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How to generate hero 3D models from text prompts with Dioramas

Create product plates and optimized GLB models with Nano Banana 2 and Meshy 7.1 on fal.

Credentials. The generation step needs your fal FAL_KEY in a local .env file, so keep that file out of version control and do not paste the key into an agent prompt.

Who it is forA designer or developer who needs a custom 3D hero object for a page and does not have a 3D modelling workflow.

The problemModelling a detailed product object by hand is slow, and the raw AI output is too heavy for a web page.

The outcomeA set of generated images and an optimized GLB file in public/models/, ready for a Dioramas page.

What you need first

  • Git and Node.js with npm, to clone the repo and run the dev server
  • A desktop GPU, which the README recommends for 60 fps
  • About 330 MB of disk space for the repo, since models ship in the clone (no LFS)
  • A fal account and FAL_KEY, only if you want to generate new models or images

Steps

  1. Run cp .env.example .env and add your key as FAL_KEY in the .env file.
  2. Add your object prompts as JSON job files in assets/jobs/, following the examples there.
  3. Run node scripts/gen.mjs all assets/jobs/ .json to generate images and meshes. The README says this step is resumable.
  4. Run node scripts/optimize-all.mjs to weld, compress and convert the models into public/models/.
  5. Point your example's main.js at the new GLB in public/models/ and check it with npm run dev.

Prompt for your agent

In the blendi-remade/dioramas repo, add a job for <object description> to assets/jobs/ following the existing job files, then run the generation and optimize scripts from the README. Expected output: a new optimized GLB in public/models/ and the generated image in the job output. Verify by confirming the GLB file size is in the 3 to 8 MB range the README gives for optimized models.

Caveat. The README gives an approximate cost of about $1.20 per textured model and says the whole collection cost roughly $90, so check your fal usage before running a full batch.

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