Diffusers
multilingual
flux.1
minicpm-o
qwenvl
internvl
text-to-image
multi-image-to-image
video-to-image
text_image-to-image
audio-to-image
speech-to-image
any-to-image
Instructions to use OPPOer/X2I with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OPPOer/X2I with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OPPOer/X2I", 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
Add library_name and update pipeline tag
#1
by nielsr HF Staff - opened
This PR updates the model card by adding the library_name to the metadata and updating the pipeline_tag to any-to-image. The model uses diffusers and can generate images from various input modalities, therefore these changes improve the metadata accuracy and discoverability.
majian0318 changed pull request status to merged