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
AutoencoderKLMochi
The 3D variational autoencoder (VAE) model with KL loss used in Mochi was introduced in Mochi 1 Preview by Tsinghua University & ZhipuAI.
The model can be loaded with the following code snippet.
from diffusers import AutoencoderKLMochi
vae = AutoencoderKLMochi.from_pretrained("genmo/mochi-1-preview", subfolder="vae", torch_dtype=torch.float32).to("cuda")
AutoencoderKLMochi
[[autodoc]] AutoencoderKLMochi - decode - all
DecoderOutput
[[autodoc]] models.autoencoders.vae.DecoderOutput