# TencentARC/PhotoMaker

PhotoMaker [CVPR 2024]

Repository: https://github.com/TencentARC/PhotoMaker
Canonical: https://ross.abutalabs.com/products/photomaker
Homepage: https://photo-maker.github.io/
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2024-10-31T09:35:15+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 71
- inputs: {"age_days": 1001, "days_push": 671, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10088, forks 810 (observed 2026-08-28T04:10:39.530420+00:00)

## What it is
PhotoMaker is a personalized text-to-image generation method that encodes multiple reference face photos into a stacked ID embedding to generate realistic or stylized human photos of a given identity. It works as an adapter on top of diffusion base models without requiring LoRA fine-tuning, producing results within seconds.

## Use cases
- generate realistic photos of a person from reference face images
- create stylized portraits or avatars of a specific identity
- customize text-to-image output to preserve a person's identity
- generate consistent character images across scenes without training a LoRA
- swap identity into different poses and styles via text prompts

## When to choose
- you need fast identity-preserving image generation without per-subject training
- you want an adapter compatible with existing diffusion base models and LoRAs
- you need both realistic and stylized human photo generation
- you have multiple reference photos of a person and want high ID fidelity

## When to avoid
- you need general-purpose image editing unrelated to human identity
- you cannot run GPU inference locally and don't want to use hosted demos
- you need video or audio generation rather than still images
- your use case requires strict licensing compliance given the non-standard license

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, machine-learning, stable-diffusion
- domain: artificial-intelligence, image-processing, deep-learning
- platform: python, cross-platform
- tags: text-to-image, identity-preservation, diffusion, personalization, cvpr-2024, id-embedding, comfyui, gpu

## Member repositories
- TencentARC/PhotoMaker (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:39.530420+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:19:44.637173+00:00, confidence not recorded.
  - readme: https://github.com/TencentARC/PhotoMaker (fetched 2026-08-28T04:10:39.530420+00:00, sha 9cf20ed55dae)
  - homepage: https://photo-maker.github.io/ (fetched 2026-08-29T08:19:28.625143+00:00, sha c8542280c8be)
- Data as of 2026-08-30T08:39:29.467469+00:00.