amber-hoardz-v01

Model Card for Amber Hoardz Text-To-3D

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Embracing the content generation is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions

Table of Contents

• Model Card for Amber Hoardz Text-To-3D
• Table of Contents
• Table of Contents
• Model Details
  • Model Description
• Uses
  • Direct Use
  • Downstream Use [Optional]
  • Out-of-Scope Use
• Bias, Risks, and Limitations
  • Recommendations
• Training Details
  • Training Data
  • Training Procedure
    • Preprocessing
    • Speeds, Sizes, Times
• Evaluation
  • Testing Data, Factors & Metrics
    • Testing Data
    • Factors
    • Metrics
  • Results
• Model Examination
• Environmental Impact
• Technical Specifications [optional]
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
• Citation
• Glossary [optional]
• More Information [optional]
• Model Card Authors [optional]
• Model Card Contact
• How to Get Started with the Model

Model Details

Model Description

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Embracing the content generation

• Developed by: More information needed
• Shared by [Optional]: More information needed
• Model type: Language model
• Language(s) (NLP): en
• License: pddl
• Parent Model: More information needed
• Resources for more information: More information needed

Uses

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Direct Use

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Downstream Use [Optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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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.

Recommendations

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Training Details

Training Data

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More information on training data needed

Training Procedure

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Preprocessing

More information needed

Speeds, Sizes, Times

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More information needed

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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More information needed

Factors

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Metrics

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Results

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Model Examination

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Environmental Impact

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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: More information needed

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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BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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How to Get Started with the Model

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<details> <summary> Click to expand </summary>

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