Text-to-Image
Diffusers
TensorBoard
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use Aminrabi/diff1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aminrabi/diff1000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Aminrabi/diff1000", 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 Aminrabi/diff1000: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/Aminrabi/diff1000/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://Aminrabi/diff1000/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/Aminrabi/diff1000/resolve/main/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- 38e3918ec38b4d08c26c1e209572ebc7ff957f65345e437ebd3dba1d0df9abec
- Size of remote file:
- 3.44 GB
- SHA256:
- 931b64a46a7020952e12acb1b7da3cc1d102aa8b2080b120d831c18e2552e8ea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.