Model Description
Mugen is a continuation of our SDXL to Flux 2 VAE conversion, renamed to signify a substantial divergence from the original NoobAI models.
It has been trained for 7 additional epochs, totaling under 8000$ for a full latent space conversion, while preserving and improving upon model anime knowledge.
In particular, we have paid attention to characters in this iteration, and developed in-house approach for benchmarking their performance, about which you can read below.
Overall, model performs particularly well with textures and patterns that were previously simply impossible due to SDXL VAE. We prioritized keeping our training as standard-friendly as possible, so local community can easily train on it like on a new Base Model, which it practically is.
We provide 4 models:
Mugen: A base model.
Mugen - Aesthetic: Slightly tuned on a limited dataset model for better quality output.
Mugen - Aesthetic - Anzhc/Selph: Further tune on opinionated dataset selection.
Developed by: Cabal Research (Bluvoll, Anzhc)
Funded by: Community
License:
Resumed from: NoobAI Flux2 VAE v0.3
Bias and Limitations
General data biases from Danbooru might apply.
Flux 2 VAE seem to have brown bias overall, which can be alleviated by adding sepia or brown theme to negative.
We will provide a Node, and hope it will be adapted natively in main repo eventually: https://github.com/Anzhc/SDXL-Flux2VAE-ComfyUI-Node
Just install it, and it will patch the model config, no node changes required.
Apparently works in SwarmUI as is.
Same as your normal inference, but with addition of SD3 sampling node, as this model is Flow-based.
Recommended Parameters: Sampler: Euler A, Euler, DPM++ SDE, etc. Steps: 20-28 CFG: 4-7 Shift: 8-12 Schedule: Normal/Simple/SGM Uniform Positive Quality Tags: masterpiece, best quality Negative Tags: worst quality, normal quality, bad anatomy, sepia
Alternative Extended Negative: (worst quality:1.1), normal quality, (bad anatomy:1.1), (blurry:1.1), watermark, sepia, (adversarial noise:1.1), jpeg artifacts (Some of our testers pointed out that they prefer longer negative)