Text-to-Image
Diffusers
ONNX
English
StableDiffusionPipeline
stable-diffusion
sd-1.5
cfg
photorealistic
Instructions to use Heliosoph/epicrealism-cfg-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Heliosoph/epicrealism-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/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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 2,673 Bytes
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license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- sd-1.5
- onnx
- text-to-image
- cfg
- photorealistic
base_model: emilianJR/epiCRealism
pipeline_tag: text-to-image
language:
- en
---
# epiCRealism (CFG, full quality) β ONNX
ONNX export of [emilianJR/epiCRealism](https://huggingface.co/emilianJR/epiCRealism) (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](https://huggingface.co/Heliosoph/epicrealism-hyper-onnx). 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 sharp, prompt-faithful output epiCRealism is known for β 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 β 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
1. Load `emilianJR/epiCRealism` via `diffusers` (uses its bundled VAE).
2. `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-epicrealism-cfg.ps1`](https://github.com/HeliosophLLC/DatumV/blob/main/scripts/export-epicrealism-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 |
| 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 + epiCRealism). License files included. By using this model you accept those terms. |