: 136 (half of those image are just portrait version of the other half) or 272 if we count flipped images
-
Dataset: https://files.catbox.moe/yc9kkh.7z
-
Steps: 20k
-
Learning rate: 5e-5:816,5e-6:10000,5e-7:20000
-
resolution: 512x512
-
Template: [filewords]
-
- Example (it's the one on the right): https://i.4cdn.org/g/1669142706465584.jpg
- Training:
- Tomoko: https://raw.githubusercontent.com/hlky/sd-embeddings/main/tomoko/tomoko.pt
- Morino Bambi (NUH) (NAI, 10k steps,learning rate: 5e-5:672,5e-6:10000,5e-7:30000, 224 images): https://files.catbox.moe/5x3icv.pt
- Example (NSFW): https://i.4cdn.org/g/1669256512125648.jpg, Prompt: https://pastebin.com/GsZA9BEa
- Example (SFW): https://i.4cdn.org/g/1669260253330069.jpg, Prompt: https://pastebin.com/DGD4DV7g
- Dataset (NSFW): https://files.catbox.moe/q3l0wl.7z
Found on Discord:
-
Art style of Rumiko Takahashi
Base: Novel AI's Final Pruned
[126 images, 40000 steps, 0.00005 rate]
Tips: "by Rumiko Takahashi" or "Shampoo from Ranma" etc. -
Amamiya Kokoro (天宮こころ) a Vtuber from Njiisanji [NSFW / SFW] (Work on WD / NAI)
(Training set: 36 Input images, 21500 Steps, 0.000005 Learning rate.
Training model: NAI-Full-Prunced
Start with nijisanji-kokoro to get a good result.
Recommend Hypernetwork Strength rate: 0.6 to 1.0 -
Haru Urara (ハルウララ) from Umamusume ウマ娘 [NSFW / SFW] (Work on WD / NAI)
Training set: 42 Input images, 21500 Steps, 0.000005 Learning rate.
Training model: NAI-Full-Prunced
Start with uma-urara to get a good result.
Recommend Hypernetwork Strength rate: 0.6 to 1.0 -
Genshin Impact [SFW]
992 images, official art including some game assets
15k steps trained on nai
use "character name genshin impact" or "genshin impact)" for best results- LINK: https://files.catbox.moe/t4ooj6.pt
- 45k step version: https://files.catbox.moe/newhp6.pt
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Ajitani Hifumi (阿慈谷 ヒフミ) from Blue Archive [NSFW / SFW] (Work on WD / NAI)
Training set: 41 Input images, 20055 Steps, 0.000005 Learning rate.
Model: NAI-Full-Prunced
Start with ba-hifumi to get a good result.
Recommend Hypernetwork Strength rate: 0.6 to 1.0
1.0 Is a little bit overkill I thought about.
If you want to go different costume like swimsuit or casual, I think 0.4 to 0.7 is the best ideal rate. -
Higurashi no Nako Koro ni // ryukishi07's artstyle
Trained on Higurashi's original VN sprites. Might do Umineko's sprites next, or mix the two together.
8k steps, 15k steps, 18k steps included. -
Trained Koharu of Blue Archive. I'm not very good at English, so it's painful to read this describe.
Training set: 41 images, 20000 steps, 0.000005 learning rate.
Model: WD1.3 merged NAI (3/7 - Sigmoid) - queencomplexstyle (no training info): https://files.catbox.moe/32s6yb.pt
- Shiroko of Blue Archive. Training set: 14 images at 20000 steps 0.000005 learning rate. The tag is 'ba-shiroko'
-
Queen Complex
(https://queencomplex.net/gallery/?avia-element-paging=2) [NSFW]
"It's a cool style, and it has nice results. Don't need to special reference anything, seems to work fine regardless of prompt."
Base Model: Novel AI
Training set: 52 images at 4300 steps 0.00005 learning rate (images sourced from link above and cropped) -
Raichiyo33 style hypernetwork. Not perfect but seems good enough.
Trained with captions from booru tags for compability with model + art by raichiyo33 in the beginning over NAI model. Use "art by raichiyo33" in the begining of prompt to triggering.
Some useful tips:- with tag "traditional media" produce more beautiful results
- try to avoid too much negativ promts. I use only "bad anatomy, bad hands, lowres, worst quality, low quality, blurry, bad quality" even that seems too much. With many UC tags (especially with full NAI set of uc) it will produce almost generic NAI result.
- Use CLIP -2 (because its trained over NAI, ofc)
-
Genshin Impact [SFW]
992 images, official art including some game assets
15k steps trained on nai
use "character name genshin impact" or "genshin impact)" for best results -
Gyokai-ZEN (aliases: Gyokai / Onono Imoko / shunin) [NSFW / SFW] (For NAI)
Includes training images
Training set: 329 Input images, Various steps included. Main model is 21,000 steps.
Training model: NAI-Full-Pruned.
Recommend Hypernetwork Strength rate: 0.6 to 1.0. Lower strength is good for the overtrained model.
Emphasise the hypernet by using the prompt words "gyokai" or "art by gyokai".Note: the prompt words "color halftone" or "halftone" can be good at adding the little patterns in the shading often seen in onono imoko's style.
HOWEVER: This often results in a noise/grain which often can be fixed if you render at a resolution higher than 768x768 (with hi-res fix)
Omit these options from your prompt if the noise is too much in the image. Your outputs will be sharper and cleaner, but unfortunately less in the style.gyokai-zen-1.0 is 16k steps at 0.000005, then up to 21k steps at 0.0000005
gyokai-zen-1.0-16000 is a bit less trained (16k steps) and sometimes outputs cleaner at full strength.
gyokai-zen-1.0-overtrain is at 22k steps all at 0.000005. It can sometimes be a bit baked in. -
yapo (ヤポ) Art Style [NSFW / SFW] (Work on WD / NAI)
Training set: 51 Input images, 8000 Steps, 0.0000005 Learning rate.
Training model: NAI-Full-Prunced
Start with in style of yapo / yapo to get a good result.
Recommend Hypernetwork Strength rate: 0.4 to 0.8- Preview Link: https://imgur.com/a/r2sOV41
- Download Link: https://anonfiles.com/N6B4d4D7y9/yapo_pt
-
Lycoris recoil chisato
Training set: 100 Input images, 21500 Steps, 0.000005 Learning rate.
Training model: NAI-Full-Prunced
Start with "cr-chisato"
Recommend Hypernetwork Strength rate: 0.4 to 0.8. clip skip : 1 Euler -
Liang Xing styled
Artstation: https://www.artstation.com/liangxing
20,000 steps at varying learning rates down to 0.000005, 449 training images. Novel AI base.
Requires that you mention Liang Xing in some form as that was what I used in the training document. "in the style of Liang Xing" as an example. -
アーニャ(anya)(SPY×FAMILY) [NSFW / SFW] (Work on WD / NAI)
Training set: 46 Input images, 20500 Steps, 0.00000005 Learning rate.
Training model: NAI-Full-Prunced
Start with Anya to get a good result.
Recommend Hypernetwork Strength rate: 0.6 to 0.9- Preview Link: https://imgur.com/a/ZbmIVRe
- Download Link: https://anonfiles.com/ZdKej8D5ya/Anya_pt
-
cp-lucy
Training set: 67 Input images, 21500 Steps, 0.000005 Learning rate.
Training model: NAI-Full-Prunced
Start with "cp-lucy" / clip skip : 1
Recommend Hypernetwork Strength rate: 0.6 to 0.9 -
バッチ (azur bache) (アズールレーン) [NSFW / SFW] (Work on WD / NAI)
Training set: 55 Input images, 20050 Steps, 0.00000005 Learning rate.
Training model: NAI-Full-Prunced
Start with azur-bache to get a good result.
Recommend Hypernetwork Strength rate: 0.6 to 1.0 -
Ixy (by ixyanon):
Hypernetwork trained on ixy's style from 100 handpicked images from them, using split oversized images.
Trained in Nai-full-pruned
recommend using white pupils in the prompt, ixy for a greater effect of their style
Uses: will generally make your output more flat in shading, very good at frilly stuff, and white pupils of course
Grid examples located in the folder. -
Blue Archives Azusa
Training set: 28 Input images, 20000 Steps, 5e-6:12000, 5e-7:30000 Learning rate.
Training model: NAI-Full-Prunced
Start with ba-azusa to get a good result.
Recommend Hypernetwork Strength rate: 0.6 to 1.0 clip skip : 1 -
Makoto Shinkai HN
trained on a roughly ~150 images, 1,2,2,1 for 30,000 steps on NAI model, use "art by makotoshinkaiv2" to trigger it (experimental, it might not be that much different from base model but I have noticed that it improved composition when paired with the aesthetic gradient)
1007 frames from the entire 5 Centimeter Per Second (Byousoku 5 Centimeter) (2007) animovie by Makoto Shinkai- Dataset (for the aesthetic gradient and for general hypernetwork training/finetuning of a model if anyone else wants to attempt to get this style down.):
- 1: https://cdn.discordapp.com/attachments/1022209206146838599/1033198526714363954/5_Centimeters_Per_Second.7z.001
- 2: https://cdn.discordapp.com/attachments/1022209206146838599/1033198659321475184/5_Centimeters_Per_Second.7z.002
- 3: https://cdn.discordapp.com/attachments/1022209206146838599/1033198735657803806/5_Centimeters_Per_Second.7z.003
- Link: https://cdn.discordapp.com/attachments/1032726084149583965/1033200762085453874/makotoshinkaiv2.pt
- Aesthetic Gradient: https://cdn.discordapp.com/attachments/1033147620966801609/1033196207478161488/makoto_shinkai.pt
- Dataset (for the aesthetic gradient and for general hypernetwork training/finetuning of a model if anyone else wants to attempt to get this style down.):
-
Hypernetwork based on the following prompts:
- cervix, urethra, puffy pussy, fat_mons, spread_pussy, gaping_anus, prolapse, gape, gaping
Released by IWillRemember#1912
This hyper network was trained for 2000 steps at different learning rates on different batches of images (usually 25 images each batch)I suggest using an HYPERNETWORK STRENGTH OF 0,5 or maybe up to 0,8 since it's really strong ; it is compatible with almost all anime like models and it performs great even with semi realistic ones.
The examples are made using the Nai model , but it works with ally , and any other anime based model if strength is adjusted acccordingly , also it COULD work with f111 and other models with the right prompts , to get really fat labia majora/minora , and/or gaping
- Link: https://mega.nz/file/pSN3mYoS#Q7e8tJWPSYGxdsyJMwhhtE5Jj8-A5e-sYZHhzbi3QAg
- Examples: https://cdn.discordapp.com/attachments/1018623945739616346/1033541564603039845/unknown.png, https://cdn.discordapp.com/attachments/1018623945739616346/1033542053444988938/unknown.png, https://cdn.discordapp.com/attachments/1018623945739616346/1033544232310411284/unknown.png, https://cdn.discordapp.com/attachments/1018623945739616346/1033550933151469688/unknown.png
- (reupload from the 4chan section) Hypernetwork trained on spacezin's art, 13 handpicked images flipped and used oversized crop, data is the rar in the link
>by ixyanon
>Trained in Nai-full-pruned, using swish activation method with dropout, 5e-6 training rate
>recommend using spacezin in the prompt, lesser steps are included for testing and usage if 20k is too much
>Uses: booba with covered nipples, sharp eyes and all that stuff you'd expect from him.
>Aesthetic gradient embedding included, helps a lot to use, nails the style significantly further if you can find good settings... - Hypernetwork trained on mikozin's art (reupload from 4chan section)
>Trained in Nai-full-pruned, using swish activation method with dropout, 5e-6 training rate
>placing mikozin in the prompt will make it have a stronger effect
>has a number of effects, but mostly gives a very soft, painted style to the output image
>Aesthetic gradient embedding included, not necessary but could be neat!
>Data it was trained on included in the mega link, if you want something specific from the data it was trained on it'll help looking at the fileword txts - yabuki_kentarou(1,1_relu_5e-5)-8750
>Source image count: 75 (white-bg, hi-res, and hi-qual)
>Dataset image count: 154 (split, 512x512)
>Dataset stress test: excellent (LR 0.0005, 2000 steps)
>Model: NAI [925997e9]
>Layer: 1, 1
>Learning rate: 0.00005
>Steps: 8750 - namori(1,1_relu_5e-5)-9000.pt
>Source image count: 50 (white-bg, hi-res, and hi-qual)
>Dataset image count: 98 (split, 512x512)
>Dataset stress test: excellent (LR 0.0005, 2000 steps)
>Model: NAI [925997e9]
>Layer: 1, 1
>Learning rate: 0.00005
>Steps: 9000
>Preview: https://i.imgur.com/MEmvDCS.jpg
>Download: https://anonfiles.com/n2W8rdF7y5/namori_1_1_relu_5e-5_-9000_pt -
Yordles:
Released by IWillRemember#1912 on discord
Trained for roughly 30000 steps at different learning rates on 80 images
Trained with NAI but they work best with Arena's Gape60
Trained on the following tags: Yordle, tristana, lulu_(league_oflegends), poppy(league_oflegends), vex(league_oflegends), shortstack
Suggested to not use negative promptsYordles = to be use with an HYPERNETWORK STRENGHT OF 0,7
Yordles-FullSTR = to be useed with an HYPERNETWORK STRENGHT OF 1Example for Poppy : masterpiece, highest quality, digital art, colored skin, blue skin, white skin, 1girl, (yordle:1.1), purple eyes, (poppy(league_of_legends):1.1), shortstack, twintails, fang, red scarf, white armor, thighs, sitting, night, gradient background , grass , blonde hair , on back, :d
I suggest not using negative prompts or use only the conditional ones like : monochrome, letterbox, ecc ecc
https://mega.nz/file/FCdiSIbI#ekOnlvox0ksEe1zzOQCFXgMJPkClEFPJFfGaAXv4rYc
Examples:
https://cdn.discordapp.com/attachments/1023082871822503966/1037513553386684527/poppy.png
https://cdn.discordapp.com/attachments/1023082871822503966/1037513571355066448/lulu.png
Colored eyes:
Aesthetic Gradients
Collection of Aesthetic Gradients: https://github.com/vicgalle/stable-diffusion-aesthetic-gradients/tree/main/aesthetic_embeddings
- Pussy improvements (Called an aesthetic embed, not sure if it's supposed to be here or in embeds):
https://files.catbox.moe/l7gclr.pt
Polar Resources
- Scat (??): https://files.catbox.moe/8hklc5.pt
- Horse (?): https://files.catbox.moe/idm0vf.pt
- MLP nsfw f16 f32 (might be pickled): https://drive.google.com/drive/folders/14JyQE36wYABH-0TSV_HBEsBJ3r8ZITrS?usp=sharing
Datasets
Datasets:
- ie_(raarami): https://mega.nz/folder/4GkVQCpL#Bg0wAxqXtHThtNDaz2c90w
- Expanded (DEAD LINK): https://litter.catbox.moe/j4mpde.zip
- Toplessness: https://litter.catbox.moe/mttar5.zip
- Reine: https://files.catbox.moe/zv6n6q.zip
- Power: https://files.catbox.moe/wcpcbu.7z
- Baffu: https://files.catbox.moe/ejh5sg.7z
- tatsuki fujimoto: https://litter.catbox.moe/k09588.zip
- Butcha-U and Hypnosis: https://files.catbox.moe/9dv0cy.7z
- (By midnanon) tagged data sets with minimal effort and you're comfortable with C# (not sure if safe): https://pastebin.com/JmZFWCUK
- Take whatever that produces and throw it into a duplicate detector.
- Take whatever remains, filter out stuff you don't like or otherwise deviates too much.
- I built up the midna dataset in about 10 minutes or so end to end.
- You can customise tags on line 248.
- Anya: https://litter.catbox.moe/o5efml.zip
- Amelia Watson: https://files.catbox.moe/vrr2sl.zip
- Henreader (NSFW + loli): https://files.catbox.moe/e0e3nk.7z
- olga discordia from kuroinu: https://www.dropbox.com/s/wir30k9oj3uvnay/process%202.rar?dl=0
- Olga (another?): https://tstorage.info/a70vikp8waeg
- LeMat and Radom (guns): https://files.catbox.moe/zen22z.zip
- AK and SCAR (gun families): https://files.catbox.moe/7vo31t.zip
- Alkemanubis, Siina You Honzuki, Tatata: https://mega.nz/folder/sTV0EI6b#hGotLRXpotvmYxqfTb_KMw
- Onono imoko (NSFW + SFW, 300 cropped images): https://files.catbox.moe/dkn85w.zip
- Moona: https://files.catbox.moe/mmrf0v.rar
- Au'ra, a playable race from Final Fantasy (~100 imgs): https://mega.nz/folder/ZWcXCYpB#Zo-dHbp_u30iIz-LxLUGyA
Old(?) Training Info
- Model merging math: https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/c250cb289c97fe303cef69064bf45899406f6a40#comments
- Old model merging: https://github.com/eyriewow/merge-models/
- Can use ckpt_merge script from https://github.com/bmaltais/dehydrate
- python3 merge.py <path to model 1> <path to model2> --alpha <value between 0.0 and 1.0> --output <output filename>
From anon: For sigmoid/inverse sigmoid interpolation between modesl, add this code starting with line 38 of merge.py:
- Model merge guide: https://rentry.org/lftbl
- anon: The Checkpoint Merger tab in webui works well. It uses standard RAM not VRAM. As a general guide, you need 2x as much RAM as the total combined size of the models you need to load.
- Supposedly empty ckpt to help with memory issues, might be pickled: https://easyupload.io/ggfxvc
- batch checkpoint merger: https://github.com/lodimasq/batch-checkpoint-merger
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
runpython 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
- Aesthetic Gradients: https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients
- Image aesthetic rating (?): https://github.com/waifu-diffusion/aesthetic
- 1 img TI: https://huggingface.co/lambdalabs/sd-image-variations-diffusers
- You can set a learning rate of "0.1:500, 0.01:1000, 0.001:10000" in textual inversion and it will follow the schedule
- Tip: combining natural language sentences and tags can create a better training
- Dreambooth on 2080ti 11GB (anon's guide): https://rentry.org/tfp6h
- Training a TI on 6gb (not sure if safe or even works, instructions by uploader anon): https://pastebin.com/iFwvy5Gy
- Have xformers enabled.
This diff does 2 things.
- enables cross attention optimizations during TI training. Voldy disabled the optimizations during training because he said it gave him bad results. However, if you use the InvokeAI optimization or xformers after the xformers fix it does not give you bad results anymore.
This saves around 1.5GB vram with xformers - 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
- enables cross attention optimizations during TI training. Voldy disabled the optimizations during training because he said it gave him bad results. However, if you use the InvokeAI optimization or xformers after the xformers fix it does not give you bad results anymore.
- Have xformers enabled.
- 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
- Anon's Guide: https://rentry.org/zcspm
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.
Training dataset with aesthetic ratings: https://github.com/JD-P/simulacra-aesthetic-captions
random training stuff that was posted months ago
- Finetune diffusion: https://github.com/YaYaB/finetune-diffusion
- Training guide: https://pastebin.com/xcFpp9Mr
- Training guide for textual inversion/embedding and hypernetworks: https://pastebin.com/dqHZBpyA
- Anon's guide: https://rentry.org/stmam
- Anon2's guide: https://rentry.org/983k3
- Full Textual Inversion folder: https://files.catbox.moe/c6502c.7z
- Tutorial 2: https://rentry.org/textard
- Another tutorial: https://imgur.com/a/kXOZeHj
- Dreambooth on 12gb no WSL: https://gist.github.com/geocine/e51fcc8511c91e4e3b257a0ebee938d0
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)
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
reddit stuff posted months ago:
- Reddit guide: https://www.reddit.com/r/StableDiffusion/comments/xzbc2h/guide_for_dreambooth_with_8gb_vram_under_windows/
- Reddit guide (2): https://www.reddit.com/r/StableDiffusion/comments/y389a5/how_do_you_train_dreambooth_locally/
- Dreambooth (8gb of vram if you have 25gb+ of ram and Windows 11): https://pastebin.com/0NHA5YTP
- Another 8gb Dreambooth: https://github.com/Ttl/diffusers/tree/dreambooth_deepspeed/examples/dreambooth#training-on-a-8-gb-gpu
- Dreambooth: https://rentry.org/dreambooth-shitguide
- Dreambooth: https://rentry.org/simple-db-elinas
- Dreambooth (Reddit): https://www.reddit.com/r/StableDiffusion/comments/ybxv7h/good_dreambooth_formula/
- Documentation: https://www.reddit.com/r/StableDiffusion/comments/wvzr7s/tutorial_fine_tuning_stable_diffusion_using_only/
- Guide on dreambooth training in comments: https://www.reddit.com/r/StableDiffusion/comments/yo05gy/cyberpunk_character_concepts/