Instructions to use xiaoxiaxu/LowLatencyMEG_Backbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xiaoxiaxu/LowLatencyMEG_Backbone with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xiaoxiaxu/LowLatencyMEG_Backbone", 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
Download MS_pruning_decoder.pth from xiaoxiaxu/LowLatencyMEG_Backbone: direct link, hf CLI and curl.
- Browser
- Download file 101 MB
-
https://huggingface.co/xiaoxiaxu/LowLatencyMEG_Backbone/resolve/main/MS_pruning_decoder.pth
- Command line
-
hf download hf://xiaoxiaxu/LowLatencyMEG_Backbone/MS_pruning_decoder.pth
-
curl -L -o MS_pruning_decoder.pth https://huggingface.co/xiaoxiaxu/LowLatencyMEG_Backbone/resolve/main/MS_pruning_decoder.pth
101 MB
- Xet hash:
- 12ee84ab5d62a18951827fcf052fd810800a3a52ca6ffa7e939dbe002e9f32f5
- Size of remote file:
- 101 MB
- SHA256:
- 575cf12d63ecabd225a74fbce39dabd422014e6636e14aaf8e10dfe86cc61804
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