Instructions to use dataautogpt3/Miniaturus_PotentiaV1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dataautogpt3/Miniaturus_PotentiaV1.2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dataautogpt3/Miniaturus_PotentiaV1.2", dtype=torch.bfloat16, device_map="cuda") prompt = "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from dataautogpt3/Miniaturus_PotentiaV1.2: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/dataautogpt3/Miniaturus_PotentiaV1.2/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://dataautogpt3/Miniaturus_PotentiaV1.2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dataautogpt3/Miniaturus_PotentiaV1.2/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- bd9fbff80f695fff352624f2c651a52b7039bf691dad93718dc2356dd60977ee
- Size of remote file:
- 492 MB
- SHA256:
- 9fa78d9b138c78c5e079bfbfa41578685f677fb717eb22cbb169fef5adb53bca
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