import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Heliosoph/epicrealism-cfg-onnx", torch_dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]epiCRealism (CFG, full quality) — ONNX
ONNX export of emilianJR/epiCRealism (which ships its own VAE). No distillation LoRA — this is the full, non-distilled UNet driven with classifier-free guidance. SD 1.5 architecture, 512×512 native, Euler scheduler, CFG ≈ 7.5, ~25 steps.
This is the quality counterpart to the 4-step Hyper export. The Hyper variant is distilled for fast, CFG-free, 1–4 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 sharp, prompt-faithful output epiCRealism is known for — at a higher per-image cost (the UNet runs twice per step, over ~25 steps).
epiCRealism is a photoreal SD 1.5 fine-tune with broad subject coverage — strongest on environments, landscapes, architecture, interiors, and natural lighting.
Converted artifact. Training credit: emilianJR (epiCRealism).
What this repo contains
model_index.json
feature_extractor/
scheduler/
text_encoder/
tokenizer/
unet/ # epiCRealism UNet, non-distilled (no LoRA)
vae_decoder/ # epiCRealism bundled VAE
vae_encoder/
How it was produced
- Load
emilianJR/epiCRealismviadiffusers(uses its bundled VAE). optimum-cli export onnx(no LoRA fusion step).
Exported at FP32 — 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-epicrealism-cfg.ps1.
Inference notes
| Setting | Value |
|---|---|
| Scheduler | Euler |
| Steps | 25 (20–30 sweet spot) |
| CFG / guidance scale | 7.5 (6–9 usual range) |
| Negative prompt | Supported — use it |
| Resolution | 512×512 native (best results); 768×768 OK |
Classifier-free guidance runs the UNet twice per step (conditional + unconditional) and combines them as uncond + guidance · (cond − uncond). The negative prompt only takes effect when guidance > 1.
License
CreativeML OpenRAIL-M (SD 1.5 + epiCRealism). License files included. By using this model you accept those terms.
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Model tree for Heliosoph/epicrealism-cfg-onnx
Base model
emilianJR/epiCRealism