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Hugging Face
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Diffusers Library Updates

13 posts · Jul 2023 – Jul 2026

What this trail covers

Hugging Face supports diffusion models for image generation. This post details the implementation of Modular Diffusers, allowing users to create custom diffusion workflows by combining different components like schedulers, UNets, and VAEs. The introduction of 'Overworld/Waypoint-1-Small' as a 2.3B parameter model further expands the available options for users, enabling real-time interactive video diffusion with low latency on consumer hardware via the WorldEngine inference library. This post also details how to fine-tune Stable Diffusion models using Dreambooth with the Diffusers library, enabling users to train custom models on their own data for personalized image generation.

All Hugging Face trails
  1. Bringing Nunchaku 4-bit Diffusion Inference to DiffusersThis post introduces the integration of Nunchaku 4-bit diffusion inference into the Diffusers library. It details the technical aspects of using Nunchaku for optimized inference, likely involving quantization techniques and specific kernel implementations to reduce memory footprint and improve speed for diffusion models. The post demonstrates the practical application of this optimization with a specific model (OzzyGT/Krea_2_Turbo_nunchaku_lite_nvfp4).
  2. Introducing Modular Diffusers - Composable Building Blocks for Diffusion PipelinesThis post introduces Modular Diffusers, a new architecture for the Diffusers library that allows for composable diffusion pipelines. It details how users can combine different components (schedulers, UNets, VAEs) to create custom workflows. The post also highlights the 'Overworld/Waypoint-1-Small' model, a 3B parameter model, as an example of a component that can be integrated into these modular pipelines.
  3. Introducing Waypoint-1: Real-time interactive video diffusion from OverworldIntroduces Waypoint-1, a real-time interactive video diffusion model trained with diffusion forcing and post-trained with self-forcing. Details the WorldEngine inference library, highlighting optimizations like AdaLN feature caching, static rolling KV cache, matmul fusion, and Torch Compile for low-latency, high-throughput video generation on consumer hardware. Provides a Python code example for using WorldEngine.
  4. AI for Game Development: Creating a Farming Game in 5 Days. Part 1This post demonstrates the use of Stable Diffusion for generating concept art for a farming game. It provides detailed instructions on setting up Stable Diffusion locally (Automatic1111 WebUI) and online, including installation steps, model weight placement, and usage. It also offers guidance on prompt engineering for concept art and outlines the process of bringing the generated art into Unity by blocking out scenes, setting up materials, lighting, cameras, and post-processing.
  5. Training Stable Diffusion with Dreambooth using DiffusersThis post details the implementation of Dreambooth fine-tuning within the Diffusers library for Stable Diffusion models. It provides a technical walkthrough of the process, including data preparation, model configuration, and training parameters, enabling users to train personalized image generation models on their own datasets. The post highlights the use of specific schedulers and optimizers for effective Dreambooth training.
  7. The Annotated Diffusion ModelThis post provides a detailed, step-by-step implementation of Denoising Diffusion Probabilistic Models (DDPMs) in PyTorch. It covers the mathematical formulation, defines the forward diffusion process, implements a PyTorch Dataset and DataLoader, outlines the neural network architecture (including ResNet blocks, attention modules, and a conditional U-Net), and demonstrates both training and sampling (inference) procedures. The post aims to demystify diffusion models by offering a practical, annotated code walkthrough.
  8. 🧨 Stable Diffusion in JAX / Flax !This post introduces the implementation of Stable Diffusion models in JAX/Flax, providing an alternative to PyTorch for training and inference. It details the architecture and components of Stable Diffusion within the JAX/Flax ecosystem, highlighting the benefits of using these frameworks for performance and flexibility.
  9. Stable Diffusion with 🧨 DiffusersThis post details the implementation of Denoising Diffusion Probabilistic Models (DDPMs) in PyTorch, covering the forward diffusion process, defining the neural network architecture (U-Net), training procedures, and inference sampling. It provides a step-by-step guide to understanding and implementing these models.
  10. Using Stable Diffusion with Core ML on Apple SiliconThis post details how to use Stable Diffusion with Core ML on Apple Silicon for efficient on-device inference. It covers converting the Stable Diffusion model to Core ML format and running inference on Apple hardware, leveraging the performance benefits of Apple Silicon for image generation tasks.
  11. VQ-DiffusionThis post introduces VQ-Diffusion, a novel diffusion model that leverages Vector Quantization (VQ) to compress the latent space, leading to more efficient and higher-quality image generation. It details the architecture, which combines a VQ-VAE for latent space compression with a diffusion model operating in this quantized space. The post provides implementation details and demonstrates its effectiveness on image generation tasks, showcasing improved FID scores and reduced computational requirements compared to standard diffusion models.
  12. What's new in Diffusers? 🎨This post details the implementation of VQ-Diffusion, a variant of diffusion models that uses Vector Quantization (VQ) to improve efficiency and quality. It highlights the model's ability to achieve high-fidelity image generation with reduced computational cost by quantizing the latent space.