I stepped upon this new arXiv preprint: “SingLoRA: Low-Rank Adaptation Using a Single Matrix.” It proposes a twist on standard LoRA by dropping the two-matrix approach (B A) in favor of a single matrix A applied as A Aᵀ. Some highlights:

• ~50 % smaller adapters – one matrix instead of two, so half the trainable parameters and lighter .safetensors files
• Only one learning rate
• Higher accuracy with fewer params.
• DreamBooth on SD 1.5 improved DINO-similarity by 5.4 % at the same rank

If I get this right it could be adopted in inference pipelines, meaning:

• Smaller LoRA checkpoints
• Less VRAM usage when merging
• Simpler and more stable training with fewer hyper-params to tweak

Now the question we always ask: Comfy when?

Link to paper: https://arxiv.org/abs/2507.05566

Hello, I was wondering what everyone was doing for storage. I have a fast M2 drive with limited space that I have the core ComfyUI files and some models saved on it, but most of the larger full checkpoints and loras on an external drive. Anyone have the optimal setup?

https://arxiv.org/pdf/2506.18899 https://filmaster-ai.github.io/

I'm not the author nor anyone involved. I just saw this and thought it was pretty cool, and wanted to hear your thoughts on it.

What do you guys think of it? Does it have the potential to surpass veo, runway, Kling, wan, vace?

Quote:

What Makes FilMaster Different?

Built-in Cinematic Expertise We don't just generate video; we apply cinematic principles in camera language design, cinematic rhythm control to create high-quality films, including a rich, dynamic audio landscape.

Fully Automated Production Pipeline From script analysis to final render, FilMaster automates the entire process and delivers project files compatible with professional editing software.

More examples on their website: https://filmaster-ai.github.io/

Is there any workflow to increase the number of frames and the quality of a video already created to make it more fluid?

(i7 14700K + 2x16GB DDR4 RAM)

During FaceFusion's initial 'Face Swapper' stage: HWiNFO reports 67C~ overall (92C~ hot spot) GPU temps + 92-97% core utilisation, alongside 7.1GB VRAM use (with 'tolerant' video memory strategy setting).

Despite evident GPU usage: 'memory controller utilisation' remains locked at 6-8% throughout, followed by the subsequent 'Face Enhancer' stage resulting in erratic fluctuations every second instead (between 2% and 30%~).

I've tried various setting combinations for execution thread count, execution queue count, video memory strategy, and system memory limit (+ maximum priority assigned to FaceFusion via ProcessLasso) with no avail. 14700K stays at 50-60C + 14% total usage regardless of stage, and ‘Physical Memory Available’ behaves similarly with a consistent 20GB (lowest hit @ 15GB, even when 'system memory limit' is set to 28GB).

I've been planning to eventually acquire either a RTX 4070 Super / 5070 for an overdue upgrade - just hasn't been a huge priority with my current chronic League of Legends addiction + only occasional usage of AI in minor tasks like upscaling (though likely due to current speeds when training models 🤕).

Any suggestions for solutions (+ alleviating general AMD AI bottleneck) in the meanwhile?

Thank you in advance!

Curious to hear what everyone is working on. Is it for work, side hustle, or hobby? What are you creating, and, if you make money, how do you do it?

I wanted to recreate photo like this on my own with my friends. I already have the background of this photo and our own that we would like to merge with to create similar effect. I wanted to use Chat-GPT for that purpose but everytime I want it to generate merged photo it changes our clothes and logos and even gives my friend a helmet because of him wearing a balaclava also turning it into a cartoonish like look. My question is is there an A.I that could do something like this without all those secondary effects. I know for a fact that Chat GPT doesn't edit these photos but only generates them according to my commands so is there an actual A.I that edits photos by command and could pull this off? And if not, is there one A.I that could make more realistic and naturally looking already edited photo that we tried to do ourselves? I'm talking about correcting shadows, figures placement e.t.c.

Okay so I've been messing around with Stable Diffusion for a few weeks now and I accidentally created some really good game assets.

Started just for fun, trying to generate random fantasy creatures. But the output was actually usable? Like, with some cleanup and editing, these could totally work in an actual game.

I'm working on this little indie project (roguelike, naturally) and I was dreading the art phase because I can barely draw stick figures. But SD is generating concept art faster than I can evaluate it.

The weird part is I feel guilty about it. Like I'm somehow cheating by not spending months learning to draw properly. But then I remember that I'm a solo dev and if this tool helps me actually finish my game instead of getting stuck on art for years...

Saw that Ocean Keeper used some AI-assisted art in their development process and it got me thinking about where the line is. If you use AI for initial concepts but then hand-draw the final versions, is that different from using photos for reference?

The art purists are gonna hate this but honestly, SD is democratizing game development in a way that feels revolutionary. Small teams can now create visual assets that would have required a full art department before.