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:
- 3832144c4d2e7245b264e4130ed6e220597247588a39093ed226892c191e074b
- Size of remote file:
- 3.44 GB
- SHA256:
- 39b04577249f9b52dcf3d672d179bee6f8db94e9dcfe987e06bfbaae0a3a13bc
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