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
Caching methods
Cache methods speedup diffusion transformers by storing and reusing intermediate outputs of specific layers, such as attention and feedforward layers, instead of recalculating them at each inference step.
CacheMixin
[[autodoc]] CacheMixin
PyramidAttentionBroadcastConfig
[[autodoc]] PyramidAttentionBroadcastConfig
[[autodoc]] apply_pyramid_attention_broadcast
FasterCacheConfig
[[autodoc]] FasterCacheConfig
[[autodoc]] apply_faster_cache
FirstBlockCacheConfig
[[autodoc]] FirstBlockCacheConfig
[[autodoc]] apply_first_block_cache