Instructions to use SherryXTChen/Instruct-CLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SherryXTChen/Instruct-CLIP with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SherryXTChen/Instruct-CLIP", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Push model using huggingface_hub.
Browse files- README.md +9 -3
- model.safetensors +3 -0
README.md
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---
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tags:
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- model_hub_mixin
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- pytorch_model_hub_mixin
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This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
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- Library: [More Information Needed]
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- Docs: [More Information Needed]
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b0bce6162c4e81ebe0f86e17821e99ca68f4cc5aeaa8b8fec3471c98f75b419d
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size 3578069780
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