Instructions to use ttttdiva/dreambooth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ttttdiva/dreambooth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ttttdiva/dreambooth", 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
Upload dreambooth-hyper-preg-v2 with huggingface_hub
Browse files
dreambooth-hyper-preg-v2/hyper_preg_4.4k_xl-lr5e-7-50ks-[hyperpreg]-fp16-v2.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:e319903ed7576a4b51a5aed18e1e93489fc6bfe3a51ac4f9d26ba76321e71703
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size 2133006562
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dreambooth-hyper-preg-v2/keyword.txt
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keyword=hyperpreg
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trained on 4.4k images for 50k steps at learning rate of 5e-7 and batch size 1 also added random flip
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