Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
animal
Instructions to use mathpn/dreambooth-friendly-otter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mathpn/dreambooth-friendly-otter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mathpn/dreambooth-friendly-otter", dtype=torch.bfloat16, device_map="cuda") prompt = "an ött3r otter flying over a city at night, neon lights, highly detailed, digital painting, artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, cinematic lighting" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DreamBooth model for the ött3r concept trained by mathpn on the mathpn/LeonardTheOtter dataset.
This is a Stable Diffusion model fine-tuned on the ött3r concept with DreamBooth. It can be used by modifying the instance_prompt: a photo of ött3r otter
This model was created as part of the DreamBooth Hackathon 🔥. Visit the organisation page for instructions on how to take part!
Description
This is a Stable Diffusion model fine-tuned on otter images for the animal theme.
Usage
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('mathpn/dreambooth-friendly-otter')
image = pipeline().images[0]
image
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