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
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Download README.md from bart1259/CityDiffusion: direct link, hf CLI and curl.
- Browser
- Download file 657 Bytes
-
https://huggingface.co/bart1259/CityDiffusion/resolve/main/README.md
- Command line
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hf download hf://bart1259/CityDiffusion/README.md
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curl -L -o README.md https://huggingface.co/bart1259/CityDiffusion/resolve/main/README.md
657 Bytes
| 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 | |
|  | |