AI Image & Video

How to Pick a Text-to-Image Model: Same-Seed Bake-Off Plus an 8-Dimension Camera-Language Prompt

A topic change flips your verdict because you were comparing luck, not models. Lock the subject, seed and resolution so only the model varies, view the results side by side, and in thirty minutes you can pick a default from evidence.

10/05/2026 ｜ 4 min read ｜ Works with ComfyUI or any image app — about a 15-minute read

The one idea that matters

When you compare models, only the model may change. Everything else stays locked.

Most people compare like this: generate one image with model A, one with model B, and pick whichever looks nicer. But those two images may differ in subject, random seed and size, so you are comparing luck plus settings. Fix the subject, the seed and the resolution, then view the results side by side. Only then are you comparing the models themselves.

Steps

Step 1　Prepare three test subjects that match your real use. Skip “a cat.” Pick what you will actually generate: a portrait close-up, a scene, a product shot.

Step 2　Write each subject as a camera-language prompt. A bare prompt can’t separate good models from bad ones. The template is below.

Step 3　Fix the seed and resolution. Pick any constant seed (say 42) for every model and every subject, and a single resolution (say 768×1344 portrait).

Step 4　Generate one image per model per subject. Name files “subject_model.”

Step 5　Build a side-by-side table. One row per subject, one column per model, so you see a whole row at once. Memory can’t hold the previous image, so don’t flip between them.

Step 6　Score with fixed criteria. Four checks: did it follow your camera language (adherence), realism and texture, broken faces or hands, and speed. Then choose a default.

The 8-dimension camera-language template

Structure: subject → action → environment → lighting → camera parameters → style. Fill in these 8 dimensions:

A filled example (portrait close-up):

Close-up portrait of a focused young woman data scientist, a glowing
holographic chart reflected in her glasses, modern dark office at night,
85mm lens, f/1.8 shallow depth of field, Rembrandt key light with a cyan
rim light, cool teal and orange tone, tack sharp eyes, photorealistic
skin texture, moody Fincher-style cinematography, ultra detailed

Reminder: keep camera-move words such as push, pull, pan, track and orbit out of image prompts. Stills don’t move; those words belong to the image-to-video layer.

Update: what I found in late July

I later ran a controlled test (same seed, only the wording changed) and found that the focal-length numbers in the list above are often ignored by Z-Image Turbo. More reliable habits:

• Describe the camera position as a physical place (“camera placed at the end of the corridor”), not only a focal length.
• Describe the action as a visible result, not a mid-turn state.
• Put at most one character with a complex pose in a frame.
• Negative composition instructions (for example “no sky visible”) don’t keep things out of frame.

So the 8 dimensions are a checklist, not a guarantee that every model obeys every field. Verify with a same-seed bake-off which fields your model actually honors.

Traps you will hit

Trap 1　Testing with a bare prompt and concluding the models are all alike. Differences only show up under information-rich prompts. Say it properly first, then choose.

Trap 2　Not locking the seed. One model gives different results on every click, so cross-model comparison means nothing.

Trap 3　Picking a non-commercial model and finding out too late. If the content will earn money (client work, courses, a monetized account), check the license before you choose:

• Commercial-safe: Apache 2.0, MIT and similar. All five I compared were: FLUX.1-schnell, FLUX.2-klein (4B), Qwen-Image, Z-Image-Turbo and Sana.
• Check the version: FLUX.2-klein 4B is Apache 2.0; the 9B is non-commercial.
• Dangerous: flux-dev, ideogram and other non-commercial models, plus the InsightFace family used for face identity (PuLID, IP-Adapter FaceID and InstantID nearly all depend on it). Useful, but a legal risk once you monetize.

Trap 4　Hardcoding the decision. Make it a default plus a switchable menu. When the subject or the need changes, swap one option instead of rebuilding the pipeline.

My five-model result (a reference, rerun it with your own subjects)

Alibaba’s Z-Image-Turbo was the dark horse: about 11 seconds per image, 7–8 times faster than its peers, quality on par with FLUX.2-klein and Qwen-Image, and it renders Chinese text inside the image, which matters for Chinese title cards. It is still my default for local image generation.

When you’re stuck, ask an AI like this

I’m new to AI image generation. I use <ComfyUI / your tool> on <Windows / Mac>. I want to check whether the license of the model “<model name>” allows use in commercial, money-making content. Please: 1) find its official license, 2) explain in plain language whether commercial use is allowed, 3) if not, suggest 2 similar models that are commercial-safe (Apache 2.0 or MIT). Go step by step in plain language.

In one sentence

Choose models with a controlled comparison (locked variables plus camera-language prompts), not by feel, and leave yourself an exit (a default plus a menu) instead of hardcoding the choice.