Instructions to use ramsrigouthamg/lora-dog-SSD-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ramsrigouthamg/lora-dog-SSD-1B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ramsrigouthamg/lora-dog-SSD-1B") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- eac93d6341fdcd0cd2627961dfc14389a3e7b726f1b49dfa5527f9fdce1321ef
- Size of remote file:
- 1.59 MB
- SHA256:
- 773bf2912c92b7d97c3c0fdc5a0247bc120d3758abd9c33b2cd57e2c1942d271
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