Instructions to use madebyollin/taesdxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use madebyollin/taesdxl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("madebyollin/taesdxl", 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
The config attributes {'shift_factor': 0.0, 'upsample_fn': 'nearest'} were passed to AutoencoderTiny, but are not expected and will be ignored. Please verify your config.json configuration file.
#4
by TanDD - opened
AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
The config attributes {'shift_factor': 0.0, 'upsample_fn': 'nearest'} were passed to AutoencoderTiny, but are not expected and will be ignored. Please verify your config.json configuration file.
