Instructions to use inclusionAI/Ming-flash-omni-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use inclusionAI/Ming-flash-omni-2.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("inclusionAI/Ming-flash-omni-2.0", 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
vLLM inference
#3
by orange0318 - opened
Hi inclusionAI team, does Ming-flash-omni-2.0 officially support vLLM inference now?
Hi, @orange0318 thanks so much for your question. At this moment not yet, we are working internally on some fundamentals and will engage with the community to make sure we could bring the best experience to our users. Stay tuned!
Hi @RichardBrian any hope for helping out with llamacpp support, even if it's text/image only at first?