A ten-person group photo where AI must place everyone correctly and keep every face right, guided by five to ten reference shots — this has long been the most failure-prone scenario in identity-preserving generation. WithEveryone, a framework released on arXiv by Tencent's Hunyuan-affiliated team (paper), takes a different tack: don't start drawing until you have planned who stands where.

Why Identity Drifts as Groups Grow

The paper opens with the core tension: identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. The model has to do three things at once — retain each identity, bind every reference face to a distinct person, and place that person at a correct location. Meanwhile, training-time identity losses must establish correspondence among several noisy predicted faces. When any of these links slips, you get face-swapping mix-ups, or worse, reference faces copied straight into the output.

Four Components: Plan First, Render Second, Supervise the Right Face

WithEveryone's design breaks into four steps:

  • Addressed tokens: each selected identity is injected as a dedicated token;
  • Identity-layout planning: the model first predicts a structured identity-layout plan, then renders that plan as a visual condition — a seating chart before the photo;
  • Layout-Grounded ID Loss: annotated face regions supervise the intended identity directly, sidestepping unstable embedding-based face matching;
  • ID Representation Forcing: a prediction is additionally trained for each identity before image synthesis.

The keyword here is grounding: identity stops being an abstract concept floating in the prompt and gets anchored to a concrete region in the layout.

The Numbers: Similarity 0.462 to 0.499, Artifacts Cut by About Two-Thirds

On an identity-disjoint benchmark, the paper reports WithEveryone achieving the highest target-context identity similarity: face similarity improves from GPT-Image-2's 0.462 to 0.499, while copy-paste artifacts fall from 0.169 to 0.055 — roughly a two-thirds reduction. On coverage, it spans 97.3% of requested identities with a duplicate rate of only 2.8%. The paper's conclusion: explicit identity-layout grounding lets identity-preserving generation scale to larger groups without relying on direct reference-face copying.

A Practical Catch: No Checkpoints Yet

The detail worth noticing sits in the GitHub repository release plan: the research version in the paper is built on a foundation model whose licensing terms do not allow the checkpoint to be released. To give the community an open alternative, the team is actively training a new version on a foundation model that supports open release; code and checkpoints will follow once ready. In other words, for now you can read the method but not run the weights — a symptom of how base-model licenses increasingly constrain research reproducibility.

The paper made Hugging Face's Daily Papers list on August 21 with 39 upvotes, and the GitHub repo has 43 stars. For teams building multi-person scene synthesis or virtual group-photo applications, this belongs on the watchlist; real adoption, though, waits on the retrained open version. When the weights actually land — and whether the open release trades away any performance — is the leap that separates this technique from paper to tool.