Instructions to use predictia/cerra_denoise_convswin2sr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use predictia/cerra_denoise_convswin2sr with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("predictia/cerra_denoise_convswin2sr", 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
- Xet hash:
- ce8d393d90f0df716f10c48e55fcf0dc0c0b5480637eb43975cb3d279586bd4e
- Size of remote file:
- 33.6 MB
- SHA256:
- 895db4bb43f3f47c66438a8a93d5003ad2ae4c97c9ef3071826994eaa70d9fbd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.