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", torch_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:
- d31c06989fb5e4c3b55fa92306ea85ff5aa0104d6569ba4cd9c5793d5f286954
- Size of remote file:
- 563 Bytes
- SHA256:
- f5bce499d55ca335a930cffce881c1932e79c22f04059c672fde785e2087d9e3
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