Instructions to use ishitangupta/test_model_10_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ishitangupta/test_model_10_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_10_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
Download checkpoint-10/optimizer.bin from ishitangupta/test_model_10_vae: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/ishitangupta/test_model_10_vae/resolve/main/checkpoint-10/optimizer.bin
- Command line
-
hf download hf://ishitangupta/test_model_10_vae/checkpoint-10/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ishitangupta/test_model_10_vae/resolve/main/checkpoint-10/optimizer.bin
6.88 GB
- Xet hash:
- a278ba54365e1bc5f2e4f6b157e2aa840ad0d87a7b9f8e42c0cea43047b6a015
- Size of remote file:
- 6.88 GB
- SHA256:
- 84ca46b967b4e01d5141b86fa169d2dee9c009f3b3fab95033f1e5e21ed69eb0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.