The email landed in my inbox at 4 PM on a Friday—naturally. The client needed twelve custom hero images for their new SaaS platform’s landing pages. Each image had to visualize abstract concepts like “seamless integration,” “enterprise security,” and “real-time collaboration.” The budget? About $200 total. The deadline? Monday morning.
Two years ago, this would have been impossible. I’d have spent the weekend cobbling together generic stock photos, probably ending up with the same handshake-over-a-laptop imagery that plagues half the B2B websites on the internet. Or I’d have had to deliver the unfortunate news that custom illustration work at that budget simply wasn’t feasible.
Instead, I spent about three hours that evening with AI image generation tools and delivered exactly what the client needed—unique visuals that actually matched their brand aesthetic and communicated the concepts they wanted to convey. Not perfect, mind you. I’ll get into the limitations. But genuinely useful, distinctly theirs, and miles better than the stock photo alternative.
That experience crystallized something I’d been gradually realizing: AI image generation had crossed from interesting novelty to practical tool. Not for everything—I still commission illustrators and photographers for plenty of projects—but for a significant and growing portion of web design work, these tools have become indispensable.
After two years of intensive use across dozens of web projects, I’ve developed strong opinions about which tools work best, how to use them effectively, and where they still fall short. This is my attempt to share what I’ve learned.
The Stock Photo Problem That Needed Solving
Before diving into specific tools, let’s acknowledge the problem they solve—because it’s a real one that has plagued web design for decades.
Stock photography was supposed to democratize visual content. Anyone could access professional imagery without hiring photographers. And for a while, it worked. But success created its own problems.
The same photos appeared everywhere. You’d recognize specific models and scenes across competing websites. That confident woman pointing at a whiteboard? She’s the face of approximately twelve thousand marketing agencies. The diverse team high-fiving in a glass-walled conference room? They’re apparently employed by every tech startup simultaneously.
Beyond the familiarity problem, stock photos often miss the mark on specificity. Your brand is playful but professional, uses a particular color palette, and needs imagery that conveys concepts unique to your product. Good luck finding stock photos that match those requirements. You end up compromising—settling for images that are close enough, adjusting your vision to fit available inventory.
Custom photography and illustration solve these problems but introduce others—primarily cost and timeline. A custom photo shoot for web imagery typically starts around $2,000 and goes up from there. Custom illustration might be $200-500 per image depending on complexity and the illustrator’s rates. For a website with fifty images, the math becomes prohibitive for many projects.
AI image generation tools slot into this gap. They can produce unique, customized imagery at a fraction of traditional costs, with turnaround measured in minutes rather than weeks. Not as good as skilled human work at its best—but often better than generic stock, and accessible for projects where custom work simply isn’t in the budget.
Understanding What These Tools Actually Do
AI image generators work by learning patterns from massive datasets of existing images, then producing new images based on text descriptions (and sometimes other inputs like reference images or sketches).
You provide a prompt describing what you want—”minimalist office workspace with natural light, soft blue color palette, clean desk with single plant”—and the system generates an image matching that description. The output has never existed before; it’s synthesized based on learned patterns rather than retrieved from a database.
Different tools use different underlying models and approaches, which is why they produce noticeably different results. Some excel at photorealistic imagery; others are better for illustration. Some handle text in images well; others struggle with it. Some offer more control over the generation process; others prioritize simplicity.
Understanding these differences matters because choosing the right tool for your specific needs dramatically affects results. Let me walk through the major options.
The Tools That Actually Matter for Web Work
Midjourney: The Quality Leader
If you care most about visual quality and artistic appeal, Midjourney remains the tool to beat. The images it produces have a distinctive aesthetic sophistication that competitors struggle to match—particularly for anything requiring mood, atmosphere, or artistic composition.
Midjourney operates through Discord, which initially felt awkward but has proven manageable. You type prompts in chat channels, and the system generates images in that conversation. The interface takes adjustment if you’re not a Discord user, but it’s hardly a dealbreaker.
What sets Midjourney apart is its ability to produce images that feel intentional rather than assembled. There’s a cohesiveness to the lighting, color relationships, and composition that suggests artistic sensibility rather than technical assembly. For hero images, backgrounds, and any imagery where aesthetic impact matters, it’s my first choice.
Version 6 (the current major release as of this writing) dramatically improved prompt understanding and detail rendering. Hands and faces—historically weak points—are now handled much better, though still not perfectly. The model also handles style consistency better than previous versions, making it more practical for creating image sets with coherent aesthetics.
Strengths:
- Exceptional aesthetic quality
- Strong composition and lighting
- Good style consistency across images
- Active development with regular improvements
- Reasonable pricing for output volume
Limitations:
- Discord interface can feel clunky for production work
- Less control than some alternatives
- Photorealism still sometimes feels “AI-ish”
- Text rendering remains imperfect
- Learning curve for effective prompting
Pricing: Plans start at $10/month for basic usage, with $30/month offering unlimited relaxed generations and $60/month for faster processing. For web design work, the $30 tier typically provides sufficient volume.
Best for: Hero images, atmospheric backgrounds, artistic visuals, any imagery where aesthetic quality is the priority.
DALL-E 3: The Easiest Starting Point
DALL-E 3, accessible through ChatGPT Plus or the API, has become remarkably capable while remaining the most approachable option for newcomers. The integration with ChatGPT means you can have a conversation about what you want rather than struggling to craft perfect prompts.
The practical advantage is significant. You can say something like “I need an image for a blog post about remote team communication—something that shows connection despite physical distance, professional but warm, illustration style rather than photo” and get genuinely useful results. If the first attempt misses the mark, you can explain what to adjust in natural language.
For web designers working with clients who want involvement in the image creation process, DALL-E 3’s conversational interface is valuable. Clients can describe what they want without learning prompt syntax; you can iterate together in real-time.
Image quality has improved substantially with version 3. Coherence is strong—images make visual sense in ways earlier versions struggled with. Text rendering is notably better than most competitors, making it useful for images that incorporate words.
Strengths:
- Natural language interface through ChatGPT
- Excellent prompt understanding
- Good text rendering
- Easy iteration through conversation
- Strong coherence and logic in outputs
Limitations:
- Less artistic refinement than Midjourney
- Requires ChatGPT Plus subscription ($20/month) or API costs
- Generation limits in ChatGPT context
- Fewer style controls than specialized tools
- Output can look somewhat generic
Pricing: Included with ChatGPT Plus at $20/month, or pay-per-image through API (pricing varies by resolution).
Best for: Blog images, concept visualization, quick iterations, client collaboration, images with text elements.
Adobe Firefly: The Professional Integration Play
Adobe Firefly matters primarily because of integration. If your workflow lives in Adobe Creative Cloud—and for many web designers, it does—Firefly’s presence inside Photoshop, Illustrator, and Express creates seamless possibilities.
The Generative Fill feature in Photoshop exemplifies this well. You can select an area of an image, describe what you want there, and Firefly generates options that blend with the existing image. For extending backgrounds, adding elements, or modifying existing imagery, this integration is genuinely useful.
Standalone Firefly generation is capable but not best-in-class for quality. Where Midjourney produces images I’d use as-is, Firefly outputs more often need refinement in Photoshop. But that’s the point—Firefly is designed as part of a larger workflow rather than a standalone solution.
Adobe’s positioning on training data also matters for commercial work. They’ve committed to training only on licensed content, Adobe Stock, and public domain materials. While the legal landscape for AI-generated images remains somewhat uncertain, Firefly’s approach reduces some concerns for commercial usage.
Strengths:
- Native integration with Adobe apps
- Generative Fill and Expand are genuinely useful
- Commercial licensing clarity
- Familiar interface for Adobe users
- Good for modifying existing images
Limitations:
- Standalone quality below top competitors
- Requires Creative Cloud subscription
- Less stylistic range than alternatives
- Generation limits even with subscription
- Features evolving rapidly (documentation sometimes lags)
Pricing: Included with most Creative Cloud plans; standalone starts at $4.99/month for 100 credits.
Best for: Extending and modifying existing images, integrated Adobe workflows, users prioritizing licensing clarity.
Leonardo AI: The Control Enthusiast’s Choice
Leonardo AI offers more granular control than most alternatives, making it appealing for users who want to fine-tune their outputs rather than accepting what the AI initially produces.
The platform provides various models optimized for different styles—some better for photography, others for various illustration approaches. You can adjust parameters like contrast, clarity, and color saturation. The Canvas feature enables inpainting and outpainting with good results.
What’s particularly useful for web work is the ability to train custom models on your own imagery. If you have a specific illustration style you want to replicate, or brand imagery you want to extend, Leonardo’s fine-tuning capabilities allow more consistency than starting fresh with each generation.
The free tier is genuinely usable—150 daily tokens allow real experimentation before committing financially. This makes Leonardo a good platform for learning AI image generation without immediate investment.
Strengths:
- Granular control over outputs
- Multiple specialized models
- Custom model training
- Useful free tier
- Strong inpainting/outpainting
Limitations:
- Interface complexity can overwhelm newcomers
- Quality varies across different models
- Learning curve for advanced features
- Token system requires monitoring
Pricing: Free tier with 150 daily tokens; paid plans start at $12/month for substantially more.
Best for: Users wanting fine control, consistent style development, extending existing brand imagery.
Ideogram: The Text Rendering Champion
If your images need to include text—and many web images do—Ideogram deserves attention. It handles typography dramatically better than alternatives, producing legible text that actually looks designed rather than garbled.
For social media graphics, quote images, title cards, and any imagery where words are central, this capability matters enormously. Other tools can produce beautiful images ruined by mangled text. Ideogram produces images where the text actually works.
The trade-off is that overall artistic quality falls somewhat below Midjourney. For images where text isn’t involved, other tools may serve better. But for text-inclusive images, Ideogram’s advantage is substantial.
Strengths:
- Exceptional text rendering
- Good for graphics with typography
- Solid overall quality
- Improving rapidly
- Reasonable pricing
Limitations:
- Artistic quality below top tier
- Newer platform, still maturing
- Smaller community and fewer resources
- Style range somewhat limited
Pricing: Free tier available; premium plans start at $7/month.
Best for: Images containing text, quote graphics, title cards, social media imagery with captions.
Stable Diffusion: The Open-Source Option
Stable Diffusion takes a fundamentally different approach—it’s open source, meaning you can run it locally on your own hardware rather than paying for cloud generation.
For users with capable GPUs (typically NVIDIA cards with 8GB+ VRAM), this enables unlimited generation without per-image costs. The control available through various interfaces (Automatic1111, ComfyUI, and others) exceeds cloud platforms substantially.
The ecosystem of custom models is remarkable. Want a model specifically trained on architectural photography? Product visualization? Anime style? Vintage illustration? The community has likely created and shared it. This specificity enables results other platforms can’t match for particular use cases.
The catch is complexity. Setting up Stable Diffusion locally requires technical comfort—installing software, managing dependencies, understanding model formats. Cloud services exist that run Stable Diffusion for you, but these reduce the cost advantage while maintaining the complexity disadvantage.
For technically inclined users with specific needs and high volume requirements, Stable Diffusion is compelling. For most web designers wanting practical results without infrastructure management, cloud tools are more appropriate.
Strengths:
- No per-image costs (local installation)
- Maximum control and customization
- Vast ecosystem of specialized models
- Open source with transparent operation
- No content restrictions (beyond what you choose)
Limitations:
- Significant technical setup required
- Requires capable hardware
- More complex than cloud alternatives
- Quality depends heavily on configuration
- Support is community-based
Pricing: Free (local) plus hardware costs; cloud services vary.
Best for: High-volume users, technical users, specialized style needs, users requiring maximum control.
Canva: The Non-Designer’s Friend
Canva has integrated AI image generation into their design platform, making it accessible to users who wouldn’t otherwise encounter these tools. For small businesses and non-designers creating their own web graphics, this matters.
The generation quality is acceptable rather than exceptional—clearly below Midjourney or DALL-E 3. But the integration with Canva’s template-based design system means users can generate images and immediately incorporate them into layouts, social graphics, and presentations without switching tools.
For web designers, Canva’s AI features are unlikely to replace dedicated generators. For clients who do their own social media graphics or simple updates, Canva’s approach is accessible in ways standalone tools aren’t.
Strengths:
- Integrated with design tools
- Accessible for non-designers
- Easy iteration within familiar interface
- Good for social media graphics
- Template integration
Limitations:
- Quality below dedicated tools
- Less control than alternatives
- Better for simple graphics than complex imagery
- Credit system limits volume
Pricing: Included in Canva Pro ($15/month) with monthly credit limits.
Best for: Non-designers, social media graphics, simple web imagery, users already in Canva.
Practical Considerations for Web Use
Choosing between tools matters, but several practical considerations affect all AI image generation for web work.
Resolution and File Quality
Most AI generators produce images at moderate resolutions—typically between 1024 and 2048 pixels on the longest edge. This works fine for blog images, social graphics, and many web uses. For full-width hero images on high-resolution displays, you may need to upscale.
Upscaling tools like Topaz Gigapixel AI or the upscaling features within platforms like Leonardo can increase resolution effectively. AI upscalers handle AI-generated images particularly well because they’re working with similar technology.
Plan your workflow: generate at native resolution, upscale if needed, then optimize for web delivery.
Commercial Licensing
The legal landscape for AI-generated images remains somewhat unsettled. Different platforms take different positions:
- Midjourney: Commercial rights granted for paid subscribers
- DALL-E: Users own outputs and can use commercially
- Adobe Firefly: Commercial license included, trained on licensed content
- Leonardo: Commercial rights vary by plan level
- Stable Diffusion: No platform restrictions (local use); model-specific licenses vary
For most commercial web work, the major paid platforms grant sufficient rights. But read terms of service carefully, particularly for high-visibility or sensitive applications. The legal framework is still developing, and some uncertainty remains.
Style Consistency Across Images
Creating cohesive image sets—consistent style across multiple images for a website—remains challenging. Even with identical prompts, AI generators produce variation. Different subjects may render with different styles.
Strategies that help:
Save and reuse effective prompts. When you find prompt formulations that produce the look you want, document them precisely.
Use style references. Some tools (particularly Midjourney) allow reference images that guide style. A single successful image can anchor the style for subsequent generations.
Plan for post-processing. Color grading, filter application, and treatment adjustment in Photoshop or similar tools can unify diverse generations into cohesive sets.
Generate more than you need. If you need five images, generate twenty and select the five that work together best.
Avoiding the AI Look
AI-generated images often share tells that mark them as synthetic—a certain smoothness, particular lighting patterns, specific kinds of detail that trained eyes recognize.
For some uses, this doesn’t matter. For others, you want images that don’t immediately read as AI-generated. Strategies include:
Emphasize illustration styles. AI handles illustration and artistic styles more convincingly than photorealism for many subjects.
Add noise and texture. A slight grain applied in post-processing can reduce the synthetic smoothness that marks AI imagery.
Use generated images as starting points. Paint over, composite, or heavily edit AI outputs rather than using them raw.
Avoid subjects where AI struggles. Hands, crowds of people, complex mechanical details, and specific real-world objects often reveal AI limitations.
Prompt Craft Matters
The quality of outputs depends substantially on prompt quality. This is a learnable skill that improves with practice.
Effective prompts typically include:
- Subject: What is the image fundamentally showing?
- Style: Photographic? Illustrated? What artistic approach?
- Mood: Bright and energetic? Calm and minimal? Dark and dramatic?
- Composition: How is the frame organized? What’s the viewpoint?
- Details: Colors, lighting, environment, specific elements to include
Specificity helps. “A woman working on a laptop” produces generic results. “A confident businesswoman in her 40s working on a laptop in a bright minimalist home office, morning light through large windows, professional but relaxed, shot from side angle” produces something more useful.
Most platforms have communities sharing effective prompts. Study what works, adapt to your needs, and build a personal library of formulations that produce results you like.
Real Use Cases for Website Imagery
Abstract tool discussion only goes so far. Here’s how I actually use AI image generation for web projects:
Hero Sections and Headers
Large, atmospheric images for homepage heroes and section backgrounds are ideal AI candidates. These images need to create mood and visual impact without necessarily showing specific real things.
I recently designed a website for an executive coaching firm. Rather than stock photos of handshakes and podiums, I generated images evoking transformation—abstract visualizations of growth, emergence, and change. The results felt unique to the brand rather than pulled from a stock library.
Blog Post Illustrations
Blog content needs images. Lots of them. And for many posts, the content doesn’t suggest obvious photography—how do you photograph a post about “5 Strategies for Reducing Technical Debt”?
AI generation allows conceptual visualization that would otherwise require illustration budget. Abstract representations of concepts, metaphorical imagery, and stylized visuals that enhance content without requiring literal interpretation.
For a client’s tech blog, I established a consistent illustration style—flat design with specific color relationships—and now generate appropriate images for each post in minutes. The visual consistency across posts creates brand coherence that scattered stock photos couldn’t achieve.
Product Mockups and Lifestyle Context
E-commerce and product websites often need lifestyle imagery showing products in context. Photographing products in every possible setting is expensive.
AI can generate lifestyle backgrounds that integrate with product photography. Generate a beautiful kitchen scene, composite your product into it, adjust lighting to match—the result shows product usage context without staging physical shoots.
This approach requires Photoshop skills for convincing integration, but the combination of AI backgrounds and traditional product photography works well.
Backgrounds and Textures
Subtle background textures, gradients, and patterns support web design without demanding attention. AI generates these efficiently—abstract patterns, textured surfaces, gradient fields that complement foreground content.
I’ve generated dozens of background variations for sites where subtle visual texture matters but specific imagery would distract. The ability to specify colors, mood, and density while getting unique results beats stock texture libraries.
Social Media Graphics
Website launches generate social media needs—announcement images, share graphics, promotional visuals. The volume required and the ephemeral nature of social content makes AI generation practical.
For clients managing their own social presence, I create templates in Canva or Figma with AI-generated graphic elements. They can quickly produce on-brand social content without designing from scratch each time.
Custom Icons and UI Elements
While I wouldn’t use AI for primary icon sets—consistency and precision matter too much—AI can generate custom illustrated elements for specific uses. A decorative illustration for an empty state, a unique visual for an error page, a custom graphic for a specific feature callout.
These one-off elements benefit from AI’s ability to match existing styles while creating something unique.
Where AI Image Generation Falls Short
Honest assessment requires acknowledging limitations. These tools don’t solve everything, and knowing when not to use them matters as much as knowing when to use them.
Specific Real-World Accuracy
Need an accurate image of a particular place, product, or person? AI won’t help. Generated images synthesize from patterns; they can’t represent specific real things accurately.
For product photography, architectural visualization, or any imagery that must accurately represent reality, traditional photography remains essential. AI generates plausible alternatives, not accurate representations.
Detailed Control
Sometimes you need precise control over every element—specific object placement, exact proportions, particular details. AI generation offers guidance rather than control. You can influence outputs but not dictate them precisely.
For design work requiring pixel-perfect specification, traditional creation methods—illustration, 3D rendering, photography—provide necessary control.
Human Details
Despite improvements, AI still struggles with hands, complex poses, crowds, and detailed human features. Results have improved dramatically, but errors remain common enough that human imagery requires careful review.
For imagery where people are central and must look fully natural, AI may require extensive cherry-picking or may simply not work.
Brand Photography
Nothing replaces actual photos of real employees, real offices, real customers for authentic brand representation. AI can supplement brand photography but not substitute for it when authenticity matters.
Websites aiming to build genuine human connection—team pages, about sections, culture content—need real photography of real people.
Complex Narrative Scenes
Images telling complex stories with multiple interacting elements often challenge AI. The relationship between elements, narrative coherence, and logical scene construction can go wrong in ways that feel subtly but significantly off.
For imagery requiring clear storytelling, simple scenes work better than complex ones.
Ethical Considerations Worth Taking Seriously
AI image generation raises legitimate ethical questions that responsible practitioners should consider.
Training Data Concerns
AI image generators learn from existing images, and questions about consent, compensation, and attribution for artists whose work trained these models are valid. Different platforms take different positions—Adobe emphasizes licensed training data; open-source models often trained on scraped content with unclear rights.
I don’t have a clean answer here. The technology exists and provides genuine value. The concerns about artist compensation and consent are real. Using platforms that respect creator rights where possible seems responsible, while acknowledging the broader questions remain unresolved.
Job Displacement
These tools affect illustrators, photographers, and designers—people whose skills I respect and often work with. The impact isn’t evenly distributed; lower-end commercial illustration has been most affected.
My perspective: AI handles work that often wasn’t reaching skilled professionals anyway—work that went to cheap stock or simply didn’t happen due to budget constraints. At the same time, it genuinely reduces some professional work that would have been paid.
Responsible use means continuing to hire skilled professionals for work that benefits from human expertise while using AI for applications where it’s genuinely the right tool.
Transparency
Should websites disclose AI-generated imagery? No current requirement exists, and I don’t believe every stock photo requires disclosure. But for images that might be assumed to represent real things—team photos, testimonials, case studies—using AI imagery without disclosure feels deceptive.
Context matters. AI-generated decorative illustrations don’t require disclosure. AI-generated images pretending to show real people or real situations do.
Environmental Impact
AI image generation requires substantial computing resources. Training these models consumed enormous energy; running them continues to consume significant resources.
For individual images, the impact is small. At scale, it’s worth considering. I don’t think environmental concerns should preclude use, but awareness is appropriate.
Getting Started: Practical Recommendations
If you’re approaching AI image generation for web work fresh, here’s how I’d suggest beginning:
Start with DALL-E 3 through ChatGPT
The conversational interface lowers barriers. You can describe what you want naturally, iterate through discussion, and develop intuition for what works without learning complex prompt syntax. The $20/month ChatGPT Plus subscription includes enough generation volume to learn effectively.
Graduate to Midjourney for Quality
Once you understand basics, Midjourney’s quality advantage matters for most web work. The Discord interface takes adjustment, but the output quality justifies learning it. Start with the $10/month plan; upgrade if volume demands.
Develop a Prompt Library
When you find prompts that produce good results, save them. Build a personal reference of formulations that work for different purposes. This compounds—your efficiency improves as your library grows.
Integrate with Your Design Workflow
Figure out how AI generation fits your existing process. Where does it save time? Where does it create new steps? How do you move images from generation to final implementation efficiently?
Know When Not to Use It
Develop judgment about when AI helps and when it doesn’t. Some projects benefit enormously; others need traditional approaches. The goal is effective outcomes, not maximum AI usage.
Looking Forward
AI image generation capabilities have improved faster than anyone expected, and that trajectory seems likely to continue. Resolution will increase. Realism will improve. Control will become more precise.
Video generation—still nascent—will mature, extending similar capabilities to motion content. Integration with design tools will deepen. The line between AI-generated and traditionally created will blur further.
For web designers and developers, this means visual creation barriers continue falling. The ability to create unique, appropriate imagery for any project becomes increasingly accessible. The advantage shifts from access to capability—from who can afford imagery to who can use imagery effectively.
What doesn’t change: the need for strategic thinking about what visuals a website needs, aesthetic judgment about what looks good, and design craft that integrates imagery into effective experiences. These tools expand what’s possible; they don’t substitute for knowing what should be done.
That’s what I keep returning to after two years with these tools. They’ve genuinely changed what I can deliver for clients at various budget levels. They’ve expanded creative possibilities. They’ve made some aspects of web design more efficient and accessible.
But they’re tools. Effective use requires vision, skill, and judgment that the tools themselves don’t provide. Used thoughtfully, they’re remarkable. Used carelessly, they produce forgettable results.
The difference, as always, is the person using them.