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Text To Image repository template

This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps

  1. Specify the requirements by defining a requirements.txt file.
  2. Implement the pipeline.py __init__ and __call__ methods. These methods are called by the Inference API. The __init__ method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The __call__ method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.

Example repos

How to start

First create a repo in https://hf.co/new. Then clone this template and push it to your repo.

git clone https://huggingface.co/templates/text-to-image
cd text-to-image
git remote set-url origin https://huggingface.co/Nymbo/Model_Repo_Template
git push --force

For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1

Doc / guide: https://huggingface.co/docs/hub/model-cards

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Model Card for {{ model_id | default("Model ID", true) }}

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Model Details

Model Description

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  • Developed by: {{ developers | default("[More Information Needed]", true)}}
  • Funded by [optional]: {{ funded_by | default("[More Information Needed]", true)}}
  • Shared by [optional]: {{ shared_by | default("[More Information Needed]", true)}}
  • Model type: {{ model_type | default("[More Information Needed]", true)}}
  • Language(s) (NLP): {{ language | default("[More Information Needed]", true)}}
  • License: {{ license | default("[More Information Needed]", true)}}
  • Finetuned from model [optional]: {{ base_model | default("[More Information Needed]", true)}}

Model Sources [optional]

  • Repository: {{ repo | default("[More Information Needed]", true)}}
  • Paper [optional]: {{ paper | default("[More Information Needed]", true)}}
  • Demo [optional]: {{ demo | default("[More Information Needed]", true)}}

Uses

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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Recommendations

{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

{{ preprocessing | default("[More Information Needed]", true)}}

Training Hyperparameters

  • Training regime: {{ training_regime | default("[More Information Needed]", true)}}

Speeds, Sizes, Times [optional]

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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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Summary

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Model Examination [optional]

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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: {{ hardware_type | default("[More Information Needed]", true)}}
  • Hours used: {{ hours_used | default("[More Information Needed]", true)}}
  • Cloud Provider: {{ cloud_provider | default("[More Information Needed]", true)}}
  • Compute Region: {{ cloud_region | default("[More Information Needed]", true)}}
  • Carbon Emitted: {{ co2_emitted | default("[More Information Needed]", true)}}

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

{{ compute_infrastructure | default("[More Information Needed]", true)}}

Hardware

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Software

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Citation [optional]

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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