Generated Photos vs InstructPix2Pix

Generated Photos sells AI-created model images for design, marketing, research, and machine learning teams that need royalty-safe human photos. The catalog lists 2.6 million pre-generated faces plus 167,000 full-body humans you can browse, filter, or regenerate with custom parameters. Face Generator and Human Generator tools build new portraits on demand without a photo shoot.

Stock photo sites license real models with release forms and recurring fees. Generated Photos trains its own synthetic humans, so downloads ship without model releases and scale through bulk pricing or API access. That trade-off suits ad mockups and dataset work, but the faces can still look synthetic close up compared with studio photography.

Designers, game studios, and researchers use the Faces and Humans databases, Anonymizer uploads, and custom datasets for training data. Subscriptions run $199 per year per product line with 15 monthly downloads, while pay-as-you-go bulk tiers drop to $1 per image above 1,000 photos. Enterprise buyers can request custom datasets and API integration from the business page.

InstructPix2Pix edits a photo from a plain-language instruction such as "make the sky sunset orange" or "turn this into a watercolor." You provide an input image and a short text command, and the model returns an edited version in seconds without training on that specific photo first. It was published as a CVPR 2023 Highlight by researchers at UC Berkeley.

Mask-based editors make you paint regions before changing anything. InstructPix2Pix runs a single forward pass from text alone, skipping per-image fine-tuning or inversion steps that slow other diffusion edit workflows. The trade-off shows up in the paper's failure cases: viewpoint changes, object swaps, and tight local edits remain weak spots.

The project ships open source code on GitHub and a Hugging Face demo space for trying edits in a browser. Training combined GPT-3 and Stable Diffusion to synthesize instruction pairs, and the released model generalizes to real photos up to about 768 pixels wide despite 256x256 training resolution.