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Diffusion Models
Generative models that learn to reverse a noising process. The dominant approach behind modern image, video, and audio synthesis.
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Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation
Diffusion · Sep 8
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LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes
Diffusion · Sep 3 · 7:30
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GenFirst: Generation Before Reconstruction for Stable End-to-End Latent Generative Modeling
Diffusion · Aug 29 · 6:36
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Video Generative Models as Geometry Learner
Diffusion · Aug 28
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Beyond Pixels: From Video Priors to 4D Worlds
Diffusion · Aug 11 · 8:32
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JoyAI-Video-Edit: Real-Time Open-Ended Video Editing with Autoregressive Diffusion
Diffusion · Aug 4 · 8:28
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Hunyuan3D-Buffalo 1.0: A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editing
Diffusion · Aug 3 · 5:59
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PhiZero: A World Model Built Around Physical Language
Diffusion · Jul 30 · 6:56
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DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation
Diffusion · Jul 29 · 7:16
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Meshy T2: Fast Native Mesh Generation with Flow Matching
Diffusion · Jul 28 · 5:42
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