metadata

license: wtfpl
datasets:
  - k4d3/furry
language:
  - en
tags:
  - not-for-all-audiences

Hotdogwolf's Yiff Toolkit

The Yiff Toolkit is a comprehensive set of tools designed to enhance your creative process in the realm of furry art. From refining artist styles to generating unique characters, the Yiff Toolkit provides a range of tools to help you cum.

NOTE: You can click on any image in this README to be instantly teleported next to the original resolution version of it! If you want the metadata for a picture and it isn't there, you need to delete the letter e before the .png in the link! If a metadata containing original image is missing, please let me know!

Click to reveal table of contents

Hotdogwolf's Yiff Toolkit

Dataset Tools

I have uploaded all of the little handy Python scripts I use to /dataset_tools. They are pretty self explanatory by just the file name but almost all of them contain an AI generated descriptions. If you want to use them you will need to edit the path to your training_dir folder, the variable will be called path or directory and look something like this:

def main():
    path = 'C:\\Users\\kade\\Desktop\\training_dir_staging'

Don't be afraid of editing Python scripts, unlike the real snake, these won't bite!

Dataset Preparation

Before you begin collecting your dataset you will need to decide what you want to teach the model, it can be a character, a style or a new concept.

For now let's imagine you want to teach your model wickerbeasts so you can generate your VRChat avatar every night.

Create the `training_dir` Directory

Before starting we need a directory where we'll organize our datasets. Open up a terminal by pressing Win + R and typing in pwsh. We will also be using git and huggingface to version control our smut. For brevity I'll refrain from giving you a tutorial on both. Once you have your newly created dataset on HF ready lets clone it. Make sure you change user in the first line to your HF username!

git clone [email protected]:/datasets/user/training_dir C:\training_dir
cd C:\training_dir
git branch wickerbeast
git checkout wickerbeast

Let's continue with downloading some wickerbeast data but don't close the terminal window just yet, for this we'll make good use of the furry booru e621.net. There are two nice ways to download data from this site with the metadata intact, I'll start with the fastest and then I will explain how you can selectively browse around the site and get the images you like one by one.

Grabber

Grabber makes your life easier when trying to compile datasets quickly from imageboards.

Clicking on the Add button on the Download tab lets you add a group which will get downloaded, Tags will be the where you can type in the search parameters like you would on e621.net, so for example the string wickerbeast solo -comic -meme -animated order:score will search for solo wickerbeast pictures without including comics, memes, and animated posts in descending order of their scores. For training SDXL LoRAs you usually won't need more than 50 images, but you should set the solo group to 40 and add a new group with -solo instead of solo and set the Image Limit to 10 for it to include some images with other characters in it. This will help the model learn a lot better!

You should also enable Separate log files for e621, this will download the metadata automatically alongside the pictures.

For Pony I've set up the Text file content like so: rating_%rating%, %all:separator=^, % for other models you might want to replace rating_%rating% with just %rating%.

You should also set the Folder into which the images will get downloaded. Let's use C:\training_dir\1_wickerbeast for both groups.

Now you are ready to right-click on each group and download the images.

Manual Method

This method requires a browser extension like ViolentMonkey and the following UserScript:

Click to reveal userscript.

// ==UserScript==
// @name e621 JSON Button
// @namespace https://cringe.live
// @version 1.0
// @description Adds a JSON button next to the download button on e621.net
// @author _ka_de
// @match https://e621.net/*
// @match https://e6ai.net/*
// @grant none
// ==/UserScript==

(function() {
  'use strict';

  function constructJSONUrl() {
    // Get the current URL
    var currentUrl = window.location.href;
    // Extract the post ID from the URL
    var postId = currentUrl.match(/^https?:\/\/(?:e621\.net|e6ai\.net)\/posts\/(\d+)/)[1];
    // Check the hostname
    var hostname = window.location.hostname;
    // Construct the JSON URL based on the hostname
    var jsonUrl = 'https://' + hostname + '/posts/' + postId + '.json';
    return jsonUrl;
  }

  function createJSONButton() {
    // Create a new button element
    var jsonButton = document.createElement('a');
    // Set the attributes for the button
    jsonButton.setAttribute('class', 'button btn-info');
    var jsonUrl = constructJSONUrl();
    // Set the JSON URL as the button's href attribute
    jsonButton.setAttribute('href', jsonUrl);
    // Set the inner HTML for the button
    jsonButton.innerHTML = '<i class="fa-solid fa-angle-double-right"></i><span>JSON</span>';

    // Find the container where we want to insert the button
    var container = document.querySelector('#post-options > li:last-child');

    // Check if the #image-extra-controls element exists
    if (document.getElementById('image-extra-controls')) {
      // Insert the button after the download button
      container = document.getElementById('image-download-link');
      container.insertBefore(jsonButton, container.children[0].nextSibling);
    } else {
      // Insert the button after the last li element in #post-options
      container.parentNode.insertBefore(jsonButton, container.nextSibling);
    }
  }

  // Run the function to create the JSON button
  createJSONButton();
})();

This will put a link to the JSON next to the download button on e621.net and e6ai.net and you can use this Python script to convert them to caption files, it uses the rating_ prefix before safe/questionable/explicit because.. you've guessed it, Pony! It also lets you ignore the tags you add into ignored_tags using the r"\btag\b", syntax, just replace tag with the tag you want it to skip.

LoRA Training Guide

Installation Tips

Firstly, download kohya_ss' sd-scripts, you need to set up your environment either like this tells you for Windows, or if you are using Linux or Miniconda on Windows, you are probably smart enough to figure out the installation for it. I recommend always installing the latest PyTorch in the virtual environment you are going to use, which at the time of writing is 2.2.2. I hope future me has faster PyTorch!

Pony Training

I'm not going to lie, it is a bit complicated to explain everything. But here is my best attempt going through some "basic" stuff and almost all lines in order.

Download Pony in Diffusers Format

I'm using the diffusers version for training I converted, you can download it using git.

git clone https://huggingface.co/k4d3/ponydiffusers

Sample Prompt File

A sample prompt file is used during training to sample images. A sample prompt for example might look like this for Pony:

# anthro female kindred
score_9, score_8_up, score_7_up, score_6_up, rating_explicit, source_furry, solo, female anthro kindred, mask, presenting, white pillow, bedroom, looking at viewer, detailed background, amazing_background, scenery porn, realistic, photo --n low quality, worst quality, blurred background, blurry, simple background --w 1024 --h 1024 --d 1 --l 6.0 --s 40
# anthro female wolf
score_9, score_8_up, score_7_up, score_6_up, rating_explicit, source_furry, solo, anthro female wolf, sexy pose, standing, gray fur, brown fur, canine pussy, black nose, blue eyes, pink areola, pink nipples, detailed background, amazing_background, realistic, photo --n low quality, worst quality, blurred background, blurry, simple background --w 1024 --h 1024 --d 1 --l 6.0 --s 40

Please note that sample prompts should not exceed 77 tokens, you can use Count Tokens in Sample Prompts from /dataset_tools to analyze your prompts.

If you are training with multiple GPUs, ensure that the total number of prompts is divisible by the number of GPUs without any remainder.

Training Commands

Click to reveal training commands.

`--lowram`

If you are running running out of system memory like I do with 2 GPUs and a really fat model that gets loaded into it per GPU, this option will help you save a bit of it and might get you out of OOM hell.

`--pretrained_model_name_or_path`

The directory containing the checkpoint you just downloaded. I recommend closing the path if you are using a local model with a /.

    --pretrained_model_name_or_path="/ponydiffusers/" \

`--output_dir`

This is where all the saved epochs or steps will be saved, including the last one. If y

    --output_dir="/output_dir" \

`--train_data_dir`

The directory containing the dataset. We prepared this earlier together.

    --train_data_dir="/training_dir" \

`--resolution`

Always set this to match the model's resolution, which in Pony's case it is 1024x1024. If you can't fit into the VRAM, you can decrease it to 512,512 as a last resort.

    --resolution="1024,1024" \

`--enable_bucket`

⚠️

    --enable_bucket \

`--min_bucket_reso` and `--max_bucket_reso`

⚠️

    --min_bucket_reso=256 \
    --max_bucket_reso=1024 \

`--network_alpha`

⚠️

    --network_alpha=4 \

`--save_model_as`

⚠️

    --save_model_as=safetensors \

`--network_module`

⚠️

    --network_module=lycoris.kohya \

`--network_args`

⚠️

    --network_args \
               "preset=full" \
               "conv_dim=256" \
               "conv_alpha=4" \
               "rank_dropout=0" \
               "module_dropout=0" \
               "use_tucker=False" \
               "use_scalar=False" \
               "rank_dropout_scale=False" \
               "algo=locon" \
               "train_norm=False" \
               "block_dims=8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8" \
               "block_alphas=0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625,0.0625" \

`--network_dropout`

⚠️

    --network_dropout=0 \

`--lr_scheduler`

⚠️

    --lr_scheduler="cosine" \

`--lr_scheduler_num_cycles`

⚠️

    --lr_scheduler_num_cycles=1 \

`--learning_rate`

⚠️

    --learning_rate=0.0001 \

`--unet_lr`

⚠️

    --unet_lr=0.0001 \

`--text_encoder_lr`

⚠️

    --text_encoder_lr=0.0001 \

`--network_dim`

⚠️

    --network_dim=8 \

`--output_name`

⚠️

    --output_name="last" \

`--scale_weight_norms`

⚠️

    --scale_weight_norms=1 \

`--no_half_vae`

⚠️

    --no_half_vae \

`--save_every_n_epochs`

⚠️

    --save_every_n_epochs=10 \

`--mixed_precision`

⚠️

    --mixed_precision="fp16" \

`--save_precision`

⚠️

    --save_precision="fp16" \

`--caption_extension`

⚠️

    --caption_extension=".txt" \

`--cache_latents` and `--cache_latents_to_disk`

⚠️

    --cache_latents \
    --cache_latents_to_disk \

`--optimizer_type`

The default optimizer is AdamW and there are a bunch of them added every month or so, therefore I'm not listing them all, you can find the list if you really want, but AdamW is the best as of this writing so we use that!

    --optimizer_type="AdamW" \

`--dataset_repeats`

Repeats the dataset when training with captions, by default it is set to 1 so we'll set this to 0 with:

    --dataset_repeats=0 \

`--max_train_steps`

Specify the number of steps or epochs to train. If both --max_train_steps and --max_train_epochs are specified, the number of epochs takes precedence.

    --max_train_steps=500 \

`--shuffle_caption`

Shuffles the captions set by --caption_separator, it is a comma , by default which will work perfectly for our case since our captions look like this:

rating_questionable, 5 fingers, anthro, bent over, big breasts, blue eyes, blue hair, breasts, butt, claws, curved horn, female, finger claws, fingers, fur, hair, huge breasts, looking at viewer, looking back, looking back at viewer, nipples, nude, pink body, pink hair, pink nipples, rear view, solo, tail, tail tuft, tuft, by lunarii, by x-leon-x, mythology, krystal (darkmaster781), dragon, scalie, wickerbeast, The image showcases a pink-scaled wickerbeast a furred dragon creature with blue eyes., She has large breasts and a thick tail., Her blue and pink horns are curved and pointy and she has a slight smiling expression on her face., Her scales are shiny and she has a blue and pink pattern on her body., Her hair is a mix of pink and blue., She is looking back at the viewer with a curious expression., She has a slight blush.,

As you can tell, I have separated the caption part not just the tags with a , to make sure everything gets shuffled. I'm at this point pretty certain this is beneficial especially when your caption file contains more than 77 tokens.

NOTE: --cache_text_encoder_outputs and --cache_text_encoder_outputs_to_disk can't be used together with --shuffle_caption. Both of these aim to reduce VRAM usage, you will need to decide between these yourself!

`--sdpa` or `--xformers` or `--mem_eff_attn`

The choice between --xformers or --mem_eff_attn and --spda will depend on your GPU. You can benchmark it by repeating a training with them!

`--sample_prompts` and `--sample_sampler` and `--sample_every_n_steps`

You have the option of generating images during training so you can check the progress, the argument let's you pick between different samplers, by default it is on ddim, so you better change it!

You can also use --sample_every_n_epochs instead which will take precedence over steps. The k_ prefix means karras and the _a suffix means ancestral.

    --sample_prompts=/training_dir/sample-prompts.txt
    --sample_sampler="euler_a" \
    --sample_every_n_steps=100

My recommendation for Pony is to use euler_a for toony and for realistic k_dpm_2.

Your sampler options include the following:

ddim, pndm, lms, euler, euler_a, heun, dpm_2, dpm_2_a, dpmsolver, dpmsolver++, dpmsingle, k_lms, k_euler, k_euler_a, k_dpm_2, k_dpm_2_a

CosXL Training

Embeddings for 1.5 and SDXL

Embeddings in Stable Diffusion are high-dimensional representations of input data, such as images or text, that capture their essential features and relationships. These embeddings are used to guide the diffusion process, enabling the model to generate outputs that closely match the desired characteristics specified in the input.

You can find in the /embeddings folder a whole bunch of them I collected for SD 1.5 that I later converted with this tool for SDXL.

ComfyUI Walkthrough any%

⚠️ Coming next year! ⚠️

AnimateDiff for Masochists

⚠️ Coming in 2026! ⚠️

Stable Cascade Furry Bible

Resonance Cascade

🍆

SDXL Furry Bible

Some Common Knowledge Stuff

Resolution Lora is a nice thing to have, it will help with consistency. For SDXL it is just a LoRA you can load in and it will do its magic. No need for a custom node or extension in this case.

Pony Diffusion V6

Requirements

Download the model and load it in to whatever you use to generate models.

Positive Prompt Stuff

score_9, score_8_up, score_7_up, score_6_up, rating_explicit, source_furry,

I just assumed you wanted explicit and furry, you can also set the rating to rating_safe or rating_questionable and the source to source_anime, source_cartoon, source_pony, source_rule34 and optionally mix them however you'd like. Its your life! score_9 is an interesting tag, the model seems to have put all it's "artsy" knowledge. You might want to check if it is for your taste. The other interesting tag is score_5_up which seems to have learned a little bit of everything regarding quality while score_4_up seems to be at the bottom of the autism spectrum regarding art, I do not recommend using it, but you can do whatever you want!

You can talk to Pony in three ways, use tags only, tags are neat, but you can also just type in The background is of full white marble towers in greek architecture style and a castle. and use natural language to the fullest extent, but the best way is to mix it both, its actually recommended since the score tags by definition are tags, and you need to use them! There are also artist styles that seeped into some random tokens during training, there is a community effort by some weebs to sort them here.

Other nice words to have in the box depending on your mood:

detailed background, amazing_background, scenery porn

Other types of backgrounds include:

simple background, abstract background, spiral background, geometric background, heart background, gradient background, monotone background, pattern background, dotted background, stripped background, textured background, blurred background

After simple background you can also define a color for the background like white background to get a simple white background.

For the character portrayal you can set many different types:

three-quarter view, full-length portrait, headshot portrait, bust portrait, half-length portrait, torso shot

Its a good thing to describe your subject or subjects start with solo or duo or maybe trio, group , and then finally start describing your character in an interesting situation.

Negative Prompt Stuff

⚠️

How to Prompt Female Anthro Lions

anthro

SeaArt Furry

⚠️

SeaArt's furry model sadly has its cons not just pros, yes it might come with artist knowledge bundled, but it seems to have trouble doing more than one character or everyone is bad at prompting, oh and it uses raw e621 tags, which just means you have to use underscores _ instead of spaces inside the tags.

⚠️ TODO: Prompting tips.

Pony Diffusion V6 LoRAs

All LoRAs listed here are actually LyCORIS with the exception of blue_frost which is just a regular LoRA. This might be important in case the software you use makes you put them in separate folders or if you are generating from a cute Python script.

Concept Loras

space-v1e500

// Keywords:
by hubble
by jwst

// Example Positive Prompts:
by jwst, a galaxy, photo
by jwst, a red and blue galaxy
by hubble, a galaxy, photo


// Negative Prompt:
cropped,

blurry, wtf, old art, where is your god now, abstract background, simple background, cropped

Click to reveal images.

spacengine-v1e500

// Keyword
by spaceengine

// Example Prompt:
by spaceengine, a planet, black background

Artist/Style LoRAs

blp-v1e400

Replicate blp's unique style of AI art without employing 40 different custom nodes to alter sigmas and noise injection. I recommend you set your CFG to 6 and use DPM++ 2M Karras for the sampler and scheduler for a more realistic look or you can use Euler a for a more cartoony/dreamy generation with with a low CFG of 6.

There have been reports that if you use this LoRA with a negative weight of -0.5 your generations will have a slight sepia tone.

blp,

// Recommended:

detailed background, amazing_background, scenery porn, feral,

blue_frost

A bit of an experiment trying to make generating kitsch winter scenes easier. Originally trained for base SDXL, but it seems to work with PonyXL just fine. If you can call kitsch fine, anyway..

butterchalk-v3e400

I'm not into young anthro I only trained this one for you, you hentai baka! ^_^

cecily_lin-v1e37

I'm honestly not familiar with this artist, I just scraped their art and let sd-scripts go wild.

chunie_ponyxl_v1e5

Everyone loves Chunie, except for that loud dumbass on Discord. 😹

cooliehigh-v1e45

Again, I'm really uncultured when it comes to furry artists.

dagasi-v1e134

Even I heard about this one!

darkgem_ponyxl_v1e4

Quality digital painting style. Some people don't like it.

I recommend first an Euler a with 40 steps, CFG set to 11 at 1024x1024 resolution and then a hi-res pass at 1536x1536 with DPM++ 2M Karras at 60 steps with denoise set at 0.69 for the highest darkgem. Please only use darkgem if you want gems to appear in the scene or maybe your character will end up holding a dark red gem.

Click to reveal images.

himari-v1e400

A tiny dumb LoRA trained on 4 images by @147Penguinmw. The keyword is by himari but you probably don't need to use it!

// Positive Prompt Example
score_9, score_8_up, score_7_up, score_6_up, source_furry, rating_explicit, on back, sexy pose, full-length portrait, pussy, solo, reptile, scalie, anthro female lizard, scales, blush, blue eyes, white body, blue body, plant, blue scales, white scales, detailed background, looking at viewer, furniture, digital media \(artwork\), This digital artwork image presents a solo anthropomorphic female reptile specifically a lizard with a white body adorned with detailed blue scales.,

Click to reveal images.

furry_sticker-v1e250

Generate an infinite amount of furry stickers for your infinite amount of telegram accounts!

// Positive prompt:

furry sticker, simple background, black background, white outline, 

// Negative prompt:

abstract background, detailed background, amazing_background, scenery porn,

Click to reveal images.

goronic-v1e1

greg_rutkowski-v1e400

hamgas-v1e400

honovy_ponyxl_v1e4

jinxit-v1e10

kame_3-v1e80

louart-v1e10

realistic-v4e400

// Positive prompt:

realistic, photo, detailed background, amazing_background, scenery porn,

// Negative prompt:

abstract background, simple background

My take on photorealistic furries. Highly experimental and extremely fun! I recommend you don't try anything but a CFG of 6 and DPM++ 2M Karras.

You can combo this with the RetouchPhoto LoRA for even more research. 📈

Click to view images

skecchiart-v1e134

spectrumshift-v1e400

squishy-v1e10

whisperingfornothing-v1e58

wjs07-v1e200

wolfy-nail-v1e400

woolrool_ponyxl_v1e4

Character LoRAs

amalia-v2e400

Some loli cat girl. Enjoy yourself!

amicus-v1e200

Gay space wolf from a visual novel everyone wants me to play.

auroth-v1e250

A dragon or wyvern thing from DOTA2

blaidd-v1e400

Half-wolf Blaidd! Bestest boy of Elden Ring! He's a very good boy! Can be a naughty boy though as well, if you like..

martlet-v1e200

ramona-v1e400

tibetan-v2e500

veemon-v1e400

hoodwink-v1e400

jayjay-v1e400

foxparks-v2e134

lovander-v3e10

skiltaire-v1e400

chillet-v3e10

maliketh-v1e1

Second best boy of Elden Ring, it took me 7 tries the first time, so this is my form of payback!

// Positive prompt:

male, anthro, maliketh \(elden ring\), white fur, white hair, head armor, red canine genitalia, knot,

// NLP version:

anthro male maliketh \(elden ring\) with white fur and white hair wearing head armor, He has a red canine genitalia with a knotty base and fluffy tail, He has claws and monotone fur with a monotone body,

Click to reveal images

Hotdogwolf's Yiff Toolkit

Table of Contents

Dataset Tools

Dataset Preparation

Create the training_dir Directory

Grabber

Manual Method

LoRA Training Guide

Installation Tips

Pony Training

Download Pony in Diffusers Format

Sample Prompt File

Training Commands

--lowram

--pretrained_model_name_or_path

--output_dir

--train_data_dir

--resolution

--enable_bucket

--min_bucket_reso and --max_bucket_reso

--network_alpha

--save_model_as

--network_module

--network_args

--network_dropout

--lr_scheduler

--lr_scheduler_num_cycles

--learning_rate

--unet_lr

--text_encoder_lr

--network_dim

--output_name

--scale_weight_norms

--no_half_vae

--save_every_n_epochs

--mixed_precision

--save_precision

--caption_extension

--cache_latents and --cache_latents_to_disk

--optimizer_type

--dataset_repeats

--max_train_steps

--shuffle_caption

--sdpa or --xformers or --mem_eff_attn

--sample_prompts and --sample_sampler and --sample_every_n_steps

CosXL Training

Embeddings for 1.5 and SDXL

ComfyUI Walkthrough any%

AnimateDiff for Masochists

Stable Cascade Furry Bible

Resonance Cascade

SDXL Furry Bible

Some Common Knowledge Stuff

Pony Diffusion V6

Requirements

Positive Prompt Stuff

Negative Prompt Stuff

How to Prompt Female Anthro Lions

SeaArt Furry

Pony Diffusion V6 LoRAs

Concept Loras

space-v1e500

spacengine-v1e500

Artist/Style LoRAs

blp-v1e400

blue_frost

butterchalk-v3e400

cecily_lin-v1e37

chunie_ponyxl_v1e5

cooliehigh-v1e45

dagasi-v1e134

darkgem_ponyxl_v1e4

himari-v1e400

furry_sticker-v1e250

goronic-v1e1

greg_rutkowski-v1e400

hamgas-v1e400

honovy_ponyxl_v1e4

jinxit-v1e10

kame_3-v1e80

Create the `training_dir` Directory

`--lowram`

`--pretrained_model_name_or_path`

`--output_dir`

`--train_data_dir`

`--resolution`

`--enable_bucket`

`--min_bucket_reso` and `--max_bucket_reso`

`--network_alpha`

`--save_model_as`

`--network_module`

`--network_args`

`--network_dropout`

`--lr_scheduler`

`--lr_scheduler_num_cycles`

`--learning_rate`

`--unet_lr`

`--text_encoder_lr`

`--network_dim`

`--output_name`

`--scale_weight_norms`

`--no_half_vae`

`--save_every_n_epochs`

`--mixed_precision`

`--save_precision`

`--caption_extension`

`--cache_latents` and `--cache_latents_to_disk`

`--optimizer_type`

`--dataset_repeats`

`--max_train_steps`

`--shuffle_caption`

`--sdpa` or `--xformers` or `--mem_eff_attn`

`--sample_prompts` and `--sample_sampler` and `--sample_every_n_steps`