Nikita Mikhaylov's picture

13 181

Nikita Mikhaylov

mihaylovnikitos

·

AI & ML interests

None yet

Recent Activity

liked a model 2 days ago

microsoft/phi-4

reacted to sanaka87's post with 🚀 3 days ago

🚀 Excited to Share Our Latest Work: 3DIS & 3DIS-FLUX for Multi-Instance Layout-to-Image Generation! ❤️❤️❤️ 🎨 Daily Paper: https://huggingface.co/papers/2501.05131#community 🔓 Code is now open source! 🌐 Project Website: https://limuloo.github.io/3DIS/ 🏠 GitHub Repository: https://github.com/limuloo/3DIS 📄 3DIS Paper: https://arxiv.org/abs/2410.12669 📄 3DIS-FLUX Tech Report: https://arxiv.org/abs/2501.05131 🔥 Why 3DIS & 3DIS-FLUX? Current SOTA multi-instance generation methods are typically adapter-based, requiring additional control modules trained on pre-trained models for layout and instance attribute control. However, with the emergence of more powerful models like FLUX and SD3.5, these methods demand constant retraining and extensive resources. ✨ Our Solution: 3DIS We introduce a decoupled approach that only requires training a low-resolution Layout-to-Depth model to convert layouts into coarse-grained scene depth maps. Leveraging community and company pre-trained models like ControlNet + SAM2, we enable training-free controllable image generation on high-resolution models such as SDXL and FLUX. 🌟 Benefits of Our Decoupled Multi-Instance Generation: 1. Enhanced Control: By constructing scenes using depth maps in the first stage, the model focuses on coarse-grained scene layout, improving control over instance placement. 2. Flexibility & Preservation: The second stage employs training-free rendering methods, allowing seamless integration with various models (e.g., fine-tuned weights, LoRA) while maintaining the generative capabilities of pre-trained models. Join us in advancing Layout-to-Image Generation! Follow and star our repository to stay updated! ⭐

reacted to sanaka87's post with 🔥 3 days ago

🚀 Excited to Share Our Latest Work: 3DIS & 3DIS-FLUX for Multi-Instance Layout-to-Image Generation! ❤️❤️❤️ 🎨 Daily Paper: https://huggingface.co/papers/2501.05131#community 🔓 Code is now open source! 🌐 Project Website: https://limuloo.github.io/3DIS/ 🏠 GitHub Repository: https://github.com/limuloo/3DIS 📄 3DIS Paper: https://arxiv.org/abs/2410.12669 📄 3DIS-FLUX Tech Report: https://arxiv.org/abs/2501.05131 🔥 Why 3DIS & 3DIS-FLUX? Current SOTA multi-instance generation methods are typically adapter-based, requiring additional control modules trained on pre-trained models for layout and instance attribute control. However, with the emergence of more powerful models like FLUX and SD3.5, these methods demand constant retraining and extensive resources. ✨ Our Solution: 3DIS We introduce a decoupled approach that only requires training a low-resolution Layout-to-Depth model to convert layouts into coarse-grained scene depth maps. Leveraging community and company pre-trained models like ControlNet + SAM2, we enable training-free controllable image generation on high-resolution models such as SDXL and FLUX. 🌟 Benefits of Our Decoupled Multi-Instance Generation: 1. Enhanced Control: By constructing scenes using depth maps in the first stage, the model focuses on coarse-grained scene layout, improving control over instance placement. 2. Flexibility & Preservation: The second stage employs training-free rendering methods, allowing seamless integration with various models (e.g., fine-tuned weights, LoRA) while maintaining the generative capabilities of pre-trained models. Join us in advancing Layout-to-Image Generation! Follow and star our repository to stay updated! ⭐

View all activity

Organizations

None yet

models

None public yet

datasets

None public yet