Instructions to use sd-dreambooth-library/abstract_nature_patterns_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sd-dreambooth-library/abstract_nature_patterns_v2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sd-dreambooth-library/abstract_nature_patterns_v2", 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
- Draw Things
- DiffusionBee
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- stable-diffusion
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### abstract_nature_patterns_v2 Dreambooth model trained by apurik-parv with https://github.com/ShivamShrirao/diffusers dreambooth implementation.
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inference prompt : **abnapa**
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The model is an attempt at teaching symmetry and scales associated with nature to SD 1.5 base model.
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This version v2 is trained on better curated images for 40,000 steps. I am still working on finding what the model really does and if it has any impact on the base model.
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With that being said, the following are my findings, at the outset it seems that
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- stable-diffusion
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---
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### abstract_nature_patterns_v2 Dreambooth model trained by apurik-parv with https://github.com/ShivamShrirao/diffusers dreambooth implementation.
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inference prompt : **abnapa**\\
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The model is an attempt at teaching symmetry and scales associated with nature to SD 1.5 base model.
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This version v2 is trained on better curated images for 40,000 steps. I am still working on finding what the model really does and if it has any impact on the base model.
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With that being said, the following are my findings, at the outset it seems that
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