Instructions to use reallylongaddress/vangoghstyle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use reallylongaddress/vangoghstyle with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("reallylongaddress/vangoghstyle", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from reallylongaddress/vangoghstyle: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/reallylongaddress/vangoghstyle/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://reallylongaddress/vangoghstyle/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/reallylongaddress/vangoghstyle/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- db3606c9002e6e8e128ab04ce9c31fed182c2e668115b217d1efac30d38bd67c
- Size of remote file:
- 492 MB
- SHA256:
- b1153136fbb8cbb45dd051f05c4fb7978b33e52522542610e85bdfe72b829153
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