Instructions to use abdd68/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use abdd68/output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("abdd68/output") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 31f6f4c58d65359a113b2235fa070fec28578f0b5b9c712caef01bfbeb40fc19
- Size of remote file:
- 1.2 kB
- SHA256:
- 0ef66287b92a854580d2493411dc2f291699afddca6767ee7acd840b92595a11
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