Instructions to use ishitangupta/test_model_2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ishitangupta/test_model_2000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ishitangupta/test_model_2000", 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
- Xet hash:
- 1a9ff7283d1eaa2572a4970993d95a07ea69087065c1b6fee1435383694acc78
- Size of remote file:
- 6.88 GB
- SHA256:
- 2f0b79c2cbe8fa49e0c65dadf7ded6c63023c32eca4324380044176995f007ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.