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--- |
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license: mit |
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library_name: diffusers |
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pipeline_tag: image-to-image |
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--- |
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# DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation |
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SIGGRAPH 2024 |
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- Project Page: https://dilightnet.github.io/ |
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- Paper: https://arxiv.org/abs/2402.11929 |
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- Full Usage: please check https://github.com/iamNCJ/DiLightNet |
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Example Usage: |
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```python |
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from diffusers.utils import get_class_from_dynamic_module |
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NeuralTextureControlNetModel = get_class_from_dynamic_module( |
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"dilightnet/model_helpers", |
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"neuraltexture_controlnet.py", |
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"NeuralTextureControlNetModel" |
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) |
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neuraltexture_controlnet = NeuralTextureControlNetModel.from_pretrained("DiLightNet/DiLightNet") |
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pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-2-1", controlnet=neuraltexture_controlnet, |
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) |
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cond_image = torch.randn((1, 16, 512, 512)) |
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image = pipe("some text prompt", image=cond_image).images[0] |
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``` |
