prompt-images

What this skill does

# 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.

## Choose a model with the API, not from memory

This skill describes general prompting techniques. To choose a model, use the [find-models](../find-models/SKILL.md) skill and query the Replicate API. The image model landscape changes weekly. Don't assume specific models exist or are still state-of-the-art based on names you've seen before. Always search the API for current options, then read the schema before running anything.

For 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