Instructions to use cuongdev/7nguoi-2500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuongdev/7nguoi-2500 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuongdev/7nguoi-2500", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download unet/diffusion_pytorch_model.bin from cuongdev/7nguoi-2500: direct link, hf CLI and curl.
- Browser
- Download file 3.46 GB
-
https://huggingface.co/cuongdev/7nguoi-2500/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://cuongdev/7nguoi-2500/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/cuongdev/7nguoi-2500/resolve/main/unet/diffusion_pytorch_model.bin
3.46 GB
- Xet hash:
- a9c28528e43cae223c851b23343d2f6e8a31387d0af29c8facd40da0fa642f29
- Size of remote file:
- 3.46 GB
- SHA256:
- fcafa7b99e52c29c940a1b9972290e8b8ddde26a71d7aacf7d16a16c951583c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.