README.md

metadata

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).

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, 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 (torch.compile decorators and the deterministic-scatter toggle are adapted for the ZeroGPU runtime, otherwise inference is unchanged).