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Text to image, Language models
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repliedto savoji's post about 7 hours ago Idol, a new Apache-2.0 image-to-3D model from @speridlabs, makes a 3D Gaussian splat from one image in 7.2 GB of peak VRAM. It is the #1 trending model on the Hub with 1,057 likes.
It writes a standard 3DGS .ply with up to 1,048,576 Gaussians, including sides the picture never showed, and does background removal and framing itself. The catch: the only quality evidence is the authors' own benchmark, on a repo created 2026-10-28.
š The numbers
ā¢ Authors' benchmark, 1 x H100: best in 6 of 8 columns vs TripoSplat, TRELLIS and LGM (SSIM 0.726, LPIPS 0.213, DINOv2 0.781, CLIP 0.885)
ā¢ LGM wins PSNR (12.46 vs 12.25) and speed (1.1 s). Idol takes 6.8 s vs 7.1 s for TRELLIS and 9.9 s for TripoSplat
ā¢ 262,144 Gaussians, 50 steps: 3.4 s on B300, 6.7 s on H100, 19.1 s on RTX 4090, 46.2 s on a 10 GB RTX 3080
š ļø Try it
ā¢ Install idol-sdk, accept the terms of the gated DINOv3 and FLUX.2-dev models, run hf auth login
ā¢ python -m idol.sample --image photo.jpg --mode real
ā¢ --num_gaussians runs from 32,768 (0.4 MB) to 1,048,576 (12.2 MB)
ā ļø Caveats
ā¢ Test set and size are not named, and no independent evaluation exists yet
ā¢ The card says "under 2 seconds", but the timing table shows 3.4 s at best
ā¢ Single objects only, NVIDIA CUDA required, no failure cases shown
https://huggingface.co/speridlabs/idol
https://speridlabs.com/research/idol
If you run it on your own messy photos, how do the unseen sides hold up?
š Read it elsewhere
ā¢ The deep dive on Medium: https://medium.com/p/5f660ae6ae56
ā¢ The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/speridlabs-idol-builds-3d-splats
ā¢ The thread on X: https://x.com/saavoji/status/2119137739580858545
ā¢ Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#speridlabs-idol repliedto savoji's post about 10 hours ago Idol, a new Apache-2.0 image-to-3D model from @speridlabs, makes a 3D Gaussian splat from one image in 7.2 GB of peak VRAM. It is the #1 trending model on the Hub with 1,057 likes.
It writes a standard 3DGS .ply with up to 1,048,576 Gaussians, including sides the picture never showed, and does background removal and framing itself. The catch: the only quality evidence is the authors' own benchmark, on a repo created 2026-10-28.
š The numbers
ā¢ Authors' benchmark, 1 x H100: best in 6 of 8 columns vs TripoSplat, TRELLIS and LGM (SSIM 0.726, LPIPS 0.213, DINOv2 0.781, CLIP 0.885)
ā¢ LGM wins PSNR (12.46 vs 12.25) and speed (1.1 s). Idol takes 6.8 s vs 7.1 s for TRELLIS and 9.9 s for TripoSplat
ā¢ 262,144 Gaussians, 50 steps: 3.4 s on B300, 6.7 s on H100, 19.1 s on RTX 4090, 46.2 s on a 10 GB RTX 3080
š ļø Try it
ā¢ Install idol-sdk, accept the terms of the gated DINOv3 and FLUX.2-dev models, run hf auth login
ā¢ python -m idol.sample --image photo.jpg --mode real
ā¢ --num_gaussians runs from 32,768 (0.4 MB) to 1,048,576 (12.2 MB)
ā ļø Caveats
ā¢ Test set and size are not named, and no independent evaluation exists yet
ā¢ The card says "under 2 seconds", but the timing table shows 3.4 s at best
ā¢ Single objects only, NVIDIA CUDA required, no failure cases shown
https://huggingface.co/speridlabs/idol
https://speridlabs.com/research/idol
If you run it on your own messy photos, how do the unseen sides hold up?
š Read it elsewhere
ā¢ The deep dive on Medium: https://medium.com/p/5f660ae6ae56
ā¢ The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/speridlabs-idol-builds-3d-splats
ā¢ The thread on X: https://x.com/saavoji/status/2119137739580858545
ā¢ Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#speridlabs-idol Organizations
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