Instructions to use drmeeseeks/dreambooth_diffusion_model-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use drmeeseeks/dreambooth_diffusion_model-v2 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://drmeeseeks/dreambooth_diffusion_model-v2") - Notebooks
- Google Colab
- Kaggle
Download inf1_v2.png from drmeeseeks/dreambooth_diffusion_model-v2: direct link, hf CLI and curl.
- Browser
- Download file 1.35 MB
-
https://huggingface.co/drmeeseeks/dreambooth_diffusion_model-v2/resolve/main/inf1_v2.png
- Command line
-
hf download hf://drmeeseeks/dreambooth_diffusion_model-v2/inf1_v2.png
-
curl -L -o inf1_v2.png https://huggingface.co/drmeeseeks/dreambooth_diffusion_model-v2/resolve/main/inf1_v2.png
1.35 MB

- Xet hash:
- 65f93e5e647fbffdf49f5c3d12448fe5aa34613819f251e5044b1da5810f7fd5
- Size of remote file:
- 1.35 MB
- SHA256:
- d7275cbc4ab5d672646350d8ff0e486d1cbf3c47542ade0da526dc1a0dcf3d5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.