---
title: "NVIDIA & Hugging Face: Scale Fine-Tuning of Video & Image Models with NeMo Automodel"
source: "Hugging Face：Blog（RSS）"
source_tier: "t1"
url: "https://huggingface.co/blog/nvidia/scale-diffusers-finetuning-nemo-automodel"
permalink: "https://feed.astralor.com/items/itm_a0bbc2c6e6f2a453cca78f56"
published: "2026-07-17T15:57:54.000Z"
score: 90
channel: "ai"
tags: ["Diffusers","Hugging Face","NeMo Automodel","NVIDIA","Distributed Training","Large-scale fine-tuning"]
---

# NVIDIA & Hugging Face: Scale Fine-Tuning of Video & Image Models with NeMo Automodel

## AI commentary

> What makes this post worth reading is that it provides a ready-to-use distributed fine-tuning solution, seamlessly bridging NVIDIA's low-level engineering with the Diffusers ecosystem. For teams hesitating on how to scale fine-tuning of video diffusion models, this is arguably the most straightforward reference available — no need to assemble distributed frameworks or wrestle with multi-GPU configs; NeMo Automodel handles it.

## At a glance

NVIDIA published a blog post on fine-tuning video and image models at scale by integrating NeMo Automodel with Hugging Face Diffusers. The approach supports multi-GPU, mixed-precision training, and distributed data parallelism, significantly boosting training efficiency and lowering barriers. It includes complete code examples from setup to fine-tuning a video diffusion model, targeting researchers and engineers.

## Key points

- NeMo Automodel automates distributed training setups, simplifying large-scale fine-tuning workflows.
- Deep integration with Diffusers supports popular models like Stable Video Diffusion.
- Complete code examples cover environment setup, data loading, training loops, and model saving.

## Full text

A Blog post by NVIDIA on Hugging Face

---

Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_a0bbc2c6e6f2a453cca78f56