ControlNet Seg Room
Big thanks to Google for lending us TPUv4s to train this model on. Big thanks to the Huggingface and Diffusers team for organising the JAX Diffusers sprint, giving support and making the JAX training scripts. Big...
Source du modèle
- Type
- ControlNet
- Taille du fichier
- 689 MB
- Version
- vdiffusion_pytorch_model
- Mis à jour
- 6 oct. 2026
- Téléchargements
- 241
- Source
- Hugging Face
Description de la source
Thanks
Big thanks to Google for lending us TPUv4s to train this model on. Big thanks to the Huggingface and Diffusers team for organising the JAX Diffusers sprint, giving support and making the JAX training scripts. Big thanks to StabilityAI for opensourcing the Stable Diffusion model, it has made a great impact on the community!
About the dataset
To make this demo as good as possible, our team spend a lot of time training a custom model. We used the LAION5B dataset to build our custom dataset, which contains 130k images of 15 types of rooms in almost 30 design styles. After fetching all these images, we started adding metadata such as captions (from the BLIP captioning model) and segmentation maps (from the HuggingFace UperNetForSemanticSegmentation model).
About the model
This dataset was then used to train the controlnet model to generate quality interior design images by using the segmentation maps and prompts as conditioning information for the model. By training on segmentation maps, the end user has a very finegrained control over which objects they want to place in their room.
The training started from the lllyasviel/control_v11p_sd15_seg checkpoint, which is a robustly trained controlnet model conditioned on segmentation maps. This checkpoint got fine-tuned on a TPUv4 with the JAX framework. Afterwards, the checkpoint was converted into a PyTorch checkpoint for easy integration with the diffusers library.
About the demo
Our team made a streamlit demo where you can test out the capabilities of this model. The resulting model is used in a community pipeline that supports image2image and inpainting, so the user can keep elements of their room and change specific parts of the image. https://huggingface.co/spaces/controlnet-interior-design/controlnet-seg
Sources
1 source
Sources
1 sourceVérifié 6 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- license: openrail library_name: diffusers tags: - image-to-image - controlnet --- ## Thanks Big thanks to `Google` for lending us TPUv4s to train this model on. Big thanks to the Huggingface and Diffusers team for organising the JAX Diffusers sprint, giving support and making the JAX training scripts. Big thanks to StabilityAI for opensourcing the Stable Diffusion model, it has made a great impact on the community! ## About the dataset To make this demo as good as possible, our team spend a lot of time training a custom model. We used the LAION5B dataset to build our custom dataset, which contains 130k images of 15 types of rooms in almost 30 design styles. After fetching all these images, we started adding metadata such as captions (from the BLIP captioning model) and segmentation maps (from the HuggingFace UperNetForSemanticSegmentation model). ## About the model This dataset was then used to train the controlnet model to generate quality interior design images by using the segmentation maps and prompts as conditioning information for the model. By training on segmentation maps, the end user has a very finegrained control over which objects they want to place in their room. The training started from the `lllyasviel/control_v11p_sd15_seg` checkpoint, which is a robustly trained controlnet model conditioned on segmentation maps. This checkpoint got fine-tuned on a TPUv4 with the JAX framework. Afterwards, the checkpoint was converted into a PyTorch checkpoint for easy integration with the diffusers library. ## About the demo Our team made a streamlit demo where you can test out the capabilities of this model. The resulting model is used in a community pipeline that supports image2image and inpainting, so the user can keep elements of their room and change specific parts of the image. https://huggingface.co/spaces/controlnet-interior-design/controlnet-seg
Source context: 241 downloads · 28 likes · Pipeline image-to-image · Library diffusers · Repo BertChristiaens/controlnet-seg-room
Source context: 90 downloads · 27 likes · Pipeline image-to-image · Library diffusers · Repo BertChristiaens/controlnet-seg-room
