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.
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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.
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 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.
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.
TunerDiT: Training-free Progressive Steering of Diffusion Transformers for Multi-Event Video Generation
TunerDiT is a training-free method for steering video diffusion transformers (DiTs) to generate long-horizon videos containing multiple sequential events. The approach identifies intrinsic turning points in the DiT denoising trajectory where text conditioning shifts from global layout to fine-grained detail, then applies two steering mechanisms: Event-Partitioned Masking and Cross-Event Prompt Fusion. The authors also introduce Meve, a benchmark prompt suite for multi-event video generation, and report state-of-the-art results across 8 metrics with improved text alignment scaling with event count.
The Annotated Diffusion Model
A Hugging Face blog post providing a detailed, annotated walkthrough of diffusion models for image generation, likely covering the mathematical foundations and implementation details of denoising diffusion probabilistic models (DDPMs). The post serves as an educational deep-dive into the architecture and training process of diffusion-based generative models. Published in mid-2022, it coincides with the period of rapid growth in diffusion model adoption.
Diffusers welcomes Stable Diffusion 3.5 Large
Hugging Face's Diffusers library has added support for Stable Diffusion 3.5 Large, Stability AI's latest image generation model. The blog post covers integration details, usage patterns, and how to run the model within the Diffusers ecosystem. This represents a standard tooling integration announcement for a recently released frontier image generation model.