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# Introduction
AI image editing has advanced rapidly. Tools like ChatGPT and Gemini have shown how powerful AI can be for creative tasks, leading many to wonder how it will change the future of graphic design. At the same time, open source image editing models are rapidly improving and closing the quality gap.
These models allow you to edit images using simple text prompts. You can remove backgrounds, replace objects, enhance images, and add artistic effects with minimal effort. Advanced design skills once required can now be done in a few steps.
In this blog, we review five open source AI models that stand out for image editing. You can run them locally, use them through an API, or access them directly in the browser, depending on your workflow and needs.
# 1. Flux 2 (clean) 9b
Flux 2 (Clean) is a high-performance open source image generation and editing model designed for speed, quality and flexibility. Developed by Black Forest Labs, it combines image generation and image editing into a single compact architecture, enabling end-to-end visualization on consumer hardware in under a second.
The Flux2 (clean) 9b base model is an uncompressed, full-featured foundation model that supports text-to-image generation and multi-reference image editing, making it well-suited for researchers, developers, and creators who want fine-grained control over output rather than relying on heavily distilled pipelines.
Key Features:
- Unified generation and editing: A single model handles text-to-image and image editing tasks within the architecture.
- Fixed Foundation Model: Full training preserves the signal, offering maximum flexibility, control and output diversity.
- Multi-reference editing support: Allows image editing guided by multiple reference images for more precise results.
- Optimized for real-time use: Delivers state-of-the-art quality with extremely low latency, even on consumer GPUs.
- Open weights and fine tuning are ready: Laura is designed for training, research, and custom pipelines, with compatibility in tools such as Diffuser and Comfy.
# 2. QWEN-IIMAGE-EDIT-2511
Kevin Image-Edit-2511 is an advanced open source image editing model focused on high consistency and precision. Developed by Alibaba Cloud as part of the Kevin model family, it builds on the Kevin Image-Edit-2509 with major improvements in image stability, character consistency, and compositional accuracy.
The model is designed for complex image editing tasks such as multi-person editing, industrial design workflows, and geometry-aware transformations, while remaining easy to integrate with diffuser and browser-based tools such as QuenChat.
Key Features:
- Improved Image and Character Consistency: Reduces image drift and preserves identity in single-person and multi-person edits.
- Multi-image and multi-person editing: Enables high-quality fusion of multiple reference images into a coherent final result.
- Built-in Laura integration: The base model contains Loras developed directly from the community, unlocking advanced effects without additional setup.
- Industrial Design and Engineering Support: Optimized for product design tasks such as material conversion, batch design, and structural modifications.
- Advanced Geometric Reasoning: Supports geometry-aware editing including construction lines and design annotations for technical use cases.
# 3. Flux 2 (Giant) Turbo
Flux 2 (Giant) Turbo is a lightweight, high-speed image generation and editing adapter designed to dramatically reduce projection time without sacrificing quality.
Built by Black Forest Labs as a distilled Laura adapter for the Flux 2 (Giant) base model, it enables high-quality results in eight inference steps. This makes it an excellent choice for real-time applications, rapid prototyping, and interactive image workflows where speed is critical.
Key Features:
- Ultra-fast 8-step assessment: Achieves six times faster generation than a standard 50-step workflow.
- Quality is reserved: Despite the heavy attenuation, the visual quality of the original Flux 2 (Dev) model is matched or exceeded.
- Laura-based adapters: Lightweight and easy to plug into existing Flux 2 pipelines with minimal overhead.
- Text to Image and Image Editing Support: Works with both generation and editing functions in a single setup.
- Wide ecosystem support: Available via host APIs, diffusers, and comfyui for flexible deployment options.
# 4. Longcut Image Edit
Longcat Image Edit is a state-of-the-art open-source image editing model designed for high-fidelity, directed editing with strong visual consistency. Developed by Meituan as an image editing counterpart to Longkat Image, it supports bilingual editing in both Chinese and English.
The model follows complex editing instructions while preserving unedited regions, making it particularly effective for multi-step and reference-guided image editing workflows.
Key Features:
- Edit based on exact instructions: Supports global edits, local edits, text editing, and reference guide editing with strong terminology understanding.
- Strong consistency protection: Maintains layout, texture, color tone and identity of subjects in unedited regions, even with multi-layered edits.
- Bilingual editing support: Handles both Chinese and English characters, enabling wider accessibility and use cases.
- State-of-the-art open source performance: Delivers SOTA results in an open-source image editing model with improved performance.
- Text rendering optimization: Uses special character-level encoding for quoted text, enabling more accurate text generation within images.
# 5. STEP1X-EDIT-V1P2
STEP1X-EDIT-V1P2 An Argument is an enhanced open source image editing model designed to improve instructional understanding and editing accuracy. Powered by step-by-step AI, it introduces spatial reasoning capabilities through structure to think And reflection Mechanism. This allows the model to interpret complex or abstract editing instructions, carefully apply the changes, and then review and correct the results before finalizing the output.
As a result, STEP1X-EDIT-V1P2 achieves strong performance on benchmarks such as ChrisBench and GedtBench, especially in scenarios that require precise, multi-dimensional editing.
Key Features:
- Logical Image Editing: Uses clear thinking and reflection steps to better understand instructions and reduce unannounced changes.
- Strong benchmark performance: Open source image editing models provide competitive results on ChrisBench and GedtBench.
- Better understanding of instructions: Excel to handle summary, detailed, or multipart editing prompts.
- Reflection based optimization: Reviews edited outputs to fix errors and decide when editing is complete.
- Research-based and scalable: Designed for experimentation, with multiple modes that trade off speed, accuracy and depth of reasoning.
# Final thoughts
Open source image editing models are rapidly maturing, offering creators and developers serious alternatives to closed tools. Now they combine speed, consistency, and fine-grained control, making it easy to experience and deploy advanced image editing.
Models at a glance:
- Flux 2 (clean) 9b Focus on high-quality generation and flexible modification in a single, immovable foundation model.
- Kevin Image-Edit-2511 Stands out for consistent, structure-aware edits, especially in multi-dimensional and design-heavy scenarios.
- Flux 2 (Giant) Turbo Laura Prioritizes speed delivering robust results in real-time with minimal inference steps.
- Longcat Image Edit Excel at precise, instruction-driven edits while preserving visual consistency across multiple turns.
- STEP1X-EDIT-V1P2 Takes image editing further by adding reasoning, and letting the model think through complex edits before finalizing the model.
Abid Ali Owan For centuries.@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunication Engineering. His vision is to create an AI product using graph neural networks for students with mental illness.