Awesome-AI-Images-Prompts: Compare Image Prompting Across Models
The same idea can produce very different images in different models. The reason is not only model capability. Models also interpret composition, text, style words, reference images, and parameters differently. dongyubin/Awesome-AI-Images-Prompts provides a cross-model entry point for comparing Chinese image prompts.
Why Compare Models Side by Side
A single task may need several models:
- One model may be better at text and layout;
- Another may be better for portraits and realism;
- Another may be useful for fast drafts;
- Another may fit a specific style or workflow.
Copying one prompt unchanged into every model often carries over wording that only works for the original model.
Four Layers to Compare
1. General Task Description
“Create a product launch poster” can usually remain, but the product, audience, and distribution channel still need to be explicit.
2. Composition and Layout
Different models execute “leave space on the left,” “put the subject in the lower-right,” or “use a three-column information structure” differently. Validate these instructions with cases instead of trusting the wording alone.
3. Model-Specific Expression
Some models are more sensitive to reference images, style, camera language, or negative constraints. When migrating a prompt, separate general semantics from habits specific to one platform.
4. Post-Processing Boundaries
If a model cannot reliably render exact text, do not keep adding prompt words indefinitely. Split background generation and final typography into two steps.
A Cross-Model Rewrite Template
Shared goal: create a 16:9 product launch visual
Shared subject: an enterprise API console and data flow
Shared constraints: subject on the right, negative space on the left, fixed brand colors
Model A: emphasize product UI and readable text
Model B: emphasize reference images and overall composition
Model C: emphasize style, materials, and camera
Keep the task, dimensions, and references fixed when comparing. Ideally change only the model-specific wording at a time; otherwise you cannot tell whether a difference came from the model or the rewrite.
Do Not Treat Case Comparisons as a Benchmark
Public prompt lists help you learn migration patterns, but they do not prove that one model is better for every task. A real comparison needs your own sample set and records:
- Whether composition meets the requirement;
- Whether text is readable;
- Whether the subject is complete;
- Whether edits preserve consistency;
- Generation speed and cost;
- Human rework time.
Summary
Cross-model prompting is not about finding one “universal prompt.” It is about knowing which goals must stay fixed, which expressions need model-specific rewriting, and which work should move to a post-processing tool. Repository contents and model coverage change, so use the current Awesome-AI-Images-Prompts repository as the source of truth.