Instructions to use tuwonga/dbluth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tuwonga/dbluth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tuwonga/dbluth", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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_prompt and settings used: **[person] in dbluth style** | **Steps: 30 - denoising stregth around 50/70 but you can play around with settings**_
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This model was trained with Dreambooth training by TheLastBen, using
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## License
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This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
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This model was trained with Dreambooth training by TheLastBen, using 40 images at 8000 steps with 20% of text encoder for each model and then merged in a single one with Automatic1111 webui checkpoint merger.
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## License
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This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
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