Instructions to use dzhov/sd-class-jellyfish-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dzhov/sd-class-jellyfish-32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dzhov/sd-class-jellyfish-32", 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:
- b2e1266594df07c15efad01aa124ced888af59a5022b58583b06c4f3b8939f04
- Size of remote file:
- 74.3 MB
- SHA256:
- 7acd0f6961aa942d1e84db0f43e5ac28e42f66ed87de0db30258e7cd79c0322b
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