flux-2/dev/edit
Advanced open-weight model enabling refined image transformation and consistent visual editing.
Z Image Turbo Inpaint: Seamless Object Editing & Scene Restoration | RunComfy
tongyi-mai/z-image/turbo/inpainting
Z Image Inpainting generates clean, photorealistic results from edited images with seamless object removal, background restoration, and structural precision for e-commerce, design, and creative retouching workflows.
Idle
The rate is $0.01 per image.
Introduction to Z Image Inpainting
Z Image Inpainting is an advanced image-to-image diffusion model optimized for seamless object editing, background restoration, and structural consistency in photorealistic visuals. Trading complex manual mask work for accelerated precision, it empowers e-commerce teams, creative agencies, and developers to refine or reconstruct visuals with minimal steps using Z Image Inpainting. For developers, Z Image Inpainting on RunComfy can be used both in the browser and via an HTTP API, so you don’t need to host or scale the model yourself.
Ideal for: Product Photo Retouching | Scene Reconstruction | Design Localization
Examples of Z Image Inpainting in Action
What makes Z Image Inpainting stand out
Image-to-image in Z Image Inpainting performs constrained synthesis inside masked regions, letting teams remove objects, restore backgrounds, and refine surfaces while preserving scene geometry and materials. By aligning control with layout cues, Z Image Inpainting delivers stable, edge-consistent results across diverse product and creative shots.
Examples:
- Z Image Inpainting localized removal: remove overhead cables; preserve rooflines and sky gradient; thin mask over wires.
- In Z Image Inpainting, restore a scuffed wall: match brick pattern and mortar tone; keep lighting neutral; mask only damaged area.
- Product cleanup: clean dust on a glossy phone; do not alter logo; strength 0.5-0.7; steps 20-24.
- Background simplification: replace busy backdrop with smooth light gray studio paper; mask background only.
- Add small prop: add a folded beige towel on the left shelf; match perspective and contact shadows; mask target zone.
Pro tips:
- Be explicit about what to preserve vs what to change; name subjects and surfaces.
- Use spatial language and scale cues: left shelf, upper-right quadrant, 20 percent of frame.
- Start concise, iterate in short passes; refine with seed held constant for A-B comparisons.
- Tune strength for nuance: 0.3-0.6 for retouching, 0.7-1.0 for full replacement; control_scale 0.6-0.9 for firm guidance.
- For compliant pipelines, keep enable_safety_checker on and prefer PNG for lossless masks and outputs.
Note: Alongside Z Image Inpainting, you can explore the turbo playground for text-to-image experimentation at Z Image Turbo or the Z Image Inpainting LoRA playground.
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Frequently Asked Questions
What is Z Image Turbo Inpainting and what does it do?
Z Image Turbo Inpainting is a high-performance AI model designed for editing and completing images through advanced diffusion processes. It supports image-to-image workflows by allowing users to repair, modify, or enhance parts of an image while maintaining visual consistency and realistic results.
How does Z Image Turbo Inpainting handle image-to-image editing tasks?
Z Image Turbo Inpainting performs image-to-image editing by analyzing the visual structure of a source image and applying context-aware diffusion over selected regions. This means users can fill in missing parts, remove objects, or swap backdrops while keeping the overall style and lighting coherent.
Is Z Image Turbo Inpainting free to use or based on a credit system?
Z Image Turbo Inpainting can be accessed through Runcomfy’s AI playground, where users typically spend credits to generate or edit images. New users receive complimentary credits for testing image-to-image tasks before deciding on a purchase plan.
What are the main advantages of using Z Image Turbo Inpainting compared to other models?
Z Image Turbo Inpainting offers fast inference with high-quality visual fidelity and supports bilingual prompt understanding, making it unique among diffusion models. Its efficient architecture allows smoother image-to-image generation even on consumer GPUs, with reduced artifacts and better consistency.
Who can benefit most from Z Image Turbo Inpainting?
Designers, marketers, photographers, and digital artists can all benefit from Z Image Turbo Inpainting. Its image-to-image functions are ideal for those who need quick visual edits, background replacements, or localized adjustments while maintaining professional-level quality.
Does Z Image Turbo Inpainting work on mobile browsers?
Yes, Z Image Turbo Inpainting can be accessed via its website through mobile browsers. Users can log into Runcomfy’s AI playground and conduct image-to-image operations on supported phones or tablets without needing a desktop setup.
What kind of outputs can I expect from Z Image Turbo Inpainting?
With Z Image Turbo Inpainting, outputs generally show strong texture continuity, realistic lighting, and accurate perspective handling. The model’s advanced image-to-image processing ensures that edited regions blend naturally into the original artwork or photograph.
Are there any limitations to Z Image Turbo Inpainting?
While highly capable, Z Image Turbo Inpainting may struggle with very fine details such as hair edges or intricate skin textures. Users performing image-to-image work should use moderate denoise parameters and smooth mask transitions for optimal results.
How can I improve the results I get with Z Image Turbo Inpainting?
To get the best results from Z Image Turbo Inpainting, structure clear prompts and apply proper mask blurring during image-to-image tasks. Maintaining moderate diffusion strength helps ensure image coherence and prevents unwanted artifacts or style mismatches.
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