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Nikita Mikhaylov

mihaylovnikitos
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liked a model 2 days ago
microsoft/phi-4
reacted to sanaka87's post with šŸš€ 3 days ago
šŸš€ Excited to Share Our Latest Work: 3DIS & 3DIS-FLUX for Multi-Instance Layout-to-Image Generation! ā¤ļøā¤ļøā¤ļø šŸŽØ Daily Paper: https://huggingface.co/papers/2501.05131#community šŸ”“ Code is now open source! šŸŒ Project Website: https://limuloo.github.io/3DIS/ šŸ  GitHub Repository: https://github.com/limuloo/3DIS šŸ“„ 3DIS Paper: https://arxiv.org/abs/2410.12669 šŸ“„ 3DIS-FLUX Tech Report: https://arxiv.org/abs/2501.05131 šŸ”„ 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. āœØ 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. šŸŒŸ 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! ā­
reacted to sanaka87's post with šŸ”„ 3 days ago
šŸš€ Excited to Share Our Latest Work: 3DIS & 3DIS-FLUX for Multi-Instance Layout-to-Image Generation! ā¤ļøā¤ļøā¤ļø šŸŽØ Daily Paper: https://huggingface.co/papers/2501.05131#community šŸ”“ Code is now open source! šŸŒ Project Website: https://limuloo.github.io/3DIS/ šŸ  GitHub Repository: https://github.com/limuloo/3DIS šŸ“„ 3DIS Paper: https://arxiv.org/abs/2410.12669 šŸ“„ 3DIS-FLUX Tech Report: https://arxiv.org/abs/2501.05131 šŸ”„ 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. āœØ 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. šŸŒŸ 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! ā­
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