Instructions to use moritzef/model_mapillary_lr1e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use moritzef/model_mapillary_lr1e5 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("moritzef/model_mapillary_lr1e5") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
controlnet-moritzef/model_mapillary_lr1e5
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. You can find some example images below.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.
prompt: A realistic streetview image.

Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for moritzef/model_mapillary_lr1e5
Base model
stabilityai/stable-diffusion-2-1-base