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
Update README.md
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README.md
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---
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license: creativeml-openrail-m
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---
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---
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license: creativeml-openrail-m
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library_name: diffusers
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tags:
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- stable-diffusion
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- sd-1.5
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- onnx
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- text-to-image
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- cfg
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- photorealistic
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base_model: emilianJR/epiCRealism
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pipeline_tag: text-to-image
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language:
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- en
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---
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# epiCRealism (CFG, full quality) — ONNX
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ONNX export of [emilianJR/epiCRealism](https://huggingface.co/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**.
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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, 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).
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epiCRealism is a photoreal SD 1.5 fine-tune with broad subject coverage — strongest on environments, landscapes, architecture, interiors, and natural lighting.
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Converted artifact. Training credit: emilianJR (epiCRealism).
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## What this repo contains
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```
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model_index.json
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feature_extractor/
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scheduler/
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text_encoder/
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tokenizer/
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unet/ # epiCRealism UNet, non-distilled (no LoRA)
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vae_decoder/ # epiCRealism bundled VAE
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vae_encoder/
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```
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## How it was produced
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1. Load `emilianJR/epiCRealism` via `diffusers` (uses its bundled VAE).
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2. `optimum-cli export onnx` (no LoRA fusion step).
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Exported at FP32 — the SD 1.5 VAE is fp16-fragile (it overflows and posterizes), so the quality export stays full precision.
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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).
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## Inference notes
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| Setting | Value |
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|---|---|
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| Scheduler | Euler |
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| Steps | 25 (20–30 sweet spot) |
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| CFG / guidance scale | 7.5 (6–9 usual range) |
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| Negative prompt | Supported — use it |
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| Resolution | 512×512 native (best results); 768×768 OK |
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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`.
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## License
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CreativeML OpenRAIL-M (SD 1.5 + epiCRealism). License files included. By using this model you accept those terms.
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