Instructions to use amayprro552/customModelsFID_tasczcimp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amayprro552/customModelsFID_tasczcimp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("amayprro552/customModelsFID_tasczcimp", 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
- Draw Things
- DiffusionBee
- Xet hash:
- 45eaa0c3459ea2c500251c3f67e7df81029d97fb3170a91355862a2194828504
- Size of remote file:
- 492 MB
- SHA256:
- 933e7ec0e887540cc1f6cae5e9ee05b0b3aa3491d54e7d6f938c19861b0aa270
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