Creative Workflows – IMG.LY Blog

AI Design Agents and Creative Automation: How to Ship a Full Campaign Without a Designer

Klaudia — Wed, 01 Apr 2026 10:14:03 GMT

You have your files, hooks, Google Ads data but still can’t actually launch a campaign because you have to go to a designer for the final assets.

That gap is exactly where campaign momentum dies. Not at the strategy stage or in copy but at the last mile, when everything is ready except the thing people will actually see.

Luckily, it no longer has to.

The AI Marketing Stack Has a Design-Shaped Hole in It

Most marketing workflows have been quietly transformed over the last two years. Copy generation, audience segmentation, keyword research, performance analysis - all of it runs faster now, with less manual input. A solo marketer can do what used to require a team. Except for one part.

Design hasn’t moved. The workflow is still: write a brief, hand it to a designer, wait, review, revise, wait again. Then do the same thing in three more formats because the 1:1 you approved doesn’t fit Stories, Display, or LinkedIn. That loop can take days. And it doesn’t matter how good your AI-generated copy is if it’s sitting in a doc waiting for someone to have bandwidth.

This isn’t a resourcing problem. Hiring more designers doesn’t fix the structural issue; it just adds capacity to a fundamentally slow process. The problem is that the tools built for design weren’t designed for the workflow a modern marketer actually runs. They expect a designer at the keyboard. They don’t expect a marketer with a campaign brief and a conversation window.

The result: campaigns that are otherwise fully automated still stall before they ship. The bottleneck moved from copy to creative. And most teams haven’t noticed yet, because design delays feel normal. They’ve always been there.

What an AI Design Agent Actually Changes

An AI design agent is an autonomous AI system, not a prompt-response tool. It plans, reasons, and executes design tasks independently. Given a goal, it breaks that goal into steps, uses the tools available to it, retains context across the session, and can self-correct when the output isn’t right. That’s the category: a system that drives the workflow rather than waiting for instruction at each step.

Most design agents deliver speed. The handoff that used to take days can happen in minutes. But the output is still a deliverable: a file you receive, use as-is, or move somewhere else. Most operate inside existing tools or generate assets you work with elsewhere. The speed is real, the editability usually isn’t. If something needs to change, you’re back to prompting from scratch.

CoDesign sits differently. It doesn’t hand you a finished asset. It gives you a working starting point on a real canvas. Layers, text boxes, placeholders - real assets you can continue to work with, in the same conversation, without leaving the session.

Brand consistency gets handled at the foundation. Load your brand kit once, including colors, fonts, logo, and layout rules, and every output that session respects those constraints. You’re not eyeballing hex codes or hoping the font looks right. The rules are applied from the start, not checked at the end.

Multi-format adaptation is where the time savings become concrete. A campaign that runs across Instagram, Google Display, LinkedIn, and print doesn’t produce four separate briefs and four separate rounds of designer revisions. You describe the formats you need, and the agent adapts the work. The campaign stays consistent across all of them.

The biggest shift isn’t speed, though that’s real. It’s that you stay in creative control throughout. There’s no handoff moment where you lose the thread and have to re-explain the brief to someone else. The context lives in the conversation.

How to Run a Campaign Production Session with CoDesign

This is the actual sequence. Walk through it once and the workflow becomes repeatable.

1.Start with the brief. Open CoDesign and describe the campaign in plain language. Audience, objective, platform, tone, any constraints. The more specific you are here, the less iteration you’ll need later. Treat it like briefing a senior designer who hasn’t worked with your brand before.

2.Generate copy variations before you open the canvas. Use whichever AI writing tool you already work with — ChatGPT, Claude, or whatever is in your stack — to produce multiple copy directions: headline hooks, body copy, CTAs. Get four or five versions per element. Copy and design are separate steps that feed into each other. Having real options ready before you start the design session means CoDesign has something specific to work with, not a blank brief waiting to be interpreted.

3.Feed the brief to the AI design companion. With copy variations ready, ask CoDesign to generate initial ad designs. Describe the format, the hierarchy you want, any layout preferences. You’ll get structured, editable designs on the canvas. Not a rendered image, but a working starting point with real layers.

4.Apply your brand kit. If you haven’t already, load your brand assets: logo, color palette, type system, approved imagery. The agent applies these rules to the designs. Every output from this point respects your brand standards automatically.

5.Adapt across formats. Tell the agent which formats you need. The social variant, the display variant, the vertical for Stories, the square for feed. Watch the layouts adapt to each context, maintaining the campaign idea and brand consistency across dimensions. If the hierarchy needs adjusting for a specific format, describe what’s not working and the agent fixes it in conversation.

6.Refine in conversation. This is where the canvas-based approach earns its value. You’re not generating new versions from scratch. You’re iterating on what’s already there, in the same session. “Move the logo to the bottom right. Try the headline in the lighter weight. Swap this layout for something with more white space.” Each exchange builds on the last, so the conversation stays grounded in what’s on the canvas rather than starting over from a new prompt.

7.Export and ship. When the designs are approved, export in the formats your media plan requires. The session context lives with the file, so if something needs to change post-launch, you’re not starting from zero.

One honest note: the quality of the output is proportional to the quality of the brief. Vague prompts produce generic starting points. Teams that invest two minutes in a specific, structured brief consistently get more usable first outputs than teams that describe the campaign in one sentence and expect the agent to fill in the gaps.

This Is What Closing the Loop Actually Looks Like

Design agents don’t replace designers. They remove the bottleneck that sits between strategy and execution.

The marketer who used to wait three days for ad assets can now produce a full campaign set in a single session. The designer who spent half their week on format resizes and small-copy tweaks can spend that time on the work that genuinely requires their judgment. That means brand-defining creative, campaign concepts, and anything where taste and experience are the actual input.

It was never about willingness to collaborate. The tools just didn’t allow for anything else. Every design change, no matter how small, had to go through a handoff. A headline adjustment on a banner resize does not need a creative director. A resize from 1:1 to 9:16 does not need a brief, a Slack message, and a 48-hour turnaround.

Thanks to design agents conversation shifts. It moves from “can you make this” to “how should this look.” And that’s a way more interesting conversation.

Interested in trying IMG.LY CoDesign? Reach out to our team.

How to Embed an Editable InDesign Template in Your Website

Jan — Tue, 28 Oct 2025 12:29:03 GMT

Why Editing InDesign Files in the Browser Matters Now

Adobe InDesign has long been the standard for high-fidelity layout and print design. Yet for many teams, its power comes with friction: it’s desktop-bound, collaboration-limited, and inaccessible to clients or non-designers who simply need to make minor edits.

Creative work requires ever shorter feedback loops and is becoming more and more accessible to the average users, hence organizations are looking for ways to bring InDesign workflows into the browser to make templates editable, collaborative, and automatable.

At the same time, businesses are under pressure to modernize creative production. Marketing teams need to localize campaigns at scale, SaaS platforms want to let users personalize assets, and agencies aim to deliver editable templates instead of static files. The question naturally arises:

“Can I edit InDesign files in a browser?”

Until recently, the answer was “not really.” Adobe offers Share for Review for commenting, InCopy on the Web for limited text changes, and Adobe Express for simplified exports, but none provide full-fidelity, browser-native editing or the ability to embed such functionality into your own product.

That’s where CE.SDK enters the picture.

CE.SDK is an embeddable creative editor that powers photo, video, and design workflows directly in the browser. It’s deeply customizable and extensible, enabling developers to tailor every aspect of the editing experience. The same SDK works cross-platform, Web, iOS, Android, Desktop, and Server, so teams can build consistent creative tools across environments.

By combining CE.SDK’s robust editing engine with the InDesign Importer, you can now bring InDesign templates (IDML files) into a fully fledged web-based design editor while preserving essential layout, style, and asset information. The result: true browser editing of InDesign content, without the limitations of traditional desktop software.

The Current Landscape: What’s Possible (and What Isn’t) with InDesign on the Web

While creative teams increasingly expect collaborative, browser-based tools, Adobe InDesign remains deeply rooted in its desktop heritage. Its powerful layout engine and proprietary file structure were never designed for real-time, cloud-native editing. As a result, teams who rely on InDesign often face friction when trying to make designs accessible to clients or other stakeholders online.

Adobe has made incremental steps toward the web, but these tools still serve limited purposes:

Share for Review – enables commenting and approval workflows in the browser, but doesn’t allow editing or layout changes.
InCopy on the Web (beta) – offers browser-based text editing within locked layouts. It’s useful for copy review, yet visual elements remain untouchable.
Adobe Express export – lets designers repurpose InDesign layouts as simplified templates for lightweight editing, but the process is one-way and loses much of InDesign’s fidelity and control.

For teams that need to deliver editable templates, enable client-side personalization, or embed creative workflows inside their own platforms, these options aren’t sufficient. They lack extensibility, API access, and the ability to maintain brand-level control in a web environment.

Beyond Adobe: Existing Alternatives

Several third-party tools have tried to fill the gap:

VivaDesigner mirrors parts of InDesign’s functionality in a browser, but operates as a self-contained product rather than an embeddable SDK.
Silicon Designer builds on InDesign Server to power web-to-print solutions, but depends on heavy backend infrastructure and costly licensing.
Photopea provides impressive browser editing for layered graphics and basic IDML files, yet lacks enterprise-grade extensibility or workflow integration.

Each of these solutions demonstrates what’s possible, but none offer a developer-friendly foundation for building editing workflows and experience on-top of InDesign in the browser.

Where IMG.LY’s CE.SDK Fits In

This is the gap that CE.SDK fills.

Instead of emulating InDesign’s desktop application, CE.SDK focuses on data translation and browser-native rendering. Its InDesign Importer converts the open IDML format into CE.SDK’s optimized scene format retaining layout structure, typography, and key design elements so users can edit and export designs directly in the browser and all other platforms supported by CE.SDK.

For developers, this approach unlocks a new level of flexibility:

Embed an editable InDesign experience in any web platform.
Integrate design editing into DAMs, CMSs, or creative automation workflows.
Customize UI, behaviors, and integrations to match existing systems.

In short, CE.SDK transforms what used to be static, desktop-bound InDesign files into interactive, web-based design templates, without compromising control or scalability.

Introducing the CE.SDK InDesign Importer

Moving professional InDesign layouts into the browser isn’t just about file conversion, it’s about accurately translating complex design data into a web-native format that can be rendered, edited, and automated.

That’s exactly what the CE.SDK InDesign Importer does.

The importer acts as a bridge between Adobe InDesign’s IDML format and CE.SDK’s scene model, transforming desktop-authored layouts into editable, browser-ready projects. Once an .idml file is exported from InDesign, the importer reconstructs its layers, assets, and properties, packaging them into a CE.SDK scene archive that can be opened instantly inside any CE.SDK instance.

Explore the InDesign Template Import Demo for a comprehensive example.

A Stand-Alone Module, Built for Integration

Unlike the core CE.SDK editor, the InDesign Importer is distributed as a stand-alone package that you can integrate into any workflow. It’s available via npm as

@imgly/idml-importer,

allowing developers to run imports in their own build systems, servers, or client-side applications before loading the resulting scene into CE.SDK.

This separation makes it easy to slot the importer into existing pipelines, for example, automated template ingestion systems, DAM integrations, or internal pre-processing tools — without requiring the full editor runtime.

How It Fits into the CE.SDK Ecosystem

CE.SDK (CreativeEditor SDK) is an embeddable creative editor powering photo, video, and design workflows across Web, iOS, Android, Desktop, and Server. It offers a modular, extensible engine and UI framework that teams can tailor to any brand or use case.

The InDesign Importer extends that ecosystem by unlocking compatibility with one of the most widely used layout tools in the world. Together, they enable a complete pipeline:

InDesign (IDML) → @imgly/idml-importer → CE.SDK Scene File → Browser Editing & Automation

This means existing InDesign templates can become live, editable browser experiences — without rebuilding designs manually or deploying heavy server infrastructure.

What the Importer Delivers

File-Format Translation – Converts IDML files into CE.SDK scene archives while preserving layout hierarchy, positioning, and grouping.
Asset Bundling – Packages fonts, embedded images, and color data for immediate use in CE.SDK.
Color Mapping – Converts CMYK values into RGB for web rendering (native CMYK support is in development).
Element Preservation – Maintains grouping, rotation, shapes (rectangles, ovals, polygons, lines), gradients, transparency, and strokes.
Developer Flexibility – Import locally or at scale, feed the resulting scene into CE.SDK’s API, or integrate into automated asset pipelines.

Real-World Use Cases & Workflows

Once an InDesign file becomes editable in the browser, entirely new workflows open up — from collaborative editing to automated content generation.

The CE.SDK InDesign Importer enables organizations to extend proven InDesign templates into scalable, web-native experiences:

Web-to-Print Platforms

Allow end users to personalize marketing collateral, business cards, or packaging directly in a browser editor while maintaining the designer’s original layout integrity.

Brand Template Portals

Empower distributed teams, agencies, or franchise partners to create on-brand materials without ever touching desktop software. Designers upload InDesign templates once; users edit and export variations on demand.

Creative Automation Systems

Combine CE.SDK with data pipelines to automatically generate localized or personalized assets at scale — replacing time-consuming manual layout work with programmable design workflows.

Client Collaboration

Deliver interactive proofing experiences where clients can adjust copy, swap images, or approve layouts in a controlled browser environment, eliminating the “export–review–revise” loop typical of InDesign-based projects.

Each of these use cases builds on the same foundation: reliable IDML translation plus CE.SDK’s flexible editing engine.

That combination makes the Importer not just a conversion tool, but a bridge to entirely new creative business models.

CE.SDK vs. Traditional InDesign Server & Other Alternatives

For teams exploring browser-based design editing, the landscape typically centers on three paths — InDesign Server, web-to-print middleware, or browser SDKs.

The CE.SDK InDesign Importer offers a modern alternative to all three.

Feature / Capability	InDesign Server	Third-Party Tools (VivaDesigner, Silicon Designer)	CE.SDK + InDesign Importer
Editing Fidelity	Full but desktop-rendered	Partial; varies by implementation	High; layout preserved via IDML
Web Accessibility	Limited; server-side only	Browser UI, but closed systems	Fully client-side, browser-native
Embeddable / SDK	No	Proprietary	Yes + modular npm packages
Infrastructure	Requires Adobe licensing & server setup	Vendor-hosted	Lightweight; deploy anywhere
Extensibility	Restricted scripting	Limited	Full API & UI customization
Cost / Licensing	High, per-instance	Varies; often enterprise-only	Predictable developer friendly licensing

CE.SDK’s approach eliminates the dependency on server-side rendering and proprietary hosting, providing a developer-first, browser-native foundation for creative editing.

By translating InDesign layouts into open CE.SDK scenes, it combines professional-grade fidelity with the flexibility of modern web architecture.

Conclusion – A New Era for InDesign Workflows

For years, creative teams have struggled to bridge the gap between InDesign’s print-grade precision and the web’s flexibility and scalability.

The CE.SDK InDesign Importer closes that gap — turning traditional .indd projects into browser-ready, editable templates that can live inside any modern application.

Whether you’re building a web-to-print platform, empowering clients through self-service editing, or connecting templates to creative-automation pipelines, CE.SDK provides the foundation to make it happen — with full developer control and a consistent experience across Web, Mobile, and Desktop.

Explore the live demo: InDesign Template Import Demo

Try the importer: @imgly/idml-importer on npm

Learn more about CE.SDK: https://img.ly/products/creative-sdk/

Frequently Asked Questions

Can I edit an InDesign file directly in a browser?

Not with Adobe’s native tools alone — Share for Review and InCopy on the Web only allow commenting or text changes. With the CE.SDK InDesign Importer, however, you can convert an exported IDML file into a browser-editable format that retains layout, fonts, and key visual elements.

What file formats does the importer support?

The importer reads IDML files exported from Adobe InDesign and converts them into CE.SDK scene archives. These can then be opened in CE.SDK for full browser editing and exported again to formats such as PDF, PNG, or JSON.

Does it require Adobe InDesign Server?

No. The @imgly/idml-importer runs independently — it’s a standalone npm package and doesn’t depend on InDesign Server or any Adobe infrastructure. You only need an IDML export from InDesign.

Is CMYK color supported?

Currently, CMYK values are automatically translated into RGB for accurate web rendering. Native CMYK support is planned in future updates.

Can I automate bulk imports?

Yes. Because the importer is installable via npm, you can integrate it into scripts or pipelines to process large template libraries automatically before loading them into CE.SDK.

Do imported InDesign templates remain editable for non-designers?

Absolutely. Once loaded into CE.SDK, templates can be edited through a customizable browser interface — ideal for client portals, marketing platforms, or self-service brand editors.

What is Visual Prompting?

Jan — Tue, 29 Jul 2025 13:06:11 GMT

A New Paradigm for Creative AI, Built by IMG.LY

To say it’s trite to refer to the impact of AI in this or that domain as disruptive or groundbreaking would be an understatement. Yet, few areas have been as profoundly affected as the creative process. With just a text prompt, anyone can produce stunning images, remix visual styles, and explore design possibilities at a scale and speed never seen before. AI has inserted itself so quickly into this process that its gone from curious novelty to an essential part of the creator toolchain.

The more serious adoption we see, however, the more key limitations of today’s AI tooling come into focus: the prompt itself.

Text alone, for all its expressive power, struggles to capture the essence of visual intent. Most creative work doesn’t begin with a sentence it begins with a sketch, a layout, a mood board, or an arrangement of elements. Visual ideas are shared by pointing, placing, showing.

At IMG.LY, we have begun to think about better ways to direct AI for visual generation, the term we use is Visual Prompting.

Visual Prompting: the practice of composing a visual scene or layout as input for a generative model.

Instead of describing what you want with paragraphs of text, you show it directly using a canvas of images, text, spatial cues, and annotations. This visual composition then becomes the prompt for the AI to generate new content in return. It’s a more natural, intuitive, and powerful way to collaborate with AI, especially when integrated directly into the creative process.

Problem: the Chat Disconnect

The current generation of AI tools has largely been shaped by language-first interfaces. Whether it’s ChatGPT for writing or Midjourney for image generation, the assumption is the same: the user will type a descriptive prompt, and the AI will generate a result based on it.

But when it comes to design, this workflow quickly runs into friction. Visual ideas are inherently spatial and non-linear. Trying to express layout, balance, mood, or specific spatial relationships through text can feel like trying to describe a painting over the phone. It’s possible but unnecessarily cumbersome.

A designer might want to:

Indicate that a certain area in the image should be blue.
Replace a background with a texture sample.
Position a character precisely in a composition.
Annotate which parts of a scene to preserve or modify.

All of these are difficult to express fluently in text. But they’re effortless in a visual interface. The truth is: an image is worth more than a thousand words when prompting an image.

What Is Visual Prompting?

Visual Prompting is a multimodal approach to generative AI, where the input to the model is not just text, but a full visual composition: images, text, annotations, and layout.

Rather than prompting AI in isolation, the user builds their intent on a canvas. This might include:

Reference images that communicate mood or style.
Text blocks indicating desired copy or instructions.
Annotations pointing to specific areas with notes like “make this glow” or “replace this object.”
Spatial composition: where elements are arranged meaningfully to convey intent.

The visual prompt is then interpreted by a multimodal model such as OpenAI’s gpt-image-1 to generate new visual content that reflects not only the textual description, but also the visual context.

How Visual Prompting Works in CE.SDK

About time for an example. As part of our recent AI released we demoed how to use OpenAIs gpt-image-1 model to build visual prompting into CreativeEditor SDK (CE.SDK).

Here’s what the process looks like inside CE.SDK:

Compose Visually: The user creates a layout with reference content, uploaded images, icons, color schemes, design elements, placeholder text, and annotations. This composition represents the “prompt” in visual form.
Add AI Layers: With a single click, the user can trigger image generation using CE.SDK’s built-in AI plugin. The plugin sends the visual context (alongside any optional text input) to a multimodal model capable of interpreting both.
Refine and Iterate: Users can adjust the layout, reposition elements, change annotations, or layer in new references, then prompt again. Because the canvas is interactive and editable, the feedback loop is tight.
Build Up Complexity: Over time, users can layer generated images with manually designed components or other generated outputs, creating rich compositions that blend AI creativity with human direction.

This workflow turns the traditional prompt/response cycle into a conversation between the designer and the model, with the canvas acting as the shared language.

Who Is Visual Prompting For?

The use cases for Visual Prompting extend across industries:

Creative teams can go from reference to generation in seconds, iterating visually instead of wrangling prompts.
Marketing teams can generate regionalized or personalized creative variants from a shared layout.
Product designers can prototype in context, turning layouts into realistic screens without leaving the editor.
Storytellers and content creators can use annotated sketches to generate detailed illustrations or scene variations.
E-commerce platforms can give sellers the power to visually customize their brand materials with AI assistance.

In every case, Visual Prompting replaces friction with flow and text-based prompting with something more expressive, more reliable, and more fun.

Built for This: Multimodal Models and CE.SDK’s Plugin System

Visual Prompting is only possible because of two parallel advancements:

Multimodal AI models, such as OpenAI’s gpt-image-1, that can interpret both images and text, understand spatial relationships, and respond to annotated cues.
A flexible, composable editor SDK like CE.SDK, which enables the construction of visual prompts on a live canvas, and makes it easy to integrate AI models directly into the design flow.

Our SDK was built from the ground up to support AI-first creative workflows. Its plugin architecture allows you to add any model or API, image generation, video generation, captioning, text rewriting and use it natively inside the editor without the need to switch tools or copy/paste.

Generative AI’s full potential is only unlocked when it is embedded directly into the tools creatives use not siloed in chatbots or separate interfaces. Visual Prompting allows that embedding to go even deeper, aligning the mode of input (visual) with the desired output (visual).

Explore It Yourself

🎨 Try out Visual Prompting in our AI Editor demo
📘 Learn How to Integrate AI into CE.SDK
💬 Contact Us to Bring Visual Prompting to Your Product

OpenAI GPT-4o Image Generation (gpt-image-1) API: A Complete Guide for Creative Workflows for 2025

Jan — Mon, 28 Apr 2025 07:55:48 GMT

Update: AI-first Visual Editing

A day after the release of the gpt-image-1 API, we took it for a spin and integrated it into CreativeEditor SDK. Users can now generate images, create variants and use the canvas to compose visual prompts with our design editor. See it in action:

Open AI Editor Demo Page

Introduction

The release of OpenAI’s gpt-image-1 model signals a pivotal shift in the creative developer landscape—one that moves beyond static, one-shot image generation and toward a more dynamic, multimodal interaction model. Until recently, most image APIs followed a predictable pattern: submit a prompt, receive a finished image. The process was useful, but flat. What’s changing now is not just image quality or style fidelity, but the shape of the workflow itself. With gpt-image-1, built on the GPT-4o foundation, developers can start designing creative tools that feel conversational and iterative. This evolution invites a new kind of interface where prompting, tweaking, and refining happen inside the canvas, not outside of it.

For teams building creative editing experience into their app, this moment coincides with the release of IMG.LY’s AI Editor SDK, a powerful, fully integrated toolkit designed for generative workflows. The SDK is already equipped to support interactive image generation, contextual editing, and multimodal inputs, and you can try it today through this live demo.

This guide is a comprehensive introduction to the gpt-image-1 API, but it also goes further. It’s not just about wiring up an endpoint, it’s about rethinking what image generation means in a user-centric product.

From prompt handling to interactive iteration, we’ll walk through how to design creative cycles, not just outputs. This guide explores how to make that shift, how to go from generating images to integrating gpt-image-1 into real creative cycles, where AI becomes a tool that bends to user intent, not the other way around.

Overview of `gpt-image-1`

OpenAI’s gpt-image-1 model, released in April 2025, is the latest evolution in the company’s generative image lineup and marks a turning point in how developers approach visual creation inside applications. Built on the same multimodal foundation as GPT-4o, this model allows applications to move beyond one-shot static generation and instead build toward more conversational, iterative image workflows.

Model Architecture and Capabilities

gpt-image-1 is rooted in GPT-4o’s ability to understand and generate across modalities. It is designed to produce high-resolution images—up to 4096×4096 pixels—based on natural language prompts. The model handles complex scenes with more fidelity than previous iterations and provides improved consistency in how it interprets detailed descriptions. This is particularly relevant for tools that need reliability when turning prompt inputs into design elements.

Parameter Control

Developers working with gpt-image-1 have access to a streamlined set of parameters, here is a subset of the most important ones:

prompt: The primary text input describing the desired image.
size: Choose between “1024x1024”, “1024x1536” (portrait), “1536x1024” (landscape), or “auto” (default, based on prompt).
n: Number of images to generate (default is 1).
response_format: Always returns b64_json. URL outputs are not supported.

Unlike DALL·E 3, gpt-image-1 does not accept style modifiers or quality settings. It is designed for straightforward, high-fidelity image creation driven purely by the text prompt and size selection.

Full documentation of these options is available via OpenAI’s official guide.

Style and Use Case Alignment

By supporting a wide range of stylistic templates, gpt-image-1 positions itself as a flexible backend for everything from marketing collateral to storyboarding tools. The output can be tailored to suit technical illustrations, concept art, or even photorealistic renderings, allowing developers to map visual outputs more directly to brand or product requirements.

Limitations and Future Direction

As of April 2025, gpt-image-1 supports only one image per request and does not offer fine-grained image editing or inpainting. However, its tight coupling with GPT-4o suggests that future iterations may embrace persistent context, conversational refinement, or even integrated image-plus-text exchanges within the same session. For developers building editors or multimodal workflows, the current model lays a strong foundation for these future capabilities.

API Setup and Usage

2.1 Get Access

To start using gpt-image-1, developers must first register for access via the OpenAI platform at platform.openai.com. Access requires an API key, which is tied to your OpenAI account and associated usage limits based on your billing tier. Be sure to confirm that your account is approved for image generation, as availability may differ by region and subscription level. Once authenticated, keys can be created in your dashboard and stored securely in your server or development environment.

2.2 First Image Generation (Node.js Example)

The image generation API for gpt-image-1 can be used directly via OpenAI’s official Node.js client. Below is a complete example showing how to send a prompt and receive an image URL in response:

import OpenAI from 'openai';
import fs from 'fs';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY, // make sure this is securely set
});

async function generateImage() {
  try {
    const prompt = `
    A studio ghibli style illustration of a cyberpunk girl holding a butterfly on her finger.
    `;

    const result = await openai.images.generate({
      model: 'gpt-image-1',
      prompt,
      size: '1024x1024', // or "1024x1536", "1536x1024", or "auto"
    });

    const image_base64 = result.data[0].b64_json;
    const image_bytes = Buffer.from(image_base64, 'base64');
    fs.writeFileSync('butterfly.png', image_bytes);
    console.log('Image saved as butterfly.png');
  } catch (err) {
    console.error('Error generating image:', err);
  }
}

generateImage();

Remember that all outputs from gpt-image-1 are delivered as base64-encoded JSON. Developers should decode this data for display, storage, or further processing within their applications. For complete parameter options and examples, consult the OpenAI Images API guide.

Integrating with CE.SDK

Embedding gpt-image-1 into a creative editor like CE.SDK is about more than just piping an image into a canvas. It reshapes how users interact with content creation, bridging manual design work and AI-driven generation within the same editing environment. Rather than operating as a standalone prompt generator, gpt-image-1 becomes a continuous creative partner inside your editor. For in in-depth technical guide on how to integrate gpt-image-1 stay tuned for our upcoming tutorial, sign up to our newsletter to be notified when it goes live.

Embedding Image Generation in a Creative Editing Workflow

The natural entry point for gpt-image-1 inside CE.SDK is through a dual-mode experience: offering users the option to start either from scratch or from existing context. In “from scratch” mode, a user might open a blank scene and initiate an image generation by writing a prompt for example, “Create a vibrant festival scene at sunset.” The result appears directly on the canvas, immediately editable like any other design element.

Where gpt-image-1 shows its real potential is in “in-context editing.” Here, users interact with existing content—a background, a product shot, or a decorative element and trigger AI enhancements based on that visual context. A user might select an image of a bird, as in the example below and ask for variants, initiate a background swap, or request a change like adding more birds in a conversational interface embedded in the editor. Because CE.SDK treats generated images as first-class canvas elements, context such as positioning, layering, and cropping is preserved throughout the process.

Let’s see what this might look like in practice. We positioned an image of a single bird on our canvas, opening the AI context menu we can now manipulate that image in place using the OpenAI API:

We edit the image and prompt the API to add more birds:

We see that the model correctly identified the type of bird in the picture (seagull) and filled it in with a swarm of flying seagulls.

We can now continue to work with the image, overlaying filters, changing the texture, cropping etc.

Switching Between Manual Edits and AI-Powered Enhancements

A critical design principle when integrating gpt-image-1 is giving users freedom to toggle between manual edits and AI suggestions. Manual edits should always remain possible after generation, e.g. cropping, masking, compositing while users can also seamlessly prompt gpt-image-1 for additional changes without losing prior work. Think of variant generation as a branch: a user picks a generated image and creates “forks” by asking for alternate styles, different lighting, or new thematic elements.

In this setup, the generated image serves as a stable node in the creative graph, while edits and regenerations can attach contextually. This workflow minimizes user frustration by avoiding the “start over” penalty typical of isolated generation APIs. It also opens up more complex creative behaviors, like blending user-drawn sketches with AI-augmented refinements, or iteratively developing an asset library around a consistent visual theme.

An upcoming in-depth tutorial will walk through implementing this multimodal workflow step-by-step, but the key takeaway is that gpt-image-1 shines brightest when it is embedded into a creative loop—not treated as a black-box generator, but as an interactive, iterative design companion.

Prompt Engineering Tips

One of the most overlooked but critical factors in successful image generation is prompt design. With gpt-image-1, prompt engineering isn’t just about describing an image—it’s about steering the model toward intent, tone, composition, and usability. Because the model is capable of rendering complex scenes and a wide range of styles, thoughtful phrasing and contextual hints can dramatically affect the outcome.

Writing for Visual Intent

Start by clarifying what the image is supposed to communicate. Are you looking for atmosphere, action, product detail, or narrative clarity? A prompt like “a city skyline at night” is a starting point, but it leaves too much to chance. Adding elements like “view from a rooftop bar, with glowing signage and overcast haze” gives the model anchors for both composition and mood.

Leveraging Artistic Language

You can further refine outputs by referencing mediums or artistic schools. Prompts that include terms like “in watercolor style,” “oil painting,” ”80s anime aesthetic,” or “studio photography” help the model lock onto a particular visual identity. These cues not only improve stylistic fidelity but also align the output with specific brand or genre expectations, which is especially important for products with a defined look and feel.

Creating Consistency in Branded Outputs

When generating a set of related images, such as social media creatives, campaign assets, or UI visuals, consistency becomes more important than variety. To achieve this, structure prompts with repeatable patterns and include brand elements such as color palettes, motifs, or reference characters. While gpt-image-1 doesn’t yet support persistent memory across requests, consistency can be enforced by prompting with the same style terms, layout descriptions, and constraints. Teams working within CE.SDK can even pair prompt templates with locked canvas layers to preserve composition between generations.

Ultimately, good prompt engineering is not about verbosity but about clarity and constraint. It’s less like writing poetry and more like drafting a product spec. The best prompts are focused, directive, and give the model just enough creative freedom within clear boundaries. However, effective prompting should not burden the user. In practice, the interface should abstract most of the complexity away. Users can be guided toward better outputs through simple UI choices—selecting predefined styles, choosing themes, or adjusting mood settings—while the system dynamically enhances and augments their input behind the scenes. By managing the technical depth invisibly, you enable a creative process that feels intuitive and powerful without ever making prompt engineering the center of the user experience.

Real-World Use Cases

The versatility of gpt-image-1 makes it especially impactful across a variety of industries where visual content creation is either a core product feature or a major operational need. Beyond isolated image generation, the model supports workflows that demand contextual awareness, brand consistency, and iterative refinement, key ingredients for modern digital products.

Web-to-Print

In web-to-print applications, customers expect to customize marketing materials, event invitations, signage, or packaging with minimal friction. By integrating gpt-image-1, developers can offer template-driven personalization where users simply select a theme or enter a few keywords, and receive ready-to-edit visual assets. Combined with CE.SDK’s layout and editing capabilities, this enables a highly interactive experience where generated backgrounds, graphical elements, or themed illustrations can be dynamically placed into editable templates.

Marketing teams rely on high-frequency content creation, often needing visually consistent, campaign-specific assets. gpt-image-1 can assist by automating the generation of background scenes, promotional visuals, and thematic graphics based on campaign briefs. Brands can define style presets aligned with their visual identity, making it easy for marketing teams to produce “on-brand” assets without heavy design overhead. Integrating image generation directly into campaign builders or social scheduling tools amplifies speed without sacrificing quality.

Digital Asset Management (DAM)

Asset libraries often suffer from gaps: missing variants, seasonal versions, or content tailored to different demographics. DAM systems can integrate gpt-image-1 to extend asset catalogs dynamically. Instead of manually commissioning variations, users can generate alternative backgrounds, localize visuals with region-specific elements, or adjust brand visuals for different markets—all from a single master file. With CE.SDK handling structured editing, teams maintain asset consistency while boosting creative flexibility.

E-Commerce

Product visualization remains a huge challenge in e-commerce, especially for smaller retailers. gpt-image-1 can be used to automatically create product lifestyle imagery, context backgrounds, or thematic campaigns without expensive photo shoots. For example, a single shoe photograph can be placed into a generated “urban,” “sporty,” or “luxury” background, customized according to target audiences. When tightly integrated into e-commerce platforms, this enables faster product launches, A/B tested visuals, and localized campaigns at scale.

E-Learning

Educational platforms can harness gpt-image-1 to generate explanatory diagrams, thematic illustrations, or scene-based visual storytelling assets. Instead of relying solely on static stock imagery, teachers, course designers, or even learners themselves can prompt the generation of custom visuals aligned with the curriculum. When embedded into authoring tools, this approach accelerates content creation and enables more engaging, visually enriched learning experiences tailored to specific topics and age groups.

Cost Optimization

While gpt-image-1 opens up impressive creative possibilities, it also introduces new cost considerations that developers and product teams must plan for carefully. Since image generation typically incurs higher API costs than text-based operations, structuring workflows efficiently becomes critical, especially at scale.

Balancing Price, Quality, and Resolution

The cost of generating an image with gpt-image-1 depends significantly on both the requested resolution and the selected quality setting. Higher resolutions like 4096×4096 produce sharper, more detailed results, but they also consume more compute resources-and therefore cost more. For many use cases, especially for previews, lower resolutions such as 1024×1024 or 2048×2048 strike an excellent balance between visual fidelity and API efficiency. Reserving the highest quality settings for final exports or premium workflows can help manage overall spend without compromising user experience.

Image Reuse and Smart Upscaling

One practical cost-saving approach is to design workflows that encourage image reuse. Instead of regenerating similar images for every small variation, applications can create high-quality master images and allow users to crop, edit, or layer additional design elements dynamically. Integrating smart upscaling techniques-for instance, using specialized image enhancement libraries after initial generation-also allows teams to work with smaller base images without sacrificing end-user quality.

Rate Limits and Batching Strategies

Every call to gpt-image-1 counts toward your usage quota, and OpenAI imposes rate limits depending on account tier. To optimize performance and cost, it’s helpful to batch generation requests thoughtfully where possible-for instance, combining multiple prompts into structured queues or allowing users to preview low-res draft versions before finalizing a high-res render. Building this logic into your app’s generation flow not only controls expenses but also improves perceived responsiveness, an important UX factor for creative applications.

By considering cost optimization as an early design constraint rather than a late-stage patch, developers can build scalable, sustainable creative tools powered by gpt-image-1.

Bonus: Starter Kit Repo

We are currently in the process of integrating the new GPT-4o-powered gpt-image-1 model into CE.SDK. As part of this effort, we are preparing a comprehensive Starter Kit will showcase a complete with CE.SDK integration, real-time prompt input, image generation workflows, and best practices for building an AI-powered creative editor.

Both a public GitHub repository and a live demo will be made available soon. If you want to be notified when the Starter Kit launches, you can subscribe to updates here.

This Starter Kit is designed to help developers move beyond simple image generation into building full creative cycles, where users can generate, edit, refine, and remix visuals seamlessly inside the editor.

FAQs

Choosing to work with gpt-image-1 raises a number of practical and strategic questions. Below, we address the most common topics for teams evaluating the model for integration into creative workflows.

How is `gpt-image-1` different from DALL·E 3?

While DALL·E 3 and gpt-image-1 both translate text prompts into images, the underlying architecture and integration paths are different. gpt-image-1 is built on GPT-4o’s multimodal framework, making it better suited for future conversational and iterative workflows. It also offers support for a wider range of styles, higher resolutions up to 4096×4096 pixels, and is positioned for deeper integration into dynamic user experiences rather than one-off generation tasks.

Can you fine-tune or train `gpt-image-1`?

As of April 2025, OpenAI does not allow fine-tuning of gpt-image-1. The model is optimized for broad creative use cases out of the box. Developers seeking more control typically customize the user-facing prompt engineering or combine outputs with structured editing tools like CE.SDK to achieve brand or project-specific consistency.

Is offline support available?

Currently, gpt-image-1 requires access to OpenAI’s cloud APIs. There is no offline inference mode or local deployment option. Teams requiring strict data residency, offline workflows, or private model hosting should consider hybrid architectures where images are generated securely via backend services and then edited locally using embedded tools like CE.SDK.

What about copyright and licensing?

Images generated by gpt-image-1 can be used commercially according to OpenAI’s usage policies, but developers are encouraged to review the latest terms. Outputs are not directly copyrighted by OpenAI or the user, and responsibility for ensuring compliance with branding, likeness, or content standards typically falls on the developer or platform operator. When deploying generation features to end-users, it is good practice to provide clear terms of use and, if needed, additional moderation or review layers.

By addressing these considerations early, teams can integrate gpt-image-1 more effectively and responsibly into creative products and workflows.

Conclusion

gpt-image-1 offers developers a significant opportunity to rethink what image generation can mean inside creative applications. It is not simply a tool for producing pictures on command, but a foundation for building interactive, iterative design workflows where users stay in control of the creative process. When combined with CE.SDK, it becomes even easier to move from static outputs to living, editable canvases that support real-world design needs. As we continue to integrate GPT-4o capabilities, the next wave of creative tooling will be about more than prompting images-it will be about shaping truly collaborative creative environments. Now is the time to start experimenting, iterating, and reimagining the user experience around this new generation of multimodal AI.

How OpenAI's Upcoming GPT-4o Image Generation API Will Change Creative Workflows

Jan — Mon, 14 Apr 2025 10:51:53 GMT

If you’ve been working with image-generation APIs over the past year, you’ve probably gotten used to a certain flow: send a prompt, wait a few seconds, and get a flat image back. It’s a one-shot deal. Useful? Definitely. But not exactly interactive. That’s what will change with OpenAI’s upcoming GPT-4o image-generation capabilities.
IMG.LY, which recently released a suite of AI features for its design editor, is eagerly awaiting the release to expand how users can interact with AI-driven creativity even further.

Update: AI-first Visual Editing

A day after the release of the gpt-image-1 API, we put the UX principles outlined in this post into practice and integrated it into CreativeEditor SDK. Users can now generate images, create variants and use the canvas to compose visual prompts with our design editor. See it in action:

Open AI Editor Demo Page

GPT-4o: Beyond the Prompt-to-Image Pipeline

GPT-4o isn’t just another version of DALL·E. It represents a shift in how developers will integrate AI into creative applications. While DALL·E 3 is powerful it is also somewhat siloed (you send a prompt, you get an image), GPT-4o looks like it will be part of a much more dynamic, conversational model one that accepts both text and image inputs, and could soon generate visual content in context, on the fly, and as part of a back-and-forth user interaction.

If you’ve used ChatGPT recently, you’ve already seen glimpses of this. You can drop an image into the chat, ask GPT to describe or edit it, and get a response that feels fluid and visual. Developers should expect the API version to follow a similar pattern. It likely won’t just be a /generate-image endpoint. Instead, we may be looking at an extension of the chat/completions endpoint that handles multimodal messages. That changes the way you integrate this capability into your application. Rather than simply placing an image generation step in your pipeline, you will have to build your app’s UX around this new user flow. This comes with its own set of unique challenges.

Rethinking the Interface: Prompting as a Conversation

So what does this mean if you’re planning to integrate multi-modal image generation into your own product? For starters, you’ll probably need to rethink how users initiate and refine prompts. In the DALL·E flow, you might offer a text box with a few style dropdowns and call it a day. But in a GPT-4o world, your UI needs to support image inputs, persistent context, and dynamic editing, image gen becomes more like a conversation than a command.

This is where the rubber meets the road. The tools that will benefit most from GPT-4o aren’t static generators but interactive editors. Think collaborative design apps, video editors with generative overlays, or product customizers that let users sketch or upload a photo and then iterate with AI. Put differently, the model output isn’t the endpoint but rather a checkpoint in the creation process.

A Typical Iteration Cycle in a Multimodal Workflow

Here’s a rough sketch of a workflow we might be seeing more of: The user starts with a prompt and an image, maybe a rough sketch or collage created inside an editor, a product photo, or a UI frame. GPT-4o returns a generated image based on that input. The user then edits or annotates the result, maybe adds new prompt text for refinement, and resubmits that combination to further develop the output. This cycle might loop several times: generate, tweak, refine, regenerate.

That’s a fundamentally different interaction model from past AI tooling. It’s less about one-off generation and more about a guided creative journey, where the user is in dialogue with the model. The result: better alignment with the original intent, more control, and more usable creative outputs.

There is an additional, more subjective benefit to this kind of workflow: it gives the user a sense of autonomy again; they are back in the driver’s seat and less at the whim of an inscrutable machine. In many contexts, that makes a difference. Most notably, as we discussed in our white paper on print personalization, the psychological benefit of personalization lies to a large extent in the investment, the sense of ownership that comes about when you create something. “Make it yours” is the common tagline attached to personalization campaigns in e-commerce. That only works if the user exerts more control over the output than iterating over a set of prompts.

The most pithy encapsulation of this paradigm that I have heard is Humans on top, AI on tap.

Persistent Elements and Visual Consistency

One particularly interesting frontier here is character and object persistence. If a user defines a character early in the workflow, either via prompt, image, or a combination, they’ll increasingly expect that character to appear consistently across assets. Think of it as visual continuity, whether you’re generating scenes in a story, slides in a deck, or frames in a video.

If the user of a creative marketing cloud creates a campaign avatar or mascot, that character needs to be consistent within and across campaigns.

Being able to reference earlier outputs, prompts, or style cues gives the user control over not just individual assets but the whole arc of the design narrative. GPT-4o’s ability to maintain that continuity is a game-changer for workflows that involve storytelling, brand identity, or serialized design work.

What to Expect from the API

Technically, if GPT-4o follows OpenAI’s recent design philosophy, you can expect a JSON-based API with a messages array, where content can include both text and image_url types. The output will likely be returned either as an image URL hosted by OpenAI or as base64-encoded image data, depending on the format you request.

That structure plays nicely with modern JavaScript front-end frameworks. React, Svelte, and Vue are all well-suited to async generation flows with visual previews. If you’re already using tools like Zustand or Jotai for local state or something like tRPC or GraphQL for structured calls, you’re in a good position to layer GPT-4o in without breaking the flow.

Trade-offs and Technical Considerations

There are trade-offs, of course. GPT-4o will probably cost more per call than a standard DALL·E 2 or 3 generation. Its latency is still an open question, and the multimodal input support will likely require more thoughtful UX decisions. What happens when a user drops an image and wants to undo just part of the generation? Where do you store prompt context for edits? How do you communicate what’s editable and what’s not?

This is where design and engineering need to work together. You’ll want to build an interface that makes AI feel like a creative partner, not just a backend service. That might mean giving users a visual prompt history or allowing partial re-generations of specific canvas elements. You’ll need sensible fallback states. What happens when generation fails or the result isn’t what the user wanted?

Where IMG.LY’s CE.SDK Fits In

We have already given the questions raised above some serious thought, and most of the complexities introduced by this new workflow are the table stakes for the Creative Editor. So, if you’ve already integrated IMG.LY’s CE.SDK, we have taken care of most of these problems, and you can seamlessly integrate with any AI model. We are actively working on an off-the-shelf integration of the GPT-4o image model once its public API launches.

In general, you can treat GPT-4o’s image outputs as just another layer in the editing canvas, positioned, styled, cropped, and ultimately editable in the same environment as everything else. That’s the real power of multimodal workflows: not just generating but integrating. And once GPT-4o’s API goes live, you’ll want your infrastructure ready to slot it in with minimal friction.

The Loop: Prompt, Generate, Refine

The era of single-shot generation is winding down. What’s coming next is a loop: edit, prompt, generate, refine, repeat. And this loop doesn’t just belong in the backend, it needs to live in the UI, in a way that invites user input, creativity, and correction.

We’ll be publishing more on how this integrates into IMG.LY’s upcoming AI workflows soon. Expect tools that don’t just generate visuals but help teams and individuals work through ideas in real time. Because especially as AI gets more potent, it needs humans on top.

3,000+ creative professionals gain early access to new features and updates—don’t miss out, and subscribe to our newsletter.

Top 5 Generative AI APIs for Creative Apps in 2025: A Developer’s Guide (GPT-4o, Gemini, Firefly, and More)

Jan — Mon, 14 Apr 2025 07:57:51 GMT

If you’re working on creative tooling right now, anything from a lightweight design editor to a marketing automation suite, you’re probably already thinking about or actively working on bringing image generation into the mix. The tech is here, expectations are rising, and if your users can’t type a prompt and get a visual back in seconds, your app might feel like it’s lagging behind.

But choosing which AI model to integrate, and how, isn’t all that straightforward. There’s a growing ecosystem of APIs out there, and they don’t all behave the same way, some are designed for open-ended creativity, others for structured workflows. Some offer pixel-perfect fidelity with fine control, others lean toward rapid ideation. And Importantly in our content not all of them are equally accessible to developers.

This is a guide to help you make sense of it all. What models are available, how do they differ, and what should you consider when embedding them into your product. This isn’t supposed to be a hype piece or a leaderboard, just a clear-eyed look at what’s out there and what’s coming.

OpenAI GPT-4o

GPT-4o is OpenAI’s next-gen multimodal model, currently only available inside ChatGPT. It can take both text and images as input and is capable of generating image outputs in context.

The potential upside is significant. With GPT-4o, you may soon be able to create deeply interactive creative tools where users chat, sketch, and prompt all within a single UI. It’s likely to support richer input types and more natural iteration flows.

The main downside is availability. There’s no API yet, so you can’t build on it directly. It also remains to be seen how OpenAI will expose generation tools—whether through a dedicated endpoint or via the chat interface.

GPT-4o is right for you if you’re planning ahead and want to design for a future where multimodal interaction is the norm. It’s not something you can use today, but it should inform how you architect your UI and prompt handling.

OpenAI DALL·E 3

DALL·E 3 is OpenAI’s current image generation API, available via both the platform and ChatGPT. It translates text prompts into images and is known for interpreting prompts accurately and producing clean, useful visuals.

Its strengths are clarity, commercial readiness, and reliability. It’s easy to use and integrates well into frontend flows that involve text-to-image generation.

However, it lacks features like inpainting, style tuning, or detailed layout control. You also don’t get deep iteration features—each image is a new generation.

DALL·E 3 is a good fit if you want high-quality results from text prompts with minimal complexity. It’s especially useful for marketing visuals, content automation, and simple design tools.

Google Gemini (Imagen)

Gemini, powered by Google’s Imagen models, is available via fal.ai, Makersuite, Vertex AI. It supports not only text prompts, but also sketches and inpainting, making it one of the more flexible APIs for creative work.

Its big advantage is control. You can use sketches to guide composition and make visual edits to generated outputs. That makes it ideal for iterative design processes.

The downside is that it can be tricky to navigate Google’s ecosystem. Access and feature sets can change quickly, and the integration overhead is higher than OpenAI.

Gemini is right for you if your product needs image refinement, visual grounding, or sketch-to-image workflows. It fits e-commerce editors, mockup tools, and design collaboration features.

Adobe Firefly

Firefly is Adobe’s generative image model, integrated tightly into Creative Cloud. It stands out for its licensing model—images are trained on Adobe Stock, meaning they’re cleared for commercial use.

The biggest strength here is trust and integration. Designers already using Photoshop or Illustrator can use Firefly to generate content directly in their layers and work non-destructively.

The drawback is API access. There is no public endpoint for Firefly yet, and its features are embedded in Adobe’s own ecosystem.

Firefly is a strong option if you’re building for agencies, brand teams, or other users with high expectations around copyright and integration with existing Adobe workflows.

Stability AI (SDXL)

Stability AI offers an open-source model suite, with SDXL as the flagship for high-resolution image generation. It supports both text and image inputs and can be run locally or hosted via services like Replicate.

Its biggest advantage is flexibility. You can fine-tune models, build custom workflows, or even run inference offline. It’s ideal for teams that want full control.

The challenge is quality consistency. Compared to closed models like DALL·E, SDXL may require more tuning, and prompt engineering matters more. Hosting and scaling also require more effort.

SDXL is right for you if you need an open, customizable system that fits into a broader pipeline. It’s a solid choice for research tools, OSS projects, and privacy-conscious applications.

Midjourney

Midjourney is a proprietary model with a focus on aesthetic, stylized image generation. It runs exclusively via Discord and is popular for its distinctive look and community-driven prompts.

Its upside is the quality of its visuals, especially for stylized scenes or concept art. Designers often use it as an ideation tool.

The limitation is integration. There’s no API, no SDK, and limited ways to embed it in your own product beyond scraping or bots.

Midjourney is best used as an inspiration engine. If your workflow includes moodboarding or creative brainstorming, it can supplement—but not power—your product.

Hugging Face

Hugging Face is a hub for open models, offering hosted APIs for SDXL variants, Playground v2, and other creative generation tools.

The main benefit is diversity. You can try multiple models, experiment with variations, and deploy quickly using their hosted inference endpoints.

That said, it’s not always ready for production. Some models lack documentation or support, and you may need to piece together features.

Hugging Face is a great choice for experimental projects, prototyping, or if you want to stay vendor-neutral and build your own stack.

Runway Gen-2 and Leonardo.Ai

Runway and Leonardo are rising players at the edge of AI and media. Runway’s Gen-2 supports text-to-video and animated image generation, while Leonardo focuses on style-consistent 2D asset generation.

These platforms bring specialization. Runway is tailored to video and cinematic scenes, while Leonardo offers structured design features for asset creators.

They’re less open from a dev perspective. APIs are limited, and integration support is still maturing.

Use these tools if your use case leans into video, motion, or asset generation for games and content libraries. They’re best when you’re not looking to build your own editor, but to enhance creative capacity.

Quick Comparison

Model/API	Input	Output	Control	API Access	Best For
GPT-4o (OpenAI)	text, image (chat)	image (likely)	medium-high	not yet	assistants, multimodal UIs
DALL·E 3	text	image	medium	yes	content tools, illustrations
Gemini (Google)	text, sketch	image	high	yes	e-commerce, product mockups
Firefly (Adobe)	text	image, layers	very high	no	professional design tools
SDXL	text, image	image	high	yes	custom tools, OSS projects
Midjourney	text	image	very high	no	stylized inspiration
Hugging Face	text, image	image	medium-high	yes	experimentation, open models
Runway Gen-2	text	video/image	medium	yes	motion design, AI video
Leonardo.Ai	text	image	high	limited	game assets, style templates

Conclusion

If you’re building for creative users, especially those used to real-time feedback and control, then how you wrap these APIs into your workflow matters more than which model you use. It’s not just about generating images. It’s about how you let users prompt, refine, iterate, and remix inside your canvas.

That’s the opportunity here. Not just plugging in a model, but designing a loop where generation feels native to creation. The APIs are improving fast. The real challenge, and the real product value, is in how you build around them.

3,000+ creative professionals gain early access to new features and updates—don’t miss out, and subscribe to our newsletter.

Creative Workflows – IMG.LY Blog

AI Design Agents and Creative Automation: How to Ship a Full Campaign Without a Designer

The AI Marketing Stack Has a Design-Shaped Hole in It

What an AI Design Agent Actually Changes

How to Run a Campaign Production Session with CoDesign

This Is What Closing the Loop Actually Looks Like

How to Embed an Editable InDesign Template in Your Website

Why Editing InDesign Files in the Browser Matters Now

The Current Landscape: What’s Possible (and What Isn’t) with InDesign on the Web

Beyond Adobe: Existing Alternatives

Where IMG.LY’s CE.SDK Fits In

Introducing the CE.SDK InDesign Importer

A Stand-Alone Module, Built for Integration

How It Fits into the CE.SDK Ecosystem

What the Importer Delivers

Real-World Use Cases & Workflows

Web-to-Print Platforms

Brand Template Portals

Creative Automation Systems

Client Collaboration

CE.SDK vs. Traditional InDesign Server & Other Alternatives

Conclusion – A New Era for InDesign Workflows

Frequently Asked Questions

Can I edit an InDesign file directly in a browser?

What file formats does the importer support?

Does it require Adobe InDesign Server?

Is CMYK color supported?

Can I automate bulk imports?

Do imported InDesign templates remain editable for non-designers?

What is Visual Prompting?

A New Paradigm for Creative AI, Built by IMG.LY

Problem: the Chat Disconnect

What Is Visual Prompting?

How Visual Prompting Works in CE.SDK

Who Is Visual Prompting For?

Built for This: Multimodal Models and CE.SDK’s Plugin System

Explore It Yourself

OpenAI GPT-4o Image Generation (gpt-image-1) API: A Complete Guide for Creative Workflows for 2025

Update: AI-first Visual Editing

Introduction

Overview of gpt-image-1

Model Architecture and Capabilities

Parameter Control

Style and Use Case Alignment

Limitations and Future Direction

API Setup and Usage

2.1 Get Access

2.2 First Image Generation (Node.js Example)

Integrating with CE.SDK

Embedding Image Generation in a Creative Editing Workflow

Switching Between Manual Edits and AI-Powered Enhancements

Prompt Engineering Tips

Writing for Visual Intent

Leveraging Artistic Language

Creating Consistency in Branded Outputs

Real-World Use Cases

Web-to-Print

Social Media Marketing and MarTech

Digital Asset Management (DAM)

E-Commerce

E-Learning

Cost Optimization

Balancing Price, Quality, and Resolution

Image Reuse and Smart Upscaling

Rate Limits and Batching Strategies

Bonus: Starter Kit Repo

FAQs

How is gpt-image-1 different from DALL·E 3?

Can you fine-tune or train gpt-image-1?

Is offline support available?

What about copyright and licensing?

Conclusion

How OpenAI's Upcoming GPT-4o Image Generation API Will Change Creative Workflows

Update: AI-first Visual Editing

GPT-4o: Beyond the Prompt-to-Image Pipeline

Rethinking the Interface: Prompting as a Conversation

A Typical Iteration Cycle in a Multimodal Workflow

Persistent Elements and Visual Consistency

What to Expect from the API

Trade-offs and Technical Considerations

Overview of `gpt-image-1`

How is `gpt-image-1` different from DALL·E 3?

Can you fine-tune or train `gpt-image-1`?