import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Heliosoph/juggernaut-xl-lightning-onnx", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]JuggernautXL Lightning — ONNX (fp32)
ONNX export of RunDiffusion/Juggernaut-XL-Lightning — an SDXL fine-tune + Lightning distillation. SDXL architecture (1024×1024 native, dual text encoders), runs in 4 inference steps at CFG = 1.
JuggernautXL is one of the most-downloaded SDXL fine-tunes for photorealistic and fantasy-character art. Lightning distillation brings it down to 4 steps (vs SDXL's native 30+) at minimal quality cost. Pick this when SDXL-Turbo's 1-step output isn't quality-stable enough but you still want fast inference.
Converted artifact. Training credit: RunDiffusion (Juggernaut-XL-Lightning).
What this repo contains
A standard SDXL ONNX diffusers pipeline layout:
model_index.json
scheduler/
text_encoder/ # CLIP-L (OpenAI)
text_encoder_2/ # OpenCLIP-G
tokenizer/
tokenizer_2/
unet/ # JuggernautXL-Lightning UNet (~2.6B params)
vae_decoder/
vae_encoder/
unet/model.onnx has external weights in unet/model.onnx_data. Same for text_encoder_2/ if present.
How it was produced
optimum-cli export onnx --model RunDiffusion/Juggernaut-XL-Lightning --task text-to-image --library diffusers --device cuda --no-post-process <output>
via scripts/export-batch-onnx.ps1 -Models juggernaut-xl-lightning.
Toolchain: optimum 1.24.0, diffusers 0.31.0, transformers 4.45.2, torch 2.4.x (CUDA 12.4).
Why fp32, not fp16
The optimum 1.24 + torch 2.4 + opset-14 fp16 export path produces a numerically broken UNet for SDXL-class models (all-NaN noise predictions from valid conditioning). The fp32 export works correctly. Revisit fp16 if/when a confirmed working toolchain ships.
Practical impact: ~13 GB on disk vs ~6.5 GB at fp16. VRAM budget at inference: ~7–8 GB with the standard scheduler.
Inference notes
| Setting | Value |
|---|---|
| Scheduler | Euler (epsilon prediction — Lightning 4-step is not sample-prediction; the 2-step variant is, but this is the 4-step) |
| Steps | 4 |
| CFG / guidance scale | 1.0 |
| Negative prompt | Skip |
| Resolution | 1024×1024 native |
License
CreativeML OpenRAIL++-M inherited from stabilityai/stable-diffusion-xl-base-1.0 + JuggernautXL's terms. The included LICENSE-creativeml-openrail-pp-m.md travels with redistribution. By using this model you accept those terms — review the use-based restrictions in section II of the license.
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Model tree for Heliosoph/juggernaut-xl-lightning-onnx
Base model
stabilityai/stable-diffusion-xl-base-1.0