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.
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Related events (8)
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.
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.
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.
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
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.
Representation-Conditioned Diffusion Models for Controllable Image Generation
This paper explores conditioning diffusion models on representations from pre-trained self-supervised models as an alternative to text prompts or semantic maps, which require large annotated datasets. The self-conditioning mechanism improves unconditional image generation quality and provides a controllable representation space. The authors identify directions of variation in this space and demonstrate smoothness and disentanglement properties, suggesting potential for fine-grained generative control without heavy annotation overhead.
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.
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.