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):

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: MTBench, for more details, see here Data Collection Method by dataset: Hybrid: Manually-collected, Synthetic Labeling Method by dataset: Hybrid: manually-labelled, Synthetic Properties: 3,300 multi-turn dialogue sequences.

Inference:

Acceleration Engine: Hugging Face Diffusers, SGLang Diffusion, vLLM-Omni, and TensorRT-LLM VisualGen

Test Hardware: NVIDIA GB200

DMD2 Distillation

Qwen-Image-Flash was trained using multi-step DMD2 (Distribution Matching Distillation) to approximate the output distribution of Qwen-Image in four denoising steps. The student retains the base transformer's architecture and parameter count. The four-step design reduces the number of transformer evaluations but does not reduce the checkpoint's parameter count or memory requirements.

The teacher target used classifier-free guidance (CFG) 4.0 during distillation. That guidance is internalized by the student, so inference uses true_cfg_scale=1.0 to avoid applying guidance a second time. The packaged static shift-3 FlowMatch Euler scheduler produces effective sigmas [1.0, 0.9, 0.75, 0.5, 0.0] over the required four steps.

Usage

Diffusers

Tested container: vllm/vllm-omni:v0.24.0 (diffusers==0.38.0, transformers==5.12.1)

import torch
from diffusers import QwenImagePipeline

pipe = QwenImagePipeline.from_pretrained(
    "nvidia/Qwen-Image-Flash",
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(
    prompt="A red fox in a snowy pine forest at golden hour, photorealistic, sharp focus, soft bokeh",
    width=1024,
    height=1024,
    num_inference_steps=4,
    true_cfg_scale=1.0,
    guidance_scale=None,
    negative_prompt=None,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]

image.save("qwen-image-flash-diffusers.png")

SGLang Diffusion

Tested container: lmsysorg/sglang:nightly-dev-cu13-20260721-8905cbd4

docker run --rm --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -e HF_TOKEN -v qwen-image-hf-cache:/root/.cache/huggingface -v "$PWD:/workspace" -w /workspace lmsysorg/sglang:nightly-dev-cu13-20260721-8905cbd4 sglang generate --model-path nvidia/Qwen-Image-Flash --prompt "A red fox in a snowy pine forest at golden hour, photorealistic, sharp focus, soft bokeh" --width 1024 --height 1024 --num-inference-steps 4 --guidance-scale 1.0 --true-cfg-scale 1.0 --seed 42 --save-output --output-file-path qwen-image-flash-sglang.png

vLLM-Omni

Tested container: vllm/vllm-omni:v0.24.0

# Server
docker run --rm --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -e HF_TOKEN -p 8091:8091 -v qwen-image-hf-cache:/root/.cache/huggingface vllm/vllm-omni:v0.24.0 vllm serve nvidia/Qwen-Image-Flash --omni --host 0.0.0.0 --port 8091

# Client
curl -fsS http://127.0.0.1:8091/v1/images/generations -H 'Content-Type: application/json' -d '{"prompt":"A red fox in a snowy pine forest at golden hour, photorealistic, sharp focus, soft bokeh","n":1,"size":"1024x1024","response_format":"b64_json","num_inference_steps":4,"true_cfg_scale":1.0,"guidance_scale":1.0,"seed":42}' -o qwen-image-flash-vllm-omni-response.json
set -o pipefail; jq -er '.data[0].b64_json' qwen-image-flash-vllm-omni-response.json | base64 --decode > qwen-image-flash-vllm-omni.png

TensorRT-LLM

Tested container: nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc21

from tensorrt_llm import VisualGen


def main():
    visual_gen = VisualGen(model="nvidia/Qwen-Image-Flash")
    try:
        params = visual_gen.default_params
        params.width = 1024
        params.height = 1024
        params.num_inference_steps = 4
        params.guidance_scale = 1.0
        params.negative_prompt = None
        params.seed = 42

        output = visual_gen.generate(
            inputs="A red fox in a snowy pine forest at golden hour, photorealistic, sharp focus, soft bokeh",
            params=params,
        )
        output.save("qwen-image-flash-tensorrt-llm.png")
    finally:
        visual_gen.shutdown()


if __name__ == "__main__":
    main()

Evaluation

The following results compare Qwen-Image-Flash with Qwen-Image and Qwen-Image-Lightning:

Evaluation was performed at 1024 × 1024 resolution on an NVIDIA B200 GPU.

Model	Qwen-Image-Bench	OneIG-EN
Qwen-Image	49.070	0.886
Qwen-Image-Lightning	46.980	0.887
Qwen-Image-Flash (Ours)	47.080	0.885

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards below.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

SUBCARDS:

Explainability

Field	Response
Intended Task/Domain	Few-step text-to-image generation.
Model Type	DMD2-distilled diffusion transformer (MMDiT) packaged as a full Diffusers pipeline.
Intended Users	Developers and researchers evaluating or integrating latency-sensitive image generation.
Output	Generated RGB image.
Describe how the model works	A Qwen2.5-VL text encoder converts the prompt to conditioning embeddings. A DMD2-distilled Qwen-Image transformer denoises image latents in four deterministic FlowMatch Euler steps, and the VAE decodes the final latents into an image.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of	Not Applicable
Technical Limitations & Mitigation	The model is designed for the packaged four-step schedule; changing the number of inference steps or scheduler trajectory may degrade image quality or produce unexpected results. It was distilled with English captions at 1024 × 1024, so performance with non-English prompts has not been evaluated and may be less reliable. The pipeline accepts other resolutions when the width and height are divisible by 16, but only 1024 × 1024 has been tested and quality may vary at other sizes. The model may inherit biases and visual artifacts from the base model and training data and does not include a safety checker; deployers should conduct use-case-specific testing and add appropriate safeguards.
Verified to have met prescribed NVIDIA quality standards	Yes
Performance Metrics	Image quality, prompt adherence, throughput, and end-to-end latency.
Potential Known Risk	This model can generate synthetic images and may produce content that is inaccurate, offensive, or otherwise inappropriate. Users should implement robust safety guardrails — including content filtering, abuse monitoring, and access controls — to reduce the risk of harmful outputs. Users are responsible for ensuring that their use of the model complies with all applicable laws and regulations, and for regularly reviewing and updating their guardrails as risks evolve.
Licensing	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.) |

Bias

Field	Response
Participation considerations from adversely impacted groups in model design and testing	None
Measures taken to mitigate against unwanted bias	None
Bias Metric	None

Safety & Security

Field	Response
Model Application Field(s)	Text-to-image generation, creative-content prototyping, and research.
Describe the life-critical impact (if present)	Not Applicable
Use Case Restrictions	Use must comply with the 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 , and applicable law. Image editing, image understanding, high-stakes decisions, and illegal or abusive content generation are outside the intended scope. | | Model and Dataset Restrictions | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |

Privacy

Field	Response
Generatable or reverse-engineerable personal data	No
Was consent obtained for any personal data used	Not Applicable
Personal data used to create this model	None Known
How often is the dataset reviewed	Before Release
Was data from user interactions with the AI model used to train the model	No
Is there provenance for all datasets used in training	Yes
Does data labeling comply with privacy laws	Yes
Is the data compliant with data-subject requests for correction or removal	No, not possible with externally-sourced data.
Applicable NVIDIA Privacy Policy	NVIDIA Privacy Policy