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:
- 6a8998d970a0dc778090df68f3715fd1dc64237f15ac8685f4efcecc8f03ea28
- Size of remote file:
- 1.5 MB
- SHA256:
- dccaae98f63e3e8bbb1af25704c7f5b1a05035ac17dfcd71195b0a0fe1f4ad94
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