Exploring Quantization Backends in Diffusers
Hugging Face published a technical overview of quantization backends available in the Diffusers library for image and video generation models. The post covers integration with multiple quantization frameworks (likely bitsandbytes, GGUF, torchao, and similar) and their trade-offs for diffusion model inference. It targets practitioners seeking to reduce memory footprint and improve throughput when deploying diffusion models.
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Related events (8)
Memory-efficient Diffusion Transformers with Quanto and Diffusers
This Hugging Face blog post describes integrating the Quanto quantization library with the Diffusers framework to reduce memory requirements for diffusion transformer models. The approach enables running large image/video generation models on consumer-grade hardware by applying int8 and int4 quantization to model weights. The post covers practical implementation details and benchmarks showing memory savings for models like Flux and others in the diffusion transformer family.
VQ-Diffusion: Vector Quantized Diffusion Models on Hugging Face
This Hugging Face blog post introduces VQ-Diffusion, a text-to-image generation approach that combines vector quantization with diffusion models. The method operates in a discrete latent space defined by a VQ-VAE codebook, applying the diffusion process to token sequences rather than continuous pixel or latent representations. The post likely covers integration into the Hugging Face diffusers ecosystem and demonstrates generation capabilities.
State of open video generation models in Diffusers
Hugging Face published a survey of open-source video generation models integrated into the Diffusers library as of January 2025. The post covers the current landscape of available open video generation models, their capabilities, and how they are supported within the Diffusers ecosystem. This serves as a reference for practitioners looking to use or compare open-weights video generation models.
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.
What's new in Diffusers? — Hugging Face Diffusers Library Second Month Update
Hugging Face published a blog post summarizing new features and updates added to the Diffusers library in its second month of development. The post covers new pipelines, model integrations, and tooling improvements for diffusion-based generative image models. This represents an early-stage ecosystem update for one of the primary open-source libraries supporting text-to-image and related diffusion model workflows.
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.