fal

GAN-based Super-Resolution for upscaling generated images, a variation of the GigaGAN paper for image-conditioned upscaling. Torch implementation is based on the unofficial lucidrains/gigagan-pytorch repository.

AuraFlow v0.1 is the fully open-sourced largest flow-based text-to-image generation model. This model achieves state-of-the-art results on GenEval. Read our blog post for more technical details. The model is currently in beta. We are working on improving it and the community's feedback is important. Join fal's Discord to give us feedback and stay in touch with the model development. Credits: A huge thank you to @cloneofsimo and @isidentical for bringing this project to life. It's incredible what two cracked engineers can achieve in such a short period of time. We also extend our gratitude to the incredible researchers whose prior work laid the foundation for our efforts.

Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

Works best at resolution 1536x1024 input images (works on other resolutions as well) Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

Weights for this model are available in Safetensors format. Training was done using fal.ai/models/fal-ai/flux-kontext-trainer/playground.

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Model Name: AuraFace Version: 1.0 Model Type: Deep Learning Model for Face Recognition Architecture: Resnet100 with Additive Angular Margin Loss (based on ArcFace) The original ArcFace model and its theoretical foundation are described in the paper ArcFace: Additive Angular Margin Loss for Deep Face Recognition. AuraFace is a highly discriminative face recognition model designed using the Additive Angular Margin Loss approach. It builds upon the principles introduced in ArcFace and has been trained on commercially and publicly available data sources to enable its usage in commercial setting. AuraFace is tailored for scenarios requiring robust and accurate face recognition capabilities with minimal computational overhead. To get a face embedding using AuraFace, it can be used via InsightFace as shown in the example: - E-commerce and Retail: Implement secure facial recognition for payment systems or personalized shopping experiences. - Digital Content Creation: Use the IP-Adapter for creating consistent digital avatars or characters in games and interactive media. - Mobile Applications: Integrate face recognition features into apps for enhanced user experiences and security. 1. The efficacy of the model in subject preservation may vary on the basis of etchnicity. 2. The generalization of the models is limited due to limitations of the training data. AuraFace was trained on a commercial dataset comprising face images from various sources. The dataset tries to include a wide range of demographics, lighting conditions, and image qualities to ensure robust performance across different scenarios, however due to the commercial limitation may not extensively cover all ethnicities. - Normalization: All images were normalized to a standard size and format. - Augmentation: Techniques such as rotation, flipping, and scaling were used to augment the data and improve the model's generalization capabilities. AuraFace has been tested on multiple face recognition benchmarks: - LFW: 0.99650 - CFP-FP: 0.95186 - AGEDB: 0.96100 - CALFW: 0.94700 - CPLFW: 0.90933 Efforts have been made to ensure that AuraFace performs equitably across different demographic groups. However, users should conduct their own assessments to confirm the model's fairness in their specific application context. AuraFace should be used in compliance with all relevant privacy laws and guidelines. Users are responsible for ensuring that their use of the model respects individual privacy and data protection regulations. AuraFace is a powerful and efficient face recognition model designed for commercial applications. It leverages the advanced Additive Angular Margin Loss technique to provide highly accurate and discriminative features. With a commitment to commercial use and ongoing improvements, AuraFace aims to set a new standard in face recognition technology in the commercial space.