Instructions to use pawlo2013/vangogh-sd-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pawlo2013/vangogh-sd-2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pawlo2013/vangogh-sd-2", 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 pawlo2013/vangogh-sd-2: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/pawlo2013/vangogh-sd-2/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://pawlo2013/vangogh-sd-2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pawlo2013/vangogh-sd-2/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- fbd896a2b8d22ec51871570a0a10cfe32d578c5055f37fc811dc08a4acf07afb
- Size of remote file:
- 681 MB
- SHA256:
- 016ba3dc953aa72bc903b53b43613cd839de0d21ed3e1a5e299d4126b2bcd222
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