Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use Qistinasofea/controlnet-floorplan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Qistinasofea/controlnet-floorplan with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Qistinasofea/controlnet-floorplan") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from Qistinasofea/controlnet-floorplan: direct link, hf CLI and curl.
- Browser
- Download file 918 Bytes
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https://huggingface.co/Qistinasofea/controlnet-floorplan/resolve/main/README.md
- Command line
-
hf download hf://Qistinasofea/controlnet-floorplan/README.md
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curl -L -o README.md https://huggingface.co/Qistinasofea/controlnet-floorplan/resolve/main/README.md
918 Bytes
metadata
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
controlnet-Qistinasofea/controlnet-floorplan
These are controlnet weights trained on stable-diffusion-v1-5/stable-diffusion-v1-5 with new type of conditioning.
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]