Instructions to use kwagh20ite/finetuned_stable_diffusion_batch2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kwagh20ite/finetuned_stable_diffusion_batch2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kwagh20ite/finetuned_stable_diffusion_batch2", 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:
- 99f0e8e47125d502ca04e2c9f9c2b0c660d4f7d77864e8032687497a873217eb
- Size of remote file:
- 4.86 kB
- SHA256:
- d18b3a6fd67965827f84020881fd76cd94e9ac344c42fd2c840a3f80f427cb2a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.