Text-to-Image
Diffusers
ONNX
English
StableDiffusionPipeline
stable-diffusion
sd-1.5
hyper-sd
4-step
photorealistic
Instructions to use Heliosoph/realistic-vision-hyper-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Heliosoph/realistic-vision-hyper-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/realistic-vision-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] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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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- hyper-sd
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- onnx
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- text-to-image
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- 4-step
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- photorealistic
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base_model: SG161222/Realistic_Vision_V6.0_B1_noVAE
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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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# Realistic Vision V6 + Hyper-SD (4-step) — ONNX
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ONNX export of [SG161222/Realistic_Vision_V6.0_B1_noVAE](https://huggingface.co/SG161222/Realistic_Vision_V6.0_B1_noVAE) paired with [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) and the [ByteDance/Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD) 4-step LoRA fused into the UNet. SD 1.5 architecture, 512×512 native, Euler scheduler, CFG = 1, **4 steps**.
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Realistic Vision V6 is the photorealistic-portrait flagship of the SD 1.5 ecosystem. Trained on a narrow distribution (people, portraits, photography aesthetics), which is exactly *why* it's more stable across seeds than base SD 1.5 for those subjects.
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> **Heads-up:** Realistic Vision is more NSFW-permissive than the other Hyper variants in this collection. Pair with content filters if that matters for your application.
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Converted artifact. Training credit: SG161222 (Realistic Vision), Stability AI (sd-vae-ft-mse), ByteDance (Hyper-SD).
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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/ # RV6 UNet + Hyper-SD-15 4-step LoRA fused in
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vae_decoder/ # sd-vae-ft-mse (RV6 ships without VAE — paired here)
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vae_encoder/
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```
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## How it was produced
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1. Load `SG161222/Realistic_Vision_V6.0_B1_noVAE` via `diffusers`.
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2. Replace the (missing) VAE with `stabilityai/sd-vae-ft-mse` — the SD 1.5 community-standard fine-tuned VAE.
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3. Load `ByteDance/Hyper-SD/Hyper-SD15-4steps-lora.safetensors` via `peft`, `fuse_lora()` into UNet.
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4. `optimum-cli export onnx`.
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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-realistic-vision-hyper.ps1`](https://github.com/HeliosophLLC/DatumIngest/blob/main/scripts/export-realistic-vision-hyper.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 | 4 |
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| CFG / guidance scale | 1.0 |
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| Negative prompt | Skip |
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| Resolution | 512×512 native (best results); 768×768 OK |
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## License
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CreativeML OpenRAIL-M (SD 1.5 + Realistic Vision + Hyper-SD). License files included. By using this model you accept those terms.
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