import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Heliosoph/epicrealism-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]epiCRealism + Hyper-SD (4-step) — ONNX
ONNX export of emilianJR/epiCRealism 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.
epiCRealism leans heavily toward photorealistic environments and natural lighting. Pick this one over Realistic Vision when the subject is "a place" rather than "a person."
Converted artifact, not a new model. Training credit: emilianJR (epiCRealism), ByteDance (Hyper-SD).
What this repo contains
model_index.json
feature_extractor/
scheduler/
text_encoder/
tokenizer/
unet/ # epiCRealism UNet + Hyper-SD-15 4-step LoRA fused in
vae_decoder/
vae_encoder/
How it was produced
- Load
emilianJR/epiCRealismviadiffusers. - Load
ByteDance/Hyper-SD/Hyper-SD15-4steps-lora.safetensorsviapeft,fuse_lora()into UNet. - Save fused pipeline; run
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-epicrealism-hyper.ps1.
Inference notes
| Setting | Value |
|---|---|
| Scheduler | Euler |
| Steps | 4 |
| CFG / guidance scale | 1.0 |
| Negative prompt | Skip |
| Resolution | 512×512 native |
License
CreativeML OpenRAIL-M, inherited from SD 1.5 + epiCRealism + Hyper-SD. License files included. By using this model you accept those terms.
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Base model
emilianJR/epiCRealism