Text-to-Image
Diffusers
Safetensors
English
Chinese
grin_qwen2_vl
MoE
Unified Generation
Multi-modal
Instructions to use HIT-TMG/Uni-MoE-2.0-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HIT-TMG/Uni-MoE-2.0-Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("HIT-TMG/Uni-MoE-2.0-Image", 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
Add `pipeline_tag`, `library_name`, and GitHub badge to model card
#1
by nielsr HF Staff - opened
This PR enhances the Uni-MoE-2.0-Image model card by adding key metadata and improving navigation:
pipeline_tag: text-to-image: This makes the model discoverable under the text-to-image pipeline filter on the Hugging Face Hub, accurately reflecting its primary function as a visual generation model.library_name: transformers: This indicates compatibility with the Hugging Face Transformers library, enabling the automated "how to use" widget on the model page for convenient code snippets.- GitHub badge: A direct link to the main GitHub repository (
https://github.com/HITsz-TMG/Uni-MoE) has been added as a prominent badge at the top of the model card, improving accessibility for users looking for the project's code.
All existing descriptive content, including the paper link, project page link, and usage examples, remains unchanged to preserve the original author's information and adhere to best practices.