Instructions to use Muapi/assup-flashing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/assup-flashing with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cocktailpeanut/pony-diffusion-v6-xl", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/assup-flashing") 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:
- e4f2209953f044696d9302d141a0c0922cf4afb5caf8c16d08b16e6851dda67b
- Size of remote file:
- 2.24 MB
- SHA256:
- da0c401b99b41709d5e14b346c42821e18e90a2f39893adef374c71453b72380
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