Unconditional Image Generation
Diffusers
diffusion
robotics
motion-planning
path-planning
obstacle-avoidance
homotopy-continuation
synthetic-data
ieee
acdsa-2027
Eval Results (legacy)
Instructions to use jantrom/tile-obstacle-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jantrom/tile-obstacle-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jantrom/tile-obstacle-diffusion", 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
- Xet hash:
- 2f3bd6df11dbda3cc71d14ba8de7927fb75d53e006223cc0d1368b4ddf36053d
- Size of remote file:
- 39.2 MB
- SHA256:
- e5037abc479f0d96ae3773b50515bebc140c8e03a233e3d4194cb22a8bee69bc
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