// Prompting techniques for AI image generation and editing models on Replicate. Use when writing prompts for image models or building image generation features.
| name | prompt-images |
| description | Prompting techniques for AI image generation and editing models on Replicate. Use when writing prompts for image models or building image generation features. |
Distilled from Replicate's blog posts on prompting image models (2024-2026). Techniques are model-agnostic and focus on transferable principles.
This skill describes general prompting techniques. To choose a model, use the find-models 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 skill.
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"
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"
Use descriptive phrases like "the woman with short black hair" or "the red car." Avoid pronouns, which are often too ambiguous for image models.
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.
Begin with basic changes. Test small edits first, then build on what works. Most editing models support iterative editing, so take advantage of that.
Modern image models understand camera and photography terminology deeply. Using this vocabulary gives you precise control over the look.
Rendering text in images is a common task. These techniques improve accuracy across models.
Maintaining the same character across multiple generations is one of the hardest challenges in image generation.
Some models can generate multiple related images in a single prompt.
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.
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."
Not specifying what to keep: When editing, always say what should stay the same. Without explicit instructions, models may change anything.
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.
Too-high guidance scale (CFG): If images look "burnt" with excessive contrast, lower the guidance scale. Each model has a recommended range.
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.
Short prompts for complex scenes: Modern models accept thousands of tokens. For complex compositions with many specific requirements, use that capacity.
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.
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 skill for guidance.
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.
The techniques above are distilled from Replicate's blog posts on prompting image models. The posts are anchored to specific models that were current at the time, but the techniques generalize across modern image models. Use these for additional context, then use the find-models skill to pick a model that fits your task today.
Find AI models on Replicate using search and curated collections.
Prompting techniques for AI video generation models on Replicate. Use when writing prompts for video models or building video generation features.
Package and build custom AI models with Cog for deployment on Replicate. Use when creating a cog.yaml or predict.py, defining model inputs and outputs, loading model weights at setup time, building Docker images for ML models, serving locally with cog serve or cog predict, or porting a HuggingFace, GitHub, or ComfyUI model to run on Replicate. Trigger on phrases like "build a model", "package a model", "create a Cog model", "wrap a model", "containerize an AI model", "predict.py", "cog.yaml", "BasePredictor", or "Cog container", and when referencing cog.run, github.com/replicate/cog, or github.com/replicate/cog-examples. Covers GPU and CUDA setup, pget for fast weight downloads, async predictors with continuous batching, streaming outputs, and cold-boot optimization for image, video, audio, and LLM models. For pushing built models to Replicate, see publish-models. For running existing models, see run-models.
Push and publish custom AI models to Replicate, and set up CI/CD for releasing new model versions safely. Use when running cog push, deploying a model to Replicate, releasing a new version, validating a model with cog-safe-push before publishing, configuring a Replicate deployment, setting up GitHub Actions for model releases, or porting a community model to an official one. Trigger on phrases like "push a model to Replicate", "publish a model", "deploy a model", "release a new version", "cog push", "cog-safe-push", "model CI", "r8.im", or "schema compatibility", and when referencing github.com/replicate/cog-safe-push or github.com/replicate/model-ci-template. Covers cog push, the full cog-safe-push config (test cases, fuzz, deployment, official_model), GitHub Actions patterns, multi-model matrix pushes, and post-publish monitoring. Assumes you already have a working Cog project; see build-models if you need to package one first.
Compare Replicate models by cost, speed, quality, and capabilities.
Run AI models on Replicate via predictions, webhooks, and streaming.
