Instructions to use Muapi/infundibulum-insertion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/infundibulum-insertion 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/infundibulum-insertion") 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:
- 7b730e6ec45e4695d69ef168e277bd38687df3411321693abc60244a464e962f
- Size of remote file:
- 57.4 MB
- SHA256:
- c56162f38bc490bb8756908339736aff06447d6d6f796a0f25a14a2cfa6115d0
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