Finally 2024 is here and this allowed me a bit of free time to work on some of my NLNet tasks. The first thing on my list to tackle was adding LCM support on the AI Horde as it provides massively reduced steps, which for a crowdsourced service like ours, it makes all the difference in how much we can deliver.
For those who don’t know, LCM is a new breakthrough in Stable Diffusion that allows to “finetune” the model in such a way where an image can be generated using 10% of the steps previously required. So an image which would require 30 steps to converge, now needs just 3! That is a massive boost for lower-range GPUs. For high range GPUs, it starts avenues such as video generation as an image can happen at millisecond speeds!
Given the benefits, I wanted to work on this as soon as possible, and given the flexibility of the FOSS GenAI technology enthusiasts, we already had a great way to use LCMs, by using LoRas to turn any SD model into an LCM version.
However there was a snag. You see while the AI Horde already supports all LoRas on CivitAI, we never supported different versions of each, as we never expected anyone would want to use more than the latest. Unfortunately people on CivitAI started using the versioning system as “alternative” versions. And the LCM LoRa was using the same approach, where there was a version for each different sampler.
So the first order of business had to be to allow the AI horde to understand and support all LoRa versions of each LoRa! This took the better part of a full work-week of development and debugging, and then another week of troubleshooting and fixing in beta.
The good news is that this lead to us also identifying and squashing a very frustrating long-running bug where workers would rarely return previous images they’d generated instead of the ones requested. Getting someone else’s image is something we definitely don’t want to ever happen so we’re very happy we figured it out.
With that out of the way, I simply had to update the AI Horde itself to be able to handle the payload for specific LoRa versions, and then add support for the LCM sampler and then some ways to urge users to switch to it.
If you’re an AI Horde integrator, we strongly suggest you change your default settings to utilize LCM LoRas in your generations. You can get them from the same API you receive the model details, under the modelVersions key. To use them, you need to send the exact version ID as a string (found in
modelVersions[#]['id]) this won’t accept a version name. You will also need to set
is_version: true for the LoRa payload. This will tell the worker to look for a version instead of a LoRa ID.
Sending the LoRa name or ID will continue working as usual, grabbing the latest version (
modelVersions) from that list, so you existing implementations should continue working as usual.
Also we recently added AlbedoXL in our model list, to provide a better baseline for SDXL generations than basic SDXL 1.0 which requires a refiner to work. Using Albedo you can get generations that do not require a refiner in your workflow at all and get much less “fuzzy” generations in the process!