ChenkinNoob-XL-v0.2- Rectified-Flow (completed epoch)

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Subido en: 18 feb 2026, 15:45
Usos: 3.7k
Reseñas: Bien (3)
Pasos de muestreo: 20
Método de muestreo: Euler
Escala CFG: 3.5
Excluir de la imagen: worst quality, low quality, lowres, twitter username, artist name, signature, watermark
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Descripción

A conversion to rectified flow of the Chenkin 0.2, all NoobAI EPS loras do work fine with this model. 20 steps 3.5 cfg is an ideal range for the model. For main model description please refer to this model repo. RF allows this model to get away from greyness of the base EPS solutions, provides vivid colors and unlocks better lighting adherence, like very dark or contrasty scenes, while not requiring training-time tricks like offset noise. It also allows to sustain high stability at wide range of CFG, while not suffering from common downfalls of other base models.

Developed by: Cabal Research (Bluvoll, Anzhc)

Compute provided by: Chenkin, Heathcliff

License: fair-ai-public-license-1.0-sd

Finetuned from model: ChenkinNoob-XL-V0.2

Bias and Limitations Standard biases and limitations of Danbooru dataset apply.

Community Guide Basic and standalone getting started guide. Reserved for Chenkin and nian__gao233

Getting Started Guide English

中文

Recommendations Inference (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

Training Training Details Samples seen(unbatched steps): ~47 million samples seen Learning Rate: 2e-5 Effective Batch size: 1376 Precision: Mixed BF16 Optimizer: AdamW8bit with Kahan Summation Weight Decay: 0.01 Schedule: Constant with warmup Timestep Sampling Strategy: Complicated, first 2 epochs are "Logit Normal", epoch 3 onwards is "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

4 full and 1 partial epochs of extended Danbooru dataset(~10m).

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.

Hardware Model was trained on 8xH20 node.

Software Custom fork of SD-Scripts(maintained by Bluvoll)

Acknowledgements 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