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AnimateDiff

camenduru

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DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. These are Vision Transformer and ConvNeXt models trained following the method described in the DINOv3 paper. 12 models are provided: - 10 models pretrained on web data (LVD-1689M dataset) - 1 ViT-7B trained from scratch, - 5 ViT-S/S+/B/L/H+ models distilled from the ViT-7B, - 4 ConvNeXt-{T/S/B/L} models distilled from the ViT-7B, - 2 models pretrained on satellite data (SAT-493M dataset) - 1 ViT-7B trained from scratch - 1 ViT-L distilled from the ViT-7B

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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IDM-VTON-F16

camenduru

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TRELLIS.2-4B

camenduru

**TRELLIS.2** is a state-of-the-art large 3D generative model designed for high-fidelity **image-to-3D** generation. It leverages a novel "field-free" sparse voxel structure termed **O-Voxel** and a large-scale flow-matching transformer (4 Billion parameters). Unlike previous methods that rely on iso-surface fields (e.g., SDF, Flexicubes) which struggle with open surfaces or non-manifold geometry, TRELLIS can reconstruct and generate **arbitrary 3D assets** with complex topologies, sharp features, and full Physical-Based Rendering (PBR) materials—including transparency/translucency. *Note: Please refer to the official GitHub Repository for installation instructions and dependencies.*

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