Instructions to use darask0/MOSS-Image_4-step_adapter_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use darask0/MOSS-Image_4-step_adapter_lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OpenMOSS-Team/MOSS-Image-SFT", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("darask0/MOSS-Image_4-step_adapter_lora") 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 images/training/000800.jpg from darask0/MOSS-Image_4-step_adapter_lora: direct link, hf CLI and curl.
- Browser
- Download file 472 kB
-
https://huggingface.co/darask0/MOSS-Image_4-step_adapter_lora/resolve/main/images/training/000800.jpg
- Command line
-
hf download hf://darask0/MOSS-Image_4-step_adapter_lora/images/training/000800.jpg
-
curl -L -o 000800.jpg https://huggingface.co/darask0/MOSS-Image_4-step_adapter_lora/resolve/main/images/training/000800.jpg
472 kB

- Xet hash:
- 271d1367a4e85b54b7f93200bb94aab43045aa866413f7d6589f65b26bd3056d
- Size of remote file:
- 472 kB
- SHA256:
- 78bfb73a3c06cadc425da2733c13e1255897e75959a4721fc9be0cec9642238a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.