import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Heliosoph/mo-di-hyper-onnx", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Mo-Di Diffusion + Hyper-SD (4-step) — ONNX
ONNX export of nitrosocke/mo-di-diffusion with the ByteDance/Hyper-SD 4-step LoRA fused into the UNet. SD 1.5 architecture, 512×512 native, Euler scheduler, CFG = 1, 4 steps.
Mo-Di is nitrosocke's "modern Disney style" fine-tune — produces character art with the late-2010s Disney/Pixar look. Activator phrase commonly used upstream: modern disney style. Worth including in prompts for stronger style adherence.
Converted artifact. Training credit: nitrosocke (Mo-Di), ByteDance (Hyper-SD).
What this repo contains
model_index.json
feature_extractor/
scheduler/
text_encoder/
tokenizer/
unet/ # Mo-Di UNet + Hyper-SD-15 4-step LoRA fused in
vae_decoder/
vae_encoder/
How it was produced
- Load
nitrosocke/mo-di-diffusionviadiffusers. - Fuse
ByteDance/Hyper-SD/Hyper-SD15-4steps-lora.safetensors. optimum-cli export onnx.
Toolchain: optimum 1.24.0, diffusers 0.31.0, transformers 4.45.2, torch 2.4.x (CUDA 12.4). Conversion script: scripts/export-mo-di-hyper.ps1.
Inference notes
| Setting | Value |
|---|---|
| Scheduler | Euler |
| Steps | 4 |
| CFG / guidance scale | 1.0 |
| Negative prompt | Skip |
| Resolution | 512×512 native |
| Activator | Include modern disney style in prompts for stronger adherence |
License
CreativeML OpenRAIL-M (SD 1.5 + Mo-Di + Hyper-SD). License files included. By using this model you accept those terms.
- Downloads last month
- 13
Model tree for Heliosoph/mo-di-hyper-onnx
Base model
nitrosocke/mo-di-diffusion