Instructions to use manycore-research/FLUX.1-Layout-ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use manycore-research/FLUX.1-Layout-ControlNet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("manycore-research/FLUX.1-Layout-ControlNet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
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# FLUX.1-Layout-ControlNet for SpatialGen
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`FLUX.1 Layout Controlnet [dev]` is a ControlNet conditioned by a semantic image. It is capable of generating a 2D image based on a text description while following the semantic image layout. This ControlNet is applicable to [FLUX.1 [dev]](https://huggingface.co/black-forest-labs/FLUX.1-dev) and is leveraged within the SpatialGen framework for 3D scene synthesis.
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* 📖 [Paper: SPATIALGEN: Layout-guided 3D Indoor Scene Generation](https://huggingface.co/papers/2509.14981)
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* 🌐 [Project Page](https://manycore-research.github.io/SpatialGen)
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# FLUX.1-Layout-ControlNet for SpatialGen
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`FLUX.1-Layout-ControlNet` is a key component of the [SpatialGen](https://manycore-research.github.io/SpatialGen/). `FLUX.1 Layout Controlnet [dev]` is a ControlNet conditioned by a semantic image. It is capable of generating a 2D image based on a text description while following the semantic image layout. This ControlNet is applicable to [FLUX.1 [dev]](https://huggingface.co/black-forest-labs/FLUX.1-dev) and is leveraged within the SpatialGen framework for 3D scene synthesis.
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* 📖 [Paper: SPATIALGEN: Layout-guided 3D Indoor Scene Generation](https://huggingface.co/papers/2509.14981)
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* 🌐 [Project Page](https://manycore-research.github.io/SpatialGen)
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