Instructions to use bart1259/CityDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bart1259/CityDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bart1259/CityDiffusion", 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
File size: 657 Bytes
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license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# City Diffusion Model
This is a diffusion model trained to generate cities.
The model will output a 256x256 image of a city where blue pixels represent buildings, red pixels represent roads, and white pixels represent empty space.
## Frontend
[City Generator Web Interface](https://github.com/bart1259/CityDiffusion)

## Sample training data

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