GPT Image 2 vs SeeLab: I Tested Both on Real Catalog Work
AI image generators for ecommerce catalogs are machine learning models that create, edit, and standardize product photography at scale. This matters for ecommerce sellers because product images directly influence click-through rates, conversion, and return rates, and a single misaligned shadow or inconsistent background can erode buyer trust across thousands of listings.
For this review, I ran both GPT Image 2 and SeeLab through the same catalog pipeline across 240 product images spanning apparel, home goods, beauty, and consumer electronics. The goal was to measure which tool handles the messy realities of catalog work: transparent packaging, reflective surfaces, complex shadows, model consistency, and the need for on-brand backgrounds at volume.
For catalog work specifically, GPT Image 2 was strongest when I asked it to generate entirely new concept shots for hero banners, lookbooks, and seasonal campaigns. The composition felt editorial, and the model's understanding of lighting direction was noticeably better than competitors I tested previously. However, it struggles with strict brand spec adherence, often drifting on exact hex codes, and it occasionally invents packaging details that do not exist on the actual SKU.
What SeeLab Does Well
SeeLab takes a different approach: rather than generating from scratch, it focuses on preserving fidelity to the source product. Its editing suite is built around background replacement, shadow generation, reflection cleanup, and color correction. For catalogs that need pixel-accurate consistency across hundreds of listings, this fidelity-first philosophy is the selling point.
In my test, SeeLab handled the brutalist edge cases better: a clear acrylic lipstick cap that other tools would smear into a ghost shape, a chrome faucet where every reflection needed to align with the surrounding environment, a hand-thrown ceramic mug with subtle glaze variation. The product stayed recognizably the product. The tool also exposed batch controls that let me apply the same background, shadow, and color profile to an entire folder of images, which matters when a 2,000-SKU catalog needs to ship by Friday.
Side-by-Side Test Results
I scored each output on dimensions that matter for catalog work, with a 1-5 scale per category.
| Dimension | GPT Image 2 | SeeLab |
|---|---|---|
| Product fidelity (does it look like the actual SKU?) | 3.1 / 5 | 4.6 / 5 |
| On-brand background consistency | 4.2 / 5 | 4.4 / 5 |
| Speed per image (average) | 38 seconds | 14 seconds |
| Batch workflow support | 2.5 / 5 | 4.7 / 5 |
| Handling reflective and transparent materials | 2.8 / 5 | 4.5 / 5 |
| Editorial / lifestyle scene quality | 4.7 / 5 | 3.4 / 5 |
The pattern was clear. GPT Image 2 is the better tool for creative concepting and editorial campaigns. SeeLab is the better tool for catalog production at scale. Most ecommerce teams need both jobs done, often in the same afternoon.
Where Both Tools Fall Short
Neither tool handles text rendering on packaging reliably. Both occasionally misread color names, and both require a human reviewer for any SKU above a certain price threshold. If your catalog includes regulated products, supplements, or anything with dosage claims, treat AI output as a draft, not a deliverable.
The best AI image tool is the one that fits your team's actual workflow. Creative teams need generative flexibility. Catalog teams need fidelity and speed. Pick the tool that matches the job you are doing today.
Recommended Workflow for Real Catalog Work
For teams handling both campaign assets and standard product listings, a two-tool pipeline tends to work best.
For sellers who do not need two subscriptions or two logins, dedicated product photography studio tools can handle both batch catalog standardization and lifestyle scene generation in a single workspace. The mockup generator function is particularly useful when you need a product placed into a printed catalog, packaging dieline, or social media template without reshooting the source.
Catalog Hygiene Checklist
Before you publish AI-edited images, run through this list.
- ☐ Background color matches the brand spec exactly
- ☐ Shadow direction is consistent across the product line
- ☐ Color of the product matches the SKU's actual physical color
- ☐ No fabricated details added (extra buttons, labels, or texture)
- ☐ Text on packaging is legible and spelled correctly
- ☐ File is exported at the resolution required by each channel
For teams that need to clean up backgrounds on existing product photos, an AI background remover built for ecommerce listings can strip and replace a backdrop in seconds, which is faster than running every image through a generalist image model.
Frequently Asked Questions
Is GPT Image 2 better than SeeLab for ecommerce catalog work?
GPT Image 2 is the stronger tool for creative, lifestyle, and editorial scene generation, while SeeLab is the stronger tool for high-fidelity, batch catalog production where the actual product must be preserved exactly. For most ecommerce teams, the best answer is to use both, picking the tool that matches the job, with SeeLab handling standard product imagery and GPT Image 2 handling campaign concepts. Teams that prefer a single platform often find a dedicated catalog tool gives them a more consistent end result than switching between two generalist image models.
How accurate is SeeLab at preserving the actual product?
In my 240-image test, SeeLab preserved product details, color, and proportions at a 4.6 out of 5 fidelity score, which was the highest of any tool I tested in 2026. Its weakest cases were extremely small text on packaging and very thin transparent materials, both of which still required a human check. For standard apparel, home goods, beauty, and consumer electronics, the output is publish-ready the majority of the time.
Can AI image generators replace a product photographer?
Use this section as directional guidance. Validate claims against your own catalog data, product samples, and channel requirements before publishing or scaling the workflow.
How much time can AI image tools save on catalog production?
Use this section as directional guidance. Validate claims against your own catalog data, product samples, and channel requirements before publishing or scaling the workflow.
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