Bias and Limitations

Standard biases and limitations of Danbooru dataset apply, dataset consists of danbooru up to January 2026.

Getting Started Guide

Recommendations

Inference

Comfy

(Workflow is available alongside model in repo)

Same as your normal inference, but with addition of SD3 sampling node, as this model is Flow-based.

Recommended Parameters:
Sampler: Euler, DPM++ SDE, etc.
Steps: 20-28
CFG: 3-6
Shift: 3-8
Schedule: Normal/Simple/SGM Uniform/Beta Positive Quality Tags: masterpiece, best quality, aesthetic

Negative Tags: worst quality, normal quality, bad anatomy, low resolution

A1111 WebUI

(All screenshots are repeating our other RF release, as there is no difference in setup)

Recommended WebUI: ReForge - has native support for Flow models, and we've PR'd our native support for Flux2vae-based SDXL modification.

How to use in ReForge:

(ignore Sigma max field at the top, this is not used in RF)

Support for RF in ReForge is being implemented through a built-in extension:

Set parameters to that, and you're good to go.

Recommended Parameters:
Sampler: Euler Comfy, Euler, DPM++ SDE Comfy, etc. ALL VARIANTS MUST BE RF OR COMFY, IF AVAILABLE. In ComfyUI routing is automatic, but not in the case of WebUI.
Steps: 20-28
CFG: 3-6
Shift: 3-8
Schedule: Normal/Simple/SGM Uniform/Beta Positive Quality Tags: masterpiece, best quality, aesthetic
Negative Tags: worst quality, normal quality, bad anatomy, low resolution

ADETAILER FIX FOR RF: By default, Adetailer discards Advanced Model Sampling extension, which breaks RF. You need to add AMS to this part of settings:

Add: advanced_model_sampling_script,advanced_model_sampling_script_backported to there.

If that does not work, go into adetailer extension, find args.py, open it, replace builtinscripts like this:

Here is a copypaste for easy copy:

_builtin_script = (
    "advanced_model_sampling_script",
    "advanced_model_sampling_script_backported",
    "hypertile_script",
    "soft_inpainting",
)

Or use this fork of Adetailer - https://github.com/Anzhc/aadetailer-reforge

Training

Training Details

Samples seen(unbatched steps): 52 million samples seen.
Learning Rate: 2e-5
Effective Batch size: 1152 Effective Batch Size, 36 Batch Size, 4 Gradient Accumulation, 8 GPUs
Precision: Mixed BF16
Optimizer: AdamW8bit with Kahan Summation
Weight Decay: 0.01
Schedule: Constant with warmup
Timestep Sampling Strategy: Uniform
SD3 Shift: 2
Text Encoders: Frozen
Keep Token: False
Tag Dropout: 10%
Uncond Dropout: 10%
Shuffle: True

Additional Features used: Protected Tags, Cosine Optimal Transport.

Training Data

Danbooru up to January of 2026.

LoRA Training

Pochi.toml is a basic TOML for usage with https://github.com/67372a/LoRA_Easy_Training_Scripts/tree/refresh MAKE SURE TO USE BRANCH REFRESH, comes ready to work.

You can also use https://github.com/bluvoll/Akegarasu-lora-scripts-RF/tree/main to train LoRAs or Finetune the model, use Example.toml as a starter configuration for training, or the example in the huggingface repo.

Hardware

Model was trained on a 8xH100 node.

Software

Custom fork of SD-Scripts(maintained by Bluvoll)

Acknowledgements

The model is still overcoming the anatomy issues first seen in ChenkinNoobXL 0.2 Epsilon and the change caused by deprecated tags in danbooru 2025, at this point in time the model has become far sharper and detailed than expected, some newer characters are promptable with helper features, we expect this to improve over the next 5 or 7 epochs as we raise LR to 4e-5 due to the high batch size we run.

Testers

Everyone in server who tested model throughout it's training and provided feedback, included but not limited to:

Shinku
yoinked
low channel
Anzhc
lylogummy
Silvelter
brittle
Darren Laurie
L_A_X
Nebulae
Francisco
WANG
youhuang
ztxzhy
Drac
user
nian__gao233
DUO
Kai Wong
Requiredforsomereason
spawner
peoscrha
waww
itterative
Nama M
Talan
Magpie
BKM Desu
花火流光
tairitsujiang
123
2222k
spawner
青苇

Showcase Images

Itterative
Ryusho
Panchovix
Talan
Silvelter
Drac

Hardware

Chenkin and Heathcliff for providing compute.

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