Instructions to use rishitdagli/diffusion-isp-model-a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rishitdagli/diffusion-isp-model-a 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-a", 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:
- a2d5e712cbff95c2f477f39745499f5aaa9c87c08ac494fa4bacd8a4badace9b
- Size of remote file:
- 3.44 GB
- SHA256:
- 3a0fb38dba46e343621286b88cb2952f79b133dd84e83d64bf8996ac07270310
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