Instructions to use Lam-Hung/controlnet_segment_interior with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lam-Hung/controlnet_segment_interior with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Lam-Hung/controlnet_segment_interior") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
controlnet-Lam-Hung/controlnet_segment_interior
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below.
prompt: A Bauhaus-inspired living room with a sleek black leather sofa, a tubular steel coffee table exemplifying modernist design, and a geometric patterned rug adding a touch of artistic flair.
prompt: A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury.

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 Lam-Hung/controlnet_segment_interior
Base model
runwayml/stable-diffusion-v1-5