The second image in showcase was generated with the CivitAI image generator.

This is a LoRA extraction based on the Chroma-DC-2K model with Chroma1-HD as base.

The following is a copy of original Chroma1-HD model page.

Hey everyone,

A while back, I posted about Chroma, my work-in-progress, open-source foundational model. I got a ton of great feedback, and I'm excited to announce that the base model training is finally complete, and the whole family of models is now ready for you to use!

A quick refresher on the promise here: these are true base models.

I haven't done any aesthetic tuning or used post-training stuff like DPO. They are raw, powerful, and designed to be the perfect, neutral starting point for you to fine-tune. We did the heavy lifting so you don't have to.

And by heavy lifting, I mean about 105,000 H100 hours of compute. All that GPU time went into packing these models with a massive data distribution, which should make fine-tuning on top of them a breeze.

As promised, everything is fully Apache 2.0 licensed—no gatekeeping.

TL;DR:

Release branch:

• Chroma1-Base: This is the core 512x512 model. It's a solid, all-around foundation for pretty much any creative project. You might want to use this one if you’re planning to fine-tune it for longer and then only train high res at the end of the epochs to make it converge faster.

• Chroma1-HD: This is the high-res fine-tune of the Chroma1-Base at a 1024x1024 resolution. If you're looking to do a quick fine-tune or LoRA for high-res, this is your starting point.

Research Branch:

• Chroma1-Flash: A fine-tuned version of the Chroma1-Base I made to find the best way to make these flow matching models faster. This is technically an experimental result to figure out how to train a fast model without utilizing any GAN-based training. The delta weights can be applied to any Chroma version to make it faster (just make sure to adjust the strength).

• Chroma1-Radiance [WIP]: A radical tuned version of the Chroma1-Base where the model is now a pixel space model which technically should not suffer from the VAE compression artifacts.

Quantization options

• Alternative option: FP8 Scaled Quant (Format used by ComfyUI with possible inference speed increase)

• Alternative option: GGUF Quantized (You will need to install ComfyUI-GGUF custom node)

Special Thanks

A massive thank you to the supporters who make this project possible.

• Anonymous donor whose incredible generosity funded the pretraining run and data collections. Your support has been transformative for open-source AI.

• Fictional.ai for their fantastic support and for helping push the boundaries of open-source AI.

Support this project!

https://ko-fi.com/lodestonerock/

BTC address: bc1qahn97gm03csxeqs7f4avdwecahdj4mcp9dytnj
ETH address: 0x679C0C419E949d8f3515a255cE675A1c4D92A3d7

my discord: discord.gg/SQVcWVbqKx