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