The project's README from github.com/speridlabs/idol-sdk. As one markdown file: README.md.
A single image to 3DGS object model
Idol turns one picture of an object into a 3D Gaussian splat. Give it a render, a generated image or a photo: it removes the background, frames the object and generates up to 1,048,576 Gaussians, including the sides the picture never showed. It runs on consumer GPUs, using 7.2 GB of VRAM and can run in under 2 seconds.
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Each pair: the input image and the generated object turning. The demo has 93 more examples ready to run and takes your own images as well.
| Output | 3D Gaussians: 32,768 to 1,048,576 (default 262,144), standard 3DGS .ply |
| DiT | 370M parameters |
| Image conditioning | DINOv3 ViT-H+ and FLUX.2 VAE |
| Decoder | 288M parameters |
| Background removal | BiRefNet |
| Sampler | Euler, 50 steps, classifier-free guidance 3.0, time shift 3.0 |
pip install -e . # Python >= 3.10, PyTorch >= 2.5
pip install -e ".[demo]" # + Gradio, to run the demo locally
The weights are downloaded from Hugging Face on first use, you need to request access to
facebook/dinov3-vith16plus-pretrain-lvd1689m
and black-forest-labs/FLUX.2-dev first, then log in with
hf auth login.
flash-attn, if installed, is used for attention; otherwise PyTorch's scaled_dot_product_attention is (same
result, slower). Set ATTN_BACKEND=sdpa or ATTN_BACKEND=flash_attn to choose.
python -m idol.sample --image photo.jpg --mode real
python -m idol.sample --image renders/ --mode synthetic --out outputs
For each image this writes <name>.ply (open it in any Gaussian splat viewer), <name>_input.png (the image the model saw, after background
removal and cropping) and <name>_camera.json (the predicted camera of the input).
Pick the mode for your input:
--mode |
for |
|---|---|
synthetic (default) |
computer generated images |
real |
real world photographs |
Other flags:
| flag | default | |
|---|---|---|
--seed |
42 | same seed, same object |
--steps |
50 | sampling steps |
--cfg_strength |
3.0 | classifier-free guidance |
--rescale_t |
3.0 | time shift: higher spends more steps at high noise |
--num_gaussians |
262144 | 32,768 to 1,048,576, a multiple of 32 |
--dinov3_dtype |
bf16 | fp32 runs the DINOv3 encoder in full precision (1.7 GB more memory) |
--weights |
speridlabs/idol |
a Hugging Face repo or a local folder |
--no_optimize |
optimized | turn off the faster sampling (see below) |
Faster sampling is on by default on GPU The first image in a process is slower while it pytorch compiles; the rest run at full speed.
from idol import IdolPipeline
pipeline = IdolPipeline.from_pretrained("speridlabs/idol")
output = pipeline("photo.jpg", mode="real", seed=42, num_gaussians=1_048_576)
output.gaussian.save_ply("photo.ply")
output.preprocessed_image.save("photo_input.png")
The Gradio app behind the Hugging Face Space is in demo/,
with the example images. It runs on ZeroGPU there and on any local GPU:
pip install -e ".[demo]"
cd demo && python app.py
| Component | Parameters | Precision on GPU | Weights in VRAM |
|---|---|---|---|
| DiT (denoiser) | 370.6M | FP16 transformer blocks; FP32 embeddings and norms | ~0.8–1.0 GB |
| Octree decoder | 50.8M | FP16 transformer blocks; FP32 embeddings and norms | ~0.1–0.2 GB |
| Gaussian decoder | 237.3M | FP16 transformer blocks; FP32 embeddings and norms | ~0.5–0.9 GB |
| BiRefNet (background removal) | 220.7M | FP16 | ~0.44 GB |
| DINOv3 ViT-H+/16 | 840.6M | bf16 | ~1.68 GB |
| FLUX.2 VAE | 84.1M | bf16 (FP32 checkpoint, cast on load) | ~0.17 GB |
| Full request | ~1.80B | mostly FP16 / bf16, some FP32 | ~3.7–4.4 GB of weights, 7.2 GB peak |
Idol, TripoSplat, TRELLIS and LGM, scored at the input view (PSNR, SSIM, LPIPS, IoU) and at seven new views (DINOv2, CLIP, sharpness distance). Mean scores, 1 × H100 per model. Bold is the best in each column.
| Model | Setting | PSNR ↑ | SSIM ↑ | LPIPS ↓ | IoU ↑ | DINOv2 ↑ | CLIP ↑ | Sharpness distance ↓ | Request time ↓ |
|---|---|---|---|---|---|---|---|---|---|
| Idol | 50 steps, 262,144 Gaussians | 12.25 | 0.726 | 0.213 | 0.862 | 0.781 | 0.885 | 0.81 | 6.8 s |
| TripoSplat | 50 steps, 262,144 Gaussians | 11.76 | 0.712 | 0.225 | 0.860 | 0.743 | 0.868 | 1.03 | 9.9 s |
| TRELLIS | Paper setting, 50 + 50 steps | 11.89 | 0.716 | 0.235 | 0.845 | 0.754 | 0.868 | 0.97 | 7.1 s |
| LGM | Default, 65,536 Gaussians | 12.46* | 0.722 | 0.237 | 0.834 | 0.575 | 0.784 | 1.22 | 1.1 s |
* LGM has the highest PSNR despite visibly blurry results, because PSNR averages squared pixel error.
One image to 262,144 Gaussians in 3.4 s. Request time at 50 steps: median over 8 images, with the same code and settings on every GPU, one GPU per machine.
| GPU | VRAM | Request time |
|---|---|---|
| B300 | 269 GB | 3.4 s |
| B200 | 179 GB | 3.6 s |
| H200 | 140 GB | 6.6 s |
| H100 | 80 GB | 6.7 s |
| RTX 5090 | 32 GB | 14.1 s |
| A100 | 80 GB | 16.3 s |
| L40S | 45 GB | 16.6 s |
| RTX 4090 | 24 GB | 19.1 s |
| RTX 3080 | 10 GB | 46.2 s |
| L4 | 22 GB | 54.6 s |
Idol builds on TripoSplat. It uses DINOv3, the FLUX.2 VAE and BiRefNet.
Made with ❤️ by speridlabs.com
Idol turns one picture of an object into a 3D Gaussian splat. Give it a render, a generated image or a photo: it removes the background, frames the object and generates up to 1,048,576 Gaussians, including the sides the picture never showed.