GPT Image 2 for Scientific Figures

Text-aware by design. GPT Image 2 is the model to reach for when a scientific figure has to explain, not just impress.

• Explanation-First Figures: It is especially useful for graphical abstracts, annotated concept figures, pathway diagrams, and any visual where labels, captions, and structured callouts carry a large share of the meaning.
• Recommended Default for Journal Work: GPT Image 2 is the recommended default in SciFig for peer-reviewed journal submission and notation-heavy figures. It tends to follow the scientific brief more tightly — every label, formula, and structural cue from the prompt has a higher chance of landing in the figure.
• Still a Full Figure Model: This is not a text-only model. It still handles full scientific figure generation and editing across prompt-led and image-led workflows.

Yes. One model, two real generation modes. GPT Image 2 covers both prompt-first generation and image-conditioned regeneration inside SciFig.

• Text to Image: In `Text-to-Figure`, SciFig can use GPT Image 2 to generate a figure directly from your scientific prompt.
• Image to Image: In `Sketch-to-Figure`, `Reference-to-Figure`, `Photo-to-Figure`, and `Figure Enhancer`, the same model can work from an uploaded image plus your prompt to regenerate, restage, or refine the figure.
• PDF Is Different: `PDF-to-Figure` remains a SciFig workflow where the paper is first interpreted into a prompt. GPT Image 2 then participates in the follow-up figure generation step, rather than reading the PDF natively itself.

Choose GPT Image 2 when the figure's logic has to stay readable under pressure.

• Labels Lead the Task: If the figure depends on annotations, panel text, callouts, arrows, and information hierarchy, GPT Image 2 is often the cleaner choice.
• Edits Matter More Than Aura: When you expect to revise the figure several times, preserve structure from an uploaded input, or make controlled redraws instead of aesthetic leaps, GPT Image 2 is usually the safer route.
• Different Defaults, Different Outputs: Nano Banana Pro is the editorial-style specialist when the figure heads to a slide deck, conference poster, or social-media thumbnail. GPT Image 2 stays the recommended default when the figure heads to a peer-reviewed journal — chemistry, math, dense labels, scale bars.

Usually, yes. This is where GPT Image 2 becomes most distinctive inside SciFig.

• Graphical Abstract Friendly: Graphical abstracts often combine many small labels, directional flow, and compressed scientific storytelling. GPT Image 2 is a strong fit for that kind of visual density.
• Annotation-Heavy Figures: It is also well suited for explanatory concept figures, educational diagrams, and multi-step mechanism visuals where embedded text is part of the figure, not an afterthought.
• Multilingual Edge: Because text inside the image matters more here, GPT Image 2 is especially relevant when the figure may need multilingual labels or a mix of scientific notation and prose.

Yes. This is one of the clearest reasons to pick it.

• Controlled Regeneration: When you already have a sketch, a rough figure, a reference image, or a lab photo, GPT Image 2 is often useful because it can reinterpret the image while still respecting your new instructions.
• Better for Keep This, Change That Tasks: Users often do not want a complete reset. They want to keep the scientific structure, preserve the rough layout, or maintain the original figure idea while tightening execution. That is a strong fit here.
• Fits Revision Work: If your figure is already halfway there and the problem is refinement, not invention, GPT Image 2 becomes more compelling.

It fits redraw-heavy workflows better than most researchers expect.

• Sketch-to-Figure: It is useful when the hand-drawn structure matters and you want the AI to respect the logic of your sketch instead of free-styling too far away from it.
• Reference-to-Figure: It is also a strong option when you want to borrow organization, composition, or explanatory rhythm from a reference image without just making a prettier version of the same thing.
• Why It Matters: These are the workflows where prompt-following and image-to-image stability matter more than dramatic visual polish on the first try.

Yes. It is not limited to prompt-only use.

• Photo-to-Figure: If you want a lab photo or microscope-adjacent input to become a cleaner scientific figure while still following explicit instructions, GPT Image 2 makes sense.
• Figure Enhancer: It is also useful in enhancement workflows where the goal is not only make this prettier, but make this clearer, cleaner, and easier to revise.
• Edit-Sensitive Workflows: The more your workflow behaves like guided scientific rewriting rather than pure visual restyling, the more relevant GPT Image 2 becomes.

This is one of its real edges, not just a marketing talking point.

• Text Inside the Image: GPT Image 2 is particularly relevant when the text is part of the visual composition itself: labels, captions, panel headers, arrows, legends, and callout boxes.
• Multilingual Use Cases: It is also useful when figures need to survive bilingual or multilingual contexts, such as teaching slides, global teams, or region-specific presentation decks.
• Still Not a Free Pass: Better text handling does not remove the need for final review. In scientific figures, one wrong protein name or mislabeled compartment is still your responsibility to catch.

Not every figure problem is a notation problem.

• Slide and Poster Work: If the figure is heading to a slide deck, conference poster, or social-media thumbnail — where readability outweighs notation density — Nano Banana Pro is usually the better first pick.
• BioRender-Style Simplification: For BioRender-style mechanism cartoons, intro-level pathway figures, or layered architecture diagrams aimed at non-specialist audiences, Nano Banana Pro's editorial polish is the more natural fit.
• Use the Model for Its Edge: GPT Image 2 stays the recommended default for journal submission and notation-heavy work — chemistry, math, dense labels, scale bars. When that is not the task, the editorial-style specialist often wins.

AI can organize the draft. You still own the science.

• Scientific Accuracy: Check names, labels, units, pathways, arrows, sequence, and biological or chemical relationships. The model can help structure the figure, but it does not become the scientific authority.
• Figure Logic: Make sure the visual flow matches the story you actually want to tell. In research figures, a clean layout can still communicate the wrong emphasis.
• Publication Readiness: Before a paper, poster, or talk, do one final pass for terminology, legend consistency, journal conventions, and any small text that could break under export or resizing.