Instructions to use rishitdagli/diffusion-isp-model-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rishitdagli/diffusion-isp-model-new with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rishitdagli/diffusion-isp-model-new", 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:
- 883bc40179cc7d2376b671b21f6cba16386e754813a43d8f3ee5772c9156d61c
- Size of remote file:
- 6.88 GB
- SHA256:
- c790ae07e206f49617c6e08880963b1e1907e43200cc845f7f809f87ec3806a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.