Model Card for lac040-sdxl-lora-v1-1
Versatile Dreambooth LoRA for SDXL based on concept images of a large fire in the center of Eindhoven, May 14th, 2023. The old Philips Lighting Application Centre went up in flames, resulting in massive smoke clouds. The dataset contains images of the remains of the building two months later. The footage was taken on July 19, 2023.
Table of Contents
- Model Card for lac040-sdxl-lora-v1-1
- Table of Contents
- Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Environmental Impact
- Technical Specifications [optional]
- Citation
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
- How to Get Started with the Model
Model Details
Model Description
Versatile Dreambooth LoRA for SDXL based on concept images of a large fire in the center of Eindhoven, May 14th, 2023. The old Philips Lighting Application Centre went up in flames, resulting in massive smoke clouds. The dataset contains images of the remains of the building two months later. The footage was taken on July 19, 2023.
- Developed by: More information needed
- Shared by [Optional]: More information needed
- Model type: Language model
- Language(s) (NLP): en
- License: creativeml-openrail-m
- Parent Model: More information needed
- Resources for more information: More information needed
Uses
Direct Use
Downstream Use [Optional]
Out-of-Scope Use
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
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Training Details
Training Data
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Training Procedure
Preprocessing
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Speeds, Sizes, Times
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Model Examination
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: \usepackage{hyperref} \subsection{CO2 Emission Related to Experiments} Experiments were conducted using Google Cloud Platform in region europe-north1, which has a carbon efficiency of 0.21 kgCO$_2$eq/kWh. A cumulative of 1 hours of computation was performed on hardware of type RTX 3090 (TDP of 350W). Total emissions are estimated to be 0.07 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider. %Uncomment if you bought additional offsets: %XX kg CO2eq were manually offset through \href{link}{Offset Provider}. Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}. @article{lacoste2019quantifying, title={Quantifying the Carbon Emissions of Machine Learning}, author={Lacoste, Alexandre and Luccioni, Alexandra and Schmidt, Victor and Dandres, Thomas}, journal={arXiv preprint arXiv:1910.09700}, year={2019} }
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation
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Glossary [optional]
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Model Card Authors [optional]
L, e, o, n, , v, a, n, , B, o, k, h, o, r, s, t
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How to Get Started with the Model
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