| --- |
| license: creativeml-openrail-m |
| library_name: diffusers |
| tags: |
| - stable-diffusion |
| - sd-1.5 |
| - hyper-sd |
| - onnx |
| - text-to-image |
| - 4-step |
| base_model: Lykon/AbsoluteReality |
| pipeline_tag: text-to-image |
| language: |
| - en |
| --- |
| |
| # AbsoluteReality + Hyper-SD (4-step) — ONNX |
|
|
| ONNX export of [Lykon/AbsoluteReality](https://huggingface.co/Lykon/AbsoluteReality) with the [ByteDance/Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD) 4-step LoRA fused into the UNet. SD 1.5 architecture, 512×512 native, designed to run with the Euler scheduler at CFG = 1 in **4 inference steps**. |
|
|
| This is a converted artifact, not a new model. All training credit belongs to Lykon (AbsoluteReality) and ByteDance (Hyper-SD). |
|
|
| ## What this repo contains |
|
|
| A standard ONNX diffusers pipeline layout: |
|
|
| ``` |
| model_index.json |
| feature_extractor/ |
| scheduler/ |
| text_encoder/ |
| tokenizer/ |
| unet/ # AbsoluteReality UNet + Hyper-SD-15 4-step LoRA fused in |
| vae_decoder/ |
| vae_encoder/ |
| ``` |
|
|
| `unet/model.onnx` is paired with `unet/model.onnx_data` (external-weights file). Both must be downloaded. |
|
|
| ## How it was produced |
|
|
| 1. Load `Lykon/AbsoluteReality` via `diffusers` (uses its bundled VAE — no separate VAE pairing needed). |
| 2. Load `ByteDance/Hyper-SD/Hyper-SD15-4steps-lora.safetensors` via `peft` and call `fuse_lora()` on the UNet. |
| 3. Save the fused pipeline to a temp directory. |
| 4. `optimum-cli export onnx --model <temp> <output>`. |
|
|
| 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. |
|
|
| ## Inference notes |
|
|
| | Setting | Value | |
| |---|---| |
| | Scheduler | Euler (sample/x0 prediction is **not** required — 4-step Hyper is epsilon) | |
| | Steps | 4 | |
| | CFG / guidance scale | 1.0 (no classifier-free guidance) | |
| | Negative prompt | Skip — CFG = 1 ignores it | |
| | Resolution | 512×512 native (768×768 works, smaller resolutions degrade fast) | |
|
|
| ## License |
|
|
| This export inherits **CreativeML OpenRAIL-M** from the base SD 1.5 lineage and AbsoluteReality. The Hyper-SD LoRA also ships under OpenRAIL-M (ByteDance). Both `LICENSE-*.md` files are included in this repo and travel with redistribution. By using this model you accept those terms — see the included license files for acceptable-use clauses. |
|
|
| ## Citation |
|
|
| If you use this in academic work, cite both the base model and the distillation method: |
|
|
| ```bibtex |
| @misc{lykon-absolutereality, |
| author = {Lykon}, |
| title = {AbsoluteReality}, |
| howpublished = {\url{https://huggingface.co/Lykon/AbsoluteReality}} |
| } |
| @article{ren2024hypersd, |
| title = {Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis}, |
| author = {Ren, Yuxi and others}, |
| journal = {arXiv preprint arXiv:2404.13686}, |
| year = {2024} |
| } |
| ``` |
