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--- |
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license: apache-2.0 |
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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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- t2i-adapter |
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- controlnet |
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- stable-diffusion |
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- image-to-image |
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--- |
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# T2I Adapter - Segment |
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T2I Adapter is a network providing additional conditioning to stable diffusion. Each t2i checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint. |
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This checkpoint provides conditioning on semantic segmentation for the stable diffusion 1.4 checkpoint. |
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## Model Details |
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- **Developed by:** T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models |
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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:** Apache 2.0 |
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- **Resources for more information:** [GitHub Repository](https://github.com/TencentARC/T2I-Adapter), [Paper](https://arxiv.org/abs/2302.08453). |
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- **Cite as:** |
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@misc{ |
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title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models}, |
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author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie}, |
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year={2023}, |
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eprint={2302.08453}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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### Checkpoints |
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| Model Name | Control Image Overview| Control Image Example | Generated Image Example | |
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|---|---|---|---| |
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|[TencentARC/t2iadapter_color_sd14v1](https://huggingface.co/TencentARC/t2iadapter_color_sd14v1)<br/> *Trained with spatial color palette* | A image with 8x8 color palette.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_canny_sd14v1](https://huggingface.co/TencentARC/t2iadapter_canny_sd14v1)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_sketch_sd14v1](https://huggingface.co/TencentARC/t2iadapter_sketch_sd14v1)<br/> *Trained with [PidiNet](https://github.com/zhuoinoulu/pidinet) edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_depth_sd14v1](https://huggingface.co/TencentARC/t2iadapter_depth_sd14v1)<br/> *Trained with Midas depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_openpose_sd14v1](https://huggingface.co/TencentARC/t2iadapter_openpose_sd14v1)<br/> *Trained with OpenPose bone image* | A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_keypose_sd14v1](https://huggingface.co/TencentARC/t2iadapter_keypose_sd14v1)<br/> *Trained with mmpose skeleton image* | A [mmpose skeleton](https://github.com/open-mmlab/mmpose) image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_output.png"/></a>| |
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|[TencentARC/t2iadapter_seg_sd14v1](https://huggingface.co/TencentARC/t2iadapter_seg_sd14v1)<br/>*Trained with semantic segmentation* | An [custom](https://github.com/TencentARC/T2I-Adapter/discussions/25) segmentation protocol image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_output.png"/></a> | |
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|[TencentARC/t2iadapter_canny_sd15v2](https://huggingface.co/TencentARC/t2iadapter_canny_sd15v2)|| |
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|[TencentARC/t2iadapter_depth_sd15v2](https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2)|| |
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|[TencentARC/t2iadapter_sketch_sd15v2](https://huggingface.co/TencentARC/t2iadapter_sketch_sd15v2)|| |
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|[TencentARC/t2iadapter_zoedepth_sd15v1](https://huggingface.co/TencentARC/t2iadapter_zoedepth_sd15v1)|| |
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## Example |
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1. Dependencies |
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```sh |
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pip install diffusers transformers |
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``` |
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2. Run code: |
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```python |
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import torch |
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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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T2IAdapter, |
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StableDiffusionAdapterPipeline |
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) |
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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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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 = Image.open('./images/seg_input.jpeg') |
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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/segment_image.png") |
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adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_seg_sd14v1", torch_dtype=torch.float16) |
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pipe = StableDiffusionAdapterPipeline.from_pretrained( |
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"CompVis/stable-diffusion-v1-4", adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16" |
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) |
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pipe.to('cuda') |
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generator = torch.Generator().manual_seed(0) |
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sketch_image_out = pipe(prompt="motorcycles driving", image=control_image, generator=generator).images[0] |
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sketch_image_out.save('./images/seg_image_out.png') |
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``` |
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