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
End of training
Browse files- README.md +2 -2
- checkpoint-100/optimizer.bin +1 -1
- checkpoint-100/pytorch_lora_weights.safetensors +1 -1
- checkpoint-200/optimizer.bin +1 -1
- checkpoint-200/pytorch_lora_weights.safetensors +1 -1
- checkpoint-300/optimizer.bin +1 -1
- checkpoint-300/pytorch_lora_weights.safetensors +1 -1
- checkpoint-400/optimizer.bin +1 -1
- checkpoint-400/pytorch_lora_weights.safetensors +1 -1
- checkpoint-500/optimizer.bin +1 -1
- checkpoint-500/pytorch_lora_weights.safetensors +1 -1
- image_0.png +0 -0
- image_1.png +0 -0
- image_2.png +0 -0
- image_3.png +0 -0
- pytorch_lora_weights.safetensors +1 -1
README.md
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---
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license: creativeml-openrail-m
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base_model: ggomma/test
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instance_prompt: 'A photo of
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tags:
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# LoRA DreamBooth - ggomma/test_lora
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These are LoRA adaption weights for ggomma/test. The weights were trained on 'A photo of
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---
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license: creativeml-openrail-m
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base_model: ggomma/test
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instance_prompt: 'A photo of c17248d0-a754-43d4-b025-e10e7688499c character'
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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# LoRA DreamBooth - ggomma/test_lora
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These are LoRA adaption weights for ggomma/test. The weights were trained on 'A photo of c17248d0-a754-43d4-b025-e10e7688499c character' using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
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