5Hugging Face Blog·
NVIDIA NeMo Automodel integrates with Hugging Face Diffusers for scalable video and image model fine-tuning
NVIDIA and Hugging Face announce integration between NVIDIA NeMo Automodel and the Diffusers library, enabling large-scale fine-tuning of video and image diffusion models. The collaboration targets practitioners who need to fine-tune generative media models at scale using enterprise-grade infrastructure. This represents a tooling bridge between NVIDIA's training framework and Hugging Face's open-source ecosystem.
Related guides (3)
Related events (8)
NVIDIA NeMo AutoModel accelerates transformer fine-tuning on Hugging Face
NVIDIA and Hugging Face published a blog post introducing NeMo AutoModel, a tool designed to accelerate fine-tuning of transformer models. The integration targets practitioners looking to speed up training workflows using NVIDIA's NeMo framework within the Hugging Face ecosystem. The post represents a tooling/infrastructure collaboration between the two companies.
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.
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.
Accelerate a World of LLMs on Hugging Face with NVIDIA NIM
NVIDIA NIM microservices are being integrated with Hugging Face to enable optimized inference deployment for a broad range of LLMs hosted on the Hub. The partnership allows developers to deploy Hugging Face models via NIM's containerized inference stack, leveraging NVIDIA's TensorRT-LLM and other optimizations. This expands the ecosystem of models accessible through NIM beyond NVIDIA's own catalog to the wider Hugging Face model repository.
NVIDIA NeMo Megatron-Bridge: Bidirectional Hugging Face Conversion for Megatron-Based Training
Megatron-Bridge is an NVIDIA NeMo training library for Megatron-based models that supports bidirectional conversion between Megatron and Hugging Face formats. The repository has accumulated 670 stars with modest daily growth (+5). It addresses a practical interoperability gap between the high-performance Megatron training stack and the broader HuggingFace ecosystem.
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.
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.
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.