Instructions to use ItsJayQz/Valorant_Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ItsJayQz/Valorant_Diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ItsJayQz/Valorant_Diffusion", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 7253f70931ec519e32a75c534ff56b9ab093fd04c83a7ed3643798358cfa7d16
- Size of remote file:
- 3.44 GB
- SHA256:
- fe3122a95c4fee72bfe94d96744005b68483f2831c571d0246124ac8b23b948a
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