nvidia/Qwen-Image-Flash

Model Overview

Description:

The NVIDIA Qwen-Image-Flash model generates images from text prompts using a four-step, DMD2-distilled version of Qwen/Qwen-Image. The distillation used DMD2 from NVIDIA FastGen, NVIDIA Model Optimizer, and NVIDIA AutoModel while retaining the base model architecture. The packaged scheduler is configured for the four-step, shift-3 trajectory.

Showcase


All showcase images were generated with nvidia/Qwen-Image-Flash.

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

License/Terms of Use:

Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement([https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/])) .

Additional Information: Apache License 2.0.

Deployment Geography:

Global

Use Case:

Developers and researchers evaluating few-step text-to-image generation, including creative-content prototyping and latency-sensitive image-generation workflows. Image editing, image understanding, and safety- or life-critical decision-making are outside the intended scope of this checkpoint.

Release Date:

Hugging Face 07/23/2026 via nvidia/Qwen-Image-Flash

Reference(s):

• Qwen-Image model card
• Qwen-Image Technical Report
• Improved Distribution Matching Distillation for Fast Image Synthesis (DMD2)
• NVIDIA Model Optimizer
• NVIDIA FastGen
• NVIDIA NeMo AutoModel

Model Architecture:

Architecture Type: Diffusion Transformer (MMDiT)

Network Architecture: QwenImageTransformer2DModel in a QwenImagePipeline

Base Model: 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 transformer has the same architecture as the base Qwen-Image transformer; its weights are replaced by the distilled student weights.

Input:

Input Type(s): Text

Input Format(s): String

Input Parameters: One-Dimensional (1D) sequence

Other Properties Related to Input: The release was distilled with English captions. Any inherited Chinese-language capability has not been evaluated for this model. The pipeline allows at most 1,024 prompt tokens and defaults to 512.

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. The tested output setting is 1024 × 1024 pixels. 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 hardware and software frameworks such as CUDA and PyTorch, the model can achieve faster inference than CPU-only execution.

Software Integration:

Supported Runtime Engine(s):

• Hugging Face Diffusers 0.38.0 with Transformers 5.12.1
• SGLang Diffusion: lmsysorg/sglang:nightly-dev-cu13-20260721-8905cbd4
• vLLM-Omni: vllm/vllm-omni:v0.24.0
• TensorRT-LLM VisualGen: nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc21

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Hopper H100
• NVIDIA Blackwell B200

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):

Qwen-Image-Flash was distilled with NVIDIA Model Optimizer 0.45.0.

Training and Evaluation Datasets:

Training Dataset:

Source Prompt Dataset: ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions, dataset is comprised of AI-generated images sourced from various websites and individuals, primarily focusing on Dalle 3 content.

Image Generator: Qwen/Qwen-Image

Internal Training Dataset Identifier: qwen-image-synthetic-dalle3-1m

Data Modality: Text, Image

Text Training Data Size: [Less than a Billion Tokens]

Image Training Data Size: [Less than a Million Images]

Data Collection Method by dataset: Hybrid: Automated, Synthetic

Labeling Method by dataset: Automated

Properties: Only text prompts from the ProGamerGov dataset were used; its original images were not used for training. Each prompt was passed to the original Qwen-Image model to generate a new synthetic image. The resulting dataset contains 1,000,000 English prompt–image pairs processed at 1024 × 1024, and a subset was used for Distribution Matching Distillation (DMD2) training.

Evaluation Dataset:

Link: Qwen-Image-Bench, OneIG-Bench-EN

Data Collection Method by dataset: Manually-collected

Labeling Method by dataset: Automated

Properties: Qwen-Image-Bench: 1,000 prompts; OneIG-Bench-EN: 1,120 English prompts across five categories.

Inference:

Acceleration Engine: Hugging Face Diffusers, SGLang Diffusion, vLLM-Omni, and...