Image Generation Best Practices

Image generation prompts are fundamentally different from text prompts. With language models, you're instructing; with image models, you're describing. The best image prompts paint a detailed picture in words — subject, setting, lighting, composition, style, mood, color palette, and technical specifications all matter. Start with the subject: what is the main focus of the image? Be specific. "A dog" is weak; "A golden retriever puppy sitting on a wooden dock, looking at a sunset over a mountain lake" gives the model far more to work with. The subject should be the first thing in your prompt — most image models weight early tokens more heavily. Add composition and perspective: "wide angle shot from low perspective," "close-up portrait with shallow depth of field," "aerial drone view." These cues help the model frame the image properly. Lighting is next: "golden hour sunlight," "soft diffused studio lighting," "neon-lit night scene," "dramatic chiaroscuro." Lighting often makes the difference between a flat image and a striking one. Style modifiers are powerful but must be used carefully. "Photorealistic," "oil painting," "watercolor," "3D render," "cinematic," "anime style" — each shifts the entire aesthetic. For consistency across multiple generations (important for products), pick a style and stick with it. Mixing styles in a single prompt ("photorealistic oil painting") produces unpredictable results. Technical parameters matter too. Resolution ("4K", "8K", "HD"), aspect ratio ("16:9", "1:1", "9:16"), and quality descriptors ("highly detailed," "sharp focus," "intricate textures") help models understand the desired output quality. Negative prompts — specifying what you don't want — can prevent common artifacts: "no blur, no distorted faces, no extra limbs." GreatRouter's prompt enhancement automatically adds these technical cues to short prompts, improving output quality without requiring users to learn prompt engineering.

Not all image models are created equal, and the "best" model depends entirely on your use case. Black Forest Labs' Flux Pro produces stunning photorealistic images with excellent prompt adherence — ideal for marketing materials, social media content, and any application where visual quality is paramount. Flux Schnell delivers 80% of the quality at 5% of the cost — perfect for thumbnails, placeholder images, and high-volume generation. Google's Imagen excels at photorealism and text rendering within images — a capability many image models struggle with. If your images need to include legible text (signs, labels, UI mockups), Imagen is often the best choice. OpenAI's DALL-E produces creative, stylistically diverse outputs and integrates naturally into OpenAI-heavy stacks. For editing tasks — modifying existing images rather than generating from scratch — model requirements change. You need models that support image-to-image workflows with masking, inpainting (filling in removed areas), and outpainting (extending beyond original bounds). GreatRouter's routing automatically selects edit-capable models when it detects editing intent in the prompt. Style consistency across multiple generations is a common challenge. Different models have different aesthetic defaults — Flux images look different from Imagen images, which look different from DALL-E images. If you're building a product where visual consistency matters (brand assets, comic series, product shots), lock in a specific model rather than letting the router choose freely. GreatRouter's model preference settings let you specify preferred models for image generation while still getting the benefits of fallback and health checking. For production deployments, consider resolution requirements. Higher resolution means higher cost and longer generation time. Many use cases don't need 4K — a 1024x1024 image is sufficient for social media, and even 512x512 works for thumbnails. Route resolution-appropriate requests to resolution-appropriate models to optimize cost without sacrificing user experience.

Image generation costs can spiral quickly at scale. At $0.08 per premium image, generating 10,000 images costs $800. At $0.004 per budget image, the same volume costs $40. The 20x cost difference demands intentional routing decisions. Implement tiered generation based on use case. Customer-facing hero images and marketing assets deserve premium models (Flux Pro, Imagen). Inline content illustrations and social media posts can use mid-tier models. Thumbnails, placeholders, and internal tools should use budget models (Flux Schnell). The router handles this automatically when you set per-request optimization preferences. Cache generated images aggressively. Many applications regenerate the same or similar images repeatedly — user avatars, product mockups, background textures. A CDN-cached generated image costs nothing on subsequent requests. Use response metadata (model ID, cost, request ID) to key your cache layer. Batch strategically. Some providers offer batch pricing with significant discounts for bulk generation. If your use case allows for asynchronous generation (generating assets ahead of time rather than on-demand), batching can reduce per-image costs by 30-50%. The trade-off is latency — batched requests may take longer to complete. Consider resolution carefully. Most users on mobile devices can't distinguish 4K from 1080p. Generating at the resolution your users actually see — rather than the maximum the model supports — can reduce costs by 50-80% with no perceptible quality loss. Progressive loading (generate a low-res preview first, then upscale on demand) delivers fast perceived performance while keeping costs low. GreatStudios uses all of these strategies to deliver a full image generation and editing suite at a fraction of the cost of individual provider subscriptions.