Instructions to use OpenMOSS-Team/MOSS-Image-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OpenMOSS-Team/MOSS-Image-Base 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-Base", 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
Thank you for supporting both natural language and tags!
#4
by TupaTaxist - opened
I just wanted to share my appreciation for training the model to understand both natural language and tags. In my experience, this makes prompting so much more responsive and user-friendly.
Many other models rely purely on natural language, which often forces you to write long, paragraph style prompts just to steer the generation in the right direction. Being able to simply add a single tag to get a specific frame composition or camera angle saves so much time and effort.
Thank you for this amazing work!