A single image turned into a 3D object you can rotate: a tiny two-person Spanish lab, 9 days, 830 stars, mostly earned for it by the ComfyUI community

In 2026 Oct 28, Speridlabs, a small Spanish company calling itself a "spatial AI lab", released Idol: give it one picture of an object and it outputs a 3D Gaussian splatting (3DGS) model you can rotate through 360 degrees, with the back side that the photo never showed also filled in. The project page lists only two people, Alejandro Cañada and Chema Garabito. 9 days later, the GitHub repo speridlabs/idol-sdk had 830 stars and 73 forks, and the model on Hugging Face hit 2 on the site-wide trending board. This piece covers what it runs on, where the stars came from, and what you can learn from it if you want to build an open-source project that gets this kind of traction, and whether to follow it.

What it is

In short: a single image in, a 3D object out, as a standard 3DGS .ply file that any splat viewer can open.

The README's first paragraph is blunt: "It runs on consumer GPUs with 7.2 GB of VRAM." The pipeline automatically removes the background and frames the object, then uses a 370M-parameter diffusion model to generate 32,768 to 1,048,576 Gaussian points. Point count is a parameter: turn it down and the file is only 0.4 MB, turn it up and it's 12.2 MB. The "one dial controls detail" pitch was repeatedly quoted by news sites afterward.

Install is pip install, the license is Apache-2.0, and commercial use is allowed. The only hurdle is requesting access to two dependency models on Hugging Face first (Meta's DINOv3 and Black Forest Labs' FLUX.2 VAE).

The README also honestly says it's built on TripoSplat, which VAST open-sourced last June.

Five things shipped the same day

Idol didn't just drop a repo. 10-28 things went live that day:

• model weights on Hugging Face;
• a live demo (free HF GPU) with 93 preloaded sample images, one click to get a 3D model;
• an SDK you can pip install, plus a local Gradio UI;
• a gallery-style project page: a gingerbread house, roller skates, a red tulip chair, every one rotating;
• a comparison table putting its predecessor TripoSplat, Microsoft's TRELLIS, and the older LGM side by side, with Idol bolded in every column except PSNR.

That table became the main image for nearly every repost after. One caveat: the numbers in the table are their own tests, and the README's "under 2 seconds" claim doesn't match the 6.8 seconds on H100 in the table. comfyui-wiki pointed this out the same day. The gap is real, but not large.

How the stars grew

(GitHub daily snapshots; X view counts as of 11-06.)

The peak came the day after Comfy-Org's official packaging. The timing lines up, but there's no direct evidence the one caused the other (inference).

The more telling numbers are the ones outside GitHub: on Hugging Face the original model has 874 likes and 19,139 downloads. The Comfy-Org packaged version has 26,645 downloads, more than the original (HF, 11-06). fal.ai has also turned it into a pay-per-call API. So the GitHub stars are a byproduct of the HF and ComfyUI mainline, and most people actually using it aren't on GitHub.

Hacker News had one post, 2 points. There's a thread on Reddit's r/StableDiffusion, score not captured. No major influencer reposts seen.

Who spread it

The ComfyUI crowd. This was the biggest amplifier. People who play with Stable Diffusion and ComfyUI already have GPUs with around 12 GB of VRAM, and they'd long wanted a local image-to-3D tool. 7.2 GB of VRAM sits right in their reach. Someone wrote a ComfyUI node the day after launch, at least 6 within a week, and eventually ComfyUI's official org stepped in to package it.

Ports to other platforms. 11-03: Italian dev Ivan Fioravanti posted on LinkedIn and X that he had "quickly ported it to Apple MLX"; the same day @Stefan_3D_AI reposted it under the headline "The best local image-to-3D model now runs on Apple silicon," adding "peak around 8.6 GB, so a 16 GB Mac is enough." There's also a Rust version, a C# version, a CPU-only idol.cpp, a LichtFeld viewer plugin, and a small tool called splat2stl that turns splats into 3D-printable STL files. Every derivative project posted about it again within its own circle.

Auto-reposting news sites and accounts. comfyui-wiki, AlphaSignal (at least 3 articles), the Chinese site xiaohu.ai, the Japanese 3D news site izutsuya, and accounts like @HuggingModels that only repost HF trending charts. Secondary spread, but high volume.

Their own previous release. Three weeks earlier they'd shipped an image model, Iris-3B, and posted it on r/StableDiffusion. The Idol project page demos "Iris makes the image, Idol turns it into a 3D model", so the audience from the previous project became the first audience for this one (inference).

Who it's competing with

Single-image-to-3D is a crowded field:

Idol occupies a narrow niche: objects only, splat output only, low VRAM requirements, commercial use allowed, complete tooling. In 9 days it reached over half of its predecessor TripoSplat's star count after five months. The difference isn't the model. It's the package. TripoSplat was tossed out by a big company with nothing prepared for the community.

Where it falls short

A few public comments show the gaps still open:

• "How do you turn a radiance field into a clean mesh, with the baked lighting stripped from the textures?" (@rianflo on X, 11-03). Splats look great, but game engines, modeling software, and 3D printing mostly need meshes.
• "Does the back of the castle hold up after rotating 90 degrees? Single-image splats usually turn to fog right about there." (on X, @rath_abhishek99,11-03)
• Two dependencies require access requests, which turns some people away, and the code hasn't been pushed since 10-29.

If it were you

Verdict: It's worth studying how they launched it. Building yet another model head-on isn't worth it. Training a single-image-to-3D model takes research depth and compute, and the field is held by Microsoft, Meta and Tencent. Its stars were also earned by the HF and ComfyUI ecosystems, not organically on GitHub.

Good fit for: developers already known in circles like ComfyUI, Blender, 3D printing, or Apple MLX, who move fast and can plug a new model into their ecosystem within a day or two of release.

Bad fit for: solo builders trying to create a comparable model from scratch, or anyone counting on GitHub to recommend them on its own.

How to do it: two paths. One is to build the half it didn't do well: splat to clean mesh with lighting removed, splat to printable STL, batch-converting e-commerce photos into rotatable product 3D models. People are already asking for these in the comments, and they aren't tied to Idol, so they'd work with the next model too. The other is to watch new models and be the "first to integrate": when a new open-source 3D or image model drops, ship a ComfyUI node, MLX port or one-click installer within 48 hours. But expect modest star counts from single projects (the first ComfyUI-Idol node got 53 stars). You'll mostly earn a name in the community.

How to get users: put an animated GIF of "one input image + rotating result" on the first screen of the README; post to r/StableDiffusion, the ComfyUI Discord, and X accounts focused on 3D AI (like @Stefan_3D_AI, who reposts local 3D tools); host a demo on HF so people can try it without installing.

When to move: the sooner the better, measured in weeks. A new model's derivative window is usually one to two weeks after release. Tool windows like splat-to-mesh last longer, a few months (inference).

What would show you read it wrong: big labs' next versions like TRELLIS or Hunyuan output clean mesh and splat in one go; or Idol's downloads on HF stall two or three weeks out and the ComfyUI crowd moves on to a new favorite; or your derivative project goes out and nobody picks it up on r/StableDiffusion or in ComfyUI circles within a week.