IOPaint: Free AI Inpainting and Outpainting Tool Guide 2025

IOPaint is a free, open-source, self-hostable AI tool for inpainting and outpainting that removes unwanted objects, extends images, and replaces elements using state-of-the-art AI models. Unlike Adobe Photoshop Generative Fill which costs $54.99/month, IOPaint runs locally on your hardware with complete privacy and zero ongoing costs.

You need to remove a photo bomber from your vacation pictures. Or extend that portrait beyond its edges. Or erase that ugly power line ruining your landscape shot.

Traditional photo editing requires manual cloning, careful masking, and hours of tedious work. Even then, results often look artificial. Professional tools like Adobe Photoshop offer AI features, but they cost $54.99 per month and send your images to Adobe's servers.

IOPaint changes everything. This free, open-source tool delivers professional inpainting and outpainting results using cutting-edge AI models, all running locally on your computer. For users who prefer cloud convenience without the technical setup, Apatero.com provides instant access to similar AI image editing capabilities with zero configuration required.

What is IOPaint?

IOPaint is a fully self-hostable web application that provides AI-powered image inpainting and outpainting capabilities. Created by developer Sanster and available on GitHub, it brings professional image editing tools to anyone with a computer, no subscription fees or cloud uploads required.

At its core, IOPaint serves as a unified interface for multiple state-of-the-art AI models. You brush over parts of an image you want to modify, and the AI intelligently fills, removes, or extends based on surrounding context. The results often match or exceed what professional editors can achieve manually in Photoshop.

The Technology Behind IOPaint

IOPaint integrates two fundamental approaches to AI image manipulation. Each serves different use cases and delivers different types of results.

Erase Models like LaMa (Large Mask Inpainting) and MAT (Mask-Aware Transformer) excel at removing unwanted elements cleanly. These models analyze surrounding pixels and generate natural-looking fills that blend seamlessly. They're perfect for removing objects, people, watermarks, or defects where you want the background to continue naturally.

Diffusion Models like Stable Diffusion and PowerPaint can replace or generate new content based on text descriptions. Instead of just removing, they create. Want to replace a dog with a cat? Change daytime to sunset? Add objects that weren't there? Diffusion models make it possible through text prompts combined with masked regions.

This dual approach gives IOPaint incredible versatility. Remove objects with erase models, extend images with outpainting, or replace elements with diffusion models, all from one interface.

IOPaint Key Features

IOPaint packs professional capabilities into an accessible package designed for both beginners and advanced users.

Why Choose IOPaint Over Paid Alternatives?

Before diving into installation, understanding IOPaint's position in the image editing landscape helps you make informed decisions about your workflow.

IOPaint vs Adobe Photoshop Generative Fill

Adobe Photoshop introduced Generative Fill as a flagship AI feature, powered by Adobe Firefly. It's impressive, but it comes with significant costs and limitations.

Photoshop Generative Fill:

• $54.99/month subscription minimum
• Images uploaded to Adobe's servers
• Limited to Adobe's single AI model
• Requires internet connection
• Subject to Adobe's terms of service
• Generative credits may be limited on lower tiers

IOPaint:

• Completely free and open source
• 100% local processing with full privacy
• Choose from 15+ different AI models
• Works offline once models are downloaded
• No usage limits or restrictions
• Full control over all aspects of processing

The quality comparison depends on the specific model you choose in IOPaint. LaMa excels at clean object removal, while Stable Diffusion models can match or exceed Photoshop Generative Fill for creative tasks. You're not locked into a single AI approach.

IOPaint vs Online Inpainting Services

Services like Cleanup.pictures, Remove.bg, and ClipDrop offer convenient browser-based inpainting. They're easy to start with but have hidden costs.

Typical Online Services:

• $9-29 per month for unlimited use
• Free tiers heavily limited (10-50 images/month)
• Images uploaded to third-party servers
• Privacy concerns for sensitive content
• Internet dependency
• Limited or no model selection
• Cannot customize or extend

IOPaint:

• Zero ongoing costs after initial setup
• Unlimited image processing
• Complete privacy for sensitive work
• Works offline after model download
• Full access to all available models
• Extensible through plugins
• Open source allows customization

For professional photographers, designers working with client images, or anyone processing sensitive content, IOPaint's local execution provides peace of mind that online services simply cannot match.

When to Consider Apatero.com Instead

IOPaint requires technical setup and hardware to run models locally. If you need instant access without configuration, platforms like Apatero.com provide cloud-based AI image generation and editing capabilities with professional results and zero setup time.

Choose IOPaint if you:

• Have suitable hardware (8GB+ RAM, decent GPU for faster processing)
• Want complete control over models and settings
• Need absolute privacy for sensitive images
• Process images frequently enough to justify setup time
• Enjoy learning and customizing your tools

Choose Apatero.com if you:

• Need to start creating immediately without technical setup
• Don't have powerful local hardware
• Want professionally curated workflows and models
• Prefer cloud convenience over local control
• Need reliable access from any device

Both approaches have merit. Many professionals use IOPaint for sensitive client work while leveraging cloud platforms for rapid iteration and testing.

How Do You Install IOPaint?

IOPaint installation takes under 10 minutes on most systems. The process is straightforward, though requirements vary based on your operating system and desired performance level.

System Requirements

Before installation, verify your system meets these minimum specifications.

Minimum Requirements:

• Python 3.8 or higher
• 8GB RAM (16GB recommended)
• 10GB free disk space
• Windows 10+, macOS 10.15+, or modern Linux distribution

For GPU Acceleration:

• NVIDIA GPU with 6GB+ VRAM (RTX 3060 or better recommended)
• CUDA 11.8+ installed
• Or AMD GPU with ROCm support (Linux only)
• Or Apple Silicon Mac (M1/M2/M3/M4) for Metal acceleration

CPU-only execution works but runs significantly slower, especially with diffusion models. Erase models like LaMa run acceptably on CPU for occasional use. For frequent processing, GPU acceleration dramatically improves workflow speed.

If you're building a system specifically for AI image work, our guide to running ComfyUI on budget hardware provides hardware recommendations that apply equally to IOPaint.

Installing IOPaint on Windows

Windows installation uses Python's pip package manager. This method works reliably across Windows 10 and 11.

Step 1 - Install Python

Download Python 3.10 or 3.11 from python.org. During installation, check "Add Python to PATH" before clicking Install Now. This ensures Python is accessible from the command line.

Step 2 - Install IOPaint

Open Command Prompt or PowerShell and run the following command.

pip install iopaint

This downloads IOPaint and all required dependencies. The process takes 2-5 minutes depending on your internet connection.

Step 3 - Launch IOPaint

Once installation completes, start IOPaint with a simple command.

iopaint start --model lama --device cpu

For NVIDIA GPU users, replace cpu with cuda.

iopaint start --model lama --device cuda

IOPaint starts a local web server and automatically opens your default browser to the interface. You're ready to start editing images immediately.

Installing IOPaint on macOS

macOS installation follows a similar pattern but leverages Apple Silicon's Metal acceleration when available.

Step 1 - Install Python

macOS typically includes Python, but you need Python 3.8+. Check your version in Terminal.

python3 --version

If you need to install or update Python, download from python.org or use Homebrew.

brew install python@3.11

Step 2 - Install IOPaint

Open Terminal and install via pip.

pip3 install iopaint

Step 3 - Launch IOPaint

Start IOPaint with CPU mode or Metal acceleration for Apple Silicon.

For Intel Macs, use CPU mode.

iopaint start --model lama --device cpu

For Apple Silicon (M1/M2/M3/M4), use MPS for Metal acceleration.

iopaint start --model lama --device mps

Metal acceleration on Apple Silicon provides excellent performance for erase models. Diffusion models benefit more modestly but still run faster than CPU-only execution. Our Flux on Apple Silicon performance guide covers optimization techniques applicable to IOPaint's diffusion models.

Installing IOPaint on Linux

Linux offers the most flexible deployment options, including Docker containers for isolated environments.

Via Pip (Traditional)

Install Python 3.8+ and pip through your distribution's package manager.

For Ubuntu/Debian systems.

sudo apt update sudo apt install python3 python3-pip

Install IOPaint.

pip3 install iopaint

Launch with appropriate device setting.

iopaint start --model lama --device cuda

Via Docker (Recommended for Production)

Docker provides clean isolation and easier deployment. Pull the official IOPaint image.

docker pull sanster/iopaint:latest

Run IOPaint in a container.

docker run -p 8080:8080 -v /path/to/images:/app/images sanster/iopaint:latest

This mounts your image directory and exposes IOPaint on port 8080. Access via browser at localhost:8080.

Verifying Installation

After launching IOPaint, your browser should open to the interface automatically. If not, manually navigate to the address shown in the terminal, typically http://localhost:8080.

You should see a clean interface with an image upload area, brush tools on the left, and model selection in the top right. Try uploading a test image and painting a small area. If the interface responds and shows the painted mask, your installation succeeded.

Understanding IOPaint Models and When to Use Each

IOPaint's power comes from its model selection. Choosing the right model for your task dramatically affects both quality and processing time. Let's break down each model type and its ideal use cases.

Erase Models for Clean Object Removal

Erase models excel at one thing above all else: making things disappear naturally. They analyze surrounding context and generate fills that blend seamlessly with the background.

LaMa (Large Mask Inpainting)

LaMa is the default model for good reason. It handles both small and large masked areas with impressive quality, processing images quickly even on CPU. LaMa works by understanding image structure at multiple scales, allowing it to recreate complex patterns and textures convincingly.

Use LaMa for:

• Removing people or objects from photos
• Erasing watermarks and text overlays
• Fixing image defects and blemishes
• Cleaning up backgrounds
• Quick edits where speed matters

LaMa occasionally struggles with highly complex scenes or when removing very large objects that occupy significant portions of the image. For these cases, try MAT instead.

MAT (Mask-Aware Transformer)

MAT uses transformer architecture similar to language models but applied to image understanding. This gives it superior ability to maintain coherence across large masked regions. Processing takes longer than LaMa but quality often justifies the wait.

Use MAT for:

• Large object removal requiring extensive inpainting
• Complex scenes with intricate patterns
• When LaMa results lack coherence
• Professional work where quality trumps speed

FcF (Fast Content Fill)

The speed-focused option when you need quick previews or are processing many images in batch. Quality doesn't match LaMa or MAT, but for simple removals on clean backgrounds, FcF delivers acceptable results in a fraction of the time.

Diffusion Models for Creative Generation

Diffusion models don't just remove content. They create new content based on text prompts and surrounding context. This opens creative possibilities far beyond simple object removal.

Stable Diffusion 1.5 and 2.1

These general-purpose diffusion models can both inpaint (fill masked areas) and outpaint (extend images beyond borders). You provide a text prompt describing what should appear in the masked region, and the model generates appropriate content.

Stable Diffusion 1.5 has wider community support and more available fine-tuned models. Version 2.1 offers better composition and fewer artifacts but has a smaller ecosystem of custom models.

Use Stable Diffusion for:

• Replacing objects with different objects via text prompts
• Extending images in any direction (outpainting)
• Adding new elements to existing scenes
• Style changes within masked regions
• Creative experimentation

The quality depends heavily on your prompt. Clear, descriptive prompts yield better results. "A golden retriever sitting on grass" works better than just "dog."

PowerPaint

PowerPaint represents specialized training for inpainting tasks. It understands context better than base Stable Diffusion models and generates fills that respect existing image style and lighting more naturally.

Use PowerPaint for:

• Professional-quality inpainting with diffusion models
• When Stable Diffusion results look out of place
• Complex scenes requiring style consistency
• Replacing objects while maintaining photorealistic quality

Manga and Anime Models

IOPaint includes specialized models trained on manga and anime art styles. These generate stylistically appropriate fills for illustrated content where photorealistic models would clash.

Use anime models for:

• Editing manga pages and comic art
• Anime-style digital illustrations
• Character art modifications
• Any non-photorealistic illustrated content

Model Performance Comparison

Different models have different hardware requirements and processing speeds. This table helps you choose based on your system capabilities.

For most users starting with IOPaint, begin with LaMa for object removal tasks. Once comfortable with the interface and workflow, experiment with Stable Diffusion models for creative projects and outpainting.

IOPaint Tutorial: Removing Unwanted Objects

Let's walk through a complete workflow for removing an unwanted object from a photo. This fundamental technique applies to countless editing scenarios from cleaning up vacation photos to preparing professional product images.

Step 1 - Upload Your Image

Click the upload area in IOPaint's interface or drag and drop your image file. IOPaint supports common formats including JPG, PNG, and WebP. The image loads into the main canvas area.

For best results, use images at their native resolution. IOPaint can handle large images, but processing time increases with resolution. If working with very large files on limited hardware, consider resizing to 2048px on the longest edge before uploading.

Step 2 - Select the Erase Tool and Model

Choose the brush tool from the left toolbar. This is your primary selection mechanism for indicating what to remove. Adjust brush size using the slider to match the scale of objects you're removing. Larger brushes work faster for big areas, while smaller brushes provide precision for detailed work.

Select your model from the dropdown in the upper right. For object removal, choose LaMa as your starting point. Its balance of speed and quality makes it ideal for learning the workflow.

Step 3 - Paint Over the Object

Carefully brush over the entire object you want to remove. You don't need perfect precision around edges. The AI understands context and will generate appropriate fills even if your mask extends slightly into the background.

For complex objects with irregular shapes, zoom in for better control. Use multiple strokes to build up your mask rather than trying to paint everything in one pass. This gives you better control and makes corrections easier.

The mask appears as a semi-transparent overlay, typically in red or blue depending on your settings. You can adjust mask opacity in settings if you need to see underlying details more clearly.

Step 4 - Process and Review Results

Click the Process or Generate button (terminology varies by version). IOPaint analyzes the masked area, considers surrounding context, and generates a fill.

Processing time depends on your hardware and image size. CPU processing of a 1080p image with LaMa typically takes 5-15 seconds. GPU acceleration reduces this to 1-3 seconds. Diffusion models take substantially longer, from 30 seconds on fast GPUs to several minutes on CPU.

When results appear, zoom in and examine the edited area carefully. Look for:

• Natural blending at edges
• Continuation of background patterns or textures
• Appropriate lighting and shadows matching the scene
• No obvious artifacts or repetitive patterns

Step 5 - Refine if Needed

If results aren't perfect on the first attempt, you have several options. You can mask and process additional areas to clean up any remaining artifacts. Alternatively, try a different model. If LaMa left artifacts, switch to MAT for a second pass.

For stubborn areas, reduce your mask size and process smaller regions. Sometimes breaking a large removal into several smaller edits yields cleaner results than attempting everything at once.

Real-World Example: Tourist Photo Cleanup

Imagine you captured a beautiful architectural shot ruined by tourists in the foreground. Here's the complete workflow.

Upload the image and select LaMa. Paint over each tourist with a moderately sized brush, being careful around complex background elements like columns or decorative details. Process the image. LaMa analyzes the scene, recognizes the architectural patterns, and generates fills that continue the building's facade naturally where tourists stood. Examine edges carefully. If any tourist remnants remain, mask those specific areas and process again. The second pass typically cleans up any artifacts the first pass missed.

The entire process takes 2-3 minutes for an image with 3-4 people to remove. The same edit in Photoshop using the clone stamp would require 20-30 minutes of careful manual work.

IOPaint Tutorial: Extending Images with Outpainting

Outpainting extends images beyond their original borders, generating new content that naturally continues the existing scene. This powerful technique solves countless creative problems from fixing poorly framed shots to creating wider aspect ratios for specific platforms.

Understanding Outpainting Concepts

Unlike inpainting which fills areas within an image, outpainting generates entirely new pixels beyond the original boundaries. The AI analyzes edge content and extrapolates what should logically continue in the extended space.

Successful outpainting requires appropriate model selection. Diffusion models handle outpainting, while erase models do not. Stable Diffusion 1.5, 2.1, and PowerPaint all support outpainting with varying quality levels.

Step 1 - Prepare Your Image

Upload the image you want to extend. Consider which direction you need to expand. IOPaint can extend in any direction, including all directions simultaneously to create a larger canvas.

Think about what should appear in the extended areas. For landscapes, this might be more sky, foreground, or landscape features. For portraits, it's typically background continuation. Clear mental visualization helps you write better prompts later.

Step 2 - Add Extension Canvas

IOPaint provides canvas extension controls in the interface. Specify how many pixels to add in each direction. For example, to extend a landscape image upward to include more sky, add 512 pixels to the top.

Common extension amounts:

• 256-512 pixels: Subtle extensions for minor composition adjustments
• 512-1024 pixels: Significant expansion for aspect ratio changes
• 1024+ pixels: Dramatic extensions creating mostly new content

The extension appears as blank canvas around your original image. This blank area is what IOPaint will fill during generation.

Step 3 - Write Your Prompt

Switch to a diffusion model like Stable Diffusion 1.5 or PowerPaint. These models accept text prompts describing what should appear in the extended areas.

Your prompt should describe the entire scene, not just the new areas. Good outpainting prompts include:

• Overall scene description
• Lighting and time of day
• Weather conditions
• Style or aesthetic qualities
• Specific elements that should appear

Example prompt for extending a mountain landscape upward: "Dramatic mountain landscape with snow-capped peaks under clear blue sky with wispy clouds, golden hour lighting, professional landscape photography, high detail."

The model uses this description along with the existing image content to generate appropriate extensions.

Step 4 - Generate and Refine

Click Generate to process the outpainted image. Diffusion model outpainting takes longer than inpainting since it's generating entirely new content. Expect 30-90 seconds on GPU, several minutes on CPU.

When results appear, examine the transition between original and generated areas. Successful outpainting blends seamlessly. You shouldn't be able to identify where the original image ends and generation begins.

If the transition looks artificial or generated content doesn't match your vision, adjust your prompt and try again. Adding more descriptive detail often improves coherence. Alternatively, try a different diffusion model. PowerPaint typically maintains style consistency better than base Stable Diffusion.

Step 5 - Multi-Direction Extensions

For extending in multiple directions simultaneously, you can either add canvas to all sides at once or work iteratively, extending one direction at a time.

Iterative extension provides more control but takes longer. Simultaneous extension is faster but gives you less ability to guide each direction individually. Choose based on your specific needs and desired level of control.

Practical Outpainting Applications

Outpainting solves numerous real-world problems. Photographers use it to rescue poorly composed shots by expanding into better framing. Social media managers extend images to fit specific platform requirements without cropping important elements. Designers create background expansions for product photography when they need more working space around subjects.

One particularly powerful application involves converting vertical images to horizontal orientations or vice versa. Rather than cropping and losing content, outpainting generates appropriate fills to achieve the desired aspect ratio while preserving all original elements.

Advanced IOPaint Techniques for Professional Results

Once you've mastered basic object removal and outpainting, these advanced techniques unlock IOPaint's full potential for professional workflows.

Combining Multiple Models for Complex Edits

Complex edits often benefit from using different models for different tasks within the same image. This multi-model approach leverages each model's strengths.

Start by using an erase model like LaMa to cleanly remove unwanted objects. Once objects are gone, switch to a diffusion model to add new elements via prompts in specific areas. This combination lets you both remove and replace in a single workflow.

For example, removing a person from a park bench (LaMa) then adding a different object like a bicycle to the same bench (Stable Diffusion with an appropriate prompt). Each model does what it does best, and the combined result exceeds what either could achieve alone.

Iterative Refinement Strategy

Professional results often require multiple passes rather than expecting perfection on the first attempt. This iterative approach progressively improves results.

First pass removes or generates primary content using broad strokes. Examine results and identify areas needing improvement. Second pass targets specific problem areas with smaller masks and adjusted settings. Third pass handles any remaining fine details or edge artifacts.

Each pass refines and polishes. This systematic approach consistently delivers cleaner results than trying to perfect everything in a single generation.

Mask Feathering and Edge Control

The quality of your mask directly affects result quality. Soft, feathered edges blend more naturally than hard edges in most scenarios. IOPaint provides mask feathering controls that soften the transition between masked and unmasked areas.

For object removal, moderate feathering (10-20 pixels) helps the AI blend fills smoothly into surrounding areas. For creative additions with diffusion models, experiment with feather amounts. Softer masks integrate new elements more naturally, while harder masks maintain more distinct boundaries.

Prompt Engineering for Diffusion Models

When using Stable Diffusion or PowerPaint for inpainting or outpainting, prompt quality dramatically affects results. Apply these prompt engineering principles for better outcomes.

Be specific about style and quality. Include terms like "professional photography," "detailed," "high quality," or specific artistic styles. These guide the model toward higher quality outputs that match your aesthetic goals.

Describe lighting explicitly. Mention time of day, light direction, and quality. "Soft morning light," "dramatic sunset," or "studio lighting" help the model match lighting in generated areas to your existing image.

Include technical photography terms. Terms like "shallow depth of field," "bokeh," "wide angle," or "telephoto compression" influence composition and rendering style.

Use negative prompts when available. If your IOPaint version supports negative prompts, describe what you don't want. "Low quality, blurry, distorted, artifacts" helps avoid common diffusion model problems.

Batch Processing Workflows

When you need to apply similar edits to multiple images, IOPaint supports batch processing through its command-line interface. This dramatically accelerates repetitive tasks like watermark removal across a photo collection.

The batch processing workflow involves preparing all images in a single directory, creating mask templates for consistent editing areas, then running IOPaint with batch processing flags. Processing time scales linearly with image count, but you can walk away and let it run unattended.

For truly large batch operations, consider running IOPaint on a dedicated machine or cloud instance. The CPU or GPU can process images continuously without tying up your primary workstation.

Using Custom Fine-Tuned Models

IOPaint supports loading custom Stable Diffusion models. If you have specialized needs, you can use community models fine-tuned for specific styles or subjects.

For architectural photography, models trained on building datasets generate more convincing architectural details. For product photography, models trained on commercial product images maintain appropriate lighting and styling. For nature photography, landscape-specific models understand terrain and vegetation better.

Custom models load through IOPaint's model management interface. Download models in Stable Diffusion checkpoint format, place them in IOPaint's model directory, and select them from the model dropdown. This opens access to thousands of specialized models from the Stable Diffusion community.

Troubleshooting Common IOPaint Issues

Even with proper installation and technique, you may encounter issues. These solutions address the most common problems users face.

IOPaint Won't Start or Crashes Immediately

If IOPaint fails to launch or crashes on startup, the issue typically relates to dependencies or hardware compatibility.

Solution 1 - Verify Python Version

IOPaint requires Python 3.8 or higher. Check your version with python --version or python3 --version. If you're running an older version, upgrade Python and reinstall IOPaint.

Solution 2 - Reinstall Dependencies

Corrupted dependencies cause startup failures. Uninstall and reinstall cleanly.

pip uninstall iopaint pip install iopaint --no-cache-dir

The no-cache-dir flag forces fresh downloads of all dependencies rather than using potentially corrupted cached versions.

Solution 3 - Check Device Compatibility

If launching with cuda device on a system without NVIDIA GPU or without CUDA installed, IOPaint crashes. Verify your device selection matches your hardware. Use cpu if you don't have compatible GPU acceleration.

Processing Extremely Slow

Slow processing stems from CPU execution of models optimized for GPU acceleration, or from hardware resource constraints.

For Users with NVIDIA GPUs

Ensure you're using cuda device selection. Verify CUDA installation with nvidia-smi in command line. If this fails, CUDA isn't properly installed even if you have an NVIDIA GPU. Install appropriate CUDA toolkit for your GPU.

For CPU Users

CPU processing is inherently slower, especially for diffusion models. Erase models like LaMa remain reasonably fast on modern CPUs, but Stable Diffusion becomes painfully slow. Consider using only erase models, or explore cloud alternatives like Apatero.com for diffusion-based work when local processing is too slow.

Reduce Image Resolution

Processing time scales with pixel count. If working with very high resolution images, consider resizing to 2048px maximum on the longest edge. Quality remains excellent for most applications while processing accelerates significantly.

Generated Results Look Distorted or Unnatural

Poor quality results typically stem from inappropriate model selection, unclear prompts, or challenging source images.

Try Different Models

If LaMa produces artifacts, try MAT. If Stable Diffusion 1.5 looks wrong, try 2.1 or PowerPaint. Different models excel in different scenarios, and experimentation helps you learn which models work best for your specific use cases.

Improve Your Prompts

For diffusion models, vague prompts yield inconsistent results. Add more descriptive detail about style, lighting, and desired elements. Reference the prompt engineering section above for specific strategies.

Check Image Quality

Low resolution or heavily compressed source images limit what any AI can achieve. IOPaint works best with reasonably high quality source material. If your source image is blurry or artifact-filled, results will reflect those limitations.

Out of Memory Errors

Memory errors occur when your hardware lacks sufficient RAM or VRAM for the selected model and image size.

Reduce Image Size

Resize your source image to smaller dimensions. 1024x1024 uses roughly 1/4 the memory of 2048x2048. For most applications, the quality difference is minimal while memory requirements drop dramatically.

Use Smaller Models

If running diffusion models, Stable Diffusion 1.5 uses less memory than 2.1. Erase models use significantly less memory than any diffusion model. Switch to more memory-efficient models if you're consistently hitting limits.

Close Other Applications

Free up RAM by closing unnecessary applications. Browser tabs, in particular, consume surprising amounts of memory. For optimal IOPaint performance, close everything else while processing.

Colors Don't Match Between Original and Generated Areas

Color matching problems appear when diffusion models generate content in different color spaces or with different white balance than the original image.

Adjust Prompts to Match Colors

Include color descriptors in your prompts that match the original image. If your photo has warm sunset tones, mention "warm golden light" or "sunset tones." This guides the model toward appropriate color palettes.

Post-Process for Color Matching

Use traditional image editing tools to adjust colors in generated areas after IOPaint processing. Slight hue, saturation, or curves adjustments can harmonize colors between original and AI-generated regions when the model doesn't match perfectly.

IOPaint vs Cloud AI Image Editing Platforms

IOPaint's local execution offers clear advantages in privacy and cost, but cloud platforms provide benefits worth considering for certain workflows and user profiles.

When Local IOPaint Makes Sense

Self-hosting IOPaint delivers maximum control and privacy at zero ongoing cost after initial setup. This makes it ideal for professionals handling sensitive content, high-volume users who would pay substantial monthly fees for cloud services, and technically inclined users who enjoy customizing and optimizing their tools.

Photographers editing client photos maintain complete confidentiality. Businesses processing proprietary product images never upload to third parties. Creative professionals working on unreleased projects keep everything internal. These use cases justify IOPaint's setup investment.

The cost advantage becomes dramatic at scale. Processing thousands of images monthly would cost hundreds on usage-based cloud platforms. With IOPaint, you pay only for electricity and hardware you already own.

When Cloud Platforms Like Apatero Make Sense

Despite IOPaint's advantages, cloud platforms serve important needs that local solutions cannot easily match. Apatero.com and similar services provide instant access without technical setup, professionally curated workflows, and reliable access from any device.

Non-technical users who want to start editing immediately without learning installation procedures benefit tremendously from cloud convenience. Beginners exploring AI image editing can try cloud platforms for free or cheap to determine if the technology meets their needs before investing in local setup and hardware.

Users without suitable hardware find cloud platforms essential. If you only have a basic laptop without dedicated GPU, IOPaint's CPU performance may frustrate while cloud platforms deliver fast results regardless of your local hardware limitations.

Mobility matters for some workflows. Cloud platforms let you work from any device anywhere with internet. IOPaint requires access to the specific machine where you installed it unless you configure remote access, adding complexity.

Hybrid Workflows Combining Both

Many professional users adopt hybrid approaches using both local and cloud tools strategically. IOPaint handles sensitive client work and high-volume processing where cost and privacy matter most. Cloud platforms handle quick tests, mobile work, and experimentation when away from the main workstation.

This combination leverages each approach's strengths while mitigating weaknesses. You maintain control where it matters while keeping convenient access for casual needs.

Frequently Asked Questions

Is IOPaint really free or are there hidden costs?

IOPaint is completely free and open source under a permissive license. You never pay for the software itself. Your only costs are the hardware to run it (which you likely already own) and electricity for processing. There are no subscriptions, usage limits, credits, or premium tiers. All features and models are available to everyone immediately after installation.

Can IOPaint match Adobe Photoshop Generative Fill quality?

Quality depends on the specific model you choose and the type of edit. For simple object removal, IOPaint's LaMa model matches or exceeds Photoshop Generative Fill in most scenarios. For creative generation and replacement tasks, results vary based on your prompts and model selection. PowerPaint specifically can match Photoshop quality in many cases. The key advantage is choice. If one IOPaint model doesn't satisfy you, try another. Photoshop gives you one AI approach with no alternatives.

How long does it take to process images in IOPaint?

Processing time varies dramatically based on your hardware and chosen model. With a modern NVIDIA GPU, LaMa processes a 1080p image in 1-3 seconds. MAT takes 3-5 seconds. Stable Diffusion models take 20-60 seconds depending on the specific model and resolution. On CPU without GPU acceleration, multiply these times by 10-30x. LaMa remains usable on CPU at 15-30 seconds per image. Diffusion models become impractically slow on CPU at several minutes per generation.

Can I use IOPaint commercially for client work?

Yes. IOPaint's license permits commercial use without restriction. You can use it for client projects, sell edited images, or incorporate it into commercial services. The models themselves may have specific licenses, so verify individual model licenses if you're concerned. The base models included with IOPaint are commercially usable. Some community models may have restrictions, so check before using third-party models for commercial purposes.

Does IOPaint work on Mac M1, M2, M3, or M4 chips?

IOPaint works on Apple Silicon Macs using the mps device setting for Metal Performance Shaders acceleration. Performance is good for erase models like LaMa and acceptable for diffusion models, though not as fast as equivalent NVIDIA GPUs. The main advantage is excellent power efficiency. You can run IOPaint on a MacBook for hours on battery while traveling. Installation follows the same pip process as other platforms.

What's the difference between inpainting and outpainting?

Inpainting fills masked areas within the existing image boundaries. You use it to remove objects, fix defects, or replace elements. Outpainting extends the image beyond its original borders, generating new content that continues the scene. You use it to change aspect ratios, improve composition, or create larger canvases. Both techniques use AI to generate pixels, but inpainting works within constraints while outpainting expands beyond them.

Can IOPaint remove watermarks and text from images?

IOPaint's erase models excel at removing watermarks and text overlays. LaMa handles simple watermarks effectively while MAT tackles more complex or large watermarks. Results depend on watermark characteristics. Simple semi-transparent overlays disappear completely. Complex, high-contrast watermarks over detailed backgrounds require more careful masking and potentially multiple passes. Always respect copyright and intellectual property rights. Just because the technology can remove watermarks doesn't mean doing so is legal or ethical without proper authorization.

Is my data private when using IOPaint?

Completely private. IOPaint runs entirely on your local hardware. Images never leave your computer. No data uploads to external servers, no cloud processing, no third-party access. This makes IOPaint ideal for sensitive content including client work under NDAs, unreleased products, private photos, medical images, or anything requiring confidentiality. The only data transmission is downloading models initially, which are generic AI models not connected to your specific images.

How do I update IOPaint to the latest version?

Update using pip's upgrade flag. Run pip install --upgrade iopaint in your terminal or command prompt. This downloads and installs the newest version while preserving your downloaded models and settings. IOPaint regularly receives updates with new models, features, and bug fixes, so updating monthly keeps you current with the latest capabilities.

Can I run IOPaint on a VPS or cloud server?

Yes. IOPaint runs on any Linux server with Python support. Cloud deployment works particularly well for batch processing or providing access to multiple users. You can install IOPaint on a VPS, configure it to accept connections from your network, and access the web interface from any device. For production deployments, use Docker for cleaner isolation and easier updates. Be aware that GPU-accelerated cloud instances cost significantly more than CPU-only instances but deliver much better performance for diffusion models.

Conclusion

IOPaint delivers professional inpainting and outpainting capabilities without the subscription costs, privacy concerns, or vendor lock-in of commercial alternatives. From removing unwanted objects to extending images creatively, this open-source tool handles diverse image editing challenges with impressive quality.

The barrier to entry is refreshingly low. Install Python, run a simple pip command, and within minutes you're editing images with state-of-the-art AI models. The learning curve remains gentle. Upload an image, paint over what you want to change, and let the AI handle the complex work.

For users seeking instant access without technical setup, platforms like Apatero.com provide professionally curated AI image generation and editing workflows with zero configuration. For those who want maximum control, complete privacy, and zero ongoing costs, IOPaint stands as the clear choice.

Start with LaMa for object removal. Master the basic workflow. Experiment with different models to understand their strengths. Explore outpainting when you need to extend images. Build skills progressively, and you'll quickly integrate IOPaint into your creative workflow.

The world of AI image editing is accelerating rapidly. Tools like IOPaint ensure that cutting-edge capabilities remain accessible to everyone, not just those who can afford expensive subscriptions. Download it from GitHub, install it this afternoon, and start creating cleaner, more creative images today.

If you found this guide valuable, explore our other AI image generation tutorials including our complete guide to getting started with AI image generation and specialized guides for tools like Qwen Image Edit for advanced editing workflows.