Stability AI and NVIDIA just rolled out a Stable Diffusion 3.5 NIM microservice to streamline enterprise image generation. Running TensorRT optimizations on H100 GPUs, it delivers a 1.8x speedup over standard PyTorch while packaging SD3.5 Large alongside Depth and Canny ControlNets in a single container.
NVIDIA and Stability AI just introduced TensorRT FP8 optimizations for Stable Diffusion 3.5, slashing Large model VRAM needs from over 18GB down to 11GB while boosting performance by up to 2.3x. Developers can also grab the new standalone TensorRT for RTX SDK with JIT compilation. This basically puts heavyweight image generation within reach for standard consumer RTX rigs.
Stability AI just teamed up with AMD to release ONNX-optimized versions of Stable Diffusion 3.5 and SDXL, engineered specifically for Radeon GPUs and Ryzen AI APUs. The new builds run up to 3.8x faster than standard PyTorch models while keeping full image quality intact. It is a major local AI performance win for AMD hardware users, ready to drop into ONNX Runtime workflows or Amuse 3.0.
AMD just launched Amuse 2.2 Beta, bringing support for Stable Diffusion 3.5 to Ryzen AI 300 and Radeon 7000 hardware. The update introduces offline high-quality modes and XDNA super-resolution, boosting native output resolutions up to 2048x2048 pixels. Why it matters: Local generative AI on everyday AMD laptops is finally getting fast, painless, and sharp.
