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
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Download README.md from ggomma/test_lora: direct link, hf CLI and curl.
- Browser
- Download file 667 Bytes
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https://huggingface.co/ggomma/test_lora/resolve/main/README.md
- Command line
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hf download hf://ggomma/test_lora/README.md
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curl -L -o README.md https://huggingface.co/ggomma/test_lora/resolve/main/README.md
667 Bytes
| license: creativeml-openrail-m | |
| base_model: ggomma/test | |
| instance_prompt: 'A photo of 76301216-c872-4193-a1c8-5e138d14e453 person' | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - lora | |
| inference: true | |
| # 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](https://dreambooth.github.io/). You can find some example images in the following. | |
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| LoRA for the text encoder was enabled: True. | |