Stable Diffusion

Open-source image generation

imageintermediateFree tier

Overview

Stable Diffusion by Stability AI is the leading open-source image generation model. Run it locally on your GPU, fine-tune with LoRAs, or use community-hosted versions. Massive ecosystem of models, extensions, and UIs (ComfyUI, Automatic1111). Fully free.

Best For

+Customization
+Local generation
+Fine-tuning
+Privacy

News & Updates

Researchers Develop Method to Detect Child Abuse Material in AI Model Uploads

Researchers have developed a technique to detect child sexual abuse material (CSAM) embedded in text-to-image LoRA adapters by analyzing model weights, without needing to generate images. The method, detailed in a preprint on arXiv, identifies known CSAM concepts by examining weight patterns in fine-tuned diffusion models. This approach allows platforms to scan uploaded LoRAs for illegal content before deployment, addressing a critical safety gap. The technique was tested on Stable Diffusion models and successfully flagged CSAM-related concepts with high accuracy, offering a proactive defense against misuse of generative AI.

breakthroughs7/29/2026

Hugging Face Hosts AI Models Creating Nonconsensual Deepfake Nudes of Minors, Report Finds

A Verge investigation found Hugging Face, the popular AI model repository, is hosting models specifically designed to undress women and children in photos without consent. These models, often fine-tuned versions of Stable Diffusion, can be downloaded and run locally with minimal technical skill. Hugging Face's moderation relies on user reports, allowing such models to remain accessible for weeks or months. The platform has removed some models after being contacted by The Verge but lacks proactive screening. This highlights a broader failure across AI hosting platforms to prevent misuse of open-source models for generating child sexual abuse material and nonconsensual intimate images.

other7/28/2026

Researchers Fix Quality Problem in AI Image Model Training Method

Researchers from multiple institutions, including Bingnan Li, Haozhe Wang, and Haozhong Xiong, have identified a fundamental flaw in how on-policy distillation (OPD) handles classifier-free guidance (CFG), a standard component in modern diffusion models like Stable Diffusion. Their analysis shows that existing OPD methods inadvertently apply CFG to student-generated trajectories using teacher CFG scales, causing a mismatch that degrades sample quality. The team proposes a corrected training procedure that aligns CFG scales between teacher and student, achieving up to 2x improvement in FID scores on ImageNet 256x256. This work, published on arxiv, provides both theoretical grounding and practical recipes for training faster, high-quality diffusion models.

breakthroughs7/28/2026