A product card is four separate jobs: look at the item and describe it, make the photo presentable, create the shots nobody took, and translate everything into the languages you sell in. No single model does all four well, because these are different kinds of model: one looks, one cleans up, one draws, one translates. Chained together they form a pipeline that digests a thousand items without hiring an editorial team.
A model that understands images and text at once looks at a shot and produces a draft: what the item is, material, colour, fittings, intended use. This does not replace the supplier data sheet, it fills the empty fields where the supplier sent only photos and an article number. Without this part descriptions are written by hand, and on a large catalogue that is the real bottleneck.
One of the strongest open vision models: reads documents, tables, charts and video, and works with user interfaces. Since Qwen3.5, vision is built directly into the main Qwen model.
A large family of Chinese vision models sized from 1B to 241B. InternVL-U (4B) combines image understanding, generation and editing.
Compact vision models that run even on a phone or laptop. Good at reading text in photos and understanding video; version 4.6 is only 1.3B.
Photo models remove backgrounds, sharpen images and scale small shots up to the required resolution. It is dull work but the most visible to buyers: they see the picture first and read the text second. Skip it and cards look inconsistent, some images fail marketplace requirements, and items come back for rework.
BRIA's background removal, trained on licensed photos. Soft edges, hair, transparency. Video versions available. Business use requires a paid agreement.
An open MIT-licensed model for precise object segmentation and background removal. RMBG-2.0 is built on it. Versions for 2K and for hair and semi-transparent edges.
The classic for upscaling photos 2–4x while cleaning noise and compression artifacts. Lightweight, runs even on a CPU. Versions for drawings and anime.
Image generation fills catalogue gaps: a seasonal background, a colour variant, a simple scene for a banner or a category cover. It should work on top of the real product photo rather than instead of it, otherwise buyers receive something that does not match the picture. Without this part you either reshoot or leave gaps in the card and lose impressions.
Image generation from the creators of Stable Diffusion. Renders text in images well and keeps the composition.
Image generation and editing, including text in images. Earlier versions allow commercial use; the latest 2.1 is non-commercial only.
The model that started open image generation. A huge ecosystem of fine-tunes, styles and plugins; runs even on a home PC. The popular SDXL-Lightning and Hyper-SD accelerators were made by ByteDance.
A dedicated translation model rolls the catalogue out into other languages more cheaply and more consistently than a general language model, and it does not paraphrase your text. Keep a glossary of your own terms and product names, or the same material ends up named three different ways across cards. Without this step entering a neighbouring market means translating thousands of descriptions by hand.
A translator for 200 languages, including rare and minor ones. Russian is supported. Strong language coverage, but the license prohibits commercial use.
Google's translator for more than 400 languages under a permissive license. Russian is supported. A good substitute for NLLB when commercial use is needed.
More than a thousand small translators, each for its own language pair. Russian-English and back are available. Fast even on a regular CPU.
The main risk is a model confidently inventing attributes the product does not have: composition, country of origin, warranty. The rule is simple: anything the seller is answerable for comes from supplier data, and the model only writes what is visible in the photo. Build in spot checks by a human and keep one glossary for the whole catalogue. Marketplace rules for images and descriptions change, so check them before a bulk upload.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
