Inpainting Models
Explore the best AI models for inpainting
GPT Image 1
GPT Image 1 is OpenAI's latest image generation model that integrates natively within the GPT architecture, combining language understanding with visual generation in a unified autoregressive framework. Unlike diffusion-based competitors, GPT Image 1 generates images token by token through an autoregressive process similar to text generation, enabling a conversational interface where users iteratively refine outputs through dialogue. The model excels at text rendering within images, producing legible and accurately placed typography that has historically been a weakness of diffusion models. It supports both generation from text descriptions and editing through natural language instructions, allowing users to upload images and describe desired modifications. GPT Image 1 understands complex compositional prompts with multiple subjects, spatial relationships, and specific attributes, producing coherent scenes accurately reflecting described elements. It handles diverse styles from photorealism to illustration, painting, graphic design, and technical diagrams. Editing capabilities include inpainting, style transformation, background replacement, object addition or removal, and color adjustment, all through conversational input. The model is accessible through the OpenAI API for application integration and through ChatGPT for consumer use. Safety systems prevent harmful content generation. Generated images belong to the user with full commercial rights under OpenAI's terms. GPT Image 1 represents a significant step toward multimodal AI systems seamlessly blending language and visual capabilities, making AI image creation more intuitive through natural conversation.
Adobe Generative Fill
Adobe Generative Fill is a generative AI feature integrated directly into Adobe Photoshop, powered by Adobe's proprietary Firefly image generation model. Introduced in 2023, it enables users to add, modify, or remove content in images using natural language text prompts within the familiar Photoshop interface. The feature works by selecting a region with any Photoshop selection tool, typing a descriptive prompt in the contextual task bar, and receiving three AI-generated variations within seconds. Generated content is placed on a separate layer, preserving Photoshop's non-destructive editing workflow that professionals rely on. A key differentiator is Firefly's training data approach, which uses exclusively licensed Adobe Stock imagery, openly licensed content, and public domain materials, providing commercial safety and IP indemnification that competing solutions cannot match. Generative Fill automatically maintains coherence with surrounding color, lighting, perspective, and texture for seamless blending. The companion Generative Expand feature enables extending images beyond their original canvas boundaries. Professional applications span advertising campaign iteration, photography post-production, real estate staging, product photography background replacement, fashion color modification, and editorial visual preparation. The feature is accessible through Photoshop's Creative Cloud subscription with a monthly generative credits system, and also available through Adobe Express and the web-based Firefly application. Content Credentials metadata indicates when AI was used, supporting transparency standards. Adobe Generative Fill represents the most commercially safe and professionally integrated approach to AI-powered image editing available today.
FLUX Fill
FLUX Fill is the specialized inpainting and outpainting model within the FLUX model family developed by Black Forest Labs, designed for professional-grade region editing, content filling, and image extension. Built on the 12-billion parameter Diffusion Transformer architecture that powers all FLUX models, FLUX Fill takes an input image along with a binary mask indicating the region to be modified and generates seamlessly blended content that matches the surrounding context in style, lighting, perspective, and detail level. The model excels at both inpainting tasks where masked areas within an image are filled with contextually appropriate content and outpainting tasks where image boundaries are extended to create larger compositions. FLUX Fill leverages the superior prompt adherence of the FLUX architecture, allowing users to guide the generation with text descriptions of what should appear in the masked region, providing precise creative control over the output. The model handles complex scenarios including filling regions that span multiple materials and textures, maintaining structural continuity of architectural elements, and generating photorealistic human features in masked face areas. As a proprietary model, FLUX Fill is accessible through Black Forest Labs' API and partner platforms including Replicate and fal.ai, with usage-based pricing. Professional photographers use FLUX Fill for removing unwanted elements and extending compositions, e-commerce teams employ it for product background replacement, digital artists leverage it for creative compositing, and marketing professionals use it for adapting images to different aspect ratios and formats without losing content quality.
SD Inpainting
Stable Diffusion Inpainting is a specialized variant of Stability AI's Stable Diffusion model fine-tuned specifically for image inpainting tasks, enabling users to fill masked regions of an image with contextually coherent content guided by text prompts. Released in 2022, the model builds upon the latent diffusion architecture but extends it with additional input channels for mask-aware processing, where the original image, mask, and masked image are fed as extra channels to the U-Net. The v1.5 inpainting model was trained on 595K curated inpainting examples in collaboration with RunwayML, while community-developed SDXL variants have since extended capabilities with higher resolution output. Common applications include removing unwanted objects from photographs, completing damaged image regions, modifying content such as adding elements to scenes, and cleaning watermarks or text overlays. Professional use cases span photography post-production, advertising visual preparation, real estate staging, product photography background replacement, and digital art workflows. The model is accessible through popular open-source interfaces including AUTOMATIC1111 WebUI, ComfyUI, InvokeAI, and the Hugging Face Diffusers library. Users can create masks manually with brush tools or automatically through segmentation models like SAM. ControlNet integration adds additional control layers for more precise output guidance. Released under the CreativeML Open RAIL-M license, the model runs on GPUs with 8GB VRAM and supports optimizations like xFormers for reduced memory usage, making it one of the most widely adopted open-source inpainting solutions available.
Lama Cleaner
Lama Cleaner is an open-source image inpainting tool built around the LaMa (Large Mask Inpainting) model, designed for removing unwanted objects, watermarks, text overlays, and blemishes from photographs with minimal effort. Developed by Sanster as an accessible desktop application, it provides a user-friendly brush-based interface where users simply paint over the area they want removed, and the AI fills the region with contextually appropriate content that blends seamlessly with the surrounding image. The underlying LaMa model uses a fast Fourier convolution-based architecture that excels at handling large masked areas, a common weakness in traditional inpainting approaches. Unlike many AI tools that require cloud processing, Lama Cleaner runs entirely locally on the user's machine, ensuring privacy and eliminating subscription costs. The tool supports multiple inpainting backends beyond LaMa, including LDM, ZITS, MAT, and Stable Diffusion-based models, giving users flexibility to choose the best engine for their specific task. It handles various image formats and can process both photographs and illustrations effectively. Common use cases include cleaning up travel photos by removing tourists, erasing power lines or signage from architectural shots, removing date stamps from scanned photographs, and eliminating skin blemishes in portraits. The tool is available as a Python package installable via pip and also offers a web-based interface for browser access. Its combination of powerful AI-driven inpainting, local processing, and zero cost makes it an essential utility for photographers, designers, and content creators who need quick object removal capabilities.
DALL-E Inpainting
DALL-E Inpainting is OpenAI's proprietary image editing capability that allows users to modify specific regions of existing images through natural language prompts, available through both the DALL-E web interface and the OpenAI API. Building on the DALL-E image generation architecture, the inpainting feature enables users to select rectangular or custom-shaped regions of an image and describe what should appear in the masked area, with the AI generating contextually appropriate content that blends with the surrounding image. The system understands complex spatial relationships, lighting conditions, and artistic styles to produce edits that maintain visual coherence with the original image. Key capabilities include adding new objects to scenes, replacing backgrounds, modifying clothing or accessories on people, changing weather conditions or time of day in landscapes, and removing unwanted elements. The API provides programmatic access for building automated editing pipelines and integrating inpainting into custom applications, with options for controlling output resolution and the number of generated variations. Unlike open-source alternatives, DALL-E Inpainting operates entirely in the cloud with no local GPU requirements, making it accessible to users without specialized hardware. The model benefits from OpenAI's continuous improvements and safety filters that prevent generation of harmful content. Commercial usage is permitted under OpenAI's terms of service, with generated images belonging to the user. While it requires a paid API subscription or credits-based usage, its ease of integration, consistent quality, and the backing of OpenAI's infrastructure make it a reliable choice for developers and businesses requiring scalable AI-powered image editing capabilities.
PowerPaint
PowerPaint is a versatile open-source inpainting model developed by researchers at Tsinghua University and HKUST under the Tencent ARC umbrella, introducing the innovative concept of learnable task prompts that enable multiple inpainting functions within a single unified model. Rather than requiring separate specialized models for each editing task, PowerPaint uses learnable task vectors that activate different behaviors within shared model weights, supporting four distinct modes: text-guided object insertion, object removal, shape-guided inpainting, and image outpainting. Built upon a Stable Diffusion backbone enriched with a ControlNet-like control mechanism, the model allows users to describe desired content through text prompts for contextual generation, cleanly remove objects while preserving surrounding textures, generate content within specific mask shapes, or extend images beyond their original boundaries. This multi-task flexibility eliminates the need to switch between different tools or models during editing workflows. In benchmark evaluations, PowerPaint achieves competitive results against separately optimized task-specific models, with its object removal quality rivaling specialized models like LaMa and MAT. Applications span photography editing, graphic design mockups, e-commerce product image preparation, digital art canvas extension, and social media content adaptation for different platform dimensions. The model is PyTorch-based and publicly available through Hugging Face with a Gradio demo interface and Diffusers library integration. GPU requirements are similar to standard Stable Diffusion models with 8GB or more VRAM recommended. PowerPaint has established a new paradigm in multi-task inpainting and continues to inspire research in unified visual editing systems.