Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
landscape
classical-art
Instructions to use avuhong/DB_Monet_style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use avuhong/DB_Monet_style with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("avuhong/DB_Monet_style", dtype=torch.bfloat16, device_map="cuda") prompt = "a painting in $M## style of a fishing village under a cherry blossom forest at sunset" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from avuhong/DB_Monet_style: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/avuhong/DB_Monet_style/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://avuhong/DB_Monet_style/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/avuhong/DB_Monet_style/resolve/main/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- b218fbe1fb52eb61464c2ecaaa8560bbb83d9b329a33e8ef14f93e8dd2582271
- Size of remote file:
- 1.36 GB
- SHA256:
- 4d5b85602a4fb8506d3d00efbcc7e8f1c967ae683ac6366ac9b929a86b017d95
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