Instructions to use ggomma/test_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ggomma/test_lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ggomma/test", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ggomma/test_lora") prompt = "A photo of 76301216-c872-4193-a1c8-5e138d14e453 person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA DreamBooth - ggomma/test_lora
These are LoRA adaption weights for ggomma/test. The weights were trained on 'A photo of 76301216-c872-4193-a1c8-5e138d14e453 person' using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: True.
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Model tree for ggomma/test_lora
Base model
KantoRegion/99mix-converted


