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.
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Related events (8)
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.
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.
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.
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.
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.
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.
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.
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.