Instructions to use keras-dreambooth/dreambooth_diffusion_minercraft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use keras-dreambooth/dreambooth_diffusion_minercraft with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("keras-dreambooth/dreambooth_diffusion_minercraft") - Diffusers
How to use keras-dreambooth/dreambooth_diffusion_minercraft with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("keras-dreambooth/dreambooth_diffusion_minercraft", dtype=torch.bfloat16, device_map="cuda") prompt = "a fishing village under a cherry blossom forest at sunset in mrf style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf4a2598a0a1e973c22b48438c8d818b554a3706b366ffd1314bb6edf20c4831
- Size of remote file:
- 861 kB
- SHA256:
- 95f3ff9e20346bf9a37b1e6d42df546535d30b4a713246418b6c72b5b07b3d47
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.