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:
- af9d23d6eb7866e2e7f48ee6d4f1d9263f0b3352892882e8cae1264362a2dab8
- Size of remote file:
- 295 MB
- SHA256:
- 506338affe7b41486a1367d29caa010f072eb507f2d221b4851a12ef5a58b1fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.