| --- |
| license: openrail |
| base_model: runwayml/stable-diffusion-v1-5 |
| tags: |
| - art |
| - controlnet |
| - stable-diffusion |
| - image-to-image |
| duplicated_from: lllyasviel/sd-controlnet-seg |
| --- |
| |
| # Controlnet - *Image Segmentation Version* |
|
|
| ControlNet is a neural network structure to control diffusion models by adding extra conditions. |
| This checkpoint corresponds to the ControlNet conditioned on **Image Segmentation**. |
|
|
| It can be used in combination with [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img). |
|
|
|  |
|
|
| ## Model Details |
| - **Developed by:** Lvmin Zhang, Maneesh Agrawala |
| - **Model type:** Diffusion-based text-to-image generation model |
| - **Language(s):** English |
| - **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. |
| - **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543). |
| - **Cite as:** |
|
|
| @misc{zhang2023adding, |
| title={Adding Conditional Control to Text-to-Image Diffusion Models}, |
| author={Lvmin Zhang and Maneesh Agrawala}, |
| year={2023}, |
| eprint={2302.05543}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| |
| ## Introduction |
|
|
| Controlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by |
| Lvmin Zhang, Maneesh Agrawala. |
|
|
| The abstract reads as follows: |
|
|
| *We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. |
| 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). |
| Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. |
| Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. |
| 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. |
| This may enrich the methods to control large diffusion models and further facilitate related applications.* |
|
|
| ## Released Checkpoints |
|
|
| The authors released 8 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) |
| on a different type of conditioning: |
|
|
| | Model Name | Control Image Overview| Control Image Example | Generated Image Example | |
| |---|---|---|---| |
| |[lllyasviel/sd-controlnet-canny](https://huggingface.co/lllyasviel/sd-controlnet-canny)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_canny.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"/></a>| |
| |[lllyasviel/sd-controlnet-depth](https://huggingface.co/lllyasviel/sd-controlnet-depth)<br/> *Trained with Midas depth estimation* |A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_depth.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_depth.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"/></a>| |
| |[lllyasviel/sd-controlnet-hed](https://huggingface.co/lllyasviel/sd-controlnet-hed)<br/> *Trained with HED edge detection (soft edge)* |A monochrome image with white soft edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_hed.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_hed.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"/></a> | |
| |[lllyasviel/sd-controlnet-mlsd](https://huggingface.co/lllyasviel/sd-controlnet-mlsd)<br/> *Trained with M-LSD line detection* |A monochrome image composed only of white straight lines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_mlsd.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_mlsd.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"/></a>| |
| |[lllyasviel/sd-controlnet-normal](https://huggingface.co/lllyasviel/sd-controlnet-normal)<br/> *Trained with normal map* |A [normal mapped](https://en.wikipedia.org/wiki/Normal_mapping) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_normal.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_normal.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"/></a>| |
| |[lllyasviel/sd-controlnet_openpose](https://huggingface.co/lllyasviel/sd-controlnet-openpose)<br/> *Trained with OpenPose bone image* |A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_openpose.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_openpose.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"/></a>| |
| |[lllyasviel/sd-controlnet_scribble](https://huggingface.co/lllyasviel/sd-controlnet-scribble)<br/> *Trained with human scribbles* |A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_scribble.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_scribble.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"/></a> | |
| |[lllyasviel/sd-controlnet_seg](https://huggingface.co/lllyasviel/sd-controlnet-seg)<br/>*Trained with semantic segmentation* |An [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/)'s segmentation protocol image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_seg.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_seg.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"/></a> | |
|
|
|
|
| ## Example |
|
|
| It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint |
| has been trained on it. |
| Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion. |
|
|
| 1. Let's install `diffusers` and related packages: |
|
|
| ``` |
| $ pip install diffusers transformers accelerate |
| ``` |
|
|
| 2. We'll need to make use of a color palette here as described in [semantic_segmentation](https://huggingface.co/docs/transformers/tasks/semantic_segmentation): |
|
|
| ```py |
| palette = np.asarray([ |
| [0, 0, 0], |
| [120, 120, 120], |
| [180, 120, 120], |
| [6, 230, 230], |
| [80, 50, 50], |
| [4, 200, 3], |
| [120, 120, 80], |
| [140, 140, 140], |
| [204, 5, 255], |
| [230, 230, 230], |
| [4, 250, 7], |
| [224, 5, 255], |
| [235, 255, 7], |
| [150, 5, 61], |
| [120, 120, 70], |
| [8, 255, 51], |
| [255, 6, 82], |
| [143, 255, 140], |
| [204, 255, 4], |
| [255, 51, 7], |
| [204, 70, 3], |
| [0, 102, 200], |
| [61, 230, 250], |
| [255, 6, 51], |
| [11, 102, 255], |
| [255, 7, 71], |
| [255, 9, 224], |
| [9, 7, 230], |
| [220, 220, 220], |
| [255, 9, 92], |
| [112, 9, 255], |
| [8, 255, 214], |
| [7, 255, 224], |
| [255, 184, 6], |
| [10, 255, 71], |
| [255, 41, 10], |
| [7, 255, 255], |
| [224, 255, 8], |
| [102, 8, 255], |
| [255, 61, 6], |
| [255, 194, 7], |
| [255, 122, 8], |
| [0, 255, 20], |
| [255, 8, 41], |
| [255, 5, 153], |
| [6, 51, 255], |
| [235, 12, 255], |
| [160, 150, 20], |
| [0, 163, 255], |
| [140, 140, 140], |
| [250, 10, 15], |
| [20, 255, 0], |
| [31, 255, 0], |
| [255, 31, 0], |
| [255, 224, 0], |
| [153, 255, 0], |
| [0, 0, 255], |
| [255, 71, 0], |
| [0, 235, 255], |
| [0, 173, 255], |
| [31, 0, 255], |
| [11, 200, 200], |
| [255, 82, 0], |
| [0, 255, 245], |
| [0, 61, 255], |
| [0, 255, 112], |
| [0, 255, 133], |
| [255, 0, 0], |
| [255, 163, 0], |
| [255, 102, 0], |
| [194, 255, 0], |
| [0, 143, 255], |
| [51, 255, 0], |
| [0, 82, 255], |
| [0, 255, 41], |
| [0, 255, 173], |
| [10, 0, 255], |
| [173, 255, 0], |
| [0, 255, 153], |
| [255, 92, 0], |
| [255, 0, 255], |
| [255, 0, 245], |
| [255, 0, 102], |
| [255, 173, 0], |
| [255, 0, 20], |
| [255, 184, 184], |
| [0, 31, 255], |
| [0, 255, 61], |
| [0, 71, 255], |
| [255, 0, 204], |
| [0, 255, 194], |
| [0, 255, 82], |
| [0, 10, 255], |
| [0, 112, 255], |
| [51, 0, 255], |
| [0, 194, 255], |
| [0, 122, 255], |
| [0, 255, 163], |
| [255, 153, 0], |
| [0, 255, 10], |
| [255, 112, 0], |
| [143, 255, 0], |
| [82, 0, 255], |
| [163, 255, 0], |
| [255, 235, 0], |
| [8, 184, 170], |
| [133, 0, 255], |
| [0, 255, 92], |
| [184, 0, 255], |
| [255, 0, 31], |
| [0, 184, 255], |
| [0, 214, 255], |
| [255, 0, 112], |
| [92, 255, 0], |
| [0, 224, 255], |
| [112, 224, 255], |
| [70, 184, 160], |
| [163, 0, 255], |
| [153, 0, 255], |
| [71, 255, 0], |
| [255, 0, 163], |
| [255, 204, 0], |
| [255, 0, 143], |
| [0, 255, 235], |
| [133, 255, 0], |
| [255, 0, 235], |
| [245, 0, 255], |
| [255, 0, 122], |
| [255, 245, 0], |
| [10, 190, 212], |
| [214, 255, 0], |
| [0, 204, 255], |
| [20, 0, 255], |
| [255, 255, 0], |
| [0, 153, 255], |
| [0, 41, 255], |
| [0, 255, 204], |
| [41, 0, 255], |
| [41, 255, 0], |
| [173, 0, 255], |
| [0, 245, 255], |
| [71, 0, 255], |
| [122, 0, 255], |
| [0, 255, 184], |
| [0, 92, 255], |
| [184, 255, 0], |
| [0, 133, 255], |
| [255, 214, 0], |
| [25, 194, 194], |
| [102, 255, 0], |
| [92, 0, 255], |
| ]) |
| ``` |
|
|
| 3. Having defined the color palette we can now run the whole segmentation + controlnet generation code: |
|
|
| ```py |
| from transformers import AutoImageProcessor, UperNetForSemanticSegmentation |
| from PIL import Image |
| import numpy as np |
| import torch |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler |
| from diffusers.utils import load_image |
| |
| image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") |
| image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") |
| |
| image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-seg/resolve/main/images/house.png").convert('RGB') |
| |
| pixel_values = image_processor(image, return_tensors="pt").pixel_values |
| |
| with torch.no_grad(): |
| outputs = image_segmentor(pixel_values) |
| |
| seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] |
| |
| color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3 |
| |
| for label, color in enumerate(palette): |
| color_seg[seg == label, :] = color |
| |
| color_seg = color_seg.astype(np.uint8) |
| |
| image = Image.fromarray(color_seg) |
| |
| controlnet = ControlNetModel.from_pretrained( |
| "lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16 |
| ) |
| |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( |
| "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 |
| ) |
| |
| pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) |
| |
| # Remove if you do not have xformers installed |
| # see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers |
| # for installation instructions |
| pipe.enable_xformers_memory_efficient_attention() |
| |
| pipe.enable_model_cpu_offload() |
| |
| image = pipe("house", image, num_inference_steps=20).images[0] |
| |
| image.save('./images/house_seg_out.png') |
| ``` |
|
|
|  |
|
|
|  |
|
|
|  |
|
|
| ### Training |
|
|
| The semantic segmentation model was trained on 164K segmentation-image, caption pairs from ADE20K. The model was trained for 200 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model. |
|
|
| ### Blog post |
|
|
| For more information, please also have a look at the [official ControlNet Blog Post](https://huggingface.co/blog/controlnet). |
