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An experimental version of IP-Adapter-FaceID: we use face ID embedding from a face recognition model instead of CLIP image embedding, additionally, we use LoRA to improve ID consistency. IP-Adapter-FaceID can generate various style images conditioned on a face with only text prompts. Update 2023/12/27:
- README.md · h94/IP-Adapter-FaceID at main - Hugging Face
An experimental version of IP-Adapter-FaceID: we use face ID...
- ziffir/IP-Adapter-FaceID-0.1 · Datasets at Hugging Face
An experimental version of IP-Adapter-FaceID: we use face ID...
- README.md · h94/IP-Adapter-FaceID at main - Hugging Face
The IP-Adapter-FaceID model, Extended IP Adapter, Generate various style images conditioned on a face with only text prompts. Face consistency and realism.
The IP-Adapter-FaceID model is a cutting-edge tool for generating images conditioned on face embeddings. It utilizes face ID embedding from a face recognition model and incorporates LoRA to improve ID consistency.
An experimental version of IP-Adapter-FaceID: we use face ID embedding from a face recognition model instead of CLIP image embedding, additionally, we use LoRA to improve ID consistency. IP-Adapter-FaceID can generate various style images conditioned on a face with only text prompts. Update 2023/12/27:
An experimental version of IP-Adapter-FaceID: we use face ID embedding from a face recognition model instead of CLIP image embedding, additionally, we use LoRA to improve ID consistency. IP-Adapter-FaceID can generate various style images conditioned on a face with only text prompts.
2 lut 2024 · If only portrait photos are used for training, ID embedding is relatively easy to learn, so we get IP-Adapter-FaceID-Portrait. This also shows that ID embedding can learn better if appropriate strategies are adopted, such as ID loss or GAN loss.
3 lip 2024 · Key Highlights: One-Click Installation & Usage: Discover the simplicity of using the IP Adapter Face ID with my custom-coded Gradio application. Cross-Platform Compatibility: Learn how to operate this tool on Linux, Windows, RunPod, and Kaggle.