Instructions to use lsmpp/kontextrefiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lsmpp/kontextrefiner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lsmpp/kontextrefiner", 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
AutoencoderKLMagvit
The 3D variational autoencoder (VAE) model with KL loss used in EasyAnimate was introduced by Alibaba PAI.
The model can be loaded with the following code snippet.
from diffusers import AutoencoderKLMagvit
vae = AutoencoderKLMagvit.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="vae", torch_dtype=torch.float16).to("cuda")
AutoencoderKLMagvit
[[autodoc]] AutoencoderKLMagvit - decode - encode - all
AutoencoderKLOutput
[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
DecoderOutput
[[autodoc]] models.autoencoders.vae.DecoderOutput