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--- |
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license: openrail |
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base_model: runwayml/stable-diffusion-v1-5 |
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tags: |
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- art |
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- controlnet |
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- stable-diffusion |
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- controlnet-v1-1 |
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- image-to-image |
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duplicated_from: ControlNet-1-1-preview/control_v11p_sd15_seg |
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--- |
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# Controlnet - v1.1 - *seg Version* |
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**Controlnet v1.1** is the successor model of [Controlnet v1.0](https://huggingface.co/lllyasviel/ControlNet) |
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and was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel). |
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This checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_seg.pth) into `diffusers` format. |
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It can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). |
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For more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet). |
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ControlNet is a neural network structure to control diffusion models by adding extra conditions. |
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This checkpoint corresponds to the ControlNet conditioned on **seg images**. |
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## Model Details |
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- **Developed by:** Lvmin Zhang, Maneesh Agrawala |
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- **Model type:** Diffusion-based text-to-image generation model |
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- **Language(s):** English |
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- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. |
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- **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543). |
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- **Cite as:** |
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@misc{zhang2023adding, |
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title={Adding Conditional Control to Text-to-Image Diffusion Models}, |
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author={Lvmin Zhang and Maneesh Agrawala}, |
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year={2023}, |
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eprint={2302.05543}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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## Introduction |
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Controlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by |
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Lvmin Zhang, Maneesh Agrawala. |
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The abstract reads as follows: |
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*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. |
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The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). |
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Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. |
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Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. |
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We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. |
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This may enrich the methods to control large diffusion models and further facilitate related applications.* |
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## Example |
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It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint |
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has been trained on it. |
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Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion. |
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**Note**: If you want to process an image to create the auxiliary conditioning, external dependencies are required as shown below: |
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1. Let's install `diffusers` and related packages: |
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``` |
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$ pip install diffusers transformers accelerate |
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``` |
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2. Let's define a color table we'll need later. |
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```py |
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import numpy as np |
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ada_palette = np.asarray([ |
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[0, 0, 0], |
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[120, 120, 120], |
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[180, 120, 120], |
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[6, 230, 230], |
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[80, 50, 50], |
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[4, 200, 3], |
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[120, 120, 80], |
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[140, 140, 140], |
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[204, 5, 255], |
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[230, 230, 230], |
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[4, 250, 7], |
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[224, 5, 255], |
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[235, 255, 7], |
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[150, 5, 61], |
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[120, 120, 70], |
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[8, 255, 51], |
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[255, 6, 82], |
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[143, 255, 140], |
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[204, 255, 4], |
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[255, 51, 7], |
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[204, 70, 3], |
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[0, 102, 200], |
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[61, 230, 250], |
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[255, 6, 51], |
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[11, 102, 255], |
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[255, 7, 71], |
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[255, 9, 224], |
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[9, 7, 230], |
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[220, 220, 220], |
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[255, 9, 92], |
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[112, 9, 255], |
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[8, 255, 214], |
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[7, 255, 224], |
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[255, 184, 6], |
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[10, 255, 71], |
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[255, 41, 10], |
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[7, 255, 255], |
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[224, 255, 8], |
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[102, 8, 255], |
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[255, 61, 6], |
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[255, 194, 7], |
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[255, 122, 8], |
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[0, 255, 20], |
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[255, 8, 41], |
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[255, 5, 153], |
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[6, 51, 255], |
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[235, 12, 255], |
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[160, 150, 20], |
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[0, 163, 255], |
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[140, 140, 140], |
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[250, 10, 15], |
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[20, 255, 0], |
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[31, 255, 0], |
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[255, 31, 0], |
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[255, 224, 0], |
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[153, 255, 0], |
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[0, 0, 255], |
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[255, 71, 0], |
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[0, 235, 255], |
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[0, 173, 255], |
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[31, 0, 255], |
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[11, 200, 200], |
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[255, 82, 0], |
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[0, 255, 245], |
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[0, 61, 255], |
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[0, 255, 112], |
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[0, 255, 133], |
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[255, 0, 0], |
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[255, 163, 0], |
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[255, 102, 0], |
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[194, 255, 0], |
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[0, 143, 255], |
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[51, 255, 0], |
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[0, 82, 255], |
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[0, 255, 41], |
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[0, 255, 173], |
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[10, 0, 255], |
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[173, 255, 0], |
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[0, 255, 153], |
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[255, 92, 0], |
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[255, 0, 255], |
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[255, 0, 245], |
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[255, 0, 102], |
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[255, 173, 0], |
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[255, 0, 20], |
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[255, 184, 184], |
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[0, 31, 255], |
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[0, 255, 61], |
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[0, 71, 255], |
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[255, 0, 204], |
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[0, 255, 194], |
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[0, 255, 82], |
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[0, 10, 255], |
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[0, 112, 255], |
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[51, 0, 255], |
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[0, 194, 255], |
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[0, 122, 255], |
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[0, 255, 163], |
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[255, 153, 0], |
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[0, 255, 10], |
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[255, 112, 0], |
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[143, 255, 0], |
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[82, 0, 255], |
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[163, 255, 0], |
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[255, 235, 0], |
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[8, 184, 170], |
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[133, 0, 255], |
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[0, 255, 92], |
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[184, 0, 255], |
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[255, 0, 31], |
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[0, 184, 255], |
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[0, 214, 255], |
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[255, 0, 112], |
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[92, 255, 0], |
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[0, 224, 255], |
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[112, 224, 255], |
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[70, 184, 160], |
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[163, 0, 255], |
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[153, 0, 255], |
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[71, 255, 0], |
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[255, 0, 163], |
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[255, 204, 0], |
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[255, 0, 143], |
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[0, 255, 235], |
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[133, 255, 0], |
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[255, 0, 235], |
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[245, 0, 255], |
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[255, 0, 122], |
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[255, 245, 0], |
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[10, 190, 212], |
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[214, 255, 0], |
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[0, 204, 255], |
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[20, 0, 255], |
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[255, 255, 0], |
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[0, 153, 255], |
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[0, 41, 255], |
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[0, 255, 204], |
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[41, 0, 255], |
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[41, 255, 0], |
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[173, 0, 255], |
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[0, 245, 255], |
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[71, 0, 255], |
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[122, 0, 255], |
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[0, 255, 184], |
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[0, 92, 255], |
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[184, 255, 0], |
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[0, 133, 255], |
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[255, 214, 0], |
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[25, 194, 194], |
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[102, 255, 0], |
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[92, 0, 255], |
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]) |
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``` |
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3. Run code: |
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```python |
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import torch |
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import os |
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from huggingface_hub import HfApi |
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from pathlib import Path |
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from diffusers.utils import load_image |
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from PIL import Image |
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import numpy as np |
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from transformers import AutoImageProcessor, UperNetForSemanticSegmentation |
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from diffusers import ( |
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ControlNetModel, |
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StableDiffusionControlNetPipeline, |
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UniPCMultistepScheduler, |
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) |
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image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") |
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image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") |
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checkpoint = "lllyasviel/control_v11p_sd15_seg" |
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image = load_image( |
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"https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/input.png" |
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) |
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prompt = "old house in stormy weather with rain and wind" |
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pixel_values = image_processor(image, return_tensors="pt").pixel_values |
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with torch.no_grad(): |
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outputs = image_segmentor(pixel_values) |
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seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] |
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color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3 |
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for label, color in enumerate(ada_palette): |
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color_seg[seg == label, :] = color |
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color_seg = color_seg.astype(np.uint8) |
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control_image = Image.fromarray(color_seg) |
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control_image.save("./images/control.png") |
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controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16) |
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pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 |
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) |
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) |
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pipe.enable_model_cpu_offload() |
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generator = torch.manual_seed(0) |
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image = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0] |
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image.save('images/image_out.png') |
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``` |
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## Other released checkpoints v1-1 |
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The authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) |
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on a different type of conditioning: |
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| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example | |
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|---|---|---|---|---| |
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|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>| |
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|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>| |
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|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>| |
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## Improvements in Segmentation 1.1: |
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- COCO protocol is supported. The previous Segmentation 1.0 supports about 150 colors, but Segmentation 1.1 supports another 182 colors from coco. |
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- Resumed from Segmentation 1.0. All previous inputs should still work. |
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## More information |
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For more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly). |
