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  license: creativeml-openrail-m
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ 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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+
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+ # epiCRealism + Hyper-SD (4-step) — ONNX
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+
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+ ONNX export of [emilianJR/epiCRealism](https://huggingface.co/emilianJR/epiCRealism) 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, Euler scheduler, CFG = 1, **4 steps**.
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+ epiCRealism leans heavily toward photorealistic environments and natural lighting. Pick this one over Realistic Vision when the subject is "a place" rather than "a person."
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+
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+ Converted artifact, not a new model. Training credit: emilianJR (epiCRealism), ByteDance (Hyper-SD).
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+ ## What this repo contains
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+
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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 + Hyper-SD-15 4-step LoRA fused in
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+ vae_decoder/
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+ vae_encoder/
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+ ```
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+
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+ ## How it was produced
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+ 1. Load `emilianJR/epiCRealism` via `diffusers`.
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+ 2. Load `ByteDance/Hyper-SD/Hyper-SD15-4steps-lora.safetensors` via `peft`, `fuse_lora()` into UNet.
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+ 3. Save fused pipeline; run `optimum-cli export onnx`.
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+
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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-hyper.ps1`](https://github.com/[YOUR_GH_ORG]/DatumIngest/blob/main/scripts/export-epicrealism-hyper.ps1).
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+
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+ ## Inference notes
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+
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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 |
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+
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+ ## License
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+
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+ CreativeML OpenRAIL-M, inherited from SD 1.5 + epiCRealism + Hyper-SD. License files included. By using this model you accept those terms.