Instructions to use Joypop/TVTSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Joypop/TVTSR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Joypop/TVTSR", 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
Improve model card: Add pipeline tag, library name, abstract, and usage instructions
#1
by nielsr HF Staff - opened
This PR significantly improves the model card by:
- Adding
pipeline_tag: image-to-imageandlibrary_name: diffusersto the metadata, enhancing discoverability on the Hub and enabling the Hugging Faceuse this modelwidget. - Including a link to the paper Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training and the official GitHub repository https://github.com/Joyies/TVT.
- Incorporating the paper's abstract for a quick overview of the model's capabilities.
- Providing detailed 'Quick Inference' instructions, including installation steps and command examples, directly from the project's GitHub README, to facilitate easy usage.