Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
food
Instructions to use dreambooth-hackathon/cburgerz-hamburger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dreambooth-hackathon/cburgerz-hamburger with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dreambooth-hackathon/cburgerz-hamburger", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of cburgerz hamburger in a car" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DreamBooth model for cburgerz trained by lewtun on the lewtun/hamburgers dataset.
This your the Stable Diffusion model fine-tuned the cburgerz concept taught to Stable Diffusion with DreamBooth.
It can be used by modifying the instance_prompt: a photo of cburgerz hamburger
This model was created as part of the DreamBooth Hackathon. Visit the organisation page for instructions on how to take part!
Description
Describe your model and concept here.
Usage
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('dreambooth-hackathon/cburgerz-hamburger')
image = pipeline().images[0]
image
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