Black Forest Labs的FLUX.1 Kontext [dev]图像编辑模型现已作为NVIDIA NIM微服务推出。该模型允许用户通过简单的语言编辑现有图像，无需进行微调或复杂的工作流程。通过NIM微服务，用户可以更轻松地获得加速的生成式AI工作流程，尤其是在RTX AI PC上。该服务还提供了模型量化，显著降低了显存需求，并利用NVIDIA TensorRT框架实现了超过2倍的性能提升，使普通用户也能享受到专业级的AI图像编辑体验。

🌟 FLUX.1 Kontext [dev]是一款开放权重生成模型，专为图像编辑设计，支持通过文本提示引导图像的演变，实现对细节的精细调整或场景的整体转变。它同时接受文本和图像输入，允许用户直观地参考视觉概念来指导编辑过程，确保图像编辑与原始概念保持高度一致性。

🚀 NVIDIA NIM微服务极大地简化了部署强大AI模型的复杂性，将模型转换为优化的推理后端软件，并连接到新的AI应用程序接口。FLUX.1 Kontext [dev] NIM微服务提供预打包、优化的文件，可通过ComfyUI NIM节点一键下载，方便用户快速上手。

💡 为了提高性能和可访问性，FLUX.1 Kontext [dev]模型经过了量化处理，将模型尺寸从24GB大幅缩减至12GB（FP8）和7GB（FP4），使其能在NVIDIA RTX GPU上更高效地运行。特别是FP8版本针对GeForce RTX 40系列GPU进行了优化，而FP4版本则针对GeForce RTX 50系列GPU，并采用了SVDQuant技术以在减小模型尺寸的同时保持图像质量。

⚡️ 结合NVIDIA TensorRT框架，FLUX.1 Kontext [dev] NIM微服务实现了比原始BF16模型通过PyTorch运行快2倍以上的加速。这一性能的巨大提升使得即使是没有高级AI基础设施知识的普通用户，也能通过NIM微服务节省大量时间并获得更优的性能表现。

💻 用户可以通过安装NVIDIA AI Workbench，获取ComfyUI，并在ComfyUI中安装NIM节点来开始使用FLUX.1 Kontext [dev] NIM微服务。随后，在Hugging Face上接受模型许可，即可通过ComfyUI的节点下载模型并进行图像编辑。

Black Forest Labs’ FLUX.1 Kontext [dev] image editing model is now available as an NVIDIA NIM microservice.

FLUX.1 models allow users to edit existing images with simple language, without the need for fine-tuning or complex workflows.

Deploying powerful AI requires curation of model variants, adaptation to manage all input and output data, and quantization to reduce VRAM requirements. Models must be converted to work with optimized inference backend software and connected to new AI application programming interfaces.

The FLUX.1 Kontext [dev] NIM microservice simplifies this process, unlocking faster generative AI workflows, and is optimized for RTX AI PCs.

Generative AI in Kontext

FLUX.1 Kontext [dev] is an open-weight generative model built for image editing. It features a guided, step-by-step generation process that makes it easier to control how an image evolves, whether refining small details or transforming an entire scene.

Because the model accepts both text and image inputs, users can easily reference a visual concept and guide how it evolves in a natural and intuitive way. This enables coherent, high-quality image edits that stay true to the original concept.

The FLUX.1 Kontext [dev] NIM microservice provides prepackaged, optimized files that are ready for one-click download through ComfyUI NIM nodes — making them easily accessible to users.

NVIDIA and Black Forest Labs worked together to quantize FLUX.1 Kontext [dev], reducing the model size from 24GB to 12GB for FP8 (NVIDIA Ada Generation GPUs) and 7GB for FP4 (NVIDIA Blackwell architecture). The FP8 checkpoint is optimized for GeForce RTX 40 Series GPUs, which have FP8 accelerators in their Tensor Cores. The FP4 checkpoint is optimized for GeForce RTX 50 Series GPUs and uses a new method called SVDQuant, which preserves image quality while reducing model size.

In addition, NVIDIA TensorRT — a framework to access the Tensor Cores in NVIDIA RTX GPUs for maximum performance — provides over 2x acceleration compared with running the original BF16 model with PyTorch.

These dramatic performance gains were previously limited to AI specialists and developers with advanced AI infrastructure knowledge. With the FLUX.1 Kontext [dev] NIM microservice, even enthusiasts can achieve these time savings with greater performance.

Get NIMble

FLUX.1 Kontext [dev] is available on Hugging Face with TensorRT optimizations and ComfyUI.

To get started, follow the directions on ComfyUI’s NIM nodes GitHub:

Install NVIDIA AI Workbench.Get ComfyUI.Install NIM nodes through the ComfyUI Manager within the app.Accept the model licenses on Black Forest Labs’ FLUX.1 Kontext’s [dev] Hugging Face.The node will prepare the desired workflow and help with downloading all necessary models after clicking “Run.”

NIM microservices are optimized for performance on NVIDIA GeForce RTX and RTX PRO GPUs and include popular models from the AI community. Explore NIM microservices on GitHub and build.nvidia.com.

Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, productivity apps and more on AI PCs and workstations. 

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