Keep your products, headlines or events in a sheet and let each row become its own generated image. The Google Sheets connector sends one row per generation to the same models as the toolkit, and the results come back as files or links you review before anything is used.
Most image lists already live in a spreadsheet: forty product names, a column of blog headlines, a calendar of events that each need a cover. Making those pictures one by one means copying a cell into a tool, waiting, saving the file and going back for the next cell. The Google Sheets connector for Picasso IA keeps the sheet as the source of truth: one row goes in, one generated image comes out.
A sheet is where a list is already organized. Each row has a name, a category, a date, a note about tone, and somebody already agreed on the order. Generating from that structure means the images inherit the organization instead of being renamed and sorted afterwards, and nothing has to be retyped between the list and the picture.
It also suits the way repetitive visual work really goes. A shop with a seasonal catalog, a newsletter with weekly headlines or a small venue with a program of events does not need thirty masterpieces. It needs thirty images that are on topic, roughly in one family and ready for a person to check. The connector uses the same models and the same credit system as the toolkit, so you can try any prompt there first and carry the one that works into the sheet.
An image prompt is a sentence assembled from parts, and a sheet already stores the parts in separate columns. The trick is to decide which column carries which job, then let every row fill the same slots. Rows that fill the slots well give you a batch that looks related. Rows that leave a slot empty give you the model guessing.
| Column | Job in the prompt | Example cell | Watch for |
|---|---|---|---|
| Subject | The thing in the picture | Ceramic coffee mug, speckled glaze | Vague names such as Item 14 |
| Setting | Where and how it sits | On a wooden shelf, morning window light | Contradicting the style column |
| Style | One shared look for the batch | Soft editorial photograph, muted colors | Changing it row by row |
| Format | Shape of the result | Wide banner, or square | Cropping that cuts the subject |
| Notes | Row-specific extras | No text in the image | Long paragraphs the model will half ignore |
Only the subject really changes from row to row. Setting, style and format are best typed once and reused, so a reader flipping through the results sees one collection and not thirty unrelated experiments.
A prompt you test on one row has to work on all of them, and that is a stricter test than it sounds. Write it as a template: a fixed frame around a single changing cell. Something like a photograph of the subject on a plain surface, soft daylight, no text, with the subject cell dropped in. Then read three or four results from different rows before you trust it.
Pick the model for the job rather than for its fame. For product-like subjects a strong general image model such as flux-2-pro, nano-banana-pro or seedream-4.5 is a sensible starting point, and p-image is a lighter option when you are only sketching directions. The full catalog lists every text-to-image model if you want to compare styles, and the same prompt can be tried in several of them side by side in the toolkit before it ever touches a sheet.
Keep the prompt free of things a model handles badly in bulk. Words inside the picture are the main one: ask for no text and add headlines in a design tool afterwards. For the ones that should look like a real item you sell, say plainly what the item is and check every result against it, because a lovely image of a slightly different mug is a misleading listing.
This is the journey from a plain list to a folder of reviewed images. It needs a Google account, a Picasso IA account and one tab of data.
Consistency in a batch comes from what you hold still, not from luck. Hold the style and format columns constant, keep subject names concrete, and let only the notes column vary. When one row looks off, resist the urge to rewrite the template for everyone. Fix that single row first and rerun it.
Add a Status column next to the data before the first run. Marking each image as keep, rerun or drop turns the sheet into a review queue, and it is the only way to know which rows still cost credits and which are done.
Review is also where the sheet earns its place beyond generation. Sorting by status shows what is left, and a Notes cell that explains why a row failed becomes the fix for the next pass. The images themselves stay in whatever place you store the downloaded files, and your own naming habit, such as the row number in the file name, keeps them traceable back to the cell that produced them.
Linking a sheet to a generator is practical, and it also has edges. Knowing them ahead of time saves credits and avoids awkward surprises later.
If a list needs ten very different images with art direction for each, a sheet is the wrong tool and the toolkit, one image at a time, is the better one.
The point of the connector is that the sheet stays the interface: you fill in rows and columns, connect your account with a key, and choose which columns feed the prompt. You do not need to write a script to describe a generation. Some thought about the prompt template is still required, because the quality of a batch depends far more on that template than on any setting.
One row sends one generation, and that generation produces one image. The result comes back as a file you can download or a link to open, along with the same kind of output the toolkit gives you. If you want several options for a single idea, put several rows for it, each with a slightly different note, and compare the results.
That depends on Google Sheets, not on Picasso IA. The connector gives you the generated image as a file or a link, and how a spreadsheet displays images in cells, at what size and with what limits, follows the rules Google sets. Plan for a link or a stored file as the dependable result, and treat in-cell display as a convenience that Google controls.
Choose one model for the whole batch so the results look related. A general model such as flux-2-pro, nano-banana-pro or seedream-4.5 is a reasonable first test for product-like subjects, and p-image works for quick drafts. Try the same prompt in the toolkit on a few real rows first, since models differ in how they handle your particular subjects and styles.
It uses credits, the same system as the toolkit. The cost of each image depends on the model and the settings, so a long list costs more than a short one and a heavier model costs more than a lighter one. New accounts get free credits to test with, and the pricing page is where plans live. Run a few rows first to see what your own template costs.
Yes, access works with a key you create in your account. Treat it like a password: do not paste it into cells that others can read, do not share the sheet with people who should not be able to run generations, and replace the key if you think it has leaked. If you are unsure where to create or manage it, support can point you to the right place.
You can generate product-style scenes, as long as the picture stays truthful about the item. A model may change a color, a proportion or a detail without telling you, and a customer who receives something different from the image has reason to complain. Check each result against the real product, and check the current requirements of the marketplace you sell on.
Failures are normal in a batch, and the status column exists for them. Mark the row, adjust the notes for that subject, and run only that row again. If the same row keeps failing the same way, take it to the toolkit, try another model there and bring the better prompt back into the sheet. Questions about a stubborn case can go to support.
You can ask for it, but lettering is where generated images are least dependable, especially in a long batch. A safer pattern is to generate the image with no text and add the headline in a design tool afterwards, so spelling and layout are fully yours. If you do generate text, read every result character by character before using it.
It is the same catalog and the same credits, with a different starting point. In the toolkit you pick a model and write one prompt. With the sheet you describe the repeated parts once and let each row fill in its own subject. The toolkit is also available today for testing any prompt or model before you connect a sheet.
Pick a few rows from your own list and try the prompt first in the toolkit, then bring it back to the sheet. If you hit a question along the way, support is the place to ask.
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