Instructions to use BiliSakura/ProMoE-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/ProMoE-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/ProMoE-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "ProMoETransformer2DModel", | |
| "architecture": "promoe_tc", | |
| "model_config": { | |
| "MoE_config": { | |
| "init_MoeMLP": false, | |
| "interleave": true, | |
| "moe_intermediate_size": 2048, | |
| "num_routed_experts": 12, | |
| "shared_expert_intermediate_size": 2048, | |
| "top_k": 1, | |
| "use_shared_expert": true, | |
| "use_uncond_expert": true | |
| }, | |
| "depth": 24, | |
| "hidden_size": 1024, | |
| "input_size": 32, | |
| "num_classes": 1000, | |
| "num_heads": 16, | |
| "patch_size": 2 | |
| } | |
| } | |