Text-to-Image
Diffusers
ONNX
English
StableDiffusionPipeline
stable-diffusion
sd-1.5
hyper-sd
4-step
Instructions to use Heliosoph/absolute-reality-hyper-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Heliosoph/absolute-reality-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/absolute-reality-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
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README.md
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3. Save the fused pipeline to a temp directory.
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4. `optimum-cli export onnx --model <temp> <output>`.
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Toolchain: `optimum 1.24.0`, `diffusers 0.31.0`, `transformers 4.45.2`, `torch 2.4.x` (CUDA 12.4), `peft` latest. Full conversion script: [`scripts/export-absolute-reality-hyper.ps1`](https://github.com/
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## Inference notes
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3. Save the fused pipeline to a temp directory.
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4. `optimum-cli export onnx --model <temp> <output>`.
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Toolchain: `optimum 1.24.0`, `diffusers 0.31.0`, `transformers 4.45.2`, `torch 2.4.x` (CUDA 12.4), `peft` latest. Full conversion script: [`scripts/export-absolute-reality-hyper.ps1`](https://github.com/HeliosophLLC/DatumIngest/blob/main/scripts/export-absolute-reality-hyper.ps1) in the DatumIngest repo.
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## Inference notes
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