Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
lora
Instructions to use Colezwhy/dreambooth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Colezwhy/dreambooth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Colezwhy/dreambooth") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- a2c0e2886b86b9766493bf33af14c894366d80c6b824956b9d0b8f35f32148bd
- Size of remote file:
- 6.59 MB
- SHA256:
- ec3e90ea2ed8d73f9c3f5aadc5197d4661b4e47678f68a81e4e156d3ec998b44
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.