Models/Collections/Best Image-to-Image
Capability · Image20 ModelsUpdated Oct 2026

Best Image-to-Image

Models that transform an existing image using text guidance, reference inputs, or structural cues. Useful for restyling, variation generation, and controlled edits that keep key visual elements intact.

The picks.Transform existing images

TXT → IMGIMG → IMG

GPT-Image-2.5 Sunburst

Premium image generation and editing with precise control across iterative edits

$0.0152/inpaint
Run
TXT → IMGIMG → IMGEDIT

Open weights.Hosted alternatives for this stack

Open-weight models covering the same tasks as this stack, running on Runware's own optimized compute and billed on compute time. License terms vary per model — check each model page before self-hosting.

Black Forest Labs

FLUX 3 Image Klein

by Black Forest Labs

Distilled open-weights model for fast image generation and editing

TXT → IMGIMG → IMG
from $0.006/text-to-image
Black Forest Labs

FLUX.2 [klein] 9B KV

by Black Forest Labs

KV-cache accelerated image generation and editing for real-time multi-reference workflows

TXT → IMGIMG → IMG
from $0.00169/text-to-image
Black Forest Labs

FLUX.2 [dev]

by Black Forest Labs

FLUX.2 dev for controllable open text to image workflows

TXT → IMGIMG → IMG
from $0.009/text-to-image
Black Forest Labs

FLUX 3 Image Klein Base

by Black Forest Labs

Undistilled open-weights foundation for fine-tuning and customization

TXT → IMGIMG → IMG
from $0.0238/bounding-boxes

Common questions

Yes — FLUX.2 [klein] 9B, FLUX.2 [klein] 4B run on Runware's own optimized compute (the platform's open-weight tier, billed on compute time). Check each model page for license terms before self-hosting.

Nano Banana 2.1, released October 2026 per the live catalog. Membership updates automatically as the catalog publishes new models to this collection.

Production notes

Every price on this page is the model's published rate from the live Runware catalog, using the cheapest listed configuration unless stated otherwise. Prices vary with resolution, duration, quality tier, or token volume, so check the pricing table on each model page before estimating unit economics.

The Runware catalog does not publish per-model latency figures, so this page does not quote end-to-end timings. Where a model's own description commits to speed (for example sub-second generation or realtime streaming), that claim is repeated here. For anything else, benchmark the exact models in the Playground with your own payload sizes before committing to an SLA.

Models are addressed by versioned AIR identifiers, so a workflow pinned to specific model versions keeps producing the same behaviour as new versions ship. Adopt upgrades deliberately by re-running your evaluation set against the new version before switching production traffic.

About this collection

Models that transform an existing image using text guidance, reference inputs, or structural cues. Useful for restyling, variation generation, and controlled edits that keep key visual elements intact.

Learn: how image-to-image works →

Membership comes directly from the Runware catalog: the 20 models on this page are the live catalog's own membership for the "Best Image-to-Image" collection. Names, descriptions, pricing, capability chips, samples, and guides are all read live from the catalog — nothing here is hand-curated.

Model guides.Learn how to use the stack

GPT-Image-2.5 Sunburst

Precision editing

How to use GPT-Image-2.5 Sunburst for detailed and repeated edits: what the longer generation time buys, and when to reach for it instead of Flare.

Read the guide →
GPT-Image-2.5 Flare

Prompting

How to prompt GPT-Image-2.5 Flare: writing a brief it can plan a layout from, pinning counts and positions, and choosing a size from its custom dimension envelope.

Read the guide →
GPT-Image-2.5 Flare

Quality levels

How to set rendering effort on GPT-Image-2.5 Flare with settings.quality: what each level changes, what it costs, and which subjects are worth the top of the ladder.

Read the guide →
GPT-Image-2.5 Flare

Editing images

How to edit an image with GPT-Image-2.5 Flare: one source, one written instruction, and the composition, lighting and detail the model holds while it changes what you named.

Read the guide →
GPT-Image-2.5 Flare

Subject consistency

How to keep one product or character recognisable across new scenes with GPT-Image-2.5 Flare: how many reference images to send, and how to say what each one is for.

Read the guide →
GPT-Image-2.5 Flare

Masked editing

How to confine an edit to a painted region with GPT-Image-2.5 Flare: what white and black mean, and why the mask is guidance rather than a hard boundary.

Read the guide →
GPT-Image-2.5 Flare

Iterative editing

How to run a chain of edits with GPT-Image-2.5 Flare: feeding each result back as the next source, what accumulates across passes, and when to restate instead of chain.

Read the guide →
GPT-Image-2.5 Flare

Transparent backgrounds

How to generate cut-out assets with GPT-Image-2.5 Flare: settings.background, the output format it forces, and prompting for edges that survive compositing.

Read the guide →
GPT Image 2

Prompting

How to write prompts for GPT Image 2 across the cases the model handles unusually well: photorealism, accurate text, world-knowledge composition, and multi-image editing workflows.

Read the guide →
Muse Image

Prompting

How to prompt Muse Image: writing a brief it can plan a layout from, setting the thinking level, picking one of the eight size pairs, and working without a seed.

Read the guide →
Muse Image

Editing images

How to edit an image with Muse Image: passing one source image, targeting a region in words, removing objects, restoring old photos, and refining a result across passes.

Read the guide →
Muse Image

Rendering exact text

How to render exact, legible copy inside an image with Muse Image: quoting the literal string, keeping it short, directing the type, and landing multi-element layouts.

Read the guide →
Muse Image

Composing with multiple reference images

How to build one image out of up to ten reference images with Muse Image: giving each reference a job in the prompt, and letting the references set the output shape.

Read the guide →
Muse Image

Grounding images with web and image search

How to ground Muse Image in real facts and real references with settings.webSearch and settings.imageSearch, and when to switch the lookups off.

Read the guide →
Muse Image

Building infographics and charts

How to build data-accurate charts and infographics with Muse Image: what settings.shell does, how to write your figures into the prompt, and when to switch it off.

Read the guide →
Nano Banana 2.1

Prompting

How to write prompts for Nano Banana 2.1: briefs with counts and positions, layered descriptions, a system prompt for a house style, thinking levels, and sizes.

Read the guide →
Nano Banana 2.1

Text and layouts

How to get exact copy and structured layouts from Nano Banana 2.1: quoting strings, setting a type hierarchy, building infographics, and translating a design.

Read the guide →
Nano Banana 2.1

Editing images

How to edit an image with Nano Banana 2.1: one reference and a written instruction, keep lists, removals and additions, numbered changes, and chained edits.

Read the guide →
Nano Banana 2.1

Composing from multiple reference images

How to combine up to 14 reference images with Nano Banana 2.1: giving each image a role, following a layout sketch, borrowing a style, and reading a video.

Read the guide →
Nano Banana 2.1

Subject consistency

How to keep one product or character identical across a series with Nano Banana 2.1: one reference into new scenes, extra views, and illustrated characters.

Read the guide →
Nano Banana 2.1

Grounding with web and image search

How to ground Nano Banana 2.1 in live facts and real appearances with settings.webSearch and settings.imageSearch, and when to leave both lookups off.

Read the guide →
Seedream 5.0 Pro

Prompting

How to prompt Seedream 5.0 Pro for text-to-image, reference-guided editing, and multi-image fusion in commercial workflows: campaign posters, product variants, and editorial still-life.

Read the guide →
Seedream 5.0 Pro

Multilingual text rendering

How to prompt Seedream 5.0 Pro for accurate in-image text across 15 native languages, from French posters with accented characters to Arabic, Thai, Korean, and Japanese scripts.

Read the guide →
Nano Banana 2

Prompting

How to write prompts for Nano Banana 2: detailed scene descriptions, structured layering, legible text rendering, and thinking-level control.

Read the guide →
Nano Banana 2

Combining multiple images into one composition

How to merge several reference images, a product, a subject, a backdrop, or a style, into a single coherent image with Nano Banana 2.

Read the guide →
Nano Banana 2

Keeping characters and products consistent

How to use Nano Banana 2 reference images to keep the same character or product identical across new scenes and styles.

Read the guide →
Nano Banana 2

Grounded generation with web and image search

How to generate images from real-time information with Nano Banana 2 using web and image search grounding via providerSettings.google.

Read the guide →
FLUX 3 Image

Prompting

How to prompt FLUX 3 Image for text-to-image: the five-part prompt structure, the detail that prompt expansion cannot invent for you, camera direction, and output size.

Read the guide →
FLUX 3 Image

Rendering text

How to render readable text with FLUX 3 Image: quoting the exact string, directing the typography, building a text hierarchy, and editing copy inside an existing image.

Read the guide →
FLUX 3 Image

Editing images

How to edit an image with FLUX 3 Image from a single reference: scoping the instruction, swapping materials and light, removing objects, reframing, and chaining edits.

Read the guide →
FLUX 3 Image

Multi-reference composition

How to combine up to ten reference images with FLUX 3 Image: assigning a role to each one, holding a face or a product across scenes, and borrowing a look.

Read the guide →
FLUX 3 Image

Bounding boxes

How to place and edit elements by region with FLUX 3 Image: the 0 to 1000 grid, composing a layout from scratch, moving a marked element, and removing one.

Read the guide →
Ideogram 4.5 Precise Edit

Masked edits

How to limit an Ideogram 4.5 Precise Edit to one region with a mask, from picking one object out of several to printing a reference graphic onto a garment.

Read the guide →
Ideogram 4.5 Precise Edit

Text and localization

How to change and translate the text in a finished design with Ideogram 4.5 Precise Edit, from one updated line to the same banner in four languages.

Read the guide →
Ideogram 4.5 Precise Edit

Iterative editing

How to run a chain of edits with Ideogram 4.5 Precise Edit, one change per round, while every pixel the instruction leaves alone is restored from the source.

Read the guide →
Ideogram 4.5

Prompting

How to write prompts for Ideogram 4.5: the order of a good prompt, space for copy, Magic Prompt, the 1K and 2K sizes and the quality tiers.

Read the guide →
Ideogram 4.5

Text and design

How to use Ideogram 4.5 for designs where the words have to be right: quoting exact copy, type hierarchy, placement, dense layouts, non-Latin scripts and brand palettes.

Read the guide →
Ideogram 4.5

Editing with reference images

How to edit an image with Ideogram 4.5 and up to four reference images: numbering the inputs, taking one attribute from each, choosing the output size and adding a mask.

Read the guide →
Ideogram 4.5

Pose control

How to repose a model photo with Ideogram 4.5 using a pose skeleton reference and joint angles in the prompt, from one raised arm to a full lookbook from a single shot.

Read the guide →
FLUX Virtual Try-On

Virtual try-on

How to dress a person in any garment from a reference image with FLUX VTO. The call takes one person photo, one garment photo, and a short prompt.

Read the guide →
P-Image-Try-On

Dressing a person in a full outfit

How to dress a person in a full outfit with Pruna P-Image-Try-On, passing each garment as its own reference and matching a target pose.

Read the guide →
Topaz Labs Wonder 3.5

Upscaling images with Wonder 3.5

How to upscale images with Topaz Wonder 3.5: the request shape, choosing an upscale factor, the input and output size limits, and what generative upscaling recovers.

Read the guide →
Topaz Labs Wonder 3.5

Upscaling text and structured graphics

How to sharpen text, charts, tables, and packaging with Topaz Wonder 3.5, recovering legible type and clean structured graphics from soft or compressed sources.

Read the guide →
Topaz Labs Wonder 3.5

Controlling detail with enhancement strength

How to control how much detail Topaz Wonder 3.5 rebuilds with settings.enhancementStrength: what low, medium, and high change, and when to dial it down for faces or up for texture.

Read the guide →
Topaz Labs Wonder 3.5

Adding film grain

How to add film grain to a Wonder 3.5 upscale with settings.grain: the silver, gaussian, and grey grain models and the strength, density, and size controls.

Read the guide →
Topaz Labs Wonder 3.5

Restoring old and degraded photos

How to restore old and degraded photos with Topaz Wonder 3.5: faded scans, over-compressed uploads, noisy low-light shots, and tiny legacy files rebuilt as they upscale.

Read the guide →