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 filesThis view is limited to 50 files because it contains too many changes. See raw diff
- README.md +2 -2
- checkpoint-100/optimizer.bin +1 -1
- checkpoint-100/pytorch_lora_weights.safetensors +1 -1
- checkpoint-100/random_states_0.pkl +1 -1
- checkpoint-1000/optimizer.bin +1 -1
- checkpoint-1000/pytorch_lora_weights.safetensors +1 -1
- checkpoint-1000/random_states_0.pkl +1 -1
- checkpoint-1100/optimizer.bin +1 -1
- checkpoint-1100/pytorch_lora_weights.safetensors +1 -1
- checkpoint-1100/random_states_0.pkl +1 -1
- checkpoint-1200/optimizer.bin +1 -1
- checkpoint-1200/pytorch_lora_weights.safetensors +1 -1
- checkpoint-1200/random_states_0.pkl +1 -1
- checkpoint-1300/optimizer.bin +1 -1
- checkpoint-1300/pytorch_lora_weights.safetensors +1 -1
- checkpoint-1300/random_states_0.pkl +1 -1
- checkpoint-1400/optimizer.bin +1 -1
- checkpoint-1400/pytorch_lora_weights.safetensors +1 -1
- checkpoint-1400/random_states_0.pkl +1 -1
- checkpoint-1500/optimizer.bin +1 -1
- checkpoint-1500/pytorch_lora_weights.safetensors +1 -1
- checkpoint-1500/random_states_0.pkl +1 -1
- checkpoint-1600/optimizer.bin +3 -0
- checkpoint-1600/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1600/random_states_0.pkl +3 -0
- checkpoint-1600/scheduler.bin +3 -0
- checkpoint-1700/optimizer.bin +3 -0
- checkpoint-1700/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1700/random_states_0.pkl +3 -0
- checkpoint-1700/scheduler.bin +3 -0
- checkpoint-1800/optimizer.bin +3 -0
- checkpoint-1800/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1800/random_states_0.pkl +3 -0
- checkpoint-1800/scheduler.bin +3 -0
- checkpoint-1900/optimizer.bin +3 -0
- checkpoint-1900/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1900/random_states_0.pkl +3 -0
- checkpoint-1900/scheduler.bin +3 -0
- checkpoint-200/optimizer.bin +1 -1
- checkpoint-200/pytorch_lora_weights.safetensors +1 -1
- checkpoint-200/random_states_0.pkl +1 -1
- checkpoint-2000/optimizer.bin +3 -0
- checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-2000/scheduler.bin +3 -0
- checkpoint-300/optimizer.bin +1 -1
- checkpoint-300/pytorch_lora_weights.safetensors +1 -1
- checkpoint-300/random_states_0.pkl +1 -1
- checkpoint-400/optimizer.bin +1 -1
- checkpoint-400/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 76301216-c872-4193-a1c8-5e138d14e453 person'
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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 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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