Text-to-Image
Diffusers
ONNX
StableDiffusionPipeline
stable-diffusion
sd-1.5
hyper-sd
4-step
midjourney-style
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Heliosoph/openjourney-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]Openjourney + Hyper-SD (4-step) — ONNX
ONNX export of prompthero/openjourney 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.
Openjourney is prompthero's Midjourney-v4-style fine-tune. Activator phrase: mdjrny-v4 style. Pick this one when you want the painterly / illustrative "MJ look."
Converted artifact. Training credit: prompthero (Openjourney), ByteDance (Hyper-SD).
What this repo contains
model_index.json
feature_extractor/
scheduler/
text_encoder/
tokenizer/
unet/ # Openjourney UNet + Hyper-SD-15 4-step LoRA fused in
vae_decoder/
vae_encoder/
How it was produced
- Load
prompthero/openjourneyviadiffusers. - 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-openjourney-hyper.ps1.
Inference notes
| Setting | Value |
|---|---|
| Scheduler | Euler |
| Steps | 4 |
| CFG / guidance scale | 1.0 |
| Negative prompt | Skip |
| Resolution | 512×512 native |
| Activator | Include mdjrny-v4 style in prompts |
License
CreativeML OpenRAIL-M (SD 1.5 + Openjourney + Hyper-SD). License files included. By using this model you accept those terms.
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Model tree for Heliosoph/openjourney-hyper-onnx
Base model
prompthero/openjourney