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
- Draw Things
- DiffusionBee
LoRA DreamBooth - ramsrigouthamg/lora-dog-SSD-1B
These are LoRA adaption weights for segmind/SSD-1B. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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Model tree for ramsrigouthamg/lora-dog-SSD-1B
Base model
segmind/SSD-1B


