Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 61
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("cyberagent/opencole-stable-diffusion-xl-base-1.0-finetune", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Model Card for Model ID
This model is a fine-tuned SDXL1.0 model. UNet of the model is fine-tuned on the OpenCOLE1.0 dataset to generate images that looks like graphic design but without texts.
Model Details
Model Description
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- Language(s) (NLP): English
- Finetuned from model: SDXL1.0.
Model Sources [optional]
- Repository: CyberAgentAILab/OpenCOLE
- Paper: OpenCOLE: Towards Reproducible Automatic Graphic Design Generation
Uses
Please refer to OpenCOLE.
Citation
@inproceedings{inoue2024opencole,
title={{OpenCOLE: Towards Reproducible Automatic Graphic Design Generation}},
author={Naoto Inoue and Kento Masui and Wataru Shimoda and Kota Yamaguchi},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
year={2024},
}
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