Instructions to use Daaaaamian/med2-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Daaaaamian/med2-trained with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Daaaaamian/med2-trained", 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 Daaaaamian/med2-trained: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/Daaaaamian/med2-trained/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://Daaaaamian/med2-trained/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Daaaaamian/med2-trained/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 151abf085aa63695a36172b2d7a26ae80278c780d684317864f593a7c162716c
- Size of remote file:
- 246 MB
- SHA256:
- 838620bf884ecb64d31fba8a28e286b470c0fc587e4e3b0566d367a756313966
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