Instructions to use XDG-XHS/distar_long_1step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use XDG-XHS/distar_long_1step with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("XDG-XHS/distar_long_1step", 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
| license: mit | |
| library_name: diffusers | |
| pipeline_tag: text-to-image | |
| This repository contains the model described in the paper [Diff-Instruct*: Towards Human-Preferred One-step Text-to-image Generative Models](https://huggingface.co/papers/2410.20898). | |
| Code: https://github.com/pkulwj1994/diff_instruct_star |