instantX-research/InstantID

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11987 stars · 885 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

InstantID is a tuning-free, zero-shot identity-preserving image generation method built on diffusion models, generating customized images in various styles from a single reference face image. It works as a plug-and-play module with pre-trained text-to-image models like SD1.5 and SDXL via a novel IdentityNet and has been merged into Hugging Face diffusers.

Use cases

• generate images of a person in different poses and styles from one photo
• create identity-preserving avatars without fine-tuning
• personalize stable diffusion outputs with a single face image
• avoid DreamBooth or LoRA training for face customization
• integrate face-consistent generation into an SDXL pipeline

When to choose

• you need high face fidelity from only a single reference image
• you want tuning-free personalization with no per-identity training
• you already use SD1.5/SDXL or diffusers and want a plug-and-play identity module

When to avoid

• you need style transfer rather than identity preservation
• you lack a GPU or cannot run diffusion model inference
• you need to preserve non-face subjects like objects or pets

Facets

library · maturity active

image-processing machine-learning deep-learning stable-diffusion artificial-intelligence image-processing computer-vision deep-learning python cross-platform identity-preservation text-to-image diffusion-models zero-shot face-generation controlnet sdxl tuning-free gpu

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