Instructions to use Heliosoph/openjourney-cfg-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Heliosoph/openjourney-cfg-onnx with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Heliosoph/openjourney-cfg-onnx", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Openjourney v4 (CFG, full quality) β ONNX
ONNX export of prompthero/openjourney-v4 (which ships its own VAE). No distillation LoRA β this is the full, non-distilled UNet driven with classifier-free guidance. SD 1.5 architecture, 512Γ512 native, Euler scheduler, CFG β 7.5, ~25 steps.
This is the quality counterpart to the 4-step Hyper export. The Hyper variant is distilled for fast, CFG-free, 1β4 step sampling; it's great for previews and batch work but caps fidelity and prompt adherence. This non-distilled export, run with classifier-free guidance, a negative prompt, and a normal step budget, recovers the cinematic, prompt-faithful output Openjourney is known for β at a higher per-image cost (the UNet runs twice per step, over ~25 steps).
Openjourney v4 is a Midjourney-v4-style SD 1.5 fine-tune. No activator phrase β unlike v1/v3 it dropped the mdjrny-v4 style trigger; prompt it like any modern fine-tune.
Converted artifact. Training credit: PromptHero (Openjourney).
What this repo contains
model_index.json
feature_extractor/
scheduler/
text_encoder/
tokenizer/
unet/ # Openjourney v4 UNet, non-distilled (no LoRA)
vae_decoder/ # Openjourney v4 bundled VAE
vae_encoder/
How it was produced
- Load
prompthero/openjourney-v4viadiffusers(uses its bundled VAE). optimum-cli export onnx(no LoRA fusion step).
Exported at FP32 β the SD 1.5 VAE is fp16-fragile (it overflows and posterizes), so the quality export stays full precision.
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-cfg.ps1.
Inference notes
| Setting | Value |
|---|---|
| Scheduler | Euler |
| Steps | 25 (20β30 sweet spot) |
| CFG / guidance scale | 7.5 (6β9 usual range) |
| Negative prompt | Supported β use it |
| Activator phrase | None (v4 dropped the v1/v3 trigger) |
| Resolution | 512Γ512 native (best results); 768Γ768 OK |
Classifier-free guidance runs the UNet twice per step (conditional + unconditional) and combines them as uncond + guidance Β· (cond β uncond). The negative prompt only takes effect when guidance > 1.
License
CreativeML OpenRAIL-M (SD 1.5 + Openjourney). License files included. By using this model you accept those terms.
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Model tree for Heliosoph/openjourney-cfg-onnx
Base model
prompthero/openjourney-v4