👋Introducing the #Meissonic# & #Menotico# model series, a collaborative pre-trained image generation model by Collov Labs! This marks another step forward in our research journey, following our work on #3D# Prior Image Synthesis and D-edit. In collaboration with University of California, Berkeley AI Research and Stanford University , this series features highly efficient image generation models based on the MIM (Masked Image Modeling) architecture. Designed to surpass traditional diffusion models (such as SDXL), these models enable efficient generation of 1024x1024 and 512x512 images even at the edge. The #Meissonic# & #Menotico# series represents a milestone for Collov Labs as we pursue advancements in hashtag#spatialdesignintelligence#. Our goal is to create stunning, efficient text-to-image models trained with minimal data and parameters, paving the way for cost-effective pre-training that delivers enterprise-grade solutions. By collaborating with clients in real estate and home decor, Collov offers customizable, compliant, and cost-controlled pre-training solutions. Through our post-training pipeline, MIM models excel in downstream tasks, including: 🖼 Precision: Perfectly scaled image generation 💡 Visual Memory: Retaining 3D and 2D concepts with fidelity, like furniture, cabinetry, and flooring textures 🚀 Spatial Reasoning: Intelligent spatial arrangement and drag-and-drop editing capabilities We’re thrilled to contribute to the open-source community, sparking discussions on YouTube and Reddit from Japan, Korea, the U.S., India, the Middle East, and the U.K. Check out this YouTube tutorial on deploying these models efficiently at the edge: We invite you to explore and discuss our work! Our code and full paper are now available: 💻 Hugging Face: 📄 Full Paper: