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How to install a Codex pet from two or more photos with Pocket Men with You

Turn your own photos into an installed Codex companion pet with one create command.

  • Top pick
  • Content
  • Intermediate
  • 1 hour
  • From the README
  • Codex

Reverse engineering. Remove or keep private the reference photos of real people; only pet.json and spritesheet.webp are meant for the install package.

Who it is forA Codex user who wants a personal animated companion based on a person or animal they photographed.

The problemMaking an animated pet for Codex by hand means drawing or animating every state, which takes skill and time.

The outcomeAn installed Codex pet with a pet.json and spritesheet.webp built from the supplied photos.

What you need first

  • Two or more reference photos of the subject (the README says raw personal references stay local by default)
  • A Windows machine for the .bat installers, which the README describes
  • Python, the repo language, for the pocketmen CLI
  • An NVIDIA GPU with enough VRAM for the neural engine; otherwise PocketMen falls back to the deterministic v0.2 engine
  • Codex, if you want to use the $pocketmen-with-you skill

Steps

  1. Run INSTALL_SKILL.bat to install the skill and create the isolated runtime.
  2. Run pocketmen doctor to check the hardware.
  3. Run pocketmen create --reference photo-1.jpg --reference photo-2.jpg --name "Mochi" --subject-type animal --style soft-real --engine auto --quality balanced --output ./pocketmen-output --install, replacing the file names, name, and subject type with your own. (inferred, check the README)

Prompt for your agent

Using the repo six-nut/PocketMen-with-you, run `pocketmen doctor`, then create a companion with `pocketmen create` from the two reference images at <photo-1> and <photo-2>, output to ./pocketmen-output with --install. Expected output: pet.json and spritesheet.webp in the output folder and an installed Codex pet. Verify the atlas is 1536x1872 with 8 columns and 9 rows.

Caveat. The README does not state a Python version or the exact VRAM needed, so check the installer output before relying on the neural path.

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