Visual Generation Models
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18 items • Updated • 1
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Check out the documentation for more information.
ProMoE — Hub custom pipeline
Load checkpoints with native Hugging Face diffusers and this folder on the Hub (or via custom_pipeline):
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"BiliSakura/ProMoE-diffusers",
trust_remote_code=True,
torch_dtype=torch.float16,
)
pipe.to("cuda")
Hub layout
| Path | Purpose |
|---|---|
pipeline.py |
ProMoEPipeline |
transformer/ |
backbone_diffmoe.py, backbone_dit.py, backbone_ecdit.py, backbone_promoe_ec.py, backbone_promoe_tc.py, backbone_tcdit.py, … |
scheduler/ |
scheduling_flow_match_promoe.py |
ImageNet class labels
Each variant keeps an English id2label map in model_index.json (DiT-style).
pipe.id2label— id → English label (comma-separated synonyms)pipe(class_labels=207, ...)— class-conditional sampling with integer ids
Copy the full 1000-class id2label block from BiliSakura/DiT-diffusers when publishing a model repo.
model_index.json
Copy entries from model_index.json.example into your model repo after save_pretrained.
Use ["_class_name"] = ["pipeline", "ProMoEPipeline"] and custom module stems for each component.
- FlowMatch scheduler:
"scheduler": ["scheduling_flow_match_promoe", "ProMoEFlowMatchScheduler"] - VAE:
"vae": ["diffusers", "AutoencoderKL"]withstabilityai/sd-vae-ft-mseweights or bundled safetensors - ProMoE-TC presets:
ProMoE_TC_S,ProMoE_TC_B,ProMoE_TC_L,ProMoE_TC_XL(see convert script)
Regenerate: python scripts/build_community_pipelines.py
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