Diff-ID: Identity Consistent Facial Image Generation and Morphing via Diffusion Models
Quick Answer
Diff-ID is a novel diffusion-based framework for generating high-resolution facial images that ensures identity consistency, utilizing a custom 210K image dataset and integrating ArcFace and CLIP embeddings.
Quick Take
While it does not surpass InstantID in raw ArcFace Face Similarity, it achieves lower FID and superior identity-realism trade-offs, emphasizing the need for joint evaluation of identity preservation and photorealism.
Key Points
- Diff-ID uses a custom dataset of 210K images from CelebA-HQ, FFHQ, and LAION-Face.
- Integrates ArcFace and CLIP embeddings with a dual cross attention adapter.
- Achieves lower FID scores while maintaining identity realism compared to InstantID.
- Introduces a pseudo discriminator loss based on ArcFace cosine similarity.
- Proposes a DDIM-based morphing pipeline for qualitative facial interpolation.
Paper Resources
Source Excerpt
Generative diffusion models have revolutionized facial image synthesis, yet robust identity preservation in high resolution outputs remains a critical challenge. This issue is especially vital for security systems, biometric authentication, and privacy sensitive applications, where any drift in identity integrity can undermine trust and functionality. We introduce Diff-ID, a diffusion based framework that enforces identity consistency while delivering photorealistic quality. Central to our appro
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