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Stable Diffusion 3
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stable-diffusion-3-a5f9a357·25 events·first seen Aliases: Stable Diffusion 3, Stable Diffusion XL, Stable Diffusion
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Recent events (25)
Large-scale study of AI nudification on 4chan finds majority of targets are now non-celebrities
A new arXiv paper presents a large-scale empirical study of AI-generated non-consensual sexually explicit imagery (SNEACI) on 4chan, identifying 24,105 items. A key finding is a demographic shift: non-celebrity individuals now constitute 55.8% of targets, up from 4.7% in prior studies, indicating the harm has expanded from public figures to people in users' personal social circles. Open-source models dominate production, with Stable Diffusion generating 42.7% of images and Wan 66.5% of videos, enabled by thousands of shared fine-tuned models and tutorials. The study characterizes the community dynamics, finding a small cohort of prolific producers drives most content and lowers barriers for new participants.
Stable Diffusion with 🧨 Diffusers
Hugging Face published a blog post introducing Stable Diffusion integration with their Diffusers library, covering the model's architecture and how to run it using the open-source tooling. The post appeared at the time of Stable Diffusion's public release in August 2022, marking a significant moment in accessible text-to-image generation. It served as both a technical introduction and a practical guide for the community to adopt the model.
Japanese Stable Diffusion
This post introduces Japanese Stable Diffusion, a fine-tuned variant of Stable Diffusion adapted for Japanese language and cultural context. The model was developed to better handle Japanese text prompts and generate images reflecting Japanese aesthetics. It represents an early example of localizing large generative image models for non-English languages.
Stable Diffusion in JAX / Flax
Hugging Face published a blog post demonstrating Stable Diffusion running in JAX/Flax, enabling efficient inference on TPU hardware. The post covers the technical implementation of diffusion pipelines using Flax's functional programming model. This represents an early effort to bring high-performance image generation to Google's TPU ecosystem via the Diffusers library.
Training Stable Diffusion with Dreambooth using Diffusers
This Hugging Face blog post describes how to fine-tune Stable Diffusion models using the DreamBooth technique via the Diffusers library. DreamBooth enables personalized text-to-image generation by training a model on a small set of reference images. The post covers the technical workflow for applying this fine-tuning approach within the Diffusers ecosystem.
Using Stable Diffusion with Core ML on Apple Silicon
Hugging Face published a guide on running Stable Diffusion models via Apple's Core ML framework on Apple Silicon hardware. The post covers converting diffusion model weights to Core ML format and integrating them into the Diffusers library for on-device inference. This represents an early effort to enable efficient local image generation on consumer Apple hardware without requiring cloud GPU resources.
From GPT2 to Stable Diffusion: Hugging Face arrives to the Elixir community
Hugging Face announces Bumblebee, a library bringing Hugging Face model support to the Elixir programming language ecosystem. The integration enables Elixir developers to run models including GPT-2 and Stable Diffusion via the Nx numerical computing library. This expands the reach of Hugging Face's model hub beyond Python-centric workflows into the BEAM/Elixir ecosystem.
Using LoRA for Efficient Stable Diffusion Fine-Tuning
This Hugging Face blog post explains how Low-Rank Adaptation (LoRA) can be applied to fine-tune Stable Diffusion models efficiently. LoRA reduces the number of trainable parameters by decomposing weight updates into low-rank matrices, enabling fine-tuning on consumer hardware with significantly less memory. The post covers practical implementation details using the diffusers library.
Swift Diffusers: Fast Stable Diffusion for Mac
Hugging Face published a blog post introducing Swift Diffusers, a native macOS/iOS application for running Stable Diffusion models locally on Apple Silicon hardware. The post covers optimizations leveraging Apple's Core ML framework to accelerate inference on Mac. This represents an effort to bring on-device diffusion model inference to consumer Apple hardware without cloud dependency.
ControlNet in 🧨 Diffusers
Hugging Face's Diffusers library added support for ControlNet, a technique that enables fine-grained spatial and structural control over diffusion model image generation. The blog post covers how ControlNet conditions image synthesis on auxiliary inputs such as edge maps, depth maps, pose skeletons, and segmentation masks. This integration makes ControlNet-based generation accessible through the standard Diffusers pipeline API.
Train your ControlNet with diffusers
This Hugging Face blog post provides a technical guide for training ControlNet models using the diffusers library. It covers the process of conditioning diffusion models on additional inputs such as edge maps, depth maps, or other spatial signals to enable fine-grained image generation control. The post targets practitioners looking to implement custom ControlNet pipelines on their own datasets.
Accelerating Stable Diffusion Inference on Intel CPUs
This Hugging Face blog post details techniques for optimizing Stable Diffusion inference on Intel CPUs, likely covering quantization, operator fusion, and Intel-specific hardware acceleration libraries. The post addresses the practical challenge of running diffusion models on CPU hardware without dedicated GPUs. This is relevant to inference economics and enterprise deployment patterns where GPU availability is constrained.
Instruction-tuning Stable Diffusion with InstructPix2Pix
This Hugging Face blog post describes a methodology for instruction-tuning Stable Diffusion using the InstructPix2Pix framework, enabling image editing via natural language instructions. The approach adapts techniques from language model instruction-tuning to the image generation domain. The post covers dataset construction, training procedures, and evaluation of the resulting models.
Optimizing Stable Diffusion for Intel CPUs with NNCF and Hugging Face Optimum
This Hugging Face blog post details techniques for optimizing Stable Diffusion inference on Intel CPUs using Neural Network Compression Framework (NNCF) and the Optimum library. The workflow covers quantization and other compression methods to reduce latency and memory footprint on CPU hardware. This is relevant to the inference-economics and enterprise-deployment threads as it addresses running diffusion models without dedicated GPU hardware.
Faster Stable Diffusion with Core ML on iPhone, iPad, and Mac
Hugging Face published a blog post detailing optimizations for running Stable Diffusion models via Core ML on Apple devices including iPhone, iPad, and Mac. The post covers techniques to accelerate on-device inference using Apple's neural engine and Core ML framework. This represents progress in deploying capable diffusion models at the edge without cloud dependency.
Fine-tuning Stable Diffusion models on Intel CPUs
This Hugging Face blog post describes a workflow for fine-tuning Stable Diffusion image generation models on Intel CPUs rather than GPUs. It covers the tooling and optimizations required to make CPU-based diffusion model training practical, relevant to inference-economics and hardware diversification trends. The post targets practitioners looking to reduce dependency on GPU hardware for generative model fine-tuning.
Stable Diffusion XL on Mac with Advanced Core ML Quantization
Hugging Face details the process of running Stable Diffusion XL (SDXL) on Apple Silicon Macs using Core ML with advanced quantization techniques. The post covers how quantization reduces model size and memory requirements to make SDXL feasible on consumer Mac hardware. This represents a practical deployment advance for running large diffusion models at the edge on Apple devices.
Open-sourcing Knowledge Distillation Code and Weights of SD-Small and SD-Tiny
Hugging Face has open-sourced knowledge distillation code and model weights for two compressed variants of Stable Diffusion: SD-Small and SD-Tiny. These distilled models are smaller and faster than the original Stable Diffusion, targeting inference efficiency. The release includes both the trained weights and the distillation training code, enabling the community to reproduce or extend the work.
Efficient Controllable Generation for SDXL with T2I-Adapters
Hugging Face published a blog post detailing T2I-Adapters for Stable Diffusion XL (SDXL), a lightweight conditioning mechanism that enables controllable image generation without full fine-tuning. The approach allows users to guide SDXL outputs using structural signals such as depth maps, edge detection, and pose estimation. T2I-Adapters offer a parameter-efficient alternative to ControlNet for the SDXL architecture, with integration into the Diffusers library.
Finetune Stable Diffusion Models with DDPO via TRL
Hugging Face's TRL library adds support for DDPO (Denoising Diffusion Policy Optimization), enabling reinforcement learning-based finetuning of Stable Diffusion models. This extends TRL's RLHF tooling beyond language models to image generation, allowing reward-driven optimization of diffusion models. The post demonstrates practical usage of the new DDPO trainer within the TRL ecosystem.
Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e
Hugging Face published a technical blog post detailing how to accelerate Stable Diffusion XL inference using JAX on Google Cloud TPU v5e hardware. The post covers the integration of JAX-based diffusion pipelines with TPU v5e, demonstrating performance gains from hardware-software co-optimization. This represents a practical deployment pattern for large image generation models on non-GPU accelerators.
Exploring Simple Optimizations for SDXL
This Hugging Face blog post explores practical optimization techniques for Stable Diffusion XL (SDXL) inference. It covers methods to improve throughput and reduce memory usage when running SDXL, targeting practitioners deploying the model. The content is oriented toward applied inference efficiency rather than novel research.
SDXL in 4 Steps with Latent Consistency LoRAs
Hugging Face demonstrates combining Latent Consistency Models (LCMs) with LoRA adapters to enable high-quality image generation with Stable Diffusion XL in as few as 4 inference steps. This approach dramatically reduces the number of diffusion steps required compared to standard SDXL, lowering inference latency and compute cost. The technique leverages consistency distillation applied via lightweight LoRA weights, making it accessible without full model retraining.
LoRA Training Scripts of the World, Unite!
Hugging Face published a blog post consolidating and comparing advanced LoRA fine-tuning scripts for Stable Diffusion XL, covering techniques such as pivotal tuning, custom captions, and various regularization strategies. The post aims to unify fragmented community training approaches into a more coherent set of best practices. It serves as a practical guide for practitioners fine-tuning SDXL models with LoRA adapters.
Diffusers welcomes Stable Diffusion 3
Hugging Face's Diffusers library adds support for Stable Diffusion 3, enabling users to run Stability AI's latest text-to-image model through the standard Diffusers API. The post covers integration details, usage patterns, and memory optimization techniques for running SD3 locally. This marks the open-weights availability of SD3 through a major ML tooling ecosystem.