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pipeline_tag: text-to-image
base_model:
- Qwen/Qwen-Image
license: other
license_name: nvidia-open-model-license
license_link:
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license
library_name: diffusers
tags:
- nvidia
- ModelOpt
- AutoModel
- qwen-image
- dmd2
- few-step
- text-to-image
---
# 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](https://huggingface.co/Qwen/Qwen-Image). The distillation used DMD2 from [NVIDIA FastGen](https://github.com/NVlabs/FastGen), [NVIDIA Model Optimizer](https://github.com/NVIDIA/Model-Optimizer), and [NVIDIA AutoModel](https://github.com/NVIDIA-NeMo/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](https://huggingface.co/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](https://choosealicense.com/licenses/apache-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](https://huggingface.co/nvidia/Qwen-Image-Flash)
# Reference(s):
- [Qwen-Image model card](https://huggingface.co/Qwen/Qwen-Image)
- [Qwen-Image Technical Report](https://arxiv.org/abs/2508.02324)
- [Improved Distribution Matching Distillation for Fast Image Synthesis (DMD2)](https://arxiv.org/abs/2405.14867)
- [NVIDIA Model Optimizer](https://github.com/NVIDIA/Model-Optimizer)
- [NVIDIA FastGen](https://github.com/NVlabs/FastGen)
- [NVIDIA NeMo AutoModel](https://github.com/NVIDIA-NeMo/Automodel)
## Model Architecture:
**Architecture Type:** Diffusion Transformer (MMDiT)
**Network Architecture:** `QwenImageTransformer2DModel` in a `QwenImagePipeline`
**Base Model:** [Qwen/Qwen-Image](https://huggingface.co/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](https://github.com/huggingface/diffusers) 0.38.0 with Transformers 5.12.1
- [SGLang Diffusion](https://github.com/sgl-project/sglang): `lmsysorg/sglang:nightly-dev-cu13-20260721-8905cbd4`
- [vLLM-Omni](https://github.com/vllm-project/vllm-omni): `vllm/vllm-omni:v0.24.0`
- [TensorRT-LLM VisualGen](https://github.com/NVIDIA/TensorRT-LLM): `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](https://huggingface.co/datasets/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](https://openai.com/index/dall-e-3/) content.
**Image Generator:** [Qwen/Qwen-Image](https://huggingface.co/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](https://arxiv.org/abs/2605.28091), [OneIG-Bench-EN](https://oneig-bench.github.io/)
**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 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`)
```python
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`
```bash
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`
```bash
# 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
```
```bash
# 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`
```python
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](https://arxiv.org/abs/2605.28091) | [OneIG-Bench-EN](https://oneig-bench.github.io/) |
| --- | ---: | ---: |
| Qwen-Image | 49.070 | 0.886 |
| [Qwen-Image-Lightning](https://huggingface.co/lightx2v/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](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).
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](https://choosealicense.com/licenses/apache-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](https://choosealicense.com/licenses/apache-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](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |
|