PixelWave FLUX.1 schnell version 04 is an aesthetic fine tune of FLUX.1-schnell. The training images were hand picked to ensure the model has a bias to eye catching images, with beautiful colors, textures and lighting. Whether it's an abstract charcoal sketch, high key fashion portrait with saturated colors, or moody cinematic cyberpunk inspired art, PixelWave FLUX.1 schnell allows you to be more flexible with the styles you need.

You can use more steps to improve finer details, but the output doesn't change much after 8 steps.

Big thanks to RunDiffusion (co-creators of Juggernaut!) for sponsoring the compute that made training this model possible! To help you try it, they're offering 250 free daily credits for a short time on their platform (~40-80 gens of PW-Schnell).

And for those needing API access for this model, we're partnering with Runware.ai.

Grateful for their support in getting this model out there!

Training was done with kohya_ss/sd-scripts. You can find my fork of Kohya here , which also contains changes to the sd-scripts submodule, make sure you clone both.

Use the fine tuning tab. I found the best results with the pagedlion8bit optimizer on my 4090 GPU. Other optimizers struggle to learn.

I have frozen the time_in, vector_in and mod/modulation parameters. This stops the 'de-distillation'.

I avoid training single blocks over 15. You can set which blocks to train in the FLUX section.

LR 5e-6 trains fast, but you have to stop after a few thousand steps as it starts to corrupt blocks and slow down learning.

You can then block merge with an earlier checkpoint, replacing the corrupt blocks, and then continue training further.

Signs of corrupt blocks: paper texture over most images, loss of background details.

For business or commercial inquiries please reach out to us at pixelwave@rundiffusion.com. Licensing flux dev fine tunes. Customer training projects. Commercial AI development. The team can do it all!

twitter: thehumblemikey

civitai: pixelwave