Instructions to use Muapi/cum-pool-concept with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/cum-pool-concept with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OnomaAIResearch/Illustrious-xl-early-release-v0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/cum-pool-concept") 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:
- 16bcf0ee98e2aa5a6a2ea2443fb96420a94c89cfb896f3cd53f91c3385d2f299
- Size of remote file:
- 1.15 MB
- SHA256:
- bbc82a22d4f5e7976cee6398872c334a82ca70c008e9b12fd00cfa2b2bf0f352
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