Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
animal
Instructions to use jefsnacker/azzy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jefsnacker/azzy with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jefsnacker/azzy", dtype=torch.bfloat16, device_map="cuda") prompt = "portrait of azzy cat anthro as a dapper bartender with a big, fluffy tail, retro futurism, art deco, detailed, painterly digital art by wlop and cory loftis and delphin enjolras, 🐿🍸🍋, furaffinity, trending on artstation" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Commit ·
a271429
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Parent(s): a5256d7
Upload the concept azzy embeds and token
Browse files- README.md +27 -0
- token_identifier.txt +1 -0
README.md
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---
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license: creativeml-openrail-m
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tags:
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- text-to-image
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---
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### azzy on Stable Diffusion via Dreambooth
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#### model by jefsnacker
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This your the Stable Diffusion model fine-tuned the azzy concept taught to Stable Diffusion with Dreambooth.
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It can be used by modifying the `instance_prompt`: **photo of azzy cat**
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You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
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And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
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Here are the images used for training this concept:
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token_identifier.txt
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photo of azzy cat
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