nvidia/Qwen-Image-NVFP4

Model Overview

Description:

The NVIDIA Qwen-Image-NVFP4 model is the quantized version of Qwen's Qwen-Image model, which is a text-to-image diffusion transformer with strong image-generation quality, complex text rendering, and prompt adherence. For more information, please check here. The NVIDIA Qwen-Image-NVFP4 model is quantized with Model Optimizer.

This model is ready for commercial or non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA (Qwen-Image) Model Card.

References

• Nvidia Model Optimizer: https://github.com/NVIDIA/Model-Optimizer
• Qwen-Image model card
• Qwen-Image Technical Report

License/Terms of Use:

GOVERNING DOWNLOAD TERMS: Use of this model is governed by the NVIDIA Open Model Agreement.

Additional Information: Apache license 2.0

Deployment Geography:

Global

Use Case:

Developers and researchers looking to take off-the-shelf, pre-quantized text-to-image models for deployment in creative-content prototyping, visual-content generation workflows, and other AI-powered image-generation applications.

Release Date:

Hugging Face 10/12/2026 via https://huggingface.co/nvidia/Qwen-Image-NVFP4

Model Architecture:

Architecture Type: Diffusion Transformer (MMDiT)

Network Architecture: QwenImageTransformer2DModel in a QwenImagePipeline

This model was developed based on: Qwen/Qwen-Image

Number of Model Parameters: 20.43B parameters in the denoising transformer; 28.85B learned parameters in the full pipeline when the text encoder and VAE are included.

The full pipeline contains a Qwen2.5-VL text encoder, Qwen tokenizer, 60-layer Qwen-Image transformer, Qwen-Image VAE, and FlowMatch Euler scheduler. The quantized release preserves the base-model architecture while reducing inference precision for supported operators.

Input:

Input Type(s): Text

Input Format(s): String

Input Parameters: One-Dimensional (1D) sequence

Other Properties Related to Input: The pipeline allows at most 1,024 prompt tokens and defaults to 512. The upstream model card documents support for both English and Chinese prompt workflows.

Output:

Output Type(s): Image

Output Format: Red, Green, Blue (RGB)

Output Parameters: Two-Dimensional (2D)

Other Properties Related to Output: The pipeline returns generated images, typically as Python Imaging Library (PIL) images. Use width and height values divisible by 16 to avoid automatic resizing.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster inference times compared to CPU-only solutions.

Software Integration:

Supported Runtime Engine(s):

• TensorRT-LLM VisualGen 1.3.0rc28

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Blackwell - GB300

Preferred Operating System(s):

• Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

The model version is Qwen-Image-NVFP4 version 1.0 and is quantized with NVIDIA Model Optimizer v0.47.0.

Training, Testing, and Evaluation Datasets:

Calibration Dataset:

Link: Gustavosta/Stable-Diffusion-Prompts

Data Collection Method by dataset: Hybrid: Automated, Synthetic

Labeling Method by dataset: Automated

Properties: Only text prompts from the dataset were used for calibration of the quantized release.

Training Dataset:

Data Modality: Text, Image

Text Training Data Size: Undisclosed

Image Training Data Size: Undisclosed

Data Collection Method by dataset: Undisclosed

Labeling Method by dataset: Undisclosed

Properties: The upstream Qwen-Image model card describes the model's image-generation and editing capabilities, but does not provide enough detail in the retrieved source to fully characterize the original training datasets for this NVIDIA quantized release.

Testing Dataset:

Data Collection Method by dataset: Undisclosed

Labeling Method by dataset: Undisclosed

Properties: Undisclosed

Evaluation Dataset:

• Datasets: Qwen-Image-Bench, OneIG-EN

Data Collection Method by dataset: Hybrid: Automated, manually-collected

Labeling Method by dataset: Qwen-Image-Bench: Expert-reviewed benchmark metadata; automated scoring of this release using Q-Judger. OneIG-EN: Model-generated evaluation questions and reasoning references, alongside curated reference assets; automated scoring using the benchmark’s dimension-specific evaluators and metrics.

Properties: Qwen-Image-Bench: A text-to-image benchmark assessing quality, aesthetics, text-image alignment, real-world fidelity, and creative generation. OneIG-EN: The English subset of OneIG-Bench, assessing alignment, text rendering, knowledge/reasoning, style, and diversity.

Inference:

Acceleration Engine: TensorRT-LLM VisualGen

Test Hardware: NVIDIA Blackwell - GB300

Post Training Quantization

This checkpoint stores 784 transformer linear weight matrices in ModelOpt NVFP4 format across blocks 2–57, with 14 quantized matrices per block. Supported inference kernels use NVFP4 weights and activations for these linear operations. Other transformer parameters, the text encoder, and the VAE retain higher precision.

Usage

This checkpoint was validated with TensorRT-LLM VisualGen 1.3.0rc28 on an NVIDIA GB300 GPU. VisualGen loads the stored ModelOpt NVFP4...