Instructions to use duja1/ismo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use duja1/ismo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("duja1/ismo", dtype=torch.bfloat16, device_map="cuda") prompt = "i123smo" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from duja1/ismo: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/duja1/ismo/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://duja1/ismo@refs/pr/2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/duja1/ismo/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 2bf9cf40995d53e0da35c5dd2ff1e73f60375e9440df13bee48bf03c20245d04
- Size of remote file:
- 492 MB
- SHA256:
- 61f375e7e1bdf3cf3477417968e8b069a77b7750ddd360413f79f34ac6f75d5d
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