Instructions to use NisargUpadhyay/ImageSuperResolution-replication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NisargUpadhyay/ImageSuperResolution-replication with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NisargUpadhyay/ImageSuperResolution-replication", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a96e559a0320ddf069d5e6daec37ce90b0cb043b321798a71c2731bddb0e9872
- Size of remote file:
- 6.59 GB
- SHA256:
- 99a11a73b598603b3af2b34789c38e313ab75b2ef911d0c7ced8794b5b1c6382
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