import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("DiffusionLight/TurboLoRA")
prompt = "a perfect mirrored reflective chrome ball sphere, a perfect black dark mirrored reflective chrome ball sphere"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
image = pipe(image=input_image, prompt=prompt).images[0]DiffusionLight's TurboLoRA
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DiffusionLight-Turbo |
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DiffusionLight-Turbo |
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DiffusionLight-Turbo |
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DiffusionLight-Turbo |
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DiffusionLight |
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DiffusionLight |
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DiffusionLight |
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DiffusionLight |
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Underexposed |
Normally exposed |
Underexposed |
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Underexposed |
Normally exposed |
Underexposed |
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Usage
This repository is the weight for an example code that shows how to implement DiffusionLight-Turbo inpaint.py in Diffusers 0.33.0
We recommend checking out Github Repository: Github
Note that the Weight is the same as 'DiffusionLight/Flicker2k', but due to diffusers 0.33.0 breaking the compatibility, we have to store it in a separate model.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Trigger words
| Chromeball | Prompt |
|---|---|
| Normally exposed | a perfect mirrored reflective chrome ball sphere |
| Underexposed | a perfect black dark mirrored reflective chrome ball sphere |
Citation
@inproceedings{Chinchuthakun2025DiffusionLightTurbo,
author = {Chinchuthakun, Worameth and Phongthawee, Pakkapon and Raj, Amit and Jampani, Varun and Khungurn, Pramook and Suwajanakorn, Supasorn},
title = {DiffusionLight-Turbo: Accelerated Light Probes for Free via Single-Pass Chrome Ball Inpainting},
booktitle = {ArXiv},
year = {2025},
}
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Base model
stabilityai/stable-diffusion-xl-base-1.0