Instructions to use milaidy/dcaa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use milaidy/dcaa with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("milaidy/dcaa", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download unet/diffusion_pytorch_model.bin from milaidy/dcaa: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/milaidy/dcaa/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://milaidy/dcaa/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/milaidy/dcaa/resolve/main/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- e46c9c8ee7eaf2c36082507ded65dc8420527e0d37937c1065b4f5305e3f5848
- Size of remote file:
- 3.44 GB
- SHA256:
- 270a675ca84d20bb48737f80d477c28588daed9a9f1f792c4a1bf15afcde972d
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