Midjourney

In recent years, mainstream text-to-image models (such as Stable Diffusion, Midjourney and the DALL·E series) have mostly adopted latent diffusion approaches, completing denoising in a compressed latent space to reduce compute costs.

In game development, a typical vibecoding workflow includes generating game logic code with ChatGPT or Claude, generating art assets with Midjourney or Stable Diffusion, generating background music with Suno, and then quickly assembling everything with Unity or Godot.

Midjourney's full parameter list contains no parameters related to transparent backgrounds.

The Prompt Image Atlas editorial team states that the portrait prompt templates it provides are editing starting points rather than tested outputs; they have not been run in ChatGPT nor compared with Midjourney.

Midjourney's web app now supports generation, variations, upscaling, and history search, so it no longer necessarily depends on Discord.

Reference image modes (such as Midjourney's --cref, Stable Diffusion's IP-Adapter and Flux's Redux) encode the reference image into an independent identity/style vector that is injected in parallel with the text prompt and is not bound to composition.

You should not write sunset, neon, and studio spotlight in the same prompt at the same time, because they are three completely different physical setups.

When debugging lighting, it is recommended to keep the subject and composition unchanged and change only the lighting description, in order to determine whether the change in the image comes from the lighting instruction.

Fixing the seed helps organize comparisons, but it cannot prove that lighting is the only visual variable; before using it, it is recommended to first read Midjourney's official Seeds guide.

Midjourney's official Prompt Basics documentation suggests that prompts should contain clear descriptions and lists lighting as a detail worth specifying.