Instructions to use Mitsua/vroid-diffusion-test-unconditional with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Mitsua/vroid-diffusion-test-unconditional with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Mitsua/vroid-diffusion-test-unconditional", 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 Settings
- Draw Things
- DiffusionBee
- Xet hash:
- cf7fc331fcd047953689ae254d71cb9ae5fa1cafbdc9441684ee7e6c4cd2695f
- Size of remote file:
- 1.13 GB
- SHA256:
- d684fc66296cb03dfa53a30c509a6b3544dda98d306729c9582a340b079c4fc9
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