Release: https://github.com/shootthesound/Fizgig/releases/tag/v8.6.0

cross-posted from: https://lemmy.world/post/53334627

Using the same exploit that recently allowed unlocking features of Nvidia mining GPUs, an unlocker for RTX 3090 has been created. Unfortunately the card doesn't have hidden VRAM to enable, but we can get some free floating point processing power.

The install instructions are a bit vague, but on my Fedora install I had to manually add the kernel command line arg (gpu_unlock.targets) and rebuild initramfs after running the build/install command.

I did some simple comparison using ComfyUI, and image generation became 33% faster when using fp8 and bf16 models. However, with an int8 convrot quantized model I only got around 8% speedup, which make sense since it would use the int8 tensor cores of the RTX 3090 which weren't locked to begin with.

I didn't write down my exact tokens/s in llama.cpp before installing the unlocker, but the numbers I got after were roughly the same as I remembered, which also makes sense since I use quantized models that afaik use integer tensor ops.

As with all hardware "hacking" you should be careful. This seemed relatively safe to me since it doesn't permanently alter anything on the GPU so it should be easy to revert. And it shouldn't mess with current limits or clock speeds, which could more easily damage the hardware.

The install procedure does involve changing some kernel boot parameters, so if you make a mistake it's possible that you soft-brick your OS.

Release: https://github.com/Comfy-Org/ComfyUI/releases/tag/v0.40.0

Abstract

Autoregressive video diffusion models have become a leading approach to real-time interactive video generation and video world modeling. However, existing methods typically allocate computation uniformly across space when generating the next video chunk, updating all patches with the same denoising schedule and number of function evaluations. This ignores the spatial heterogeneity of video content: static, simple, or recurring regions are often easier to denoise, while other regions require further refinement or additional context. A uniform budget thus leads to redundant computation on easy patches, whereas reducing the fixed step count can leave difficult patches insufficiently refined. We introduce ForcingBooster, a general framework that dynamically allocates denoising steps to individual patches: easy patches stop early to improve efficiency, while difficult patches receive more refinement steps. This design accelerates generation with negligible quality degradation. Our method is compatible with most existing few-step autoregressive diffusion models, requiring only a lightweight routing module with modest training. Experiments demonstrate that ForcingBooster reduces the total number of denoising steps applied across video patches by about 50% while maintaining visual quality comparable to the four-step baseline.

Paper: https://arxiv.org/abs/2610.12625

Code: https://github.com/IGL-HKUST/ForcingBooster/tree/main

Checkpoints: https://huggingface.co/EXCAI/ForcingBooster

Project Page: https://igl-hkust.github.io/ForcingBooster/

Abstract

Layout-to-Image generation has made substantial progress in spatial and object-level control. However, existing methods still struggle with complex scenes containing many overlapping and interacting objects. We argue that training data is a particular bottleneck: existing datasets lack examples with dense, complex object interactions. To address this gap, we introduce OverLay++, a large-scale Layout-to-Image dataset with structurally complex scenes. OverLay++ contains approximately 500K images with an average of 6.6 objects per image, exceeding existing datasets by 1.67× in annotation density. Beyond annotation density, OverLay++ provides rich semantic detail with object captions over six times longer than in current datasets. Our dataset generation pipeline is simple and produces dense, overlapping object annotations with rich per-object captions. Across multiple benchmarks, state-of-the-art Layout-to-Image methods trained on the OverLay++ dataset show consistent improvement and faster convergence, demonstrating the importance of dense, overlap-aware, and caption-rich supervision for controllable image generation.

Paper: https://arxiv.org/abs/2610.09071

Code: https://github.com/mlpc-ucsd/OverLayPP

Project Page: https://mlpc-ucsd.github.io/OverLayPP/

https://www.youtube.com/watch?v=1GfSTYlKpSo