Instructions to use ishitangupta/test_model_2000_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ishitangupta/test_model_2000_vae 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_vae", 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:
- 7a8277da040ad5aae389bdb986da0f4be31bd63d0205a2f066cebf0ce4ed69be
- Size of remote file:
- 172 MB
- SHA256:
- 6f1e63c6f5336f6eba33412c1f15af6e4966b3ab1bedea789fb11851263cd036
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