# instantx-research/instantid

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11,951 stars · 883 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/instantX-research/InstantID
- Homepage: https://instantid.github.io/
- awesome-repositories: https://awesome-repositories.com/repository/instantx-research-instantid.md

## Description

InstantID is a diffusion-based identity preservation framework designed for zero-shot image generation. It allows for the synthesis of images featuring a specific person's facial identity using a single reference photo without requiring additional model training or fine-tuning.

The project distinguishes itself through the use of consistency model distillation to accelerate inference, reducing the number of steps needed to produce high-quality results. It combines identity-preserving feature extraction with multi-modal prompt integration to merge visual embeddings from a reference image with textual scene descriptions.

The system's broader capabilities include spatial guidance via facial landmarks and depth maps, as well as visual style transfer tools that apply artistic aesthetics to images while maintaining the subject's structural identity.

## Tags

### Artificial Intelligence & ML

- [Zero-Shot Identity Synthesis](https://awesome-repositories.com/f/artificial-intelligence-ml/zero-shot-inference/zero-shot-identity-synthesis.md) — Enables zero-shot identity synthesis by injecting facial features from a single reference image without fine-tuning.
- [Feature Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-extraction.md) — Extracts high-level facial embeddings from reference photos to maintain subject consistency across different poses.
- [Identity Adapters](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-models/diffusion-models/identity-adapters.md) — Employs identity adapters within a diffusion framework to maintain facial consistency from a single reference photo.
- [Latent Diffusion Models](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-models/latent-diffusion-models.md) — Utilizes latent diffusion models to generate images by denoising representations in a compressed latent space.
- [Generative Identity Models](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-identity-models.md) — Generates new images of specific individuals by preserving facial characteristics from a single reference photo. ([source](https://github.com/instantx-research/instantid#readme))
- [Personalized Image Synthesis](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-image-models/personalized-image-synthesis.md) — Creates images that synthesize a specific person's appearance within detailed, text-described environments.
- [Identity-Driven Image Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/identity-driven-image-generation.md) — A feature in this project that combines a specific person's appearance from a reference image with detailed text descriptions of a scene. ([source](https://github.com/instantx-research/instantid#readme))
- [Multi-Modal Prompt Integration](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-modal-tokenizers/multi-modal-prompt-integration.md) — Combines textual descriptions with visual identity embeddings to control the final output of the diffusion process.
- [Structural Guidance](https://awesome-repositories.com/f/artificial-intelligence-ml/structural-guidance.md) — Provides structural guidance using facial landmarks and depth maps to constrain the generated image layout.
- [Inference Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-models/diffusion-models/inference-acceleration.md) — Reduces the time and computational steps required for high-quality image generation using consistency models.
- [Generation Accelerators](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-models/generation-accelerators.md) — Accelerates image generation by using consistency models to lower the number of required sampling steps. ([source](https://github.com/instantx-research/instantid#readme))
- [Model Inference Accelerators](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/inference-servers-and-runtimes/model-inference-accelerators.md) — Provides a performance layer via consistency models that accelerates high-quality image generation.
- [Neural Style Transfer](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-style-transfer.md) — Applies artistic aesthetics to images through neural style transfer while maintaining the subject's structural identity. ([source](https://github.com/instantx-research/instantid#readme))

### Part of an Awesome List

- [Consistency](https://awesome-repositories.com/f/awesome-lists/ai/model-distillation/consistency.md) — Implements consistency model distillation to significantly reduce the number of inference steps required for image generation.