Instructions to use Jersonm89/Avatar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jersonm89/Avatar with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jersonm89/Avatar", 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
Commit History
-
Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default
Since `diffusers==0.9.0` the width and height is automatically inferred from the `sample_size` attribute of your unet's config. It seems like your diffusion model has the same architecture as Stable Diffusion 1 which means that when using this model, by default an image size of 512x512 should be generated. This in turn means the unet's sample size should be **64**. In order to suppress to update your configuration on the fly and to suppress the deprecation warning added in this PR: https://github.com/huggingface/diffusers/pull/1406/files#r1035703505 it is strongly recommended to merge this PR.e483f73
-
Update README.md
f6154b1
-
Update README.md
a53e93f -
Update README.md
ea8500f
-
Update README.md
a87fa26 -
Update README.md
2b2f898 -
Update README.md
aea6c46 -
Update README.md
344fe88 -
Upload 6 files
7179c32 -
Update README.md
b94b54f -
Prueba de Carga
a3e15b9 -
initial commit
86e6ad4