Instructions to use schrum2/MarioDiffusion-MLM-absence0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use schrum2/MarioDiffusion-MLM-absence0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("schrum2/MarioDiffusion-MLM-absence0", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Details on the code used to produce and use this model are available at:
https://github.com/schrum2/MarioDiffusion
That repo has instructions to check out this model and apply it to the generation of Super Mario Bros. level scenes. There is also an interactive GUI for constructing complete levels out of model-generated scenes.
This model had good performance in our experiments, but it challenging to use because it was trained with lengthy "absence" captions that list various elements that are absent in each game scene. When that information is not supplied, the results can be counterintuitive. We recommend https://huggingface.co/schrum2/MarioDiffusion-MLM-regular0 as a simpler starter model for generating Mario levels.
- Downloads last month
- 1