AI multimodale

Qwen-Image: text-to-image generation and multi-image editing

QwenLM/Qwen-Image

Qwen image generation and editing models with Diffusers examples for text-rich artwork, product imagery and reference-guided edits.

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Panoramica

The repository provides distinct models for generating new images and editing existing ones. Its examples cover Qwen-Image-2512 for text-to-image generation and Qwen-Image-Edit-2511 for editing with multiple reference images. Choose between a prompt-only workflow and an image-plus-instruction workflow before loading a pipeline. Product announcements in the repository are separate from the checkpoints available in its deployment examples.

Funzionalità principali

  • Targets image generation scenarios involving Chinese and other text.
  • Provides a dedicated text-to-image pipeline.
  • Edit-2511 examples accept multiple reference images.
  • Exposes prompts, seeds and inference-step settings.
  • Includes generation examples for multiple aspect ratios.
  • Supports Diffusers integration and local demo workflows.

Requisiti, installazione e avvio rapido

1. Prepare an isolated Python/PyTorch environment and weight storage.
2. Install compatible Transformers as documented and run pip install git+https://github.com/huggingface/diffusers.
3. For generation, load Qwen/Qwen-Image-2512 with QwenImagePipeline.
4. For editing, load Qwen/Qwen-Image-Edit-2511 with QwenImageEditPlusPipeline.
5. Configure device, precision, image dimensions and seed for the selected pipeline.
6. Run the official minimal example before substituting your own prompt or reference images.

Utilizzo

For a product graphic, describe the product, layout, exact wording and text placement separately. For existing product photography, use the editing pipeline and specify which background elements should change and which subject details should remain. Inspect lettering, logos, geometry and reference consistency. Retain seeds and settings when iterating on a campaign.

Implementation notes
Generated lettering can still be wrong, and multi-image edits need checks for identity and placement. Change a small number of requirements per iteration and retain intermediate outputs. Measure peak memory, processing time and retry behavior before deploying a service.

Compatibilità del modello e casi d'uso

The generation example includes CPU and CUDA paths, while the editing example uses CUDA with bfloat16. Large checkpoints require substantial memory and storage; offloading and quantization change the deployment configuration and performance.

Note su licenza e rischi

The official README identifies Qwen-Image as Apache-2.0. Apply the terms associated with each downloaded checkpoint, dependency and input asset.

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