update v0.0.4

example/__init__.py

@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+from . import impls

example/example.yaml

@@ -0,0 +1,260 @@
+ENV:
+  USE_PL: False
+# SET GLOBAL SYSTEM
+SOLVER:
+  # NAME DESCRIPTION:  TYPE:  default: 'TrainValSolver'
+  NAME: TrainValSolver
+  # RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
+  RESUME_FROM:
+  # MAX_EPOCHS DESCRIPTION: Max epochs for training. TYPE: int default: 10
+  MAX_EPOCHS: 200
+  # NUM_FOLDS DESCRIPTION: Num folds for training. TYPE: int default: 0
+  NUM_FOLDS: 1
+  # WORK_DIR DESCRIPTION: Save dir of the training log or model. TYPE: str default: ''
+  WORK_DIR: ./exp12/
+  LOG_FILE: std_log.txt
+  # EVAL_INTERVAL DESCRIPTION: Eval the model interval. TYPE: int default: 1
+  EVAL_INTERVAL: 1
+  ACCU_STEP: 1
+  # DO_FINAL_EVAL DESCRIPTION: If do final evaluation or not. TYPE: bool default: False
+  DO_FINAL_EVAL: True
+  # SAVE_EVAL_DATA DESCRIPTION: If save the evaluation data or not. TYPE: bool default: False
+  SAVE_EVAL_DATA: True
+  # EXTRA_KEYS DESCRIPTION: The extra keys for metric. TYPE: list default: []
+  EXTRA_KEYS: []
+  # TRAIN_DATA DESCRIPTION: Train data config. TYPE:  default: ''
+  TRAIN_DATA:
+    # NAME DESCRIPTION:  TYPE:  default: 'ImageClassifyPublicDataset'
+    NAME: ImageClassifyExampleDataset
+    # DATASET DESCRIPTION: the public dataset name TYPE: str default: 'cifar10'
+    DATASET: cifar10
+    # DATA_ROOT DESCRIPTION: the download data save path TYPE: str default: ''
+    DATA_ROOT: cifar10
+    # MODE DESCRIPTION: test TYPE: str default: test
+    MODE: train
+    # PIN_MEMORY DESCRIPTION: pin_memory for data loader TYPE: bool default: False
+    PIN_MEMORY: True
+    # BATCH_SIZE DESCRIPTION: batch size for data TYPE: int default: 4
+    BATCH_SIZE: 96
+    # NUM_WORKERS DESCRIPTION: num workers for fetching data! TYPE: int default: 1
+    NUM_WORKERS: 4
+    # TRANSFORMS DESCRIPTION:  TYPE:  default:
+    TRANSFORMS:
+      # - DESCRIPTION:  TYPE:  default:
+      - # NAME DESCRIPTION:  TYPE:  default: 'RandomResizedCrop'
+        NAME: RandomResizedCrop
+        SIZE: 32
+        # RATIO DESCRIPTION: ratio TYPE: list default: [0.75, 1.3333333333333333]
+        RATIO: [0.75, 1.33]
+        # SCALE DESCRIPTION: scale TYPE: list default: [0.08, 1.0]
+        SCALE: [0.8, 1.0]
+        # INTERPOLATION DESCRIPTION: interpolation TYPE: str default: 'blilinear'
+        INTERPOLATION: bilinear
+        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        INPUT_KEY: img
+        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        OUTPUT_KEY: img
+        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
+        BACKEND: pillow
+      - # NAME DESCRIPTION:  TYPE:  default: 'RandomHorizontalFlip'
+        NAME: RandomHorizontalFlip
+        # P DESCRIPTION: P TYPE: float default: 0.5
+        P: 0.5
+        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        INPUT_KEY: img
+        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        OUTPUT_KEY: img
+        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
+        BACKEND: pillow
+      - # NAME DESCRIPTION:  TYPE:  default: 'ImageToTensor'
+        NAME: ImageToTensor
+        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        INPUT_KEY: img
+        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        OUTPUT_KEY: img
+        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
+        BACKEND: pillow
+      - # NAME DESCRIPTION:  TYPE:  default: 'Normalize'
+        NAME: Normalize
+        # MEAN DESCRIPTION: mean TYPE: list default: []
+        MEAN: [0.4914, 0.4822, 0.4465]
+        # STD DESCRIPTION: std TYPE: list default: []
+        STD: [0.2023, 0.1994, 0.2010]
+        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        INPUT_KEY: img
+        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        OUTPUT_KEY: img
+        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
+        BACKEND: pillow
+      - NAME: ToTensor
+        # KEYS DESCRIPTION: keys TYPE: list default: []
+        KEYS: ["img", "label"]
+      - # NAME DESCRIPTION:  TYPE:  default: 'Select'
+        NAME: Select
+        # KEYS DESCRIPTION: keys TYPE: list default: []
+        KEYS: ["img", "label"]
+        # META_KEYS DESCRIPTION: meta keys TYPE: list default: []
+        META_KEYS: []
+  # EVAL_DATA DESCRIPTION: Eval data config. TYPE:  default: ''
+  EVAL_DATA:
+    # NAME DESCRIPTION:  TYPE:  default: 'ImageClassifyPublicDataset'
+    NAME: ImageClassifyPublicDataset
+    # DATASET DESCRIPTION: the public dataset name TYPE: str default: 'cifar10'
+    DATASET: cifar10
+    # DATA_ROOT DESCRIPTION: the download data save path TYPE: str default: ''
+    DATA_ROOT: ./local_data/cifar10
+    # MODE DESCRIPTION: test TYPE: str default: test
+    MODE: test
+    # PIN_MEMORY DESCRIPTION: pin_memory for data loader TYPE: bool default: False
+    PIN_MEMORY: True
+    # BATCH_SIZE DESCRIPTION: batch size for data TYPE: int default: 4
+    BATCH_SIZE: 96
+    # NUM_WORKERS DESCRIPTION: num workers for fetching data! TYPE: int default: 1
+    NUM_WORKERS: 4
+    # TRANSFORMS DESCRIPTION:  TYPE:  default:
+    TRANSFORMS:
+      # - DESCRIPTION:  TYPE:  default:
+      - # NAME DESCRIPTION:  TYPE:  default: 'Resize'
+        NAME: Resize
+        SIZE: 32
+        # INTERPOLATION DESCRIPTION: interpolation TYPE: str default: 'blilinear'
+        INTERPOLATION: bilinear
+        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        INPUT_KEY: img
+        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        OUTPUT_KEY: img
+        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
+        BACKEND: pillow
+      - # NAME DESCRIPTION:  TYPE:  default: 'ImageToTensor'
+        NAME: ImageToTensor
+        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        INPUT_KEY: img
+        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        OUTPUT_KEY: img
+        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
+        BACKEND: pillow
+      - # NAME DESCRIPTION:  TYPE:  default: 'Normalize'
+        NAME: Normalize
+        # MEAN DESCRIPTION: mean TYPE: list default: []
+        MEAN: [0.4914, 0.4822, 0.4465]
+        # STD DESCRIPTION: std TYPE: list default: []
+        STD: [0.2023, 0.1994, 0.2010]
+        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        INPUT_KEY: img
+        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
+        OUTPUT_KEY: img
+        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
+        BACKEND: pillow
+      - NAME: ToTensor
+        # KEYS DESCRIPTION: keys TYPE: list default: []
+        KEYS: ["img", "label"]
+      - # NAME DESCRIPTION:  TYPE:  default: 'Select'
+        NAME: Select
+        # KEYS DESCRIPTION: keys TYPE: list default: []
+        KEYS: ["img", "label"]
+        # META_KEYS DESCRIPTION: meta keys TYPE: list default: []
+        META_KEYS: []
+  # TRAIN_HOOKS DESCRIPTION:  TYPE:  default: ''
+  TRAIN_HOOKS:
+    - # NAME DESCRIPTION:  TYPE:  default: 'LogHook'
+      NAME: LogHook
+      # LOG_INTERVAL DESCRIPTION: the interval for log print! TYPE: int default: 10
+      LOG_INTERVAL: 10
+  # EVAL_HOOKS DESCRIPTION:  TYPE:  default: ''
+  EVAL_HOOKS:
+    - # NAME DESCRIPTION:  TYPE:  default: 'LogHook'
+      NAME: LogHook
+      # LOG_INTERVAL DESCRIPTION: the interval for log print! TYPE: int default: 10
+      LOG_INTERVAL: 10
+  # TEST_HOOKS DESCRIPTION:  TYPE:  default: ''
+  MODEL:
+    # NAME DESCRIPTION:  TYPE:  default: 'Classifier'
+    NAME: Classifier
+    # ACT_NAME DESCRIPTION: the activation function for logits, select from [softmax, sigmoid]! TYPE: str default: 'softmax'
+    ACT_NAME: softmax
+    # FREEZE_BN DESCRIPTION: if freeze bn of not TYPE: bool default: False
+    FREEZE_BN: False
+    # BACKBONE DESCRIPTION:  TYPE:  default: ''
+    BACKBONE:
+      # NAME DESCRIPTION:  TYPE:  default: 'ResNet'
+      NAME: ResNet
+      # DEPTH DESCRIPTION: the depth of network for resnet! TYPE: int default: 18
+      DEPTH: 18
+      # PRETRAINED DESCRIPTION: if load the official pretrained model or not. TYPE: bool default: False
+      PRETRAINED: false
+      #
+      KERNEL_SIZE: 3
+      # USE_RELU DESCRIPTION: use relu or not! TYPE: bool default: True
+      USE_RELU: True
+      # USE_MAXPOOL DESCRIPTION: use maxpool or not! TYPE: bool default: True
+      USE_MAXPOOL: false
+      # FIRST_CONV_STRIDE DESCRIPTION: first conv stride 1 or 2! TYPE: int default: 1
+      FIRST_CONV_STRIDE: 1
+      # FIRST_MAX_POOL_STRIDE DESCRIPTION: first max pool stride 1 or 2! TYPE: int default: 1
+      FIRST_MAX_POOL_STRIDE: 1
+    # NECK DESCRIPTION:  TYPE:  default: ''
+    NECK:
+      # NAME DESCRIPTION:  TYPE:  default: 'GlobalAveragePooling'
+      NAME: GlobalAveragePooling
+      # DIM DESCRIPTION: GlobalAveragePooling dim! TYPE: int default: 2
+      DIM: 2
+    # HEAD DESCRIPTION:  TYPE:  default: ''
+    HEAD:
+      # NAME DESCRIPTION:  TYPE:  default: 'ClassifierHead'
+      NAME: ClassifierHead
+      # DIM DESCRIPTION: representation dim! TYPE: int default: 512
+      DIM: 512
+      # NUM_CLASSES DESCRIPTION: number of classes. TYPE: int default: 10
+      NUM_CLASSES: 10
+      # DROPOUT_RATE DESCRIPTION: dropout rate, default 0. TYPE: float default: 0.0
+      DROPOUT_RATE: 0.0
+    METRIC:
+      # NAME DESCRIPTION:  TYPE:  default: 'AccuracyMetric'
+      NAME: AccuracyMetric
+      # TOPK DESCRIPTION: topk accuracy! TYPE: int default: 1
+      TOPK: 1
+    # LOSS DESCRIPTION:  TYPE:  default: ''
+    LOSS:
+      # NAME DESCRIPTION:  TYPE:  default: 'CrossEntropy'
+      NAME: CrossEntropy
+      # REDUCE DESCRIPTION: reduce is False, returns a loss per batch element instead and ignores :attr: size_average. Default: True TYPE: NoneType default: None
+      # REDUCE: None
+      # SIZE_AVERAGE DESCRIPTION: Deprecated (see :attr: reduction). By default,the losses are averaged over each loss element in the batch. Note that forsome losses, there are multiple elements per sample. If the field :attr: size_averageis set to False, the losses are instead summed for each minibatch. Ignoredwhen :attr: reduce is False. Default: True TYPE: NoneType default: None
+      # SIZE_AVERAGE: None
+      # IGNORE_INDEX DESCRIPTION: Specifies a target value that is ignoredand does not contribute to the input gradient. When :attr: size_average isTrue, the loss is averaged over non-ignored targets. Note that:attr: ignore_index is only applicable when the target contains class indices. TYPE: int default: -100
+      # IGNORE_INDEX: -100
+      # REDUCTION DESCRIPTION: Specifies the reduction to apply to the output:'none' | 'mean' | 'sum'. 'none': no reduction willbe applied, 'mean': the weighted mean of the output is taken,'sum': the output will be summed. Note: :attr: size_averageand :attr:`reduce` are in the process of being deprecated, and inthe meantime, specifying either of those two args will override:attr:`reduction`. Default: 'mean' TYPE: str default: 'mean'
+      # REDUCTION: mean
+      # LABEL_SMOOTHING DESCRIPTION: A float in [0.0, 1.0]. Specifies the amountof smoothing when computing the loss, where 0.0 means no smoothing.  TYPE: float default: 0.0
+      # LABEL_SMOOTHING: 0.0
+  # OPTIMIZER DESCRIPTION:  TYPE:  default: ''
+  OPTIMIZER:
+    # NAME DESCRIPTION:  TYPE:  default: 'SGD'
+    NAME: SGD
+    # LEARNING_RATE DESCRIPTION: the initial learning rate! TYPE: float default: 0.1
+    LEARNING_RATE: 0.01
+    # MOMENTUM DESCRIPTION: the momentum! TYPE: int default: 0
+    MOMENTUM: 0.9
+    # DAMPENING DESCRIPTION: the dampening! TYPE: int default: 0
+    DAMPENING: 0
+    # WEIGHT_DECAY DESCRIPTION: the weight decay! TYPE: int default: 0
+    WEIGHT_DECAY: 5e-4
+    # NESTEROV DESCRIPTION: the nesterov! TYPE: bool default: False
+    NESTEROV: False
+  # LR_SCHEDULER DESCRIPTION:  TYPE:  default: ''
+  LR_SCHEDULER:
+    # NAME DESCRIPTION:  TYPE:  default: 'CosineAnnealingLR'
+    NAME: CosineAnnealingLR
+    # T_MAX DESCRIPTION: the T max! TYPE: float default: 1.0
+    T_MAX: 200.0
+    # ETA_MIN DESCRIPTION: the eta min! TYPE: int default: 0
+    ETA_MIN: 0
+    # LAST_EPOCH DESCRIPTION: the last epoch! TYPE: int default: -1
+    LAST_EPOCH: -1
+  # METRICS DESCRIPTION:  TYPE:  default: ''
+  METRICS:
+    - # NAME DESCRIPTION:  TYPE:  default: 'AccuracyMetric'
+       NAME: AccuracyMetric
+       # TOPK DESCRIPTION: topk accuracy! TYPE: int default: 1
+       TOPK: 1
+       KEYS: ["logits", "label"]

example/impls/__init__.py

@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+from . import dataset

example/impls/dataset/__init__.py

@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+from .classifier_dataset import ImageClassifyExampleDataset

example/impls/dataset/classifier_dataset.py

@@ -0,0 +1,80 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import numpy as np
+import torchvision
+
+from scepter.modules.data.dataset.base_dataset import BaseDataset
+from scepter.modules.data.dataset.registry import DATASETS
+from scepter.modules.utils.config import dict_to_yaml
+
+
+@DATASETS.register_class()
+class ImageClassifyExampleDataset(BaseDataset):
+    """
+    Dataset for image classification wrapper
+
+    Args:
+        json_path (str): json file which contains all instances, should be a list of dict
+            which contains img_path and gt_label
+        image_dir (str or None): image directory, if None, img_path in json_path will be considered as absolute path
+        classes (list[str] or None): image class description
+    """
+    para_dict = {
+        'DATASET': {
+            'value': 'cifar10',
+            'description': 'the public dataset name'
+        },
+        'DATA_ROOT': {
+            'value': '',
+            'description': 'the download data save path'
+        }
+    }
+
+    para_dict.update(BaseDataset.para_dict)
+
+    def __init__(self, cfg, logger=None):
+
+        super(ImageClassifyExampleDataset, self).__init__(cfg, logger=logger)
+
+        self.dataset_name = cfg.DATASET
+        self.data_root = cfg.DATA_ROOT
+        self.phase = cfg.MODE
+        if self.dataset_name == 'cifar10':
+            self.dataset = torchvision.datasets.CIFAR10(
+                root=self.data_root,
+                train=self.phase == 'train',
+                download=True)
+
+    def __len__(self) -> int:
+        return len(self.dataset)
+
+    def _get(self, index: int):
+        img, target = self.dataset.__getitem__(index)
+        ret = {
+            'meta': {},
+            'label': np.asarray(target, dtype=np.int64),
+            'img': img
+        }
+        return ret
+
+    def worker_init_fn(self, worker_id, num_workers=1):
+        super(ImageClassifyExampleDataset,
+              self).worker_init_fn(worker_id, num_workers=num_workers)
+
+    @staticmethod
+    def get_config_template():
+        '''
+        { "ENV" :
+            { "description" : "",
+              "A" : {
+                    "value": 1.0,
+                    "description": ""
+               }
+            }
+        }
+        :return:
+        '''
+        return dict_to_yaml('modename_DATA',
+                            __class__.__name__,
+                            ImageClassifyExampleDataset.para_dict,
+                            set_name=True)

example/run.py

@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+from scepter.tools.run_train import run
+
+if __name__ == '__main__':
+    run()

readme.md

@@ -9,8 +9,8 @@
 </p>
 
 ## 📖 Table of Contents
-- [Introduction](#-introduction)
 - [News](#-news)
+- [Introduction](#-introduction)
 - [Installation](#%EF%B8%8F-installation)
 - [Getting Started](#-getting-started)
 - [SCEPTER Studio](#%EF%B8%8F-scepter-studio)
@@ -18,6 +18,15 @@
 - [Features](#-features)
 - [Learn More](#-learn-more)
 - [License](#license)
+- [Acknowledgement](#acknowledgement)
+
+## 🎉 News
+- [2024.03]: We optimize the training UI and checkpoint management. New [LAR-Gen](https://arxiv.org/abs/2403.19534) model has been added on SCEPTER Studio, supporting `zoom-out`, `virtual try on`, `inpainting`.
+- [2024.02]: We release new SCEdit controllable image synthesis models for SD v2.1 and SD XL. Multiple strategies applied to accelerate inference time for SCEPTER Studio.
+- [2024.01]: We release **SCEPTER Studio**, an integrated toolkit for data management, model training and inference based on [Gradio](https://www.gradio.app/).
+- [2024.01]: [SCEdit](https://arxiv.org/abs/2312.11392) support controllable image synthesis for training and inference.
+- [2023.12]: We propose [SCEdit](https://arxiv.org/abs/2312.11392), an efficient and controllable generation framework.
+- [2023.12]: We release [🪄SCEPTER](https://github.com/modelscope/scepter/) library.
 
 ## 📝 Introduction
 
@@ -28,7 +37,7 @@ Main Feature:
 - Task:
   - Text-to-image generation
   - Controllable image synthesis
-  - Image editing (TODO)
+  - Image editing
 - Training / Inference:
   - Distribute: DDP / FSDP / FairScale / Xformers
   - File system: Local / Http / OSS / Modelscope
@@ -40,15 +49,9 @@ Main Feature:
 Currently supported approaches (and counting):
 
 1. SD Series: [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) / [Stable Diffusion v2.1](https://huggingface.co/runwayml/stable-diffusion-v1-5) / [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
-2. SCEdit: [SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing](https://arxiv.org/abs/2312.11392)  [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=SCEdit&color=red&logo=arxiv)](https://arxiv.org/abs/2312.11392) [![Page link](https://img.shields.io/badge/Page-SCEdit-Gree)](https://scedit.github.io/)
-3. Res-Tuning(TODO): [Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone](https://arxiv.org/abs/2310.19859) [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=ResTuning&color=red&logo=arxiv)](https://arxiv.org/abs/2310.19859) [![Page link](https://img.shields.io/badge/Page-ResTuning-Gree)](https://res-tuning.github.io/)
-
-## 🎉 News
-- [2024.02]: We release new SCEdit controllable image synthesis models for SD v2.1 and SD XL. Multiple strategies applied to accelerate inference time for SCEPTER Studio.
-- [2024.01]: We release **SCEPTER Studio**, an integrated toolkit for data management, model training and inference based on [Gradio](https://www.gradio.app/).
-- [2024.01]: [SCEdit](https://arxiv.org/abs/2312.11392) support controllable image synthesis for training and inference.
-- [2023.12]: We propose [SCEdit](https://arxiv.org/abs/2312.11392), an efficient and controllable generation framework.
-- [2023.12]: We release [🪄SCEPTER](https://github.com/modelscope/scepter/) library.
+2. SCEdit(CVPR2024): [SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing](https://arxiv.org/abs/2312.11392)  [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=SCEdit&color=red&logo=arxiv)](https://arxiv.org/abs/2312.11392) [![Page link](https://img.shields.io/badge/Page-SCEdit-Gree)](https://scedit.github.io/)
+3. Res-Tuning(NeurIPS2023 TODO): [Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone](https://arxiv.org/abs/2310.19859) [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=ResTuning&color=red&logo=arxiv)](https://arxiv.org/abs/2310.19859) [![Page link](https://img.shields.io/badge/Page-ResTuning-Gree)](https://res-tuning.github.io/)
+4. LAR-Gen: [Locate, Assign, Refine: Taming Customized Image Inpainting with Text-Subject Guidance](https://arxiv.org/abs/2403.19534)  [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=LARGen&color=red&logo=arxiv)](https://arxiv.org/abs/2403.19534) [![Page link](https://img.shields.io/badge/Page-LARGen-Gree)](https://ali-vilab.github.io/largen-page/)
 
 ## 🛠️ Installation
 
@@ -94,7 +97,7 @@ For the data format used by SCEPTER Studio, please refer to [3D_example_csv.zip]
 To facilitate starting training in command-line mode, you can use a dataset in text format, please refer to [3D_example_txt.zip](https://modelscope.cn/api/v1/models/damo/scepter/repo?Revision=master&FilePath=datasets/3D_example_txt.zip)
 
 ```shell
-mkdir -p cache/dataset/ && wget 'https://modelscope.cn/api/v1/models/damo/scepter_scedit/repo?Revision=master&FilePath=dataset/3D_example_txt.zip' -O cache/dataset/3D_example_txt.zip && unzip cache/dataset/3D_example_txt.zip -d cache/dataset/ && rm cache/dataset/3D_example_txt.zip
+mkdir -p cache/datasets/ && wget 'https://modelscope.cn/api/v1/models/damo/scepter_scedit/repo?Revision=master&FilePath=dataset/3D_example_txt.zip' -O cache/datasets/3D_example_txt.zip && unzip cache/datasets/3D_example_txt.zip -d cache/datasets/ && rm cache/datasets/3D_example_txt.zip
 ```
 
 ### Training
@@ -165,6 +168,14 @@ python scepter/tools/run_inference.py --cfg scepter/methods/scedit/ctr/sd21_768_
 python scepter/tools/run_inference.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_pose.yaml --num_samples 1 --prompt 'super mario' --save_folder 'test_mario_pose' --image_size 768 --task control --image 'asset/images/pose_source.png' --control_mode source --pretrained_model ms://damo/scepter_scedit@controllable_model/SD2.1/pose_control/0_SwiftSCETuning/pytorch_model.bin   # pose
 ```
 
+### Customize Modules
+Refer to `example`, build your modules in `example/impls`.
+```python
+cd example
+python run.py --cfg example.yaml
+```
+
+
 ## 🖥️ SCEPTER Studio
 
 ### Launch
@@ -181,16 +192,94 @@ git clone https://github.com/modelscope/scepter.git
 PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ui.yaml
 ```
 
-The startup of **SCEPTER Studio** eliminates the need for manual downloading and organizing of models; it will automatically load the corresponding models and store them in a local directory. 
-Depending on the network and hardware situation, the initial startup usually requires 15-60 minutes, primarily involving the download and processing of SDv1.5, SDv2.1, and SDXL models. 
+The startup of **SCEPTER Studio** eliminates the need for manual downloading and organizing of models; it will automatically load the corresponding models and store them in a local directory.
+Depending on the network and hardware situation, the initial startup usually requires 15-60 minutes, primarily involving the download and processing of SDv1.5, SDv2.1, and SDXL models.
 Therefore, subsequent startups will become much faster (about one minute) as downloading is no longer required.
 
+* LAR-Gen: we release `zoom-out`, `virtual try on`, `inpainting(text guided)`, `inpainting(text + reference image guided)` image editing capabilities.
+Please note that the **Data Preprocess** button must be clicked before clicking the **Generate** button.
+<p align="center">
+<img src="./asset/images/largen.gif">
+</p>
+
 ### Modelscope Studio
 
 We deploy a work studio on Modelscope that includes only the inference tab, please refer to [ms_scepter_studio](https://www.modelscope.cn/studios/damo/scepter_studio/summary)
 
 ## 🖼️ Gallery
 
+### LAR-Gen: Zoom-Out
+<table>
+  <tr>
+    <td><strong>Origin Image</strong><br>Prompt: a temple on fire</td>
+    <td><strong>Zoom-Out</strong><br>CenterAround:0.75</td>
+    <td><strong>Zoom-Out</strong><br>CenterAround:0.75</td>
+    <td><strong>Zoom-Out</strong><br>CenterAround:0.75</td>
+    <td><strong>Zoom-Out</strong><br>CenterAround:0.75</td>
+  </tr>
+  <tr>
+    <td><img src="asset/images/zoom_out/ex1_scene_im.jpeg" width="240"></td>
+    <td><img src="asset/images/zoom_out/ex1_zoom_out1.jpeg" width="240"></td>
+    <td><img src="./asset/images/zoom_out/ex1_zoom_out2.jpeg" width="240"></td>
+    <td><img src="./asset/images/zoom_out/ex1_zoom_out3.jpeg" width="240"></td>
+    <td><img src="./asset/images/zoom_out/ex1_zoom_out4.jpeg" width="240"></td>
+  </tr>
+</table>
+
+### LAR-Gen: Virtual-Try-on
+<table>
+  <tr>
+    <td><strong>Model Image</strong></td>
+    <td><strong>Model Mask</strong></td>
+    <td><strong>Clothing Image</strong></td>
+    <td><strong>Clothing Mask</strong></td>
+    <td><strong>Try-on Output</strong></td>
+  </tr>
+  <tr>
+    <td><img src="asset/images/virtual_try_on/model.jpg" width="240"></td>
+    <td><img src="asset/images/virtual_try_on/ex2_scene_mask.jpg" width="240"></td>
+    <td><img src="asset/images/virtual_try_on/tshirt.jpg" width="240"></td>
+    <td><img src="asset/images/virtual_try_on/ex2_subject_mask.jpg" width="240"></td>
+    <td><img src="asset/images/virtual_try_on/try_on_out.jpeg" width="240"></td>
+  </tr>
+</table>
+
+### LAR-Gen: Inpainting(Text-guided)
+<table>
+  <tr>
+    <td><strong>Origin Image</strong><br>Prompt: a blue and white porcelain</td>
+    <td><strong>Inpainting Mask1</strong></td>
+    <td><strong>Inpainting Output1</strong></td>
+    <td><strong>Inpainting Mask2</strong><br>Prompt: a clock</td>
+    <td><strong>Inpainting Output2</strong></td>
+  </tr>
+  <tr>
+    <td><img src="asset/images/inpainting_text/ex3_scene_im.jpg" width="240"></td>
+    <td><img src="asset/images/inpainting_text/ex3_scene_mask.jpg" width="240"></td>
+    <td><img src="asset/images/inpainting_text/inpainting_text.jpeg" width="240"></td>
+    <td><img src="asset/images/inpainting_text/ex3_scene_mask2.jpg" width="240"></td>
+    <td><img src="asset/images/inpainting_text/inpainting_text2.jpeg" width="240"></td>
+  </tr>
+</table>
+
+### LAR-Gen: Inpainting(Text + Reference Image Guided)
+<table>
+  <tr>
+    <td><strong>Origin Image</strong><br>Prompt: a dog wearing sunglasses</td>
+    <td><strong>Origin Mask</strong></td>
+    <td><strong>Reference Image</strong></td>
+    <td><strong>Reference Mask</strong></td>
+    <td><strong>Inpainting Output</strong></td>
+  </tr>
+  <tr>
+    <td><img src="asset/images/inpainting_text_ref/ex4_scene_im.jpg" width="240"></td>
+    <td><img src="asset/images/inpainting_text_ref/ex4_scene_mask.jpg" width="240"></td>
+    <td><img src="asset/images/inpainting_text_ref/ex4_subject_im.jpg" width="240"></td>
+    <td><img src="asset/images/inpainting_text_ref/ex4_subject_mask.jpg" width="240"></td>
+    <td><img src="asset/images/inpainting_text_ref/inpainting_text_ref.jpeg" width="240"></td>
+  </tr>
+</table>
+
 ### Dragon Year Special: Dragon Tuner
 
 <table>
@@ -244,22 +333,31 @@ We deploy a work studio on Modelscope that includes only the inference tab, plea
 |   SD 2.1  |     🪄     |    🪄    |     🪄     |     🪄    |     🪄     |
 |   SD XL   |     🪄     |    🪄    |     🪄     |     🪄    |     🪄     |
 
+### Image Editing
+- LAR-Gen
+
+| **Model** | **Locate** | **Assign** | **Refine** |
+|:---------:|:----------:|:----------:|:----------:|
+|   SD XL   |     🪄     |    🪄       |     ⏳     |
+
 ### Model URL
 
 - ✅ indicates support for both training and inference.
 - 🪄 denotes that the model has been published.
+- ⏳ denotes that the module has not been integrated currently.
 - More models will be released in the future.
 
 | Model  | URL                                                                                                                                            |
 |--------|------------------------------------------------------------------------------------------------------------------------------------------------|
-| SCEdit | [ModelScope](https://modelscope.cn/models/damo/scepter_scedit/summary) [HuggingFace](https://huggingface.co/scepter-studio/scepter_scedit) |
+| SCEdit | [ModelScope](https://modelscope.cn/models/iic/scepter_scedit/summary) [HuggingFace](https://huggingface.co/scepter-studio/scepter_scedit) |
+| LAR-Gen | [ModelScope](https://www.modelscope.cn/models/iic/LARGEN/summary) |
 
 PS: Scripts running within the SCEPTER framework will automatically fetch and load models based on the required dependency files, eliminating the need for manual downloads.
 
 
 ## 🔍 Learn More
 
-- [Alibaba TongYi Vision Intelligence Lab](https://github.com/damo-vilab)
+- [Alibaba TongYi Vision Intelligence Lab](https://github.com/ali-vilab)
 
   Discover more about open-source projects on image generation, video generation, and editing tasks.
 
@@ -271,7 +369,20 @@ PS: Scripts running within the SCEPTER framework will automatically fetch and lo
 
   SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) is an extensible framwork designed to faciliate lightweight model fine-tuning and inference.
 
+## BibTeX
+If our work is useful for your research, please consider citing:
+```bibtex
+@misc{scepter,
+    title = {SCEPTER, https://github.com/modelscope/scepter},
+    author = {SCEPTER},
+    year = {2023}
+}
+```
+
 
 ## License
 
 This project is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
+
+## Acknowledgement
+Thanks to [Stability-AI](https://github.com/Stability-AI), [SWIFT library](https://github.com/modelscope/swift/) and [Fooocus](https://github.com/lllyasviel/Fooocus) for their awesome work.

requirements/framework.txt

@@ -1,3 +1,5 @@
+albumentations
+bezier
 einops
 modelscope
 ms-swift>=1.5.2
@@ -6,6 +8,7 @@ open_clip_torch
 opencv-python
 opencv_transforms>=0.0.6
 oss2>=2.15.0
+pycocotools
 pyyaml>=5.3.1
 scikit-image
 torchsde

requirements/recommended.txt

@@ -1,3 +1,4 @@
+git+https://github.com/cocodataset/panopticapi.git
 torch==2.0.1
 torchvision==0.15.2
 xformers==0.0.21

requirements/scepter_studio.txt

@@ -1,2 +1,3 @@
 gradio>=3.47.1,<4.0.0
 imagehash
+psutil

scepter/methods/studio/inference/inference.yaml

@@ -79,6 +79,7 @@ EXTENSION_PARAS:
   MANTRA_BOOK: scepter/methods/studio/extensions/mantra_book/mantra_book.yaml
   OFFICIAL_TUNERS: scepter/methods/studio/extensions/tuners/official_tuners.yaml
   OFFICIAL_CONTROLLERS: scepter/methods/studio/extensions/controllers/official_controllers.yaml
+  TUNER_MANAGER: scepter/methods/studio/tuner_manager/tuner_manager.yaml
 CONTROLABLE_ANNOTATORS:
   -
     NAME: "CannyAnnotator"

scepter/methods/studio/inference/largen/largen_pro.yaml

@@ -0,0 +1,268 @@
+NAME: LARGEN
+IS_DEFAULT: True
+DEFAULT_PARAS:
+  PARAS:
+    RESOLUTIONS: [[1024, 1024]]
+  INPUT:
+    IMAGE:
+    ORIGINAL_SIZE_AS_TUPLE: [1024, 1024]
+    TARGET_SIZE_AS_TUPLE: [1024, 1024]
+    AESTHETIC_SCORE: 6.0
+    NEGATIVE_AESTHETIC_SCORE: 2.5
+    PROMPT: ""
+    NEGATIVE_PROMPT: ""
+    PROMPT_PREFIX: ""
+    CROP_COORDS_TOP_LEFT: [0, 0]
+    SAMPLE: ddim
+    SAMPLE_STEPS: 50
+    GUIDE_SCALE: 7.5
+    GUIDE_RESCALE: 0.5
+    DISCRETIZATION: trailing
+    REFINE_SAMPLE: ddim
+    REFINE_GUIDE_SCALE: 7.5
+    REFINE_GUIDE_RESCALE: 0.5
+    REFINE_DISCRETIZATION: trailing
+  OUTPUT:
+    LATENT:
+    BEFORE_REFINE_IMAGES:
+    IMAGES:
+    SEED:
+  MODULES_PARAS:
+    FIRST_STAGE_MODEL:
+      FUNCTION:
+        -
+          NAME: encode
+          DTYPE: float32
+          INPUT: ["IMAGE"]
+        -
+          NAME: decode
+          DTYPE: float32
+          INPUT: ["LATENT"]
+      PARAS:
+        # SCALE_FACTOR DESCRIPTION: The vae embeding scale. TYPE: float default: 0.18215
+        SCALE_FACTOR: 0.13025
+        SIZE_FACTOR: 8
+    DIFFUSION_MODEL:
+      FUNCTION:
+        -
+          NAME: forward
+          DTYPE: float16
+          INPUT: ["SAMPLE_STEPS", "SAMPLE", "GUIDE_SCALE", "GUIDE_RESCALE", "DISCRETIZATION"]
+    COND_STAGE_MODEL:
+      FUNCTION:
+        -
+          NAME: encode
+          DTYPE: float16
+          INPUT: ["ORIGINAL_SIZE_AS_TUPLE", "CROP_COORDS_TOP_LEFT", "PROMPT", "NEGATIVE_PROMPT"]
+    REFINER_MODEL:
+      FUNCTION:
+        -
+          NAME: forward
+          DTYPE: float16
+          INPUT: ["SAMPLE_STEPS", "REFINE_SAMPLE", "REFINE_GUIDE_SCALE", "REFINE_GUIDE_RESCALE", "REFINE_DISCRETIZATION"]
+    REFINER_COND_MODEL:
+      FUNCTION:
+        -
+          NAME: encode
+          DTYPE: float16
+          INPUT: ["ORIGINAL_SIZE_AS_TUPLE", "AESTHETIC_SCORE", "NEGATIVE_AESTHETIC_SCORE", "CROP_COORDS_TOP_LEFT", "PROMPT", "NEGATIVE_PROMPT"]
+
+MODEL:
+  PRETRAINED_MODEL: ms://damo/LARGEN@models/largen_ckpt_s22k.pth
+  # SCHEDULE_ARGS DESCRIPTION:  TYPE:  default: ''
+  SCHEDULE:
+    PARAMETERIZATION: "eps"
+    TIMESTEPS: 1000
+    ZERO_TERMINAL_SNR: False
+    SCHEDULE_ARGS:
+      # NAME DESCRIPTION:  TYPE:  default: ''
+      NAME: "scaled_linear"
+      BETA_MIN: 0.00085
+      BETA_MAX: 0.0120
+  # DIFFUSION_MODEL DESCRIPTION:  TYPE:  default: ''
+  DIFFUSION_MODEL:
+    # NAME DESCRIPTION:  TYPE:  default: 'DiffusionUNetXL'
+    NAME: LargenUNetXL
+    # PRETRAINED_MODEL DESCRIPTION: Whole model's pretrained model path. TYPE: NoneType default: None
+    PRETRAINED_MODEL:
+    # IN_CHANNELS DESCRIPTION: Unet channels for input, considering the input image's channels. TYPE: int default: 4
+    IN_CHANNELS: 9
+    # OUT_CHANNELS DESCRIPTION: Unet channels for output, considering the input image's channels. TYPE: int default: 4
+    OUT_CHANNELS: 4
+    # NUM_RES_BLOCKS DESCRIPTION: The blocks's number of res. TYPE: int default: 2
+    NUM_RES_BLOCKS: 2
+    # MODEL_CHANNELS DESCRIPTION: base channel count for the model. TYPE: int default: 320
+    MODEL_CHANNELS: 320
+    # ATTENTION_RESOLUTIONS DESCRIPTION: A collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attentio will be used. TYPE: list default: [4, 2]
+    ATTENTION_RESOLUTIONS: [4, 2]
+    # DROPOUT DESCRIPTION: The dropout rate. TYPE: int default: 0
+    DROPOUT: 0
+    # CHANNEL_MULT DESCRIPTION: channel multiplier for each level of the UNet. TYPE: list default: [1, 2, 4]
+    CHANNEL_MULT: [1, 2, 4]
+    # CONV_RESAMPLE DESCRIPTION: Use conv to resample when downsample. TYPE: bool default: True
+    CONV_RESAMPLE: True
+    # DIMS DESCRIPTION: The Conv dims which 2 represent Conv2D. TYPE: int default: 2
+    DIMS: 2
+    # NUM_CLASSES DESCRIPTION: The class num for class guided setting, also can be set as continuous. TYPE: str default: 'sequential'
+    NUM_CLASSES: sequential
+    # USE_CHECKPOINT DESCRIPTION: Use gradient checkpointing to reduce memory usage. TYPE: bool default: False
+    USE_CHECKPOINT: False
+    # NUM_HEADS DESCRIPTION: The number of attention heads in each attention layer. TYPE: int default: -1
+    NUM_HEADS: -1
+    # NUM_HEADS_CHANNELS DESCRIPTION: If specified, ignore num_heads and instead use a fixed channel width per attention head. TYPE: int default: 64
+    NUM_HEADS_CHANNELS: 64
+    # USE_SCALE_SHIFT_NORM DESCRIPTION: The scale and shift for the outnorm of RESBLOCK, use a FiLM-like conditioning mechanism. TYPE: bool default: False
+    USE_SCALE_SHIFT_NORM: False
+    # RESBLOCK_UPDOWN DESCRIPTION: Use residual blocks for up/downsampling, if False use Conv. TYPE: bool default: False
+    RESBLOCK_UPDOWN: False
+    # USE_NEW_ATTENTION_ORDER DESCRIPTION: Whether use new attention(qkv before split heads or not) or not. TYPE: bool default: True
+    USE_NEW_ATTENTION_ORDER: True
+    # USE_SPATIAL_TRANSFORMER DESCRIPTION: Custom transformer which support the context, if context_dim is not None, the parameter must set True TYPE: bool default: True
+    USE_SPATIAL_TRANSFORMER: True
+    # TRANSFORMER_DEPTH DESCRIPTION: Custom transformer's depth, valid when USE_SPATIAL_TRANSFORMER is True. TYPE: list default: [1, 2, 10]
+    TRANSFORMER_DEPTH: [1, 2, 10]
+    # TRANSFORMER_DEPTH_MIDDLE DESCRIPTION: Custom transformer's depth of middle block, If set None, use TRANSFORMER_DEPTH last value. TYPE: NoneType default: None
+    # TRANSFORMER_DEPTH_MIDDLE: None
+    # CONTEXT_DIM DESCRIPTION: Custom context info, if set, USE_SPATIAL_TRANSFORMER also set True. TYPE: int default: 2048
+    CONTEXT_DIM: 2048
+    # DISABLE_SELF_ATTENTIONS DESCRIPTION: Whether disable the self-attentions on some level, should be a list, [False, True, ...] TYPE: NoneType default: None
+    # DISABLE_SELF_ATTENTIONS: None
+    # NUM_ATTENTION_BLOCKS DESCRIPTION: The number of attention blocks for attention layer. TYPE: NoneType default: None
+    # NUM_ATTENTION_BLOCKS: None
+    # DISABLE_MIDDLE_SELF_ATTN DESCRIPTION: Whether disable the self-attentions in middle blocks. TYPE: bool default: False
+    DISABLE_MIDDLE_SELF_ATTN: False
+    # USE_LINEAR_IN_TRANSFORMER DESCRIPTION: Custom transformer's parameter, valid when USE_SPATIAL_TRANSFORMER is True. TYPE: bool default: True
+    USE_LINEAR_IN_TRANSFORMER: True
+    # ADM_IN_CHANNELS DESCRIPTION: Used when num_classes == 'sequential' or 'timestep'. TYPE: int default: 2816
+    ADM_IN_CHANNELS: 2816
+    # USE_SENTENCE_EMB DESCRIPTION: Used sentence emb or not, default False. TYPE: bool default: False
+    USE_SENTENCE_EMB: False
+    # USE_WORD_MAPPING DESCRIPTION: Used word mapping or not, default False. TYPE: bool default: False
+    USE_WORD_MAPPING: False
+    TRANSFORMER_BLOCK_TYPE: att_v2
+    IMAGE_SCALE: 1.0
+    USE_REFINE: False
+  # FIRST_STAGE_MODEL DESCRIPTION:  TYPE:  default: ''
+  FIRST_STAGE_MODEL:
+    NAME: AutoencoderKL
+    EMBED_DIM: 4
+    IGNORE_KEYS: [ ]
+    BATCH_SIZE: 1
+    #
+    ENCODER:
+      NAME: Encoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DOUBLE_Z: True
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+    #
+    DECODER:
+      NAME: Decoder
+      CH: 128
+      OUT_CH: 3
+      NUM_RES_BLOCKS: 2
+      IN_CHANNELS: 3
+      ATTN_RESOLUTIONS: [ ]
+      CH_MULT: [ 1, 2, 4, 4 ]
+      Z_CHANNELS: 4
+      DROPOUT: 0.0
+      RESAMP_WITH_CONV: True
+      GIVE_PRE_END: False
+      TANH_OUT: False
+  # COND_STAGE_MODEL DESCRIPTION:  TYPE:  default: ''
+  COND_STAGE_MODEL:
+    # NAME DESCRIPTION:  TYPE:  default: 'GeneralConditioner'
+    NAME: GeneralConditioner
+    USE_GRAD: False
+    # EMBEDDERS DESCRIPTION:  TYPE:  default: ''
+    EMBEDDERS:
+      -
+        # NAME DESCRIPTION:  TYPE:  default: 'FrozenCLIPEmbedder'
+        NAME: FrozenCLIPEmbedder
+        # PRETRAINED_MODEL DESCRIPTION:  TYPE: str default: ''
+        PRETRAINED_MODEL: ms://AI-ModelScope/clip-vit-large-patch14
+        TOKENIZER_PATH: ms://AI-ModelScope/clip-vit-large-patch14
+        # MAX_LENGTH DESCRIPTION:  TYPE: int default: 77
+        MAX_LENGTH: 77
+        # FREEZE DESCRIPTION:  TYPE: bool default: True
+        FREEZE: True
+        # LAYER DESCRIPTION:  TYPE: str default: 'last'
+        LAYER: hidden
+        # LAYER_IDX DESCRIPTION:  TYPE: NoneType default: None
+        LAYER_IDX: 11
+        # USE_FINAL_LAYER_NORM DESCRIPTION:  TYPE: bool default: False
+        USE_FINAL_LAYER_NORM: False
+        UCG_RATE: 0.0
+        INPUT_KEYS: ["prompt"]
+        LEGACY_UCG_VALUE:
+      -
+        # NAME DESCRIPTION:  TYPE:  default: 'FrozenOpenCLIPEmbedder2'
+        NAME: FrozenOpenCLIPEmbedder2
+        # ARCH DESCRIPTION:  TYPE: str default: 'ViT-H-14'
+        ARCH: ViT-bigG-14
+        # MAX_LENGTH DESCRIPTION:  TYPE: int default: 77
+        MAX_LENGTH: 77
+        # FREEZE DESCRIPTION:  TYPE: bool default: True
+        FREEZE: True
+        # ALWAYS_RETURN_POOLED DESCRIPTION: Whether always return pooled results or not ,default False. TYPE: bool default: False
+        ALWAYS_RETURN_POOLED: True
+        # LEGACY DESCRIPTION: Whether use legacy returnd feature or not ,default True. TYPE: bool default: True
+        LEGACY: False
+        # LAYER DESCRIPTION:  TYPE: str default: 'last'
+        LAYER: penultimate
+        UCG_RATE: 0.0
+        INPUT_KEYS: ["prompt"]
+        LEGACY_UCG_VALUE:
+      -
+        # NAME DESCRIPTION:  TYPE:  default: 'ConcatTimestepEmbedderND'
+        NAME: ConcatTimestepEmbedderND
+        # OUT_DIM DESCRIPTION: Output dim TYPE: int default: 256
+        OUT_DIM: 256
+        UCG_RATE: 0.0
+        INPUT_KEYS: ["original_size_as_tuple"]
+        LEGACY_UCG_VALUE:
+      -
+        # NAME DESCRIPTION:  TYPE:  default: 'ConcatTimestepEmbedderND'
+        NAME: ConcatTimestepEmbedderND
+        # OUT_DIM DESCRIPTION: Output dim TYPE: int default: 256
+        OUT_DIM: 256
+        UCG_RATE: 0.0
+        INPUT_KEYS: ["crop_coords_top_left"]
+        LEGACY_UCG_VALUE:
+      -
+        # NAME DESCRIPTION:  TYPE:  default: 'ConcatTimestepEmbedderND'
+        NAME: ConcatTimestepEmbedderND
+        # OUT_DIM DESCRIPTION: Output dim TYPE: int default: 256
+        OUT_DIM: 256
+        UCG_RATE: 0.0
+        INPUT_KEYS: ["target_size_as_tuple"]
+        LEGACY_UCG_VALUE:
+      -
+        NAME: IPAdapterPlusEmbedder
+        CLIP_DIR: ms://damo/LARGEN@models/clip_encoder/
+        PRETRAINED_MODEL: ms://damo/LARGEN@models/ip-adapter-plus_sdxl_vit-h.bin
+        INPUT_KEYS: [ "ref_ip", "ref_detail" ]
+        IN_DIM: 1280
+        HEADS: 20
+        CROSSATTN_DIM: 2048
+      -
+        NAME: TransparentEmbedder
+        INPUT_KEYS: [ "tar_x0", "tar_mask_latent" ]
+      -
+        NAME: NoiseConcatEmbedder
+        INPUT_KEYS: [ "tar_mask_latent", "masked_x0" ]
+      -
+        NAME: TransparentEmbedder
+        INPUT_KEYS: [ "ref_x0" ]
+      -
+        NAME: TransparentEmbedder
+        INPUT_KEYS: [ "task" ]
+      -
+        NAME: TransparentEmbedder
+        INPUT_KEYS: [ "image_scale" ]

scepter/methods/studio/scepter_ui.yaml

@@ -49,11 +49,11 @@ BANNER: |
       <div class="qr-codes">
           <div class="qr-code-container">
               <img src="https://modelscope.cn/api/v1/models/damo/scepter/repo?Revision=master&FilePath=assets/scepter_studio/ms_scepter_studio_qr.png" alt="ms_scepter_studio_qr">
-              <div class="caption">Modelscope Studio</div>
+              <div class="caption"><a href="https://www.modelscope.cn/studios/iic/scepter_studio">Modelscope Studio</a></div>
           </div>
           <div class="qr-code-container">
               <img src="https://modelscope.cn/api/v1/models/damo/scepter/repo?Revision=master&FilePath=assets/scepter_studio/scepter_github_qr.png" alt="scepter_github_qr">
-              <div class="caption">Github</div>
+              <div class="caption"><a href="https://github.com/modelscope/scepter">Github</a></div>
           </div>
       </div>
   </div>
@@ -79,6 +79,10 @@ INTERFACE:
     NAME_EN: Train
     IFID: self_train
     CONFIG: scepter/methods/studio/self_train/self_train.yaml
+  - NAME: 模型管理
+    NAME_EN: Tuner Management
+    IFID: tuner_manager
+    CONFIG: scepter/methods/studio/tuner_manager/tuner_manager.yaml
   - NAME: 推理
     NAME_EN: Inference
     IFID: inference

scepter/methods/studio/self_train/sd_xl/sdxl_pro.yaml

@@ -11,13 +11,13 @@ META:
     DEFAULT_SAMPLER: "dpmpp_2s_ancestral"
     DEFAULT_SAMPLE_STEPS: 40
     INFERENCE_N_PROMPT: ""
-    RESOLUTION: 1024
+    RESOLUTION: [1024, 1024]
   PARAS:
     -
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 1024
+      RESOLUTION: [1024, 1024]
       MEMORY: 29000
       EPOCHS: 50
       SAVE_INTERVAL: 25
@@ -29,7 +29,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 1024
+      RESOLUTION: [1024, 1024]
       MEMORY: 29000
       EPOCHS: 50
       SAVE_INTERVAL: 25
@@ -41,7 +41,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 1024
+      RESOLUTION: [1024, 1024]
       MEMORY: 29000
       EPOCHS: 200
       SAVE_INTERVAL: 25
@@ -53,7 +53,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 1024
+      RESOLUTION: [1024, 1024]
       MEMORY: 29000
       EPOCHS: 200
       SAVE_INTERVAL: 25
@@ -65,7 +65,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 1024
+      RESOLUTION: [1024, 1024]
       MEMORY: 29000
       EPOCHS: 50
       SAVE_INTERVAL: 25
@@ -146,10 +146,12 @@ SOLVER:
   MAX_EPOCHS: -1
   # NUM_FOLDS DESCRIPTION: Num folds for training. TYPE: int default: 1
   NUM_FOLDS: 1
+  #
+  EVAL_INTERVAL: -1
   # WORK_DIR DESCRIPTION: Save dir of the training log or model. TYPE: str default: ''
   WORK_DIR:
   # LOG_FILE DESCRIPTION: Save log path. TYPE: str default: ''
-  LOG_FILE: stg_log.txt
+  LOG_FILE: std_log.txt
   FILE_SYSTEM:
     NAME: "ModelscopeFs"
     TEMP_DIR: "./cache/data"
@@ -586,15 +588,44 @@ SOLVER:
         INPUT_KEY: [ 'img', 'img_original_size_as_tuple', 'img_target_size_as_tuple', 'img_crop_coords_top_left']
         OUTPUT_KEY: [ 'image', 'original_size_as_tuple', 'target_size_as_tuple', 'crop_coords_top_left' ]
   #
+  EVAL_DATA:
+    NAME: Text2ImageDataset
+    MODE: eval
+    PROMPT_FILE:
+    PROMPT_DATA: [ "a boy wearing a jacket", "a dog running on the lawn" ]
+    IMAGE_SIZE: [ 1024, 1024 ]
+    FIELDS: [ "prompt" ]
+    DELIMITER: '#;#'
+    PROMPT_PREFIX: ''
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'index', 'prompt' ]
+        META_KEYS: [ 'image_size' ]
+  #
   TRAIN_HOOKS:
-    - NAME: BackwardHook
-      # GRADIENT_CLIP: 1.0
+    -
+      NAME: BackwardHook
       PRIORITY: 0
-    - NAME: LogHook
-      LOG_INTERVAL: 50
+    -
+      NAME: LogHook
+      LOG_INTERVAL: 10
       SHOW_GPU_MEM: True
+    -
+      NAME: TensorboardLogHook
     -
       NAME: CheckpointHook
-      SAVE_LAST: True
       INTERVAL: 10000
       PRIORITY: 200
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+  #
+  EVAL_HOOKS:
+    -
+      NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+      SAVE_PROBE_PREFIX: 'image'

scepter/methods/studio/self_train/self_train.yaml

@@ -8,3 +8,13 @@ SAMPLERS:
     NAME: 'dpmpp_2m_sde'
   -
     NAME: 'dpmpp_2s_ancestral'
+TRAIN_PARAS:
+  RESOLUTIONS:
+    VALUES: [[256, 256], [320, 180], [180, 320],
+             [512, 512], [640, 360], [360, 640],
+             [768, 768], [960, 540], [540, 960],
+             [1024, 1024], [1280, 720], [720, 1280]]
+    DEFAULT: [1024, 1024]
+  EVAL_PROMPTS:
+    - a boy wearing a jacket
+    - a dog running on the lawn

scepter/methods/studio/self_train/stable_diffusion/sd15_pro.yaml

@@ -10,13 +10,13 @@ META:
     DEFAULT_SAMPLER: "ddim"
     DEFAULT_SAMPLE_STEPS: 40
     INFERENCE_N_PROMPT: ""
-    RESOLUTION: 512
+    RESOLUTION: [512, 512]
   PARAS:
     -
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 512
+      RESOLUTION: [512, 512]
       MEMORY: 29000
       EPOCHS: 50
       SAVE_INTERVAL: 25
@@ -28,7 +28,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 512
+      RESOLUTION: [512, 512]
       MEMORY: 29000
       EPOCHS: 50
       SAVE_INTERVAL: 25
@@ -40,7 +40,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 512
+      RESOLUTION: [512, 512]
       MEMORY: 29000
       EPOCHS: 200
       SAVE_INTERVAL: 25
@@ -53,7 +53,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 512
+      RESOLUTION: [512, 512]
       MEMORY: 29000
       EPOCHS: 200
       SAVE_INTERVAL: 25
@@ -66,7 +66,7 @@ META:
       TRAIN_BATCH_SIZE: 4
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 512
+      RESOLUTION: [512, 512]
       MEMORY: 29000
       EPOCHS: 50
       SAVE_INTERVAL: 25
@@ -135,6 +135,7 @@ SOLVER:
   MAX_EPOCHS: -1
   NUM_FOLDS: 1
   ACCU_STEP: 1
+  EVAL_INTERVAL: -1
   #
   WORK_DIR:
   LOG_FILE: std_log.txt
@@ -298,15 +299,44 @@ SOLVER:
         KEYS: [ 'image', 'prompt' ]
         META_KEYS: [ 'data_key' ]
   #
+  EVAL_DATA:
+    NAME: Text2ImageDataset
+    MODE: eval
+    PROMPT_FILE:
+    PROMPT_DATA: [ "a boy wearing a jacket", "a dog running on the lawn" ]
+    IMAGE_SIZE: [ 512, 512 ]
+    FIELDS: [ "prompt" ]
+    DELIMITER: '#;#'
+    PROMPT_PREFIX: ''
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'index', 'prompt' ]
+        META_KEYS: [ 'image_size' ]
+  #
   TRAIN_HOOKS:
-    - NAME: BackwardHook
-      # GRADIENT_CLIP: 1.0
+    -
+      NAME: BackwardHook
       PRIORITY: 0
-    - NAME: LogHook
-      LOG_INTERVAL: 50
+    -
+      NAME: LogHook
+      LOG_INTERVAL: 10
       SHOW_GPU_MEM: True
+    -
+      NAME: TensorboardLogHook
     -
       NAME: CheckpointHook
-      SAVE_LAST: True
       INTERVAL: 10000
       PRIORITY: 200
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+  #
+  EVAL_HOOKS:
+    -
+      NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+      SAVE_PROBE_PREFIX: 'image'

scepter/methods/studio/self_train/stable_diffusion/sd21_pro.yaml

@@ -10,13 +10,13 @@ META:
     DEFAULT_SAMPLER: "ddim"
     DEFAULT_SAMPLE_STEPS: 40
     INFERENCE_N_PROMPT: ""
-    RESOLUTION: 768
+    RESOLUTION: [768, 768]
   PARAS:
     -
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 768
+      RESOLUTION: [768, 768]
       MEMORY: 29000
       EPOCHS: 50
       SAVE_INTERVAL: 25
@@ -28,7 +28,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 768
+      RESOLUTION: [768, 768]
       MEMORY: 29000
       EPOCHS: 50
       SAVE_INTERVAL: 25
@@ -40,7 +40,7 @@ META:
       TRAIN_BATCH_SIZE: 2
       TRAIN_PREFIX: ""
       TRAIN_N_PROMPT: ""
-      RESOLUTION: 768
+      RESOLUTION: [768, 768]
       MEMORY: 29000
       EPOCHS: 200
       SAVE_INTERVAL: 25
@@ -78,6 +78,7 @@ SOLVER:
   MAX_EPOCHS: -1
   NUM_FOLDS: 1
   ACCU_STEP: 1
+  EVAL_INTERVAL: -1
   #
   WORK_DIR:
   LOG_FILE: std_log.txt
@@ -240,15 +241,44 @@ SOLVER:
         KEYS: [ 'image', 'prompt' ]
         META_KEYS: [ 'data_key' ]
   #
+  EVAL_DATA:
+    NAME: Text2ImageDataset
+    MODE: eval
+    PROMPT_FILE:
+    PROMPT_DATA: [ "a boy wearing a jacket", "a dog running on the lawn" ]
+    IMAGE_SIZE: [ 768, 768 ]
+    FIELDS: [ "prompt" ]
+    DELIMITER: '#;#'
+    PROMPT_PREFIX: ''
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+    TRANSFORMS:
+      - NAME: Select
+        KEYS: [ 'index', 'prompt' ]
+        META_KEYS: [ 'image_size' ]
+  #
   TRAIN_HOOKS:
-    - NAME: BackwardHook
-      # GRADIENT_CLIP: 1.0
+    -
+      NAME: BackwardHook
       PRIORITY: 0
-    - NAME: LogHook
-      LOG_INTERVAL: 50
+    -
+      NAME: LogHook
+      LOG_INTERVAL: 10
       SHOW_GPU_MEM: True
+    -
+      NAME: TensorboardLogHook
     -
       NAME: CheckpointHook
-      SAVE_LAST: True
       INTERVAL: 10000
       PRIORITY: 200
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+  #
+  EVAL_HOOKS:
+    -
+      NAME: ProbeDataHook
+      PROB_INTERVAL: 100
+      SAVE_LAST: True
+      SAVE_NAME_PREFIX: 'step'
+      SAVE_PROBE_PREFIX: 'image'

scepter/methods/studio/tuner_manager/tuner_manager.yaml

@@ -0,0 +1,2 @@
+WORK_DIR: "tuner_manager"
+TUNER_LIST_YAML: "tuner_list.yaml"

scepter/modules/data/dataset/dataset.py

@@ -244,12 +244,18 @@ class Text2ImageDataset(BaseDataset):
             image_size = [image_size, image_size]
         assert isinstance(image_size, Iterable) and len(image_size) == 2
 
-        prompt_file = cfg.PROMPT_FILE
-        with FS.get_object(prompt_file) as local_data:
+        if cfg.PROMPT_FILE is not None and cfg.PROMPT_FILE != '':
+            prompt_file = cfg.PROMPT_FILE
+            with FS.get_object(prompt_file) as local_data:
+                rows = [
+                    i.split(delimiter,
+                            len(fields) - 1)
+                    for i in local_data.decode('utf-8').strip().split('\n')
+                ]
+        else:
             rows = [
                 i.split(delimiter,
-                        len(fields) - 1)
-                for i in local_data.decode('utf-8').strip().split('\n')
+                        len(fields) - 1) for i in cfg.PROMPT_DATA
             ]
 
         self.items = list()
@@ -263,10 +269,9 @@ class Text2ImageDataset(BaseDataset):
                     item['meta']['img_path'] = os.path.join(path_prefix, value)
                 elif key in ['width', 'height']:
                     item['meta'][key] = int(value)
-                elif key != 'meta':
-                    item[key] = value
                 else:
-                    continue
+                    item['meta'][key] = value
+
             self.items.append(item)
         if use_num > 0:
             self.items = self.items[:use_num]

scepter/modules/inference/control_inference.py

@@ -2,18 +2,23 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import copy
 import os
+import warnings
 
 import torch
 import torch.nn as nn
 import torchvision.transforms as TT
 from PIL.Image import Image
-from swift import SwiftModel
 
 from scepter.modules.model.registry import TUNERS
 from scepter.modules.utils.config import Config
 from scepter.modules.utils.distribute import we
 from scepter.modules.utils.file_system import FS
 
+try:
+    from swift import SwiftModel
+except Exception:
+    warnings.warn('Import swift failed, please check it.')
+
 
 class ControlInference():
     def __init__(self, logger=None):

scepter/modules/inference/diffusion_inference.py

@@ -474,10 +474,14 @@ class DiffusionInference():
                             enabled=dtype == 'float16',
                             dtype=getattr(torch, dtype)):
             if self.tokenizer:
+                if not hasattr(get_model(self.cond_stage_model), 'tokenizer'):
+                    setattr(get_model(self.cond_stage_model), 'tokenizer',
+                            self.tokenizer)
                 context = getattr(get_model(self.cond_stage_model),
                                   function_name)(batch['tokens'])
                 null_context = getattr(get_model(self.cond_stage_model),
                                        function_name)(batch_uc['tokens'])
+
             else:
                 context = getattr(get_model(self.cond_stage_model),
                                   function_name)(batch)
@@ -558,12 +562,13 @@ class DiffusionInference():
                         seed=seed,
                         condition_fn=None,
                         clamp=None,
+                        sharpness=value_input.get('sharpness', 0.0),
                         percentile=None,
                         t_max=None,
                         t_min=None,
                         discard_penultimate_step=None,
                         intermediate_callback=intermediate_callback,
-                        cat_uc=cat_uc,
+                        cat_uc=value_input.get('cat_uc', cat_uc),
                         **kwargs)
 
                 self.dynamic_unload(self.diffusion_model,

scepter/modules/inference/largen_inference.py

@@ -0,0 +1,595 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import copy
+import os.path
+import random
+from collections import OrderedDict
+
+import torch
+import torchvision.transforms.functional as TF
+from PIL.Image import Image
+
+from scepter.modules.model.network.diffusion.diffusion import GaussianDiffusion
+from scepter.modules.model.network.diffusion.schedules import noise_schedule
+from scepter.modules.model.registry import (BACKBONES, EMBEDDERS, MODELS,
+                                            TOKENIZERS)
+from scepter.modules.model.utils.data_utils import crop_back
+from scepter.modules.utils.distribute import we
+from scepter.modules.utils.file_system import FS
+
+
+def get_model(model_tuple):
+    assert 'model' in model_tuple
+    return model_tuple['model']
+
+
+class LargenInference():
+    '''
+        define vae, unet, text-encoder, tuner, refiner components
+        support to load the components dynamicly.
+        create and load model when run this model at the first time.
+    '''
+    def __init__(self, logger=None):
+        self.logger = logger
+        self.loaded_model = {}
+        self.loaded_model_name = [
+            'diffusion_model', 'first_stage_model', 'cond_stage_model'
+        ]
+
+    def init_from_cfg(self, cfg):
+        self.name = cfg.NAME
+        self.is_default = cfg.get('IS_DEFAULT', False)
+        module_paras = self.load_default(cfg.get('DEFAULT_PARAS', None))
+        assert cfg.have('MODEL')
+        cfg.MODEL = self.redefine_paras(cfg.MODEL)
+        self.diffusion = self.load_schedule(cfg.MODEL.SCHEDULE)
+        self.diffusion_model = self.infer_model(
+            cfg.MODEL.DIFFUSION_MODEL, module_paras.get(
+                'DIFFUSION_MODEL',
+                None)) if cfg.MODEL.have('DIFFUSION_MODEL') else None
+        self.first_stage_model = self.infer_model(
+            cfg.MODEL.FIRST_STAGE_MODEL,
+            module_paras.get(
+                'FIRST_STAGE_MODEL',
+                None)) if cfg.MODEL.have('FIRST_STAGE_MODEL') else None
+        self.cond_stage_model = self.infer_model(
+            cfg.MODEL.COND_STAGE_MODEL,
+            module_paras.get(
+                'COND_STAGE_MODEL',
+                None)) if cfg.MODEL.have('COND_STAGE_MODEL') else None
+        self.refiner_cond_model = self.infer_model(
+            cfg.MODEL.REFINER_COND_MODEL,
+            module_paras.get(
+                'REFINER_COND_MODEL',
+                None)) if cfg.MODEL.have('REFINER_COND_MODEL') else None
+        self.refiner_diffusion_model = self.infer_model(
+            cfg.MODEL.REFINER_MODEL, module_paras.get(
+                'REFINER_MODEL',
+                None)) if cfg.MODEL.have('REFINER_MODEL') else None
+        self.tokenizer = TOKENIZERS.build(
+            cfg.MODEL.TOKENIZER,
+            logger=self.logger) if cfg.MODEL.have('TOKENIZER') else None
+
+        if self.tokenizer is not None:
+            self.cond_stage_model['cfg'].KWARGS = {
+                'vocab_size': self.tokenizer.vocab_size
+            }
+
+    def redefine_paras(self, cfg):
+        if cfg.get('PRETRAINED_MODEL', None):
+            assert FS.isfile(cfg.PRETRAINED_MODEL)
+            with FS.get_from(cfg.PRETRAINED_MODEL,
+                             wait_finish=True) as local_path:
+                if local_path.endswith('safetensors'):
+                    from safetensors.torch import load_file as load_safetensors
+                    sd = load_safetensors(local_path)
+                else:
+                    sd = torch.load(local_path, map_location='cpu')
+
+                if 'model' in sd:
+                    sd = sd['model']
+
+                first_stage_model_path = os.path.join(
+                    os.path.dirname(local_path), 'first_stage_model.pth')
+                cond_stage_model_path = os.path.join(
+                    os.path.dirname(local_path), 'cond_stage_model.pth')
+                diffusion_model_path = os.path.join(
+                    os.path.dirname(local_path), 'diffusion_model.pth')
+                if (not os.path.exists(first_stage_model_path)
+                        or not os.path.exists(cond_stage_model_path)
+                        or not os.path.exists(diffusion_model_path)):
+                    self.logger.info(
+                        'Now read the whole model and rearrange the modules, it may take several mins.'
+                    )
+                    first_stage_model = OrderedDict()
+                    cond_stage_model = OrderedDict()
+                    diffusion_model = OrderedDict()
+                    for k, v in sd.items():
+                        if k.startswith('first_stage_model.'):
+                            first_stage_model[k.replace(
+                                'first_stage_model.', '')] = v
+                        elif k.startswith('conditioner.'):
+                            cond_stage_model[k.replace('conditioner.', '')] = v
+                        elif k.startswith('cond_stage_model.'):
+                            if k.startswith('cond_stage_model.model.'):
+                                cond_stage_model[k.replace(
+                                    'cond_stage_model.model.', '')] = v
+                            else:
+                                cond_stage_model[k.replace(
+                                    'cond_stage_model.', '')] = v
+                        elif k.startswith('model.diffusion_model.'):
+                            diffusion_model[k.replace('model.diffusion_model.',
+                                                      '')] = v
+                        elif k.startswith('model.'):
+                            diffusion_model[k.replace('model.', '')] = v
+                        else:
+                            continue
+                    if cfg.have('FIRST_STAGE_MODEL'):
+                        with open(first_stage_model_path + 'cache', 'wb') as f:
+                            torch.save(first_stage_model, f)
+                        os.rename(first_stage_model_path + 'cache',
+                                  first_stage_model_path)
+                        self.logger.info(
+                            'First stage model has been processed.')
+                    if cfg.have('COND_STAGE_MODEL'):
+                        with open(cond_stage_model_path + 'cache', 'wb') as f:
+                            torch.save(cond_stage_model, f)
+                        os.rename(cond_stage_model_path + 'cache',
+                                  cond_stage_model_path)
+                        self.logger.info(
+                            'Cond stage model has been processed.')
+                    if cfg.have('DIFFUSION_MODEL'):
+                        with open(diffusion_model_path + 'cache', 'wb') as f:
+                            torch.save(diffusion_model, f)
+                        os.rename(diffusion_model_path + 'cache',
+                                  diffusion_model_path)
+                        self.logger.info('Diffusion model has been processed.')
+                if not cfg.FIRST_STAGE_MODEL.get('PRETRAINED_MODEL', None):
+                    cfg.FIRST_STAGE_MODEL.PRETRAINED_MODEL = first_stage_model_path
+                else:
+                    cfg.FIRST_STAGE_MODEL.RELOAD_MODEL = first_stage_model_path
+                if not cfg.COND_STAGE_MODEL.get('PRETRAINED_MODEL', None):
+                    cfg.COND_STAGE_MODEL.PRETRAINED_MODEL = cond_stage_model_path
+                else:
+                    cfg.COND_STAGE_MODEL.RELOAD_MODEL = cond_stage_model_path
+                if not cfg.DIFFUSION_MODEL.get('PRETRAINED_MODEL', None):
+                    cfg.DIFFUSION_MODEL.PRETRAINED_MODEL = diffusion_model_path
+                else:
+                    cfg.DIFFUSION_MODEL.RELOAD_MODEL = diffusion_model_path
+        return cfg
+
+    def init_from_modules(self, modules):
+        for k, v in modules.items():
+            self.__setattr__(k, v)
+
+    def infer_model(self, cfg, module_paras=None):
+        module = {
+            'model': None,
+            'cfg': cfg,
+            'device': 'offline',
+            'name': cfg.NAME,
+            'function_info': {},
+            'paras': {}
+        }
+        if module_paras is None:
+            return module
+        function_info = {}
+        paras = {
+            k.lower(): v
+            for k, v in module_paras.get('PARAS', {}).items()
+        }
+        for function in module_paras.get('FUNCTION', []):
+            input_dict = {}
+            for inp in function.get('INPUT', []):
+                if inp.lower() in self.input:
+                    input_dict[inp.lower()] = self.input[inp.lower()]
+            function_info[function.NAME] = {
+                'dtype': function.get('DTYPE', 'float32'),
+                'input': input_dict
+            }
+        module['paras'] = paras
+        module['function_info'] = function_info
+        return module
+
+    def init_from_ckpt(self, path, model, ignore_keys=list()):
+        if path.endswith('safetensors'):
+            from safetensors.torch import load_file as load_safetensors
+            sd = load_safetensors(path)
+        else:
+            sd = torch.load(path, map_location='cpu')
+
+        new_sd = OrderedDict()
+        for k, v in sd.items():
+            ignored = False
+            for ik in ignore_keys:
+                if ik in k:
+                    if we.rank == 0:
+                        self.logger.info(
+                            'Ignore key {} from state_dict.'.format(k))
+                    ignored = True
+                    break
+            if not ignored:
+                new_sd[k] = v
+
+        missing, unexpected = model.load_state_dict(new_sd, strict=False)
+        if we.rank == 0:
+            self.logger.info(
+                f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+            )
+            if len(missing) > 0:
+                self.logger.info(f'Missing Keys:\n {missing}')
+            if len(unexpected) > 0:
+                self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
+
+    def load(self, module):
+        if module['device'] == 'offline':
+            if module['cfg'].NAME in MODELS.class_map:
+                model = MODELS.build(module['cfg'], logger=self.logger).eval()
+            elif module['cfg'].NAME in BACKBONES.class_map:
+                model = BACKBONES.build(module['cfg'],
+                                        logger=self.logger).eval()
+            elif module['cfg'].NAME in EMBEDDERS.class_map:
+                model = EMBEDDERS.build(module['cfg'],
+                                        logger=self.logger).eval()
+            else:
+                raise NotImplementedError
+            if module['cfg'].get('RELOAD_MODEL', None):
+                self.init_from_ckpt(module['cfg'].RELOAD_MODEL, model)
+            module['model'] = model
+            module['device'] = 'cpu'
+        if module['device'] == 'cpu':
+            module['device'] = we.device_id
+            module['model'] = module['model'].to(we.device_id)
+        return module
+
+    def unload(self, module):
+        if module is None:
+            return module
+        module['model'] = module['model'].to('cpu')
+        module['device'] = 'cpu'
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+        return module
+
+    def dynamic_load(self, module=None, name=''):
+        self.logger.info('Loading {} model'.format(name))
+        if name == 'all':
+            for subname in self.loaded_model_name:
+                self.loaded_model[subname] = self.dynamic_load(
+                    getattr(self, subname), subname)
+        elif name in self.loaded_model_name:
+            if name in self.loaded_model:
+                if module['cfg'] != self.loaded_model[name]['cfg']:
+                    self.unload(self.loaded_model[name])
+                    module = self.load(module)
+                    self.loaded_model[name] = module
+                    return module
+                elif module['device'] == 'cpu':
+                    module = self.load(module)
+                    return module
+                else:
+                    return module
+            else:
+                module = self.load(module)
+                self.loaded_model[name] = module
+                return module
+        else:
+            return self.load(module)
+
+    def dynamic_unload(self, module=None, name='', skip_loaded=False):
+        self.logger.info('Unloading {} model'.format(name))
+        if name == 'all':
+            for name, module in self.loaded_model.items():
+                module = self.unload(self.loaded_model[name])
+                self.loaded_model[name] = module
+        elif name in self.loaded_model_name:
+            if name in self.loaded_model:
+                if not skip_loaded:
+                    module = self.unload(self.loaded_model[name])
+                    self.loaded_model[name] = module
+            else:
+                self.unload(module)
+        else:
+            self.unload(module)
+
+    def load_default(self, cfg):
+        module_paras = {}
+        if cfg is not None:
+            self.paras = cfg.PARAS
+            self.input = {k.lower(): v for k, v in cfg.INPUT.items()}
+            self.output = {k.lower(): v for k, v in cfg.OUTPUT.items()}
+            module_paras = cfg.MODULES_PARAS
+        return module_paras
+
+    def load_schedule(self, cfg):
+        parameterization = cfg.get('PARAMETERIZATION', 'eps')
+        assert parameterization in [
+            'eps', 'x0', 'v'
+        ], 'currently only supporting "eps" and "x0" and "v"'
+        num_timesteps = cfg.get('TIMESTEPS', 1000)
+
+        schedule_args = {
+            k.lower(): v
+            for k, v in cfg.get('SCHEDULE_ARGS', {
+                'NAME': 'logsnr_cosine_interp',
+                'SCALE_MIN': 2.0,
+                'SCALE_MAX': 4.0
+            }).items()
+        }
+
+        zero_terminal_snr = cfg.get('ZERO_TERMINAL_SNR', False)
+        if zero_terminal_snr:
+            assert parameterization == 'v', 'Now zero_terminal_snr only support v-prediction mode.'
+        sigmas = noise_schedule(schedule=schedule_args.pop('name'),
+                                n=num_timesteps,
+                                zero_terminal_snr=zero_terminal_snr,
+                                **schedule_args)
+        diffusion = GaussianDiffusion(sigmas=sigmas,
+                                      prediction_type=parameterization)
+        return diffusion
+
+    def get_batch(self, value_dict, num_samples=1):
+        batch = {}
+        batch_uc = {}
+        N = num_samples
+        device = we.device_id
+        for key in value_dict:
+            if key == 'prompt':
+                if not self.tokenizer:
+                    batch['prompt'] = value_dict['prompt']
+                    batch_uc['prompt'] = value_dict['negative_prompt']
+                else:
+                    batch['tokens'] = self.tokenizer(value_dict['prompt']).to(
+                        we.device_id)
+                    batch_uc['tokens'] = self.tokenizer(
+                        value_dict['negative_prompt']).to(we.device_id)
+            elif key == 'original_size_as_tuple':
+                batch['original_size_as_tuple'] = (torch.tensor(
+                    value_dict['original_size_as_tuple']).to(device).repeat(
+                        N, 1))
+            elif key == 'crop_coords_top_left':
+                batch['crop_coords_top_left'] = (torch.tensor(
+                    value_dict['crop_coords_top_left']).to(device).repeat(
+                        N, 1))
+            elif key == 'aesthetic_score':
+                batch['aesthetic_score'] = (torch.tensor(
+                    [value_dict['aesthetic_score']]).to(device).repeat(N, 1))
+                batch_uc['aesthetic_score'] = (torch.tensor([
+                    value_dict['negative_aesthetic_score']
+                ]).to(device).repeat(N, 1))
+
+            elif key == 'target_size_as_tuple':
+                batch['target_size_as_tuple'] = (torch.tensor(
+                    value_dict['target_size_as_tuple']).to(device).repeat(
+                        N, 1))
+            elif key == 'image':
+                batch[key] = self.load_image(value_dict[key], num_samples=N)
+            else:
+                batch[key] = value_dict[key]
+
+        for key in batch.keys():
+            if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+                batch_uc[key] = torch.clone(batch[key])
+        return batch, batch_uc
+
+    def load_image(self, image, num_samples=1):
+        if isinstance(image, torch.Tensor):
+            pass
+        elif isinstance(image, Image):
+            pass
+        elif isinstance(image, Image):
+            pass
+
+    def get_function_info(self, module, function_name=None):
+        all_function = module['function_info']
+        if function_name in all_function:
+            return function_name, all_function[function_name]['dtype']
+        if function_name is None and len(all_function) == 1:
+            for k, v in all_function.items():
+                return k, v['dtype']
+
+    def encode_first_stage(self, x, **kwargs):
+        _, dtype = self.get_function_info(self.first_stage_model, 'encode')
+        with torch.autocast('cuda',
+                            enabled=dtype == 'float16',
+                            dtype=getattr(torch, dtype)):
+            z = get_model(self.first_stage_model).encode(x)
+            return self.first_stage_model['paras']['scale_factor'] * z
+
+    def decode_first_stage(self, z):
+        _, dtype = self.get_function_info(self.first_stage_model, 'encode')
+        with torch.autocast('cuda',
+                            enabled=dtype == 'float16',
+                            dtype=getattr(torch, dtype)):
+            z = 1. / self.first_stage_model['paras']['scale_factor'] * z
+            return get_model(self.first_stage_model).decode(z)
+
+    @torch.no_grad()
+    def __call__(self,
+                 input,
+                 num_samples=1,
+                 intermediate_callback=None,
+                 refine_strength=0,
+                 img_to_img_strength=0,
+                 cat_uc=True,
+                 tuner_model=None,
+                 control_model=None,
+                 **kwargs):
+
+        value_input = copy.deepcopy(self.input)
+        value_input.update(input)
+        print(value_input)
+        height, width = value_input['target_size_as_tuple']
+        value_output = copy.deepcopy(self.output)
+        batch, batch_uc = self.get_batch(value_input, num_samples=1)
+
+        # first stage encode
+        task = kwargs.get('largen_task', 'Text_Guided_Inpainting')
+        image_scale = kwargs.get('largen_image_scale', 1.0)
+        tar_image = kwargs.get('largen_tar_image', None)
+        tar_mask = kwargs.get('largen_tar_mask', None)
+        masked_image = kwargs.get('largen_masked_image', None)
+        ref_image = kwargs.get('largen_ref_image', None)
+        ref_mask = kwargs.get('largen_ref_mask', None)
+        ref_clip = kwargs.get('largen_ref_clip', None)
+
+        base_image = kwargs.get('largen_base_image', None)
+        extra_sizes = kwargs.get('largen_extra_sizes', None)
+        bbox_yyxx = kwargs.get('largen_bbox_yyxx', None)
+
+        device = we.device_id
+        tar_image = tar_image.to(device)
+        tar_mask = tar_mask.to(device)
+        masked_image = masked_image.to(device)
+        if 'Subject' in task:
+            ref_image = ref_image.to(device)
+            ref_mask = ref_mask.to(device)
+            ref_clip = ref_clip.to(device)
+
+        self.dynamic_load(self.first_stage_model, 'first_stage_model')
+
+        tar_x0 = self.encode_first_stage(tar_image)
+        masked_x0 = self.encode_first_stage(masked_image)
+        b, _, h, w = tar_x0.shape
+        tar_mask_latent = TF.resize(tar_mask, (h, w), antialias=True)
+        tar_mask_latent = (tar_mask_latent > 0.5).float()
+
+        batch.update({
+            'tar_x0': tar_x0,
+            'tar_mask_latent': tar_mask_latent,
+            'masked_x0': masked_x0,
+            'task': task
+        })
+        batch_uc.update({
+            'tar_x0': tar_x0,
+            'tar_mask_latent': tar_mask_latent,
+            'masked_x0': masked_x0,
+            'task': task
+        })
+
+        if 'Subject' in task and ref_image is not None:
+            ref_x0 = self.encode_first_stage(ref_image)
+            batch.update({
+                'ref_ip': ref_clip,
+                'ref_detail': ref_clip,
+                'ref_x0': ref_x0,
+                'ref_mask': ref_mask,
+                'image_scale': image_scale,
+            })
+            batch_uc.update({
+                'ref_ip': torch.zeros_like(ref_clip),
+                'ref_detail': ref_clip,
+                'ref_x0': ref_x0,
+                'ref_mask': ref_mask,
+                'image_scale': image_scale,
+            })
+
+        self.dynamic_unload(self.first_stage_model,
+                            'first_stage_model',
+                            skip_loaded=True)
+
+        # cond stage
+        self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
+        function_name, dtype = self.get_function_info(self.cond_stage_model)
+        with torch.autocast('cuda',
+                            enabled=dtype == 'float16',
+                            dtype=getattr(torch, dtype)):
+            context = getattr(get_model(self.cond_stage_model),
+                              function_name)(batch)
+            null_context = getattr(get_model(self.cond_stage_model),
+                                   function_name)(batch_uc)
+
+        self.dynamic_unload(self.cond_stage_model,
+                            'cond_stage_model',
+                            skip_loaded=True)
+
+        # get noise
+        seed = kwargs.pop('seed', -1)
+        g = torch.Generator(device=we.device_id)
+        seed = seed if seed >= 0 else random.randint(0, 2**32 - 1)
+        g.manual_seed(seed)
+        if 'seed' in value_output:
+            value_output['seed'] = seed
+        for sample_id in range(num_samples):
+            if self.diffusion_model is not None:
+                noise = torch.empty(
+                    1,
+                    4,
+                    height // self.first_stage_model['paras']['size_factor'],
+                    width // self.first_stage_model['paras']['size_factor'],
+                    device=we.device_id).normal_(generator=g)
+
+                self.dynamic_load(self.diffusion_model, 'diffusion_model')
+                # UNet use input n_prompt
+                function_name, dtype = self.get_function_info(
+                    self.diffusion_model)
+                with torch.autocast('cuda',
+                                    enabled=dtype == 'float16',
+                                    dtype=getattr(torch, dtype)):
+                    latent = self.diffusion.sample(
+                        noise=noise,
+                        x=None,
+                        denoising_strength=1.0,
+                        refine_strength=refine_strength,
+                        solver=value_input.get('sample', 'ddim'),
+                        model=get_model(self.diffusion_model),
+                        model_kwargs=[{
+                            'cond': context
+                        }, {
+                            'cond': null_context
+                        }],
+                        steps=value_input.get('sample_steps', 50),
+                        guide_scale=value_input.get('guide_scale', 7.5),
+                        guide_rescale=value_input.get('guide_rescale', 0.5),
+                        discretization=value_input.get('discretization',
+                                                       'trailing'),
+                        show_progress=True,
+                        seed=seed,
+                        condition_fn=None,
+                        clamp=None,
+                        percentile=None,
+                        t_max=None,
+                        t_min=None,
+                        discard_penultimate_step=None,
+                        intermediate_callback=intermediate_callback,
+                        cat_uc=cat_uc,
+                        **kwargs)
+
+                self.dynamic_unload(self.diffusion_model,
+                                    'diffusion_model',
+                                    skip_loaded=True)
+
+            if 'latent' in value_output:
+                if value_output['latent'] is None or (
+                        isinstance(value_output['latent'], list)
+                        and len(value_output['latent']) < 1):
+                    value_output['latent'] = []
+                value_output['latent'].append(latent)
+
+            self.dynamic_load(self.first_stage_model, 'first_stage_model')
+            x_samples = self.decode_first_stage(latent).float()
+            self.dynamic_unload(self.first_stage_model,
+                                'first_stage_model',
+                                skip_loaded=True)
+            images = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+            if base_image is not None:
+                stitch_images = []
+                for img in images:
+                    stitch_img = crop_back(img, copy.deepcopy(base_image),
+                                           extra_sizes, bbox_yyxx)
+                    stitch_images.append(stitch_img)
+                images = torch.stack(stitch_images, dim=0)
+            if 'images' in value_output:
+                if value_output['images'] is None or (
+                        isinstance(value_output['images'], list)
+                        and len(value_output['images']) < 1):
+                    value_output['images'] = []
+                value_output['images'].append(images)
+
+        for k, v in value_output.items():
+            if isinstance(v, list):
+                value_output[k] = torch.cat(v, dim=0)
+            if isinstance(v, torch.Tensor):
+                value_output[k] = v.cpu()
+
+        return value_output

scepter/modules/model/backbone/autoencoder/__init__.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from scepter.modules.model.backbone.autoencoder.ae_module import (Decoder,
-                                                                  Encoder)
+                                                                  Encoder,
+                                                                  RDecoder)

scepter/modules/model/backbone/autoencoder/ae_module.py

@@ -3,6 +3,7 @@
 
 import torch
 import torch.nn as nn
+from einops import repeat
 from torch.utils.checkpoint import checkpoint
 
 from scepter.modules.model.backbone.autoencoder.ae_utils import (
@@ -244,6 +245,7 @@ class Decoder(BaseModel):
 
         # compute in_ch_mult, block_in and curr_res at lowest res
         block_in = self.ch * self.ch_mult[self.num_resolutions - 1]
+        self.block_in = block_in
         curr_res = 1
         # z to block_in
         self.conv_in = torch.nn.Conv2d(self.z_channels,
@@ -340,3 +342,48 @@ class Decoder(BaseModel):
                             __class__.__name__,
                             Decoder.para_dict,
                             set_name=True)
+
+
+@BACKBONES.register_class()
+class RDecoder(Decoder):
+    def construct_model(self):
+        super().construct_model()
+        self.resize_level = nn.Sequential(
+            nn.Linear(self.block_in, self.block_in),
+            nn.SiLU(),
+            nn.Linear(self.block_in, self.block_in),
+        )
+
+    def forward(self, z, rembed=None):
+        # timestep embedding
+        temb = None
+        h = self.conv_in(z)
+        bs, channel, hdim, wdim = h.size()
+        if rembed is not None:
+            rembed = self.resize_level(rembed)
+            rembed = repeat(rembed, 'b e->  b e hd wd', hd=hdim, wd=wdim)
+            h = h + rembed
+
+        # middle
+        if not self.use_checkpoint:
+            h = self.mid_upsclae_transform(h, temb)
+        else:
+            h = checkpoint(self.mid_upsclae_transform, h, temb)
+
+        # end
+        if self.give_pre_end:
+            return h
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        if self.tanh_out:
+            h = torch.tanh(h)
+        return h
+
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('BACKBONE',
+                            __class__.__name__,
+                            Decoder.para_dict,
+                            set_name=True)

scepter/modules/model/backbone/unet/__init__.py

@@ -1,3 +1,5 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
-from scepter.modules.model.backbone.unet.unet_module import DiffusionUNet
+from scepter.modules.model.backbone.unet.unet_module import (DiffusionUNet,
+                                                             DiffusionUNetXL,
+                                                             LargenUNetXL)

scepter/modules/model/backbone/unet/unet_module.py

@@ -8,8 +8,9 @@ import torch
 import torch.nn as nn
 
 from scepter.modules.model.backbone.unet.unet_utils import (
-    Downsample, ResBlock, SpatialTransformer, Timestep,
-    TimestepEmbedSequential, Upsample, conv_nd, linear, normalization,
+    BasicTransformerBlock, Downsample, ResBlock, SpatialTransformer,
+    SpatialTransformerV2, Timestep, TimestepEmbedSequential,
+    TransformerBlockV2, Upsample, conv_nd, linear, normalization,
     timestep_embedding, zero_module)
 from scepter.modules.model.base_model import BaseModel
 from scepter.modules.model.registry import BACKBONES
@@ -951,3 +952,443 @@ class DiffusionUNetXL(DiffusionUNet):
                             __class__.__name__,
                             DiffusionUNetXL.para_dict,
                             set_name=True)
+
+
+@BACKBONES.register_class()
+class LargenUNetXL(DiffusionUNetXL):
+    para_dict = {
+        'TRANSFORMER_BLOCK_TYPE': {
+            'value': 'att_v1'
+        },
+        'IMAGE_SCALE': {
+            'value': 0.0,
+        },
+    }
+    para_dict.update(DiffusionUNetXL.para_dict)
+
+    def __init__(self, cfg, logger):
+        super().__init__(cfg, logger=logger)
+        self.init_params(cfg)
+        self.construct_network()
+
+    def init_params(self, cfg):
+        super().init_params(cfg)
+        self.transformer_block_type = cfg.get('TRANSFORMER_BLOCK_TYPE',
+                                              'att_v1')
+        TRANSFORMER_BLOCKS = {
+            'att_v1': BasicTransformerBlock,
+            'att_v2': TransformerBlockV2,
+        }
+        assert self.transformer_block_type in list(TRANSFORMER_BLOCKS.keys())
+        self.transformer_block = TRANSFORMER_BLOCKS[
+            self.transformer_block_type]
+        self.image_scale = cfg.get('IMAGE_SCALE', 0.0)
+        self.use_refine = cfg.get('USE_REFINE', False)
+
+    def construct_network(self):
+        in_channels = self.in_channels
+        model_channels = self.model_channels
+        out_channels = self.out_channels
+        attention_resolutions = self.attention_resolutions
+        channel_mult = self.channel_mult
+        num_classes = self.num_classes
+        num_heads = self.num_heads
+        num_head_channels = self.num_head_channels
+        dims = self.dims
+        dropout = self.dropout
+        use_checkpoint = self.use_checkpoint
+        use_scale_shift_norm = self.use_scale_shift_norm
+        disable_self_attentions = self.disable_self_attentions
+        disable_middle_self_attn = self.disable_middle_self_attn
+        transformer_depth = self.transformer_depth
+        transformer_depth_middle = self.transformer_depth_middle
+        context_dim = self.context_dim
+        use_linear_in_transformer = self.use_linear_in_transformer
+        resblock_updown = self.resblock_updown
+        conv_resample = self.conv_resample
+        adm_in_channels = self.adm_in_channels
+        transformer_block = self.transformer_block
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            if isinstance(self.num_classes, int):
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+            elif self.num_classes == 'continuous':
+                print('setting up linear c_adm embedding layer')
+                self.label_emb = nn.Linear(1, time_embed_dim)
+            elif self.num_classes == 'timestep':
+                self.label_emb = nn.Sequential(
+                    Timestep(model_channels),
+                    nn.Sequential(
+                        linear(model_channels, time_embed_dim),
+                        nn.SiLU(),
+                        linear(time_embed_dim, time_embed_dim),
+                    ),
+                )
+            elif self.num_classes == 'sequential':
+                assert adm_in_channels is not None
+                self.label_emb = nn.Sequential(
+                    nn.Sequential(
+                        linear(adm_in_channels, time_embed_dim),
+                        nn.SiLU(),
+                        linear(time_embed_dim, time_embed_dim),
+                    ))
+            else:
+                raise ValueError()
+
+        self.input_blocks = nn.ModuleList([
+            TimestepEmbedSequential(
+                conv_nd(dims, in_channels, model_channels, 3, padding=1))
+        ])
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        input_down_flag = [False]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    disabled_sa = disable_self_attentions[level] if exists(
+                        disable_self_attentions) else False
+
+                    layers.append(
+                        SpatialTransformerV2(
+                            ch,
+                            num_heads,
+                            dim_head,
+                            transformer_block=transformer_block,
+                            depth=transformer_depth[level],
+                            context_dim=context_dim,
+                            disable_self_attn=disabled_sa,
+                            use_linear=use_linear_in_transformer,
+                            use_checkpoint=use_checkpoint))
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+                input_down_flag.append(False)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        ) if resblock_updown else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch))
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                input_down_flag.append(True)
+                ds *= 2
+                self._feature_size += ch
+        self._input_block_chans = copy.deepcopy(input_block_chans)
+        self._input_down_flag = input_down_flag
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            SpatialTransformerV2(ch,
+                                 num_heads,
+                                 dim_head,
+                                 transformer_block=transformer_block,
+                                 depth=transformer_depth_middle,
+                                 context_dim=context_dim,
+                                 disable_self_attn=disable_middle_self_attn,
+                                 use_linear=use_linear_in_transformer,
+                                 use_checkpoint=use_checkpoint),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+        self._middle_block_chans = [ch]
+
+        self._output_block_chans = []
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(self.num_res_blocks[level] + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    disabled_sa = disable_self_attentions[level] if exists(
+                        disable_self_attentions) else False
+                    layers.append(
+                        SpatialTransformerV2(
+                            ch,
+                            num_heads,
+                            dim_head,
+                            transformer_block=transformer_block,
+                            depth=transformer_depth[level],
+                            context_dim=context_dim,
+                            disable_self_attn=disabled_sa,
+                            use_linear=use_linear_in_transformer,
+                            use_checkpoint=use_checkpoint))
+                if level and i == self.num_res_blocks[level]:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        ) if resblock_updown else Upsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch))
+                    ds //= 2
+
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+
+                self._feature_size += ch
+                self._output_block_chans.append(ch)
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(
+                conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+
+        if self.use_refine:
+            self.ref_time_embed = copy.deepcopy(self.time_embed)
+            self.ref_label_emb = copy.deepcopy(self.label_emb)
+            self.ref_input_blocks = copy.deepcopy(self.input_blocks)
+            self.ref_input_blocks[0] = TimestepEmbedSequential(
+                conv_nd(dims, 4, model_channels, 3, padding=1))
+            self.ref_middle_block = copy.deepcopy(self.middle_block)
+            self.ref_output_blocks = copy.deepcopy(self.output_blocks)
+
+    def load_pretrained_model(self, pretrained_model):
+        if pretrained_model is not None:
+            with FS.get_from(pretrained_model,
+                             wait_finish=True) as local_model:
+                self.init_from_ckpt(local_model, ignore_keys=self.ignore_keys)
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        if path.endswith('safetensors'):
+            from safetensors.torch import load_file as load_safetensors
+            sd = load_safetensors(path)
+        else:
+            sd = torch.load(path, map_location='cpu')
+
+        new_sd = OrderedDict()
+        for k, v in sd.items():
+            ignored = False
+            for ik in ignore_keys:
+                if ik in k:
+                    if we.rank == 0:
+                        self.logger.info(
+                            'Ignore key {} from state_dict.'.format(k))
+                    ignored = True
+                    break
+            if not ignored:
+                if k == 'input_blocks.0.0.weight':
+                    if we.rank == 0:
+                        self.logger.info(
+                            'Partial initial key {} from state_dict.'.format(
+                                k))
+                    new_v = torch.empty(320, self.in_channels, 3, 3)
+                    nn.init.zeros_(new_v)
+                    new_v[:, :v.shape[1]] = v
+                    new_sd[k] = new_v
+                    if self.use_refine:
+                        new_sd['ref_' + k] = v
+                else:
+                    new_sd[k] = v
+                    if self.use_refine:
+                        new_sd['ref_' + k] = v
+
+        missing, unexpected = self.load_state_dict(new_sd, strict=False)
+        if we.rank == 0:
+            self.logger.info(
+                f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+            )
+            if len(missing) > 0:
+                self.logger.info(f'Missing Keys:\n {missing}')
+            if len(unexpected) > 0:
+                self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
+
+    def forward(self, x, t=None, cond=dict(), **kwargs):
+        t_emb = timestep_embedding(t,
+                                   self.model_channels,
+                                   repeat_only=False,
+                                   legacy=True)
+        emb = self.time_embed(t_emb)
+
+        if isinstance(cond, dict):
+            if 'y' in cond:
+                assert self.num_classes is not None
+                emb = emb + self.label_emb(cond['y'])
+                if self.use_refine:
+                    ref_emb = self.ref_time_embed(t_emb)
+                    assert 'null_y' in cond
+                    cond_y = cond['y'].clone()
+                    cond_y[:, :cond['null_y'].shape[1]] = cond['null_y']
+                    ref_emb = ref_emb + self.ref_label_emb(cond_y)
+
+            if 'concat' in cond:
+                c = cond['concat']
+                x = torch.cat([x, c], dim=1)
+
+            context = cond.get('crossattn', None)
+            img_context = cond.get('img_crossattn', None)
+
+            task = cond['task']
+            image_scale = cond.get('image_scale', self.image_scale)
+            if 'Subject' in task and img_context is not None:
+                ip_enc_scale = image_scale
+                ip_dec_scale = image_scale
+                num_img_tokens = img_context.shape[1]
+                context = torch.cat([context, img_context], dim=1)
+            else:
+                ip_enc_scale = None
+                ip_dec_scale = None
+                num_img_tokens = None
+
+            ref = cond.get('ref_xt', None)
+            ref_context = cond.get('ref_crossattn', None)
+        else:
+            raise TypeError
+
+        hs = []
+        refs = []
+        h = x
+
+        if self.use_refine:
+            assert ref is not None and ref_context is not None
+            for i, (ref_module, module) in enumerate(
+                    zip(self.ref_input_blocks, self.input_blocks)):
+                ref = ref_module(ref, ref_emb, ref_context, caching=None)
+                h = module(h,
+                           emb,
+                           context,
+                           caching=None,
+                           scale=ip_enc_scale,
+                           num_img_token=num_img_tokens)
+                refs.append(ref)
+                hs.append(h)
+
+            ref = self.ref_middle_block(ref,
+                                        ref_emb,
+                                        ref_context,
+                                        caching=None)
+            h = self.middle_block(h,
+                                  emb,
+                                  context,
+                                  caching=None,
+                                  scale=ip_enc_scale,
+                                  num_img_token=num_img_tokens)
+
+            for i, (ref_module, module) in enumerate(
+                    zip(self.ref_output_blocks, self.output_blocks)):
+                cache = []
+                ref = torch.cat([ref, refs.pop()], dim=1)
+                ref = ref_module(ref,
+                                 ref_emb,
+                                 ref_context,
+                                 caching='write',
+                                 cache=cache)
+                h = torch.cat([h, hs.pop()], dim=1)
+                h = module(h,
+                           emb,
+                           context,
+                           caching='read',
+                           cache=cache,
+                           scale=ip_dec_scale,
+                           num_img_token=num_img_tokens)
+        else:
+            for module in self.input_blocks:
+                h = module(h,
+                           emb,
+                           context,
+                           caching=None,
+                           scale=ip_enc_scale,
+                           num_img_token=num_img_tokens)
+                hs.append(h)
+            h = self.middle_block(h,
+                                  emb,
+                                  context,
+                                  caching=None,
+                                  scale=ip_enc_scale,
+                                  num_img_token=num_img_tokens)
+            for module in self.output_blocks:
+                h = torch.cat([h, hs.pop()], dim=1)
+                h = module(h,
+                           emb,
+                           context,
+                           caching=None,
+                           scale=ip_dec_scale,
+                           num_img_token=num_img_tokens)
+
+        out = self.out(h)
+        return out
+
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('BACKBONE',
+                            __class__.__name__,
+                            LargenUNetXL.para_dict,
+                            set_name=True)

scepter/modules/model/backbone/unet/unet_utils.py

@@ -10,6 +10,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import torchvision.transforms.functional as TF
 from einops import rearrange, repeat
 from packaging import version
 
@@ -171,12 +172,14 @@ class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
     A sequential module that passes timestep embeddings to the children that
     support it as an extra input.
     """
-    def forward(self, x, emb, context=None, target_size=None):
+    def forward(self, x, emb, context=None, target_size=None, **kwargs):
         for layer in self:
             if isinstance(layer, TimestepBlock):
                 x = layer(x, emb)
             elif isinstance(layer, SpatialTransformer):
                 x = layer(x, context)
+            elif isinstance(layer, SpatialTransformerV2):
+                x = layer(x, context, **kwargs)
             elif isinstance(layer, Upsample):
                 x = layer(x, target_size)
             else:
@@ -864,6 +867,92 @@ class MemoryEfficientCrossAttention(nn.Module):
         return self.to_out(out)
 
 
+class XFormersMHA_IP(nn.Module):
+    def __init__(self,
+                 query_dim,
+                 context_dim=None,
+                 heads=8,
+                 dim_head=64,
+                 dropout=0.0):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim),
+                                    nn.Dropout(dropout))
+        self.attention_op = None
+
+    def forward(self,
+                x,
+                context=None,
+                mask=None,
+                scale=None,
+                num_img_token=None):
+        q = self.to_q(x)
+        context = default(context, x)
+
+        if scale is not None and num_img_token is not None:
+            eos = context.shape[1] - num_img_token
+            txt_context = context[:, :eos, :]
+            img_context = context[:, eos:, :]
+
+            k = self.to_k(txt_context)
+            v = self.to_v(txt_context)
+            k_i = self.to_k_ip(img_context)
+            v_i = self.to_v_ip(img_context)
+
+            b, _, _ = q.shape
+            q, k, v, k_i, v_i = map(
+                lambda t: t.unsqueeze(3).reshape(b, t.shape[
+                    1], self.heads, self.dim_head).permute(0, 2, 1, 3).reshape(
+                        b * self.heads, t.shape[1], self.dim_head).contiguous(
+                        ),
+                (q, k, v, k_i, v_i),
+            )
+
+            # actually compute the attention, what we cannot get enough of
+            txt_out = xformers.ops.memory_efficient_attention(
+                q, k, v, attn_bias=None, op=self.attention_op)
+            img_out = xformers.ops.memory_efficient_attention(
+                q, k_i, v_i, attn_bias=None, op=self.attention_op)
+            out = txt_out + scale * img_out
+        else:
+            k = self.to_k(context)
+            v = self.to_v(context)
+            b, _, _ = q.shape
+            q, k, v = map(
+                lambda t: t.unsqueeze(3).reshape(b, t.shape[
+                    1], self.heads, self.dim_head).permute(0, 2, 1, 3).reshape(
+                        b * self.heads, t.shape[1], self.dim_head).contiguous(
+                        ),
+                (q, k, v),
+            )
+            out = xformers.ops.memory_efficient_attention(q,
+                                                          k,
+                                                          v,
+                                                          attn_bias=None,
+                                                          op=self.attention_op)
+
+        # TODO: Use this directly in the attention operation, as a bias
+        if exists(mask):
+            raise NotImplementedError
+        out = (out.unsqueeze(0).reshape(
+            b, self.heads, out.shape[1],
+            self.dim_head).permute(0, 2, 1,
+                                   3).reshape(b, out.shape[1],
+                                              self.heads * self.dim_head))
+        return self.to_out(out)
+
+
 class BasicTransformerBlock(nn.Module):
     def __init__(self,
                  dim,
@@ -908,6 +997,65 @@ class BasicTransformerBlock(nn.Module):
         return x
 
 
+class TransformerBlockV2(nn.Module):
+    def __init__(self,
+                 query_dim,
+                 n_heads,
+                 d_head,
+                 dropout=0.,
+                 context_dim=None,
+                 gated_ff=True,
+                 use_checkpoint=False,
+                 disable_self_attn=False):
+        super().__init__()
+        self.disable_self_attn = disable_self_attn
+        self.attn1 = MemoryEfficientCrossAttention(query_dim=query_dim,
+                                                   heads=n_heads,
+                                                   dim_head=d_head,
+                                                   dropout=dropout,
+                                                   context_dim=None)
+        self.ff = FeedForward(query_dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = XFormersMHA_IP(query_dim=query_dim,
+                                    heads=n_heads,
+                                    dim_head=d_head,
+                                    context_dim=context_dim)
+        self.norm1 = nn.LayerNorm(query_dim)
+        self.norm2 = nn.LayerNorm(query_dim)
+        self.norm3 = nn.LayerNorm(query_dim)
+        self.use_checkpoint = use_checkpoint
+
+    def forward(self,
+                x,
+                context,
+                caching=None,
+                cache=None,
+                scale=None,
+                num_img_token=None,
+                **kwargs):
+        y = self.norm1(x)
+        if caching == 'write':
+            assert isinstance(cache, list)
+            cache.append(y)
+            x = self.attn1(y, context=None) + x
+        elif caching == 'read':
+            assert isinstance(cache, list) and len(cache) > 0
+            c = cache.pop(0)
+            self_ctx = torch.cat([y, c], dim=1)
+            x = self.attn1(y, context=self_ctx) + x
+        elif caching is None:
+            x = self.attn1(y, context=None) + x
+        else:
+            assert False
+
+        x = self.attn2(self.norm2(x),
+                       context=context,
+                       scale=scale,
+                       num_img_token=num_img_token) + x
+        x = self.ff(self.norm3(x)) + x
+
+        return x
+
+
 class SpatialTransformer(nn.Module):
     """
     Transformer block for image-like data.
@@ -1003,3 +1151,108 @@ class SpatialTransformer(nn.Module):
         if not self.use_linear:
             x = self.proj_out(x)
         return x + x_in
+
+
+class SpatialTransformerV2(nn.Module):
+    """
+    Transformer block for image-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    NEW: use_linear for more efficiency instead of the 1x1 convs
+    """
+    def __init__(self,
+                 in_channels,
+                 n_heads,
+                 d_head,
+                 transformer_block,
+                 depth=1,
+                 dropout=0.,
+                 context_dim=None,
+                 disable_self_attn=False,
+                 use_linear=False,
+                 use_checkpoint=True):
+        super().__init__()
+        if exists(context_dim) and not isinstance(context_dim, list):
+            context_dim = [context_dim]
+
+        if exists(context_dim) and not isinstance(context_dim, (list)):
+            context_dim = [context_dim]
+        if exists(context_dim) and isinstance(context_dim, list):
+            if depth != len(context_dim):
+                print(
+                    f'WARNING: {self.__class__.__name__}: Found context dims {context_dim} of'
+                    f" depth {len(context_dim)}, which does not match the specified 'depth' of"
+                    f' {depth}. Setting context_dim to {depth * [context_dim[0]]} now.'
+                )
+                # depth does not match context dims.
+                assert all(
+                    map(lambda x: x == context_dim[0], context_dim)
+                ), 'need homogenous context_dim to match depth automatically'
+                context_dim = depth * [context_dim[0]]
+        elif context_dim is None:
+            context_dim = [None] * depth
+
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = normalization(in_channels)
+        if not use_linear:
+            self.proj_in = nn.Conv2d(in_channels,
+                                     inner_dim,
+                                     kernel_size=1,
+                                     stride=1,
+                                     padding=0)
+        else:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList([
+            transformer_block(inner_dim,
+                              n_heads,
+                              d_head,
+                              dropout=dropout,
+                              context_dim=context_dim[d],
+                              disable_self_attn=disable_self_attn,
+                              use_checkpoint=use_checkpoint)
+            for d in range(depth)
+        ])
+        if not use_linear:
+            self.proj_out = zero_module(
+                nn.Conv2d(inner_dim,
+                          in_channels,
+                          kernel_size=1,
+                          stride=1,
+                          padding=0))
+        else:
+            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None, **kwargs):
+        # note: if no context is given, cross-attention defaults to self-attention
+        if not isinstance(context, list):
+            context = [context]
+        b, c, h, w = x.shape
+
+        ref_mask = kwargs.pop('ref_mask', None)
+        if ref_mask is not None:
+            ref_mask = TF.resize(ref_mask, (h, w), antialias=True)
+            ref_mask = (ref_mask > 0.5).float()
+            ref_mask = rearrange(ref_mask, 'b c h w -> b (h w) c').contiguous()
+
+        x_in = x
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+        for i, block in enumerate(self.transformer_blocks):
+            if i > 0 and len(context) == 1:
+                i = 0  # use same context for each block
+            x = block(x, context=context[i], ref_mask=ref_mask, **kwargs)
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+        if not self.use_linear:
+            x = self.proj_out(x)
+        return x + x_in

scepter/modules/model/backbone/video/video_transformer.py

@@ -1,17 +1,5 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional
-from einops import rearrange
-
-from scepter.modules.model.backbone.video.init_helper import (
-    _init_transformer_weights, trunc_normal_)
-from scepter.modules.model.registry import BACKBONES, BRICKS, STEMS
-from scepter.modules.utils.config import dict_to_yaml
 '''
 The implementations of vivit as https://arxiv.org/abs/2103.15691.
 The following setting alined the proposed model in the paper above.
@@ -39,6 +27,18 @@ TimesFormer:
     complexity: (n_h * n_w) ** 2 + O(attn_temp)
 '''
 
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional
+from einops import rearrange
+
+from scepter.modules.model.backbone.video.init_helper import (
+    _init_transformer_weights, trunc_normal_)
+from scepter.modules.model.registry import BACKBONES, BRICKS, STEMS
+from scepter.modules.utils.config import dict_to_yaml
+
 
 @BACKBONES.register_class()
 class VideoTransformer(nn.Module):

scepter/modules/model/embedder/__init__.py

@@ -1,7 +1,10 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
+
 from scepter.modules.model.embedder.embedder import (ConcatTimestepEmbedderND,
                                                      FrozenCLIPEmbedder,
                                                      FrozenOpenCLIPEmbedder,
                                                      FrozenOpenCLIPEmbedder2,
-                                                     GeneralConditioner)
+                                                     GeneralConditioner,
+                                                     IPAdapterPlusEmbedder,
+                                                     RefCrossEmbedder)

scepter/modules/model/embedder/embedder.py

@@ -22,9 +22,10 @@ from scepter.modules.utils.distribute import we
 from scepter.modules.utils.file_system import FS
 
 from .base_embedder import BaseEmbedder
+from .resampler import Resampler
 
 try:
-    from transformers import CLIPTextModel, CLIPTokenizer
+    from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 except Exception as e:
     warnings.warn(
         f'Import transformers error, please deal with this problem: {e}')
@@ -513,6 +514,93 @@ class ConcatTimestepEmbedderND(BaseEmbedder):
                             set_name=True)
 
 
+@EMBEDDERS.register_class()
+class IPAdapterPlusEmbedder(BaseEmbedder):
+    def __init__(self, cfg, logger=None):
+        super().__init__(cfg, logger=logger)
+
+        with FS.get_dir_to_local_dir(cfg.CLIP_DIR,
+                                     wait_finish=True) as local_path:
+            self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+                local_path)
+
+        self.image_proj_model = Resampler(
+            dim=self.cfg.get('IN_DIM', 768),
+            depth=self.cfg.get('DEPTH', 4),
+            dim_head=64,
+            heads=self.cfg.get('HEADS', 12),
+            num_queries=self.cfg.get('NUM_TOKENS', 16),
+            embedding_dim=self.image_encoder.config.hidden_size,
+            output_dim=self.cfg.get('CROSSATTN_DIM', 768),
+            ff_mult=4,
+        )
+
+        with FS.get_from(cfg.PRETRAINED_MODEL, wait_finish=True) as local_path:
+            ckpt = torch.load(local_path, map_location='cpu')
+            self.image_proj_model.load_state_dict(ckpt['image_proj'],
+                                                  strict=True)
+
+        self.patch_projector = nn.Linear(self.image_encoder.config.hidden_size,
+                                         self.cfg.get('CROSSATTN_DIM', 768))
+
+    def encode(self, ref_ip, ref_detail):
+        encoder_output = self.image_encoder(ref_ip, output_hidden_states=True)
+        image_prompt_embeds = self.image_proj_model(
+            encoder_output.hidden_states[-2])
+        encoder_output_2 = self.image_encoder(ref_detail,
+                                              output_hidden_states=True)
+        image_patch_embeds = self.patch_projector(
+            encoder_output_2.last_hidden_state)
+        out = {
+            'img_crossattn': image_prompt_embeds,
+            'ref_crossattn': image_patch_embeds,
+        }
+        return out
+
+    def forward(self, ref_ip, ref_detail):
+        return self.encode(ref_ip, ref_detail)
+
+
+class RefCrossEmbedder(BaseEmbedder):
+    def __init__(self, cfg, logger=None):
+        super().__init__(cfg, logger=logger)
+
+        with FS.get_dir_to_local_dir(cfg.CLIP_DIR,
+                                     wait_finish=True) as local_path:
+            self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+                local_path)
+
+        self.patch_projector = nn.Linear(self.image_encoder.config.hidden_size,
+                                         self.cfg.get('CROSSATTN_DIM', 768))
+
+    def encode(self, img):
+        encoder_output = self.image_encoder(img, output_hidden_states=True)
+        image_patch_embeds = self.patch_projector(
+            encoder_output.last_hidden_state)
+        out = {
+            'ref_crossattn': image_patch_embeds,
+        }
+        return out
+
+    def forward(self, img):
+        return self.encode(img)
+
+
+@EMBEDDERS.register_class()
+class TransparentEmbedder(BaseEmbedder):
+    def forward(self, *args):
+        out = dict()
+        for key, val in zip(self.input_keys, args):
+            out[key] = val
+        return out
+
+
+@EMBEDDERS.register_class()
+class NoiseConcatEmbedder(BaseEmbedder):
+    def forward(self, *args):
+        return {'concat': torch.cat(args, dim=1)}
+
+
 @EMBEDDERS.register_class()
 class GeneralConditioner(BaseEmbedder):
     OUTPUT_DIM2KEYS = {2: 'y', 3: 'crossattn', 4: 'concat', 5: 'concat'}
@@ -598,42 +686,60 @@ class GeneralConditioner(BaseEmbedder):
             with embedding_context():
                 if hasattr(embedder, 'input_key') and (embedder.input_key
                                                        is not None):
+                    if embedder.input_key not in batch:
+                        continue
                     if embedder.legacy_ucg_val is not None:
                         batch = self.possibly_get_ucg_val(embedder, batch)
                     emb_out = embedder(batch[embedder.input_key])
                 elif hasattr(embedder, 'input_keys'):
+                    if any([k not in batch for k in embedder.input_keys]):
+                        continue
                     emb_out = embedder(
                         *[batch[k] for k in embedder.input_keys])
-            assert isinstance(
-                emb_out, (torch.Tensor, list, tuple)
-            ), f'encoder outputs must be tensors or a sequence, but got {type(emb_out)}'
-            if not isinstance(emb_out, (list, tuple)):
-                emb_out = [emb_out]
-            for emb in emb_out:
-                # print("emb.shape", emb.shape)
-                # print("emb.input_keys", embedder.input_keys)
-                out_key = self.OUTPUT_DIM2KEYS[emb.dim()]
-                if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None:
-                    emb = (expand_dims_like(
-                        torch.bernoulli(
-                            (1.0 - embedder.ucg_rate) *
-                            torch.ones(emb.shape[0], device=emb.device)),
-                        emb,
-                    ) * emb)
-                if (hasattr(embedder, 'input_keys')):
-                    if np.sum(
-                            np.array([
-                                key in force_zero_embeddings
-                                for key in embedder.input_keys
-                            ])) > 0:
-                        emb = torch.zeros_like(emb)
-                if out_key in output:
-                    output[out_key] = torch.cat((output[out_key], emb),
-                                                self.KEY2CATDIM[out_key])
-                else:
-                    output[out_key] = emb
-            # if "y" in output:
-            #     print("out.shape", output["y"].shape)
+
+            if isinstance(emb_out, dict):
+                for key, val in emb_out.items():
+                    if key in output:
+                        # 重复出现的key必须在（y, crossattn, concat）中，否则raise error
+                        assert key in self.KEY2CATDIM
+                        output[key] = torch.cat([output[key], val],
+                                                dim=self.KEY2CATDIM[key])
+                    else:
+                        output[key] = val
+            else:
+                assert isinstance(
+                    emb_out, (torch.Tensor, list, tuple)
+                ), f'encoder outputs must be tensors or a sequence, but got {type(emb_out)}'
+
+                if not isinstance(emb_out, (list, tuple)):
+                    emb_out = [emb_out]
+
+                for emb in emb_out:
+                    # 根据emb的维度，判断该cond归属于 (y, concat, crossattn）中的哪一种
+                    out_key = self.OUTPUT_DIM2KEYS[emb.dim()]
+
+                    if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None:
+                        emb = (expand_dims_like(
+                            torch.bernoulli(
+                                (1.0 - embedder.ucg_rate) *
+                                torch.ones(emb.shape[0], device=emb.device)),
+                            emb,
+                        ) * emb)
+
+                    if (hasattr(embedder, 'input_keys')):
+                        if np.sum(
+                                np.array([
+                                    key in force_zero_embeddings
+                                    for key in embedder.input_keys
+                                ])) > 0:
+                            emb = torch.zeros_like(emb)
+
+                    if out_key in output:
+                        output[out_key] = torch.cat((output[out_key], emb),
+                                                    self.KEY2CATDIM[out_key])
+                    else:
+                        output[out_key] = emb
+
         return output
 
     def get_unconditional_conditioning(self,

scepter/modules/model/embedder/resampler.py

@@ -0,0 +1,160 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import math
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(
+            -2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1024,
+        depth=8,
+        dim_head=64,
+        heads=16,
+        num_queries=8,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+        max_seq_len: int = 257,  # CLIP tokens + CLS token
+        apply_pos_emb: bool = False,
+        num_latents_mean_pooled:
+        int = 0,  # number of latents derived from mean pooled representation of the sequence
+    ):
+        super().__init__()
+        self.pos_emb = nn.Embedding(max_seq_len,
+                                    embedding_dim) if apply_pos_emb else None
+
+        self.latents = nn.Parameter(
+            torch.randn(1, num_queries, dim) / dim**0.5)
+
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.to_latents_from_mean_pooled_seq = (nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, dim * num_latents_mean_pooled),
+            Rearrange('b (n d) -> b n d', n=num_latents_mean_pooled),
+        ) if num_latents_mean_pooled > 0 else None)
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList([
+                    PerceiverAttention(dim=dim, dim_head=dim_head,
+                                       heads=heads),
+                    FeedForward(dim=dim, mult=ff_mult),
+                ]))
+
+    def forward(self, x):
+        if self.pos_emb is not None:
+            n, device = x.shape[1], x.device
+            pos_emb = self.pos_emb(torch.arange(n, device=device))
+            x = x + pos_emb
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        x = self.proj_in(x)
+
+        if self.to_latents_from_mean_pooled_seq:
+            meanpooled_seq = masked_mean(x,
+                                         dim=1,
+                                         mask=torch.ones(x.shape[:2],
+                                                         device=x.device,
+                                                         dtype=torch.bool))
+            meanpooled_latents = self.to_latents_from_mean_pooled_seq(
+                meanpooled_seq)
+            latents = torch.cat((meanpooled_latents, latents), dim=-2)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+
+def masked_mean(t, *, dim, mask=None):
+    if mask is None:
+        return t.mean(dim=dim)
+
+    denom = mask.sum(dim=dim, keepdim=True)
+    mask = rearrange(mask, 'b n -> b n 1')
+    masked_t = t.masked_fill(~mask, 0.0)
+
+    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)

scepter/modules/model/head/__init__.py

@@ -1,5 +1,8 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
-from scepter.modules.model.head.classifier_head import (
-    ClassifierHead, CosineLinearHead, TransformerHead, TransformerHeadx2,
-    VideoClassifierHead, VideoClassifierHeadx2)
+from scepter.modules.model.head.classifier_head import (ClassifierHead,
+                                                        CosineLinearHead,
+                                                        TransformerHead,
+                                                        TransformerHeadx2,
+                                                        VideoClassifierHead,
+                                                        VideoClassifierHeadx2)

scepter/modules/model/metric/__init__.py

@@ -1,5 +1,4 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
-from scepter.modules.model.metric.classification import (AccuracyMetric,
-                                                         EnsembleAccuracyMetric
-                                                         )
+from scepter.modules.model.metric.classification import (
+    AccuracyMetric, EnsembleAccuracyMetric)

scepter/modules/model/network/diffusion/diffusion.py

@@ -13,8 +13,7 @@ from .schedules import karras_schedule
 from .solvers import (sample_ddim, sample_dpm_2, sample_dpm_2_ancestral,
                       sample_dpmpp_2m, sample_dpmpp_2m_sde,
                       sample_dpmpp_2s_ancestral, sample_dpmpp_sde,
-                      sample_euler, sample_euler_ancestral, sample_heun,
-                      sample_img2img_euler, sample_img2img_euler_ancestral)
+                      sample_euler, sample_euler_ancestral, sample_heun)
 
 __all__ = ['GaussianDiffusion']
 
@@ -27,6 +26,148 @@ def _i(tensor, t, x):
     return tensor[t.to(tensor.device)].view(shape).to(x.device)
 
 
+def _unpack_2d_ks(kernel_size):
+    if isinstance(kernel_size, int):
+        ky = kx = kernel_size
+    else:
+        assert len(
+            kernel_size) == 2, '2D Kernel size should have a length of 2.'
+        ky, kx = kernel_size
+
+    ky = int(ky)
+    kx = int(kx)
+    return ky, kx
+
+
+def _compute_zero_padding(kernel_size):
+    ky, kx = _unpack_2d_ks(kernel_size)
+    return (ky - 1) // 2, (kx - 1) // 2
+
+
+def _bilateral_blur(
+    input,
+    guidance,
+    kernel_size,
+    sigma_color,
+    sigma_space,
+    border_type='reflect',
+    color_distance_type='l1',
+):
+
+    if isinstance(sigma_color, torch.Tensor):
+        sigma_color = sigma_color.to(device=input.device,
+                                     dtype=input.dtype).view(-1, 1, 1, 1, 1)
+
+    ky, kx = _unpack_2d_ks(kernel_size)
+    pad_y, pad_x = _compute_zero_padding(kernel_size)
+
+    padded_input = torch.nn.functional.pad(input, (pad_x, pad_x, pad_y, pad_y),
+                                           mode=border_type)
+    unfolded_input = padded_input.unfold(2, ky, 1).unfold(3, kx, 1).flatten(
+        -2)  # (B, C, H, W, Ky x Kx)
+
+    if guidance is None:
+        guidance = input
+        unfolded_guidance = unfolded_input
+    else:
+        padded_guidance = torch.nn.functional.pad(guidance,
+                                                  (pad_x, pad_x, pad_y, pad_y),
+                                                  mode=border_type)
+        unfolded_guidance = padded_guidance.unfold(2, ky, 1).unfold(
+            3, kx, 1).flatten(-2)  # (B, C, H, W, Ky x Kx)
+
+    diff = unfolded_guidance - guidance.unsqueeze(-1)
+    if color_distance_type == 'l1':
+        color_distance_sq = diff.abs().sum(1, keepdim=True).square()
+    elif color_distance_type == 'l2':
+        color_distance_sq = diff.square().sum(1, keepdim=True)
+    else:
+        raise ValueError('color_distance_type only acceps l1 or l2')
+    color_kernel = (-0.5 / sigma_color**2 *
+                    color_distance_sq).exp()  # (B, 1, H, W, Ky x Kx)
+
+    space_kernel = get_gaussian_kernel2d(kernel_size,
+                                         sigma_space,
+                                         device=input.device,
+                                         dtype=input.dtype)
+    space_kernel = space_kernel.view(-1, 1, 1, 1, kx * ky)
+
+    kernel = space_kernel * color_kernel
+    out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1)
+    return out
+
+
+def get_gaussian_kernel1d(
+    kernel_size,
+    sigma,
+    force_even,
+    *,
+    device=None,
+    dtype=None,
+):
+
+    return gaussian(kernel_size, sigma, device=device, dtype=dtype)
+
+
+def gaussian(window_size, sigma, *, device=None, dtype=None):
+
+    batch_size = sigma.shape[0]
+
+    x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) -
+         window_size // 2).expand(batch_size, -1)
+
+    if window_size % 2 == 0:
+        x = x + 0.5
+
+    gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))
+
+    return gauss / gauss.sum(-1, keepdim=True)
+
+
+def get_gaussian_kernel2d(
+    kernel_size,
+    sigma,
+    force_even=False,
+    *,
+    device=None,
+    dtype=None,
+):
+
+    sigma = torch.Tensor([[sigma, sigma]]).to(device=device, dtype=dtype)
+
+    ksize_y, ksize_x = _unpack_2d_ks(kernel_size)
+    sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None]
+
+    kernel_y = get_gaussian_kernel1d(ksize_y,
+                                     sigma_y,
+                                     force_even,
+                                     device=device,
+                                     dtype=dtype)[..., None]
+    kernel_x = get_gaussian_kernel1d(ksize_x,
+                                     sigma_x,
+                                     force_even,
+                                     device=device,
+                                     dtype=dtype)[..., None]
+
+    return kernel_y * kernel_x.view(-1, 1, ksize_x)
+
+
+def adaptive_anisotropic_filter(x, g=None):
+    if g is None:
+        g = x
+    s, m = torch.std_mean(g, dim=(1, 2, 3), keepdim=True)
+    s = s + 1e-5
+    guidance = (g - m) / s
+    y = _bilateral_blur(x,
+                        guidance,
+                        kernel_size=(13, 13),
+                        sigma_color=3.0,
+                        sigma_space=3.0,
+                        border_type='reflect',
+                        color_distance_type='l1')
+    return y
+
+
 class GaussianDiffusion(object):
     def __init__(self, sigmas, prediction_type='eps'):
         assert prediction_type in {'x0', 'eps', 'v'}
@@ -53,6 +194,7 @@ class GaussianDiffusion(object):
                 guide_scale=None,
                 guide_rescale=None,
                 clamp=None,
+                sharpness=0.0,
                 percentile=None,
                 cat_uc=False,
                 **kwargs):
@@ -79,54 +221,99 @@ class GaussianDiffusion(object):
 
         # prediction
         if guide_scale is None:
-            assert isinstance(model_kwargs, dict)
-            out = model(xt, t=t, **model_kwargs, **kwargs)
+            if isinstance(model_kwargs, dict):
+                out = model(xt, t=t, **model_kwargs, **kwargs)
+            elif isinstance(model_kwargs, list) and len(model_kwargs) > 0:
+                out = model(xt, t=t, **model_kwargs[0], **kwargs)
+            else:
+                raise Exception('Error')
         else:
             # classifier-free guidance (arXiv:2207.12598)
             # model_kwargs[0]: conditional kwargs
             # model_kwargs[1]: non-conditional kwargs
-            assert isinstance(model_kwargs, list) and len(model_kwargs) == 2
-
-            if guide_scale == 1.:
-                out = model(xt, t=t, **model_kwargs[0], **kwargs)
-            else:
-                if cat_uc:
-
-                    def parse_model_kwargs(prev_value, value):
-                        if isinstance(value, torch.Tensor):
-                            prev_value = torch.cat([prev_value, value], dim=0)
-                        elif isinstance(value, dict):
-                            for k, v in value.items():
-                                prev_value[k] = parse_model_kwargs(
-                                    prev_value[k], v)
-                        elif isinstance(value, list):
-                            for idx, v in enumerate(value):
-                                prev_value[idx] = parse_model_kwargs(
-                                    prev_value[idx], v)
-                        return prev_value
-
-                    all_model_kwargs = copy.deepcopy(model_kwargs[0])
-                    for model_kwarg in model_kwargs[1:]:
-                        for key, value in model_kwarg.items():
-                            all_model_kwargs[key] = parse_model_kwargs(
-                                all_model_kwargs[key], value)
-                    all_out = model(xt.repeat(2, 1, 1, 1),
-                                    t=t.repeat(2),
-                                    **all_model_kwargs,
-                                    **kwargs)
-                    y_out, u_out = all_out.chunk(2)
+            assert isinstance(model_kwargs, list) and len(model_kwargs) >= 2
+            if isinstance(guide_scale, float) or isinstance(guide_scale, int):
+                assert len(model_kwargs) == 2
+                if guide_scale == 1.:
+                    out = model(xt, t=t, **model_kwargs[0], **kwargs)
                 else:
-                    y_out = model(xt, t=t, **model_kwargs[0], **kwargs)
-                    u_out = model(xt, t=t, **model_kwargs[1], **kwargs)
-                out = u_out + guide_scale * (y_out - u_out)
+                    if cat_uc:
 
-                # rescale the output according to arXiv:2305.08891
-                if guide_rescale is not None:
-                    assert guide_rescale >= 0 and guide_rescale <= 1
-                    ratio = (y_out.flatten(1).std(dim=1) /
-                             (out.flatten(1).std(dim=1) +
-                              1e-12)).view((-1, ) + (1, ) * (y_out.ndim - 1))
-                    out *= guide_rescale * ratio + (1 - guide_rescale) * 1.0
+                        def parse_model_kwargs(prev_value, value):
+                            if isinstance(value, torch.Tensor):
+                                prev_value = torch.cat([prev_value, value],
+                                                       dim=0)
+                            elif isinstance(value, dict):
+                                for k, v in value.items():
+                                    prev_value[k] = parse_model_kwargs(
+                                        prev_value[k], v)
+                            elif isinstance(value, list):
+                                for idx, v in enumerate(value):
+                                    prev_value[idx] = parse_model_kwargs(
+                                        prev_value[idx], v)
+                            return prev_value
+
+                        all_model_kwargs = copy.deepcopy(model_kwargs[0])
+                        for model_kwarg in model_kwargs[1:]:
+                            for key, value in model_kwarg.items():
+                                all_model_kwargs[key] = parse_model_kwargs(
+                                    all_model_kwargs[key], value)
+                        all_out = model(xt.repeat(2, 1, 1, 1),
+                                        t=t.repeat(2),
+                                        **all_model_kwargs,
+                                        **kwargs)
+                        y_out, u_out = all_out.chunk(2)
+                    else:
+                        y_out = model(xt, t=t, **model_kwargs[0], **kwargs)
+                        u_out = model(xt, t=t, **model_kwargs[1], **kwargs)
+                    # todo sharpness
+                    # sharpness sampling
+                    if sharpness is not None and sharpness > 0:
+                        positive_x0 = alphas * xt - sigmas * y_out
+                        negative_x0 = alphas * xt - sigmas * u_out
+
+                        positive_eps = xt - positive_x0
+                        negative_eps = xt - negative_x0
+
+                        global_diffusion_progress = (
+                            1 - t / 999.0).detach().cpu().numpy().tolist()[0]
+                        alpha = 0.001 * sharpness * global_diffusion_progress
+                        positive_eps_degraded = adaptive_anisotropic_filter(
+                            x=positive_eps, g=positive_x0)
+                        positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (
+                            1.0 - alpha)
+
+                        final_eps = negative_eps + guide_scale * (
+                            positive_eps_degraded_weighted - negative_eps)
+                        final_x0 = xt - final_eps
+                        out = (alphas * xt - final_x0) / sigmas
+                    else:
+                        out = u_out + guide_scale * (y_out - u_out)
+            elif isinstance(guide_scale, dict):
+                assert len(model_kwargs) == 3
+                y_out = model(xt, t=t, **model_kwargs[0], **kwargs)
+                m_out = model(xt, t=t, **model_kwargs[1], **kwargs)
+                u_out = model(xt, t=t, **model_kwargs[2], **kwargs)
+                out = u_out + guide_scale['image'] * (
+                    m_out - u_out) + guide_scale['text'] * (y_out - m_out)
+            elif isinstance(guide_scale, list):
+                assert len(guide_scale) == len(model_kwargs) - 1
+                y_out = model(xt, t=t, **model_kwargs[0], **kwargs)
+                outs = [y_out]
+                for i in range(1, len(model_kwargs)):
+                    outs.append(model(xt, t=t, **model_kwargs[i], **kwargs))
+                out = outs[-1]
+                for i in range(len(guide_scale)):
+                    out += guide_scale[i] * (outs[-i - 2] - outs[-i - 1])
+
+            # rescale the output according to arXiv:2305.08891
+            if guide_rescale is not None and guide_rescale > 0.0:
+                assert guide_rescale >= 0 and guide_rescale <= 1
+                ratio = (
+                    y_out.flatten(1).std(dim=1) /
+                    (out.flatten(1).std(dim=1) + 1e-12)).view((-1, ) + (1, ) *
+                                                              (y_out.ndim - 1))
+                out *= guide_rescale * ratio + (1 - guide_rescale) * 1.0
         # compute x0
         if self.prediction_type == 'x0':
             x0 = out
@@ -197,6 +384,7 @@ class GaussianDiffusion(object):
                guide_scale=None,
                guide_rescale=None,
                clamp=None,
+               sharpness=0.0,
                percentile=None,
                solver='euler_a',
                steps=20,
@@ -209,12 +397,16 @@ class GaussianDiffusion(object):
                seed=-1,
                intermediate_callback=None,
                cat_uc=False,
+               add_noise=False,
+               free_steps=None,
+               step_offset=None,
                **kwargs):
         # sanity check
         assert isinstance(steps, (int, torch.LongTensor))
         assert t_max is None or (t_max > 0 and t_max <= self.num_timesteps - 1)
         assert t_min is None or (t_min >= 0 and t_min < self.num_timesteps - 1)
-        assert discretization in (None, 'leading', 'linspace', 'trailing')
+        assert discretization in (None, 'leading', 'linspace', 'trailing',
+                                  'free')
         assert discard_penultimate_step in (None, True, False)
         assert return_intermediate in (None, 'x0', 'xt')
 
@@ -255,17 +447,50 @@ class GaussianDiffusion(object):
         def model_fn(xt, sigma):
             # denoising
             t = self._sigma_to_t(sigma).repeat(len(xt)).round().long()
-            x0 = self.denoise(xt,
-                              t,
-                              None,
-                              model,
-                              model_kwargs,
-                              guide_scale,
-                              guide_rescale,
-                              clamp,
-                              percentile,
-                              cat_uc=cat_uc,
-                              **kwargs)[-2]
+
+            if isinstance(
+                    model_kwargs[0]['cond'], dict) and \
+                    'tar_x0' in model_kwargs[0]['cond'] and \
+                    'tar_mask_latent' in model_kwargs[0]['cond']:
+                tar_x0 = model_kwargs[0]['cond']['tar_x0']
+                tar_mask = model_kwargs[0]['cond']['tar_mask_latent']
+
+                tar_xt = self.diffuse(x0=tar_x0, t=t)
+                xt = tar_xt * (1.0 - tar_mask) + xt * tar_mask
+
+            if isinstance(model_kwargs[0]['cond'],
+                          dict) and 'ref_x0' in model_kwargs[0]['cond']:
+                model_kwargs[0]['cond']['ref_xt'] = self.diffuse(
+                    x0=model_kwargs[0]['cond']['ref_x0'], t=t)
+                model_kwargs[1]['cond']['ref_xt'] = self.diffuse(
+                    x0=model_kwargs[1]['cond']['ref_x0'], t=t)
+
+            if solver in ('onestep', 'multistep', 'multistep2', 'multistep3'):
+                x0 = self.denoise(xt,
+                                  t,
+                                  None,
+                                  model,
+                                  model_kwargs,
+                                  guide_scale,
+                                  guide_rescale,
+                                  clamp,
+                                  sharpness,
+                                  percentile,
+                                  cat_uc=cat_uc,
+                                  **kwargs)[-3]
+            else:
+                x0 = self.denoise(xt,
+                                  t,
+                                  None,
+                                  model,
+                                  model_kwargs,
+                                  guide_scale,
+                                  guide_rescale,
+                                  clamp,
+                                  sharpness,
+                                  percentile,
+                                  cat_uc=cat_uc,
+                                  **kwargs)[-2]
 
             # collect intermediate outputs
             if return_intermediate == 'xt':
@@ -291,10 +516,14 @@ class GaussianDiffusion(object):
             elif discretization == 'trailing':
                 steps = torch.arange(t_max, t_min - 1,
                                      -((t_max - t_min + 1) / steps))
+            elif discretization == 'free':
+                steps = torch.tensor(free_steps)
             else:
                 raise NotImplementedError(
                     f'{discretization} discretization not implemented')
             steps = steps.clamp_(t_min, t_max)
+        elif isinstance(steps, list):
+            steps = torch.tensor(steps)
         steps = torch.as_tensor(steps,
                                 dtype=torch.float32,
                                 device=noise.device)
@@ -335,6 +564,23 @@ class GaussianDiffusion(object):
         if discard_penultimate_step:
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
         kwargs['seed'] = seed
+        # add noise to x0
+        if add_noise:
+            if 'dm_steps' in kwargs:
+                if step_offset:
+                    add_noise_step = -kwargs['dm_steps'] + step_offset
+                    if add_noise_step < 0:
+                        noise = self.diffuse(
+                            noise,
+                            torch.full((noise.shape[0], 1),
+                                       steps[add_noise_step],
+                                       dtype=torch.int))
+                else:
+                    noise = self.diffuse(
+                        noise,
+                        torch.full((noise.shape[0], 1),
+                                   steps[-kwargs['dm_steps'] - 1],
+                                   dtype=torch.int))
         # sampling
         x0 = solver_fn(noise,
                        model_fn,
@@ -373,168 +619,6 @@ class GaussianDiffusion(object):
                   | torch.isinf(log_sigma)] = float('inf')
         return log_sigma.exp()
 
-    @torch.no_grad()
-    def stochastic_encode(self, x0, t, steps):
-        # fast, but does not allow for exact reconstruction
-        # t serves as an index to gather the correct alphas
-
-        t_max = None
-        t_min = None
-
-        # discretization method
-        discretization = 'trailing' if self.prediction_type == 'v' else 'leading'
-
-        # timesteps
-        if isinstance(steps, int):
-            t_max = self.num_timesteps - 1 if t_max is None else t_max
-            t_min = 0 if t_min is None else t_min
-            steps = discretize_timesteps(t_max, t_min, steps, discretization)
-        steps = torch.as_tensor(steps).round().long().flip(0).to(x0.device)
-        # steps = torch.as_tensor(steps).round().long().to(x0.device)
-
-        # self.alphas_bar = torch.cumprod(1 - self.sigmas ** 2, dim=0)
-        # print('sigma: ', self.sigmas, len(self.sigmas))
-        # print('alpha_bar: ', self.alphas_bar, len(self.alphas_bar))
-        # print('steps: ', steps, len(steps))
-        # sqrt_alphas_cumprod = torch.sqrt(self.alphas_bar).to(x0.device)[steps]
-        # sqrt_one_minus_alphas_cumprod = torch.sqrt(1 - self.alphas_bar).to(x0.device)[steps]
-
-        sqrt_alphas_cumprod = self.alphas.to(x0.device)[steps]
-        sqrt_one_minus_alphas_cumprod = self.sigmas.to(x0.device)[steps]
-        # print('sigma: ', self.sigmas, len(self.sigmas))
-        # print('alpha: ', self.alphas, len(self.alphas))
-        # print('steps: ', steps, len(steps))
-
-        noise = torch.randn_like(x0)
-        return (
-            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
-            extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) *
-            noise)
-
-    @torch.no_grad()
-    def sample_img2img(self,
-                       x,
-                       noise,
-                       model,
-                       denoising_strength=1,
-                       model_kwargs={},
-                       condition_fn=None,
-                       guide_scale=None,
-                       guide_rescale=None,
-                       clamp=None,
-                       percentile=None,
-                       solver='euler_a',
-                       steps=20,
-                       t_max=None,
-                       t_min=None,
-                       discretization=None,
-                       discard_penultimate_step=None,
-                       return_intermediate=None,
-                       show_progress=False,
-                       seed=-1,
-                       **kwargs):
-        # sanity check
-        assert isinstance(steps, (int, torch.LongTensor))
-        assert t_max is None or (t_max > 0 and t_max <= self.num_timesteps - 1)
-        assert t_min is None or (t_min >= 0 and t_min < self.num_timesteps - 1)
-        assert discretization in (None, 'leading', 'linspace', 'trailing')
-        assert discard_penultimate_step in (None, True, False)
-        assert return_intermediate in (None, 'x0', 'xt')
-        # function of diffusion solver
-        solver_fn = {
-            'euler_ancestral': sample_img2img_euler_ancestral,
-            'euler': sample_img2img_euler,
-        }[solver]
-        # options
-        schedule = 'karras' if 'karras' in solver else None
-        discretization = discretization or 'linspace'
-        seed = seed if seed >= 0 else random.randint(0, 2**31)
-        if isinstance(steps, torch.LongTensor):
-            discard_penultimate_step = False
-        if discard_penultimate_step is None:
-            discard_penultimate_step = True if solver in (
-                'dpm2', 'dpm2_ancestral', 'dpmpp_2m_sde', 'dpm2_karras',
-                'dpm2_ancestral_karras', 'dpmpp_2m_sde_karras') else False
-
-        # function for denoising xt to get x0
-        intermediates = []
-
-        def get_scalings(sigma):
-            c_out = -sigma
-            c_in = 1 / (sigma**2 + 1.**2)**0.5
-            return c_out, c_in
-
-        def model_fn(xt, sigma):
-            # denoising
-            c_out, c_in = get_scalings(sigma)
-            t = self._sigma_to_t(sigma).repeat(len(xt)).round().long()
-
-            x0 = self.denoise(xt * c_in, t, None, model, model_kwargs,
-                              guide_scale, guide_rescale, clamp, percentile,
-                              **kwargs)[-2]
-            # collect intermediate outputs
-            if return_intermediate == 'xt':
-                intermediates.append(xt)
-            elif return_intermediate == 'x0':
-                intermediates.append(x0)
-            return xt + x0 * c_out
-
-        # get timesteps
-        if isinstance(steps, int):
-            steps += 1 if discard_penultimate_step else 0
-            t_max = self.num_timesteps - 1 if t_max is None else t_max
-            t_min = 0 if t_min is None else t_min
-            # discretize timesteps
-            if discretization == 'leading':
-                steps = torch.arange(t_min, t_max + 1,
-                                     (t_max - t_min + 1) / steps).flip(0)
-            elif discretization == 'linspace':
-                steps = torch.linspace(t_max, t_min, steps)
-            elif discretization == 'trailing':
-                steps = torch.arange(t_max, t_min - 1,
-                                     -((t_max - t_min + 1) / steps))
-            else:
-                raise NotImplementedError(
-                    f'{discretization} discretization not implemented')
-            steps = steps.clamp_(t_min, t_max)
-        steps = torch.as_tensor(steps, dtype=torch.float32, device=x.device)
-        # get sigmas
-        sigmas = self._t_to_sigma(steps)
-        sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
-        t_enc = int(min(denoising_strength, 0.999) * len(steps))
-        sigmas = sigmas[len(steps) - t_enc - 1:]
-        noise = x + noise * sigmas[0]
-
-        if schedule == 'karras':
-            if sigmas[0] == float('inf'):
-                sigmas = karras_schedule(
-                    n=len(steps) - 1,
-                    sigma_min=sigmas[sigmas > 0].min().item(),
-                    sigma_max=sigmas[sigmas < float('inf')].max().item(),
-                    rho=7.).to(sigmas)
-                sigmas = torch.cat([
-                    sigmas.new_tensor([float('inf')]), sigmas,
-                    sigmas.new_zeros([1])
-                ])
-            else:
-                sigmas = karras_schedule(
-                    n=len(steps),
-                    sigma_min=sigmas[sigmas > 0].min().item(),
-                    sigma_max=sigmas.max().item(),
-                    rho=7.).to(sigmas)
-                sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
-        if discard_penultimate_step:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
-        # sampling
-        x0 = solver_fn(noise,
-                       model_fn,
-                       sigmas,
-                       seed=seed,
-                       show_progress=show_progress,
-                       **kwargs)
-        return (x0, intermediates) if return_intermediate is not None else x0
-
 
 def extract_into_tensor(a, t, x_shape):
     b, *_ = t.shape

scepter/modules/model/utils/data_utils.py

@@ -0,0 +1,148 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import cv2
+import numpy as np
+import torchvision.transforms.functional as TF
+
+
+def get_bbox_from_mask(mask):
+    h, w = mask.shape[0], mask.shape[1]
+    if mask.sum() < 10:
+        return 0, h, 0, w
+    rows = np.any(mask, axis=1)
+    cols = np.any(mask, axis=0)
+    y1, y2 = np.where(rows)[0][[0, -1]]
+    x1, x2 = np.where(cols)[0][[0, -1]]
+    return (y1, y2, x1, x2)
+
+
+def pad_to_square(image, pad_value=255, random=False):
+    H, W = image.shape[0], image.shape[1]
+    if H == W:
+        return image, 0, 0
+
+    padd = abs(H - W)
+    if random:
+        padd_1 = int(np.random.randint(0, padd))
+    else:
+        padd_1 = int(padd / 2)
+    padd_2 = padd - padd_1
+
+    if H > W:
+        pad_param = ((0, 0), (padd_1, padd_2), (0, 0))
+    else:
+        pad_param = ((padd_1, padd_2), (0, 0), (0, 0))
+
+    # print(pad_param, pad_value)
+    image = np.pad(image, pad_param, 'constant', constant_values=pad_value)
+    return image, padd_1, padd_2
+
+
+def box_in_box(small_box, big_box):
+    y1, y2, x1, x2 = small_box
+    y1_b, _, x1_b, _ = big_box
+    y1, y2, x1, x2 = y1 - y1_b, y2 - y1_b, x1 - x1_b, x2 - x1_b
+    return (y1, y2, x1, x2)
+
+
+def box2squre(image, box):
+    H, W = image.shape[0], image.shape[1]
+    y1, y2, x1, x2 = box
+    cx = (x1 + x2) // 2
+    cy = (y1 + y2) // 2
+    h, w = y2 - y1, x2 - x1
+
+    if h >= w:
+        x1 = cx - h // 2
+        x2 = x1 + h
+    else:
+        y1 = cy - w // 2
+        y2 = y1 + w
+    x1 = max(0, x1)
+    x2 = min(W, x2)
+    y1 = max(0, y1)
+    y2 = min(H, y2)
+    return (y1, y2, x1, x2)
+
+
+def expand_bbox(mask,
+                yyxx,
+                ratio=1.0,
+                min_crop=0,
+                expand_type='center',
+                to_square=False):
+    y1, y2, x1, x2 = yyxx
+    h = y2 - y1 + 1
+    w = x2 - x1 + 1
+
+    H, W = mask.shape[0], mask.shape[1]
+    xc, yc = 0.5 * (x1 + x2), 0.5 * (y1 + y2)
+
+    def expand(k):
+        if isinstance(ratio, tuple) or isinstance(ratio, list):
+            r = np.random.uniform(*ratio)
+            k = k * r
+        else:
+            k = ratio * k
+        return k
+
+    new_h = expand(h)
+    new_w = expand(w)
+    new_h = max(new_h, min_crop)
+    new_w = max(new_w, min_crop)
+
+    if to_square:
+        if new_w / new_h < 0.334:
+            new_w = new_w + 1.0 / 3.0 * new_h
+        elif new_h / new_w < 0.334:
+            new_h = new_h + 1.0 / 3.0 * new_w
+
+    if expand_type == 'center':
+        x1 = max(0, int(xc - new_w * 0.5))
+        x2 = min(W, int(xc + new_w * 0.5))
+        y1 = max(0, int(yc - new_h * 0.5))
+        y2 = min(H, int(yc + new_h * 0.5))
+    else:
+        x1 = max(0, min(x1,
+                        int(x2 - new_w * np.random.uniform(w / new_w, 1.0))))
+        x2 = min(W, max(x2, x1 + new_w))
+        y1 = max(0, min(y1,
+                        int(y2 - new_h * np.random.uniform(h / new_h, 1.0))))
+        y2 = min(H, max(y2, y1 + new_h))
+
+    return (int(y1), int(y2), int(x1), int(x2))
+
+
+def crop_back(pred, tar_image, extra_sizes, tar_box_yyxx_crop):
+    H1, W1, H2, W2, pad1, pad2 = extra_sizes
+    y1, y2, x1, x2 = tar_box_yyxx_crop
+    pred = TF.resize(pred, (H2, W2), antialias=True)
+    # if W1 == H1:
+    #     tar_image[:, y1:y2, x1:x2] = pred
+    #     return tar_image
+    # if W1 < W2:
+    #     pad1 = int((W2 - W1) / 2)
+    #     pad2 = W2 - W1 - pad1
+    #     pred = pred[:, :,pad1:-pad2]
+    # else:
+    #     pad1 = int((H2 - H1) / 2)
+    #     pad2 = H2 - H1 - pad1
+    #     pred = pred[:, pad1:-pad2, :]
+    if W1 < W2:
+        # pad width
+        assert H1 == H2 and (pad1 + W1) == (W2 - pad2)
+        pred = pred[:, :, pad1 + 2:(W2 - pad2 - 2)]
+        tar_image[:, y1:y2, x1 + 2:x2 - 2] = pred
+    elif H1 < H2:
+        # pad height
+        assert W1 == W2 and (pad1 + H1) == (H2 - pad2)
+        pred = pred[:, pad1 + 2:(H2 - pad2 - 2), :]
+        tar_image[:, y1 + 2:y2 - 2, x1:x2] = pred
+    else:
+        tar_image[:, y1:y2, x1:x2] = pred
+    return tar_image
+
+
+def save_image(image, save_path):
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    cv2.imwrite(save_path, image)

scepter/modules/opt/optimizers/__init__.py

@@ -1,7 +1,10 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-from scepter.modules.opt.optimizers.official_optimizers import (
-    ASGD, LBFGS, SGD, Adadelta, Adagrad, Adam, Adamax, AdamW, RMSprop, Rprop,
-    SparseAdam)
+from scepter.modules.opt.optimizers.official_optimizers import (ASGD, LBFGS,
+                                                                SGD, Adadelta,
+                                                                Adagrad, Adam,
+                                                                Adamax, AdamW,
+                                                                RMSprop, Rprop,
+                                                                SparseAdam)
 from scepter.modules.opt.optimizers.registry import OPTIMIZERS

scepter/modules/solver/diffusion_solver.py

@@ -333,12 +333,12 @@ class LatentDiffusionSolver(BaseSolver):
         data_iter = iter(self.datas[self._mode].dataloader)
         self.print_memory_status()
         for step in range(self.max_steps):
-            if 'eval' in self._mode_set and (step % self.eval_interval == 0
-                                             or step == self.max_steps - 1):
+            if 'eval' in self._mode_set and (self.eval_interval > 0 and
+                                             step % self.eval_interval == 0):
                 self.run_eval()
                 self.train_mode()
-            self.before_iter(self.hooks_dict[self._mode])
             batch_data = next(data_iter)
+            self.before_iter(self.hooks_dict[self._mode])
             if self.sample_args:
                 batch_data.update(self.sample_args.get_lowercase_dict())
             if 'meta' in batch_data:
@@ -364,6 +364,9 @@ class LatentDiffusionSolver(BaseSolver):
             self.after_iter(self.hooks_dict[self._mode])
             if we.debug:
                 self.print_trainable_params_status(prefix='model.')
+            if 'eval' in self._mode_set and (self.eval_interval > 0
+                                             and step == self.max_steps - 1):
+                self.run_eval()
         self.after_all_iter(self.hooks_dict[self._mode])
 
     @torch.no_grad()

scepter/modules/solver/hooks/__init__.py

@@ -4,12 +4,14 @@
 from scepter.modules.solver.hooks.backward import BackwardHook
 from scepter.modules.solver.hooks.checkpoint import CheckpointHook
 from scepter.modules.solver.hooks.data_probe import ProbeDataHook
+from scepter.modules.solver.hooks.ema import ModelEmaHook
 from scepter.modules.solver.hooks.hook import Hook
 from scepter.modules.solver.hooks.log import LogHook, TensorboardLogHook
 from scepter.modules.solver.hooks.lr import LrHook
 from scepter.modules.solver.hooks.registry import HOOKS
 from scepter.modules.solver.hooks.safetensors import SafetensorsHook
 from scepter.modules.solver.hooks.sampler import DistSamplerHook
+
 """
 Normally, hooks have priorities, below we recommend priority that runs fine (low score MEANS high priority)
 BackwardHook: 0
@@ -47,5 +49,6 @@ after solve:
 
 __all__ = [
     'HOOKS', 'BackwardHook', 'CheckpointHook', 'Hook', 'LrHook', 'LogHook',
-    'TensorboardLogHook', 'DistSamplerHook', 'ProbeDataHook', 'SafetensorsHook'
+    'TensorboardLogHook', 'DistSamplerHook', 'ProbeDataHook',
+    'SafetensorsHook', 'ModelEmaHook'
 ]

scepter/modules/solver/hooks/checkpoint.py

@@ -147,7 +147,9 @@ class CheckpointHook(Hook):
 
                 if self.save_last and solver.total_iter == solver.max_steps - 1:
                     with FS.get_fs_client(save_path) as client:
-                        last_path = osp.join(solver.work_dir, 'checkpoint.pth')
+                        last_path = osp.join(
+                            solver.work_dir,
+                            f'checkpoints/{self.save_name_prefix}-last')
                         client.make_link(last_path, save_path)
                 self.last_ckpt = save_path
 

scepter/modules/solver/hooks/data_probe.py

@@ -30,7 +30,10 @@ class ProbeDataHook(Hook):
     def __init__(self, cfg, logger=None):
         super(ProbeDataHook, self).__init__(cfg, logger=logger)
         self.priority = cfg.get('PRIORITY', _DEFAULT_PROBE_PRIORITY)
-        self.log_interval = cfg.get('PROB_INTERVAL', 1000)
+        self.prob_interval = cfg.get('PROB_INTERVAL', 1000)
+        self.save_name_prefix = cfg.get('SAVE_NAME_PREFIX', 'step')
+        self.save_probe_prefix = cfg.get('SAVE_PROBE_PREFIX', None)
+        self.save_last = cfg.get('SAVE_LAST', False)
 
     def before_all_iter(self, solver):
         pass
@@ -39,19 +42,23 @@ class ProbeDataHook(Hook):
         pass
 
     def after_iter(self, solver):
-        if solver.mode == 'train' and solver.total_iter % self.log_interval == 0:
+        if solver.mode == 'train' and solver.total_iter % self.prob_interval == 0:
             probe_dict = solver.probe_data
             if we.rank == 0:
                 save_folder = os.path.join(
                     solver.work_dir,
-                    f'{solver.mode}_probe/step_{solver.total_iter}')
+                    f'{solver.mode}_probe/{self.save_probe_prefix}-{solver.total_iter}'
+                )
                 ret_data = {}
                 for k, v in probe_dict.items():
-                    ret_one = v.to_log(
-                        os.path.join(
+                    if self.save_probe_prefix is not None:
+                        ret_prefix = os.path.join(save_folder,
+                                                  self.save_probe_prefix)
+                    else:
+                        ret_prefix = os.path.join(
                             save_folder,
-                            k.replace('/', '_') +
-                            f'_step_{solver.total_iter}'))
+                            k.replace('/', '_') + f'_step_{solver.total_iter}')
+                    ret_one = v.to_log(ret_prefix)
                     if (isinstance(ret_one, list)
                             or isinstance(ret_one, dict)) and len(ret_one) < 1:
                         continue
@@ -71,13 +78,19 @@ class ProbeDataHook(Hook):
             if we.rank == 0:
                 step = solver._total_iter[
                     'train'] if 'train' in solver._total_iter else 0
-                save_folder = os.path.join(solver.work_dir,
-                                           f'{solver.mode}_probe/step_{step}')
+                save_folder = os.path.join(
+                    solver.work_dir,
+                    f'{solver.mode}_probe/{self.save_name_prefix}-{step}')
                 ret_data = {}
                 for k, v in probe_dict.items():
-                    ret_one = v.to_log(
-                        os.path.join(save_folder,
-                                     k.replace('/', '_') + f'_step_{step}'))
+                    if self.save_probe_prefix is not None:
+                        ret_prefix = os.path.join(save_folder,
+                                                  self.save_probe_prefix)
+                    else:
+                        ret_prefix = os.path.join(
+                            save_folder,
+                            k.replace('/', '_') + f'_step_{step}')
+                    ret_one = v.to_log(ret_prefix)
                     if (isinstance(ret_one, list)
                             or isinstance(ret_one, dict)) and len(ret_one) < 1:
                         continue
@@ -87,6 +100,16 @@ class ProbeDataHook(Hook):
                     json.dump(ret_data,
                               open(local_path, 'w'),
                               ensure_ascii=False)
+
+                if self.save_last and step == solver.max_steps:
+                    with FS.get_fs_client(save_folder) as client:
+                        last_save_folder = os.path.join(
+                            solver.work_dir,
+                            f'{solver.mode}_probe/{self.save_name_prefix}-last'
+                        )
+                        print(last_save_folder, save_folder)
+                        client.make_link(last_save_folder, save_folder)
+
             solver.clear_probe()
             torch.cuda.synchronize()
             barrier()

scepter/modules/solver/hooks/ema.py

@@ -0,0 +1,60 @@
+# -*- coding: utf-8 -*-
+import torch
+from torch.distributed.fsdp import (FullStateDictConfig,
+                                    FullyShardedDataParallel, StateDictType)
+
+from scepter.modules.solver.hooks.hook import Hook
+from scepter.modules.solver.hooks.registry import HOOKS
+from scepter.modules.utils.config import dict_to_yaml
+from scepter.modules.utils.distribute import we
+
+
+@HOOKS.register_class()
+class ModelEmaHook(Hook):
+    para_dict = [{
+        'PRIORITY': {
+            'value': 100,
+            'description': 'the priority for processing!'
+        },
+        'BETA': {
+            'value': 0.9999,
+            'description': ''
+        },
+    }]
+
+    def __init__(self, cfg, logger=None):
+        super(ModelEmaHook, self).__init__(cfg, logger=logger)
+        self.priority = cfg.get('PRIORITY', 100)
+        self.beta = cfg.get('BETA', 0.9999)
+
+    def after_iter(self, solver):
+        if solver.model.use_ema:
+            model_ema = solver.model.model_ema
+            model = solver.model.model
+            self.ema(model_ema, model, use_fsdp=solver.use_fsdp)
+
+    @torch.no_grad()
+    def ema(self, net_ema, net, use_fsdp=True):
+        if we.is_distributed:
+            if use_fsdp:
+                save_policy = FullStateDictConfig(offload_to_cpu=False,
+                                                  rank0_only=False)
+                with FullyShardedDataParallel.state_dict_type(
+                        net, StateDictType.FULL_STATE_DICT, save_policy):
+                    nonema_state = net.state_dict()
+            elif hasattr(net, 'module'):
+                nonema_state = net.module.state_dict()
+            else:
+                nonema_state = net.state_dict()
+        else:
+            nonema_state = net.state_dict()
+
+        for k, v in net_ema.named_parameters():
+            v.copy_(nonema_state[k].lerp(v, self.beta))
+
+    @staticmethod
+    def get_config_template():
+        return dict_to_yaml('hook',
+                            __class__.__name__,
+                            ModelEmaHook.para_dict,
+                            set_name=True)

scepter/modules/transform/image.py

@@ -10,11 +10,16 @@ import torchvision.transforms as transforms
 import torchvision.transforms.functional as TF
 
 from scepter.modules.transform.registry import TRANSFORMS
-from scepter.modules.transform.utils import (
-    BACKEND_CV2, BACKEND_PILLOW, BACKEND_TORCHVISION, INPUT_CV2_TYPE_WARNING,
-    INPUT_PIL_TYPE_WARNING, INPUT_TENSOR_TYPE_WARNING, INTERPOLATION_STYLE,
-    INTERPOLATION_STYLE_CV2, TORCHVISION_CAPABILITY, is_cv2_image,
-    is_pil_image, is_tensor)
+from scepter.modules.transform.utils import (BACKEND_CV2, BACKEND_PILLOW,
+                                             BACKEND_TORCHVISION,
+                                             INPUT_CV2_TYPE_WARNING,
+                                             INPUT_PIL_TYPE_WARNING,
+                                             INPUT_TENSOR_TYPE_WARNING,
+                                             INTERPOLATION_STYLE,
+                                             INTERPOLATION_STYLE_CV2,
+                                             TORCHVISION_CAPABILITY,
+                                             is_cv2_image, is_pil_image,
+                                             is_tensor)
 from scepter.modules.utils.config import dict_to_yaml
 
 if TORCHVISION_CAPABILITY:

scepter/modules/transform/io.py

@@ -6,13 +6,15 @@ import os
 import cv2
 import numpy as np
 import torch
-from PIL import Image
+from PIL import Image, ImageFile
 
 from scepter.modules.transform.registry import TRANSFORMS
 from scepter.modules.utils.config import dict_to_yaml
 from scepter.modules.utils.distribute import we
 from scepter.modules.utils.file_system import DATA_FS as FS
 
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
 
 def pillow_convert(image, rgb_order):
     if image.mode != rgb_order:

scepter/modules/utils/config.py

@@ -603,3 +603,6 @@ class Config(object):
             return cfg_new
         else:
             return cfg
+
+    def pop(self, name):
+        self.cfg_dict.pop(name)

scepter/modules/utils/export_model.py

@@ -4,10 +4,10 @@ import io
 from io import BytesIO
 
 import onnx
+import onnxruntime
 import torch
 from torch.onnx import OperatorExportTypes
 
-import onnxruntime
 from scepter.modules.utils.distribute import we
 
 type_map = {

scepter/modules/utils/file_clients/local_fs.py

@@ -293,6 +293,9 @@ class LocalFs(BaseFs):
         return True
 
     def get_logging_handler(self, target_logging_path):
+        dirname = os.path.dirname(target_logging_path)
+        if not os.path.exists(dirname):
+            os.makedirs(dirname, exist_ok=True)
         return logging.FileHandler(target_logging_path)
 
     def put_dir_from_local_dir(self,
@@ -303,11 +306,11 @@ class LocalFs(BaseFs):
         target_dir = self.reconstruct_path(target_dir)
         if local_dir == target_dir:
             return True
-        # cp -f local_dir/* target_dir/*
-        if not osp.exists(target_dir):
-            status = os.system(f'mkdir -p {target_dir}')
-            if status != 0:
-                return False
+        # # cp -f local_dir/* target_dir/*
+        # if not osp.exists(target_dir):
+        #     status = os.system(f'mkdir -p {target_dir}')
+        #     if status != 0:
+        #         return False
         try:
             shutil.copytree(local_dir, target_dir, symlinks=True)
         except Exception:

scepter/modules/utils/file_system.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
+import io
 import os
 import threading
 import time
@@ -327,6 +328,10 @@ class FileSystem(object):
                                                target_path_list):
                 if local_path is None or target_path is None:
                     flg = False
+                elif isinstance(local_path, io.BytesIO):
+                    flg = FS.put_object(local_path.getvalue(), target_path)
+                elif isinstance(local_path, bytes):
+                    flg = FS.put_object(local_path, target_path)
                 elif self.exists(local_path):
                     local_cache = self.get_from(local_path,
                                                 local_path + f'{time.time()}',

scepter/modules/utils/index.py

@@ -0,0 +1,44 @@
+# -*- coding: utf-8 -*-
+import os.path as osp
+
+from tqdm import tqdm
+
+from scepter.modules.utils.file_system import FS
+
+
+def init_1level_llfs(list_file,
+                     max_lines=1024,
+                     index_name='index',
+                     delimiter='\n'):
+    r"""Construct large-list-file index.
+    """
+    index_dir = osp.splitext(list_file)[0]
+    print(list_file)
+    with FS.get_from(list_file, wait_finish=True) as local_path:
+        print(local_path)
+        _num_split, index_stack, save_files = 0, [], []
+        with open(local_path, 'r', buffering=1000000) as f:
+            for line in tqdm(f):
+                index_stack.append(line.strip())
+                if len(index_stack) >= max_lines:
+                    save_file = f'{index_dir}/{index_name}/{_num_split + 1:09d}.txt'
+                    with FS.put_to(save_file) as cache_path:
+                        with open(cache_path, 'w') as f_w:
+                            f_w.write('\n'.join(index_stack))
+                    index_stack = []
+                    _num_split += 1
+                    save_files.append(save_file)
+
+    if len(index_stack) > 0:
+        save_file = f'{index_dir}/{index_name}/{_num_split + 1:06d}.txt'
+        with FS.put_to(save_file) as cache_path:
+            with open(cache_path, 'w') as f_w:
+                f_w.write('\n'.join(index_stack))
+        save_files.append(save_file)
+
+    # output meta-file
+    index_file = osp.join(index_dir, f'{index_name}.txt')
+    with FS.put_to(index_file) as cache_path:
+        with open(cache_path, 'w') as f_w:
+            f_w.write('\n'.join(save_files))
+    return index_file

scepter/modules/utils/probe.py

@@ -263,7 +263,8 @@ class ProbeData():
                         url = FS.get_url(one_path,
                                          lifecycle=3600 * 365 * 24).replace(
                                              '.oss-internal.aliyun-inc.',
-                                             '.oss.aliyuncs.')
+                                             '.oss.aliyuncs.').replace(
+                                                 '-internal', '')
                         one_rank += (
                             f'<td align="center"><input type="image" src="{url}" >'
                             f'<br><font size="4"><strong>{save_id}-{idx}|{one_label}<strong></font><br/></td>'

scepter/studio/inference/inference.py

@@ -16,6 +16,7 @@ from scepter.studio.inference.inference_ui.component_names import \
 from scepter.studio.inference.inference_ui.control_ui import ControlUI
 from scepter.studio.inference.inference_ui.diffusion_ui import DiffusionUI
 from scepter.studio.inference.inference_ui.gallery_ui import GalleryUI
+from scepter.studio.inference.inference_ui.largen_ui import LargenUI
 from scepter.studio.inference.inference_ui.mantra_ui import MantraUI
 from scepter.studio.inference.inference_ui.model_manage_ui import ModelManageUI
 from scepter.studio.inference.inference_ui.refiner_ui import RefinerUI
@@ -23,7 +24,7 @@ from scepter.studio.inference.inference_ui.tuner_ui import TunerUI
 from scepter.studio.utils.env import init_env
 
 UI_MAP = [('diffusion', DiffusionUI), ('mantra', MantraUI), ('tuner', TunerUI),
-          ('control', ControlUI), ('refiner', RefinerUI)]
+          ('control', ControlUI), ('refiner', RefinerUI), ('largen', LargenUI)]
 
 
 class InferenceUI():
@@ -54,6 +55,19 @@ class InferenceUI():
             cfg_general.EXTENSION_PARAS.OFFICIAL_CONTROLLERS))
         cfg_general.CONTROLLERS = official_controllers.CONTROLLERS
 
+        # customized tuners
+        tuner_manager = Config(
+            cfg_file=os.path.join(os.path.dirname(scepter.dirname),
+                                  cfg_general.EXTENSION_PARAS.TUNER_MANAGER))
+        tuner_manager = os.path.join(root_work_dir, tuner_manager.WORK_DIR,
+                                     tuner_manager.TUNER_LIST_YAML)
+        if FS.exists(tuner_manager):
+            with FS.get_from(tuner_manager) as local_path:
+                custom_tuners = Config(cfg_file=local_path)
+            cfg_general.CUSTOM_TUNERS = custom_tuners.get('TUNERS', [])
+        else:
+            cfg_general.CUSTOM_TUNERS = []
+
         pipe_manager = PipelineManager()
         config_list = glob(os.path.join(config_dir, '*/*_pro.yaml'),
                            recursive=True)
@@ -68,6 +82,12 @@ class InferenceUI():
         for one_controller in cfg_general.CONTROLLERS:
             pipe_manager.register_controllers(one_controller)
 
+        for one_tuner in cfg_general.CUSTOM_TUNERS:
+            pipe_manager.register_tuner(
+                one_tuner,
+                name=one_tuner.NAME_ZH if language == 'zh' else one_tuner.NAME,
+                is_customized=True)
+
         self.model_manage_ui = ModelManageUI(cfg_general,
                                              pipe_manager,
                                              is_debug=is_debug,
@@ -88,7 +108,8 @@ class InferenceUI():
             self.tab_ui_kwargs[f'{name}_ui'] = ui
             self.__setattr__(f'{name}_ui', ui)
 
-        self.check_box_controlled_tabs = ['mantra', 'tuner', 'control']
+        self.check_box_controlled_tabs = ['mantra', 'tuner', 'control', 'largen']
+        self.pipe_manager = pipe_manager
         assert len(self.component_names.check_box_for_setting) == len(
             self.check_box_controlled_tabs)
 
@@ -96,6 +117,7 @@ class InferenceUI():
         # create model
         self.model_manage_ui.create_ui()
         self.gallery_ui.create_ui()
+        self.infer_info = gr.State(value=None)
 
         # create tabs
         def create_tab(name, ui):
@@ -123,20 +145,65 @@ class InferenceUI():
         for name, ui in self.tab_ui_kwargs.items():
             ui.set_callbacks(self.model_manage_ui,
                              **self.tab_ui_kwargs,
-                             gallery_ui=self.gallery_ui)
+                             gallery_ui=self.gallery_ui,
+                             manager=manager)
 
-        def change_setting_tab(check_box, *args):
+        def change_setting_tab(check_box, default_diffusion_model, *args):
             selected_tab = 'diffusion_ui'
             ui_tabs_state = [False] * len(args)
+            largen_index = self.check_box_controlled_tabs.index('largen')
+            largen_key = self.component_names.check_box_for_setting[largen_index]
+            largen_status = args[largen_index]
             for key in check_box:
                 i = self.component_names.check_box_for_setting.index(key)
                 ui_tabs_state[i] = True
                 if ui_tabs_state[i] != args[i]:
                     selected_tab = self.check_box_controlled_tabs[i] + '_ui'
+            new_check_box_value = check_box
+            for key in check_box:
+                i = self.component_names.check_box_for_setting.index(key)
+                if ui_tabs_state[i] != args[i]:
+                    if i in [largen_index]:
+                        new_check_box_value = [key]
+                        for j in range(len(ui_tabs_state)):
+                            ui_tabs_state[j] = j == i
+                    else:
+                        new_check_box_value = [
+                            k for k in check_box
+                            if k not in [largen_key]
+                        ]
+                        ui_tabs_state[largen_index] = False
+
             ui_tabs_updates = [gr.update(visible=v) for v in ui_tabs_state]
 
-            return gr.update(
-                selected=selected_tab), *ui_tabs_state, *ui_tabs_updates
+            if ui_tabs_state[largen_index]:
+                diffusion_model = gr.Dropdown(
+                    label=self.model_manage_ui.component_names.diffusion_model,
+                    choices=self.model_manage_ui.
+                    default_choices['diffusion_model']['choices'],
+                    value='LARGEN_LargenUNetXL',
+                    interactive=False)
+            elif largen_status:
+                diffusion_model = gr.Dropdown(
+                    label=self.model_manage_ui.component_names.diffusion_model,
+                    choices=self.model_manage_ui.
+                    default_choices['diffusion_model']['choices'],
+                    value=self.model_manage_ui.
+                    default_choices['diffusion_model']['default'],
+                    interactive=True)
+            else:
+                diffusion_model = gr.Dropdown(
+                    label=self.model_manage_ui.component_names.diffusion_model,
+                    choices=self.model_manage_ui.
+                    default_choices['diffusion_model']['choices'],
+                    value=default_diffusion_model,
+                    interactive=True)
+
+            return gr.CheckboxGroup(
+                choices=self.component_names.check_box_for_setting,
+                value=new_check_box_value,
+                show_label=False), gr.update(
+                selected=selected_tab), *ui_tabs_state, *ui_tabs_updates, diffusion_model
 
         gr_states = [
             self.tab_ui[name].state for name in self.check_box_controlled_tabs
@@ -146,8 +213,9 @@ class InferenceUI():
         ]
         self.check_box_for_setting.change(
             change_setting_tab,
-            inputs=[self.check_box_for_setting, *gr_states],
-            outputs=[self.setting_tab, *gr_states, *gr_tabs],
+            inputs=[self.check_box_for_setting, self.model_manage_ui.diffusion_model, *gr_states],
+            outputs=[self.check_box_for_setting, self.setting_tab, *gr_states,
+                     *gr_tabs, self.model_manage_ui.diffusion_model],
             queue=False)
 
 

scepter/studio/inference/inference_manager/infer_runer.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from scepter.modules.inference.diffusion_inference import DiffusionInference
+from scepter.modules.inference.largen_inference import LargenInference
 from scepter.modules.utils.logger import get_logger
 
 
@@ -95,7 +96,12 @@ class PipelineManager():
         pass
 
     def register_pipeline(self, cfg):
-        new_inference = DiffusionInference(logger=self.logger)
+        pipeline_name = cfg.NAME
+        if 'LARGEN' in pipeline_name:
+            PipelineBuilder = LargenInference
+        else:
+            PipelineBuilder = DiffusionInference
+        new_inference = PipelineBuilder(logger=self.logger)
         new_inference.init_from_cfg(cfg)
         self.contruct_models_index(cfg.NAME, new_inference)
 

scepter/studio/inference/inference_ui/component_names.py

@@ -1,16 +1,17 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 # For dataset manager
-from scepter.modules.utils.file_system import FS
 from scepter.modules.utils.directory import get_md5
+from scepter.modules.utils.file_system import FS
 
 
 def download_image(image):
     if image is not None:
         client = FS.get_fs_client(image)
-        if client.tmp_dir.startswith("/home"):
+        if client.tmp_dir.startswith('/home'):
             name = get_md5(image)
-            local_path = FS.get_from(image, f"/tmp/gradio/scepter_examples/{name}")
+            local_path = FS.get_from(image,
+                                     f'/tmp/gradio/scepter_examples/{name}')
         else:
             local_path = FS.get_from(image)
         return local_path
@@ -23,21 +24,23 @@ class InferenceUIName():
         if language == 'en':
             self.advance_block_name = 'Advance Setting'
             self.check_box_for_setting = [
-                'Use Mantra', 'Use Tuners', 'Use Controller'
+                'Use Mantra', 'Use Tuners', 'Use Controller', 'LAR-Gen'
             ]
             self.diffusion_paras = 'Generation Setting'
             self.mantra_paras = 'Mantra Book'
             self.tuner_paras = 'Tuners'
             self.control_paras = 'Controlable Generation'
             self.refiner_paras = 'Refiner Setting'
+            self.largen_paras = 'LAR-Gen'
         elif language == 'zh':
             self.advance_block_name = '生成选项'
-            self.check_box_for_setting = ['使用咒语', '使用微调', '使用控制']
+            self.check_box_for_setting = ['使用咒语', '使用微调', '使用控制', 'LAR-Gen']
             self.diffusion_paras = '生成参数设置'
             self.mantra_paras = '咒语书'
             self.tuner_paras = '微调模型'
             self.control_paras = '可控生成'
             self.refiner_paras = 'Refine设置'
+            self.largen_paras = 'LAR-Gen'
 
 
 class ModelManageUIName():
@@ -216,6 +219,7 @@ class TunerUIName():
             self.example_block_name = 'Examples'
             self.examples = [[['Pencil Sketch Drawing'], 'a girl in a jacket'],
                              [['Flat 2D Art'], 'a cat']]
+            self.save_button = 'Save'
 
         elif language == 'zh':
             self.tuner_model = '微调模型'
@@ -231,6 +235,7 @@ class TunerUIName():
             self.example_block_name = '样例'
             self.examples = [[['铅笔素描'], 'a girl in a jacket'],
                              [['扁平2D艺术'], 'a cat']]
+            self.save_button = '保存'
 
 
 class ControlUIName():
@@ -342,3 +347,155 @@ class ControlUIName():
             self.advance_block_name = '高级设置'
             self.control_scale = '控制强度'
             self.example_block_name = '样例'
+
+
+class LargenUIName():
+    def __init__(self, language='en'):
+
+        self.tasks = [
+            'Text_Guided_Outpainting', 'Subject_Guided_Inpainting',
+            'Text_Guided_Inpainting', 'Text_Subject_Guided_Inpainting'
+        ]
+
+        if language == 'en':
+            self.apps = [
+                'Zoom Out', 'Virtual Try On', 'Inpainting (text guided)',
+                'Inpainting (text + reference image guided)'
+            ]
+            self.dropdown_name = 'Application'
+            self.subject_image = 'Reference Image'
+            self.subject_mask = 'Reference Mask'
+            self.scene_image = 'Scene Image'
+            self.scene_mask = 'Scene Mask'
+            self.prompt = 'Prompt'
+            self.masked_image = 'Masked Image'
+            self.preprocess = 'Input Preprocess'
+            self.button_name = 'Data Preprocess'
+            self.direction = (
+                'Instruction: \n\n'
+                'For customized data: \n\n'
+                'a.1) Select the task; \n\n'
+                'a.1) Upload scene image; \n\n'
+                'a.2) Upload reference image (if needed); \n\n'
+                'a.3) Use the brush tool to cover the areas on the scene'
+                'image and reference image (to generate corresponding scene'
+                'mask and reference mask); \n\n'
+                'a.4) Click Data Preprocess button; \n\n'
+                'a.5) Input text prompt; \n\n'
+                'For example data: \n\n'
+                'b.1) click example row \n\n'
+                'Finally, click Generate button and get the output image!')
+            self.out_direction_label = 'Out Direction'
+            self.out_directions = [
+                'CenterAround',
+                'RightDown',
+                'LeftDown',
+                'RightUp',
+                'LeftUp',
+            ]
+        elif language == 'zh':
+            self.apps = ['图像扩展', '虚拟试衣', '图像补全（文本引导）', '图像补全（文本+参考图引导）']
+            self.dropdown_name = '应用'
+            self.subject_image = '参考图片'
+            self.subject_mask = '参考图掩码'
+            self.scene_image = '背景图片'
+            self.scene_mask = '背景图掩码'
+            self.prompt = '提示文本'
+            self.masked_image = '掩码图片'
+            self.preprocess = '输入图片预处理'
+            self.button_name = '数据预处理'
+            self.direction = ('使用说明：\n'
+                              '针对自定义数据：\n'
+                              'a.1）上传背景图片（待编辑）；\n'
+                              'a.2）上传参考图片（如需要）；\n'
+                              'a.3）使用笔刷功能涂抹图像中的特定位置（获取对应的掩码图像）；\n'
+                              'a.4）点击数据预处理按钮；\n'
+                              'a.5）输入文本提示；\n'
+                              '针对提供的样例数据: \n'
+                              'b.1）点击样例数据 \n'
+                              '最后，点击生成按钮获取生成图片')
+            self.out_direction_label = '扩展方向'
+            self.out_directions = [
+                '中心向外',
+                '右下',
+                '左下',
+                '右上',
+                '左上',
+            ]
+
+        self.examples = [
+            [
+                self.apps[0],
+                'a temple on fire',
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex1_scene_im.png'  # noqa
+                ),
+                None,
+                None,
+                None,
+                1.0,
+                0.75,
+                'CenterAround',
+                1024,
+                1024
+            ],
+            [
+                self.apps[1],
+                '',
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex2_scene_im.jpg'  # noqa
+                ),
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex2_scene_mask.png'  # noqa
+                ),
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex2_subject_im.jpg'  # noqa
+                ),
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex2_subject_mask.jpg'  # noqa
+                ),
+                1.0,
+                0.0,
+                '',
+                1024,
+                1024
+            ],
+            [
+                self.apps[2],
+                'a blue and white porcelain',
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex3_scene_im.png'  # noqa
+                ),
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex3_scene_mask.png'  # noqa
+                ),
+                None,
+                None,
+                1.0,
+                0.0,
+                '',
+                1024,
+                1024
+            ],
+            [
+                self.apps[3],
+                'a dog wearing sunglasses',
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex4_scene_im.png'  # noqa
+                ),
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex4_scene_mask.png'  # noqa
+                ),
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex4_subject_im.png'  # noqa
+                ),
+                download_image(
+                    'https://modelscope.cn/api/v1/models/iic/LARGEN/repo?Revision=master&FilePath=examples/ex4_subject_mask.png'  # noqa
+                ),
+                0.45,
+                0.0,
+                '',
+                1024,
+                1024
+            ],
+        ]

scepter/studio/inference/inference_ui/gallery_ui.py

@@ -6,6 +6,7 @@ import gradio as gr
 import numpy as np
 from PIL import Image
 
+from scepter.modules.utils.file_system import FS
 from scepter.studio.inference.inference_ui.component_names import GalleryUIName
 from scepter.studio.utils.uibase import UIBase
 
@@ -15,6 +16,9 @@ class GalleryUI(UIBase):
         self.pipe_manager = pipe_manager
         self.component_names = GalleryUIName(language)
         self.cfg = cfg
+        self.work_dir = cfg.WORK_DIR
+        self.local_work_dir, _ = FS.map_to_local(self.work_dir)
+        os.makedirs(self.local_work_dir, exist_ok=True)
 
     def create_ui(self, *args, **kwargs):
         with gr.Group():
@@ -41,6 +45,7 @@ class GalleryUI(UIBase):
                         container=False,
                         autofocus=True,
                         elem_classes='type_row',
+                        submit_on_enter=True,
                         lines=1)
 
                 with gr.Column(scale=3, min_width=0):
@@ -87,6 +92,19 @@ class GalleryUI(UIBase):
                          style_template,
                          style_negative_template,
                          image_seed,
+                         largen_state,
+                         largen_task,
+                         largen_image_scale,
+                         largen_tar_image,
+                         largen_tar_mask,
+                         largen_masked_image,
+                         largen_ref_image,
+                         largen_ref_mask,
+                         largen_ref_clip,
+                         largen_base_image,
+                         largen_extra_sizes,
+                         largen_bbox_yyxx,
+                         largen_history,
                          show_jpeg_image=True):
         if control_state and control_cond_image is None:
             raise gr.Error(self.component_names.control_err1)
@@ -111,9 +129,8 @@ class GalleryUI(UIBase):
         for tuner_m in tuner_model:
             if tuner_m is None or tuner_m == '':
                 continue
-            if (now_pipeline in self.pipe_manager.model_level_info['tuners']
-                    and tuner_m in self.pipe_manager.model_level_info['tuners']
-                [now_pipeline]):
+            if now_pipeline in self.pipe_manager.model_level_info['tuners'] and \
+               tuner_m in self.pipe_manager.model_level_info['tuners'][now_pipeline]:
                 tuner_m = self.pipe_manager.model_level_info['tuners'][
                     now_pipeline][tuner_m]['model_info']
                 used_tuner_model.append(tuner_m)
@@ -163,6 +180,23 @@ class GalleryUI(UIBase):
             pipeline_input['refine_guide_rescale'] = refine_guide_rescale
         else:
             refine_strength = 0
+        if largen_state:
+            largen_cfg = {
+                'largen_task': largen_task,
+                'largen_image_scale': largen_image_scale,
+                'largen_tar_image': largen_tar_image,
+                'largen_tar_mask': largen_tar_mask,
+                'largen_ref_image': largen_ref_image,
+                'largen_ref_mask': largen_ref_mask,
+                'largen_masked_image': largen_masked_image,
+                'largen_ref_clip': largen_ref_clip,
+                'largen_base_image': largen_base_image,
+                'largen_extra_sizes': largen_extra_sizes,
+                'largen_bbox_yyxx': largen_bbox_yyxx,
+            }
+        else:
+            largen_cfg = {}
+
         results = current_pipeline(
             pipeline_input,
             num_samples=image_number,
@@ -177,7 +211,8 @@ class GalleryUI(UIBase):
             if tuner_state or control_state else None,
             control_cond_image=control_cond_image if control_state else None,
             crop_type=crop_type if control_state else None,
-            seed=int(image_seed))
+            seed=int(image_seed),
+            **largen_cfg)
         images = []
         before_images = []
         if 'images' in results:
@@ -198,27 +233,34 @@ class GalleryUI(UIBase):
         if 'seed' in results:
             print(results['seed'])
         print(images, before_images)
+        largen_history.extend(images)
+        if len(largen_history) > 10:
+            largen_history = largen_history[-10:]
         if show_jpeg_image:
             save_list = []
             for i, img in enumerate(images):
-                save_image = os.path.join(self.cfg.WORK_DIR,
+                save_image = os.path.join(self.local_work_dir,
                                           f'cur_gallery_{i}.jpg')
                 img.save(save_image)
                 save_list.append(save_image)
             images = save_list
+
         return (
             gr.Column(visible=len(before_images) > 0),
             before_images,
             images,
+            largen_history,
+            gr.update(value=largen_history),
         )
 
     def generate_image(self, *args, **kwargs):
         gallery_result = self.generate_gallery(*args, **kwargs)
-        before_refine_panel, before_refine_gallery, output_gallery = gallery_result
+        before_refine_panel, before_refine_gallery, output_gallery, _ = gallery_result
         return (before_refine_panel, before_refine_gallery, output_gallery[0])
 
     def set_callbacks(self, inference_ui, model_manage_ui, diffusion_ui,
-                      mantra_ui, tuner_ui, refiner_ui, control_ui, **kwargs):
+                      mantra_ui, tuner_ui, refiner_ui, control_ui, largen_ui,
+                      **kwargs):
 
         self.gen_inputs = [
             self.prompt, mantra_ui.state, tuner_ui.state, control_ui.state,
@@ -237,12 +279,20 @@ class GalleryUI(UIBase):
             refiner_ui.refine_strength, refiner_ui.refine_sampler,
             refiner_ui.refine_discretization, refiner_ui.refine_guide_scale,
             refiner_ui.refine_guide_rescale, mantra_ui.style_template,
-            mantra_ui.style_negative_template, diffusion_ui.image_seed
+            mantra_ui.style_negative_template, diffusion_ui.image_seed,
+            largen_ui.state, largen_ui.task, largen_ui.image_scale,
+            largen_ui.tar_image, largen_ui.tar_mask, largen_ui.masked_image,
+            largen_ui.ref_image, largen_ui.ref_mask, largen_ui.ref_clip,
+            largen_ui.base_image, largen_ui.extra_sizes, largen_ui.bbox_yyxx,
+            largen_ui.image_history
         ]
 
         self.gen_outputs = [
-            self.before_refine_panel, self.before_refine_gallery,
-            self.output_gallery
+            self.before_refine_panel,
+            self.before_refine_gallery,
+            self.output_gallery,
+            largen_ui.image_history,
+            largen_ui.gallery,
         ]
 
         self.generate_button.click(self.generate_gallery,

scepter/studio/inference/inference_ui/largen_ui.py

@@ -0,0 +1,468 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import albumentations as A
+import cv2
+import gradio as gr
+import numpy as np
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+from PIL import Image
+
+from scepter.modules.annotator.registry import ANNOTATORS
+from scepter.modules.model.utils.data_utils import (box2squre, expand_bbox,
+                                                    get_bbox_from_mask,
+                                                    pad_to_square)
+from scepter.modules.utils.distribute import we
+from scepter.studio.inference.inference_ui.component_names import LargenUIName
+from scepter.studio.utils.uibase import UIBase
+
+refresh_symbol = '\U0001f504'  # 🔄
+
+
+class LargenUI(UIBase):
+    def __init__(self, cfg, pipe_manager, is_debug=False, language='en'):
+        self.cfg = cfg
+        self.pipe_manager = pipe_manager
+
+        self.component_names = LargenUIName(language)
+
+    def load_annotator(self, annotator):
+        if annotator['device'] == 'offline':
+            annotator['model'] = ANNOTATORS.build(annotator['cfg'])
+            annotator['device'] = 'cpu'
+        if annotator['device'] == 'cpu':
+            annotator['model'] = annotator['model'].to(we.device_id)
+            annotator['device'] = we.device_id
+        return annotator
+
+    def unload_annotator(self, annotator):
+        if not annotator['device'] == 'offline' and not annotator[
+                'device'] == 'cpu':
+            annotator['model'] = annotator['model'].to('cpu')
+            annotator['device'] = 'cpu'
+        return annotator
+
+    def create_ui(self, *args, **kwargs):
+        self.state = gr.State(value=False)
+        self.tar_image = gr.State(value=None)
+        self.tar_mask = gr.State(value=None)
+        self.ref_image = gr.State(value=None)
+        self.ref_mask = gr.State(value=None)
+        self.ref_clip = gr.State(value=None)
+        self.task = gr.State(value=self.component_names.tasks[0])
+        self.masked_image = gr.State(value=None)
+        self.base_image = gr.State(value=None)
+        self.extra_sizes = gr.State(value=None)
+        self.bbox_yyxx = gr.State(value=None)
+        self.image_history = gr.State(value=[])
+
+        with gr.Column(visible=False) as self.tab:
+            with gr.Row():
+                self.select_app = gr.Dropdown(
+                        label=self.component_names.dropdown_name,
+                        choices=self.component_names.apps,
+                        value=self.component_names.apps[0],
+                        type='index')
+            with gr.Row(equal_height=True):
+                with gr.Column(scale=2, min_width=0):
+                    with gr.Row():
+                        with gr.Column(scale=1, min_width=0):
+                            self.scene_image = gr.Image(
+                                label=self.component_names.scene_image,
+                                type='pil',
+                                tool='sketch',
+                                source='upload',
+                                height=400,
+                                interactive=True)
+                            self.cache_button = gr.Button(value='Use Last Generated Image', visible=True)
+                        with gr.Column(scale=1, min_width=0):
+                            self.subject_image = gr.Image(
+                                label=self.component_names.subject_image,
+                                type='pil',
+                                tool='sketch',
+                                source='upload',
+                                interactive=True,
+                                height=400,
+                                visible=False)
+
+                    self.gallery = gr.Gallery(label='Image History', value=[], columns=1, rows=1, height=500)
+                    self.clear_button = gr.Button(value='Clear History', visible=True)
+
+                with gr.Column(scale=1, min_width=0):
+                    self.image_scale = gr.Slider(label='Image Strength',
+                                                 minimum=0.0,
+                                                 maximum=1.0,
+                                                 value=1.0,
+                                                 visible=False)
+                    self.image_ratio = gr.Slider(minimum=0.5,
+                                                 maximum=1.0,
+                                                 value=0.75,
+                                                 label='Image Resize Ratio',
+                                                 visible=True)
+                    self.out_direction = gr.Dropdown(label=self.component_names.out_direction_label,
+                                                     choices=self.component_names.out_directions,
+                                                     value=self.component_names.out_directions[0],
+                                                     visible=True)
+
+                    self.proc_button = gr.Button(value=self.component_names.button_name)
+                    self.proc_status = gr.Markdown(value='', visible=False)
+                    self.task_desc = gr.Markdown(self.component_names.direction, visible=True)
+
+                    self.eg = gr.Column(visible=True)
+
+    def set_callbacks(self, model_manage_ui, diffusion_ui, **kwargs):
+
+        def example_data_process(select_app_id, prompt, scene_image, scene_mask, subject_image,
+                                 subject_mask, image_scale, image_ratio, out_direction,
+                                 output_height, output_width):
+            task = self.component_names.tasks[select_app_id]
+            if scene_mask is not None:
+                scene_mask = (scene_mask > 128).astype(np.uint8)
+            if subject_mask is not None:
+                subject_mask = (subject_mask > 128).astype(np.uint8)
+
+            if task == 'Text_Guided_Inpainting':
+                data = self.data_preprocess_inpaint(scene_image,
+                                                    scene_mask,
+                                                    None,
+                                                    None,
+                                                    False,
+                                                    1.3,
+                                                    output_height, output_width)
+            elif task == 'Subject_Guided_Inpainting':
+                data = self.data_preprocess_inpaint(scene_image,
+                                                    scene_mask,
+                                                    subject_image,
+                                                    subject_mask,
+                                                    False,
+                                                    1.3,
+                                                    output_height,
+                                                    output_width)
+            elif task == 'Text_Subject_Guided_Inpainting':
+                data = self.data_preprocess_inpaint(scene_image,
+                                                    scene_mask,
+                                                    subject_image,
+                                                    subject_mask,
+                                                    True,
+                                                    1.3,
+                                                    output_height,
+                                                    output_width)
+            elif task == 'Text_Guided_Outpainting':
+                data = self.data_preprocess_outpaint(scene_image,
+                                                     out_direction,
+                                                     image_ratio,
+                                                     output_height,
+                                                     output_width)
+
+            subject_image_show = None if subject_image is None else Image.fromarray(subject_image.astype(np.uint8))
+            return *data, gr.update(value='Data Process Succeed!', visible=True), \
+                gr.update(value=Image.fromarray(scene_image.astype(np.uint8))), \
+                gr.update(value=subject_image_show), \
+                task, gr.update(value=self.component_names.apps[select_app_id]), \
+                gr.update(value=prompt), gr.update(value=image_scale), gr.update(value=image_ratio)
+
+        gallery_ui = kwargs.pop('gallery_ui')
+        with self.eg:
+            self.scene_image_eg = gr.Image(label=self.component_names.scene_image, type='numpy', visible=False) # noqa
+            self.scene_mask_eg = gr.Image(label=self.component_names.scene_mask, type='numpy', image_mode='L', visible=False) # noqa
+            self.subject_image_eg = gr.Image(label=self.component_names.subject_image, type='numpy', visible=False) # noqa
+            self.subject_mask_eg = gr.Image(label=self.component_names.subject_mask, type='numpy', image_mode='L', visible=False) # noqa
+            self.prompt = gr.Textbox(label=self.component_names.prompt, visible=False)
+            self.examples = gr.Examples(
+                examples=self.component_names.examples,
+                inputs=[self.select_app, self.prompt,
+                        self.scene_image_eg, self.scene_mask_eg,
+                        self.subject_image_eg, self.subject_mask_eg,
+                        self.image_scale,
+                        self.image_ratio,
+                        self.out_direction,
+                        diffusion_ui.output_height,
+                        diffusion_ui.output_width,
+                        ],
+                outputs=[self.tar_image,
+                         self.tar_mask,
+                         self.masked_image,
+                         self.ref_image,
+                         self.ref_mask,
+                         self.ref_clip,
+                         self.base_image,
+                         self.extra_sizes,
+                         self.bbox_yyxx,
+                         self.proc_status,
+                         self.scene_image,
+                         self.subject_image,
+                         self.task,
+                         self.select_app,
+                         gallery_ui.prompt,
+                         self.image_scale,
+                         self.image_ratio,
+                         ],
+                fn=example_data_process,
+                cache_examples=False,
+                run_on_click=True)
+
+        def change_app(select_app_id):
+            select_task = self.component_names.tasks[select_app_id]
+            return gr.update(visible=('Subject' in select_task)), \
+                gr.update(visible=('Subject' in select_task)), \
+                gr.update(visible=('Outpainting' in select_task)), \
+                gr.update(visible=('Outpainting' in select_task)), select_task
+
+        self.select_app.change(
+            change_app,
+            inputs=[self.select_app],
+            outputs=[
+                self.subject_image,
+                self.image_scale,
+                self.image_ratio,
+                self.out_direction,
+                self.task
+            ],
+            queue=False
+        )
+
+        def read_gallery_image(gallery):
+            if len(gallery) == 0:
+                last_image = None
+            else:
+                last_image = gallery[-1]['name']
+            return gr.update(value=last_image)
+
+        self.cache_button.click(read_gallery_image,
+                                inputs=[self.gallery],
+                                outputs=[self.scene_image])
+
+        def clear_gallery(image_history, gallery):
+            image_history.clear()
+            gallery.clear()
+            return image_history, gallery
+
+        self.clear_button.click(
+            fn=clear_gallery,
+            inputs=[self.image_history, self.gallery],
+            outputs=[self.image_history, self.gallery]
+        )
+
+        def data_process(scene_image, subject_image, task, image_ratio, out_direction, output_height, output_width):
+            tar_image = scene_image['image'].convert('RGB')
+            tar_mask = scene_image['mask'].convert('L')
+            tar_image = np.asarray(tar_image)
+            tar_mask = np.asarray(tar_mask)
+            tar_mask = np.where(tar_mask > 128, 1, 0).astype(np.uint8)
+
+            if task == 'Text_Guided_Inpainting':
+                data = self.data_preprocess_inpaint(tar_image,
+                                                    tar_mask,
+                                                    None,
+                                                    None,
+                                                    False,
+                                                    1.3,
+                                                    output_height,
+                                                    output_width)
+            elif task == 'Subject_Guided_Inpainting':
+                ref_image = subject_image['image'].convert('RGB')
+                ref_mask = subject_image['mask'].convert('L')
+                ref_image = np.asarray(ref_image)
+                ref_mask = np.asarray(ref_mask)
+                ref_mask = np.where(ref_mask > 128, 1, 0).astype(np.uint8)
+                data = self.data_preprocess_inpaint(tar_image,
+                                                    tar_mask,
+                                                    ref_image,
+                                                    ref_mask,
+                                                    False,
+                                                    1.3,
+                                                    output_height,
+                                                    output_width)
+            elif task == 'Text_Subject_Guided_Inpainting':
+                ref_image = subject_image['image'].convert('RGB')
+                ref_mask = subject_image['mask'].convert('L')
+                ref_image = np.asarray(ref_image)
+                ref_mask = np.asarray(ref_mask)
+                ref_mask = np.where(ref_mask > 128, 1, 0).astype(np.uint8)
+                data = self.data_preprocess_inpaint(tar_image,
+                                                    tar_mask,
+                                                    ref_image,
+                                                    ref_mask,
+                                                    True,
+                                                    1.3,
+                                                    output_height,
+                                                    output_width)
+            elif task == 'Text_Guided_Outpainting':
+                data = self.data_preprocess_outpaint(tar_image,
+                                                     out_direction,
+                                                     image_ratio,
+                                                     output_height,
+                                                     output_width)
+
+            return *data, gr.update(value='Data Process Succeed!', visible=True)
+
+        self.proc_button.click(data_process,
+                               inputs=[
+                                   self.scene_image,
+                                   self.subject_image,
+                                   self.task,
+                                   self.image_ratio,
+                                   self.out_direction,
+                                   diffusion_ui.output_height,
+                                   diffusion_ui.output_width
+                               ],
+                               outputs=[
+                                   self.tar_image,
+                                   self.tar_mask,
+                                   self.masked_image,
+                                   self.ref_image,
+                                   self.ref_mask,
+                                   self.ref_clip,
+                                   self.base_image,
+                                   self.extra_sizes,
+                                   self.bbox_yyxx,
+                                   self.proc_status,
+                                ])
+
+    def data_preprocess_inpaint(self,
+                                tar_image,
+                                tar_mask,
+                                ref_image,
+                                ref_mask,
+                                use_rectangle_mask,
+                                tar_crop_ratio,
+                                output_height,
+                                output_width):
+        tar_mask = np.expand_dims(tar_mask, 2).astype(np.float32)
+
+        # Zoom-In
+        tar_yyxx = get_bbox_from_mask(tar_mask)
+        tar_yyxx_crop = expand_bbox(tar_mask, tar_yyxx, ratio=tar_crop_ratio)
+        tar_yyxx_crop = box2squre(tar_mask, tar_yyxx_crop)
+        y1, y2, x1, x2 = tar_yyxx_crop
+        crop_tar_image = tar_image[y1:y2, x1:x2, :]
+        crop_tar_mask = tar_mask[y1:y2, x1:x2, :]
+
+        # 得到pad之前的HW
+        H1, W1 = crop_tar_image.shape[:2]
+
+        # 对tar_mask进行一些预处理
+        if use_rectangle_mask:
+            tar_bbox_yyxx = get_bbox_from_mask(crop_tar_mask)
+            y1, y2, x1, x2 = tar_bbox_yyxx
+            crop_tar_mask[y1:y2, x1:x2] = 1
+
+        crop_tar_image, pad1, pad2 = pad_to_square(crop_tar_image.astype(np.uint8), pad_value=0)
+        crop_tar_mask, _, _ = pad_to_square(crop_tar_mask, pad_value=0)
+        # 得到pad之后的HW
+        H2, W2 = crop_tar_image.shape[:2]
+
+        aug_tar_image = cv2.resize(crop_tar_image.astype(np.uint8), (output_width, output_height))
+        aug_tar_mask = cv2.resize(crop_tar_mask, (output_width, output_height))
+
+        final_tar_image = TF.to_tensor(aug_tar_image)
+        final_tar_image = TF.normalize(final_tar_image, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+        final_tar_mask = TF.to_tensor((aug_tar_mask > 0.5).astype(np.float32))
+
+        masked_image = final_tar_image.clone()
+        masked_image = masked_image * (1 - final_tar_mask)
+
+        final_tar_image = final_tar_image.unsqueeze(0)
+        final_tar_mask = final_tar_mask.unsqueeze(0)
+        masked_image = masked_image.unsqueeze(0)
+
+        if ref_image is not None and ref_mask is not None:
+            ref_mask = np.expand_dims(ref_mask, 2).astype(np.float32)
+            # background-free
+            ref_image = ref_image * ref_mask + np.ones_like(ref_image) * 255. * (1 - ref_mask)
+
+            ref_yyxx = get_bbox_from_mask(ref_mask)
+            y1, y2, x1, x2 = ref_yyxx
+
+            crop_ref_image_i = ref_image[y1:y2, x1:x2, :]
+            crop_ref_mask_i = ref_mask[y1:y2, x1:x2, :]
+
+            # 通过ref_expand_ratio这个值pad subject image, 从而调整clip输入图像中物体的大小
+            # ref_expand_ratio越大，对应物体越小
+            h, w = crop_ref_mask_i.shape[:2]
+            ref_expand_size = int(max(h, w) * 1.02)
+            pad_op = A.PadIfNeeded(ref_expand_size, ref_expand_size,
+                                   border_mode=cv2.BORDER_CONSTANT,
+                                   value=(255, 255, 255), mask_value=0)
+            out = pad_op(image=crop_ref_image_i, mask=crop_ref_mask_i)
+            crop_ref_image = out['image']
+
+            to_clip_input = T.Compose([
+                T.ToTensor(),
+                T.Resize((224, 224)),
+                T.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711)),
+            ])
+            ref_clip = to_clip_input(crop_ref_image.astype(np.uint8))
+
+            output_size = max(output_height, output_width)
+            ref_resize_op = A.Compose([
+                A.LongestMaxSize(output_size),
+                A.PadIfNeeded(output_size, output_size,
+                              border_mode=cv2.BORDER_CONSTANT,
+                              value=(255, 255, 255), mask_value=0),
+            ])
+            aug_out = ref_resize_op(image=crop_ref_image_i.astype(np.uint8), mask=crop_ref_mask_i)
+            aug_ref_image = aug_out['image']
+            aug_ref_mask = aug_out['mask']
+
+            final_ref_image = TF.to_tensor(aug_ref_image)
+            final_ref_image = TF.normalize(final_ref_image, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+            final_ref_mask = TF.to_tensor(aug_ref_mask)
+
+            final_ref_image = final_ref_image.unsqueeze(0)
+            final_ref_mask = final_ref_mask.unsqueeze(0)
+            ref_clip = ref_clip.unsqueeze(0)
+        else:
+            final_ref_image = None
+            final_ref_mask = None
+            ref_clip = None
+
+        return final_tar_image, final_tar_mask, masked_image, final_ref_image, final_ref_mask, ref_clip, \
+            TF.to_tensor(tar_image), torch.LongTensor([H1, W1, H2, W2, pad1, pad2]), torch.LongTensor(tar_yyxx_crop)
+
+    def data_preprocess_outpaint(self,
+                                 tar_image,
+                                 direction,
+                                 img_ratio,
+                                 output_height,
+                                 output_width):
+        oh, ow = output_height, output_width
+        h, w = tar_image.shape[:2]
+        ratio = max(h/(oh*img_ratio), w/(ow*img_ratio))
+
+        ih, iw = int(h / ratio), int(w / ratio)
+
+        masked_image = np.zeros((oh, ow, 3), dtype=np.uint8)
+        mask = np.zeros((oh, ow, 1))
+
+        if direction in ['CenterAround', '中心向外']:
+            y1, x1 = (oh-ih)//2, (ow-iw)//2
+        elif direction in ['RightDown', '右下']:
+            y1, x1 = 0, 0
+        elif direction in ['LeftDown', '左下']:
+            y1, x1 = 0, ow-iw
+        elif direction in ['RightUp', '右上']:
+            y1, x1 = oh-ih, 0
+        elif direction in ['LeftUp', '左上']:
+            y1, x1 = oh-ih, ow-iw
+        else:
+            y1, x1 = 0, 0
+
+        tar_image = cv2.resize(tar_image.astype(np.uint8), (iw, ih))
+        masked_image[y1:y1+ih, x1:x1+iw] = tar_image
+        mask[y1+5:y1+ih-5, x1+5:x1+iw-5] = 1
+
+        final_tar_image = TF.to_tensor(masked_image.astype(np.uint8))
+        final_tar_image = TF.normalize(final_tar_image, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+        final_tar_mask = TF.to_tensor(((1.0-mask) > 0.5).astype(np.float32))
+
+        masked_image = final_tar_image.clone()
+        masked_image = masked_image * (1 - final_tar_mask)
+
+        final_tar_image = final_tar_image.unsqueeze(0)
+        final_tar_mask = final_tar_mask.unsqueeze(0)
+        masked_image = masked_image.unsqueeze(0)
+
+        return final_tar_image, final_tar_mask, masked_image, None, None, None, None, None, None

scepter/studio/inference/inference_ui/mantra_ui.py

@@ -47,14 +47,15 @@ class MantraUI(UIBase):
 
     def create_ui(self, *args, **kwargs):
         self.state = gr.State(value=False)
-        with gr.Column(visible=False) as self.tab:
-            with gr.Row():
+        with gr.Column(equal_height=True, visible=False) as self.tab:
+            with gr.Row(scale=1):
                 with gr.Column(scale=1):
                     with gr.Group(visible=True):
                         with gr.Row(equal_height=True):
                             self.style = gr.Dropdown(
                                 label=self.component_names.mantra_styles,
-                                choices=self.all_styles[self.default_pipeline],
+                                choices=self.all_styles.get(
+                                    self.default_pipeline, []),
                                 value=None,
                                 multiselect=True,
                                 interactive=True)

scepter/studio/inference/inference_ui/model_manage_ui.py

@@ -146,11 +146,20 @@ class ModelManageUI(UIBase):
                     continue
                 model_name = f"{now_pipeline}_{module['name']}"
                 all_module_name[module_name] = model_name
+            tunner_choices = []
             if now_pipeline in self.default_choices['tuners']:
                 tunner_choices = self.default_choices['tuners'][now_pipeline][
                     'choices']
-            else:
-                tunner_choices = []
+            custom_tunner_choices = []
+            custom_tunner_default = []
+            if now_pipeline in self.default_choices.get(
+                    'customized_tuners', []):
+                custom_tunner_choices = self.default_choices[
+                    'customized_tuners'][now_pipeline]['choices']
+                custom_tunner_default = self.default_choices[
+                    'customized_tuners'][now_pipeline]['default']
+                if isinstance(custom_tunner_default, str):
+                    custom_tunner_default = [custom_tunner_default]
 
             if now_pipeline in self.default_choices[
                     'controllers'] and control_mode in self.default_choices[
@@ -179,9 +188,11 @@ class ModelManageUI(UIBase):
                 gr.Dropdown(value=all_module_name['first_stage_model']),
                 gr.Dropdown(value=all_module_name['cond_stage_model']),
                 gr.Dropdown(choices=tunner_choices, value=[]),
+                gr.Dropdown(choices=custom_tunner_choices,
+                            value=custom_tunner_default),
                 gr.Dropdown(choices=controller_choices,
                             value=controller_default),
-                gr.Dropdown(choices=mantra_ui.all_styles[now_pipeline],
+                gr.Dropdown(choices=mantra_ui.all_styles.get(now_pipeline, []),
                             value=[]),
                 gr.Textbox(choices=cur_paras.NEGATIVE_PROMPT.get('VALUES', []),
                            value=cur_paras.NEGATIVE_PROMPT.get('DEFAULT', '')),
@@ -206,10 +217,11 @@ class ModelManageUI(UIBase):
             outputs=[
                 self.diffusion_state, self.first_stage_model,
                 self.cond_stage_model, tuner_ui.tuner_model,
-                control_ui.control_model, mantra_ui.style,
-                diffusion_ui.negative_prompt, diffusion_ui.prompt_prefix,
-                diffusion_ui.output_height, diffusion_ui.sampler,
-                diffusion_ui.discretization, diffusion_ui.sample_steps,
-                diffusion_ui.guide_scale, diffusion_ui.guide_rescale
+                tuner_ui.custom_tuner_model, control_ui.control_model,
+                mantra_ui.style, diffusion_ui.negative_prompt,
+                diffusion_ui.prompt_prefix, diffusion_ui.output_height,
+                diffusion_ui.sampler, diffusion_ui.discretization,
+                diffusion_ui.sample_steps, diffusion_ui.guide_scale,
+                diffusion_ui.guide_rescale
             ],
             queue=True)

scepter/studio/inference/inference_ui/tuner_ui.py

@@ -21,17 +21,21 @@ class TunerUI(UIBase):
             'default']
         self.default_pipeline = pipe_manager.model_level_info[
             default_diffusion_model]['pipeline'][0]
+        self.tunner_choices = []
         if self.default_pipeline in self.default_choices['tuners']:
             self.tunner_choices = self.default_choices['tuners'][
                 self.default_pipeline]['choices']
             self.tunner_default = self.default_choices['tuners'][
                 self.default_pipeline]['default']
-        else:
-            self.tunner_choices = []
+        self.custom_tuner_choices = []
+        if self.default_pipeline in self.default_choices.get(
+                'customized_tuners', []):
+            self.custom_tuner_choices = self.default_choices[
+                'customized_tuners'][self.default_pipeline]['choices']
 
         self.tunner_default = None
         self.component_names = TunerUIName(language)
-        self.cfg_tuners = cfg.TUNERS
+        self.cfg_tuners = cfg.TUNERS + cfg.CUSTOM_TUNERS
         self.name_level_tuners = {}
         for one_tuner in tqdm(self.cfg_tuners):
             if one_tuner.BASE_MODEL not in self.name_level_tuners:
@@ -47,8 +51,8 @@ class TunerUI(UIBase):
 
     def create_ui(self, *args, **kwargs):
         self.state = gr.State(value=False)
-        with gr.Column(visible=False) as self.tab:
-            with gr.Row():
+        with gr.Column(equal_height=True, visible=False) as self.tab:
+            with gr.Row(scale=1):
                 with gr.Column(variant='panel', scale=1, min_width=0):
                     with gr.Group(visible=True):
                         with gr.Row(equal_height=True):
@@ -63,10 +67,16 @@ class TunerUI(UIBase):
                                 self.custom_tuner_model = gr.Dropdown(
                                     label=self.component_names.
                                     custom_tuner_model,
-                                    choices=[],
+                                    choices=self.custom_tuner_choices,
                                     value=None,
                                     multiselect=True,
                                     interactive=True)
+                                self.save_button = gr.Button(
+                                    label=self.component_names.save_button,
+                                    value=self.component_names.save_button,
+                                    elem_classes='type_row',
+                                    elem_id='save_button',
+                                    visible=True)
                         with gr.Row(equal_height=True):
                             with gr.Column(scale=1):
                                 self.tuner_type = gr.Text(
@@ -110,6 +120,7 @@ class TunerUI(UIBase):
                 label=self.component_names.example_block_name, open=True)
 
     def set_callbacks(self, model_manage_ui, **kwargs):
+        manager = kwargs.pop('manager')
         gallery_ui = kwargs.pop('gallery_ui')
         with self.example_block:
             gr.Examples(examples=self.component_names.examples,
@@ -121,7 +132,7 @@ class TunerUI(UIBase):
             now_pipeline = diffusion_model_info['pipeline'][0]
             tuner_info = {}
             if tuner_model is not None and len(tuner_model) > 0:
-                tuner_info = self.name_level_tuners[now_pipeline].get(
+                tuner_info = self.name_level_tuners.get(now_pipeline, {}).get(
                     tuner_model[-1], {})
             if tuner_info.get(
                     'IMAGE_PATH',
@@ -141,3 +152,45 @@ class TunerUI(UIBase):
                 self.tuner_example, self.tuner_prompt_example
             ],
             queue=False)
+
+        self.custom_tuner_model.change(
+            tuner_model_change,
+            inputs=[self.custom_tuner_model, model_manage_ui.diffusion_model],
+            outputs=[
+                self.tuner_type, self.base_model, self.tuner_desc,
+                self.tuner_example, self.tuner_prompt_example
+            ],
+            queue=False)
+
+        def save_customized_tuner(tuner_model, diffusion_model):
+            diffusion_model_info = self.pipe_manager.model_level_info[
+                diffusion_model]
+            now_pipeline = diffusion_model_info['pipeline'][0]
+            tuner_info = {}
+            if tuner_model is not None and len(tuner_model) > 0:
+                tuner_info = self.name_level_tuners.get(now_pipeline, {}).get(
+                    tuner_model[-1], {})
+            if tuner_info.get(
+                    'IMAGE_PATH',
+                    None) and not os.path.exists(tuner_info.IMAGE_PATH):
+                tuner_info.IMAGE_PATH = FS.get_from(tuner_info.IMAGE_PATH)
+            return (gr.Tabs(selected='tuner_manager'),
+                    gr.Text(value=tuner_info.NAME), gr.Text(value=''),
+                    gr.Text(value=tuner_info.get('TUNER_TYPE', '')),
+                    gr.Text(value=tuner_info.get('BASE_MODEL', '')),
+                    gr.Text(value=tuner_info.get('DESCRIPTION', '')),
+                    gr.Image(value=tuner_info.get('IMAGE_PATH', None)),
+                    gr.Text(value=tuner_info.get('PROMPT_EXAMPLE', '')))
+
+        self.save_button.click(
+            save_customized_tuner,
+            inputs=[self.custom_tuner_model, model_manage_ui.diffusion_model],
+            outputs=[
+                manager.tabs, manager.tuner_manager.info_ui.tuner_name,
+                manager.tuner_manager.info_ui.new_name,
+                manager.tuner_manager.info_ui.tuner_type,
+                manager.tuner_manager.info_ui.base_model,
+                manager.tuner_manager.info_ui.tuner_desc,
+                manager.tuner_manager.info_ui.tuner_example,
+                manager.tuner_manager.info_ui.tuner_prompt_example
+            ])

scepter/studio/preprocess/caption_editor_ui/create_dataset_ui.py

@@ -24,7 +24,6 @@ refresh_symbol = '\U0001f504'  # 🔄
 class CreateDatasetUI(UIBase):
     def __init__(self, cfg, is_debug=False, language='en'):
         self.work_dir = cfg.WORK_DIR
-        self.dir_list = FS.walk_dir(self.work_dir, recurse=False)
         self.cache_file = {}
         self.meta_dict = {}
         self.dataset_list = self.load_history()
@@ -71,7 +70,8 @@ class CreateDatasetUI(UIBase):
             json.dump(save_meta, open(local_path, 'w'))
         return meta_file
 
-    def construct_meta(self, cursor, file_list, dataset_folder, user_name):
+    def construct_meta(self, cursor, file_list, dataset_folder, user_name,
+                       login_user_name):
         '''
             {
                 "dataset_name": "xxxx",
@@ -86,11 +86,17 @@ class CreateDatasetUI(UIBase):
         save_file_list = os.path.join(dataset_folder, 'file.csv')
         save_file_list = self.write_file_list(file_list, save_file_list)
         meta = {
-            'dataset_name': user_name,
-            'cursor': cursor,
-            'file_list': file_list,
-            'train_csv': train_csv,
-            'save_file_list': save_file_list
+            'dataset_name':
+            user_name if login_user_name == '' or login_user_name is None else
+            '_'.join([login_user_name, user_name]),
+            'cursor':
+            cursor,
+            'file_list':
+            file_list,
+            'train_csv':
+            train_csv,
+            'save_file_list':
+            save_file_list
         }
         self.save_meta(meta, dataset_folder)
         return meta
@@ -180,7 +186,7 @@ class CreateDatasetUI(UIBase):
         file_folder = None
         train_list = None
         hit_dir = None
-        raw_list = []
+        raw_list = {}
         mac_osx = os.path.join(local_dataset_folder, '__MACOSX')
         if os.path.exists(mac_osx):
             res = os.popen(f"rm -rf '{mac_osx}'")
@@ -191,28 +197,45 @@ class CreateDatasetUI(UIBase):
                 res = res.readlines()
                 continue
             if FS.isdir(one_dir):
-                sub_dir = FS.walk_dir(one_dir)
-                for one_s_dir in sub_dir:
-                    if FS.isdir(one_s_dir) and one_s_dir.split(
-                            one_dir)[1].replace('/', '') == 'images':
-                        file_folder = one_s_dir
-                        hit_dir = one_dir
-                    if FS.isfile(one_s_dir) and one_s_dir.split(
-                            one_dir)[1].replace('/', '') == 'train.csv':
-                        train_list = one_s_dir
-                    if file_folder is not None and train_list is not None:
-                        break
-                    if (one_s_dir.endswith('.jpg')
-                            or one_s_dir.endswith('.jpeg')
-                            or one_s_dir.endswith('.png')
-                            or one_s_dir.endswith('.webp')):
-                        raw_list.append(one_s_dir)
+                if one_dir.endswith('images') or one_dir.endswith('images/'):
+                    file_folder = one_dir
+                    hit_dir = one_dir
+                else:
+                    sub_dir = FS.walk_dir(one_dir)
+                    for one_s_dir in sub_dir:
+                        if FS.isdir(one_s_dir) and one_s_dir.split(
+                                one_dir)[1].replace('/', '') == 'images':
+                            file_folder = one_s_dir
+                            hit_dir = one_dir
+                        if FS.isfile(one_s_dir) and one_s_dir.split(
+                                one_dir)[1].replace('/', '') == 'train.csv':
+                            train_list = one_s_dir
+                        if file_folder is not None and train_list is not None:
+                            break
+                        if (one_s_dir.endswith('.jpg')
+                                or one_s_dir.endswith('.jpeg')
+                                or one_s_dir.endswith('.png')
+                                or one_s_dir.endswith('.webp')):
+                            file_name, surfix = os.path.splitext(one_s_dir)
+                            txt_file = file_name + '.txt'
+                            if os.path.exists(txt_file):
+                                raw_list[one_s_dir] = txt_file
+                            else:
+                                raw_list[one_s_dir] = None
+            elif one_dir.endswith('train.csv'):
+                train_list = one_dir
             else:
                 if (one_dir.endswith('.jpg') or one_dir.endswith('.jpeg')
                         or one_dir.endswith('.png')
                         or one_dir.endswith('.webp')):
-                    raw_list.append(one_dir)
-
+                    file_name, surfix = os.path.splitext(one_dir)
+                    txt_file = file_name + '.txt'
+                    if os.path.exists(txt_file):
+                        raw_list[one_dir] = txt_file
+                    else:
+                        raw_list[one_dir] = None
+            if file_folder is not None and train_list is not None:
+                break
         if file_folder is None and len(raw_list) < 1:
             raise gr.Error(
                 "images folder or train.csv doesn't exists, or nothing exists in your zip"
@@ -222,17 +245,21 @@ class CreateDatasetUI(UIBase):
         if file_folder is not None:
             _ = FS.get_dir_to_local_dir(file_folder, new_file_folder)
         elif len(raw_list) > 0:
-            raw_list = list(set(raw_list))
+            raw_list = [[k, v] for k, v in raw_list.items()]
             for img_id, cur_image in enumerate(raw_list):
-                _, surfix = os.path.splitext(cur_image)
+                image_name, surfix = os.path.splitext(cur_image[0])
+                if cur_image[1] is not None and os.path.exists(cur_image[1]):
+                    prompt = open(cur_image[1], 'r').read()
+                else:
+                    prompt = image_name.split('/')[-1]
                 try:
                     os.rename(
-                        os.path.abspath(cur_image),
-                        f'{new_file_folder}/{get_md5(cur_image)}{surfix}')
+                        os.path.abspath(cur_image[0]),
+                        f'{new_file_folder}/{get_md5(cur_image[0])}{surfix}')
                     raw_list[img_id] = [
                         os.path.join('images',
-                                     f'{get_md5(cur_image)}{surfix}'),
-                        cur_image.split('/')[-1]
+                                     f'{get_md5(cur_image[0])}{surfix}'),
+                        prompt
                     ]
                 except Exception as e:
                     print(e)
@@ -251,13 +278,14 @@ class CreateDatasetUI(UIBase):
             res = res.readlines()
         if not os.path.exists(new_train_list):
             raise gr.Error(f'{str(res)}')
-        try:
-            res = os.popen(f"rm -rf '{hit_dir}/images/*'")
-            _ = res.readlines()
-            res = os.popen(f"rm -rf '{hit_dir}'")
-            _ = res.readlines()
-        except Exception:
-            pass
+        if not file_folder == hit_dir:
+            try:
+                res = os.popen(f"rm -rf '{hit_dir}/images/*'")
+                _ = res.readlines()
+                res = os.popen(f"rm -rf '{hit_dir}'")
+                _ = res.readlines()
+            except Exception:
+                pass
         file_list = self.load_train_csv(new_train_list, data_folder)
         return file_list
 
@@ -317,21 +345,25 @@ class CreateDatasetUI(UIBase):
                 return True, file_path
         return False, file_path
 
-    def load_history(self):
+    def load_history(self, login_user_name=''):
         dataset_list = []
+        self.dir_list = FS.walk_dir(self.work_dir, recurse=False)
         for one_dir in self.dir_list:
             if FS.isdir(one_dir):
                 meta_file = os.path.join(one_dir, 'meta.json')
                 if FS.exists(meta_file):
                     local_dataset_folder, _ = FS.map_to_local(one_dir)
-                    local_dataset_folder = FS.get_dir_to_local_dir(
-                        one_dir, local_dataset_folder, multi_thread=True)
+                    if not FS.exists(
+                            os.path.join(local_dataset_folder, 'meta.json')):
+                        local_dataset_folder = FS.get_dir_to_local_dir(
+                            one_dir, local_dataset_folder, multi_thread=True)
                     meta_data = self.load_meta(
                         os.path.join(local_dataset_folder, 'meta.json'))
                     meta_data['local_work_dir'] = local_dataset_folder
                     meta_data['work_dir'] = one_dir
-                    dataset_list.append(meta_data['dataset_name'])
-                    self.meta_dict[meta_data['dataset_name']] = meta_data
+                    if meta_data['dataset_name'].startswith(login_user_name):
+                        dataset_list.append(meta_data['dataset_name'])
+                        self.meta_dict[meta_data['dataset_name']] = meta_data
         return dataset_list
 
     def create_ui(self):
@@ -396,7 +428,7 @@ class CreateDatasetUI(UIBase):
         self.file_panel = file_panel
         self.modify_panel = modify_panel
 
-    def set_callbacks(self, gallery_dataset, export_dataset):
+    def set_callbacks(self, gallery_dataset, export_dataset, manager):
         def show_dataset_panel():
             return (gr.Column(visible=False), gr.Column(visible=True),
                     gr.Column(visible=True),
@@ -419,17 +451,19 @@ class CreateDatasetUI(UIBase):
                 datetime.datetime.now())
             return data_name
 
-        def refresh():
-            return gr.Dropdown(value=self.dataset_list[-1]
-                               if len(self.dataset_list) > 0 else '',
-                               choices=self.dataset_list)
+        def refresh(login_user_name):
+            dataset_list = self.load_history(login_user_name=login_user_name)
+            return gr.Dropdown(
+                value=dataset_list[-1] if len(dataset_list) > 0 else '',
+                choices=dataset_list)
 
         self.refresh_dataset_name.click(refresh,
+                                        inputs=[manager.user_name],
                                         outputs=[self.dataset_name],
                                         queue=False)
 
         def confirm_create_dataset(user_name, create_mode, file_url, file_path,
-                                   panel_state):
+                                   panel_state, login_user_name):
             if user_name.strip() == '' or ' ' in user_name or '/' in user_name:
                 raise gr.Error(self.components_name.illegal_data_name_err1)
 
@@ -442,7 +476,9 @@ class CreateDatasetUI(UIBase):
             if not file_url.strip() == '' and file_path is not None:
                 raise gr.Error(self.components_name.illegal_data_name_err4)
             if create_mode == 3 and not file_url.strip() == '':
-                file_name, surfix = os.path.splitext(file_url.split('?')[0])
+                if 'oss' in file_url:
+                    file_url = file_url.split('?')[0]
+                file_name, surfix = os.path.splitext(file_url)
                 save_file = os.path.join(self.work_dir, f'{user_name}{surfix}')
                 local_path, _ = FS.map_to_local(save_file)
                 res = os.popen(f"wget -c '{file_url}' -O '{local_path}'")
@@ -490,7 +526,7 @@ class CreateDatasetUI(UIBase):
 
             cursor = 0 if len(file_list) > 0 else -1
             meta = self.construct_meta(cursor, file_list, dataset_folder,
-                                       user_name)
+                                       user_name, login_user_name)
 
             meta['local_work_dir'] = local_dataset_folder
             meta['work_dir'] = dataset_folder
@@ -498,13 +534,13 @@ class CreateDatasetUI(UIBase):
             self.meta_dict[meta['dataset_name']] = meta
             if meta['dataset_name'] not in self.dataset_list:
                 self.dataset_list.append(meta['dataset_name'])
-            return (
-                gr.Checkbox(value=True, visible=False),
-                gr.Dropdown(value=user_name, choices=self.dataset_list),
-            )
+            return (gr.Checkbox(value=True, visible=False),
+                    gr.Dropdown(value=meta['dataset_name'],
+                                choices=self.dataset_list),
+                    gr.Text(value=meta['dataset_name']))
 
         def clear_file():
-            return gr.Text(visible=True)
+            return gr.Text(visible=False)
 
         # Click Create
         self.btn_create_datasets.click(show_dataset_panel, [], [
@@ -545,9 +581,9 @@ class CreateDatasetUI(UIBase):
         # Click Confirm
         self.confirm_data_button.click(confirm_create_dataset, [
             self.user_data_name, self.create_mode, self.file_path_url,
-            self.file_path, self.panel_state
-        ], [self.panel_state, self.dataset_name],
-                                       queue=True)
+            self.file_path, self.panel_state, manager.user_name
+        ], [self.panel_state, self.dataset_name, self.user_data_name],
+                                       queue=False)
 
         def show_edit_panel(panel_state, data_name):
             if panel_state:
@@ -568,7 +604,7 @@ class CreateDatasetUI(UIBase):
             ],
             queue=False)
 
-        def modify_data_name(user_name, prev_data_name):
+        def modify_data_name(user_name, prev_data_name, login_user_name):
             print(
                 f'Current file name {prev_data_name}, new file name {user_name}.'
             )
@@ -600,7 +636,8 @@ class CreateDatasetUI(UIBase):
                         raise gr.Error(self.components_name.illegal_data_err3)
                     cursor = ori_meta['cursor']
                     meta = self.construct_meta(cursor, file_list,
-                                               dataset_folder, user_name)
+                                               dataset_folder, user_name,
+                                               login_user_name)
                     meta['local_work_dir'] = local_dataset_folder
                     meta['work_dir'] = dataset_folder
 
@@ -624,7 +661,10 @@ class CreateDatasetUI(UIBase):
 
         self.modify_data_button.click(
             modify_data_name,
-            inputs=[self.user_data_name, self.user_data_name_state],
+            inputs=[
+                self.user_data_name, self.user_data_name_state,
+                manager.user_name
+            ],
             outputs=[self.user_data_name_state, self.dataset_name],
             queue=False)
 
@@ -647,3 +687,14 @@ class CreateDatasetUI(UIBase):
                                      gallery_dataset.gallery_state
                                  ],
                                  queue=False)
+
+        def login_user_name_change(login_user_name):
+            dataset_list = self.load_history(login_user_name=login_user_name)
+            return gr.Dropdown(
+                value=dataset_list[-1] if len(dataset_list) > 0 else '',
+                choices=dataset_list)
+
+        manager.user_name.change(login_user_name_change,
+                                 inputs=[manager.user_name],
+                                 outputs=[self.dataset_name],
+                                 queue=False)

scepter/studio/preprocess/preprocess.py

@@ -46,7 +46,7 @@ class PreprocessUI():
 
     def set_callbacks(self, manager):
         self.create_dataset.set_callbacks(self.dataset_gallery,
-                                          self.export_dataset)
+                                          self.export_dataset, manager)
         self.dataset_gallery.set_callbacks(self.create_dataset)
         self.export_dataset.set_callbacks(self.create_dataset, manager)
 

scepter/studio/self_train/scripts/run_task.py

@@ -8,6 +8,7 @@ import numpy as np
 import torch
 
 from scepter.modules.solver.hooks.checkpoint import CheckpointHook
+from scepter.modules.solver.hooks.data_probe import ProbeDataHook
 from scepter.modules.solver.registry import SOLVERS
 from scepter.modules.utils.config import Config
 from scepter.modules.utils.distribute import we
@@ -27,6 +28,7 @@ def run_task(cfg):
         solver.set_up_pre()
         solver.set_up()
         ori_steps = solver.max_steps
+
         if 'train' in solver.datas:
             dataset = solver.datas['train'].dataset
             if hasattr(dataset, 'real_number'):
@@ -48,6 +50,19 @@ def run_task(cfg):
                                 f'checkpoint save interval is changed from {ori_interval} '
                                 f'to {hook.interval} according to the setting epoches '
                                 f'interval {ori_interval}')
+
+                if 'eval' in solver.hooks_dict:
+                    for hook in solver.hooks_dict['eval']:
+                        if isinstance(hook, ProbeDataHook):
+                            ori_interval = hook.prob_interval
+                            hook.prob_interval = int(hook.prob_interval *
+                                                     solver.max_steps /
+                                                     cfg.SOLVER.MAX_EPOCHS)
+                            std_logger.info(
+                                f'prob interval is changed from {ori_interval} '
+                                f'to {hook.prob_interval} according to the setting epoches '
+                                f'interval {ori_interval}')
+                        solver.eval_interval = hook.prob_interval
             # size 为无限的时候，使用默认值。
         solver.solve()
 

scepter/studio/self_train/scripts/sleep.py

@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+import time
+
+for i in range(180):
+    time.sleep(1)
+    print('sleep', i)

scepter/studio/self_train/scripts/trainer.py

@@ -0,0 +1,336 @@
+# -*- coding: utf-8 -*-
+import json
+import os
+import subprocess
+import threading
+import time
+
+import psutil
+import torch
+
+from scepter.modules.utils.file_system import FS
+from scepter.modules.utils.logger import as_time
+
+
+class TaskStatus():
+    def __init__(self):
+        self.pid = -1
+        self.retcode = -999
+        self.error_msg = None
+        self.out_msg = None
+
+    def __repr__(self):
+        return f'Process {self.pid} retcode {self.retcode}, error msg: {self.error_msg}.'
+
+
+def kill_job(pid):
+    try:
+        current_process = psutil.Process(pid)
+        children = current_process.children(recursive=True)
+        for child in children:
+            child.terminate()
+        current_process.terminate()
+    except Exception:
+        try:
+            os.system(f'kill -9 {pid}')
+        except Exception:
+            pass
+
+
+class Trainer():
+    def __init__(self, run_script, status_message):
+        self.run_script = run_script
+        self.status_message = status_message
+        self.proc = None
+
+    def __call__(self, task_name):
+        torch.cuda.empty_cache()
+        cmd = f'PYTHONPATH=. python {self.run_script} ' \
+              f'--cfg={task_name}/train.yaml'
+        # cmd = [f"python {self.run_script}"]
+        print(cmd)
+        try:
+            # self.proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+            self.proc = subprocess.Popen(cmd, shell=True)
+            self.status_message.pid = self.proc.pid
+            self.status_message.retcode = self.proc.wait(
+            )  # self.proc.wait(3600*24*14)
+            # self.status_message.error_msg = self.proc.stderr.read()
+            self.status_message.error_msg = ''
+        except Exception:
+            self.status_message.retcode = -2
+            # self.status_message.error_msg = self.proc.stderr.read()
+            self.status_message.error_msg = ''
+
+    def terminate(self):
+        print(f'Terminate {self.proc.pid} ...')
+        kill_job(self.proc.pid)
+        self.proc.terminate()
+        print(f'Terminate {self.proc.pid} success.')
+
+
+class TrainManager():
+    '''
+        Manage all the submitted training tasks and control the status of tasks.
+        When instance this class, we should start the threading, which used to get the task name
+        and start the training task.
+    '''
+    def __init__(self, run_script, work_dir):
+        self.task_queue = []
+        self.runing_tasks = {}
+        self.run_script = run_script
+        self.work_dir = work_dir
+
+        def task_dispatch():
+            while True:
+                all_running_tasks = [k for k in self.runing_tasks]
+                for k in all_running_tasks:
+                    now_task = self.runing_tasks[k]
+                    # print(now_task["train_status"].pid, psutil.pid_exists(now_task["train_status"].pid))
+                    if (not psutil.pid_exists(now_task['train_status'].pid) and
+                            not now_task['train_status'].retcode in (-1, 0)):
+                        now_task['train_status'].retcode = -2
+                    # print(now_task["train_status"])
+                    if not now_task['train_status'].retcode == -999:
+                        status_file = os.path.join(self.work_dir, k,
+                                                   'status.json')
+                        if FS.exists(status_file):
+                            with FS.get_from(status_file) as local_status:
+                                task_status = json.load(open(
+                                    local_status, 'r'))
+                            code = now_task['train_status'].retcode
+                            task_status['code'] = code
+                            task_status['end_time'] = time.time()
+                            duration = task_status['end_time'] - task_status[
+                                'start_time']
+                            if code == 0:
+                                message = f'''
+                                            Training completed! \n
+                                            Save in [ {k} ] \n
+                                            Detail log please export the log. \n
+                                            Take time [ {duration:.4f}s ] \n
+                                            {self.check_memory()}
+                                        '''
+                                task_status['msg'] = message
+                                task_status['status'] = 'success'
+                            else:
+                                err_msg = now_task['train_status'].error_msg
+                                message = f'''
+                                            Training failed! \n
+                                            Error msg: {err_msg[-1000:]} \n
+                                            Take time [ {duration:.4f}s ] \n
+                                            {self.check_memory()}
+                                        '''
+                                task_status['msg'] = message
+                                task_status['status'] = 'failed'
+                            with FS.put_to(status_file) as local_path:
+                                json.dump(task_status,
+                                          open(local_path, 'w'),
+                                          ensure_ascii=False)
+                            train_ins = now_task['train_ins']
+                            train_ins.terminate()
+                            kill_job(now_task['train_status'].pid)
+                        self.runing_tasks.pop(k)
+                # print(len(self.task_queue))
+                if len(self.task_queue) > 0 and len(self.runing_tasks) == 0:
+                    task_name = self.task_queue.pop(0)
+                    print(f'start task {task_name}')
+                    status_message = TaskStatus()
+                    train_ins = Trainer(self.run_script, status_message)
+                    train_thread = threading.Thread(target=train_ins,
+                                                    args=(os.path.join(
+                                                        self.work_dir,
+                                                        task_name), ))
+                    train_thread.start()
+                    time.sleep(10)
+                    self.runing_tasks[task_name] = {
+                        'train_ins': train_ins,
+                        'train_status': status_message
+                    }
+                    status_file = os.path.join(self.work_dir, task_name,
+                                               'status.json')
+                    with FS.get_from(status_file) as local_status:
+                        task_status = json.load(open(local_status, 'r'))
+                    task_status['status'] = 'running'
+                    for _ in range(10):
+                        if status_message.pid > 0:
+                            task_status['pid'] = status_message.pid
+                            break
+                        time.sleep(10)
+                    task_status['update_time'] = time.time()
+                    with FS.put_to(status_file) as local_path:
+                        json.dump(task_status,
+                                  open(local_path, 'w'),
+                                  ensure_ascii=False)
+                time.sleep(5)
+
+        self.task_manage = threading.Thread(target=task_dispatch, daemon=True)
+        self.task_manage.start()
+
+    def check_memory(self):
+        # Check Cuda Memory
+        mem_msg = ''
+        if torch.cuda.is_available():
+            for device_id in range(torch.cuda.device_count()):
+                free_mem, total_mem = torch.cuda.mem_get_info(device_id)
+                free_mem = free_mem / (1024**3)
+                total_mem = total_mem / (1024**3)
+                mem_msg += f'GPU {device_id}: free mem {free_mem:.3f}G, total mem {total_mem:.3f}G \n'
+        else:
+            mem_msg += 'GPU is not available!'
+        return mem_msg
+
+    def get_status(self, task_name):
+        if task_name is None:
+            return ''
+        status_file = os.path.join(self.work_dir, task_name, 'status.json')
+        if not FS.exists(status_file):
+            return ''
+        with FS.get_from(status_file) as local_status:
+            task_status = json.load(open(local_status, 'r'))
+        return task_status['status']
+
+    def get_log(self, task_name):
+        if task_name is None:
+            return ''
+        status_file = os.path.join(self.work_dir, task_name, 'status.json')
+        if not FS.exists(status_file):
+            return ''
+        with FS.get_from(status_file) as local_status:
+            task_status = json.load(open(local_status, 'r'))
+        time.sleep(2)
+        log_msg = ''
+        if task_status['status'] == 'queue':
+            start_time = task_status['start_time']
+            if task_name in self.task_queue:
+                log_msg += f'Task status: Queuing. Still have {self.task_queue.index(task_name) + 1} tasks.\n'
+            else:
+                log_msg += 'Task status: Queuing. Still have 0 tasks.\n'
+            log_msg += f'Have waited for {as_time(time.time() - start_time)}\n\n'
+            log_msg += f'Memory status: {self.check_memory()}\n'
+        elif task_status['status'] == 'running':
+            start_time = task_status['start_time']
+            update_time = task_status['update_time']
+            log_msg += (
+                f'Task status: Running. Have run for {as_time(time.time() - update_time)} after waiting'
+                f'for {as_time(update_time - start_time)}.\n\n')
+            std_log = os.path.join(self.work_dir, task_name, 'std_log.txt')
+            out_log = os.path.join(self.work_dir, task_name,
+                                   'output_std_log.txt')
+            output_msg = []
+            if os.path.exists(std_log):
+                fp_w = open(out_log, 'w')
+                output_msg.append(f'Model {task_name} Start Training....')
+                with open(std_log, 'r') as f:
+                    for line in f:
+                        line = line.strip()
+                        if line == '':
+                            continue
+                        if 'OSS_AK' in line or 'OSS_AK'.lower() in line:
+                            continue
+                        if 'OSS_SK' in line or 'OSS_SK'.lower() in line:
+                            continue
+                        if 'Restored' in line or 'Restored'.lower() in line:
+                            continue
+                        # if 'Stage' in line:
+                        output_msg.append(line)
+                        fp_w.write(f'{line}\n')
+                fp_w.close()
+            else:
+                fp_w = open(out_log, 'w')
+                fp_w.close()
+            log_msg += f'Memory status: {self.check_memory()}\n\n'
+            log_msg += 'Recent output as follows:\n\n'
+            log_msg += '\n\n'.join(output_msg)
+        elif task_status['status'] == 'success':
+            start_time = task_status['start_time']
+            update_time = task_status['update_time']
+            end_time = task_status['end_time']
+            output_msg = []
+            out_log = os.path.join(self.work_dir, task_name,
+                                   'output_std_log.txt')
+            std_log = os.path.join(self.work_dir, task_name, 'std_log.txt')
+            if os.path.exists(std_log):
+                fp_w = open(out_log, 'w')
+                with open(std_log, 'r') as f:
+                    for line in f:
+                        line = line.strip()
+                        if line == '':
+                            continue
+                        if 'OSS_AK' in line or 'OSS_AK'.lower() in line:
+                            continue
+                        if 'OSS_SK' in line or 'OSS_SK'.lower() in line:
+                            continue
+                        if 'Restored' in line or 'Restored'.lower() in line:
+                            continue
+                        output_msg.append(line)
+                        fp_w.write(f'{line}\n')
+                fp_w.close()
+            else:
+                fp_w = open(out_log, 'w')
+                fp_w.close()
+            log_msg += (
+                f'Task status: Success. Have run for {as_time(end_time - update_time)} after waiting'
+                f'for {as_time(update_time - start_time)}.\n\n')
+            log_msg += f'Memory status: {self.check_memory()}\n\n'
+            log_msg += '\n\n'.join(output_msg)
+        elif task_status['status'] == 'failed':
+            start_time = task_status['start_time']
+            update_time = task_status['update_time']
+            end_time = task_status['end_time']
+            err_msg = task_status['msg']
+            log_msg += (
+                f'Task status: Failed. Have run for {as_time(end_time - update_time)} after waiting'
+                f'for {as_time(update_time - start_time)}.\n\n')
+            log_msg += f'Memory status: {self.check_memory()}\n\n'
+            log_msg += 'The error msg is as follows:\n\n'
+            log_msg += f'{err_msg}'
+        return log_msg
+
+    def start_task(self, task_name):
+        if task_name is None:
+            return
+        self.task_queue.append(task_name)
+        task_status = {
+            'status': 'queue',
+            'position': len(self.task_queue),
+            'start_time': time.time(),
+            'end_time': time.time()
+        }
+        status_file = os.path.join(self.work_dir, task_name, 'status.json')
+        with FS.put_to(status_file) as local_path:
+            json.dump(task_status, open(local_path, 'w'), ensure_ascii=False)
+
+    def stop_task(self, task_name):
+        if task_name in self.runing_tasks:
+            task_info = self.runing_tasks.pop(task_name)
+            train_status = task_info['train_status']
+            train_ins = task_info['train_ins']
+            kill_job(train_status.pid)
+            train_ins.terminate()
+        elif task_name in self.task_queue:
+            self.task_queue.remove(task_name)
+        else:
+            pass
+        # modify the task's status met
+
+    def __del__(self):
+        for task_name in self.task_queue:
+            status_file = os.path.join(self.work_dir, task_name, 'status.json')
+            with FS.get_from(status_file) as local_status:
+                task_status = json.load(open(local_status, 'r'))
+            task_status['status'] = 'failed'
+            task_status['msg'] = 'Main process has been killed.'
+            with FS.put_to(status_file) as local_path:
+                json.dump(task_status,
+                          open(local_path, 'w'),
+                          ensure_ascii=False)
+
+        pass
+
+
+if __name__ == '__main__':
+    train_ins = TrainManager('', '')
+    for i in range(5):
+        train_ins.start_task(f'{i}')
+    time.sleep(300)

scepter/studio/self_train/self_train.py

@@ -7,7 +7,7 @@ import gradio as gr
 import scepter
 from scepter.modules.utils.config import Config
 from scepter.modules.utils.file_system import FS
-from scepter.studio.self_train.self_train_ui.inference_ui import InferenceUI
+from scepter.studio.self_train.self_train_ui.model_ui import ModelUI
 from scepter.studio.self_train.self_train_ui.trainer_ui import TrainerUI
 from scepter.studio.self_train.utils.config_parser import get_all_config
 from scepter.studio.utils.env import init_env
@@ -34,20 +34,20 @@ class SelfTrainUI():
                                     BASE_CFG_VALUE,
                                     is_debug=is_debug,
                                     language=language)
-        self.inference_ui = InferenceUI(cfg_general,
-                                        BASE_CFG_VALUE,
-                                        is_debug=is_debug,
-                                        language=language)
+        self.model_ui = ModelUI(cfg_general,
+                                BASE_CFG_VALUE,
+                                is_debug=is_debug,
+                                language=language)
 
     def create_ui(self):
         with gr.Row():
             self.trainer_ui.create_ui()
         with gr.Row():
-            self.inference_ui.create_ui()
+            self.model_ui.create_ui()
 
     def set_callbacks(self, manager):
-        self.trainer_ui.set_callbacks(self.inference_ui)
-        self.inference_ui.set_callbacks(self.trainer_ui, manager)
+        self.trainer_ui.set_callbacks(self.model_ui, manager)
+        self.model_ui.set_callbacks(self.trainer_ui, manager)
 
 
 if __name__ == '__main__':

scepter/studio/self_train/self_train_ui/component_names.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 # For dataset manager
-class InferenceUIName():
+class ModelUIName():
     def __init__(self, language='en'):
         if language == 'en':
             self.output_model_block = 'Model Output'
             self.output_model_name = 'Output Model Name'
+            self.output_ckpt_name = 'Output Ckpt Name'
             self.test_prompt = 'Test Prompt'
             self.test_prefix = 'Test Prefix'
             self.test_n_prompt = 'Negative Prompt'
@@ -21,15 +22,23 @@ class InferenceUIName():
             self.extra_model_gbtn = 'Add Model'
             self.refresh_model_gbtn = 'Refresh Model'
             self.go_to_inference = 'Go to inference'
+            self.btn_export_log = 'Export Log'
+            self.export_file = 'Log File'
+            self.log_block = 'Training Log...'
+            self.gallery_block = 'Gallery Log...'
+            self.eval_gallery = 'Eval Gallery'
             # Error or Warning
             self.inference_err1 = 'Inference failed, please try again.'
             self.inference_err2 = 'Test prompt is empty.'
-            self.inference_err3 = "Doesn't surpport this base model"
-            self.inference_err4 = "This model maybe not finish training, because model doesn't exist."
+            self.model_err3 = "Doesn't surpport this base model"
+            self.model_err4 = "This model maybe not finish training, because model doesn't exist."
+            self.model_err5 = "Model {} doesn't exist."
+            self.training_warn1 = 'No log message util now.'
 
         elif language == 'zh':
             self.output_model_block = '模型产出'
-            self.output_model_name = '产出模型名称'
+            self.output_model_name = '产出名称'
+            self.output_ckpt_name = '产出检查点名称'
             self.test_prompt = '测试提示词'
             self.test_prefix = '测试前缀'
             self.test_n_prompt = '负向提示词'
@@ -44,12 +53,20 @@ class InferenceUIName():
             self.extra_model_gtxt = '额外模型'
             self.extra_model_gbtn = '添加模型'
             self.refresh_model_gbtn = '刷新模型'
+            self.btn_export_log = '导出日志'
+            self.export_file = '日志文件'
+            self.log_block = '训练日志...'
+            self.training_button = '开始训练'
+            self.gallery_block = '图像日志...'
+            self.eval_gallery = '评测图像'
             # Error or Warning
             self.inference_err1 = '推理失败，请重试。'
             self.inference_err2 = '测试提示词为空。'
-            self.inference_err3 = '不支持的基础模型'
+            self.model_err3 = '不支持的基础模型'
             self.go_to_inference = '使用模型'
-            self.inference_err4 = '模型可能没有训练完成或者模型不存在'
+            self.model_err4 = '模型可能没有训练完成或者模型不存在'
+            self.model_err5 = '模型{}不存在'
+            self.training_warn1 = '暂时没有日志文件；任务启动中或失败！'
 
 
 class TrainerUIName():
@@ -80,7 +97,8 @@ class TrainerUIName():
             self.base_model = 'Base Model'
             self.tuner_name = 'Fine-tuning Method'
             self.base_model_revision = 'Model Version Number'
-            self.resolution = 'Resolution'
+            self.resolution_height = 'Resolution Height'
+            self.resolution_width = 'Resolution Width'
             self.train_epoch = 'Number of Training Epochs'
             self.learning_rate = 'Learning Rate'
             self.save_interval = 'Save Interval'
@@ -89,9 +107,8 @@ class TrainerUIName():
             self.replace_keywords = 'Trigger Keywords'
             self.work_name = 'Save Model Name (refresh to get a random value)'
             self.push_to_hub = 'Push to hub'
-            self.log_block = 'Training Log...'
             self.training_button = 'Start Training'
-
+            self.eval_prompts = 'Eval Prompts'
             # Error or Warning
             self.training_err1 = 'CUDA is unavailable.'
             self.training_err2 = 'Currently insufficient VRAM, training failed!'
@@ -121,7 +138,8 @@ class TrainerUIName():
             self.base_model = '基础模型'
             self.tuner_name = '微调方法'
             self.base_model_revision = '模型版本号'
-            self.resolution = '分辨率'
+            self.resolution_height = '训练高度'
+            self.resolution_width = '训练宽度'
             self.train_epoch = '训练轮数'
             self.learning_rate = '学习率'
             self.save_interval = '存储间隔'
@@ -130,8 +148,7 @@ class TrainerUIName():
             self.replace_keywords = '触发关键词'
             self.work_name = '保存模型名称（刷新获得随机值）'
             self.push_to_hub = '推送魔搭社区'
-            self.log_block = '训练日志...'
-            self.training_button = '开始训练'
+            self.eval_prompts = '评测文本'
             # Error or Warning
             self.training_err1 = 'CUDA不可用.'
             self.training_err2 = '目前显存不足，训练失败！'

scepter/studio/self_train/self_train_ui/inference_ui.py

@@ -1,145 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Alibaba, Inc. and its affiliates.
-import os
-
-import gradio as gr
-
-from scepter.modules.utils.config import Config
-from scepter.modules.utils.file_system import FS
-from scepter.studio.self_train.self_train_ui.component_names import \
-    InferenceUIName
-from scepter.studio.self_train.utils.config_parser import (
-    get_base_model_list, get_inference_para_by_model_version)
-from scepter.studio.utils.uibase import UIBase
-
-
-class InferenceUI(UIBase):
-    def __init__(self, cfg, all_cfg_value, is_debug=False, language='en'):
-        self.BASE_CFG_VALUE = all_cfg_value
-        self.language = language
-        self.base_model_info = get_base_model_list(self.BASE_CFG_VALUE)
-        self.work_dir, _ = FS.map_to_local(cfg.WORK_DIR)
-        os.makedirs(self.work_dir, exist_ok=True)
-        self.model_list = []
-        # self.model_list.extend(self.base_model_info.get('model_choices', []))
-        have_model_list = []
-        if not self.work_dir.endswith('/'):
-            self.work_dir += '/'
-        for one_dir in FS.walk_dir(self.work_dir):
-            if one_dir.startswith(self.work_dir):
-                one_dir = one_dir[len(self.work_dir):]
-            if not os.path.isdir(os.path.join(self.work_dir, one_dir)):
-                continue
-            if len(one_dir.split('/')) > 1:
-                continue
-            if '@' in one_dir and os.path.exists(
-                    os.path.join(self.work_dir, one_dir, 'checkpoint.pth')):
-                if len(one_dir.split('@')) > 4:
-                    have_model_list.append([one_dir, one_dir.split('@')[-1]])
-        have_model_list.sort(key=lambda x: -int(x[-1][:-3]))
-        self.model_list.extend([v[0].split('/')[-1]
-                                for v in have_model_list][:50])
-        self.infer_para_data = get_inference_para_by_model_version(
-            self.BASE_CFG_VALUE, self.base_model_info.get('model_name', []),
-            self.base_model_info.get('version_name', []))
-        self.is_debug = is_debug
-        self.component_names = InferenceUIName(language)
-
-    def create_ui(self, *args, **kwargs):
-        with gr.Box():
-            gr.Markdown(self.component_names.output_model_block)
-            with gr.Row():
-                with gr.Column(scale=2, min_width=0):
-                    self.output_model_name = gr.Dropdown(
-                        label=self.component_names.output_model_name,
-                        choices=self.model_list,
-                        value=self.base_model_info.get('model_default', ''),
-                        interactive=True)
-                with gr.Column(scale=1, min_width=0):
-                    self.refresh_model_gbtn = gr.Button(
-                        self.component_names.refresh_model_gbtn)
-            with gr.Row():
-                with gr.Column(scale=2, min_width=0):
-                    self.extra_model_gtxt = gr.Text(
-                        label=self.component_names.extra_model_gtxt,
-                        show_label=False,
-                        placeholder='Add Extra Model')
-                with gr.Column(scale=1, min_width=0):
-                    self.extra_model_gbtn = gr.Button(
-                        self.component_names.extra_model_gbtn)
-            with gr.Row():
-                with gr.Column(scale=1, min_width=0):
-                    self.go_to_inferece_btn = gr.Button(
-                        self.component_names.go_to_inference)
-
-    def set_callbacks(self, trainer_ui, manager):
-        self.manager = manager
-
-        def add_model(model_name):
-            if model_name not in self.model_list:
-                self.model_list.append(model_name)
-            return '', gr.Dropdown(choices=self.model_list)
-
-        def refresh_model():
-            return gr.Dropdown(choices=self.model_list)
-
-        self.extra_model_gbtn.click(
-            fn=add_model,
-            inputs=[self.extra_model_gtxt],
-            outputs=[self.extra_model_gtxt, self.output_model_name],
-            queue=False)
-        self.refresh_model_gbtn.click(fn=refresh_model,
-                                      inputs=[],
-                                      outputs=[self.output_model_name],
-                                      queue=False)
-
-        def go_to_inferece(output_model):
-            output_model_path = os.path.join(self.work_dir, output_model)
-            _, _, base_model, _, resolution, _ = output_model_path.split('@')
-            tuner_cfg = Config(cfg_dict={}, load=False)
-            tuner_cfg.NAME = output_model
-            tuner_cfg.NAME_ZH = output_model
-            tuner_cfg.BASE_MODEL = base_model
-            model_path = os.path.join(output_model_path, 'checkpoint.pth')
-            if not os.path.exists(model_path):
-                gr.Error(self.component_names.inference_err4)
-            tuner_cfg.MODEL_PATH = model_path
-            self.manager.inference.model_manage_ui.pipe_manager.register_tuner(
-                tuner_cfg,
-                name=tuner_cfg.NAME_ZH
-                if self.language == 'zh' else tuner_cfg.NAME,
-                is_customized=True)
-
-            pipeline_level_modules = self.manager.inference.model_manage_ui.pipe_manager.pipeline_level_modules
-            if tuner_cfg.BASE_MODEL not in pipeline_level_modules:
-                gr.Error(self.component_names.inference_err3 +
-                         tuner_cfg.BASE_MODEL)
-            pipeline_ins = pipeline_level_modules[tuner_cfg.BASE_MODEL]
-            diffusion_model = f"{tuner_cfg.BASE_MODEL}_{pipeline_ins.diffusion_model['name']}"
-
-            default_choices = self.manager.inference.model_manage_ui.pipe_manager.module_level_choices
-            if 'customized_tuners' in default_choices and tuner_cfg.BASE_MODEL in default_choices[
-                    'customized_tuners']:
-                tunner_choices = default_choices['customized_tuners'][
-                    tuner_cfg.BASE_MODEL]['choices']
-                tunner_default = default_choices['customized_tuners'][
-                    tuner_cfg.BASE_MODEL]['default']
-                if not isinstance(tunner_default, list):
-                    tunner_default = [tunner_default]
-            else:
-                tunner_choices = []
-                tunner_default = ''
-            return (gr.Tabs(selected='inference'),
-                    gr.Dropdown(choices=tunner_choices, value=tunner_default),
-                    gr.Dropdown(value=diffusion_model),
-                    gr.Tabs(selected='tuner_ui'))
-
-        self.go_to_inferece_btn.click(
-            go_to_inferece,
-            inputs=[self.output_model_name],
-            outputs=[
-                manager.tabs, manager.inference.tuner_ui.custom_tuner_model,
-                manager.inference.model_manage_ui.diffusion_model,
-                manager.inference.setting_tab
-            ],
-            queue=False)

scepter/studio/self_train/self_train_ui/model_ui.py

@@ -0,0 +1,459 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import copy
+import json
+import os
+import queue
+import time
+
+import gradio as gr
+import yaml
+
+from scepter.modules.utils.config import Config
+from scepter.modules.utils.file_system import FS
+from scepter.studio.self_train.self_train_ui.component_names import ModelUIName
+from scepter.studio.self_train.utils.config_parser import get_base_model_list
+from scepter.studio.utils.uibase import UIBase
+
+refresh_symbol = '\U0001f504'  # 🔄
+delete_symbol = '\U0001f5d1'  # 🗑️
+add_symbol = '\U00002795'  # ➕
+confirm_symbol = '\U00002714'  # ✔️
+
+
+class ModelUI(UIBase):
+    def __init__(self, cfg, all_cfg_value, is_debug=False, language='en'):
+        self.BASE_CFG_VALUE = all_cfg_value
+        self.language = language
+        self.base_model_info = get_base_model_list(self.BASE_CFG_VALUE)
+        self.work_dir, _ = FS.map_to_local(cfg.WORK_DIR)
+        os.makedirs(self.work_dir, exist_ok=True)
+        self.default_model_name = 'step-last'
+        self.old_default_model_name = 'checkpoint.pth'
+        self.delete_folder_queue = queue.Queue()
+        self.model_list = []
+        # self.model_list.extend(self.base_model_info.get('model_choices', []))
+        have_model_list = []
+        if not self.work_dir.endswith('/'):
+            self.work_dir += '/'
+        for one_dir in FS.walk_dir(self.work_dir):
+            if one_dir.startswith(self.work_dir):
+                one_dir = one_dir[len(self.work_dir):]
+            if not os.path.isdir(os.path.join(self.work_dir, one_dir)):
+                continue
+            if len(one_dir.split('/')) > 1:
+                continue
+            if ('@' in one_dir
+                    and (os.path.exists(
+                        os.path.join(self.work_dir, one_dir, 'checkpoints',
+                                     self.default_model_name))
+                         or os.path.exists(
+                             os.path.join(self.work_dir, one_dir,
+                                          self.old_default_model_name)))):
+                if len(one_dir.split('@')) > 4:
+                    have_model_list.append([one_dir, one_dir.split('@')[-1]])
+        have_model_list.sort(key=lambda x: -int(x[-1][:-3]))
+        self.model_list.extend([v[0].split('/')[-1]
+                                for v in have_model_list][:50])
+        self.is_debug = is_debug
+        self.component_names = ModelUIName(language)
+
+    def get_ckpt_list(self, output_model):
+        all_ckpt_list = []
+        if output_model is None or output_model == '':
+            return all_ckpt_list
+        output_model_path = os.path.join(self.work_dir, output_model)
+        all_ckpt_path = os.path.join(output_model_path, 'checkpoints')
+        if os.path.exists(all_ckpt_path):
+            for name in os.listdir(all_ckpt_path):
+                if name == self.default_model_name:
+                    continue
+                path = os.path.join(all_ckpt_path, name)
+                if os.path.isdir(path):
+                    all_ckpt_list.append(name)
+        all_ckpt_list = sorted(all_ckpt_list,
+                               key=lambda x: int(x.split('-')[-1]))
+        if os.path.exists(os.path.join(all_ckpt_path,
+                                       self.default_model_name)):
+            all_ckpt_list.append(self.default_model_name)
+        return all_ckpt_list
+
+    def get_gallery_list(self, model_name, ckpt_name):
+        ckpt_probe_dir = os.path.join(self.work_dir, model_name, 'eval_probe',
+                                      ckpt_name, 'image')
+        all_gallery_list = []
+        if os.path.exists(ckpt_probe_dir):
+            for name in os.listdir(ckpt_probe_dir):
+                path = os.path.join(ckpt_probe_dir, name)
+                all_gallery_list.append(path)
+        return all_gallery_list
+
+    def create_ui(self, *args, **kwargs):
+        with gr.Box():
+            gr.Markdown(self.component_names.output_model_block)
+            with gr.Row(variant='panel', equal_height=True):
+                with gr.Column(scale=7, min_width=0):
+                    self.output_model_name = gr.Dropdown(
+                        label=self.component_names.output_model_name,
+                        choices=self.model_list,
+                        value=self.base_model_info.get('model_default', ''),
+                        show_label=False,
+                        container=False,
+                        interactive=True)
+                with gr.Column(scale=2, min_width=0):
+                    self.output_ckpt_name = gr.Dropdown(
+                        label=self.component_names.output_ckpt_name,
+                        value='',
+                        choices=[],
+                        show_label=False,
+                        container=False,
+                        interactive=True)
+                with gr.Column(scale=1, min_width=0):
+                    self.refresh_model_gbtn = gr.Button(value=refresh_symbol)
+                with gr.Column(scale=1, min_width=0):
+                    self.add_model_gbtn = gr.Button(value=add_symbol)
+                with gr.Column(scale=1, min_width=0):
+                    self.delete_model_gbtn = gr.Button(value=delete_symbol)
+
+            with gr.Row(variant='panel', equal_height=True,
+                        visible=False) as self.extra_model_panel:
+                with gr.Column(scale=4, min_width=0):
+                    self.extra_model_txt = gr.Text(
+                        label=self.component_names.extra_model_gtxt,
+                        show_label=False,
+                        container=False,
+                        placeholder='Add Extra Model')
+                with gr.Column(scale=1, min_width=0):
+                    self.confirm_add = gr.Button(value=confirm_symbol)
+            with gr.Row(variant='panel', equal_height=True):
+                with gr.Column(scale=3, min_width=0):
+                    with gr.Box():
+                        self.log_message = gr.Text(
+                            placeholder='Please select model'
+                            'or press export button.',
+                            autoscroll=True,
+                            lines=10,
+                            label=self.component_names.log_block)
+                with gr.Column(scale=1, min_width=0,
+                               visible=False) as self.export_log_panel:
+                    # with gr.Column(scale=1, min_width=0):
+                    self.export_log = gr.Button(
+                        value=self.component_names.btn_export_log)
+                    # with gr.Column(scale=1, min_width=0):
+                    self.export_url = gr.File(
+                        label=self.component_names.export_file,
+                        visible=False,
+                        value=None,
+                        interactive=False,
+                        show_label=True)
+
+            with gr.Row(variant='panel', equal_height=True):
+                with gr.Accordion(label=self.component_names.gallery_block,
+                                  open=True):
+                    self.eval_gallery = gr.Gallery(
+                        label=self.component_names.eval_gallery,
+                        value=[],
+                        preview=True,
+                        selected_index=None)
+
+            with gr.Row():
+                with gr.Column(scale=1, min_width=0):
+                    self.go_to_inferece_btn = gr.Button(
+                        self.component_names.go_to_inference)
+
+    def set_callbacks(self, trainer_ui, manager):
+        self.manager = manager
+
+        def model_name_change(model_name):
+            if model_name is None:
+                return '', gr.Column(), '', []
+            message = trainer_ui.trainer_ins.get_log(model_name)
+            status = trainer_ui.trainer_ins.get_status(model_name)
+            ckpt_list = self.get_ckpt_list(model_name)
+            ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else ''
+            return (message, gr.Column(visible=status in ('running',
+                                                          'success')),
+                    gr.Dropdown(choices=ckpt_list, value=ckpt_value))
+
+        self.output_model_name.change(fn=model_name_change,
+                                      inputs=[self.output_model_name],
+                                      outputs=[
+                                          self.log_message,
+                                          self.export_log_panel,
+                                          self.output_ckpt_name
+                                      ],
+                                      queue=False)
+
+        def ckpt_name_change(model_name, ckpt_value):
+            if ckpt_value is not None and len(ckpt_value) > 0:
+                gallery_value = self.get_gallery_list(model_name, ckpt_value)
+            else:
+                gallery_value = []
+            if len(gallery_value) > 0:
+                return gr.Gallery(value=gallery_value,
+                                  preview=True,
+                                  selected_index=0)
+            else:
+                return gr.Gallery(value=gallery_value,
+                                  preview=True,
+                                  selected_index=None)
+
+        self.output_ckpt_name.change(
+            fn=ckpt_name_change,
+            inputs=[self.output_model_name, self.output_ckpt_name],
+            outputs=[self.eval_gallery],
+            queue=False)
+
+        def add_model():
+            return gr.Row(visible=True)
+
+        self.add_model_gbtn.click(fn=add_model,
+                                  inputs=[],
+                                  outputs=[self.extra_model_panel],
+                                  queue=False)
+
+        def confirm_add(model_name):
+            model_folder = os.path.join(self.work_dir, model_name)
+            have_model = os.path.exists(model_folder)
+            if not have_model:
+                gr.Error(self.component_names.model_err5.format(model_name))
+            if model_name not in self.model_list and have_model:
+                self.model_list.append(model_name)
+            return gr.Row(visible=False), gr.Dropdown(choices=self.model_list,
+                                                      value=model_name)
+
+        self.confirm_add.click(
+            fn=confirm_add,
+            inputs=[self.extra_model_txt],
+            outputs=[self.extra_model_panel, self.output_model_name],
+            queue=False)
+
+        def refresh_model(model_name):
+            if not self.delete_folder_queue.empty():
+                try:
+                    del_folder = self.delete_folder_queue.get_nowait()
+                    if os.path.exists(del_folder):
+                        os.system(f'rm -rf {del_folder}')
+                        self.delete_folder_queue.put_nowait(del_folder)
+                except Exception:
+                    pass
+
+            message = trainer_ui.trainer_ins.get_log(model_name)
+            status = trainer_ui.trainer_ins.get_status(model_name)
+            ckpt_list = self.get_ckpt_list(model_name)
+            ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else ''
+            ret_gallery = ckpt_name_change(model_name, ckpt_value)
+            return (message, gr.Column(visible=status in ('running',
+                                                          'success')),
+                    gr.Dropdown(choices=ckpt_list,
+                                value=ckpt_value), ret_gallery)
+
+        self.refresh_model_gbtn.click(
+            fn=refresh_model,
+            inputs=[self.output_model_name],
+            outputs=[
+                self.log_message,
+                self.export_log_panel,
+                # self.output_model_name,
+                self.output_ckpt_name,
+                self.eval_gallery
+            ],
+            queue=False)
+
+        def delete_model(model_name):
+            index = 0
+            trainer_ui.trainer_ins.stop_task(model_name)
+            if model_name in self.model_list:
+                index = self.model_list.index(model_name)
+                self.model_list.remove(model_name)
+                folder = os.path.join(self.work_dir, model_name)
+                for _ in range(1):
+                    if os.path.exists(folder):
+                        try:
+                            os.system(f'rm -rf {folder}')
+                        except Exception:
+                            time.sleep(2)
+                    else:
+                        break
+                self.delete_folder_queue.put_nowait(folder)
+            if index <= len(self.model_list) - 1:
+                model_name = self.model_list[index]
+            elif len(self.model_list) > 0:
+                model_name = self.model_list[0]
+            else:
+                model_name = None
+
+            return gr.Dropdown(choices=self.model_list, value=model_name)
+
+        self.delete_model_gbtn.click(fn=delete_model,
+                                     inputs=[self.output_model_name],
+                                     outputs=[self.output_model_name])
+
+        def go_to_inferece(output_model, output_ckpt_name):
+            params_path = os.path.join(self.work_dir, output_model,
+                                       'params.json')
+            if os.path.exists(params_path):
+                params_info = json.loads(open(params_path).read())
+                assert params_info['work_name'] == output_model
+                # base_model = params_info['base_model']
+                base_model_revision = params_info['base_model_revision']
+                tuner_name = params_info['tuner_name']
+                model_path = os.path.join(self.work_dir, output_model,
+                                          'checkpoints', output_ckpt_name)
+                eval_prompts = params_info[
+                    'eval_prompts'] if 'eval_prompts' in params_info else []
+                image_dir = os.path.join(self.work_dir, output_model,
+                                         'eval_probe', output_ckpt_name,
+                                         'image')
+                image_path = [
+                    os.path.join(image_dir, name)
+                    for name in os.listdir(image_dir)
+                ]
+            else:
+                _, base_model, base_model_revision, tuner_name, _ = output_model.split(
+                    '@', 4)
+                model_path = os.path.join(self.work_dir, output_model,
+                                          self.old_default_model_name)
+                output_ckpt_name = self.old_default_model_name
+                image_path = []
+                eval_prompts = []
+                params_info = {}
+
+            if isinstance(image_path, list):
+                image_path = image_path[0] if len(image_path) > 0 else None
+            if isinstance(eval_prompts, list):
+                eval_prompts = eval_prompts[0] if len(eval_prompts) > 0 else ''
+            cfg_file = os.path.join(self.work_dir, output_model,
+                                    f'meta_{output_ckpt_name}.yaml')
+            output_model = output_model + '@' + output_ckpt_name
+            tuner_dict = {
+                'NAME': output_model,
+                'NAME_ZH': output_model,
+                # 'BASE_MODEL': base_model,
+                'BASE_MODEL': base_model_revision,
+                'TUNER_TYPE': tuner_name,
+                'DESCRIPTION': '',
+                'MODEL_PATH': model_path,
+                'IMAGE_PATH': image_path,
+                'PROMPT_EXAMPLE': eval_prompts,
+                'SOURCE': 'self_train',
+                'CKPT_NAME': output_ckpt_name,
+                'PARAMS': params_info
+            }
+            tuner_cfg = Config(cfg_dict=tuner_dict, load=False)
+
+            if not os.path.exists(model_path):
+                gr.Error(self.component_names.model_err4)
+            self.manager.inference.model_manage_ui.pipe_manager.register_tuner(
+                tuner_cfg,
+                name=tuner_cfg.NAME_ZH
+                if self.language == 'zh' else tuner_cfg.NAME,
+                is_customized=True)
+
+            pipeline_level_modules = self.manager.inference.model_manage_ui.pipe_manager.pipeline_level_modules
+            if tuner_cfg.BASE_MODEL not in pipeline_level_modules:
+                gr.Error(self.component_names.model_err3 +
+                         tuner_cfg.BASE_MODEL)
+            pipeline_ins = pipeline_level_modules[tuner_cfg.BASE_MODEL]
+            diffusion_model = f"{tuner_cfg.BASE_MODEL}_{pipeline_ins.diffusion_model['name']}"
+
+            default_choices = self.manager.inference.model_manage_ui.pipe_manager.module_level_choices
+            if 'customized_tuners' in default_choices:
+                if tuner_cfg.BASE_MODEL not in default_choices[
+                        'customized_tuners']:
+                    default_choices['customized_tuners'] = {}
+                tunner_choices = default_choices['customized_tuners'][
+                    tuner_cfg.BASE_MODEL]['choices']
+                tunner_default = default_choices['customized_tuners'][
+                    tuner_cfg.BASE_MODEL]['default']
+                if not isinstance(tunner_default, list):
+                    tunner_default = [tunner_default]
+            else:
+                tunner_choices = []
+                tunner_default = []
+
+            with open(cfg_file, 'w') as f_out:
+                yaml.dump(copy.deepcopy(tuner_cfg.cfg_dict),
+                          f_out,
+                          encoding='utf-8',
+                          allow_unicode=True,
+                          default_flow_style=False)
+
+            # example_image = tuner_cfg.get('IMAGE_PATH', None)
+            # if isinstance(example_image, list) and len(example_image)>0:
+            #     example_image = example_image[0]
+            #
+            # prompt_example = tuner_cfg.get('PROMPT_EXAMPLE', None)
+            # if isinstance(prompt_example, list) and len(prompt_example)>0:
+            #     prompt_example = prompt_example[0]
+
+            base_model = tuner_cfg.get('BASE_MODEL', '')
+
+            if not base_model == '':
+                if base_model not in manager.inference.tuner_ui.name_level_tuners:
+                    manager.inference.tuner_ui.name_level_tuners[
+                        base_model] = {}
+                manager.inference.tuner_ui.name_level_tuners[base_model][
+                    output_model] = tuner_cfg
+
+            return (
+                gr.Tabs(selected='inference'), cfg_file,
+                gr.Tabs(selected='tuner_ui'),
+                gr.CheckboxGroup(
+                    value='使用微调' if self.language == 'zh' else 'Use Tuners'),
+                gr.Dropdown(value=diffusion_model),
+                gr.Dropdown(choices=tunner_choices, value=tunner_default)
+                # gr.Text(value=tuner_cfg.get('TUNER_TYPE', '')),
+                # gr.Text(value=tuner_cfg.get('BASE_MODEL', '')),
+                # gr.Image(value=example_image),
+                # gr.Text(value=tuner_cfg.get('DESCRIPTION', '')),
+                # gr.Text(value=prompt_example)
+            )
+
+        self.go_to_inferece_btn.click(
+            go_to_inferece,
+            inputs=[self.output_model_name, self.output_ckpt_name],
+            outputs=[
+                manager.tabs, manager.inference.infer_info,
+                manager.inference.setting_tab,
+                manager.inference.check_box_for_setting,
+                manager.inference.model_manage_ui.diffusion_model,
+                manager.inference.tuner_ui.custom_tuner_model
+                # manager.inference.tuner_ui.tuner_type,
+                # manager.inference.tuner_ui.base_model,
+                # manager.inference.tuner_ui.tuner_example,
+                # manager.inference.tuner_ui.tuner_desc,
+                # manager.inference.tuner_ui.tuner_prompt_example
+            ],
+            queue=False)
+
+        def export_train_log(model_name):
+            current_log_folder = os.path.join(self.work_dir, model_name)
+            _ = trainer_ui.trainer_ins.get_log(model_name)
+            out_log = os.path.join(current_log_folder, 'output_std_log.txt')
+            if os.path.exists(out_log):
+                zip_path = f'{trainer_ui.current_train_model}_log.zip'
+                cache_folder = f'{trainer_ui.current_train_model}_log'
+                if not os.path.exists(
+                        os.path.join(current_log_folder, 'tensorboard')):
+                    os.makedirs(os.path.join(current_log_folder,
+                                             'tensorboard'),
+                                exist_ok=True)
+                res = os.popen(
+                    f"cd {current_log_folder} && mkdir -p '{cache_folder}' "
+                    f"&& cp -rf tensorboard '{cache_folder}/tensorboard' "
+                    f"&& cp -rf 'output_std_log.txt' '{cache_folder}/output_std_log.txt' "
+                    f"&& zip -r '{zip_path}' '{cache_folder}'/* "
+                    f"&& rm -rf '{cache_folder}'")
+                print(res.readlines())
+                return gr.File(value=os.path.join(current_log_folder,
+                                                  zip_path),
+                               visible=True)
+            else:
+                gr.Error(self.component_names.training_warn1)
+                return gr.File()
+
+        self.export_log.click(export_train_log,
+                              inputs=[self.output_model_name],
+                              outputs=[self.export_url],
+                              queue=False)

scepter/studio/self_train/self_train_ui/trainer_ui.py

@@ -2,9 +2,10 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import copy
 import datetime
+import json
 import os
 import random
-import time
+from collections import OrderedDict
 
 import gradio as gr
 import torch
@@ -12,12 +13,12 @@ import yaml
 
 import scepter
 from scepter.modules.utils.file_system import FS
+from scepter.studio.self_train.scripts.trainer import TrainManager
 from scepter.studio.self_train.self_train_ui.component_names import \
     TrainerUIName
 from scepter.studio.self_train.utils.config_parser import (
     get_default, get_values_by_model, get_values_by_model_version,
-    get_values_by_model_version_tuner,
-    get_values_by_model_version_tuner_resolution)
+    get_values_by_model_version_tuner)
 from scepter.studio.utils.uibase import UIBase
 
 
@@ -30,11 +31,17 @@ def print_memory_status(is_debug):
     return gpu_mem
 
 
+def is_basic_or_container_type(obj):
+    basic_and_container_types = (int, float, str, bool, complex, list, tuple,
+                                 dict, set)
+    return isinstance(obj, basic_and_container_types)
+
+
 refresh_symbol = '\U0001f504'  # 🔄
 
 
-def get_work_name(model, version, tuner, resolution):
-    model_prefix = f'Swift@{model}@{version}@{tuner}@{resolution}'
+def get_work_name(model, version, tuner):
+    model_prefix = f'Swift@{model}@{version}@{tuner}'
     return model_prefix + '@' + '{0:%Y%m%d%H%M%S%f}'.format(
         datetime.datetime.now()) + ''.join(
             [str(random.randint(1, 10)) for i in range(3)])
@@ -44,12 +51,26 @@ class TrainerUI(UIBase):
     def __init__(self, cfg, all_cfg_value, is_debug=False, language='en'):
         self.BASE_CFG_VALUE = all_cfg_value
         self.para_data = get_default(self.BASE_CFG_VALUE)
+        self.train_para_data = cfg.TRAIN_PARAS
         self.run_script = os.path.join(os.path.dirname(scepter.dirname),
                                        cfg.SCRIPT_DIR, 'run_task.py')
         self.work_dir_pre, _ = FS.map_to_local(cfg.WORK_DIR)
         self.is_debug = is_debug
+        self.current_train_model = None
+        self.trainer_ins = TrainManager(self.run_script, self.work_dir_pre)
         self.component_names = TrainerUIName(language=language)
 
+        self.h_level_dict = {}
+        for hw_tuple in self.train_para_data.RESOLUTIONS.get('VALUES', []):
+            h, w = hw_tuple
+            if h not in self.h_level_dict:
+                self.h_level_dict[h] = []
+            self.h_level_dict[h].append(w)
+        self.h_level_dict = OrderedDict(
+            sorted(self.h_level_dict.items(),
+                   key=lambda x: int(x[0]),
+                   reverse=False))
+
     def create_ui(self):
         with gr.Box():
             with gr.Row(variant='panel', equal_height=True):
@@ -88,15 +109,6 @@ class TrainerUI(UIBase):
                                         'model_default', ''),
                                     label=self.component_names.base_model,
                                     interactive=True)
-                            with gr.Column(scale=1, min_width=0):
-                                self.tuner_name = gr.Dropdown(
-                                    choices=self.para_data.get(
-                                        'tuner_choices', []),
-                                    value=self.para_data.get(
-                                        'tuner_default', ''),
-                                    label=self.component_names.tuner_name,
-                                    interactive=True)
-                        with gr.Row():
                             with gr.Column(scale=1, min_width=0):
                                 self.base_model_revision = gr.Dropdown(
                                     choices=self.para_data.get(
@@ -107,13 +119,35 @@ class TrainerUI(UIBase):
                                     base_model_revision,
                                     interactive=True)
 
+                        with gr.Row():
                             with gr.Column(scale=1, min_width=0):
-                                self.resolution = gr.Dropdown(
+                                self.tuner_name = gr.Dropdown(
                                     choices=self.para_data.get(
-                                        'resolution_choices', []),
+                                        'tuner_choices', []),
                                     value=self.para_data.get(
-                                        'resolution_default', 1024),
-                                    label=self.component_names.resolution,
+                                        'tuner_default', ''),
+                                    label=self.component_names.tuner_name,
+                                    interactive=True)
+
+                        with gr.Row():
+                            with gr.Column(scale=1, min_width=0):
+                                self.resolution_height = gr.Dropdown(
+                                    choices=list(self.h_level_dict.keys()),
+                                    value=self.para_data.get(
+                                        'RESOLUTION', 1024)[0],
+                                    label=self.component_names.
+                                    resolution_height,
+                                    allow_custom_value=True,
+                                    interactive=True)
+
+                            with gr.Column(scale=1, min_width=0):
+                                self.resolution_width = gr.Dropdown(
+                                    choices=self.h_level_dict[
+                                        self.resolution_height.value],
+                                    value=self.para_data.get(
+                                        'RESOLUTION', 1024)[1],
+                                    label=self.component_names.
+                                    resolution_width,
                                     allow_custom_value=True,
                                     interactive=True)
 
@@ -161,19 +195,36 @@ class TrainerUI(UIBase):
                                     value='')
 
                         with gr.Row():
-                            with gr.Column(scale=5, min_width=0):
-                                self.work_name = gr.Text(
-                                    label=self.component_names.work_name,
-                                    value=None,
-                                    interactive=False)
                             with gr.Column(scale=1, min_width=0):
-                                self.work_name_button = gr.Button(
-                                    value=refresh_symbol)
-                            with gr.Column(scale=2, min_width=0):
-                                self.push_to_hub = gr.Checkbox(
-                                    label=self.component_names.push_to_hub,
-                                    value=False,
-                                    visible=False)
+                                self.eval_prompts = gr.Dropdown(
+                                    value=None,
+                                    choices=self.train_para_data.get(
+                                        'EVAL_PROMPTS', []),
+                                    label=self.component_names.eval_prompts,
+                                    interactive=True,
+                                    multiselect=True,
+                                    allow_custom_value=True,
+                                    max_choices=20)
+
+            with gr.Row(variant='panel', equal_height=True):
+                with gr.Box():
+                    with gr.Row(variant='panel', equal_height=True):
+                        gr.Markdown(self.component_names.work_name)
+                    with gr.Row(variant='panel', equal_height=True):
+                        with gr.Column(scale=6, min_width=0):
+                            self.work_name = gr.Text(value=None,
+                                                     container=False,
+                                                     interactive=False)
+                        with gr.Column(scale=2, min_width=0):
+                            self.work_name_button = gr.Button(
+                                value=refresh_symbol, size='lg')
+
+            with gr.Row(variant='panel', visible=False, equal_height=True):
+                with gr.Column(scale=2, min_width=0):
+                    self.push_to_hub = gr.Checkbox(
+                        label=self.component_names.push_to_hub,
+                        value=False,
+                        visible=False)
 
             with gr.Row(variant='panel', equal_height=True):
                 self.examples = gr.Examples(
@@ -193,15 +244,10 @@ class TrainerUI(UIBase):
                         self.data_type, self.ms_data_space, self.ms_data_name,
                         self.ms_data_subname
                     ])
-
-            with gr.Row(variant='panel', equal_height=True):
-                with gr.Box():
-                    gr.Markdown(self.component_names.log_block)
-                    self.training_message = gr.Markdown()
             with gr.Row(variant='panel', equal_height=True):
                 self.training_button = gr.Button()
 
-    def set_callbacks(self, inference_ui):
+    def set_callbacks(self, inference_ui, manager):
         def change_data_type(data_type):
             if data_type == self.component_names.data_type_choices[0]:
                 return gr.Box(visible=False)
@@ -217,7 +263,7 @@ class TrainerUI(UIBase):
                                     inputs=[
                                         self.base_model,
                                         self.base_model_revision,
-                                        self.tuner_name, self.resolution
+                                        self.tuner_name
                                     ],
                                     outputs=[self.work_name],
                                     queue=False)
@@ -245,8 +291,12 @@ class TrainerUI(UIBase):
                             interactive=True), \
                 gr.Dropdown(value=ret_data.get('tuner_default', ''), choices=ret_data.get('tuner_choices', []),
                             interactive=True), \
-                gr.Dropdown(value=ret_data.get('resolution_default', 1024),
-                            choices=ret_data.get('resolution_choices', []), interactive=True)
+                gr.Dropdown(value=ret_data.get('RESOLUTION', 1024)[0],
+                            choices=list(self.h_level_dict.keys()),
+                            interactive=True), \
+                gr.Dropdown(value=ret_data.get('RESOLUTION', 1024)[1],
+                            choices=self.h_level_dict[ret_data.get('RESOLUTION', 1024)[0]],
+                            interactive=True)
 
         self.base_model.change(fn=change_train_value_by_model,
                                inputs=[self.base_model],
@@ -255,7 +305,8 @@ class TrainerUI(UIBase):
                                    self.save_interval, self.train_batch_size,
                                    self.prompt_prefix,
                                    self.base_model_revision, self.tuner_name,
-                                   self.resolution
+                                   self.resolution_height,
+                                   self.resolution_width
                                ],
                                queue=False)
 
@@ -282,10 +333,15 @@ class TrainerUI(UIBase):
                 ret_data.get('SAVE_INTERVAL', 10), \
                 ret_data.get('TRAIN_BATCH_SIZE', 4), \
                 ret_data.get('TRAIN_PREFIX', ''), \
-                gr.Dropdown(value=ret_data.get('tuner_default', ''), choices=ret_data.get('tuner_choices', []),
+                gr.Dropdown(value=ret_data.get('tuner_default', ''),
+                            choices=ret_data.get('tuner_choices', []),
                             interactive=True), \
-                gr.Dropdown(value=ret_data.get('resolution_default', 1024),
-                            choices=ret_data.get('resolution_choices', []), interactive=True)
+                gr.Dropdown(value=ret_data.get('RESOLUTION', 1024)[0],
+                            choices=list(self.h_level_dict.keys()),
+                            interactive=True), \
+                gr.Dropdown(value=ret_data.get('RESOLUTION', 1024)[1],
+                            choices=self.h_level_dict[ret_data.get('RESOLUTION', 1024)[0]],
+                            interactive=True)
 
         #
         self.base_model_revision.change(
@@ -294,11 +350,10 @@ class TrainerUI(UIBase):
             outputs=[
                 self.train_epoch, self.learning_rate, self.save_interval,
                 self.train_batch_size, self.prompt_prefix, self.tuner_name,
-                self.resolution
+                self.resolution_height, self.resolution_width
             ],
             queue=False)
 
-        #
         #
         def change_train_value_by_model_version_tuner(base_model,
                                                       base_model_revision,
@@ -323,8 +378,12 @@ class TrainerUI(UIBase):
                 ret_data.get('SAVE_INTERVAL', 10), \
                 ret_data.get('TRAIN_BATCH_SIZE', 4), \
                 ret_data.get('TRAIN_PREFIX', ''), \
-                gr.Dropdown(value=ret_data.get('resolution_default', 1024),
-                            choices=ret_data.get('resolution_choices', []), interactive=True)
+                gr.Dropdown(value=ret_data.get('RESOLUTION', 1024)[0],
+                            choices=list(self.h_level_dict.keys()),
+                            interactive=True), \
+                gr.Dropdown(value=ret_data.get('RESOLUTION', 1024)[1],
+                            choices=self.h_level_dict[ret_data.get('RESOLUTION', 1024)[0]],
+                            interactive=True)
 
         #
         self.tuner_name.change(fn=change_train_value_by_model_version_tuner,
@@ -335,56 +394,30 @@ class TrainerUI(UIBase):
                                outputs=[
                                    self.train_epoch, self.learning_rate,
                                    self.save_interval, self.train_batch_size,
-                                   self.prompt_prefix, self.resolution
+                                   self.prompt_prefix, self.resolution_height,
+                                   self.resolution_width
                                ],
                                queue=False)
 
-        #
-        def change_train_value_by_model_version_tuner_resolution(
-                base_model, base_model_revision, tuner_name, resolution):
-            '''
-                Changes to the base model will affect the training parameters,
-                and it is best to define the related default values in the YAML.
-                Training Iterations:
-                Learning Rate:
-                Training Prefix Used:
-                Training Batch Size:
-                Supported Resolutions:
-                Supported Fine-tuning Methods:
-                Supported Fine-tuning Methods:
-                Prefix for Saved Model:
-            '''
-            ret_data = get_values_by_model_version_tuner_resolution(
-                self.BASE_CFG_VALUE, base_model, base_model_revision,
-                tuner_name, resolution)
-            print('change_train_value_by_model_version_tuner_resolution',
-                  ret_data)
-            # work_name = get_work_name(base_model, base_model_revision,
-            #                           tuner_name, resolution)
-            return ret_data.get('EPOCHS', 10), \
-                ret_data.get('LEARNING_RATE', 0.0001), \
-                ret_data.get('SAVE_INTERVAL', 10), \
-                ret_data.get('TRAIN_BATCH_SIZE', 4), \
-                ret_data.get('TRAIN_PREFIX', '')
+        def change_resolution(h):
+            if h not in self.h_level_dict:
+                return gr.Dropdown()
+            all_choices = self.h_level_dict[h]
+            default = all_choices[0]
+            return gr.Dropdown(choices=all_choices, value=default)
 
-        #
-        self.resolution.change(
-            fn=change_train_value_by_model_version_tuner_resolution,
-            inputs=[
-                self.base_model, self.base_model_revision, self.tuner_name,
-                self.resolution
-            ],
-            outputs=[
-                self.train_epoch, self.learning_rate, self.save_interval,
-                self.train_batch_size, self.prompt_prefix
-            ],
-            queue=False)
+        self.resolution_height.change(change_resolution,
+                                      inputs=[self.resolution_height],
+                                      outputs=[self.resolution_width],
+                                      queue=False)
 
         def run_train(work_name, data_type, ms_data_space, ms_data_name,
                       ms_data_subname, base_model, base_model_revision,
-                      tuner_name, resolution, train_epoch, learning_rate,
-                      save_interval, train_batch_size, prompt_prefix,
-                      replace_keywords, push_to_hub):
+                      tuner_name, resolution_height, resolution_width,
+                      train_epoch, learning_rate, save_interval,
+                      train_batch_size, prompt_prefix, replace_keywords,
+                      push_to_hub, eval_prompts):
+
             # Check Cuda
             if not torch.cuda.is_available() and not self.is_debug:
                 raise gr.Error(self.component_names.training_err1)
@@ -392,10 +425,25 @@ class TrainerUI(UIBase):
             if work_name == 'custom' or work_name is None or work_name == '':
                 raise gr.Error(self.component_names.training_err4)
             work_dir = os.path.join(self.work_dir_pre, work_name)
-            if not os.path.exists(work_dir):
-                os.makedirs(work_dir)
-            else:
+            self.current_train_model = work_name
+            if os.path.exists(work_dir) or os.path.exists(
+                    f'.flag/{work_name}.tmp'):
                 raise gr.Error(self.component_names.training_err4)
+            else:
+                os.makedirs(work_dir)
+            os.makedirs('.flag', exist_ok=True)
+            with open(f'.flag/{work_name}.tmp', 'w') as f:
+                f.write('new line.')
+            # save params
+            with open(os.path.join(work_dir, 'params.json'), 'w') as f_out:
+                json.dump(
+                    {
+                        key: val
+                        for key, val in locals().items()
+                        if is_basic_or_container_type(val)
+                    },
+                    f_out,
+                    ensure_ascii=False)
 
             if push_to_hub:
                 model_id = work_name
@@ -404,28 +452,23 @@ class TrainerUI(UIBase):
             else:
                 hub_model_id = ''
 
-            # Check Cuda Memory
-            if torch.cuda.is_available() and not self.is_debug:
-                device = torch.device('cuda:0')
-                required_memory_bytes = 4 * (1024**3)
-                try:
-                    tensor = torch.empty(  # noqa
-                        (required_memory_bytes // 4, ), device=device
-                    )  # create 4GB tensor to check the memory if enough
-                    del tensor
-                except RuntimeError:
-                    raise gr.Error(self.component_names.training_err2)
-
             # Check Instance Valid
             if ms_data_name is None:
                 raise gr.Error(self.component_names.training_err3)
 
-            st_time = time.time()
-
-            def prepare_data(data_cfg):
+            def prepare_train_data(data_cfg):
                 data_cfg['BATCH_SIZE'] = int(train_batch_size)
                 data_cfg['PROMPT_PREFIX'] = prompt_prefix
                 data_cfg['REPLACE_KEYWORDS'] = replace_keywords
+                for trans in data_cfg['TRANSFORMS']:
+                    if trans['NAME'] in [
+                            'Resize', 'FlexibleResize', 'CenterCrop',
+                            'FlexibleCenterCrop'
+                    ]:
+                        trans['SIZE'] = [
+                            int(resolution_height),
+                            int(resolution_width)
+                        ]
                 if data_type in self.component_names.data_type_choices:
                     if ms_data_name.startswith(
                             'http') or ms_data_name.endswith('zip'):
@@ -482,7 +525,19 @@ class TrainerUI(UIBase):
                             data_cfg['MS_REMAP_KEYS'] = {'Text': 'Prompt'}
                         else:
                             data_cfg['MS_REMAP_KEYS'] = None
-                    data_cfg['OUTPUT_SIZE'] = int(resolution)
+                    data_cfg['OUTPUT_SIZE'] = [
+                        int(resolution_height),
+                        int(resolution_width)
+                    ]
+                return data_cfg
+
+            def prepare_eval_data(data_cfg):
+                data_cfg['PROMPT_PREFIX'] = prompt_prefix
+                data_cfg['IMAGE_SIZE'] = [
+                    int(resolution_height),
+                    int(resolution_width)
+                ]
+                data_cfg['PROMPT_DATA'] = eval_prompts
                 return data_cfg
 
             def prepare_train_config():
@@ -490,7 +545,7 @@ class TrainerUI(UIBase):
                 current_model_info = self.BASE_CFG_VALUE[base_model][
                     base_model_revision]
                 modify_para = current_model_info['modify_para']
-                cfg = current_model_info['config_value']
+                cfg = copy.deepcopy(current_model_info['config_value'])
                 if isinstance(modify_para, dict) and tuner_name in modify_para:
                     modify_c = modify_para[tuner_name]
                     if isinstance(modify_c, dict) and 'TRAIN' in modify_c:
@@ -518,18 +573,33 @@ class TrainerUI(UIBase):
                 cfg['SOLVER']['MAX_EPOCHS'] = int(train_epoch)
                 cfg['SOLVER']['TRAIN_DATA']['BATCH_SIZE'] = int(
                     train_batch_size)
-                cfg['SOLVER']['TUNER'] = current_model_info[
-                    'tuner_para'][tuner_name] if isinstance(
-                        current_model_info['tuner_para'],
-                        dict) and tuner_name in current_model_info[
-                            'tuner_para'] else None
-                cfg['SOLVER']['TRAIN_DATA'] = prepare_data(
+                if 'TUNER' in cfg['SOLVER']:
+                    cfg['SOLVER']['TUNER'] = current_model_info['tuner_para'][
+                        tuner_name] if isinstance(
+                            current_model_info['tuner_para'],
+                            dict) and tuner_name in current_model_info[
+                                'tuner_para'] else None
+                cfg['SOLVER']['TRAIN_DATA'] = prepare_train_data(
                     cfg['SOLVER']['TRAIN_DATA'])
+                if eval_prompts is not None and len(eval_prompts) > 0:
+                    cfg['SOLVER']['EVAL_DATA'] = prepare_eval_data(
+                        cfg['SOLVER']['EVAL_DATA'])
+                else:
+                    cfg['SOLVER'].pop('EVAL_DATA')
+                if 'SAMPLE_ARGS' in cfg['SOLVER']:
+                    cfg['SOLVER']['SAMPLE_ARGS']['IMAGE_SIZE'] = [
+                        int(resolution_height),
+                        int(resolution_width)
+                    ]
                 for hook in cfg['SOLVER']['TRAIN_HOOKS']:
                     if hook['NAME'] == 'CheckpointHook':
                         hook['INTERVAL'] = save_interval
                         hook['PUSH_TO_HUB'] = push_to_hub
                         hook['HUB_MODEL_ID'] = hub_model_id
+                if 'EVAL_HOOKS' in cfg['SOLVER']:
+                    for hook in cfg['SOLVER']['EVAL_HOOKS']:
+                        if hook['NAME'] == 'ProbeDataHook':
+                            hook['PROB_INTERVAL'] = save_interval
 
                 with open(cfg_file, 'w') as f_out:
                     yaml.dump(cfg,
@@ -539,48 +609,33 @@ class TrainerUI(UIBase):
                               default_flow_style=False)
                 return cfg_file
 
-            cfg = prepare_train_config()
-
-            def train_fn(cfg_file):
-                torch.cuda.empty_cache()
-                cmd = f'PYTHONPATH=. python {self.run_script} ' \
-                      f'--cfg={cfg_file}  2> {self.work_dir_pre}/std_out.txt'
-                print(cmd)
-                if not self.is_debug:
-                    res = os.system(cmd)
-                else:
-                    res = 0
-                if res != 0:
-                    error_info = '\n'.join(
-                        open(f'{self.work_dir_pre}/std_out.txt',
-                             'r').read().split('\n')[-20:])
-                    raise gr.Error(
-                        f'{self.component_names.training_err5} ({error_info}) '
-                    )
-
-            train_fn(cfg)
-
+            before_kill_inference = self.trainer_ins.check_memory()
+            for k, v in manager.inference.pipe_manager.pipeline_level_modules.items(
+            ):
+                if hasattr(v, 'dynamic_unload'):
+                    v.dynamic_unload(name='all')
+            after_kill_inference = self.trainer_ins.check_memory()
+            message = f'GPU info: {before_kill_inference}. \n\n'
+            message += f'After unloading inference models, the GPU info: {after_kill_inference}. \n\n'
+            _ = prepare_train_config()
+            self.trainer_ins.start_task(work_name)
+            message += self.trainer_ins.get_log(work_name)
             if work_name not in inference_ui.model_list:
                 inference_ui.model_list.append(work_name)
-            message = f'''
-                        Training completed! \n
-                        Save in [ {work_name} ] \n
-                        Take time [ {time.time() - st_time:.4f}s ] \n
-                        Mem: [ {print_memory_status(self.is_debug)} ]
-                        '''
-            print(message)
-            return message, gr.Dropdown.update(choices=inference_ui.model_list,
-                                               value=work_name)
+            gr.Info('Start Training!' + message)
+            return gr.Dropdown.update(choices=inference_ui.model_list,
+                                      value=work_name)
 
         self.training_button.click(
             run_train,
             inputs=[
                 self.work_name, self.data_type, self.ms_data_space,
                 self.ms_data_name, self.ms_data_subname, self.base_model,
-                self.base_model_revision, self.tuner_name, self.resolution,
+                self.base_model_revision, self.tuner_name,
+                self.resolution_height, self.resolution_width,
                 self.train_epoch, self.learning_rate, self.save_interval,
                 self.train_batch_size, self.prompt_prefix,
-                self.replace_keywords, self.push_to_hub
+                self.replace_keywords, self.push_to_hub, self.eval_prompts
             ],
-            outputs=[self.training_message, inference_ui.output_model_name],
+            outputs=[inference_ui.output_model_name],
             queue=True)

scepter/studio/self_train/utils/config_parser.py

@@ -10,7 +10,6 @@ paras_keys = [
     'MEMORY', 'EPOCHS', 'SAVE_INTERVAL', 'EPSEC', 'LEARNING_RATE',
     'IS_DEFAULT', 'TUNER'
 ]
-
 control_paras_keys = ['CONTROL_MODE', 'RESOLUTION', 'IS_DEFAULT']
 
 
@@ -25,10 +24,9 @@ def build_meta_index(meta_cfg, config_file):
                         f'Para key {key} not defined in {config_file} META/RARAS[{idx}]'
                     )
                 assert key in para
-            tuner_type[para['TUNER'] + '@' + str(para['RESOLUTION'])] = para
+            tuner_type[para['TUNER']] = para
             if para['IS_DEFAULT']:
-                tuner_type['default'] = para['TUNER'] + '@' + str(
-                    para['RESOLUTION'])
+                tuner_type['default'] = para['TUNER']
 
     tuner_type['choices'] = list(tuner_type.keys())
     if 'default' in tuner_type['choices']:
@@ -37,14 +35,6 @@ def build_meta_index(meta_cfg, config_file):
         tuner_type['default'] = tuner_type['choices'][0] if len(
             tuner_type['choices']) > 0 else ''
 
-    # 重新组织选项
-    choices = {}
-    for t_type in tuner_type['choices']:
-        t_name, resolution = t_type.split('@')
-        if t_name not in choices:
-            choices[t_name] = []
-        choices[t_name].append(int(resolution))
-    tuner_type['choices'] = choices
     tuner_paras = meta_cfg.get('TUNERS', None)
     return tuner_type, tuner_paras
 
@@ -60,13 +50,10 @@ def build_meta_index_control(meta_cfg, config_file):
                         f'Para key {key} not defined in {config_file} META/RARAS[{idx}]'
                     )
                 assert key in para
-            control_type[para['CONTROL_MODE'] + '@' +
-                         str(para['RESOLUTION'])] = para
+            control_type[para['CONTROL_MODE']] = para
             # control_type[para["CONTROL_MODE"]] = para
             if para['IS_DEFAULT']:
-                control_type['default'] = para['CONTROL_MODE'] + '@' + str(
-                    para['RESOLUTION'])
-                # control_type["default"] = para["CONTROL_MODE"]
+                control_type['default'] = para['CONTROL_MODE']
 
     control_type['choices'] = list(control_type.keys())
     if 'default' in control_type['choices']:
@@ -75,14 +62,6 @@ def build_meta_index_control(meta_cfg, config_file):
         control_type['default'] = control_type['choices'][0] if len(
             control_type['choices']) > 0 else ''
 
-    # 重新组织选项
-    choices = {}
-    for t_type in control_type['choices']:
-        t_name, resolution = t_type.split('@')
-        if t_name not in choices:
-            choices[t_name] = []
-        choices[t_name].append(int(resolution))
-    control_type['choices'] = choices
     return control_type, paras
 
 
@@ -166,26 +145,19 @@ def get_default(config_dict):
         return ret_data
     ret_data['version_choices'] = default_version_cfg['choices']
     ret_data['version_default'] = default_version_cfg['default']
-    default_tuner_cfg = default_version_cfg.get(default_version_cfg['default'],
+    default_model_cfg = default_version_cfg.get(default_version_cfg['default'],
                                                 None)
-    if default_tuner_cfg is None:
+    if default_model_cfg is None:
         return ret_data
-    if 'tuner_type' in default_tuner_cfg and default_tuner_cfg['tuner_type'][
+    if 'tuner_type' in default_model_cfg and default_model_cfg['tuner_type'][
             'default'] != '':
-        default_tuner_cfg = default_tuner_cfg['tuner_type']
+        default_tuner_cfg = default_model_cfg['tuner_type']
     else:
         return ret_data
-    ret_data['tuner_choices'] = list(default_tuner_cfg['choices'].keys())
+    ret_data['tuner_choices'] = default_tuner_cfg['choices']
     defalt_t_type = default_tuner_cfg['default']
+    ret_data['tuner_default'] = defalt_t_type
     type_paras = default_tuner_cfg.get(defalt_t_type, None)
-
-    default_t_n = defalt_t_type.split('@')[0]
-    default_r_n = int(defalt_t_type.split('@')[1])
-
-    ret_data['resolution_choices'] = default_tuner_cfg['choices'].get(
-        default_t_n, [])
-    ret_data['tuner_default'] = default_t_n
-    ret_data['resolution_default'] = default_r_n
     if type_paras is not None:
         ret_data.update(type_paras)
     return ret_data
@@ -198,21 +170,15 @@ def get_values_by_model(config_dict, model_name):
         return ret_data
     ret_data['version_choices'] = version_cfg['choices']
     ret_data['version_default'] = version_cfg['default']
-    default_tuner_cfg = version_cfg.get(version_cfg['default'], None)
-    if default_tuner_cfg is None:
+    default_model_cfg = version_cfg.get(version_cfg['default'], None)
+    if default_model_cfg is None:
         return ret_data
-    default_tuner_cfg = default_tuner_cfg['tuner_type']
-    ret_data['tuner_choices'] = list(default_tuner_cfg['choices'].keys())
+
+    default_tuner_cfg = default_model_cfg['tuner_type']
+    ret_data['tuner_choices'] = default_tuner_cfg['choices']
     defalt_t_type = default_tuner_cfg['default']
+    ret_data['tuner_default'] = defalt_t_type
     type_paras = default_tuner_cfg.get(defalt_t_type, None)
-
-    default_t_n = defalt_t_type.split('@')[0]
-    default_r_n = int(defalt_t_type.split('@')[1])
-
-    ret_data['resolution_choices'] = default_tuner_cfg['choices'].get(
-        default_t_n, [])
-    ret_data['tuner_default'] = default_t_n
-    ret_data['resolution_default'] = default_r_n
     if type_paras is not None:
         ret_data.update(type_paras)
     return ret_data
@@ -226,18 +192,12 @@ def get_values_by_model_version(config_dict, model_name, version):
     tuner_cfg = version_cfg.get(version, None)
     if tuner_cfg is None:
         return ret_data
+
     default_tuner_cfg = tuner_cfg['tuner_type']
-    ret_data['tuner_choices'] = list(default_tuner_cfg['choices'].keys())
+    ret_data['tuner_choices'] = default_tuner_cfg['choices']
     defalt_t_type = default_tuner_cfg['default']
+    ret_data['tuner_default'] = defalt_t_type
     type_paras = default_tuner_cfg.get(defalt_t_type, None)
-
-    default_t_n = defalt_t_type.split('@')[0]
-    default_r_n = int(defalt_t_type.split('@')[1])
-
-    ret_data['resolution_choices'] = default_tuner_cfg['choices'].get(
-        default_t_n, [])
-    ret_data['tuner_default'] = default_t_n
-    ret_data['resolution_default'] = default_r_n
     if type_paras is not None:
         ret_data.update(type_paras)
     return ret_data
@@ -249,34 +209,11 @@ def get_values_by_model_version_tuner(config_dict, model_name, version,
     version_cfg = config_dict.get(model_name, None)
     if version_cfg is None:
         return ret_data
-    tuner_cfg = version_cfg.get(version, None)
-    if tuner_cfg is None:
+    model_cfg = version_cfg.get(version, None)
+    if model_cfg is None:
         return ret_data
-    tuner_cfg = tuner_cfg['tuner_type']
-
-    ret_data['resolution_choices'] = tuner_cfg['choices'].get(tuner_name, [])
-
-    if len(ret_data['resolution_choices']) > 0:
-        t_type = '{}@{}'.format(tuner_name, ret_data['resolution_choices'][0])
-        ret_data['resolution_default'] = ret_data['resolution_choices'][0]
-        type_paras = tuner_cfg.get(t_type, None)
-        if type_paras is not None:
-            ret_data.update(type_paras)
-    return ret_data
-
-
-def get_values_by_model_version_tuner_resolution(config_dict, model_name,
-                                                 version, tuner_name,
-                                                 resolution):
-    ret_data = {}
-    version_cfg = config_dict.get(model_name, None)
-    if version_cfg is None:
-        return ret_data
-    tuner_cfg = version_cfg.get(version, None)
-    if tuner_cfg is None:
-        return ret_data
-    tuner_cfg = tuner_cfg['tuner_type']
-    type_paras = tuner_cfg.get('{}@{}'.format(tuner_name, resolution), None)
+    tuner_cfg = model_cfg['tuner_type']
+    type_paras = tuner_cfg.get(tuner_name, None)
     if type_paras is not None:
         ret_data.update(type_paras)
     return ret_data
@@ -371,15 +308,8 @@ def get_control_default(config_dict):
 
     ret_data['control_choices'] = list(default_control_cfg['choices'].keys())
     defalt_t_type = default_control_cfg['default']
+    ret_data['control_default'] = defalt_t_type
     type_paras = default_control_cfg.get(defalt_t_type, None)
-
-    default_t_n = defalt_t_type.split('@')[0]
-    default_r_n = int(defalt_t_type.split('@')[1])
-
-    ret_data['resolution_choices'] = default_control_cfg['choices'].get(
-        default_t_n, [])
-    ret_data['control_default'] = default_t_n
-    ret_data['resolution_default'] = default_r_n
     if type_paras is not None:
         ret_data.update(type_paras)
     return ret_data

scepter/studio/tuner_manager/manager_ui/browser_ui.py

@@ -0,0 +1,322 @@
+# -*- coding: utf-8 -*-
+import os
+from collections import OrderedDict
+
+import gradio as gr
+
+from scepter.modules.utils.config import Config
+from scepter.modules.utils.file_system import FS
+from scepter.studio.tuner_manager.manager_ui.component_names import \
+    TunerManagerNames
+from scepter.studio.tuner_manager.utils.dict import (delete_2level_dict,
+                                                     update_2level_dict)
+from scepter.studio.tuner_manager.utils.path import is_valid_filename
+from scepter.studio.tuner_manager.utils.yaml import save_yaml
+from scepter.studio.utils.uibase import UIBase
+
+
+class BrowserUI(UIBase):
+    def __init__(self, cfg, language='en'):
+        self.work_dir = cfg.WORK_DIR
+        self.yaml = os.path.join(self.work_dir, cfg.TUNER_LIST_YAML)
+        if not FS.exists(self.yaml):
+            self.saved_tuners = []
+            with FS.put_to(self.yaml) as local_path:
+                save_yaml({'TUNERS': self.saved_tuners}, local_path)
+        else:
+            with FS.get_from(self.yaml) as local_path:
+                self.saved_tuners = Config.get_plain_cfg(
+                    Config(cfg_file=local_path).TUNERS)
+        self.saved_tuners_to_category()
+        self.component_names = TunerManagerNames(language)
+        self.language = language
+
+    def saved_tuners_to_category(self):
+        self.saved_tuners_category = OrderedDict()
+        for tuner in self.saved_tuners:
+            first_level = f"{tuner['BASE_MODEL']}-{tuner['TUNER_TYPE']}"
+            second_level = f"{tuner['NAME']}"
+            update_2level_dict(self.saved_tuners_category,
+                               {first_level: {
+                                   second_level: tuner
+                               }})
+
+    def category_to_saved_tuners(self):
+        self.saved_tuners = []
+        for k, v in self.saved_tuners_category.items():
+            for kk, vv in v.items():
+                self.saved_tuners.append(vv)
+
+    def get_choices_and_values(self):
+        diffusion_models_choice = list(self.saved_tuners_category.keys())
+        diffusion_model = diffusion_models_choice[0] if len(
+            diffusion_models_choice) > 0 else None
+        tuner_models_choice = []
+        tuner_model = None
+        if diffusion_model:
+            tuner_models_choice = list(
+                self.saved_tuners_category.get(diffusion_model, {}).keys())
+            tuner_model = tuner_models_choice[0] if len(
+                tuner_models_choice) > 0 else None
+        return diffusion_models_choice, diffusion_model, tuner_models_choice, tuner_model
+
+    def create_ui(self, *args, **kwargs):
+        diffusion_models_choice, diffusion_model, tuner_models_choice, tuner_model = self.get_choices_and_values(
+        )
+        with gr.Column():
+            with gr.Box():
+                gr.Markdown(self.component_names.browser_block_name)
+                with gr.Row(variant='panel', equal_height=True):
+                    with gr.Column(scale=4, min_width=0):
+                        self.diffusion_models = gr.Dropdown(
+                            label=self.component_names.base_models,
+                            choices=diffusion_models_choice,
+                            value=diffusion_model,
+                            multiselect=False,
+                            interactive=True)
+                    with gr.Column(scale=4, min_width=0):
+                        self.tuner_models = gr.Dropdown(
+                            label=self.component_names.tuner_name,
+                            choices=tuner_models_choice,
+                            value=tuner_model,
+                            multiselect=False,
+                            interactive=True)
+                    with gr.Column(scale=1, min_width=0):
+                        self.save_button = gr.Button(
+                            label='Save',
+                            value=self.component_names.save_symbol,
+                            elem_classes='type_row',
+                            elem_id='save_button',
+                            visible=True)
+                        self.delete_button = gr.Button(
+                            label='Delete',
+                            value=self.component_names.delete_symbol,
+                            elem_classes='type_row',
+                            elem_id='delete_button',
+                            visible=False)
+                    with gr.Column(scale=1, min_width=0):
+                        self.refresh_button = gr.Button(
+                            label='Delete',
+                            value=self.component_names.refresh_symbol,
+                            elem_classes='type_row',
+                            elem_id='refresh_button',
+                            visible=True)
+
+    def check_new_name(self, tuner_name):
+        if tuner_name.strip() == '':
+            return False, f"'{tuner_name}' is whitespace! Only support 'a-z', 'A-Z', '0-9' and '_'."
+        if not is_valid_filename(tuner_name):
+            return False, f"'{tuner_name}' is not a valid tuner name! Only support 'a-z', 'A-Z', '0-9' and '_'."
+        for tuner in self.saved_tuners:
+            if tuner_name == tuner['NAME']:
+                return False, f"Tuner name '{tuner_name}' has been taken!"
+        return True, 'legal'
+
+    def save_tuner(self, src_path, sub_dir, tuner_name, tuner_example):
+        tar_path = os.path.join(self.work_dir, sub_dir)
+        if not FS.exists(tar_path):
+            FS.make_dir(tar_path)
+        tar_path = os.path.join(tar_path, tuner_name)
+
+        FS.put_dir_from_local_dir(src_path, tar_path)
+
+        tuner_example_path = None
+        if tuner_example is not None:
+            from PIL import Image
+            tuner_example_path = os.path.join(tar_path, f'{tuner_name}.jpg')
+            tuner_example = Image.fromarray(tuner_example)
+            with FS.put_to(tuner_example_path) as local_path:
+                tuner_example.save(local_path)
+        return tar_path, tuner_example_path
+
+    def add_tuner(self, new_tuner, manager, now_diffusion_model):
+        self.saved_tuners.append(new_tuner)
+        self.saved_tuners_to_category()
+        with FS.put_to(self.yaml) as local_path:
+            save_yaml({'TUNERS': self.saved_tuners}, local_path)
+
+        # register to pipeline
+        new_tuner = Config(cfg_dict=new_tuner, load=False)
+        manager.inference.model_manage_ui.pipe_manager.register_tuner(
+            new_tuner,
+            name=new_tuner.NAME_ZH
+            if self.language == 'zh' else new_tuner.NAME,
+            is_customized=True)
+
+        # update choices
+        pipe_manager = manager.inference.model_manage_ui.pipe_manager
+        now_pipeline = pipe_manager.model_level_info[now_diffusion_model][
+            'pipeline'][0]
+        default_choices = pipe_manager.module_level_choices
+        custom_tunner_choices = []
+        if 'customized_tuners' in default_choices and now_pipeline in default_choices[
+                'customized_tuners']:
+            custom_tunner_choices = default_choices['customized_tuners'][
+                now_pipeline]['choices']
+
+        # update tuner ui name_level_tuners
+        name_level_tuners = manager.inference.tuner_ui.name_level_tuners
+        if new_tuner.BASE_MODEL not in name_level_tuners:
+            name_level_tuners[new_tuner.BASE_MODEL] = {}
+        if self.language == 'zh':
+            name_level_tuners[new_tuner.BASE_MODEL][
+                new_tuner.NAME_ZH] = new_tuner
+        else:
+            name_level_tuners[new_tuner.BASE_MODEL][new_tuner.NAME] = new_tuner
+
+        return custom_tunner_choices
+
+    def delete_tuner(self, first_level, second_level, manager,
+                     now_diffusion_model):
+        self.saved_tuners_category, del_tuner = delete_2level_dict(
+            self.saved_tuners_category, first_level, second_level)
+        self.category_to_saved_tuners()
+        save_yaml({'TUNERS': self.saved_tuners}, self.yaml)
+
+        # update choices
+        pipe_manager = manager.inference.model_manage_ui.pipe_manager
+        now_pipeline = pipe_manager.model_level_info[now_diffusion_model][
+            'pipeline'][0]
+        default_choices = pipe_manager.module_level_choices
+        custom_tuner_choices = []
+        if 'customized_tuners' in default_choices and now_pipeline in default_choices[
+                'customized_tuners']:
+            custom_tuner_choices = default_choices['customized_tuners'][
+                now_pipeline]['choices']
+
+        # update tuner ui name_level_tuners
+        del_tuner = Config(cfg_dict=del_tuner, load=False)
+        name_level_tuners = manager.inference.tuner_ui.name_level_tuners
+        if del_tuner.BASE_MODEL in name_level_tuners:
+            if self.language == 'zh':
+                del name_level_tuners[del_tuner.BASE_MODEL][del_tuner.NAME_ZH]
+            else:
+                del name_level_tuners[del_tuner.BASE_MODEL][del_tuner.NAME]
+
+        return custom_tuner_choices
+
+    def set_callbacks(self, manager, info_ui):
+        def refresh_browser():
+            diffusion_models_choice, diffusion_model, tuner_models_choice, tuner_model = self.get_choices_and_values(
+            )
+            return (gr.Dropdown(choices=diffusion_models_choice,
+                                value=diffusion_model),
+                    gr.Dropdown(choices=tuner_models_choice,
+                                value=tuner_model))
+
+        self.refresh_button.click(
+            refresh_browser,
+            inputs=[],
+            outputs=[self.diffusion_models, self.tuner_models])
+
+        def diffusion_model_change(diffusion_model):
+            choices = list(
+                self.saved_tuners_category.get(diffusion_model, {}).keys())
+            return gr.Dropdown(choices=choices,
+                               value=choices[-1] if len(choices) > 0 else None)
+
+        self.diffusion_models.change(diffusion_model_change,
+                                     inputs=[self.diffusion_models],
+                                     outputs=[self.tuner_models],
+                                     queue=True)
+
+        def tuner_model_change(tuner_model, diffusion_model):
+            tuner_info = {}
+            if tuner_model is not None:
+                tuner_info = self.saved_tuners_category[diffusion_model][
+                    tuner_model]
+            image_path = tuner_info.get('IMAGE_PATH', None)
+            if image_path is not None:
+                image_path = FS.get_from(image_path)
+            return (gr.Text(value=tuner_info.get('NAME', '')),
+                    gr.Text(value=tuner_info.get('NAME', ''),
+                            interactive=True),
+                    gr.Text(value=tuner_info.get('TUNER_TYPE', '')),
+                    gr.Text(value=tuner_info.get('BASE_MODEL', '')),
+                    gr.Text(value=tuner_info.get('DESCRIPTION', ''),
+                            interactive=True), gr.Image(value=image_path),
+                    gr.Text(value=tuner_info.get('PROMPT_EXAMPLE', ''),
+                            interactive=True))
+
+        self.tuner_models.change(
+            tuner_model_change,
+            inputs=[self.tuner_models, self.diffusion_models],
+            outputs=[
+                info_ui.tuner_name, info_ui.new_name, info_ui.tuner_type,
+                info_ui.base_model, info_ui.tuner_desc, info_ui.tuner_example,
+                info_ui.tuner_prompt_example
+            ],
+            queue=False)
+
+        def save_tuner(tuner_name, new_name, tuner_desc, tuner_example,
+                       tuner_prompt_example, now_diffusion_model, info_path):
+            is_legal, msg = self.check_new_name(new_name)
+            if not is_legal:
+                gr.Info('Save failed because ' + msg)
+                return (gr.Dropdown(), gr.Text(), gr.Dropdown())
+
+            info = Config(cfg_file=info_path)
+            model_dir = info.MODEL_PATH
+            sub_dir = f'{info.BASE_MODEL}-{info.TUNER_TYPE}'
+            model_dir, tuner_example = self.save_tuner(model_dir, sub_dir,
+                                                       new_name, tuner_example)
+
+            # config info update
+            new_tuner = {
+                'NAME': new_name,
+                'NAME_ZH': new_name,
+                'SOURCE': 'self_train',
+                'DESCRIPTION': tuner_desc,
+                'BASE_MODEL': info.BASE_MODEL,
+                'MODEL_PATH': model_dir,
+                'IMAGE_PATH': tuner_example,
+                'TUNER_TYPE': info.TUNER_TYPE,
+                'PROMPT_EXAMPLE': tuner_prompt_example
+            }
+            custom_tuner_choices = self.add_tuner(new_tuner, manager,
+                                                  now_diffusion_model)
+
+            return (gr.Dropdown(choices=list(
+                self.saved_tuners_category.keys()),
+                                value=sub_dir),
+                    gr.Dropdown(choices=list(
+                        self.saved_tuners_category.get(sub_dir, {}).keys()),
+                                value=new_name), gr.Text(value=new_name),
+                    gr.Dropdown(choices=custom_tuner_choices))
+
+        self.save_button.click(
+            save_tuner,
+            inputs=[
+                info_ui.tuner_name, info_ui.new_name, info_ui.tuner_desc,
+                info_ui.tuner_example, info_ui.tuner_prompt_example,
+                manager.inference.model_manage_ui.diffusion_model,
+                manager.inference.infer_info
+            ],
+            outputs=[
+                self.diffusion_models, self.tuner_models, info_ui.tuner_name,
+                manager.inference.tuner_ui.custom_tuner_model
+            ],
+            queue=True)
+
+        def delete_tuner(tuner_name, tuner_type, base_model,
+                         now_diffusion_model):
+            first_level = f'{base_model}-{tuner_type}'
+            second_level = f'{tuner_name}'
+            custom_tuner_choices = self.delete_tuner(first_level, second_level,
+                                                     manager,
+                                                     now_diffusion_model)
+            return (gr.Dropdown(
+                choices=list(self.saved_tuners_category.keys()),
+                value=None), gr.Dropdown(choices=custom_tuner_choices))
+
+        self.delete_button.click(
+            delete_tuner,
+            inputs=[
+                info_ui.tuner_name, info_ui.tuner_type, info_ui.base_model,
+                manager.inference.model_manage_ui.diffusion_model
+            ],
+            outputs=[
+                self.diffusion_models,
+                manager.inference.tuner_ui.custom_tuner_model
+            ],
+            queue=True)

scepter/studio/tuner_manager/manager_ui/component_names.py

@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+# For dataset manager
+
+
+class TunerManagerNames():
+    def __init__(self, language='en'):
+        self.save_symbol = '\U0001F4BE'  # 💾
+        self.delete_symbol = '\U0001f5d1'  # 🗑️
+        self.refresh_symbol = '\U0001f504'  # 🔄
+        if language == 'en':
+            self.browser_block_name = 'Tuner Browser'
+            self.base_models = 'Base Model-Tuner Type'
+            self.tuner_models = 'Tuner Name'
+            self.info_block_name = 'Tuner Info'
+            self.tuner_name = 'Tuner Name'
+            self.rename = 'Rename'
+            self.tuner_type = 'Tuner Type'
+            self.base_model_name = 'Base Model Name'
+            self.tuner_desc = 'Tuner Description'
+            self.tuner_example = 'Results Example'
+            self.tuner_prompt_example = 'Prompt Example'
+            self.save = 'save changes'
+            self.delete = 'Delete'
+        elif language == 'zh':
+            self.browser_block_name = '微调模型查找'
+            self.base_models = '基模型-微调类型'
+            self.tuner_models = '微调模型名称'
+            self.info_block_name = '微调模型详情'
+            self.tuner_name = '微调模型名称'
+            self.rename = '重命名'
+            self.tuner_type = '微调模型类型'
+            self.base_model_name = '基模型名称'
+            self.tuner_desc = '微调模型描述'
+            self.tuner_example = '示例结果'
+            self.tuner_prompt_example = '示例提示词'
+            self.save = '保存修改'
+            self.delete = '删除'

scepter/studio/tuner_manager/manager_ui/info_ui.py

@@ -0,0 +1,64 @@
+# -*- coding: utf-8 -*-
+
+import gradio as gr
+
+from scepter.studio.tuner_manager.manager_ui.component_names import \
+    TunerManagerNames
+from scepter.studio.utils.uibase import UIBase
+
+
+class InfoUI(UIBase):
+    def __init__(self, cfg, language='en'):
+        self.component_names = TunerManagerNames(language)
+
+    def create_ui(self, *args, **kwargs):
+        with gr.Column():
+            with gr.Box():
+                gr.Markdown(self.component_names.info_block_name)
+                with gr.Row(variant='panel', equal_height=True):
+                    with gr.Column(variant='panel', scale=1, min_width=0):
+                        with gr.Group(visible=True):
+                            with gr.Row(equal_height=True):
+                                with gr.Column(scale=1):
+                                    self.tuner_name = gr.Text(
+                                        value='',
+                                        label=self.component_names.tuner_name,
+                                        interactive=False)
+                                with gr.Column(scale=1):
+                                    self.new_name = gr.Text(
+                                        value='',
+                                        label=self.component_names.rename)
+                            with gr.Row(equal_height=True):
+                                with gr.Column(scale=1):
+                                    self.tuner_type = gr.Text(
+                                        value='',
+                                        label=self.component_names.tuner_type,
+                                        interactive=False)
+                                with gr.Column(scale=1):
+                                    self.base_model = gr.Text(
+                                        value='',
+                                        label=self.component_names.
+                                        base_model_name,
+                                        interactive=False)
+                                with gr.Column(scale=1):
+                                    self.tuner_desc = gr.Text(
+                                        value='',
+                                        label=self.component_names.tuner_desc,
+                                        lines=4)
+                    with gr.Column(variant='panel', scale=1, min_width=0):
+                        with gr.Group(visible=True):
+                            with gr.Row(equal_height=True):
+                                self.tuner_example = gr.Image(
+                                    label=self.component_names.tuner_example,
+                                    source='upload',
+                                    value=None,
+                                    interactive=True)
+                            with gr.Row(equal_height=True):
+                                self.tuner_prompt_example = gr.Text(
+                                    value='',
+                                    label=self.component_names.
+                                    tuner_prompt_example,
+                                    lines=2)
+
+    def set_callbacks(self, manager):
+        pass

scepter/studio/tuner_manager/tuner_manager.py

@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+import os.path
+
+from scepter.modules.utils.config import Config
+from scepter.modules.utils.file_system import FS
+from scepter.studio.tuner_manager.manager_ui.browser_ui import BrowserUI
+from scepter.studio.tuner_manager.manager_ui.info_ui import InfoUI
+from scepter.studio.utils.env import init_env
+
+
+class TunerManagerUI():
+    def __init__(self,
+                 cfg_general_file,
+                 is_debug=False,
+                 language='en',
+                 root_work_dir='./'):
+        cfg_general = Config(cfg_file=cfg_general_file)
+        cfg_general.WORK_DIR = os.path.join(root_work_dir,
+                                            cfg_general.WORK_DIR)
+        if not FS.exists(cfg_general.WORK_DIR):
+            FS.make_dir(cfg_general.WORK_DIR)
+
+        cfg_general = init_env(cfg_general)
+        self.info_ui = InfoUI(cfg_general, language=language)
+        self.browser_ui = BrowserUI(cfg_general, language=language)
+
+    def create_ui(self):
+        self.browser_ui.create_ui()
+        self.info_ui.create_ui()
+
+    def set_callbacks(self, manager):
+        self.info_ui.set_callbacks(manager)
+        self.browser_ui.set_callbacks(manager, self.info_ui)

scepter/studio/tuner_manager/utils/dict.py

@@ -0,0 +1,16 @@
+# -*- coding: utf-8 -*-
+def update_2level_dict(d, new_dict):
+    for first, v in new_dict.items():
+        if first in d:
+            d[first].update(v)
+        else:
+            d[first] = v
+    return d
+
+
+def delete_2level_dict(d, first_key, second_key):
+    first = d.pop(first_key)
+    second = first.pop(second_key)
+    if len(first) > 0:
+        d.update({first_key: first})
+    return d, second

scepter/studio/tuner_manager/utils/path.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+import re
+
+
+def is_valid_filename(filename):
+    if re.match('^[A-Za-z0-9_@]+$', filename):
+        return True
+    else:
+        return False

scepter/studio/tuner_manager/utils/yaml.py

@@ -0,0 +1,11 @@
+# -*- coding: utf-8 -*-
+import yaml
+
+
+def save_yaml(data, file_path):
+    with open(file_path, 'w') as f_out:
+        yaml.dump(data,
+                  f_out,
+                  encoding='utf-8',
+                  allow_unicode=True,
+                  default_flow_style=False)