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
ConsisIDTransformer3DModel
A Diffusion Transformer model for 3D data from ConsisID was introduced in Identity-Preserving Text-to-Video Generation by Frequency Decomposition by Peking University & University of Rochester & etc.
The model can be loaded with the following code snippet.
from diffusers import ConsisIDTransformer3DModel
transformer = ConsisIDTransformer3DModel.from_pretrained("BestWishYsh/ConsisID-preview", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
ConsisIDTransformer3DModel
[[autodoc]] ConsisIDTransformer3DModel
Transformer2DModelOutput
[[autodoc]] models.modeling_outputs.Transformer2DModelOutput