Instructions to use briannlongzhao/smile_custom_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use briannlongzhao/smile_custom_diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("briannlongzhao/smile_custom_diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "a painting of <smile> style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from briannlongzhao/smile_custom_diffusion: direct link, hf CLI and curl.
- Browser
- Download file 713 Bytes
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https://huggingface.co/briannlongzhao/smile_custom_diffusion/resolve/main/README.md
- Command line
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hf download hf://briannlongzhao/smile_custom_diffusion/README.md
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curl -L -o README.md https://huggingface.co/briannlongzhao/smile_custom_diffusion/resolve/main/README.md
713 Bytes
metadata
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a painting of <smile> style
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
Custom Diffusion - briannlongzhao/smile_custom_diffusion
These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on a painting of style using Custom Diffusion. You can find some example images in the following.
For more details on the training, please follow this link.