---
title: Lyra-2 Explorable Scene
emoji: 🌀
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
suggested_hardware: a100-large
suggested_storage: large
sleep_time: 7200
license: other
license_name: nvidia-source-code-license-for-lyra-2-0
license_link: https://huggingface.co/nvidia/Lyra-2.0/blob/main/LICENSE
hf_oauth: false
startup_duration_timeout: 2h
models:
  - nvidia/Lyra-2.0
tags:
  - gaussian-splatting
  - 3d-reconstruction
  - video-diffusion
  - text-to-3d
  - lyra
  - nvidia
---

# Lyra-2 — Image to Explorable 3D Scene

Upload a single image and a short caption; this Space produces an exploration
video and a walkable Gaussian-splat scene of what's around your viewpoint.
Powered by [`nvidia/Lyra-2.0`](https://huggingface.co/nvidia/Lyra-2.0).

## What you get

| Artifact | Description |
|---|---|
| `mp4` video | Lyra-2's generated camera trajectory through the scene. |
| `reconstructed_scene.ply` | Standard binary Gaussian-splat PLY. Works with any GS viewer. |

## Runtime

- **Cold-boot takes ~60–65 min** before the UI is available — the DCP checkpoint
  load alone is ~60 min on A100 80GB. Space status stays `APP_STARTING` during
  that time; this is covered by `startup_duration_timeout: 2h` below.
- **Once warm: ~13 min per request** on A100 80GB (DMD distillation always on).
- The Lyra-2 graph + DA3 stay resident in GPU memory between requests —
  the 60-min warmup is amortized across every user the Space serves until
  it scales to zero.
- Queue is serial (concurrency 1) — wait for the request ahead of yours.
- Scale-to-zero after 2h idle (next request pays the warmup again).

## Walk the scene on macOS — *coming soon*

The downloaded `.ply` is a standard GS format today; you can drop it into
any GS viewer (Supersplat, antimatter15/splat, etc.).

A dedicated **first-person, walkable** macOS viewer (WASD + mouse-look) is
in the works — stay tuned.

<!--
Internal note (not rendered on Space page): the LyraViewerMac + exporter
pipeline below is ready but not yet published. Unhide this block when the
viewer goes public.

```bash
# After downloading reconstructed_scene.ply + (if provided) cameras.npz:
python3 tools/export_lyra_scene_bundle.py \
  --scene   reconstructed_scene.ply \
  --cameras cameras.npz \
  --output  ~/Desktop/LyraScenes/my_scene.lyrascene \
  --camera-convention opencv
open ~/Code/lyra/LyraVisionViewer/build/LyraViewer.app \
  --args ~/Desktop/LyraScenes/my_scene.lyrascene
```
-->

## Source & attribution

- **Model**: [`nvidia/Lyra-2.0`](https://huggingface.co/nvidia/Lyra-2.0) — weights
  released under NVIDIA Source Code License.
- **Inference code**: [`nv-tlabs/lyra`](https://github.com/nv-tlabs/lyra) (Lyra-2
  subfolder).
- **This Space**: wraps the upstream inference pipeline in a Gradio UI; no model
  modifications.

## Tips for good results

- **Composition matters**. Scenes with visible depth cues (e.g. hallways,
  foreground objects, parallax between planes) reconstruct better than flat
  frontal shots.
- **Captions guide the diffusion**. Describe the setting, mood, and any
  specific elements you want preserved. 1–2 sentences is enough.
- **Hallucinations are expected** in regions the input image can't see. Lyra-2
  fills unseen areas plausibly but not faithfully.
- **DMD vs quality**: fast mode occasionally produces repetitive textures.
  Disable DMD for production-grade results at the cost of ~10× wall-clock.