Instructions to use saivs/qwen_segmentation_LoRa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use saivs/qwen_segmentation_LoRa with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Phr00t/Qwen-Image-Edit-Rapid-AIO", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("saivs/qwen_segmentation_LoRa") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- dd07b1ff144bfa284891d7a468361dc66cd20117da88dfc9ba8f75c5b3920540
- Size of remote file:
- 590 MB
- SHA256:
- a9a0860a61aee38fbabeabcd6624ea40e797145b14237c7187cd4ac8820623ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.