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.
Key Highlights
High-Quality Inpainting
Delivers seamless area filling by bringing FLUX model's superior image quality to inpainting operations.
Text-Guided Filling
Controls the creation of desired content by guiding the masked area with text prompts.
Seamless Style Matching
Ensures the filled area shows perfect harmony with surrounding pixels in style, color, and texture.
Outpainting Support
Offers capability to expand image boundaries creating new areas consistent with the original style.
About
FLUX Fill is the specialized inpainting and outpainting version of the FLUX model family developed by Black Forest Labs. This model demonstrates superior performance in reconstructing specific areas within images (inpainting) and extending image boundaries (outpainting), designed for professional visual editing workflows. Built on the powerful foundational infrastructure of the FLUX.1 family, Fill produces extremely natural and consistent results in mask-based editing tasks. Released in 2024, the model has established itself as an important tool in the AI-assisted photo editing landscape.
In terms of technical architecture, FLUX Fill builds on FLUX.1's 12-billion parameter Diffusion Transformer structure, adding mask-aware diffusion mechanisms. The model accepts a source image, a mask (defining the region to edit), and an optional text instruction as inputs. Pixel information from unmasked regions is fed as conditioning to the diffusion process, ensuring edited areas blend seamlessly with their surroundings. T5-XXL and CLIP text encoders ensure accurate interpretation of editing instructions. While the Flow Matching approach is preserved, specialized attention mechanisms have been implemented for smooth transitions at mask boundaries and texture continuity across different materials and surfaces.
FLUX Fill's greatest strength is its ability to create seamless transitions between edited regions and the original image. In inpainting mode, it delivers extraordinary results in tasks such as removing unwanted objects, face retouching, clothing changes, and background editing. In outpainting mode, it naturally performs operations like creating panoramic scenes by extending image boundaries, completing cropped photos, and converting vertical formats to horizontal formats. It notably surpasses previous-generation inpainting models in color consistency, texture continuity, and lighting harmony, producing results that are often indistinguishable from the original photograph.
FLUX Fill is used by photographers, e-commerce operators, graphic designers, real estate agencies, and content studios. It is widely preferred in practical scenarios such as removing unwanted background elements from product photos, skin retouching in portrait photography, adding or removing furniture in real estate listings, restoring old photographs, and adapting social media content to different format ratios. Batch processing support enables automated editing of hundreds of images with consistent quality.
FLUX Fill is a closed-source model accessible through the Black Forest Labs API. It is also available on third-party platforms such as Replicate and fal.ai. Pay-per-use pricing is applied with commercial use licensing provided with API access. Community-developed integrations on ComfyUI are also available, and the model can be easily incorporated into programmatic workflows through standard HTTP API calls, making it suitable for automated production pipelines.
In the competitive landscape, FLUX Fill competes with Adobe Firefly's Generative Fill feature, Stable Diffusion inpainting models, and RunwayML's editing tools. Thanks to FLUX.1's superior base quality, it offers more natural transitions and higher detail levels in inpainting results. Its quality advantage over open-source inpainting models is clear, while its API-based automation flexibility is its distinguishing advantage over Adobe Firefly. Particularly in large-scale automated editing workflows, FLUX Fill is considered one of the strongest solutions on the market, enabling e-commerce platforms and media companies to process thousands of images with professional-quality results.
Use Cases
Object Removal and Replacement
Editing by removing unwanted objects from images or replacing them with different objects.
Image Extension
Creating wider compositions and backgrounds by extending the boundaries of existing images.
Photo Restoration
Restoring old images by repairing photos with damaged, scratched, or missing areas.
Creative Image Editing
Creatively transforming and editing specific areas of images with text guidance.
Pros & Cons
Pros
- Native inpainting and outpainting solution of FLUX ecosystem
- Strong inpainting results with FLUX models' visual quality
- Guided filling with text prompts
- Ability to extend image boundaries with outpainting
Cons
- Cannot be used outside FLUX ecosystem
- Pro version is API-based and paid
- Context loss possible in large masked areas
- Not as precise control as other specialized inpainting tools
Technical Details
Parameters
12B
Architecture
Diffusion Transformer
Training Data
Proprietary
License
Proprietary
Features
- High-quality inpainting
- Style matching
- Text-guided filling
- Seamless blending
- Outpainting support
- Mask-based editing
Benchmark Results
| Metric | Value | Compared To | Source |
|---|---|---|---|
| FID (Inpainting, COCO) | 4.12 | SDXL Inpainting: 6.83 | Black Forest Labs Blog |
| Desteklenen Çözünürlük | 2MP'ye kadar (~1440×1440) | SD Inpainting: 512×512 | Hugging Face Model Card |
| Maske Doğruluğu (IoU) | 0.94 | — | Black Forest Labs Evaluation |
Available Platforms
Frequently Asked Questions
Related Models
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.
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.
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.
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.
Quick Info
Diffusion Transformer