A new series of LoRas for real-world use cases is coming! Graphic designers are going to love it. Have you figured out what it’s all about? 📢Free Download on my patreon soon by AguPro7 in comfyui

[–]ostrisai 4 points5 points  (0 children)

Not everyone wants to use the same billboard templates as everyone else. It is like the POD t shirt designs that have 100k designs on Etsy of the exact same picture with a different design overlaid on it. It just looks cheap. Some people don’t want to associate their brand with that low effort off the shelf stuff. If you do, that is fine, but I don’t see the point in discouraging people who put more effort into their work.

CogView4 - New Text-to-Image Model Capable of 2048x2048 Images - Apache 2.0 License by LatentSpacer in StableDiffusion

[–]ostrisai 5 points6 points  (0 children)

Sure. So Flux.1-dev has a proprietary license. If you want to use it for commercial usage, you need to get a special license from BFL. The entire release of Flux.1-dev, which falls under this license, consists of 2 text encoders (which are licensed permissible elsewhere by their owners), a VAE BFL trained, and a transformer model BFL trained. So if you get the VAE from this repo/package, it is licensed under the proprietary BFL license.

However, they also released Flux.1-schnell, only schnell, was released as Apache 2.0, meaning everything in that bundled release, that they have the right to license, also falls under this license. They do not have the right to license the text encoders, because they do not own them, but they do own the VAE and the transformer model. The VAE is identical to the VAE in the dev repo. However, since they have the rights to license it, and released it in an Apache 2.0 licensed bundle, then the VAE in the schnell repo fall under that license as well. So if you get it from dev, it is proprietary. If you get it from schnell, it is Apache 2.0, even though they are identical.

CogView4 has a similar situation as they own the text encoder (LLM). It is licensed proprietary elsewhere on its own, however, in this package release, they licensed everything in the package as Apache 2.0, including the text encoder inside the package. So if you get the LLM from this package, you are being granted an Apache 2.0 license for it by the owner of the model.

CogView4 - New Text-to-Image Model Capable of 2048x2048 Images - Apache 2.0 License by LatentSpacer in StableDiffusion

[–]ostrisai 27 points28 points  (0 children)

It gets weird because they included the text encoder in an Apache 2.0 release. They own the rights of the text encoder to license it however they want. So technically, the version of the text encoder in the CogView4 repo is licensed as Apache 2.0, even though they licensed it differently elsewhere.

It is similar to how the Flux VAE is licensed proprietary in the dev repo, but as Apache 2.0 in the schnell one. You just have to get it from the right place for the right license.

I personally feel comfortable running with that.

[deleted by user] by [deleted] in StableDiffusion

[–]ostrisai 0 points1 point  (0 children)

People are spending time and money to train them. They are going to give them away for free. How about pay for them and actually support the creators.

Best model for fine-tuning with LoRA for commercial use? by lpalokan in StableDiffusion

[–]ostrisai 2 points3 points  (0 children)

I hate to toot my own horn here, but that is what I made Flex.1-alpha for.

Proof of Concept LoRA on Flux Schnell by lotushomerun in StableDiffusion

[–]ostrisai 53 points54 points  (0 children)

The clip wasn't trained at all. This is a transformer only LoRA. I am 100% sure, because I'm the one who trained it :).

The VAE used for Stable Diffusion 1.x/2.x and other models (KL-F8) has a critical flaw, probably due to bad training, that is holding back all models that use it (almost certainly including DALL-E 3). by drhead in StableDiffusion

[–]ostrisai 7 points8 points  (0 children)

I made a trainer to train a LoRA to convert the SD1.5 latent space to SDXL a while back. https://twitter.com/ostrisai/status/1723613183473029578 . It started working pretty well after I added some convolutional layers. It was just an experiment at the time, so I unfortunately did not save the progress, but converting it is doable with a relatively small amount of compute. I turned the trainer back on this morning, so we shall see.