Speridlabs Idol

Install

pip install "libreyolo[gen]"

The gen extra installs diffusers, transformers and accelerate. Speridlabs Idol reads its DINOv3 encoder through transformers and its FLUX.2 VAE through diffusers. Importing LibreGen does not import either; they load on first use.

Predict

from libreyolo import LibreGen # An example image from the Speridlabs Idol SDK. A local path, a PIL# image, a NumPy array or a folder of images works the same way.IMAGE = "https://raw.githubusercontent.com/speridlabs/idol-sdk/4702a7f00850e34b6e83a2170cc123c3e9570744/demo/examples/nb-armchair.webp" model = LibreGen("speridlabs-idol")   # weights download on first useresult = model(IMAGE, seed=0) result.save("armchair.ply")           # standard 3DGS layoutresult.save("armchair.splat")         # compact web formatprint(len(result.gaussians))          # 262144 at the defaults

# Saves the object as .ply under runs/generate/predict.libreyolo generate source=https://raw.githubusercontent.com/speridlabs/idol-sdk/4702a7f00850e34b6e83a2170cc123c3e9570744/demo/examples/nb-armchair.webp seed=0

from libreyolo import LibreGen IMAGE = "https://raw.githubusercontent.com/speridlabs/idol-sdk/4702a7f00850e34b6e83a2170cc123c3e9570744/demo/examples/nb-armchair.webp" model = LibreGen("speridlabs-idol") result = model(IMAGE, mode="real")             # photographs: a looser cropresult = model(IMAGE, gaussians=1_048_576)     # more detailresult = model(IMAGE, steps=25)                # faster, a little less detail # Two objects from one image, seeds 7 and 8, written to runs/generate/predict.results = model(IMAGE, seed=7, n=2, save=True)

from libreyolo import LibreGen IMAGE = "https://raw.githubusercontent.com/speridlabs/idol-sdk/4702a7f00850e34b6e83a2170cc123c3e9570744/demo/examples/nb-armchair.webp" model = LibreGen("speridlabs-idol")result = model(IMAGE) g = result.gaussiansprint(g.xyz.shape, g.scales.shape, g.rotations.shape)   # (N, 3), (N, 3), (N, 4) wxyzprint(g.opacities.shape, g.colors.shape)                 # (N,), (N, 3) RGBprint(g.camera)                                          # the inferred input viewprint(g.seed, g.params)                                  # how to make it again g.input_image.save("crop.png")         # what the model saw after its cropresult.plot().save("preview.png")      # point-render previewg[g.opacities > 0.1].save("opaque.ply")

The source is anything predict takes: a path, URL, PIL.Image, NumPy array (BGR), tensor, a folder of images, or a list of them. An image file or PIL.Image with real transparency is used as the object's cutout. Any other input has its background removed first, by LibreYOLO's BiRefNet network with the BiRefNet weights from the Idol snapshot. The input resolution is fixed, so imgsz raises, and so does prompt=: the model has no text input.

The first call downloads about 5 GB at pinned revisions, with nothing gated and no Hugging Face login: Idol's own weights (about 3.1 GB) from Speridlabs' repository speridlabs/idol into weights/LibreSperidlabsIdol0.6b/, plus two image encoders into the Hugging Face cache, the DINOv3 ViT-H+/16 encoder (1.7 GB) and the FLUX.2 VAE (0.2 GB). LibreGen(path) loads a local directory that holds the snapshot files instead.

One source with n=1 returns a Results; several sources or n > 1 return a list, and stream=True a generator. result.gaussians holds the object in its own frame: z up, centered in the cube [-0.5, 0.5]^3. save() picks the format from the extension. A .ply is the standard 3D Gaussian Splatting layout, turned y-up the way splat viewers and trainers expect, with the seed, camera and settings stored in a header comment. A .splat is the compact web format, 32 bytes per Gaussian. result.plot() returns a point render from the inferred input view, a quick look rather than a Gaussian rasterization. The 3D generation task page describes the payload in full.

A seed, with the same model and settings, gives the same object. Object i of a source uses seed + i; without a seed, a fresh one is drawn and recorded in result.gaussians.seed. As in the SDK, the seed drives a random generator on the model's device, so one seed gives different objects on CUDA, MPS and CPU. On CUDA a seed gives the object the Speridlabs SDK gives for the same seed and settings. Rarely, part of the Gaussians land differently from one run to the next while the object stays the same: one attention step runs on a cuDNN kernel whose result depends on GPU memory history, in the SDK as in the port.

Examples

Eleven objects, each generated from one photo of a sculpture or building in the Basque Country. Pick a photo to load its object.

Peine del Viento

Eduardo Chillida

260,901 Gaussians, seed 7, 10.1 s on an H100

Drag to turn, right-drag to pan
Pinch or ctrl + scroll to zoom, double-click to reset

Peine del VientoGuggenheim Museum BilbaoPuppyMamanEl CaminanteConstrucción vacíaVariante ovoidePaloma de la PazTall Tree & The EyeGure Aitaren EtxeaSanctuary of Loyola

Each object was generated by Speridlabs Idol in LibreYOLO from the one photo shown with it, with mode="real" and the default settings, then stored at about 4 MB for the web. The objects are shared under CC BY-SA 4.0. Photos via Wikimedia Commons: Peine del Viento by Ermell, CC BY-SA 4.0; Guggenheim Museum Bilbao by Jules Verne Times Two, CC BY-SA 4.0; Puppy by José Ligero Loarte, CC BY-SA 4.0; Maman by Didier Descouens, CC BY-SA 4.0; El Caminante by Oscar Lafuente, CC BY-SA 3.0; Construcción vacía by Iñaki LLM, CC BY-SA 4.0; Variante ovoide by Kamahele, CC BY-SA 3.0 de; Paloma de la Paz by DONOSTIA KULTURA, CC BY-SA 2.0; Tall Tree & The Eye by Zarateman, CC0; Gure Aitaren Etxea by Andy Roberts from East London, England, CC BY 2.0; Sanctuary of Loyola by Jose Luis Filpo Cabana, CC BY 3.0.

The article Speridlabs Idol in LibreYOLO: one image to a 3D object walks through how they were made.

Settings

Every setting has a default, so all are optional. A name the model does not have raises and lists the supported ones.

The constructor takes device ("auto" picks CUDA, then MPS, then CPU) and compile. compile=True needs CUDA: it compiles the denoiser blocks, runs the guided and unguided passes in one batch, and uses cuDNN attention and TF32 matmuls while sampling, the same path as the SDK's optimized sampling. It reproduces that compiled path; against the eager path it gives the same object, not the same Gaussians. The model runs in its published mixed precision (fp16 transformer blocks, bf16 encoders), so dtype accepts only "auto".

Speed and memory

Time per object at the default settings, one object per call, in the published mixed precision:

The M4 Pro figures are two objects on one machine, the first including the model load from local disk. Speridlabs Idol runs in under 8 GB of GPU memory. Speridlabs publishes request times for its SDK on ten GPUs, from 3.4 s on a B300 to 54.6 s on an L4, on the Idol research page.

Export

Export is not implemented: the model is a sampling loop over several networks, and LibreYOLO has no exportable graph contract for that yet (ADR 0031 in the library). export(), train() and val() raise NotImplementedError. train(data=...) is reserved for fine-tuning on image and 3D object pairs, and val(data=...) for rendered-view metrics.

Licensing

Citation

@misc{canada2026idol,
  title  = {Idol: Single Image to 3D Gaussian Splats},
  author = {Ca{\~n}ada, Alejandro and Garabito, Chema},
  year   = {2026},
  url    = {https://speridlabs.com/research/idol}
}