Instructions to use Pixel390/BOYV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Pixel390/BOYV1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Yntec/Genuine", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Pixel390/BOYV1") prompt = "a uxz high quality, anime style, a boy" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
End of training
Browse files- README.md +21 -0
- pytorch_lora_weights.bin +3 -0
README.md
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---
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license: creativeml-openrail-m
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base_model: Yntec/Genuine
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instance_prompt: a uxz high quality, anime style, a boy
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- text-to-image
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- diffusers
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- lora
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inference: true
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---
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# LoRA DreamBooth - Pixel390/BOYV1
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These are LoRA adaption weights for Yntec/Genuine. The weights were trained on a uxz high quality, anime style, a boy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
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LoRA for the text encoder was enabled: True.
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pytorch_lora_weights.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:056913a4a4539dec91b3f3516099cbcc6c4d20f2d223b67e324dd59f9bcac641
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size 4504891
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