Instructions to use S1T4L/Zero123pp_custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use S1T4L/Zero123pp_custom with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("S1T4L/Zero123pp_custom", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download vision_encoder/pytorch_model.bin from S1T4L/Zero123pp_custom: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/S1T4L/Zero123pp_custom/resolve/main/vision_encoder/pytorch_model.bin
- Command line
-
hf download hf://S1T4L/Zero123pp_custom/vision_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S1T4L/Zero123pp_custom/resolve/main/vision_encoder/pytorch_model.bin
1.26 GB
- Xet hash:
- 1304b1f372f6b7799b0651cb76d8ad47de8c523cb9ab33314945149fbacdb697
- Size of remote file:
- 1.26 GB
- SHA256:
- 0c626d61a7660d2f86a1f0b5f74f513f93789a99469f1af641cc1f77810427f7
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