Instructions to use gdumas/cla1rec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use gdumas/cla1rec with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("gdumas/cla1rec") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- c20231ccbe3300df696329e01198bfbc366e8312494caa669bc70de64c540570
- Size of remote file:
- 170 MB
- SHA256:
- c0666aca027be27e94b00552732eb811e2768064012c3c5a78f515e71fe84552
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