Instructions to use jzli/simpmaker2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jzli/simpmaker2000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jzli/simpmaker2000", 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
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 95dc89262160260207339eaf47a0ca3e3c09185d93a5f650d818c977b9a0f163
- Size of remote file:
- 335 MB
- SHA256:
- 54d3f4c8f83698b32dba84240a928b6de0623de143b596600f1c42d8e453fe01
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