What do the IP-Adapter-FaceID variants (FaceID, Plus, PlusV2, Portrait) condition on, which parameters tune them, and can I use them commercially?
Problem. Keeping one actor's face across generated stills needs an identity signal stronger than a CLIP style/content embedding. Conditions. SD 1.5 or SDXL generation where you have one or a few clean face photos. Method. (1) FaceID uses a face-recognition embedding extracted with insightface (FaceAnalysis name='buffalo_l', det_size=(640,640), faces[0].normed_embedding) instead of a CLIP image embedding, plus a LoRA to improve ID consistency. (2) FaceID-Plus adds a CLIP image embedding for face structure; PlusV2 makes that structure weight controllable: in the model-card code pass shortcut=True and s_scale (example 1.0) to generate(). (3) FaceID-Portrait takes multiple face images (default n_cond=5, num_tokens=16), needs no LoRA and no ControlNet. (4) Typical call parameters shown: num_inference_steps=30, width=512, height=768, seed=2023 with DDIMScheduler and a Realistic Vision base. Failure modes. The card itself says generalisation is limited by training data, base model and face...
Structured fields
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"citations": [
{
"url": "https://huggingface.co/h94/IP-Adapter-FaceID/raw/main/README.md",
"note": "Introduction; Update 2023/12/28; Update 2024/01/19; Usage: FaceID-Plus code; Non-commercial use; License; License, 2025-11-24 Update"
},
{
"url": "https://raw.githubusercontent.com/deepinsight/insightface/master/README.md",
"note": "Introduction; Update 2023/12/28; Update 2024/01/19; Usage: FaceID-Plus code; Non-commercial use; License; License, 2025-11-24 Update"
}
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}Evidence claims
- [sourced_fact] Problem. Keeping one actor's face across generated stills needs an identity signal stronger than a CLIP style/content embedding. Conditions. SD 1.5 or SDXL generation where you have one or a few clean face photos. Method. (1) FaceID uses a face-recognition embedding extracted with insightface (FaceAnalysis name='buffalo_l', det_size=(640,640), faces[0].normed_embedding) instead of a CLIP image embedding, plus a LoRA to improve ID consistency. (2) FaceID-Plus adds a CLIP image embedding for face structure; PlusV2 makes that structure weight controllable: in the model-card code pass shortcut=True and s_scale (example 1.0) to generate(). (3) FaceID-Portrait takes multiple face images (default n_cond=5, num_tokens=16), needs no LoRA and no ControlNet. (4) Typical call parameters shown: num_inference_steps=30, width=512, height=768, seed=2023 with DDIMScheduler and a Realistic Vision base. Failure modes. The card itself says generalisation is limited by training data, base model and face-recognition model. Rights. The card states the FaceID models are research-only because InsightFace pretrained models are non-commercial; InsightFace's README says its trained models and annotated data are non-commercial research only, and (2025-11-24 update) directs licensing requests for buffalo_l to the vendor. Limitations. 'Experimental' per the card; no quantitative identity scores in these sources. Version/date. Model card updates through 2024/01/19; InsightFace README update 2025-11-24. · locator: Introduction; Update 2023/12/28; Update 2024/01/19; Usage: FaceID-Plus code; Non-commercial use; License; License, 2025-11-24 Update source
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |