AI Briefing#stable-diffusion
The latest AI and developer news about #stable-diffusion, with the original source and a short summary.
The latest AI and developer news about #stable-diffusion, with the original source and a short summary.
Box, a security-enhanced Android AI app that integrates LiteRT, llama.cpp, and more to operate fully offline, has been released.
Stability AI has released its annual integrity transparency report to demonstrate the safety and transparency of its model development and deployment process.
In collaboration with AMD, ONNX-optimized versions of Stable Diffusion models were released, optimized for AMD Radeon GPUs and Ryzen AI APUs.
Hugging Face's Diffusers library officially supports the Stable Diffusion 3.5 Large model.
SegMoE, a Mixture-of-Experts (MoE) framework for diffusion models, has been released.
Inference performance of SD Turbo and SDXL Turbo models was significantly improved through ONNX Runtime and Microsoft Olive.
Hugging Face has released a new script that maximizes SDXL Dreambooth LoRA training performance.
HuggingFace kept the base model in memory and dynamically swapped only LoRA adapters, improving inference speed by 300%.
With LCM LoRA, SDXL models can generate images quickly using just 4-8 steps without any separate retraining.
It introduces how to apply fp16 and use memory-efficient attention to speed up inference and reduce memory usage for the SDXL model.
HuggingFace's TRL library now supports DDPO, enabling RLHF fine-tuning of Stable Diffusion models.
Segmind has released SD-Small and SD-Tiny, lightweight Stable Diffusion models with reduced parameters achieved through knowledge distillation.
Marking the first anniversary of its release, Hugging Face's Diffusers library summarized its major feature updates and development over the past year.
This introduces a method for fine-tuning Stable Diffusion models by leveraging the AMX accelerator in Intel Sapphire Rapids CPUs.
Through Apple's Core ML optimizations and 6-bit palettization, Stable Diffusion can run faster and more efficiently on Apple devices.
This presents an optimization method using NNCF and Optimum that boosts Stable Diffusion inference speed on Intel CPUs by 5.1x.
This introduces an instruction-tuning methodology that enables Stable Diffusion to understand and carry out users' image editing instructions.
This introduces a method for speeding up Stable Diffusion inference by more than 2x on Intel Sapphire Rapids CPUs using OpenVINO.
Hugging Face's diffusers library provides a step-by-step guide for training custom ControlNet models.
Hugging Face's Diffusers library now supports ControlNet, a feature that enables precise control over Stable Diffusion's image generation process.
Hugging Face has released a native app that runs Stable Diffusion quickly on Mac by leveraging Core ML.
It introduces a method of applying LoRA technology to Stable Diffusion to speed up training and dramatically reduce model size.
A non-technical founder built an AI art commerce startup based on **Stable Diffusion** in 60 days through **Replit Bounties**.
Reveals how to run Stable Diffusion on Apple Silicon via Core ML and releases the converted models.