Instructions to use adipanda/ochaco-simpletuner-lora-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use adipanda/ochaco-simpletuner-lora-1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("adipanda/ochaco-simpletuner-lora-1") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Model card auto-generated by SimpleTuner
Browse files
README.md
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## Training settings
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- Training epochs:
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- Training steps:
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- Learning rate: 0.0003
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- Effective batch size: 48
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- Micro-batch size: 48
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## Training settings
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- Training epochs: 133
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- Training steps: 2400
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- Learning rate: 0.0003
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- Effective batch size: 48
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- Micro-batch size: 48
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