# Prompt Images > Prompting techniques for AI image generation and editing models on Replicate. Use when writing prompts for image models or building image generation features. - Skill: `luokai0/prompt-images` (Agent Skill) - Install (CLI): `npx skillmds@latest add luokai0/prompt-images` - Raw SKILL.md: https://api.skillmd.com/api/skills/luokai0/prompt-images/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: luokai0 (https://skillmd.com/u/luokai0) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/luokai0/prompt-images --- # Prompting image models on Replicate Distilled from Replicate's blog posts on prompting image models (2024-2026). Techniques are model-agnostic and focus on transferable principles. For model selection, pricing, and feature comparison, see the [compare-models](../compare-models/SKILL.md) skill. ## Writing prompts ### Use natural language, not keyword lists Write full sentences describing what you want. Modern image models understand grammar and context far better than keyword-stuffed prompts. Good: "A woman standing in a Tokyo alleyway at dusk, neon signs reflecting off wet pavement" Bad: "woman, Tokyo, alleyway, dusk, neon, wet pavement" ### Be specific and unambiguous Name exact colors, materials, lighting setups, camera equipment, and spatial relationships. Vague terms like "make it better" or "artistic" give unpredictable results. Good: "A brutalist concrete building reflected in a perfectly still puddle after rain. A single figure with a red umbrella walks along the edge, the only color in an otherwise monochrome scene. Overcast sky, flat diffused light, tilt-shift lens effect on the edges." Bad: "Cool building with a person near it, rainy day" ### Name subjects directly Use descriptive phrases like "the woman with short black hair" or "the red car." Avoid pronouns, which are often too ambiguous for image models. ### Use long, detailed prompts Most modern models accept thousands of tokens. Long descriptive prompts with clear structure outperform short ones. A prompt with 12+ specific requirements (text on objects, labeled diagrams, color-coded elements, specific materials) can work if each requirement is stated clearly. But be aware: the longer and more complex the prompt, the more likely something will be missed. ### Start simple, then iterate Begin with basic changes. Test small edits first, then build on what works. Most editing models support iterative editing, so take advantage of that. ## Photographic language Modern image models understand camera and photography terminology deeply. Using this vocabulary gives you precise control over the look. ### Camera and lens - Film stocks: Kodak Portra 800, Fuji Velvia 50, Ilford HP5 - Lens characteristics: 50mm Summilux wide open, 85mm f/1.4, 24mm wide-angle - Depth of field: shallow (subject sharp, background blurred), deep (everything in focus) - Shooting techniques: golden hour, blue hour, long exposure, double exposure ### Lighting setups - Rembrandt lighting: classic portrait lighting with a triangle of light on the cheek - Soft diffused studio lighting: crisp highlights and gentle shadows - Rim lighting / backlight: subject outlined with light from behind - Flat diffused light: overcast, even illumination, minimal shadows - Volumetric lighting: visible light beams, fog, haze ### Composition - Rule of thirds, centered composition, symmetry - Wide shot, medium shot, close-up, macro - High angle, low angle, eye level, bird's-eye view - Tilt-shift for miniature effects ## Text rendering Rendering text in images is a common task. These techniques improve accuracy across models. - Wrap desired text in double quotation marks within the prompt: "Design a poster with the title \"BLUE NOTE SESSIONS\" in bold condensed sans-serif" - Stick to readable fonts. Highly stylized text may not work as well. - When editing text in an existing image, use the pattern: "Change 'old text' to 'new text'" - Match text length when possible: big shifts in character count can change layout - Be explicit about preserving font style if it matters - For complex typography (posters, editorial layouts), look for models that treat text as part of the composition rather than stamping it on top - Some models can inpaint text: mask the text region, prompt with new text, and it matches the original font and style ## Style transfer - Name the exact style: "impressionist painting," "1960s pop art," "Sumi-e ink wash" - Reference specific artists or movements for clearer guidance - If a style label doesn't work, describe its key traits: "visible brushstrokes, thick paint texture, rich color depth" - State what should stay the same: "keep the original composition" - When a style is hard to describe in words, some models support example-based editing: provide a before/after pair, then a third image. The model infers the transformation and applies it. - Some models accept style reference images: upload visuals capturing the color palette, texture, composition, and mood you want ## Character consistency Maintaining the same character across multiple generations is one of the hardest challenges in image generation. - Start with a clear reference description: "the woman with short black hair and green eyes wearing a navy blazer" - Say what's changing (setting, activity, style) and what should stay the same (face, expression, clothing) - Use reference images when the model supports them. Some models handle multiple reference images simultaneously for stronger consistency. - Break complex character changes into steps: change outfit first, then change scene - Generate synthetic training data: create many images of a character, pick the best ones, and use them for fine-tuning or as references ## Image editing ### General principles - Specify what to keep: explicitly state what should remain unchanged. Use phrases like "keeping the pose and expression unchanged" or "maintain the original composition." - Choose verbs carefully: "transform" suggests a full rework. Use specific actions like "change the clothes to a blue jacket" or "replace the background with a beach." - Be precise about scope: "Change the background to a beach while keeping the person in the exact same position, maintain identical subject placement, camera angle, framing, and perspective. Only replace the environment around them." ### Object removal - Describe what should fill the space left behind, not just what to remove - Some editing models handle removal cleanly; others leave structural artifacts. If one model struggles, try another. ### Background editing - Describe the new background in detail: lighting, time of day, environment - Specify that the subject should remain in the exact same position with the same lighting ### Perspective and angle changes - These are among the hardest edits. Not all models handle them well. - Some models restrict themselves to the initial composition and struggle with new angles ### Inpainting and outpainting - For inpainting: mask the region to edit, then prompt with what should fill it - Some models have a "magic prompt" or auto-rewrite feature. When this is on, you can focus on describing just the edited region. When it's off, describe the whole scene. - Describing only the masked region makes the model emphasize the prompt more, which can produce better results for targeted edits - ControlNet-style conditioning (edge detection, depth maps) helps preserve structure during generation ## Multi-image and storyboard generation Some models can generate multiple related images in a single prompt. - Ask for "a series," "a set," or specify a grid layout (e.g., "2x2 storyboard grid") - Describe each panel individually with consistent character descriptions - Maintain consistent style and character continuity by repeating exact descriptions - Some models support example-based editing: show a before/after pair for one image, then apply the same transformation to others ## Product photography and commercial work - Specify materials precisely: "brushed steel," "matte aluminum," "kraft paper," "frosted glass" - Describe lighting setup: "soft diffused studio lighting, crisp highlights and gentle shadows" - For brand assets and icons, look for models that produce native SVG output (real editable vector files) - For layouts with branding and copy placement, look for models with strong typography and design composition ## Fine-tuning and LoRAs - Use trigger words from your trained model in every prompt - When combining multiple LoRAs, balance their influence with scale parameters (typically 0.9-1.1) - Generate synthetic training data: generate many images, pick the best, retrain - Use consistent-character workflows to generate training data from a single reference image ## Common pitfalls 1. **Keyword-stuffed prompts**: Modern models respond better to natural language sentences than comma-separated keyword lists. Write like you're describing a scene, not tagging a photo. 2. **Using "transform" when you want a small edit**: "Transform the person into a Viking" may swap the entire identity. Use targeted language: "change her outfit to Viking armor, keeping her face and expression unchanged." 3. **Not specifying what to keep**: When editing, always say what should stay the same. Without explicit instructions, models may change anything. 4. **Negative prompts on models not trained for them**: Some models were not trained with negative prompts. Using them on these models introduces noise rather than removing unwanted elements. Check the model's documentation. 5. **Too-high guidance scale (CFG)**: If images look "burnt" with excessive contrast, lower the guidance scale. Each model has a recommended range. 6. **Expecting real-time knowledge**: No image model has internet access. Some have strong world knowledge baked in from training data, but it's not live. 7. **Short prompts for complex scenes**: Modern models accept thousands of tokens. For complex compositions with many specific requirements, use that capacity. 8. **Ignoring aspect ratio**: Most models have specific resolutions they work best at (commonly ~1 megapixel). Going too large produces edge artifacts. Going too small produces harsh crops. Use the model's recommended aspect ratios. 9. **Wrong model for the task**: Not every model is good at every task. Some excel at text rendering but struggle with object removal. Some are great at style transfer but poor at background editing. If a model struggles with a specific edit type, try a different one rather than fighting the prompt. See the [compare-models](../compare-models/SKILL.md) skill for guidance. 10. **Not iterating**: The best results come from iterative workflows. Make a small change, evaluate, refine, repeat. Don't try to get everything right in a single generation. ## Sources All techniques in this skill are sourced from Replicate's blog: - [How to prompt Seedream 5.0](https://replicate.com/blog/how-to-prompt-seedream-5) (Feb 2026) - [Recraft V4](https://replicate.com/blog/recraft-v4) (Feb 2026) - [Run FLUX.2 on Replicate](https://replicate.com/blog/run-flux-2-on-replicate) (Nov 2025) - [How to prompt Nano Banana Pro](https://replicate.com/blog/how-to-prompt-nano-banana-pro) (Nov 2025) - [Which image editing model should I use?](https://replicate.com/blog/compare-image-editing-models) (Sep 2025) - [Generate consistent characters](https://replicate.com/blog/generate-consistent-characters) (Jul 2025) - [Use FLUX.1 Kontext to edit images with words](https://replicate.com/blog/flux-kontext) (May 2025) - [Imagen 4](https://replicate.com/blog/google-imagen-4) (May 2025) - [Ideogram 3.0 on Replicate](https://replicate.com/blog/ideogram-v3) (May 2025) - [FLUX.1 Tools](https://replicate.com/blog/flux-tools) (Nov 2024) - [Ideogram v2 inpainting](https://replicate.com/blog/ideogram-v2-inpainting) (Oct 2024) - [Using synthetic data to improve Flux finetunes](https://replicate.com/blog/using-synthetic-data-to-improve-flux-finetunes) (Sep 2024) - [FLUX.1: First Impressions](https://replicate.com/blog/flux-first-impressions) (Aug 2024) - [How to get the best results from Stable Diffusion 3](https://replicate.com/blog/get-the-best-from-stable-diffusion-3) (Jun 2024)