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Running on Zero
Running on Zero
| title: PixelMoE | |
| emoji: 🧩 | |
| colorFrom: green | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 6.31.0 | |
| app_file: app.py | |
| short_description: Class-conditional 512x512 pixel-space MoE diffusion | |
| python_version: "3.12" | |
| startup_duration_timeout: 30m | |
| # PixelMoE | |
| Interactive demo for **PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style | |
| Mixture-of-Experts** ([arXiv 2610.18857](https://arxiv.org/abs/2610.18857)). | |
| Pick any of the 1000 ImageNet-1K classes and the Space samples a 512×512 image | |
| with the released **PixelMoE-B-E32A4/16** base model from | |
| [KlingTeam/PixelMoE](https://huggingface.co/KlingTeam/PixelMoE), using the | |
| authors' official sampling settings (Heun solver, 25 steps per hierarchical | |
| stage, CFG 3.7 applied while t < 0.8, second-EMA weights). | |
| PixelMoE runs diffusion entirely in pixel space: every image token activates | |
| 4 of 32 routed experts plus a shared expert in each MoE block, and images are | |
| generated by a two-stage hierarchical flow — coarse structure first, then | |
| details at the target resolution. | |
| ## Advanced settings | |
| - **Steps per stage** — denoising steps per hierarchical stage (25 in the | |
| official `scripts/sample.sh`; each Heun step is two model evaluations). | |
| - **CFG scale / CFG interval max** — classifier-free guidance and the global | |
| timestep below which it is active. | |
| - **Sigma min** — lower bound clamped into the velocity denominator. | |
| The model code is vendored from the MIT-licensed official repository | |
| [KlingAIResearch/PixelMoE](https://github.com/KlingAIResearch/PixelMoE) | |
| (`torch.compile` decorators and the deterministic-scatter toggle are adapted | |
| for the ZeroGPU runtime, otherwise inference is unchanged). | |