Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
wildcard
Instructions to use dadosdq/wbchaop-wallbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dadosdq/wbchaop-wallbed with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dadosdq/wbchaop-wallbed", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of a white wbchaop wallbed" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DreamBooth model for the wbchaop concept trained by dadosdq on the dadosdq/wallbed_dataset dataset.
This is a Stable Diffusion model fine-tuned on the wbchaop concept with DreamBooth. It can be used by modifying the instance_prompt: a photo of wbchaop wallbed
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 wallbed images for the wildcard theme.
Usage
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('dadosdq/wbchaop-wallbed')
image = pipeline().images[0]
image
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