Instructions to use RobinWZQ/backdoor_KMMD_len_8_a_man with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobinWZQ/backdoor_KMMD_len_8_a_man with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RobinWZQ/backdoor_KMMD_len_8_a_man") model = AutoModel.from_pretrained("RobinWZQ/backdoor_KMMD_len_8_a_man") - Notebooks
- Google Colab
- Kaggle
Improve model card: add pipeline tag, library name, paper, code link, and usage
#1
by nielsr HF Staff - opened
This PR significantly improves the model card by:
- Adding
pipeline_tag: text-to-imagefor better discoverability of the model's domain. - Specifying
library_name: transformersbased on theCLIPTextModelarchitecture, which enables the automated "how to use" widget. - Including a direct link to the associated paper: Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models.
- Providing a link to the GitHub repository: https://github.com/Robin-WZQ/DAA.
- Incorporating an "Overview" and "Usage" section directly from the GitHub README, including code snippets for detecting samples and visualization, along with relevant images to illustrate the method.
- Adding the academic citation.
This makes the model card much more informative and user-friendly.
RobinWZQ changed pull request status to merged