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https://liming-ai.github.io/ControlNet_Plus_Plus/

Official PyTorch implementation of ECCV 2024 Paper: ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback.
https://liming-ai.github.io/ControlNet_Plus_Plus/

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Official PyTorch implementation of ECCV 2024 Paper: ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback.

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README 

        
# [ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback (ECCV 2024)](https://liming-ai.github.io/ControlNet_Plus_Plus/)






[![arXiv](https://img.shields.io/badge/arXiv-2404.07987-b31b1b.svg)](https://arxiv.org/abs/2404.07987) 

[![huggingface demo](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-ControlNet++-yellow)](https://huggingface.co/spaces/limingcv/ControlNet-Plus-Plus) 






## 🕹️ Environments

```bash

git clone https://github.com/liming-ai/ControlNet_Plus_Plus.git

pip3 install -r requirements.txt

pip3 install -U openmim

mim install mmengine

mim install "mmcv==2.1.0"

pip3 install "mmsegmentation>=1.0.0"

pip3 install mmdet

```



## 🕹️ Data Preperation

**All the organized data has been put on Huggingface and will be automatically downloaded during training or evaluation.** You can preview it in advance to check the data samples and disk space occupied with following links.

|   Task    | Training Data 🤗 | Evaluation Data 🤗 |

|:----------:|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|

|  LineArt, Hed, Canny   | [Data](https://huggingface.co/datasets/limingcv/MultiGen-20M_train), 1.14 TB | [Data](https://huggingface.co/datasets/limingcv/MultiGen-20M_canny_eval), 2.25GB |

|  Depth   |  [Data](https://huggingface.co/datasets/limingcv/MultiGen-20M_depth), 1.22 TB | [Data](https://huggingface.co/datasets/limingcv/MultiGen-20M_depth_eval), 2.17GB |

|  Segmentation ADE20K   | [Data](https://huggingface.co/datasets/limingcv/Captioned_ADE20K), 7.04 GB | Same Path as Training Data |

|  Segmentation COCOStuff   | [Data](https://huggingface.co/datasets/limingcv/Captioned_COCOStuff), 61.9 GB | Same Path as Training Data |



## 🕹️ Training

By default, our training is based on 8 A100-80G GPUs. If your computational resources are insufficient for training, you may need to reduce the batch size and increase gradient accumulation at the same time, and we have not observed any performance degradation. Reducing the training resolution will result in performance degradation.



### For segmentation task

[ControlNet V1.1 Seg](https://github.com/lllyasviel/ControlNet-v1-1-nightly/blob/main/README.md#controlnet-11-segmentation) is trained on both ADE20K and COCOStuff, and these two datasets have different masks. To this end, we first perform normal model fine-tuning on each dataset, and then perform reward fine-tuning.

```bash

# Please refer to the reward script for details

bash train/reward_ade20k.sh

bash train/reward_cocostuff.sh

```



### For other tasks

We can directly perform reward fine-tuning.

```bash

bash train/reward_canny.sh

bash train/reward_depth.sh

bash train/reward_hed.sh

bash train/reward_linedrawing.sh

```



### Core Code

Please refer to the [core code here](https://github.com/liming-ai/ControlNet_Plus_Plus/blob/9167f0d85ccc5ad1eb9a83f3e7fa8d3422d5d9d5/train/reward_control.py#L1429), in summary:

#### Step 1: Predict the single-step denoised RGB image with noise sampler:

```python

# Predict the single-step denoised latents

pred_original_sample = [

    noise_scheduler.step(noise, t, noisy_latent).pred_original_sample.to(weight_dtype) \

        for (noise, t, noisy_latent) in zip(model_pred, timesteps, noisy_latents)

]

pred_original_sample = torch.stack(pred_original_sample)



# Map the denoised latents into RGB images

pred_original_sample = 1 / vae.config.scaling_factor * pred_original_sample

image = vae.decode(pred_original_sample.to(weight_dtype)).sample

image = (image / 2 + 0.5).clamp(0, 1)

```

#### Step 2: Normalize the single-step denoised images according to different reward models

```python

# The normalization depends on different reward models.

if args.task_name == 'depth':

    image = torchvision.transforms.functional.resize(image, (384, 384))

    image = normalize(image, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

elif args.task_name in ['canny', 'lineart', 'hed']:

    pass

else:

    image = normalize(image, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225))

```

#### Step 3: Apply both diffusion training loss and reward loss:

```python

# reward model inference

if args.task_name == 'canny':

    outputs = reward_model(image.to(accelerator.device), low_threshold, high_threshold)

else:

    outputs = reward_model(image.to(accelerator.device))



# Determine which samples in the current batch need to calculate reward loss

timestep_mask = (args.min_timestep_rewarding <= timesteps.reshape(-1, 1)) & (timesteps.reshape(-1, 1) <= args.max_timestep_rewarding)



# Calculate reward loss

reward_loss = get_reward_loss(outputs, labels, args.task_name, reduction='none')



# Calculate final loss

reward_loss = reward_loss.reshape_as(timestep_mask)

reward_loss = (timestep_mask * reward_loss).sum() / (timestep_mask.sum() + 1e-10)

loss = pretrain_loss + reward_loss * args.grad_scale

```



## 🕹️ Evaluation

Please download the model weights and put them into each subset of `checkpoints`:

|   model    |HF weights🤗                                                                        |

|:----------:|:------------------------------------------------------------------------------------|

|  LineArt   | [model](https://huggingface.co/limingcv/reward_controlnet/tree/main/checkpoints/lineart) |

|  Depth   |  [model](https://huggingface.co/limingcv/reward_controlnet/tree/main/checkpoints/depth) |

|  Hed (SoftEdge)   | [model](https://huggingface.co/limingcv/reward_controlnet/tree/main/checkpoints/hed) |

| Canny | [model](https://huggingface.co/limingcv/reward_controlnet/tree/main/checkpoints/canny) |

|  Segmentation (ADE20K)   | [UperNet-R50](https://huggingface.co/limingcv/reward_controlnet/tree/main/checkpoints/ade20k_reward-model-UperNet-R50/checkpoint-5000/controlnet), [FCN-R101](https://huggingface.co/limingcv/reward_controlnet/tree/main/checkpoints/ade20k_reward-model-FCN-R101-d8/checkpoint-5000/controlnet) |

| Segmentation (COCOStuff) | [model](https://huggingface.co/limingcv/reward_controlnet/tree/main/checkpoints/cocostuff/reward_5k) |



Please make sure the folder directory is consistent with the test script, then you can eval each model by:

```bash

bash eval/eval_ade20k.sh

bash eval/eval_cocostuff.sh

bash eval/eval_canny.sh

bash eval/eval_depth.sh

bash eval/eval_hed.sh

bash eval/eval_linedrawing.sh

```



**The segmentation mIoU results of ControlNet and ControlNet++ in the arXiv v1 version of the paper were tested using images and labels saved in `.jpg` format, which resulted in errors.**



**We retested and reported the results using images and labels saved in `.png` format, please refer to our latest arXiv and ECCV Camera Ready releases.**



**Other comparison methods (Gligen/T2I-Adapter/UniControl/UniControlNet) and other evaluation metrics (FID/CLIP-score) were not affected by this error.**



## 🕹️ Inference

Please refer to the [Inference Branch](https://github.com/liming-ai/ControlNet_Plus_Plus/tree/inference) or try our [online Huggingface demo](https://huggingface.co/spaces/limingcv/ControlNet-Plus-Plus)



## License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE.txt) file for details.



## 🙏 Acknowledgements

We sincerely thank the [Huggingface](https://huggingface.co), [ControlNet](https://github.com/lllyasviel/ControlNet) and [ImageReward](https://github.com/THUDM/ImageReward) communities for their open source code and contributions. Our project would not be possible without these amazing works.



## Citation

If our work assists your research, feel free to give us a star ⭐ or cite us using:

```

@inproceedings{controlnet_plus_plus,

    author    = {Ming Li, Taojiannan Yang, Huafeng Kuang, Jie Wu, Zhaoning Wang, Xuefeng Xiao, Chen Chen},

    title     = {ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback},

    booktitle = {European Conference on Computer Vision (ECCV)},

    year      = {2024},

}

```