Zero-Shot Image Classification
Transformers
Safetensors
English
clip
noisyclip
diffusion
latent-alignment
text-to-image
vision
Instructions to use asiimo/noisyclip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asiimo/noisyclip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="asiimo/noisyclip") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("asiimo/noisyclip") model = AutoModelForZeroShotImageClassification.from_pretrained("asiimo/noisyclip", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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If you find NoisyCLIP useful for your research, please cite:
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```bibtex
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```
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If you find NoisyCLIP useful for your research, please cite:
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```bibtex
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@misc{ramos2026earlyestimationlanguagelatent,
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title={Early Estimation of Language to Latent Alignment in Diffusion Models},
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author={Vasco Ramos and Regev Cohen and Idan Szpektor and Joao Magalhaes},
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year={2026},
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eprint={2512.08505},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2512.08505},
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}
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```
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