diffbir

c18c4eb6 · mashun1 · 7bd56758 · c18c4eb6 · c18c4eb6 · c18c4eb6
Commit c18c4eb6 authored Nov 06, 2023 by mashun1
18 changed files
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,4 @@ __pycache__
 /temp
 /results
 .ipynb_checkpoints/
+*checkpoints
--- a/Dockerfile
+++ b/Dockerfile
+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04.1-py39-latest
+COPY . /tmp/
+WORKDIR /tmp
+RUN pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
--- a/README.md
+++ b/README.md
--- a/README_official.md
+++ b/README_official.md
+<p align="center">
+    <img src="assets/logo.png" width="400">
+</p>
+## DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
+[Paper](https://arxiv.org/abs/2308.15070) | [Project Page](https://0x3f3f3f3fun.github.io/projects/diffbir/)
+![visitors](https://visitor-badge.laobi.icu/badge?page_id=XPixelGroup/DiffBIR) [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/linxinqi/DiffBIR-official) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/camenduru/DiffBIR-colab/blob/main/DiffBIR_colab.ipynb)
+[Xinqi Lin](https://0x3f3f3f3fun.github.io/)<sup>1,\*</sup>, [Jingwen He](https://github.com/hejingwenhejingwen)<sup>2,\*</sup>, [Ziyan Chen](https://orcid.org/0000-0001-6277-5635)<sup>2</sup>, [Zhaoyang Lyu](https://scholar.google.com.tw/citations?user=gkXFhbwAAAAJ&hl=en)<sup>2</sup>, [Ben Fei](https://scholar.google.com/citations?user=skQROj8AAAAJ&hl=zh-CN&oi=ao)<sup>2</sup>, [Bo Dai](http://daibo.info/)<sup>2</sup>, [Wanli Ouyang](https://wlouyang.github.io/)<sup>2</sup>, [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao)<sup>2</sup>, [Chao Dong](http://xpixel.group/2010/01/20/chaodong.html)<sup>1,2</sup>
+<sup>1</sup>Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences<br><sup>2</sup>Shanghai AI Laboratory
+<p align="center">
+    <img src="assets/architecture.png" style="border-radius: 15px">
+</p>
+:star:If DiffBIR is helpful for you, please help star this repo. Thanks!:hugs:
+## :book:Table Of Contents
+- [Visual Results On Real-world Images](#visual_results)
+- [Update](#update)
+- [TODO](#todo)
+- [Installation](#installation)
+- [Pretrained Models](#pretrained_models)
+- [Quick Start (gradio demo)](#quick_start)
+- [Inference](#inference)
+- [Train](#train)
+## <a name="visual_results"></a>:eyes:Visual Results On Real-world Images
+<!-- <details close>
+<summary>General Image Restoration</summary> -->
+### General Image Restoration
+[<img src="assets/visual_results/general6.png" height="223px"/>](https://imgsli.com/MTk5ODI3) [<img src="assets/visual_results/general7.png" height="223px"/>](https://imgsli.com/MTk5ODI4) [<img src="assets/visual_results/general4.png" height="223px"/>](https://imgsli.com/MTk5ODI1)
+[<img src="assets/visual_results/general1.png" height="223px"/>](https://imgsli.com/MTk5ODIy) [<img src="assets/visual_results/general2.png" height="223px"/>](https://imgsli.com/MTk5ODIz)
+[<img src="assets/visual_results/general3.png" height="223px"/>](https://imgsli.com/MTk5ODI0) [<img src="assets/visual_results/general5.png" height="223px"/>](https://imgsli.com/MjAxMjM0)
+<!-- </details> -->
+<!-- <details close> -->
+<!-- <summary>Face Image Restoration</summary> -->
+### Face Image Restoration
+[<img src="assets/visual_results/face1.png" height="223px"/>](https://imgsli.com/MTk5ODI5) [<img src="assets/visual_results/face2.png" height="223px"/>](https://imgsli.com/MTk5ODMw) [<img src="assets/visual_results/face4.png" height="223px"/>](https://imgsli.com/MTk5ODM0)
+[<img src="assets/visual_results/whole_image1.png" height="370"/>](https://imgsli.com/MjA2MTU0) 
+[<img src="assets/visual_results/whole_image2.png" height="370"/>](https://imgsli.com/MjA2MTQ4)
+<!-- [<img src="assets/visual_results/whole_image3.png" height="268"/>](https://imgsli.com/MjA1OTY2) -->
+<!-- [<img src="assets/visual_results/face3.png" height="223px"/>](https://imgsli.com/MTk5ODMy) -->
+ <!-- [<img src="assets/visual_results/face5.png" height="223px"/>](https://imgsli.com/MTk5ODM1)  -->
+<!-- [<img src="assets/visual_results/whole_image1.png" height="410"/>](https://imgsli.com/MjA1OTU5) -->
+:star: Face and the background enhanced by DiffBIR.
+<!-- </details> -->
+## <a name="update"></a>:new:Update
+- **2023.09.19**: ✅ Add support for Apple Silicon! Check [installation_xOS.md](assets/docs/installation_xOS.md) to work with **CPU/CUDA/MPS** device!
+- **2023.09.14**: ✅ Integrate a patch-based sampling strategy ([mixture-of-diffusers](https://github.com/albarji/mixture-of-diffusers)). [**Try it!**](#general_image_inference) Here is an [example](https://imgsli.com/MjA2MDA1) with a resolution of 2396 x 1596. GPU memory usage will continue to be optimized in the future and we are looking forward to your pull requests!
+- **2023.09.14**: ✅ Add support for background upsampler(DiffBIR/[RealESRGAN](https://github.com/xinntao/Real-ESRGAN)) in face enhancement! :rocket: [**Try it!**](#unaligned_face_inference)
+- **2023.09.13**: :rocket: Provide online demo (DiffBIR-official) in [OpenXLab](https://openxlab.org.cn/apps/detail/linxinqi/DiffBIR-official), which integrates both general model and face model. Please have a try! [camenduru](https://github.com/camenduru) also implements an online demo, thanks for his work.:hugs:
+- **2023.09.12**: ✅ Upload inference code of latent image guidance and release [real47](inputs/real47) testset.
+- **2023.09.08**: ✅ Add support for restoring unaligned faces.
+- **2023.09.06**: :rocket: Update [colab demo](https://colab.research.google.com/github/camenduru/DiffBIR-colab/blob/main/DiffBIR_colab.ipynb). Thanks to [camenduru](https://github.com/camenduru)!:hugs:
+- **2023.08.30**: This repo is released.
+<!-- - [**History Updates** >]() -->
+## <a name="todo"></a>:climbing:TODO
+- [x] Release code and pretrained models:computer:.
+- [x] Update links to paper and project page:link:.
+- [x] Release real47 testset:minidisc:.
+- [ ] Provide webui and reduce the memory usage of DiffBIR:fire::fire::fire:.
+- [ ] Provide HuggingFace demo:notebook::fire::fire::fire:.
+- [x] Add a patch-based sampling schedule:mag:.
+- [x] Upload inference code of latent image guidance:page_facing_up:.
+- [ ] Improve the performance:superhero:.
+- [x] Support MPS acceleration for MacOS users.
+## <a name="installation"></a>:gear:Installation
+<!-- - **Python** >= 3.9
+- **CUDA** >= 11.3
+- **PyTorch** >= 1.12.1
+- **xformers** == 0.0.16 -->
+```shell
+# clone this repo
+git clone https://github.com/XPixelGroup/DiffBIR.git
+cd DiffBIR
+# create an environment with python >= 3.9
+conda create -n diffbir python=3.9
+conda activate diffbir
+pip install -r requirements.txt
+```
+Note the installation is only compatible with **Linux** users. If you are working on different platforms, please check [xOS Installation](assets/docs/installation_xOS.md).
+<!-- ```shell
+# clone this repo
+git clone https://github.com/XPixelGroup/DiffBIR.git
+cd DiffBIR
+# create a conda environment with python >= 3.9
+conda create -n diffbir python=3.9
+conda activate diffbir
+conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
+conda install xformers==0.0.16 -c xformers
+# other dependencies
+pip install -r requirements.txt
+``` -->
+## <a name="pretrained_models"></a>:dna:Pretrained Models
+| Model Name | Description | HuggingFace | BaiduNetdisk | OpenXLab |
+| :--------- | :---------- | :---------- | :---------- | :---------- |
+| general_swinir_v1.ckpt | Stage1 model (SwinIR) for general image restoration. | [download](https://huggingface.co/lxq007/DiffBIR/resolve/main/general_swinir_v1.ckpt) | [download](https://pan.baidu.com/s/1uvSvJgcoL_Knj0h22-9TvA?pwd=v3v6) (pwd: v3v6) | [download](https://download.openxlab.org.cn/models/linxinqi/DiffBIR/weight//diffbir_general_swinir_v1) |
+| general_full_v1.ckpt | Full model for general image restoration. "Full" means it contains both the stage1 and stage2 model. | [download](https://huggingface.co/lxq007/DiffBIR/resolve/main/general_full_v1.ckpt) | [download](https://pan.baidu.com/s/1gLvW1nvkJStdVAKROqaYaA?pwd=86zi) (pwd: 86zi) | [download](https://download.openxlab.org.cn/models/linxinqi/DiffBIR/weight//diffbir_general_full_v1) |
+| face_swinir_v1.ckpt | Stage1 model (SwinIR) for face restoration. | [download](https://huggingface.co/lxq007/DiffBIR/resolve/main/face_swinir_v1.ckpt) | [download](https://pan.baidu.com/s/1cnBBC8437BJiM3q6suaK8g?pwd=xk5u) (pwd: xk5u) | [download](https://download.openxlab.org.cn/models/linxinqi/DiffBIR/weight//diffbir_face_swinir_v1) |
+| face_full_v1.ckpt | Full model for face restoration. | [download](https://huggingface.co/lxq007/DiffBIR/resolve/main/face_full_v1.ckpt) | [download](https://pan.baidu.com/s/1pc04xvQybkynRfzK5Y8K0Q?pwd=ov8i) (pwd: ov8i) | [download](https://download.openxlab.org.cn/models/linxinqi/DiffBIR/weight//diffbir_face_full_v1) |
+## <a name="quick_start"></a>:flight_departure:Quick Start
+Download [general_full_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/general_full_v1.ckpt) and [general_swinir_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/general_swinir_v1.ckpt) to `weights/`, then run the following command to interact with the gradio website.
+```shell
+python gradio_diffbir.py \
+--ckpt weights/general_full_v1.ckpt \
+--config configs/model/cldm.yaml \
+--reload_swinir \
+--swinir_ckpt weights/general_swinir_v1.ckpt \
+--device cuda
+```
+<div align="center">
+    <kbd><img src="assets/gradio.png"></img></kbd>
+</div>
+## <a name="inference"></a>:crossed_swords:Inference
+### Full Pipeline (Remove Degradations & Refine Details)
+<a name="general_image_inference"></a>
+#### General Image
+Download [general_full_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/general_full_v1.ckpt) and [general_swinir_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/general_swinir_v1.ckpt) to `weights/` and run the following command.
+```shell
+python inference.py \
+--input inputs/demo/general \
+--config configs/model/cldm.yaml \
+--ckpt weights/general_full_v1.ckpt \
+--reload_swinir --swinir_ckpt weights/general_swinir_v1.ckpt \
+--steps 50 \
+--sr_scale 4 \
+--color_fix_type wavelet \
+--output results/demo/general \
+--device cuda [--tiled --tile_size 512 --tile_stride 256]
+```
+Remove the brackets to enable tiled sampling. If you are confused about where the `reload_swinir` option came from, please refer to the [degradation details](#degradation-details).
+#### Face Image
+<!-- Download [face_full_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/face_full_v1.ckpt) to `weights/` and run the following command. -->
+The [face_full_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/face_full_v1.ckpt) will be downloaded from HuggingFace automatically.
+```shell
+# for aligned face inputs
+python inference_face.py \
+--input inputs/demo/face/aligned \
+--sr_scale 1 \
+--output results/demo/face/aligned \
+--has_aligned \
+--device cuda
+```
+<a name="unaligned_face_inference"></a>
+```shell
+# for unaligned face inputs
+python inference_face.py \
+--input inputs/demo/face/whole_img \
+--sr_scale 2 \
+--output results/demo/face/whole_img \
+--bg_upsampler DiffBIR \
+--device cuda
+```
+### Latent Image Guidance (Quality-fidelity trade-off)
+Latent image guidance is used to achieve a trade-off bwtween quality and fidelity. We default to closing it since we prefer quality rather than fidelity. Here is an example:
+```shell
+python inference.py \
+--input inputs/demo/general \
+--config configs/model/cldm.yaml \
+--ckpt weights/general_full_v1.ckpt \
+--reload_swinir --swinir_ckpt weights/general_swinir_v1.ckpt \
+--steps 50 \
+--sr_scale 4 \
+--color_fix_type wavelet \
+--output results/demo/general \
+--device cuda \
+--use_guidance --g_scale 400 --g_t_start 200
+```
+You will see that the results become more smooth.
+### Only Stage1 Model (Remove Degradations)
+Download [general_swinir_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/general_swinir_v1.ckpt), [face_swinir_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/face_swinir_v1.ckpt) for general, face image respectively, and run the following command.
+```shell
+python scripts/inference_stage1.py \
+--config configs/model/swinir.yaml \
+--ckpt [swinir_ckpt_path] \
+--input [lq_dir] \
+--sr_scale 1 --image_size 512 \
+--output [output_dir_path]
+```
+### Only Stage2 Model (Refine Details)
+Since the proposed two-stage pipeline is very flexible, you can utilize other awesome models to remove degradations instead of SwinIR and then leverage the Stable Diffusion to refine details.
+```shell
+# step1: Use other models to remove degradations and save results in [img_dir_path].
+# step2: Refine details of step1 outputs.
+python inference.py \
+--config configs/model/cldm.yaml \
+--ckpt [full_ckpt_path] \
+--steps 50 --sr_scale 1 \
+--input [img_dir_path] \
+--color_fix_type wavelet \
+--output [output_dir_path] \
+--disable_preprocess_model \
+--device cuda
+```
+## <a name="train"></a>:stars:Train
+### Degradation Details
+For general image restoration, we first train both the stage1 and stage2 model under codeformer degradation to enhance the generative capacity of the stage2 model. In order to improve the ability for degradation removal, we train another stage1 model under Real-ESRGAN degradation and utilize it during inference.
+For face image restoration, we adopt the degradation model used in [DifFace](https://github.com/zsyOAOA/DifFace/blob/master/configs/training/swinir_ffhq512.yaml) for training and directly utilize the SwinIR model released by them as our stage1 model.
+### Data Preparation
+1. Generate file list of training set and validation set.
+    ```shell
+    python scripts/make_file_list.py \
+    --img_folder [hq_dir_path] \
+    --val_size [validation_set_size] \
+    --save_folder [save_dir_path] \
+    --follow_links
+    ```
+    This script will collect all image files in `img_folder` and split them into training set and validation set automatically. You will get two file lists in `save_folder`, each line in a file list contains an absolute path of an image file:
+    ```
+    save_folder
+    ├── train.list # training file list
+    └── val.list   # validation file list
+    ```
+2. Configure training set and validation set.
+    For general image restoration, fill in the following configuration files with appropriate values.
+    - [training set](configs/dataset/general_deg_codeformer_train.yaml) and [validation set](configs/dataset/general_deg_codeformer_val.yaml) for **CodeFormer** degradation.
+    - [training set](configs/dataset/general_deg_realesrgan_train.yaml) and [validation set](configs/dataset/general_deg_realesrgan_val.yaml) for **Real-ESRGAN** degradation.
+    For face image restoration, fill in the face [training set](configs/dataset/face_train.yaml) and [validation set](configs/dataset/face_val.yaml) configuration files with appropriate values.
+### Train Stage1 Model
+1. Configure training-related information.
+    Fill in the configuration file of [training](configs/train_swinir.yaml) with appropriate values.
+2. Start training.
+    ```shell
+    python train.py --config [training_config_path]
+    ```
+    :bulb::Checkpoints of SwinIR will be used in training stage2 model.
+### Train Stage2 Model
+1. Download pretrained [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) to provide generative capabilities.
+    ```shell
+    wget https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt --no-check-certificate
+    ```
+2. Create the initial model weights.
+    ```shell
+    python scripts/make_stage2_init_weight.py \
+    --cldm_config configs/model/cldm.yaml \
+    --sd_weight [sd_v2.1_ckpt_path] \
+    --swinir_weight [swinir_ckpt_path] \
+    --output [init_weight_output_path]
+    ```
+    You will see some [outputs](assets/init_weight_outputs.txt) which show the weight initialization.
+3. Configure training-related information.
+    Fill in the configuration file of [training](configs/train_cldm.yaml) with appropriate values.
+4. Start training.
+    ```shell
+    python train.py --config [training_config_path]
+    ```
+## Citation
+Please cite us if our work is useful for your research.
+```
+@article{2023diffbir,
+  author    = {Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Ben Fei, Bo Dai, Wanli Ouyang, Yu Qiao, Chao Dong},
+  title     = {DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior},
+  journal   = {arxiv},
+  year      = {2023},
+}
+```
+## License
+This project is released under the [Apache 2.0 license](LICENSE).
+## Acknowledgement
+This project is based on [ControlNet](https://github.com/lllyasviel/ControlNet) and [BasicSR](https://github.com/XPixelGroup/BasicSR). Thanks for their awesome work.
+## Contact
+If you have any questions, please feel free to contact with me at linxinqi@tju.edu.cn.
--- a/configs/dataset/general_deg_realesrgan_train.yaml
+++ b/configs/dataset/general_deg_realesrgan_train.yaml
@@ -2,7 +2,7 @@ dataset:
  target: dataset.realesrgan.RealESRGANDataset
  params:
    # Path to the file list.
-    file_list: 
+    file_list: train_data_path/train.list
    out_size: 512
    crop_type: center
@@ -28,9 +28,9 @@ dataset:
    final_sinc_prob: 0.8
 data_loader:
-  batch_size: 32
+  batch_size: 1
  shuffle: true
-  num_workers: 16
+  num_workers: 2
  prefetch_factor: 2
  drop_last: true
@@ -40,7 +40,7 @@ batch_transform:
    use_sharpener: false
    resize_hq: false
    # Queue size of training pool, this should be multiples of batch_size.
-    queue_size: 256
+    queue_size: 128
    # the first degradation process
    resize_prob: [0.2, 0.7, 0.1] # up, down, keep
    resize_range: [0.15, 1.5]

--- a/configs/dataset/general_deg_realesrgan_val.yaml
+++ b/configs/dataset/general_deg_realesrgan_val.yaml
@@ -2,7 +2,7 @@ dataset:
  target: dataset.realesrgan.RealESRGANDataset
  params:
    # Path to the file list.
-    file_list: 
+    file_list: train_data_path/val.list
    out_size: 512
    crop_type: center
@@ -28,9 +28,9 @@ dataset:
    final_sinc_prob: 0.8
 data_loader:
-  batch_size: 32
+  batch_size: 1
  shuffle: false
-  num_workers: 16
+  num_workers: 2
  prefetch_factor: 2
  drop_last: true
@@ -40,7 +40,7 @@ batch_transform:
    use_sharpener: false
    resize_hq: false
    # Queue size of training pool, this should be multiples of batch_size.
-    queue_size: 256
+    queue_size: 128
    # the first degradation process
    resize_prob: [0.2, 0.7, 0.1] # up, down, keep
    resize_range: [0.15, 1.5]

--- a/configs/train_cldm.yaml
+++ b/configs/train_cldm.yaml
@@ -2,16 +2,16 @@ data:
  target: dataset.data_module.BIRDataModule
  params:
    # Path to training set configuration file.
-    train_config: 
+    train_config: configs/dataset/general_deg_realesrgan_train.yaml
    # Path to validation set configuration file.
-    val_config: 
+    val_config: configs/dataset/general_deg_realesrgan_val.yaml
 model:
  # You can set learning rate in the following configuration file.
  config: configs/model/cldm.yaml
  # Path to the checkpoints or weights you want to resume. At the begining, 
  # this should be set to the initial weights created by scripts/make_stage2_init_weight.py.
-  resume: 
+  resume: weights/init.pth
 lightning:
  seed: 231
@@ -20,28 +20,28 @@ lightning:
    accelerator: ddp
    precision: 32
    # Indices of GPUs used for training.
-    gpus: [0, 1, 2, 3, 4, 5]
+    gpus: [5,6,7]
    # Path to save logs and checkpoints.
-    default_root_dir: 
+    default_root_dir: stage2_checkpoints
    # Max number of training steps (batches).
-    max_steps: 25001
+    max_steps: 5000
    # Validation frequency in terms of training steps.
-    val_check_interval: 500
+    val_check_interval: 100
-    log_every_n_steps: 50
+    log_every_n_steps: 500
    # Accumulate gradients from multiple batches so as to increase batch size.
-    accumulate_grad_batches: 1
+    accumulate_grad_batches: 4
  callbacks:
    - target: model.callbacks.ImageLogger
      params:
        # Log frequency of image logger.
-        log_every_n_steps: 1000
+        log_every_n_steps: 100
-        max_images_each_step: 4
+        max_images_each_step: 2
        log_images_kwargs: ~
    - target: model.callbacks.ModelCheckpoint
      params:
        # Frequency of saving checkpoints.
-        every_n_train_steps: 5000
+        every_n_train_steps: 500
        save_top_k: -1
        filename: "{step}"
--- a/configs/train_swinir.yaml
+++ b/configs/train_swinir.yaml
@@ -2,9 +2,9 @@ data:
  target: dataset.data_module.BIRDataModule
  params:
    # Path to training set configuration file.
-    train_config: 
+    train_config: configs/dataset/general_deg_realesrgan_train.yaml
    # Path to validation set configuration file.
-    val_config: 
+    val_config: configs/dataset/general_deg_realesrgan_val.yaml
 model:
  # You can set learning rate in the following configuration file.
@@ -19,29 +19,29 @@ lightning:
    accelerator: ddp
    precision: 32
    # Indices of GPUs used for training.
-    gpus: [0, 1, 2]
+    gpus: [5,7]
    # Path to save logs and checkpoints.
-    default_root_dir: 
+    default_root_dir: stage1_checkpoints
    # Max number of training steps (batches).
-    max_steps: 150001
+    max_steps: 10000
    # Validation frequency in terms of training steps.
-    val_check_interval: 500
+    val_check_interval: 0.25
    # Log frequency of tensorboard logger.
-    log_every_n_steps: 50
+    log_every_n_steps: 100
    # Accumulate gradients from multiple batches so as to increase batch size.
-    accumulate_grad_batches: 1
+    accumulate_grad_batches: 2
  callbacks:
    - target: model.callbacks.ImageLogger
      params:
        # Log frequency of image logger.
        log_every_n_steps: 1000
-        max_images_each_step: 4
+        max_images_each_step: 2
        log_images_kwargs: ~
    - target: model.callbacks.ModelCheckpoint
      params:
        # Frequency of saving checkpoints.
-        every_n_train_steps: 10000
+        every_n_train_steps: 1000
        save_top_k: -1
        filename: "{step}"
--- a/images/image.png
+++ b/images/image.png
--- a/images/lq_step-004900_e-000008_b-001203.png
+++ b/images/lq_step-004900_e-000008_b-001203.png
--- a/images/samples_step-004900_e-000008_b-001203.png
+++ b/images/samples_step-004900_e-000008_b-001203.png
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -6,6 +6,7 @@ from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextMod
 import open_clip
 from ldm.util import default, count_params
+from pathlib import Path
 class AbstractEncoder(nn.Module):
@@ -145,7 +146,11 @@ class FrozenOpenCLIPEmbedder(AbstractEncoder):
                 freeze=True, layer="last"):
        super().__init__()
        assert layer in self.LAYERS
-        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
+        # model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
+        # modify
+        pretrained = Path(__file__).resolve().parent.parent.parent.parent / "weights" / "open_clip_pytorch_model.bin"
+        print(str(pretrained))
+        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=str(pretrained))
        del model.visual
        self.model = model

--- a/model/cldm.py
+++ b/model/cldm.py
@@ -375,25 +375,20 @@ class ControlLDM(LatentDiffusion):
        log["decoded_control"] = (self.decode_first_stage(c_latent) + 1) / 2
        log["lq"] = c_lq
        log["text"] = (log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16) + 1) / 2
+        log["samples"] = self.sample_log(
-        samples = self.sample_log(
+            cond_img=c_cat,            
-            # TODO: remove c_concat from cond
-            cond={"c_concat": [c_cat], "c_crossattn": [c], "c_latent": [c_latent]},
            steps=sample_steps
        )
-        x_samples = self.decode_first_stage(samples)
-        log["samples"] = (x_samples + 1) / 2
        return log
    @torch.no_grad()
-    def sample_log(self, cond, steps):
+    def sample_log(self, cond_img, steps):
        sampler = SpacedSampler(self)
-        b, c, h, w = cond["c_concat"][0].shape
+        b, c, h, w = cond_img.shape
        shape = (b, self.channels, h // 8, w // 8)
        samples = sampler.sample(
-            steps, shape, cond, unconditional_guidance_scale=1.0,
+            steps, shape, cond_img, positive_prompt="", negative_prompt="",
-            unconditional_conditioning=None
+            cfg_scale=1.0, color_fix_type="wavelet"
        )
        return samples

--- a/model/spaced_sampler.py
+++ b/model/spaced_sampler.py
@@ -501,15 +501,14 @@ class SpacedSampler:
        steps: int,
        shape: Tuple[int],
        cond_img: torch.Tensor,
-        positive_prompt: str,
+        positive_prompt: str = '',
-        negative_prompt: str,
+        negative_prompt: str = '',
        x_T: Optional[torch.Tensor]=None,
        cfg_scale: float=1.,
        cond_fn: Optional[Guidance]=None,
        color_fix_type: str="none"
    ) -> torch.Tensor:
        self.make_schedule(num_steps=steps)
        device = next(self.model.parameters()).device
        b = shape[0]
        if x_T is None:

--- a/nohup.txt
+++ b/nohup.txt
--- a/requirements.txt
+++ b/requirements.txt
--extra-index-url https://download.pytorch.org/whl/cu116
-torch==1.13.1+cu116
-torchvision==0.14.1+cu116
-xformers==0.0.16
 pytorch_lightning==1.4.2
 einops
 open-clip-torch
 omegaconf
 torchmetrics==0.6.0
-triton==2.0.0
 opencv-python-headless
 scipy
 matplotlib

--- a/train_data_path/train.list
+++ b/train_data_path/train.list
--- a/train_data_path/val.list
+++ b/train_data_path/val.list
+../datasets/images/german_shorthaired_3.jpg
+../datasets/images/Sphynx_164.jpg
+../datasets/images/english_cocker_spaniel_185.jpg
+../datasets/images/english_setter_76.jpg
+../datasets/images/chihuahua_75.jpg
+../datasets/images/chihuahua_172.jpg
+../datasets/images/german_shorthaired_78.jpg
+../datasets/images/beagle_52.jpg
+../datasets/images/keeshond_165.jpg
+../datasets/images/Siamese_173.jpg
+../datasets/images/beagle_47.jpg
+../datasets/images/boxer_44.jpg
+../datasets/images/great_pyrenees_92.jpg
+../datasets/images/beagle_99.jpg
+../datasets/images/yorkshire_terrier_67.jpg
+../datasets/images/american_pit_bull_terrier_118.jpg
+../datasets/images/english_setter_186.jpg
+../datasets/images/samoyed_130.jpg
+../datasets/images/havanese_131.jpg
+../datasets/images/pug_89.jpg
+../datasets/images/chihuahua_6.jpg
+../datasets/images/great_pyrenees_197.jpg
+../datasets/images/english_setter_158.jpg
+../datasets/images/wheaten_terrier_69.jpg
+../datasets/images/boxer_51.jpg
+../datasets/images/Bombay_11.jpg
+../datasets/images/Persian_185.jpg
+../datasets/images/miniature_pinscher_196.jpg
+../datasets/images/boxer_131.jpg
+../datasets/images/pomeranian_125.jpg
+../datasets/images/Birman_114.jpg
+../datasets/images/newfoundland_119.jpg
+../datasets/images/British_Shorthair_77.jpg
+../datasets/images/Bombay_131.jpg
+../datasets/images/pomeranian_130.jpg
+../datasets/images/english_setter_193.jpg
+../datasets/images/miniature_pinscher_19.jpg
+../datasets/images/great_pyrenees_59.jpg
+../datasets/images/pug_57.jpg
+../datasets/images/newfoundland_41.jpg
+../datasets/images/great_pyrenees_87.jpg
+../datasets/images/Russian_Blue_155.jpg
+../datasets/images/Abyssinian_119.jpg
+../datasets/images/miniature_pinscher_183.jpg
+../datasets/images/samoyed_125.jpg
+../datasets/images/beagle_120.jpg
+../datasets/images/samoyed_23.jpg
+../datasets/images/Sphynx_73.jpg
+../datasets/images/leonberger_158.jpg
+../datasets/images/Ragdoll_121.jpg
+../datasets/images/Siamese_64.jpg
+../datasets/images/Siamese_85.jpg
+../datasets/images/english_cocker_spaniel_164.jpg
+../datasets/images/american_bulldog_207.jpg
+../datasets/images/english_setter_63.jpg
+../datasets/images/Bombay_213.jpg
+../datasets/images/staffordshire_bull_terrier_147.jpg
+../datasets/images/Sphynx_59.jpg
+../datasets/images/beagle_202.jpg
+../datasets/images/english_setter_49.jpg
+../datasets/images/wheaten_terrier_56.jpg
+../datasets/images/great_pyrenees_149.jpg
+../datasets/images/Persian_30.jpg
+../datasets/images/english_setter_82.jpg
+../datasets/images/german_shorthaired_135.jpg
+../datasets/images/great_pyrenees_182.jpg
+../datasets/images/havanese_64.jpg
+../datasets/images/Bengal_158.jpg
+../datasets/images/yorkshire_terrier_142.jpg
+../datasets/images/Persian_164.jpg
+../datasets/images/Russian_Blue_80.jpg
+../datasets/images/Bengal_50.jpg
+../datasets/images/keeshond_191.jpg
+../datasets/images/Bengal_193.jpg
+../datasets/images/Sphynx_92.jpg
+../datasets/images/english_cocker_spaniel_37.jpg
+../datasets/images/Maine_Coon_238.jpg
+../datasets/images/shiba_inu_29.jpg
+../datasets/images/samoyed_36.jpg
+../datasets/images/Sphynx_246.jpg
+../datasets/images/Egyptian_Mau_188.jpg
+../datasets/images/miniature_pinscher_177.jpg
+../datasets/images/newfoundland_126.jpg
+../datasets/images/leonberger_22.jpg
+../datasets/images/yorkshire_terrier_157.jpg
+../datasets/images/english_cocker_spaniel_22.jpg
+../datasets/images/german_shorthaired_99.jpg
+../datasets/images/pomeranian_43.jpg
+../datasets/images/Abyssinian_126.jpg
+../datasets/images/saint_bernard_50.jpg
+../datasets/images/Abyssinian_133.jpg
+../datasets/images/chihuahua_186.jpg
+../datasets/images/pomeranian_88.jpg
+../datasets/images/Bengal_167.jpg
+../datasets/images/basset_hound_155.jpg
+../datasets/images/chihuahua_81.jpg
+../datasets/images/keeshond_184.jpg
+../datasets/images/Ragdoll_36.jpg
+../datasets/images/leonberger_167.jpg
+../datasets/images/Russian_Blue_262.jpg
+../datasets/images/basset_hound_22.jpg
+../datasets/images/chihuahua_158.jpg
+../datasets/images/american_bulldog_212.jpg
+../datasets/images/Siamese_159.jpg
+../datasets/images/beagle_135.jpg
+../datasets/images/miniature_pinscher_162.jpg
+../datasets/images/staffordshire_bull_terrier_152.jpg
+../datasets/images/Egyptian_Mau_85.jpg
+../datasets/images/Bengal_45.jpg
+../datasets/images/wheaten_terrier_4.jpg
+../datasets/images/newfoundland_133.jpg
+../datasets/images/British_Shorthair_54.jpg
+../datasets/images/havanese_71.jpg
+../datasets/images/german_shorthaired_90.jpg
+../datasets/images/wheaten_terrier_43.jpg
+../datasets/images/american_bulldog_23.jpg
+../datasets/images/wheaten_terrier_88.jpg
+../datasets/images/japanese_chin_132.jpg
+../datasets/images/wheaten_terrier_94.jpg
+../datasets/images/beagle_129.jpg
+../datasets/images/Sphynx_2.jpg
+../datasets/images/english_setter_97.jpg
+../datasets/images/keeshond_153.jpg
+../datasets/images/British_Shorthair_83.jpg
+../datasets/images/pug_127.jpg
+../datasets/images/Persian_39.jpg
+../datasets/images/Persian_171.jpg
+../datasets/images/Abyssinian_33.jpg
+../datasets/images/boxer_67.jpg
+../datasets/images/keeshond_198.jpg
+../datasets/images/British_Shorthair_136.jpg
+../datasets/images/japanese_chin_91.jpg
+../datasets/images/pug_74.jpg
+../datasets/images/yorkshire_terrier_51.jpg
+../datasets/images/scottish_terrier_9.jpg
+../datasets/images/yorkshire_terrier_86.jpg
+../datasets/images/Abyssinian_232.jpg
+../datasets/images/Bombay_32.jpg
+../datasets/images/Maine_Coon_36.jpg
+../datasets/images/Russian_Blue_95.jpg
+../datasets/images/havanese_112.jpg
+../datasets/images/Russian_Blue_176.jpg
+../datasets/images/Siamese_78.jpg
+../datasets/images/British_Shorthair_41.jpg
+../datasets/images/english_cocker_spaniel_178.jpg
+../datasets/images/wheaten_terrier_138.jpg
+../datasets/images/pomeranian_106.jpg
+../datasets/images/keeshond_146.jpg
+../datasets/images/havanese_78.jpg
+../datasets/images/wheaten_terrier_81.jpg
+../datasets/images/Birman_122.jpg
+../datasets/images/german_shorthaired_129.jpg
+../datasets/images/scottish_terrier_48.jpg
+../datasets/images/english_setter_55.jpg
+../datasets/images/pug_61.jpg
+../datasets/images/staffordshire_bull_terrier_185.jpg
+../datasets/images/scottish_terrier_83.jpg
+../datasets/images/staffordshire_bull_terrier_190.jpg
+../datasets/images/staffordshire_bull_terrier_22.jpg
+../datasets/images/British_Shorthair_123.jpg
+../datasets/images/basset_hound_149.jpg
+../datasets/images/boxer_107.jpg
+../datasets/images/shiba_inu_35.jpg
+../datasets/images/havanese_3.jpg
+../datasets/images/basset_hound_182.jpg
+../datasets/images/japanese_chin_84.jpg
+../datasets/images/saint_bernard_107.jpg
+../datasets/images/Birman_83.jpg
+../datasets/images/Egyptian_Mau_6.jpg
+../datasets/images/boxer_86.jpg
+../datasets/images/scottish_terrier_77.jpg
+../datasets/images/english_cocker_spaniel_14.jpg
+../datasets/images/havanese_138.jpg
+../datasets/images/scottish_terrier_197.jpg
+../datasets/images/german_shorthaired_71.jpg
+../datasets/images/leonberger_144.jpg
+../datasets/images/basset_hound_14.jpg
+../datasets/images/great_pyrenees_140.jpg
+../datasets/images/pug_132.jpg
+../datasets/images/Bengal_66.jpg
+../datasets/images/Bombay_18.jpg
+../datasets/images/Sphynx_178.jpg
+../datasets/images/Russian_Blue_68.jpg
+../datasets/images/american_bulldog_113.jpg
+../datasets/images/american_bulldog_139.jpg
+../datasets/images/american_bulldog_224.jpg
+../datasets/images/newfoundland_48.jpg
+../datasets/images/Egyptian_Mau_160.jpg
+../datasets/images/pug_80.jpg
+../datasets/images/chihuahua_69.jpg
+../datasets/images/wheaten_terrier_75.jpg
+../datasets/images/Abyssinian_110.jpg
+../datasets/images/english_setter_144.jpg
+../datasets/images/Egyptian_Mau_175.jpg
+../datasets/images/Birman_62.jpg
+../datasets/images/newfoundland_83.jpg
+../datasets/images/Maine_Coon_231.jpg
+../datasets/images/Birman_108.jpg
+../datasets/images/american_pit_bull_terrier_104.jpg
+../datasets/images/Bengal_144.jpg
+../datasets/images/leonberger_14.jpg
+../datasets/images/Abyssinian_105.jpg
+../datasets/images/great_pyrenees_50.jpg
+../datasets/images/Persian_147.jpg
+../datasets/images/basset_hound_176.jpg
+../datasets/images/Sphynx_45.jpg
+../datasets/images/Abyssinian_19.jpg
+../datasets/images/samoyed_139.jpg
+../datasets/images/boxer_93.jpg
+../datasets/images/leonberger_151.jpg
+../datasets/images/pomeranian_60.jpg
+../datasets/images/Maine_Coon_106.jpg
+../datasets/images/boxer_58.jpg
+../datasets/images/great_pyrenees_155.jpg
+../datasets/images/english_cocker_spaniel_147.jpg
+../datasets/images/beagle_103.jpg
+../datasets/images/scottish_terrier_62.jpg
+../datasets/images/yorkshire_terrier_174.jpg
+../datasets/images/beagle_64.jpg
+../datasets/images/Siamese_47.jpg
+../datasets/images/basset_hound_113.jpg
+../datasets/images/american_pit_bull_terrier_111.jpg
+../datasets/images/pug_168.jpg
+../datasets/images/Abyssinian_175.jpg
+../datasets/images/Abyssinian_4.jpg
+../datasets/images/Maine_Coon_149.jpg
+../datasets/images/boxer_196.jpg
+../datasets/images/great_pyrenees_9.jpg
+../datasets/images/Birman_12.jpg
+../datasets/images/samoyed_149.jpg
+../datasets/images/pomeranian_10.jpg
+../datasets/images/miniature_pinscher_124.jpg
+../datasets/images/scottish_terrier_139.jpg
+../datasets/images/Egyptian_Mau_218.jpg
+../datasets/images/leonberger_134.jpg
+../datasets/images/yorkshire_terrier_104.jpg
+../datasets/images/wheaten_terrier_10.jpg
+../datasets/images/Maine_Coon_182.jpg
+../datasets/images/basset_hound_64.jpg
+../datasets/images/keeshond_24.jpg
+../datasets/images/Persian_63.jpg
+../datasets/images/beagle_173.jpg
+../datasets/images/german_shorthaired_14.jpg
+../datasets/images/american_pit_bull_terrier_174.jpg
+../datasets/images/Persian_76.jpg
+../datasets/images/great_pyrenees_20.jpg
+../datasets/images/pomeranian_197.jpg
+../datasets/images/Egyptian_Mau_110.jpg
+../datasets/images/Birman_1.jpg
+../datasets/images/japanese_chin_15.jpg
+../datasets/images/Birman_178.jpg
+../datasets/images/basset_hound_71.jpg
+../datasets/images/Siamese_202.jpg
+../datasets/images/havanese_183.jpg
+../datasets/images/samoyed_197.jpg
+../datasets/images/leonberger_64.jpg
+../datasets/images/great_pyrenees_125.jpg
+../datasets/images/shiba_inu_175.jpg
+../datasets/images/Ragdoll_5.jpg
+../datasets/images/Egyptian_Mau_105.jpg
+../datasets/images/Russian_Blue_139.jpg
+../datasets/images/Abyssinian_69.jpg
+../datasets/images/Bombay_148.jpg
+../datasets/images/keeshond_7.jpg
+../datasets/images/havanese_37.jpg
+../datasets/images/staffordshire_bull_terrier_114.jpg
+../datasets/images/boxer_148.jpg
+../datasets/images/leonberger_121.jpg
+../datasets/images/american_bulldog_79.jpg
+../datasets/images/yorkshire_terrier_111.jpg
+../datasets/images/Bengal_121.jpg
+../datasets/images/Bombay_68.jpg
+../datasets/images/staffordshire_bull_terrier_78.jpg
+../datasets/images/Bombay_183.jpg
+../datasets/images/Sphynx_20.jpg
+../datasets/images/miniature_pinscher_60.jpg
+../datasets/images/basset_hound_106.jpg
+../datasets/images/saint_bernard_16.jpg
+../datasets/images/keeshond_31.jpg
+../datasets/images/chihuahua_19.jpg
+../datasets/images/english_cocker_spaniel_137.jpg
+../datasets/images/Siamese_37.jpg
+../datasets/images/beagle_4.jpg
+../datasets/images/great_pyrenees_130.jpg
+../datasets/images/german_shorthaired_166.jpg
+../datasets/images/Russian_Blue_18.jpg
+../datasets/images/Persian_137.jpg
+../datasets/images/Maine_Coon_46.jpg
+../datasets/images/saint_bernard_162.jpg
+../datasets/images/Ragdoll_193.jpg
+../datasets/images/Sphynx_137.jpg
+../datasets/images/american_bulldog_176.jpg
+../datasets/images/english_setter_30.jpg
+../datasets/images/beagle_14.jpg
+../datasets/images/Abyssinian_181.jpg
+../datasets/images/chihuahua_121.jpg
+../datasets/images/Ragdoll_158.jpg
+../datasets/images/japanese_chin_142.jpg
+../datasets/images/Egyptian_Mau_22.jpg
+../datasets/images/Birman_147.jpg
+../datasets/images/american_pit_bull_terrier_180.jpg
+../datasets/images/Bombay_177.jpg
+../datasets/images/Bombay_42.jpg
+../datasets/images/beagle_192.jpg
+../datasets/images/pug_157.jpg
+../datasets/images/Persian_97.jpg
+../datasets/images/havanese_177.jpg
+../datasets/images/Maine_Coon_53.jpg
+../datasets/images/basset_hound_139.jpg
+../datasets/images/Bombay_57.jpg
+../datasets/images/Maine_Coon_98.jpg
+../datasets/images/Bombay_89.jpg
+../datasets/images/Birman_152.jpg
+../datasets/images/newfoundland_181.jpg
+../datasets/images/Siamese_120.jpg
+../datasets/images/samoyed_163.jpg
+../datasets/images/Abyssinian_56.jpg
+../datasets/images/boxer_17.jpg
+../datasets/images/Persian_49.jpg
+../datasets/images/basset_hound_90.jpg
+../datasets/images/pomeranian_163.jpg
+../datasets/images/Russian_Blue_32.jpg
+../datasets/images/Russian_Blue_113.jpg
+../datasets/images/Bengal_9.jpg
+../datasets/images/pomeranian_176.jpg
+../datasets/images/beagle_187.jpg
+../datasets/images/boxer_162.jpg
+../datasets/images/chihuahua_33.jpg
+../datasets/images/english_setter_4.jpg
+../datasets/images/Maine_Coon_176.jpg
+../datasets/images/american_pit_bull_terrier_189.jpg
+../datasets/images/Ragdoll_259.jpg
+../datasets/images/shiba_inu_4.jpg
+../datasets/images/english_cocker_spaniel_90.jpg
+../datasets/images/yorkshire_terrier_34.jpg
+../datasets/images/shiba_inu_181.jpg
+../datasets/images/Birman_38.jpg
+../datasets/images/miniature_pinscher_3.jpg
+../datasets/images/Russian_Blue_27.jpg
+../datasets/images/american_pit_bull_terrier_45.jpg
+../datasets/images/leonberger_47.jpg
+../datasets/images/Abyssinian_81.jpg
+../datasets/images/scottish_terrier_38.jpg
+../datasets/images/British_Shorthair_198.jpg
+../datasets/images/basset_hound_125.jpg
+../datasets/images/Ragdoll_84.jpg
+../datasets/images/great_pyrenees_16.jpg
+../datasets/images/english_setter_25.jpg
+../datasets/images/pug_195.jpg
+../datasets/images/newfoundland_156.jpg
+../datasets/images/samoyed_84.jpg
+../datasets/images/english_cocker_spaniel_114.jpg
+../datasets/images/staffordshire_bull_terrier_47.jpg
+../datasets/images/miniature_pinscher_112.jpg
+../datasets/images/english_cocker_spaniel_200.jpg
+../datasets/images/beagle_150.jpg
+../datasets/images/japanese_chin_157.jpg
+../datasets/images/american_bulldog_46.jpg
+../datasets/images/wheaten_terrier_148.jpg
+../datasets/images/Persian_40.jpg
+../datasets/images/shiba_inu_45.jpg
+../datasets/images/Siamese_228.jpg
+../datasets/images/Maine_Coon_84.jpg
+../datasets/images/american_pit_bull_terrier_142.jpg
+../datasets/images/shiba_inu_156.jpg
+../datasets/images/newfoundland_188.jpg
+../datasets/images/shiba_inu_87.jpg
+../datasets/images/staffordshire_bull_terrier_90.jpg
+../datasets/images/great_pyrenees_113.jpg
+../datasets/images/British_Shorthair_184.jpg
+../datasets/images/Birman_31.jpg
+../datasets/images/pug_18.jpg
+../datasets/images/British_Shorthair_38.jpg
+../datasets/images/saint_bernard_20.jpg
+../datasets/images/basset_hound_52.jpg
+../datasets/images/Birman_24.jpg
+../datasets/images/american_pit_bull_terrier_157.jpg
+../datasets/images/american_pit_bull_terrier_87.jpg
+../datasets/images/leonberger_52.jpg
+../datasets/images/staffordshire_bull_terrier_137.jpg
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