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Step1X-Edit - A general-purpose image editing framework open sourced by Step1X.

Step1X-Edit is a general-purpose image editing framework developed by the Step1X team. It narrows the performance gap between open-source image editing models and closed-source models (such as GPT-4o and Gemini2 Flash). Step1X-Edit combines multi-mode...

What is Step1X-Edit?

Step1X-Edit, developed by the Step1X team, is a general-purpose image editing framework that narrows the performance gap between open-source image editing models and closed-source models (such as GPT-4o and Gemini2 Flash). Step1X-Edit combines a multimodal large language model (MLLM) and a diffusion model, extracting latent embeddings to generate target images based on processing reference images and user editing commands. To train the model, researchers built a large-scale, high-quality data generation pipeline, generating over 1 million image-command pairs. Step1X-Edit introduces a new benchmark, GEdit-Bench, for evaluating real-world user commands.

Main functions of Step1X-Edit

  • Diverse editing capabilitiesIt supports a variety of image editing tasks, including adding, removing, and replacing subjects, changing backgrounds, adjusting colors, modifying materials, changing styles, beautifying portraits, modifying text, and changing tones.
  • Natural language instruction driverUsers describe their editing needs using natural language, and the model can understand and execute complex editing instructions.
  • High-quality image generationSupports generating high-fidelity, lifelike image results.
  • Real-world scene adaptationTrained on a large-scale, high-quality dataset, it can handle various complex editing scenarios in the real world.

The technical principles of Step1X-Edit

  • Multimodal Large Language Model (MLLM)This approach uses MLLM to process reference images and user editing instructions, extracting semantic information. Leveraging MLLM's powerful semantic understanding capabilities, it generates embedding vectors relevant to the editing task.
  • Diffusion ModelImage generation is achieved by combining diffusion models (such as DiT-style architectures). The high-fidelity generation capabilities of diffusion models are used to decode the embedding vectors provided by MLLM into the target image.
  • Data generation pipelineBuild a large-scale, high-quality data generation pipeline to generate over 1 million image-instruction pairs. The data pipeline covers a variety of editing task categories, ensuring the model can learn diverse editing operations.
  • Training strategyThe text-to-image model is initialized while preserving aesthetic quality and visual consistency. Overall performance is optimized based on jointly trained connection modules and downstream diffusion models.
  • Benchmarking (GEdit-Bench)We've launched GEdit-Bench, a benchmark based on real user commands, to evaluate the model's actual performance. GEdit-Bench includes various editing tasks to ensure the model's effectiveness in real-world scenarios.

Step1X-Edit project address

Application Scenarios of Step1X-Edit

  • Creative DesignQuickly generate creative images, such as changing backgrounds, adjusting colors, and adding elements, improving design efficiency.
  • Post-production of film and televisionUsed in special effects production, such as adding/removing objects, changing appearance, or adjusting color tone, saving post-production costs.
  • social mediaEnhance photos, add fun elements, or adjust the style to increase the appeal of the content.
  • Game developmentGenerate characters, scenes, and props; quickly adjust equipment or style; and reduce art asset development time.
  • EducationGenerate teaching materials, such as modifying historical photos and creating scientific illustrations, to enhance teaching effectiveness.
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