Text-to-Image
Diffusers
Safetensors
stable-diffusion-xl
z-image
diffusion distillation
one-step generation
rlhf
Instructions to use Junyi-W/DIDR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Junyi-W/DIDR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Junyi-W/DIDR", 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 assets/teaser.jpg from Junyi-W/DIDR: direct link, hf CLI and curl.
- Browser
- Download file 1.1 MB
-
https://huggingface.co/Junyi-W/DIDR/resolve/main/assets/teaser.jpg
- Command line
-
hf download hf://Junyi-W/DIDR/assets/teaser.jpg
-
curl -L -o teaser.jpg https://huggingface.co/Junyi-W/DIDR/resolve/main/assets/teaser.jpg
1.1 MB

- Xet hash:
- d7a71a9e04f651edc3e4126213b0419a873b0899bf36923715f1323732468313
- Size of remote file:
- 1.1 MB
- SHA256:
- 2e61fca116dad262636151d69f0244d1c0c0b6edd85443f669899ed4a4880379
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.