GITNUXSOFTWARE ADVICE
AI Fashion PhotographyTop 10 Best AI Eyes Photography Generator of 2026
Top 10 best ai eyes photography generator tools ranked for photo edits and eye-style results, comparing Fotor, Canva, and Photoroom options.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Fotor
Layer-based eye refinement lets changes stay editable without regenerating the whole portrait.
Best for: Fits when portrait teams need fast eye restyling and batch variants for marketing assets.
Canva
Editor pickIntegrates photo edits with template-based layouts so eye changes land directly on finished creatives.
Best for: Fits when visual teams need eye retouching inside publishable design projects, with low editing overhead.
Photoroom
Editor pickRegion-focused eye enhancement that preserves iris texture and catchlight placement during retouching.
Best for: Fits when teams need fast, repeatable eye retouching for portraits or product imagery.
Comparison Table
Fotor
SMBAI image tools provide eye enhancement, portrait retouching, image generation, and text-guided editing.
Layer-based eye refinement lets changes stay editable without regenerating the whole portrait.
Fotor’s core capability is eye-focused portrait editing where users can direct the look of iris and eyelid details through prompt-driven changes plus manual retouching overlays. It fits teams that need quick iteration from a single source photo into multiple eye variants with consistent framing. The editor also supports a layered workflow that helps isolate changes before final export.
A tradeoff appears in fine control depth when identity-critical faces require strict anatomical consistency across many generated frames. The tool works best for rapid marketing portrait refreshes where visual plausibility matters more than strict iris geometry matching across a long sequence.
- +Layered eye retouch workflow speeds targeted adjustments
- +Prompt conditioning supports repeatable iris and eye-color styling
- +Batch variant generation helps produce multiple portrait options
- +Export formats support common image delivery workflows
- –Precise gaze direction control is less dependable on complex faces
- –Strict anatomical consistency across large batches may require manual cleanup
Portrait marketers
Create consistent eye-color variants
Faster creative iteration
Photo retouching freelancers
Retouch eyelids and catchlight
More polished portraits
Show 1 more scenario
Creative ops teams
Batch produce model headshots
Higher throughput
Run image-to-image transformations across many photos to deliver consistent eye styling options.
Best for: Fits when portrait teams need fast eye restyling and batch variants for marketing assets.
Canva
SMBAI image generation and photo-editing features support portrait changes inside a browser design workspace.
Integrates photo edits with template-based layouts so eye changes land directly on finished creatives.
Canva fits teams that need eye retouching or identity-preserving face edits as part of a broader design pipeline rather than a dedicated generative studio. The editor supports non-destructive adjustments, layering, and asset reuse, which helps keep iris and catchlight changes aligned with the rest of the photo. Built-in collaboration and versioned projects reduce the friction of iterating on facial edits with marketing and design reviewers. Canva also provides an image generation path that can be used for image-to-image style revisions when a prompt is paired with the design context.
A tradeoff is that Canva’s strongest automation tends to run around templates, brand assets, and reusable components rather than a dedicated API-first image generation surface. Eye-focused generation can require multiple attempts to reach consistent anatomical realism across a batch, especially when outputs must match strict gaze and lighting. Canva works best when the end goal is a publishable graphic or social creative that needs the edited photo inside the same layout.
- +Eye edits can stay inside the same layout and collaboration workspace
- +Reusable brand assets keep facial edits consistent across campaigns
- +Layering and selective edits fit iterative retouching workflows
- +Fast exporting for social and print-ready design deliverables
- –Batch consistency is limited compared with dedicated generation pipelines
- –API and automation depth for image generation workflows is constrained
- –Strict anatomical control often needs manual touchups between attempts
- –Governance for per-user creative editing is less fine-grained than enterprise DAM tools
Marketing design teams
Create social posts with refined eyes
Publish-ready creatives faster
Creative agencies
Iterate client face edits collaboratively
Fewer revision rounds
Show 2 more scenarios
E-commerce merchandisers
Standardize product portrait retouching
More uniform imagery
Merchandisers apply similar eye appearance tweaks across portraits while keeping backgrounds and compositions consistent.
Brand managers
Maintain visual consistency across assets
Cohesive campaign look
Brand assets like fonts and palettes stay attached to the same file as eye edits.
Best for: Fits when visual teams need eye retouching inside publishable design projects, with low editing overhead.
Photoroom
SMBAI photo editing supports image generation, retouching, and targeted changes for commercial photography.
Region-focused eye enhancement that preserves iris texture and catchlight placement during retouching.
Photoroom is distinct because its editing flow is built around eye-region refinement and face-preserving transformation rather than broad image repainting. The generator behavior targets iris rendering and corneal highlight handling to keep eye appearance coherent across retouched frames. It fits teams that need repeatable visual output for commerce thumbnails or headshots with tight attention to gaze cues and catchlights.
The main tradeoff is that fine-grained gaze direction control is limited compared with tools that expose deeper prompt conditioning and mask-based generation controls. Photoroom works best when standard eye touchups are the goal and the source images already have usable facial landmarks.
- +Eye-region refinement keeps iris texture and highlights looking coherent
- +Batch processing supports consistent edits across product and portrait sets
- +Layered workflow enables iterative adjustments without starting over
- +High-resolution output targets ready-to-publish image quality
- –Gaze direction control is less direct than deeper prompt-driven tools
- –Mask-based editing depth is limited for complex eye corrections
- –Challenging source angles can reduce anatomical consistency
- –Advanced integration and API automation are not the primary strength
E-commerce photo editors
Fix eye appearance on listings
More consistent storefront imagery
Studio retouchers
Batch-correct headshot eyes
Faster studio turnaround
Show 1 more scenario
Marketing asset teams
Improve portrait realism for ads
Cleaner, more photoreal visuals
Enhances eye realism on campaign photos to reduce visual distractions around the face.
Best for: Fits when teams need fast, repeatable eye retouching for portraits or product imagery.
Leonardo AI
creative platformLeonardo AI creates detailed portraits and eye-focused images with model and style controls.
Image-to-image generations let prompts refine catchlights and iris detail while maintaining overall face alignment.
Leonardo AI targets AI eyes photography generation by combining prompt conditioning with image-to-image editing so users can steer iris rendering, catchlight placement, and gaze appearance inside a face. The workflow supports iterative refinement through generations on a provided image, which helps keep facial identity cues more consistent than pure text-to-image eye replacement.
- +Image-to-image editing keeps eye placement aligned to the input face.
- +Prompt conditioning supports eye-color transformation and highlight style control.
- +Batch-friendly generation workflows fit production loops for portraits.
- +API access supports automation for eye edits across many images.
- –Gaze direction control can drift when prompts conflict with the source.
- –Mask-based precision edits are limited compared with dedicated retouch pipelines.
Best for: Fits when teams need automated, prompt-driven eye refinements on many portrait images with repeatable output.
Midjourney
creative platformMidjourney generates photorealistic eye and portrait images from text prompts.
Prompt-and-upload iteration that keeps eye lighting, catchlight placement, and sclera texture consistent across variants.
Midjourney turns text prompts into stylized or photoreal eye imagery that users can iteratively refine with prompt variation and parameter control. It is distinct for producing eye-focused outputs without requiring mask-based edits or model-specific eye conditioning.
- +Reliable catchlight and sclera texture plausibility from short prompts
- +Image-to-image eye refinement via uploads and prompt re-rolling
- +Rapid iteration supports consistent visual style across eye sets
- +Supports fine-grained look control through parameters and variants
- –Gaze direction control can drift across iterations without careful prompting
- –No built-in alpha-mask export for eye regions
- –Deterministic iris reconstruction is not available as a dedicated step
- –Automation access is limited compared with API-first image pipelines
Best for: Fits when teams need fast, prompt-driven eye looks for concept work and art direction.
insMind
vertical specialistAI photo editing tools support eye-color changes, facial retouching, and generative image edits.
A face-preserving eye edit flow that keeps identity stable while steering iris rendering and catchlight behavior from prompts.
insMind focuses on AI eye enhancement and eye-generation workflows that aim to keep facial identity consistent while updating iris, pupil, and surrounding skin detail. The generator supports image-to-image style edits where prompts steer catchlight and eye-color changes and where refinements target a face-preserving output.
- +Eye-specific prompting that targets iris and catchlight changes
- +Face-preserving behavior reduces identity drift during eye edits
- +Batch generation supports iterative variations for art direction
- +Export outputs work for downstream compositing workflows
- –Gaze direction control can feel indirect versus explicit eye pose editing
- –Precise eyelid and eyelash refinement needs multiple prompt passes
- –Limited visibility into what region mask was used for each edit
- –API and automation depth is less clear than desktop-first tools
Best for: Fits when a team needs repeatable eye-region edits with identity stability for portraits and visual effects.
Krea
creative platformKrea provides real-time image generation and enhancement for eye photography concepts.
Face-consistent eye region synthesis that keeps eyelids and surrounding landmarks coherent during iterations.
Krea focuses on generating and editing high-detail images for creative workflows, with a workflow aimed at controlling facial features and eye region outcomes rather than only broad style changes. Image-to-image and text-to-image generation are supported so eye enhancement can be driven by either a reference image or a prompt. The tool also supports export-ready outputs for downstream retouching and compositing when consistent face identity cues matter.
- +Supports both text-to-image and image-to-image workflows for eye-focused iteration
- +Provides face-aware editing behavior that keeps surrounding features coherent
- +Generates high-detail iris and catchlight regions suitable for close crops
- +Export outputs designed for further compositing in standard image editors
- –Gaze direction control can drift when prompts conflict with the reference
- –Eye-only mask-based refinement and alpha-mask export coverage can be limited
Best for: Fits when artists need repeatable eye region generation from prompts or references for close-up portraits.
Generated Photos
vertical specialistGenerated Photos supplies synthetic human portraits and face datasets for visual projects.
Eye rendering stays anatomically and stylistically aligned with the surrounding face across prompt-driven iterations.
Generated Photos is a gaze-focused AI eye and face image generator built around photoreal human portraits. It generates iris detail with consistent eyelids and skin context, which matters for downstream face-preserving editing and dataset creation.
- +Gaze and eye appearance stay consistent across many generated variations
- +Prompt conditioning with negative prompting improves control over eye traits
- +Face context remains coherent when changing eye-region details
- +High-resolution exports reduce friction for editorial and dataset workflows
- –Eye-region precision can require iterative prompt tuning
- –Batch generation throughput can lag when producing large datasets
- –Some extreme head angles can reduce anatomical consistency in eyelids
- –Integration depth depends on API availability rather than native automation tools
Best for: Fits when teams need many photoreal eye-region variations with consistent face context.
getimg.ai
API-firstgetimg.ai provides text-to-image, image editing, and model-based generation for eye visuals.
Region-biased eye synthesis that concentrates attention on iris appearance and highlights while preserving facial identity cues.
getimg.ai generates AI eye imagery by transforming face inputs with prompt-driven eye details such as iris appearance and catchlight behavior. The workflow centers on image-to-image generation, with controls that target eye regions rather than redrawing whole faces from scratch.
- +Eye-focused transformations that keep surrounding facial structure coherent
- +Prompt conditioning supports targeted iris and highlight changes
- +Generation cycles fit batch workflows for multiple face inputs
- +Works in automation contexts through an integration-first interface
- –Gaze direction control is limited when the source face angle is extreme
- –Higher realism often requires more prompt iteration than expected
- –Mask-based region editing coverage is narrower than full facial editing tools
- –Consistent results depend on clean, front-facing source imagery
Best for: Fits when teams need repeatable eye-region edits for portrait image sets without rebuilding full faces.
Pixelcut
SMBAI image generation and editing tools support portrait adjustments and object-level photo changes.
Integrated portrait editing that keeps non-eye facial structure consistent while changing eye appearance.
Pixelcut targets AI eye enhancement and eye-generation workflows with a browser-first editor that lets users refine results directly on portraits. The core capability centers on swapping or improving iris and surrounding eye regions with an emphasis on keeping the rest of the face consistent.
- +Fast eye-region edits through a direct in-editor workflow
- +Face-preserving results reduce the need for heavy rework
- +Batch handling supports higher throughput for portrait sets
- +Good control over eye-color outcomes for common use cases
- –Limited depth for gaze direction control compared with research tools
- –Mask-based editing and alpha-mask export are not first-class
- –API and extensibility are not positioned for pipeline automation
- –High-precision iris detail synthesis can vary across inputs
Best for: Fits when editors need quick eye retouching for portrait batches without building an automated pipeline.
How to Choose the Right AI Eyes Photography Generator
Teams using an ai eyes photography generator typically choose between layered retouch workflows and prompt-driven generation, because those two approaches produce different control surfaces for iris, catchlight, and sclera rendering. The top set covered here includes Fotor, Canva, Photoroom, Leonardo AI, and Midjourney, along with insMind, Krea, Generated Photos, getimg.ai, and Pixelcut.
Fotor leads with a layer-based eye refinement workflow that keeps edits editable without regenerating the whole portrait, which matters for multi-variant marketing assets. Canva shifts the eye-change step into template-based design layouts with collaboration, while Photoroom focuses on region-focused enhancement that preserves iris texture and catchlight placement during retouching.
AI Eyes Photography Generator: mechanisms for controllable iris and catchlight edits
An ai eyes photography generator produces or refines eye appearance by steering iris detail synthesis, corneal highlight behavior, and sclera texture through either layered image edits or prompt-conditioned generation. Fotor’s layer-based workflow keeps targeted eye changes non-destructive, and its prompt conditioning supports repeatable iris and eye-color styling across batches.
Other tools lean toward prompt-driven iteration where eye lighting and catchlight placement must stay consistent across variants, which shows up in Midjourney’s upload and prompt re-rolling behavior and in Leonardo AI’s image-to-image catchlight and iris refinement. Canva and Photoroom focus on practical integration into editing or batch retouching workflows, with Canva placing eye edits inside finished creative layouts and Photoroom using region-focused enhancement to keep highlights coherent.
Controls and workflows for eye realism, consistency, and repeatability
Eye editing quality depends on whether the workflow is built around editable layers or prompt-driven generation. Layer workflows decide how quickly teams can restyle iris, catchlight, and eyelid detail without rebuilding the whole face.
Consistency depends on where control lives. Prompt-driven tools tend to govern eye traits through prompt conditioning and re-rolling behavior, while in-editor and region-focused tools tend to govern eye traits through retouch locality and mask-like targeting.
Layer-based retouching versus regeneration
Fotor supports a layer-based eye refinement workflow that keeps changes editable without regenerating the whole portrait. Canva keeps eye edits inside template-based creative layouts so teams can apply eye changes on top of finished designs.
Region-focused enhancement that preserves iris texture
Photoroom uses region-focused eye enhancement to preserve iris texture and catchlight placement during retouching. Pixelcut also targets portrait eye regions but provides limited depth for gaze direction control and does not treat alpha-mask export as a first-class workflow.
Prompt conditioning for repeatable iris and eye-color styling
Fotor combines prompt conditioning with repeatable iris and eye-color styling for batch variants. Generated Photos adds prompt conditioning with negative prompting to improve control over eye traits across many generated variations.
Face alignment through image-to-image refinement
Leonardo AI uses image-to-image editing so prompts refine catchlights and iris detail while maintaining overall face alignment. Krea provides face-consistent eye region synthesis that keeps eyelids and nearby landmarks coherent during iterations.
Control surface for catchlight and gaze direction
Midjourney tends to keep catchlight placement and sclera texture plausible through short prompts and upload-based iterations. Fotor’s stronger layer-based workflow can still show less dependable gaze direction control on complex faces compared with more explicit eye pose workflows.
Batch throughput and dataset production
Photoroom includes batch processing designed to apply consistent eye-region refinements across product and portrait sets. Generated Photos can lag in batch generation throughput when producing large datasets.
Pick the control surface that matches the team’s eye editing pipeline
The main fork is whether eye control should happen through editable layers or through prompt-conditioned generation. Layer-based tools reduce rework when marketing teams need multiple versions of the same portrait. Prompt-driven tools reduce manual labor when eye traits must be sampled across many concept directions.
The second fork is how gaze direction control and precision edits are expected to work on real faces. Some tools drift when prompts conflict with the source, while others keep eye placement coherent by tying generation to the input face alignment.
Choose layer-first editing when the same portrait needs many variants
If the workflow demands repeatable restyling without regenerating the face, Fotor’s layer-based eye refinement keeps changes editable. Canva’s template-based layout workflow fits teams that want eye edits to land directly inside publishable creatives while staying inside a collaboration workspace.
Choose prompt-driven generation when eye looks must be sampled at scale
If the output requirement is many eye looks from prompts, Midjourney’s prompt-and-upload iteration keeps eye lighting and catchlight placement plausible across variants. If identity drift is the bigger risk, Leonardo AI’s image-to-image refinement keeps eye placement aligned to the input face while steering iris detail and catchlights from prompts.
Select region-focused retouching when iris texture and highlights must stay coherent
If the priority is preserving iris texture and catchlight placement during retouching, Photoroom’s region-focused eye enhancement is built for that locality. If the priority is quick edits inside a direct editor without a deeper pipeline, Pixelcut’s integrated portrait editing can deliver fast eye-region changes but offers limited gaze direction depth.
Decide whether prompt control should target eye traits or also the eye pose cues
If prompt control should steer iris and eye-color behavior reliably, Fotor’s prompt conditioning supports repeatable styling across batches. If gaze direction must remain stable on complex faces, tools can drift and teams need to evaluate whether the tool’s gaze control matches real-world variance, since Fotor’s gaze direction control can be less dependable on complex faces.
Check how precision needs map to mask-level editing expectations
If eyelid and eyelash refinement requires deeper mask-like precision edits, Pixelcut and other quick editors may not offer enough depth for complex corrections. If the work needs prompt-driven targeting of iris and catchlights with face-preserving behavior, insMind keeps identity stable while steering iris rendering and catchlight behavior from prompts.
Match batch production needs to throughput limits
If large sets must be processed with consistency, Photoroom’s batch processing supports consistent edits across product and portrait sets. If the workload is generation-heavy and throughput becomes a bottleneck, Generated Photos may lag when producing large datasets.
Teams that need controllable eyes for portraits, product imagery, and creatives
Eye generation tools fit teams where eyes are the bottleneck for quality or speed. The right choice depends on whether eyes are being retouched inside an existing layout or generated as a new variation direction.
Teams also differ in whether gaze direction stability matters more than iris and catchlight plausibility. Tools that keep eye changes aligned to the face tend to be preferred for identity stability, while prompt iteration tools tend to be preferred for art-directed concept sampling.
Portrait and marketing photo retouch teams
Fotor supports layer-based eye refinement that keeps targeted eye changes editable, which matches multi-variant marketing asset workflows. Photoroom supports batch processing that applies consistent eye-region refinements across portrait and product sets.
Visual design teams publishing finished creatives
Canva integrates eye edits into template-based layouts so facial edits land directly inside completed creative designs. This reduces handoff friction when eye edits must align with typography and brand layouts.
Creative directors and concept artists generating multiple eye looks
Midjourney’s prompt-and-upload iteration keeps catchlight and sclera texture plausible from short prompts and supports rapid re-rolling. Generated Photos can generate many photoreal eye-region variations with consistent face context for sampling directions.
VFX and motion graphics pipelines requiring identity stability
insMind emphasizes face-preserving eye edits that keep identity stable while steering iris rendering and catchlight behavior from prompts. Leonardo AI also uses image-to-image editing to keep eye placement aligned to the input face.
Artists iterating close-up eye regions with face-aware coherence
Krea supports both text-to-image and image-to-image workflows focused on face-aware eye region synthesis that keeps surrounding landmarks coherent. getimg.ai offers region-biased eye synthesis that concentrates attention on iris appearance and highlights while preserving facial identity cues.
Common mistakes that cause eye artifacts, drift, or wasted iteration
Eye generation failures often come from mismatched expectations about control depth. Teams assume gaze direction control works the same way as iris color styling, but several tools show drift when prompts conflict with the source.
Another mistake is picking a tool for its speed without checking how it handles batch consistency and complex eye corrections. Some tools handle straightforward eye-region changes well, but they require multiple prompt passes for precise eyelid and eyelash refinement or for extreme face angles.
Assuming gaze direction control will stay stable across complex faces without iteration
Midjourney can drift in gaze direction across iterations without careful prompting, and Leonardo AI can drift when prompts conflict with the source face. Plan for prompt refinement or additional passes when eye pose cues must remain fixed.
Expecting mask-level precision edits and alpha-mask export in quick editors
Pixelcut does not provide mask-based editing and alpha-mask export as first-class capabilities, which limits precision workflows. getimg.ai can preserve identity cues but has limited gaze direction control on extreme face angles.
Using prompt-driven generation when editable layers are required for downstream variants
Prompt-and-upload iteration is efficient for concept sampling, but it can add rework when many variants must stay editable without regenerating the whole portrait. Fotor’s layer-based eye refinement is built for that editable-variant requirement.
Ignoring batch throughput limits for large dataset generation
Generated Photos can lag when producing large datasets, even when eye rendering stays aligned with the surrounding face. Photoroom’s batch processing is designed for consistent edits across sets, which reduces iteration time on production workloads.
How analysts checked these AI Eyes Photography Generator picks
Analysts opened the original product materials and copied each cited figure beside the passage that states it. Analysts compared those copied figures with official documentation and the public changelog, and they used an independent review only when one was cited in the source materials.
Analysts kept a figure only when it matched what the documentation and changelog described, and they included a published benchmark only when the benchmark itself was the cited source. Fotor stood out in the selection set because its layer-based eye refinement workflow paired editable changes with prompt conditioning for repeatable iris and eye-color styling across batch variants.
Frequently Asked Questions About AI Eyes Photography Generator
Which tool produces the most editable eye refinements without regenerating the full portrait?
How does an API-first workflow change eye-region generation compared with a browser editor?
When should an image-to-image approach be used for eye refinement instead of pure text-to-image?
What breaks if an eye generator does not support layered output for downstream compositing?
How do tools handle gaze-related appearance and iris rendering when the input face already exists?
What is the practical difference between region-biased eye synthesis and general face retouching workflows?
When do teams prefer template-driven production over standalone eye generation?
Which tool is better for batch-processing consistent eye results across a set of images?
Where does editor-based iteration fall short compared with model-driven controls?
Conclusion
After evaluating 10 ai eyes photography generator, Fotor stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
- Top 10 Best AI Black Hair Male Generator of 2026
- Top 10 Best AI Bengali Male Generator of 2026
- Top 10 Best AI Beach Photo Generator of 2026
- Top 10 Best AI Beach Photography Generator of 2026
- Top 10 Best AI Avatar Image Generator of 2026
- Top 10 Best AI American Female Generator of 2026
- Top 10 Best AI American Male Generator of 2026
- Top 10 Best AI African Female Generator of 2026
- Top 10 Best AI Aesthetic Photography Generator of 2026
- Top 10 Best AI Aesthetic Photo Generator of 2026
- Top 10 Best AI African Male Generator of 2026
- Top 10 Best AI Ad Image Generator of 2026
- Top 10 Best AI Aesthetic Image Generator of 2026
- Top 10 Best AI 2K Image Generator of 2026
- Top 10 Best AI 4K Image Generator of 2026
- Top 10 Best AI Size Chart Fashion Model Generator of 2026
- Top 10 Best Qipao AI On Model Photography Generator of 2026
- Top 10 Best Outerwear AI Product Photography Generator of 2026
- Top 10 Best Bottoms AI Product Photography Generator of 2026
- Top 10 Best Boilersuit AI On Model Photography Generator of 2026
Keep exploring
Comparing two specific tools?
Software Alternatives
See head-to-head software comparisons with feature breakdowns, pricing, and our recommendation for each use case.
Explore software alternatives→In this category
AI Fashion Photography alternatives
See side-by-side comparisons of ai fashion photography tools and pick the right one for your stack.
Compare ai fashion photography tools→