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