Instructions to use AVIIAX/upscale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AVIIAX/upscale 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("AVIIAX/upscale", 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:
- 129e795064bdfc6e4cb3e9be7a54922c401f6163b8f42b82a185ab7aef523cc8
- Size of remote file:
- 111 MB
- SHA256:
- fac10c7b4287c0bffe5707905b28641f2093ce07f4a7a2cf7c037b485bf46ea3
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