FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. FastComposer - новый проект MIT, который обеспечивает высококачественное, персонализированное, многопредметное преобразование текста в изображение без тонкой настройки. 🖥 Github: https://github.com/mit-han-lab/fastcomposer ⏩ Paper: https://arxiv.org/abs/2305.10431v1 📌 Dataset: https://paperswithcode.com/dataset/ffhq ⭐️ Project: https://fastcomposer.mit.edu/ ai_machinelearning_big_data