modify edit

2024-12-08 00:31:50 +08:00 · 2024-12-08 00:28:11 +08:00 · 2024-12-08 00:22:43 +08:00 · 2024-12-08 00:16:03 +08:00 · 2024-12-05 15:20:00 +08:00 · 2024-11-25 14:24:54 +08:00
470 changed files with 62863 additions and 4507 deletions
@@ -9,12 +9,12 @@
 *.bin
 *.idea
 *.csv
 cache
 build
 dist
 dev
 scepter.egg-info
 .readthedocs.yml
 1.9
 MANIFEST.in
 *resources
 *.ipynb_checkpoints*
 *.vscode
@@ -1 +1,2 @@
 recursive-include scepter *.yaml
 recursive-include scepter *.md
@@ -0,0 +1,2 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
@@ -0,0 +1,163 @@
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        "This is a sample for quickly setting up ComfyUI using the Scepter open-source library:\n\n1) The first run involves downloading the models. By default, we will automatically pull models from ModelScope. The initial download may take some time, and you can also adjust the model source to change the model address.\n\n2) Currently, it supports various base models, basic settings for some inference hyperparameters, mantra settings, tuning model settings, and conditional generation node.\n\n3) In the future, we will gradually integrate interesting features to enrich the use cases.\n\n"
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@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 # Configuration file for the Sphinx documentation builder.
 #
 # This file only contains a selection of the most common options. For a full
@@ -14,8 +14,8 @@ Model modules are divided into backbones, necks, heads, loss, metrics, networks,
 Subclass registration:
 ```python
-from scepter.model.registry import BACKBONES
+from scepter.modules.model.registry import BACKBONES
-from scepter.model.base_model import BaseModel
+from scepter.modules.model.base_model import BaseModel
@BACKBONES.register_class("ResNet")
@@ -25,8 +25,8 @@ class ResNet(BaseModel):
 ```
 ```python
-from scepter.model.registry import NECKS
+from scepter.modules.model.registry import NECKS
-from scepter.model.base_model import BaseModel
+from scepter.modules.model.base_model import BaseModel
@NECKS.register_class()
@@ -36,8 +36,8 @@ class GlobalAveragePooling(BaseModel):
 ```
 ```python
-from scepter.model.registry import HEADS
+from scepter.modules.model.registry import HEADS
-from scepter.model.base_model import BaseModel
+from scepter.modules.model.base_model import BaseModel
@HEADS.register_class()
@@ -47,7 +47,7 @@ class ClassifierHead(BaseModel):
 ```
 ```python
-from scepter.model.registry import LOSSES
+from scepter.modules.model.registry import LOSSES
 import torch.nn as nn
@@ -59,7 +59,7 @@ class CrossEntropy(nn.Module):
 Actual usage：
 ```python
-from scepter.model.registry import BACKBONES, NECKS, HEADS, LOSSES
+from scepter.modules.model.registry import BACKBONES, NECKS, HEADS, LOSSES
 backbone = BACKBONES.build(cfg.BACKBONE, logger=logger)
 neck = NECKS.build(cfg.NECK, logger=logger)
@@ -83,8 +83,8 @@ To be implemented specifically as needed;
 Basic Usage Subclass registration:
 ```python
-from scepter.model.metrics.registry import METRICS
+from scepter.modules.model.metrics.registry import METRICS
-from scepter.model.metrics.base_metric import BaseMetric
+from scepter.modules.model.metrics.base_metric import BaseMetric
@METRICS.register_class("AccuracyMetric")
@@ -95,7 +95,7 @@ class AccuracyMetric(BaseMetric):
 Actual usage:
 ```python
-from scepter.model.metrics.registry import METRICS
+from scepter.modules.model.metrics.registry import METRICS
 metric = METRICS.build(cfgs, logger)
 ```
@@ -117,8 +117,8 @@ Typically takes logits and labels as well as other necessary variables as inputs
 Subclass registration:
 ```python
-from scepter.model.registry import TOKENIZERS
+from scepter.modules.model.registry import TOKENIZERS
-from scepter.model.tokenizers import BaseTokenizer
+from scepter.modules.model.tokenizers import BaseTokenizer
@TOKENIZERS.register_class()
@@ -129,7 +129,7 @@ class BaseBertTokenizer(BaseTokenizer):
 Actual usage：
 ```python
-from scepter.model.registry import TOKENIZERS
+from scepter.modules.model.registry import TOKENIZERS
 tokenizer = TOKENIZERS.build(cfgs, logger)
 ```
@@ -147,8 +147,8 @@ Takes a list of texts that need tokenization as input and outputs token id seque
 Subclass registration:
 ```python
-from scepter.model.registry import MODELS
+from scepter.modules.model.registry import MODELS
-from scepter.model.networks.train_module import TrainModule
+from scepter.modules.model.networks.train_module import TrainModule
@MODELS.register_class()
@@ -159,7 +159,7 @@ class Classifier(TrainModule):
 Actual usage：
 ```python
-from scepter.model.registry import MODELS
+from scepter.modules.model.registry import MODELS
 model = MODELS.build(self.cfg.MODEL, logger=self.logger)
 ```
@@ -9,8 +9,8 @@
 Usage when subclassing lr_schedulers:
 ```python
-from scepter.opt.lr_schedulers import LR_SCHEDULERS
+from scepter.modules.opt.lr_schedulers import LR_SCHEDULERS
-from scepter.opt.lr_schedulers.base_scheduler import BaseScheduler
+from scepter.modules.opt.lr_schedulers.base_scheduler import BaseScheduler
@LR_SCHEDULERS.register_class()
@@ -48,8 +48,8 @@ Sets up the schedule for the passed-in optimizer object;
 Usage when subclassing optimizers:
 ```python
-from scepter.opt.optimizers.base_optimizer import BaseOptimize
+from scepter.modules.opt.optimizers.base_optimizer import BaseOptimize
-from scepter.opt.optimizers.registry import OPTIMIZERS
+from scepter.modules.opt.optimizers.registry import OPTIMIZERS
@OPTIMIZERS.register_class()
@@ -6,17 +6,17 @@ This is the File System Module, designed to handle file transfer functionalities
 The component currently supports three types of IO Handler:
-1. scepter.utils.file_clients.AliyunOssFs
+1. scepter.modules.utils.file_clients.AliyunOssFs
-2. scepter.utils.file_clients.LocalFs
+2. scepter.modules.utils.file_clients.LocalFs
-3. scepter.utils.file_clients.HttpFs
+3. scepter.modules.utils.file_clients.HttpFs
 <hr/>
 ## Basic Usage
 ```python
-from scepter.utils.file_system import FS
+from scepter.modules.utils.file_system import FS
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
 fs_cfg = Config(load=False, cfg_dict={
    "NAME": "AliyunOssFs",
@@ -4,18 +4,18 @@ Relies on SDKs, which are used to organize modules and SDKs that are frequently
 ## Overview
-1. Parameter sdk (scepter.utils.config)
+1. Parameter sdk (scepter.modules.utils.config)
-2. Path sdk (scepter.utils.directory)
+2. Path sdk (scepter.modules.utils.directory)
-3. PyTorch distributed sdk (scepter.utils.distribute)
+3. PyTorch distributed sdk (scepter.modules.utils.distribute)
-4. Model export sdk (scepter.utils.export_model)
+4. Model export sdk (scepter.modules.utils.export_model)
-5. File system sdk (scepter.utils.file_system)
+5. File system sdk (scepter.modules.utils.file_system)
-6. Logging sdk (scepter.utils.logger)
+6. Logging sdk (scepter.modules.utils.logger)
-7. Video processing sdk (scepter.utils.video_reader), see the document (video_reader.md)
+7. Video processing sdk (scepter.modules.utils.video_reader), see the document (video_reader.md)
-8. Module registration sdk (scepter.utils.registry)
+8. Module registration sdk (scepter.modules.utils.registry)
-9. Data sdk (scepter.utils.data)
+9. Data sdk (scepter.modules.utils.data)
-10. Model sdk (scepter.utils.model)
+10. Model sdk (scepter.modules.utils.model)
-11. Sampler sdk (scepter.utils.sampler)
+11. Sampler sdk (scepter.modules.utils.sampler)
-12. Probing sdk (scepter.utils.probe)
+12. Probing sdk (scepter.modules.utils.probe)
 <hr/>
@@ -24,7 +24,7 @@ Relies on SDKs, which are used to organize modules and SDKs that are frequently
 ### Basic Usage
 ```python
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
 # Initialize Config object from a dict
 fs_cfg = Config(load=False, cfg_dict={"NAME": "LocalFs"})
 print(fs_cfg.NAME)
@@ -105,7 +105,7 @@ print(fs_cfg.args)
 Some commonly used path functions
 ### Basic Usage
 ```python
-from scepter.utils.directory import osp_path
+from scepter.modules.utils.directory import osp_path
 # Automatically join paths based on the path prefix
 prefix = "xxxx"
 data_file = "example_videos/1.mp4"
@@ -114,13 +114,13 @@ print(osp_path(prefix, data_file))
 # Also outputs as xxxx/example_videos/1.mp4
 data_file = "xxxx/example_videos/1.mp4"
 print(osp_path(prefix, data_file))
-from scepter.utils.directory import get_relative_folder
+from scepter.modules.utils.directory import get_relative_folder
 # Get the folder path at a specified level according to the path
 # By default, the last level xxxx/example_videos/
 print(get_relative_folder(data_file))
 # The second last level xxxx/
 print(get_relative_folder(data_file, keep_index=-2))
-from scepter.utils.directory import get_md5
+from scepter.modules.utils.directory import get_md5
 # Get the md5 code of the text/path 34a447fb46d0b786a3999c9dad01d470
 print(get_md5(data_file))
 ```
@@ -175,8 +175,8 @@ PyTorch distributed initialization SDK. By using this SDK, users can avoid focus
 ### Basic Usage
 ```python
-from scepter.utils.distribute import we
+from scepter.modules.utils.distribute import we
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
 cfg = Config(cfg_dict={}, load=False)
@@ -304,12 +304,12 @@ Since cloning is involved, this may cause additional GPU memory waste.
 **Returns**
  - **tensor** —— The output tensor on the CPU for process rank=0.
-## 4. 模型导出sdk(scepter.utils.export_model)
+## 4. 模型导出sdk(scepter.modules.utils.export_model)
 APIs for exporting models to TorchScript/ONNX formats.
 ### Basic Usage
 ```python
-from scepter.utils.export_model import save_develop_model_multi_io
+from scepter.modules.utils.export_model import save_develop_model_multi_io
 save_develop_model_multi_io(
    model,
@@ -345,16 +345,16 @@ Supports importing and exporting models with multiple inputs and outputs
 **Returns**
  - **tensor** —— The output tensor on the CPU for process rank=0.
-## 5. 文件系统sdk(scepter.utils.file_system)
+## 5. 文件系统sdk(scepter.modules.utils.file_system)
 Refer to [file_clients](file_clients.md)
-## 6. Logging SDK(scepter.utils.logger)
+## 6. Logging SDK(scepter.modules.utils.logger)
 Used to instantiate a standard logging instance for printing information.
 ### Basic Usage
 ```python
-from scepter.utils.logger import get_logger, init_logger
+from scepter.modules.utils.logger import get_logger, init_logger
 std_logger = get_logger(name="scepter")
 init_logger(std_logger, log_file="", dist_launcher="pytorch")
@@ -405,14 +405,14 @@ Calculate the time remaining until completion based on the current usage time an
 **Returns**
  - **str** —— Formatted output.
-## 7. Video Processing SDK (scepter.utils.video_reader)
+## 7. Video Processing SDK (scepter.modules.utils.video_reader)
 APIs for handling video reading.
 ### Basic Usage
 ```python
-from scepter.utils.video_reader.frame_sampler import do_frame_sample
+from scepter.modules.utils.video_reader.frame_sampler import do_frame_sample
-from scepter.utils.video_reader.video_reader import (
+from scepter.modules.utils.video_reader.video_reader import (
    VideoReaderWrapper, EasyVideoReader, FramesReaderWrapper
 )
 ```
@@ -554,14 +554,14 @@ Iterator, with each iteration returning a tensor of a segment.
 **Returns**
  - **tensor** —— The tensor of the video segment.
-## 8. Module Registration SDK (scepter.utils.registry)
+## 8. Module Registration SDK (scepter.modules.utils.registry)
 Used for managing various registered classes.
 ### Basic Usage
 ```python
-from scepter.utils.registry import Registry
+from scepter.modules.utils.registry import Registry
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
 MODELS = Registry('MODELS')
@@ -614,14 +614,14 @@ Register a function
 **Returns**
  - **name** —— Registration name.
-## 9. Data SDK(scepter.utils.data)
+## 9. Data SDK(scepter.modules.utils.data)
 Used for transferring data between devices
 ### Basic Usage
 ```python
 import torch
-from scepter.utils.data import transfer_data_to_numpy, transfer_data_to_cpu, transfer_data_to_cuda
+from scepter.modules.utils.data import transfer_data_to_numpy, transfer_data_to_cpu, transfer_data_to_cuda
 data = {"a": torch.Tensor([0])}
 transfer_data_to_numpy(data)
@@ -668,7 +668,7 @@ Used for operations such as loading and evaluating models
 ```python
 import torch
-from scepter.utils.model import move_model_to_cpu, load_pretrained,
+from scepter.modules.utils.model import move_model_to_cpu, load_pretrained,
    count_params, init_weights
 ```
 <hr/>
@@ -716,14 +716,14 @@ Initialize the parameters of the model modules.
 **Parameters**
  - **module** —— The torch.nn.Module model instance.
-## 11. Sampler SDK(scepter.utils.sampler)
+## 11. Sampler SDK(scepter.modules.utils.sampler)
 Samplers are quite universal, and in most cases, custom development is not required. Here are provided several common types of sampler.
 ### Basic Usage
 ```python
 import torch
-from scepter.utils.sampler import MultiFoldDistributedSampler,
+from scepter.modules.utils.sampler import MultiFoldDistributedSampler,
    EvalDistributedSampler, MultiLevelBatchSampler, MixtureOfSamplers
 ```
 <hr/>
@@ -830,17 +830,17 @@ A sampler for multi-level indexing of large-scale data.
 Iterator, each iteration returns an index of a sample.
-## 12. Prober SDK(scepter.utils.probe)
+## 12. Prober SDK(scepter.modules.utils.probe)
 Used for probing variable statistics of various components.
 ### Basic Usage
 ```python
 import numpy as np
-from scepter.model.base_model import BaseModel
+from scepter.modules.model.base_model import BaseModel
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
-from scepter.utils.file_system import FS
+from scepter.modules.utils.file_system import FS
-from scepter.utils.probe import ProbeData
+from scepter.modules.utils.probe import ProbeData
 class TestModel(BaseModel):
@@ -0,0 +1,133 @@
 <h1 align="center"> Locate, Assign, Refine: Taming Customized Image Inpainting with Text-Subject Guidance </h1>
 <p align="center">
    <strong>Yulin Pan</strong>
    ·
    <strong>Chaojie Mao</strong>
    ·
    <strong>Zeyinzi Jiang</strong>
    ·
    <strong>Zhen Han</strong>
    ·
    <strong>Jingfeng Zhang</strong>
    <br>
    <a href="https://arxiv.org/abs/2403.19534"><img src="https://img.shields.io/static/v1?label=arXiv&message=LARGen&color=red&logo=arxiv"></a>
    <a href="https://ali-vilab.github.io/largen-page/"><img src="https://img.shields.io/badge/Page-LARGen-Gree"></a>
 </p>
 LARGen is a unified image inpainting framework that supports text-guided, subject-guided and text-subject-guided inpainting simutaneously.
 Four LARGen-based fantastic applications are now supported by SCEPTER Studio:
 1. Zoom Out
 2. Virtual Try On
 3. Text-Guided Inpainting
 4. Text-Subject-Guided Inpainting
 ## Basic Usage
 Here's a demo showcasing the use of LARGen-based functions.
 <p align="left">
 <img src="https://raw.githubusercontent.com/ali-vilab/largen-page/main/public/images/largen.gif" width="1300">
 </p>
 ## 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.jpg" width="240"></td>
    <td><img src="../../../asset/images/zoom_out/ex1_zoom_out1.jpg" width="240"></td>
    <td><img src="../../../asset/images/zoom_out/ex1_zoom_out2.jpg" width="240"></td>
    <td><img src="../../../asset/images/zoom_out/ex1_zoom_out3.jpg" width="240"></td>
    <td><img src="../../../asset/images/zoom_out/ex1_zoom_out4.jpg" 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.jpg" 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.jpg" 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.jpg" width="240"></td>
  </tr>
 </table>
 ### LAR-Gen: Inpainting (Text and Subject 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.jpg" width="240"></td>
  </tr>
 </table>
 ## Features
 | **Model** | **Locate** | **Assign** | **Refine** |
 |:---------:|:----------:|:----------:|:----------:|
 |   SD v1.5 |     ⏳     |    ⏳       |     ⏳     |
 |   SD XL   |     🪄     |    🪄       |     ⏳     |
 - 🪄 denotes that the feature has been supported.
 - ⏳ denotes that the feature has not been integrated currently.
 ## Pretrained Models
 | **Model**  | **URL** |
 |:----------:|:-------:|
 | largen-sdxl-s22k | [ModelScope](https://www.modelscope.cn/models/iic/LARGEN/summary)  |
 ## BibTeX
 If our work is useful for your research, please consider citing:
 ```bibtex
@article{pan2024locate,
  title={Locate, Assign, Refine: Taming Customized Image Inpainting with Text-Subject Guidance},
  author={Pan, Yulin and Mao, Chaojie and Jiang, Zeyinzi and Han, Zhen and Zhang, Jingfeng},
  journal={arXiv preprint arXiv:2403.19534},
  year={2024}
 }
 ```
@@ -0,0 +1,125 @@
 <p align="center">
  <h2 align="center">SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing</h2>
  <h3 align="center">(CVPR 2024 Highlight)</h3>
  <p align="center">
    <strong>Zeyinzi Jiang</strong>
    ·
    <strong>Chaojie Mao</strong>
    ·
    <strong>Yulin Pan</strong>
    ·
    <strong>Zhen Han</strong>
    ·
    <strong>Jingfeng Zhang</strong>
    <br>
    <b>Alibaba Group</b>
    <br>
        <a href="https://arxiv.org/abs/2312.11392"><img src='https://img.shields.io/badge/arXiv-SCEdit-red' alt='Paper PDF'></a>
        <a href='https://scedit.github.io/'><img src='https://img.shields.io/badge/Project_Page-SCEdit-green' alt='Project Page'></a>
        <a href='https://github.com/modelscope/scepter'><img src='https://img.shields.io/badge/scepter-SCEdit-yellow'></a>
        <a href='https://github.com/modelscope/swift'><img src='https://img.shields.io/badge/swift-SCEdit-blue'></a>
    <br>
  </p>
 SCEdit is an efficient generative fine-tuning framework proposed by Alibaba TongYi Vision Intelligence Lab. This framework enhances the fine-tuning capabilities for text-to-image generation downstream tasks and enables quick adaptation to specific generative scenarios, **saving 30%-50% of training memory costs compared to LoRA**. Furthermore, it can be directly extended to controllable image generation tasks, **requiring only 7.9% of the parameters that ControlNet needs for conditional generation and saving 30% of memory usage**. It supports various conditional generation tasks including edge maps, depth maps, segmentation maps, poses, color maps, and image completion.
 ## Usage
 ### Text-to-Image Generation
 ```shell
 # SD v1.5
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd15_512_sce_t2i.yaml
 # SD v2.1
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd21_768_sce_t2i.yaml
 # SD XL
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sdxl_1024_sce_t2i.yaml
 ```
 ### Controllable Image Synthesis
 ```shell
 # SD v1.5 + hed
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd15_512_sce_ctr_hed.yaml
 # SD v2.1 + canny
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_canny.yaml
 # SD XL + depth
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_depth.yaml
 ```
 ### Gradio
 ```shell
 python -m scepter.tools.webui  # Then click [Use Tuners] or [Use Controller]
 ```
 ## Models
 ### Model URL
 | Model  | URL                                                                                                                                       |
 |--------|-------------------------------------------------------------------------------------------------------------------------------------------|
 | SCEdit | [ModelScope](https://modelscope.cn/models/iic/scepter_scedit/summary) [HuggingFace](https://huggingface.co/scepter-studio/scepter_scedit) |
 ### Text-to-Image Generation
 | **Model** | **SCEdit** |
 |:---------:|:----------:|
 |   SD 1.5  |      🪄     |
 |   SD 2.1  |      🪄     |
 |   SD XL   |      🪄     |
 ### Controllable Image Synthesis
 | **Model** | **Canny** | **HED** | **Depth** | **Pose** | **Color** |
 |:---------:|:---------:|:-------:|:---------:|:--------:|:---------:|
 |   SD 2.1  |     🪄     |    🪄    |     🪄     |     🪄    |     🪄     |
 |   SD XL   |     🪄     |    🪄    |     🪄     |     🪄    |     🪄     |
 ## Application Gallery
 ### Dragon Year Special: Dragon Tuner
 <table>
  <tr>
    <td><strong>Gold Dragon Tuner</strong></td>
    <td><strong>Sloppy Dragon Tuner</strong></td>
    <td><strong>Red Dragon Tuner</strong><br> + Papercraft Mantra</td>
    <td><strong>Azure Dragon Tuner</strong><br> + Pose Control</td>
  </tr>
  <tr>
    <td><img src="../../../asset/images/scedit/tuner_gold_dragon.jpeg" width="300"></td>
    <td><img src="../../../asset/images/scedit/tuner_sloppy_dragon.jpeg" width="300"></td>
    <td><img src="../../../asset/images/scedit/tuner_mantra_papercraft_dragon.jpeg" width="300"></td>
    <td><img src="../../../asset/images/scedit/tuner_pose.jpeg" width="300"></td>
  </tr>
 </table>
 ### Text Effect Image
 <table>
  <tr>
    <td><strong>Conditional Image</strong></td>
    <td><strong>Midas Control</strong><br>"Race track, top view"</td>
    <td><strong>Midas Control</strong><br> + Watercolor Mantra<br>"white lilies"</td>
    <td><strong>Midas Control</strong><br> + Dragon Tuner<br>"Spring Festival, Chinese dragon"</td>
  </tr>
  <tr>
    <td><img src="../../../asset/images/scedit/word_condition.png" width="300"></td>
    <td><img src="../../../asset/images/scedit/word_race.jpeg" width="300"></td>
    <td><img src="../../../asset/images/scedit/word_lilies.jpeg" width="300"></td>
    <td><img src="../../../asset/images/scedit/word_festival.jpeg" width="300"></td>
  </tr>
 </table>
 ## BibTeX
 ```bibtex
@article{jiang2023scedit,
    title = {SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing},
    author = {Jiang, Zeyinzi and Mao, Chaojie and Pan, Yulin and Han, Zhen and Zhang, Jingfeng},
    year = {2023},
    journal = {arXiv preprint arXiv:2312.11392}
 }
 ```
@@ -0,0 +1,130 @@
 # StyleBooth: Image Style Editing with Multimodal Instruction
 Zhen Han, Chaojie Mao, Zeyinzi Jiang, Yulin Pan, Jingfeng Zhang
 Alibaba Group
 [[paper](https://arxiv.org/abs/2404.12154)][[Model](https://modelscope.cn/models/iic/stylebooth/summary)] [[Dataset](https://modelscope.cn/models/iic/stylebooth/summary)]
 ## Abstract
 Given an original image, image editing aims to generate an image that align with the provided instruction. The challenges are to accept multimodal inputs as instructions and a scarcity of high-quality training data, including crucial triplets of source/target image pairs and multimodal (text and image) instructions. In this paper, we focus on image style editing and present <strong>StyleBooth</strong>, a method that proposes a comprehensive framework for image editing and a feasible strategy for building a high-quality style editing dataset. We integrate encoded textual instruction and image exemplar as a unified condition for diffusion model, enabling the editing of original image following <strong>multimodal instructions</strong>. Furthermore, by <strong>iterative style-destyle tuning and editing</strong> and usability filtering, the StyleBooth dataset provides content-consistent stylized/plain image pairs in various categories of styles. To show the flexibility of StyleBooth, we conduct experiments on diverse tasks, such as textbased style editing, exemplar-based style editing and compositional style editing. The results demonstrate that the quality and variety of training data significantly enhance the ability to preserve content and improve the overall quality of generated images in editing tasks.
 ![head](https://ali-vilab.github.io/stylebooth-page/public/images/head.jpg "head")
 ## Gallery
 <table>
  <tr>
    <td><strong>Origin Image</strong><br>Gold Dragon Tuner</td>
    <td><strong>Graffiti Art</strong></td>
    <td><strong>Adorable Kawaii</strong></td>
    <td><strong>game-retro game</strong></td>
    <td><strong>Vincent van Gogh</strong></td>
  </tr>
  <tr>
    <td><img src="../../../asset/images/scedit/tuner_gold_dragon.jpeg" width="240"></td>
    <td><img src="../../../asset/images/stylebooth/graffiti.jpeg" width="240"></td>
    <td><img src="../../../asset/images/stylebooth/kawaii.jpeg" width="240"></td>
    <td><img src="../../../asset/images/stylebooth/retrogame.jpeg" width="240"></td>
    <td><img src="../../../asset/images/stylebooth/vangogh.jpeg" width="240"></td>
  </tr>
  <tr>
    <td><strong>Origin Image</strong></td>
    <td><strong>Lowpoly</strong></td>
    <td><strong>Colored Pencil Art</strong></td>
    <td><strong>Watercolor</strong></td>
    <td><strong>misc-disco</strong></td>
  </tr>
  <tr>
    <td><img src="../../../asset/images/stylebooth/mountain.jpg" width="240"></td>
    <td><img src="../../../asset/images/stylebooth/lowpoly.jpg" width="240"></td>
    <td><img src="../../../asset/images/stylebooth/colorpencil.jpeg" width="240"></td>
    <td><img src="../../../asset/images/stylebooth/watercolor.jpeg" width="240"></td>
    <td><img src="../../../asset/images/stylebooth/disco.jpeg" width="240"></td>
  </tr>
 </table>
 ## Features
 | **Text-Based** | **Exemplar-Based** |
 |:--------------:|:-----------------:|
 |   🪄           |         ⏳         |
 - ✅ 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.
 ## Run StyleBooth
 - Code implementation: See model configuration and code based on [🪄SCEPTER](https://github.com/modelscope/scepter/blob/main/docs/en/tasks/stylebooth.md).
 - Demo: Try [🖥️SCEPTER Studio](https://github.com/modelscope/scepter/tree/main?tab=readme-ov-file#%EF%B8%8F-scepter-studio).
 - Easy run:
 Try the following example script to run StyleBooth modified from [tests/modules/test_diffusion_inference.py](https://github.com/modelscope/scepter/blob/main/tests/modules/test_diffusion_inference.py):
 ```python
 # `pip install scepter>0.0.4` or
 # clone newest SCEPTER and run `PYTHONPATH=./ python <this_script>` at the main branch root.
 import os
 import unittest
 from PIL import Image
 from torchvision.utils import save_image
 from scepter.modules.inference.stylebooth_inference import StyleboothInference
 from scepter.modules.utils.config import Config
 from scepter.modules.utils.file_system import FS
 from scepter.modules.utils.logger import get_logger
 class DiffusionInferenceTest(unittest.TestCase):
    def setUp(self):
        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
        self.logger = get_logger(name='scepter')
        config_file = 'scepter/methods/studio/scepter_ui.yaml'
        cfg = Config(cfg_file=config_file)
        if 'FILE_SYSTEM' in cfg:
            for fs_info in cfg['FILE_SYSTEM']:
                FS.init_fs_client(fs_info)
        self.tmp_dir = './cache/save_data/diffusion_inference'
        if not os.path.exists(self.tmp_dir):
            os.makedirs(self.tmp_dir)
    def tearDown(self):
        super().tearDown()
    # uncomment this line to skip this module.
    # @unittest.skip('')
    def test_stylebooth(self):
        config_file = 'scepter/methods/studio/inference/edit/stylebooth_tb_pro.yaml'
        cfg = Config(cfg_file=config_file)
        diff_infer = StyleboothInference(logger=self.logger)
        diff_infer.init_from_cfg(cfg)
        output = diff_infer({'prompt': 'Let this image be in the style of sai-lowpoly'},
                            style_edit_image=Image.open('asset/images/inpainting_text_ref/ex4_scene_im.jpg'),
                            style_guide_scale_text=7.5,
                            style_guide_scale_image=1.5)
        save_path = os.path.join(self.tmp_dir,
                                 'stylebooth_test_lowpoly_cute_dog.png')
        save_image(output['images'], save_path)
 if __name__ == '__main__':
    unittest.main()
 ```
 ## StyleTuner and De-StyleTuner.
 ### Base I2I Model.
 For style and de-style tuning, we use a private high-resolution I2I model trained with [InstructPix2Pix dataset](https://instruct-pix2pix.eecs.berkeley.edu/) as base model. However, one can try the same tunning process using this [yaml](https://github.com/modelscope/scepter/blob/main/scepter/methods/edit/edit_512_lora.yaml) based on any other I2I model, such as StyleBooth (shown in this yaml), [InstructPix2Pix](https://github.com/timothybrooks/instruct-pix2pix) or [MagicBrush](https://github.com/OSU-NLP-Group/MagicBrush).
 ### Training Data.
 Please check the zips for correct format: [De-Text](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets%2Fdetext.zip), [Image2Hed](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets%2Fhed_pair.zip), [Image2Depth](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets%2Fimage2depth.zip), [Depth2Image](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets%2Fdepth2image.zip).
 ### Launch.
 See [code](https://github.com/modelscope/scepter/blob/cbbef4a2da5b66fc33b9f8ece7f2fb4aac9d6e3c/tests/tools/test_train.py#L220) for more information.
@@ -0,0 +1,31 @@
 <h1 align="center">Dataset Management</h1>
 SCEPTER supports three types of dataset formats: TXT, CSV, and ModelScope.
 Below are examples for each format, illustrating their details and basic usage.
 ## Modelscope Format
 We use a [custom-stylized dataset](https://modelscope.cn/datasets/iic/style_custom_dataset/summary), which included classes 3D, anime, flat illustration, oil painting, sketch, and watercolor, each with 30 image-text pairs.
 ```python
 # pip install modelscope
 from modelscope.msdatasets import MsDataset
 ms_train_dataset = MsDataset.load('style_custom_dataset', namespace='damo', subset_name='3D', split='train_short')
 print(next(iter(ms_train_dataset)))
 ```
 ## CSV Format
 For the data format used by SCEPTER Studio, please refer to [3D_example_csv.zip](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip) and [hed_pair.zip](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets%2Fhed_pair.zip).
 ```shell
 mkdir -p cache/datasets/ && wget 'https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip' -O cache/datasets/3D_example_csv.zip && unzip cache/datasets/3D_example_csv.zip -d cache/datasets/ && rm cache/datasets/3D_example_csv.zip
 mkdir -p cache/datasets/ && wget 'https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/hed_pair.zip' -O cache/datasets/hed_pair.zip && unzip cache/datasets/hed_pair.zip -d cache/datasets/ && rm cache/datasets/hed_pair.zip
 ```
 ## TXT Format
 To facilitate starting training in command-line mode, you can use a dataset in text format, please refer to [3D_example_txt.zip](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_txt.zip)
 ```shell
 mkdir -p cache/datasets/ && wget 'https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/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
 ```
@@ -0,0 +1,45 @@
 # Inference
 In this tutorial, we'll cover the use of the scepter framework for convenient inference, including inference using the command line or specific method classes, and we'll give examples of inference methods for additional tasks.
 ## Command Line
 Inference of SDXL generation models using the command line.
 ```shell
 python scepter/tools/run_inference.py --cfg scepter/methods/examples/generation/stable_diffusion_xl_1024.yaml --prompt 'a cute dog' --save_folder 'inference'  # generation on SD XL
 ```
 ## Class Instantiation
 Inference of SD2.1 generation models using the class instantiation.
 ```python
 from torchvision.utils import save_image
 from scepter.modules.utils.config import Config
 from scepter.modules.utils.file_system import FS
 from scepter.modules.utils.logger import get_logger
 from scepter.modules.inference.diffusion_inference import DiffusionInference
 # init file system - modelscope
 FS.init_fs_client(Config(load=False, cfg_dict={'NAME': 'ModelscopeFs', 'TEMP_DIR': 'cache/data'}))
 # init model config
 logger = get_logger(name='scepter')
 cfg = Config(cfg_file='scepter/methods/studio/inference/stable_diffusion/sd21_pro.yaml')
 diff_infer = DiffusionInference(logger)
 diff_infer.init_from_cfg(cfg)
 # start inference
 output = diff_infer({'prompt': 'a cute dog'})
 save_image(output['images'], 'sd21_test_prompt_a_cute_dog.png')
 ```
 ## Additional Tasks
 ### Fine-tuned Model Inference
 ```shell
 python scepter/tools/run_inference.py --cfg scepter/methods/scedit/t2i/sd15_512_sce_t2i_swift.yaml --pretrained_model 'cache/save_data/sd15_512_sce_t2i_swift/checkpoints/ldm_step-100.pth' --prompt 'A close up of a small rabbit wearing a hat and scarf' --save_folder 'trained_test_prompt_rabbit'
 ```
 ### Controllable Image Synthesis Inference
 - SCEdit
 ```shell
 python scepter/tools/run_inference.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_canny.yaml --num_samples 1 --prompt 'a single flower is shown in front of a tree' --save_folder 'test_flower_canny' --image_size 768 --task control --image 'asset/images/flower.jpg' --control_mode canny --pretrained_model ms://iic/scepter_scedit@controllable_model/SD2.1/canny_control/0_SwiftSCETuning/pytorch_model.bin   # canny
 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://iic/scepter_scedit@controllable_model/SD2.1/pose_control/0_SwiftSCETuning/pytorch_model.bin   # pose
 ```
@@ -0,0 +1,84 @@
 # Training
 We provide a framework for training and validation.
 The scripts below are just for illustration purposes. To achieve better results, you can modify the corresponding parameters as needed.
 ## Start Training
 There are different ways to start a training:
 - calling scepter/tools/run_train.py:
 ```bash
 # calling at SCEPTER root:
 PYTHONPATH=./ python scepter/tools/run_train.py --cfg [path-to-your-yaml]
 # calling scepter library:
 pip install scepter
 python -m scepter.tools.run_train --cfg [path-to-your-yaml]
 ```
 - calling your own script:
 ```bash
 # calling at SCEPTER root:
 PYTHONPATH=./ python [path-to-your-script] --cfg [path-to-your-yaml]
 # calling scepter library:
 pip install scepter
 python [path-to-your-script] --cfg [path-to-your-yaml]
 ```
 your scepter should be like:
 ```python
 from scepter.tools.run_train import run
 if __name__ == '__main__':
    run()
 ```
 ## Popular Tasks
 ### Text-to-Image Generation
 - SCEdit
 ```bash
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd15_512_sce_t2i.yaml  # SD v1.5
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd21_768_sce_t2i.yaml  # SD v2.1
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sdxl_1024_sce_t2i.yaml  # SD XL
 ```
 - Existing Tuning Strategies
 ```bash
 python scepter/tools/run_train.py --cfg scepter/methods/examples/generation/stable_diffusion_1.5_512.yaml  # fully-tuning on SD v1.5
 python scepter/tools/run_train.py --cfg scepter/methods/examples/generation/stable_diffusion_2.1_768_lora.yaml  # lora-tuning on SD v2.1
 ```
 - Data Text Format
 ```bash
 # Download the 3D_example_txt.zip as previously mentioned
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sdxl_1024_sce_t2i_datatxt.yaml
 ```
 ### Controllable Image Synthesis
 - SCEdit
 The YAML configuration can be modified to combine different base models and conditions. The following is provided as an example.
 ```bash
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd15_512_sce_ctr_hed.yaml  # SD v1.5 + hed
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_canny.yaml  # SD v2.1 + canny
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_pose.yaml  # SD v2.1 + pose
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_depth.yaml  # SD XL + depth
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_color.yaml  # SD XL + color
 ```
 - Data Text Format
 ```bash
 # Download the 3D_example_txt.zip as previously mentioned
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_color_datatxt.yaml
 ```
 ## Customize Modules
 You can register your own Modules like DATASET, SAMPLERS, TRANSFORMS, MODELS, SOVLERS, HOOKS, OPTIMIZERS into SCEPTER.
 Refer to `example/`, build the modules of your task in `example/{task}`.
 ```bash
 cd example/classifier
 python run.py --cfg classifier.yaml
 ```
@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 # Configuration file for the Sphinx documentation builder.
 #
 # This file only contains a selection of the most common options. For a full
@@ -15,8 +15,8 @@
 子类注册：
 ```python
-from scepter.model.registry import BACKBONES
+from scepter.modules.model.registry import BACKBONES
-from scepter.model.base_model import BaseModel
+from scepter.modules.model.base_model import BaseModel
@BACKBONES.register_class("ResNet")
@@ -26,8 +26,8 @@ class ResNet(BaseModel):
 ```
 ```python
-from scepter.model.registry import NECKS
+from scepter.modules.model.registry import NECKS
-from scepter.model.base_model import BaseModel
+from scepter.modules.model.base_model import BaseModel
@NECKS.register_class()
@@ -37,8 +37,8 @@ class GlobalAveragePooling(BaseModel):
 ```
 ```python
-from scepter.model.registry import HEADS
+from scepter.modules.model.registry import HEADS
-from scepter.model.base_model import BaseModel
+from scepter.modules.model.base_model import BaseModel
@HEADS.register_class()
@@ -48,7 +48,7 @@ class ClassifierHead(BaseModel):
 ```
 ```python
-from scepter.model.registry import LOSSES
+from scepter.modules.model.registry import LOSSES
 import torch.nn as nn
@@ -60,7 +60,7 @@ class CrossEntropy(nn.Module):
 实际调用：
 ```python
-from scepter.model.registry import BACKBONES, NECKS, HEADS, LOSSES, TUNERS
+from scepter.modules.model.registry import BACKBONES, NECKS, HEADS, LOSSES, TUNERS
 backbone = BACKBONES.build(cfg.BACKBONE, logger=logger)
 neck = NECKS.build(cfg.NECK, logger=logger)
@@ -85,8 +85,8 @@ tuner = TUNERS.build(cfg.TUNER, logger=logger)
 子类注册：
 ```python
-from scepter.model.metrics.registry import METRICS
+from scepter.modules.model.metrics.registry import METRICS
-from scepter.model.metrics.base_metric import BaseMetric
+from scepter.modules.model.metrics.base_metric import BaseMetric
@METRICS.register_class("AccuracyMetric")
@@ -97,7 +97,7 @@ class AccuracyMetric(BaseMetric):
 实际用法：
 ```python
-from scepter.model.metrics.registry import METRICS
+from scepter.modules.model.metrics.registry import METRICS
 metric = METRICS.build(cfgs, logger)
 ```
@@ -119,8 +119,8 @@ metric = METRICS.build(cfgs, logger)
 子类注册：
 ```python
-from scepter.model.registry import TOKENIZERS
+from scepter.modules.model.registry import TOKENIZERS
-from scepter.model.tokenizers import BaseTokenizer
+from scepter.modules.model.tokenizers import BaseTokenizer
@TOKENIZERS.register_class()
@@ -131,7 +131,7 @@ class BaseBertTokenizer(BaseTokenizer):
 实际用法：
 ```python
-from scepter.model.registry import TOKENIZERS
+from scepter.modules.model.registry import TOKENIZERS
 tokenizer = TOKENIZERS.build(cfgs, logger)
 ```
@@ -149,8 +149,8 @@ tokenizer = TOKENIZERS.build(cfgs, logger)
 子类注册：
 ```python
-from scepter.model.registry import MODELS
+from scepter.modules.model.registry import MODELS
-from scepter.model.networks.train_module import TrainModule
+from scepter.modules.model.networks.train_module import TrainModule
@MODELS.register_class()
@@ -161,7 +161,7 @@ class Classifier(TrainModule):
 实际用法：
 ```python
-from scepter.model.registry import MODELS
+from scepter.modules.model.registry import MODELS
 model = MODELS.build(self.cfg.MODEL, logger=self.logger)
 ```
@@ -9,8 +9,8 @@
 子lr_schedulers继承时用法：
 ```python
-from scepter.opt.lr_schedulers import LR_SCHEDULERS
+from scepter.modules.opt.lr_schedulers import LR_SCHEDULERS
-from scepter.opt.lr_schedulers.base_scheduler import BaseScheduler
+from scepter.modules.opt.lr_schedulers.base_scheduler import BaseScheduler
@LR_SCHEDULERS.register_class()
@@ -48,8 +48,8 @@ lr_schedulers的基类，支持注册操作，可根据需要自定义；
 子optimizers继承时用法：
 ```python
-from scepter.opt.optimizers.base_optimizer import BaseOptimize
+from scepter.modules.opt.optimizers.base_optimizer import BaseOptimize
-from scepter.opt.optimizers.registry import OPTIMIZERS
+from scepter.modules.opt.optimizers.registry import OPTIMIZERS
@OPTIMIZERS.register_class()
@@ -6,10 +6,10 @@
 支持3类文件IO Handler:
-1. scepter.utils.file_clients.AliyunOssFs
+1. scepter.modules.utils.file_clients.AliyunOssFs
-2. scepter.utils.file_clients.LocalFs
+2. scepter.modules.utils.file_clients.LocalFs
-3. scepter.utils.file_clients.HttpFs
+3. scepter.modules.utils.file_clients.HttpFs
-4. scepter.utils.file_clients.ModelscopeFs
+4. scepter.modules.utils.file_clients.ModelscopeFs
 <hr/>
@@ -17,8 +17,8 @@
 ## 基础用法
 ```python
-from scepter.utils.file_system import FS
+from scepter.modules.utils.file_system import FS
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
 fs_cfg = Config(load=False, cfg_dict={
    "NAME": "AliyunOssFs",
@@ -3,18 +3,18 @@
 依赖SDK，该部分用于对框架全局经常复用的模块和sdk进行整理，并根据功能相关性进行聚合。
 ## 总览
-1. 参数sdk(scepter.utils.config)
+1. 参数sdk(scepter.modules.utils.config)
-2. 路径sdk(scepter.utils.directory)
+2. 路径sdk(scepter.modules.utils.directory)
-3. torch分布式sdk(scepter.utils.distribute)
+3. torch分布式sdk(scepter.modules.utils.distribute)
-4. 模型导出sdk(scepter.utils.export_model)
+4. 模型导出sdk(scepter.modules.utils.export_model)
-5. 文件系统sdk(scepter.utils.file_system)
+5. 文件系统sdk(scepter.modules.utils.file_system)
-6. 日志sdk(scepter.utils.logger)
+6. 日志sdk(scepter.modules.utils.logger)
-7. 视频处理sdk(scepter.utils.video_reader)，文档参考(video_reader.md)
+7. 视频处理sdk(scepter.modules.utils.video_reader)，文档参考(video_reader.md)
-8. 模块注册sdk(scepter.utils.registry)
+8. 模块注册sdk(scepter.modules.utils.registry)
-9. 数据sdk(scepter.utils.data)
+9. 数据sdk(scepter.modules.utils.data)
-10. 模型sdk(scepter.utils.model)
+10. 模型sdk(scepter.modules.utils.model)
-11. 采样器sdk(scepter.utils.sampler)
+11. 采样器sdk(scepter.modules.utils.sampler)
-12. 探针器sdk(scepter.utils.probe)
+12. 探针器sdk(scepter.modules.utils.probe)
 <hr/>
@@ -23,7 +23,7 @@
 ### 基础用法
 ```python
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
 # 从一个dict对象 初始化 Config对象
 fs_cfg = Config(load=False, cfg_dict={"NAME": "LocalFs"})
@@ -97,7 +97,7 @@ print(fs_cfg.args)
 ### 基础用法
 ```python
-from scepter.utils.directory import osp_path
+from scepter.modules.utils.directory import osp_path
 # 根据路径前缀进行自动化路径拼接
 prefix = "xxxx"
@@ -108,7 +108,7 @@ print(osp_path(prefix, data_file))
 data_file = "xxxx/example_videos/1.mp4"
 print(osp_path(prefix, data_file))
-from scepter.utils.directory import get_relative_folder
+from scepter.modules.utils.directory import get_relative_folder
 # 根据路径获取指定层级的文件夹路径
 # 默认最后一级 xxxx/example_videos/
@@ -116,7 +116,7 @@ print(get_relative_folder(data_file))
 # 倒数第二级 xxxx/
 print(get_relative_folder(data_file, keep_index=-2))
-from scepter.utils.directory import get_md5
+from scepter.modules.utils.directory import get_md5
 # 获取文本/路径的md5码 34a447fb46d0b786a3999c9dad01d470
 print(get_md5(data_file))
@@ -172,8 +172,8 @@ torch分布式初始化sdk，使用该sdk，可以让用户不要关注torch的
 ### 基础用法
 ```python
-from scepter.utils.distribute import we
+from scepter.modules.utils.distribute import we
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
 cfg = Config(cfg_dict={}, load=False)
@@ -304,12 +304,12 @@ we.init_env(cfg, fn, logger=None)
 **Returns**
  - **tensor** —— 输出的在进程rank=0上的cpu的tensor。
-## 4. 模型导出sdk(scepter.utils.export_model)
+## 4. 模型导出sdk(scepter.modules.utils.export_model)
 用于模型导出为torchscript/Onnx格式的api。
 ### 基础用法
 ```python
-from scepter.utils.export_model import save_develop_model_multi_io
+from scepter.modules.utils.export_model import save_develop_model_multi_io
 save_develop_model_multi_io(
    model,
@@ -347,16 +347,16 @@ input_type 一一对应。
 **Returns**
  - **tensor** —— 输出的在进程rank=0上的cpu的tensor。
-## 5. 文件系统sdk(scepter.utils.file_system)
+## 5. 文件系统sdk(scepter.modules.utils.file_system)
 参考[file_clients](file_clients.md)
-## 6. 日志sdk(scepter.utils.logger)
+## 6. 日志sdk(scepter.modules.utils.logger)
 用于实例化一个标准的日志实例，用于打印信息。
 ### 基础用法
 ```python
-from scepter.utils.logger import get_logger, init_logger
+from scepter.modules.utils.logger import get_logger, init_logger
 std_logger = get_logger(name="scepter")
 init_logger(std_logger, log_file="", dist_launcher="pytorch")
@@ -407,14 +407,14 @@ init_logger(std_logger, log_file="", dist_launcher="pytorch")
 **Returns**
  - **str** —— 格式化的输出。
-## 7. 视频处理sdk(scepter.utils.video_reader)
+## 7. 视频处理sdk(scepter.modules.utils.video_reader)
 用于处理视频读取的api。
 ### 基础用法
 ```python
-from scepter.utils.video_reader.frame_sampler import do_frame_sample
+from scepter.modules.utils.video_reader.frame_sampler import do_frame_sample
-from scepter.utils.video_reader.video_reader import (
+from scepter.modules.utils.video_reader.video_reader import (
    VideoReaderWrapper, EasyVideoReader, FramesReaderWrapper
 )
 ```
@@ -556,14 +556,14 @@ overlap: Union[float, Fraction, str] = Fraction(0), transforms: Optional[Callabl
 **Returns**
  - **tensor** —— 视频片段的tensor。
-## 8. 模块注册sdk(scepter.utils.registry)
+## 8. 模块注册sdk(scepter.modules.utils.registry)
 用于管理各种注册的类。
 ### 基础用法
 ```python
-from scepter.utils.registry import Registry
+from scepter.modules.utils.registry import Registry
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
 MODELS = Registry('MODELS')
@@ -616,14 +616,14 @@ build目标类的实例
 **Returns**
  - **name** —— 注册名称。
-## 9. 数据sdk(scepter.utils.data)
+## 9. 数据sdk(scepter.modules.utils.data)
 用于数据在设备间转移
 ### 基础用法
 ```python
 import torch
-from scepter.utils.data import transfer_data_to_numpy, transfer_data_to_cpu, transfer_data_to_cuda
+from scepter.modules.utils.data import transfer_data_to_numpy, transfer_data_to_cpu, transfer_data_to_cuda
 data = {"a": torch.Tensor([0])}
 transfer_data_to_numpy(data)
@@ -670,7 +670,7 @@ transfer_data_to_cuda(data)
 ```python
 import torch
-from scepter.utils.model import move_model_to_cpu, load_pretrained,
+from scepter.modules.utils.model import move_model_to_cpu, load_pretrained,
    count_params, init_weights
 ```
 <hr/>
@@ -718,14 +718,14 @@ from scepter.utils.model import move_model_to_cpu, load_pretrained,
 **Parameters**
  - **module** —— torch.nn.Module模型实例。
-## 11. 采样器sdk(scepter.utils.sampler)
+## 11. 采样器sdk(scepter.modules.utils.sampler)
 采样器比较具有通用性，大多数情况下不会进行定制开发，这里提供了几类常用的sampler采样器。
 ### 基础用法
 ```python
 import torch
-from scepter.utils.sampler import MultiFoldDistributedSampler,
+from scepter.modules.utils.sampler import MultiFoldDistributedSampler,
    EvalDistributedSampler, MultiLevelBatchSampler, MixtureOfSamplers
 ```
 <hr/>
@@ -832,17 +832,17 @@ from scepter.utils.sampler import MultiFoldDistributedSampler,
 迭代器，每迭代一次得到一个样本的index
-## 12. 探针器sdk(scepter.utils.probe)
+## 12. 探针器sdk(scepter.modules.utils.probe)
 用于探针各个组件的变量统计
 ### 基础用法
 ```python
 import numpy as np
-from scepter.model.base_model import BaseModel
+from scepter.modules.model.base_model import BaseModel
-from scepter.utils.config import Config
+from scepter.modules.utils.config import Config
-from scepter.utils.file_system import FS
+from scepter.modules.utils.file_system import FS
-from scepter.utils.probe import ProbeData
+from scepter.modules.utils.probe import ProbeData
 class TestModel(BaseModel):
@@ -914,7 +914,7 @@ data = {
 _model(data)
 probe = _model.probe_data()
 for key in probe:
-    print(key, probe[key].to_log(prefix=f"xxx/dev_easytorch/{key}"))
+    print(key, probe[key].to_log(prefix=f"xxx/{key}"))
 ```
 <hr/>
@@ -6,4 +6,5 @@ dependencies:
  - pip>=20.3
  - numpy>=1.23.1
  - pip:
      - -r requirements/recommended.txt
      - -r requirements.txt
@@ -0,0 +1,2 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
@@ -0,0 +1,3 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from .classifier_dataset import ImageClassifyExampleDataset
@@ -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"]
@@ -0,0 +1,79 @@
 # -*- 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)
@@ -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()
@@ -0,0 +1,22 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import os
 import shutil
 import subprocess
 import sys
 if sys.argv[0] == 'install.py':
    sys.path.append('.')   # for portable version
 source_folder = os.path.join(os.path.dirname(__file__), "scepter/workflow")
 current_dir = os.path.dirname(__file__)
 destination_folder = os.path.join(os.path.dirname(current_dir), "ComfyUI-Scepter")
 if not os.path.exists(destination_folder):
    shutil.copytree(source_folder, destination_folder)
    print(f"{os.path.abspath(source_folder)} copy to {os.path.abspath(destination_folder)} success!")
 else:
    print(f"{os.path.abspath(destination_folder)} exist.")
 # pip install scepter
 subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'scepter'])
@@ -8,156 +8,235 @@
 <a href="https://github.com/modelscope/scepter/"><img src="https://img.shields.io/badge/scepter-Build from source-6FEBB9.svg"></a>
 </p>
-## 📖 Table of Contents
+🪄SCEPTER is an open-source code repository dedicated to generative training, fine-tuning, and inference, encompassing a suite of downstream tasks such as image generation, transfer, editing.
- [Introduction](#-introduction)
+SCEPTER integrates popular community-driven implementations as well as proprietary methods by Tongyi Lab of Alibaba Group, offering a comprehensive toolkit for researchers and practitioners in the field of AIGC. This versatile library is designed to facilitate innovation and accelerate development in the rapidly evolving domain of generative models.
 - [News](#-news)
 - [Installation](#-Installation)
 - [Getting Started](#-getting-started)
 - [SCEPTER Studio](#-scepter-studio)
 - [Features](#-features)
 - [Learn More](#-learn-more)
 - [License](#license)
-## 📝 Introduction
+SCEPTER offers 3 core components:
 - [Generative training and inference framework](#tutorials)
 - [Easy implementation of popular approaches](#currently-supported-approaches)
 - [Interactive user interface: SCEPTER Studio & Comfy UI](#launch)
 SCEPTER is an open-source code repository dedicated to generative training, fine-tuning, and inference, encompassing a suite of downstream tasks such as image generation, transfer, editing. It integrates popular community-driven implementations as well as proprietary methods by Tongyi Lab of Alibaba Group, offering a comprehensive toolkit for researchers and practitioners in the field of AIGC. This versatile library is designed to facilitate innovation and accelerate development in the rapidly evolving domain of generative models.
 Main Feature:
 - Task:
  - Text-to-image generation
  - Controllable image synthesis
  - Image editing (TODO)
 - Training / Inference:
  - Distribute: DDP / FSDP / FairScale / Xformers
  - File system: Local / Http / OSS / Modelscope
 - Deploy:
  - Data management
  - Training
  - Inference
 Currently supported approches (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
 - [🔥🔥🔥2024.11]: We're excited to announce the upcoming release of the [ACE-0.6b-1024px](https://huggingface.co/scepter-studio/ACE-0.6B-1024px) model, 
 which significantly enhances image generation quality compared with [ACE-0.6b-512px](https://huggingface.co/scepter-studio/ACE-0.6B-512px). The detailed documents can be found at [ACE repo](https://github.com/ali-vilab/ACE.git).
 At the same time, based on the editing results of ACE, combined with the powerful text-to-image capabilities of the [FLUX-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) model through SDEdit as an image quality refiner, the quality of image editing can be further enhanced.
 - [🔥2024.11]: Supports video files, video annotation, caption translation in data management, and inference & training of the [CogVideoX](https://arxiv.org/abs/2408.06072).
 - [2024.10]: We are pleased to announce the release of the code for [ACE](https://arxiv.org/abs/2410.00086), supporting Customized Training / Comfy UI Workflow / gradio-based ChatBot Interface. 
 - [2024.10]: Support for inference and tuning with [FLUX](https://huggingface.co/black-forest-labs/FLUX.1-dev), as well as for building [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflows using this framework.
 - [2024.09]: We introduce **ACE**, an **A**ll-round **C**reator and **E**ditor adept at executing a diverse array of image editing tasks tailored to your specifications. Built upon the cutting-edge Diffusion Transformer architecture, ACE has been extensively trained on a comprehensive dataset to seamlessly interpret and execute any natural language instruction. For further information, please consult the [project page](https://ali-vilab.github.io/ace-page/).
 - [2024.07]: Support the inference and training of open-source generative models based on the [DiT](https://arxiv.org/abs/2212.09748) architecture, such as [SD3](https://arxiv.org/pdf/2403.03206) and [PixArt](https://arxiv.org/abs/2310.00426).
 - [2024.05]: Introducing SCEPTER v1, supporting customized image edit tasks! Simply provide 10 image pairs, SCEPTER will tune an edit tuner for your own Image-to-Image tasks, like `Clay Style`, `De-Text`, `Segmentation`, etc.
 - [2024.04]: New [StyleBooth](https://ali-vilab.github.io/stylebooth-page/) demo on SCEPTER Studio for`Text-Based Style Editing`.
 - [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.
 ## 🪄ACE
 ACE is a unified foundational model framework that supports a wide range of visual generation tasks. By defining CU for unifying multi-modal inputs across different tasks and incorporating long-context CU, we introduce historical contextual information into visual generation tasks, paving the way for ChatGPT-like dialog systems in visual generation.
 [![Watch the demo](https://ali-vilab.github.io/ace-page/static/images/tasks.png)](https://ali-vilab.github.io/ace-page/)
 ### ACE Models
 |    **Model**     |                                                                                                                                                                                                            **Status**                                                                                                                                                                                                             | 
 |:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
 |  ACE-0.6B-512px  |          [![Demo link](https://img.shields.io/badge/Demo-ACE_Chat-purple)](https://huggingface.co/spaces/scepter-studio/ACE-Chat)<br>[![ModelScope link](https://img.shields.io/badge/ModelScope-Model-blue)](https://www.modelscope.cn/models/iic/ACE-0.6B-512px)  [![HuggingFace link](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-yellow)](https://huggingface.co/scepter-studio/ACE-0.6B-512px)          |
 | ACE-0.6B-1024px  | [![Demo link](https://img.shields.io/badge/Demo-ACE_Refiner_Chat-purple)](https://huggingface.co/spaces/scepter-studio/ACE-Refiner-Chat)<br>[![ModelScope link](https://img.shields.io/badge/ModelScope-Model-blue)](https://www.modelscope.cn/models/iic/ACE-0.6B-1024px)  [![HuggingFace link](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-yellow)](https://huggingface.co/scepter-studio/ACE-0.6B-1024px) |             |
 | ACE-12B-FLUX-dev |                                                                                                                                                                                                            Coming Soon                                                                                                                                                                                                            |
 ### ACE Training
 We offer a demonstration training YAML that enables the end-to-end training of ACE using a toy dataset. For a comprehensive overview of the hyperparameter configurations, please consult `scepter/methods/edit/dit_ace_0.6b_512.yaml`.
 #### Prepare datasets
 Please find the dataset class located in `scepter/modules/data/dataset/ms_dataset.py`,
 designed to facilitate end-to-end training using an open-source toy dataset.
 Download a dataset zip file from [modelscope](https://www.modelscope.cn/models/iic/scepter/resolve/master/datasets/hed_pair.zip), and then extract its contents into the `cache/datasets/` directory.
 Should you wish to prepare your own datasets, we recommend consulting `scepter/modules/data/dataset/ms_dataset.py` for detailed guidance on the required data format.
 #### Prepare initial weight
 The ACE checkpoint has been uploaded to both ModelScope and HuggingFace platforms:
 * [ModelScope](https://www.modelscope.cn/models/iic/ACE-0.6B-512px)
 * [HuggingFace](https://huggingface.co/scepter-studio/ACE-0.6B-512px)
 In the provided training YAML configuration, we have designated the Modelscope URL as the default checkpoint URL. Should you wish to transition to Hugging Face, you can effortlessly achieve this by modifying the PRETRAINED_MODEL value within the YAML file (replace the prefix "ms://iic" to "hf://scepter-studio").
 #### Start training
 You can easily start training procedure by executing the following command:
 ```bash
 # ACE-0.6B-512px
 PYTHONPATH=. python scepter/tools/run_train.py --cfg scepter/methods/edit/dit_ace_0.6b_512.yaml
 # ACE-0.6B-1024px
 PYTHONPATH=. python scepter/tools/run_train.py --cfg scepter/methods/edit/dit_ace_0.6b_1024.yaml
 ```
 ### ACE Chat Bot
 We have developed a chatbot interface utilizing Gradio, designed to convert user input in natural language into visually captivating images that align semantically with the specified instructions. You can easily access this functionality by launching Scepter Studio with the following command:
 ```bash
 PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ui.yaml --language zh --tab chatbot
 ```
 Upon starting, you will find a "ChatBot" tab within the Gradio application, which serves as a chat-based interface to handle any requests related to image editing or generation.
 ### ACE ComfyUI Workflow
 ![Workflow](https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_example.jpg)
 <table><tbody>
  <tr>
    <th align="center" colspan="4">ACE Workflow Examples</th>
  </tr>
  <tr>
    <th align="center" colspan="1">Control</th>
    <th align="center" colspan="1">Semantic</th>
    <th align="center" colspan="1">Element</th>
  </tr>
  <tr>
    <td>
      <a href="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_control.png" target="_blank">
        <img src="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_control.png" width="200">
      </a>
    </td>
    <td>
      <a href="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_semantic.png" target="_blank">
        <img src="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_semantic.png" width="200">
      </a>
    </td>
    <td>
      <a href="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_element.png" target="_blank">
        <img src="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_element.png" width="200">
      </a>
    </td>
  </tr>
 </tbody>
 </table>
 ## 🖼 Gallery for Recent Works
 ### FLUX Tuners
 <table><tbody>
  <tr>
    <th align="center" colspan="3">Yarn Style</th>
    <th align="center" colspan="3">Soft Watercolor Style</th>
  </tr>
  <tr>
    <td><img src="asset/images/flux_tuner/flux_tuner_2_1.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_2_2.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_2_3.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_1_1.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_1_2.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_1_3.webp" width="200"></td>
  </tr>
  <tr>
    <th align="center" colspan="3">Travel Style</th>
    <th align="center" colspan="3">WuKong Style</th>
  </tr>
  <tr>
    <td><img src="asset/images/flux_tuner/flux_tuner_3_1.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_3_2.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_3_3.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_4_1.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_4_2.webp" width="200"></td>
    <td><img src="asset/images/flux_tuner/flux_tuner_4_3.webp" width="200"></td>
  </tr>
 </tbody>
 </table>
 ### ComfyUI Workflow
 ![Workflow](asset/workflow/workflow.jpg)
 <table><tbody>
  <tr>
    <th align="center" colspan="4">Example Workflow Case</th>
  </tr>
  <tr>
    <th align="center" colspan="1">Base</th>
    <th align="center" colspan="1">+Mantra</th>
    <th align="center" colspan="1">+Tuner</th>
    <th align="center" colspan="1">+Control</th>
  </tr>
  <tr>
    <td>
      <a href="asset/workflow/sdxl_base.json" target="_blank">
        <img src="asset/workflow/sdxl_base.jpg" width="200">
      </a>
    </td>
    <td>
      <a href="asset/workflow/sdxl_base_mantra.json" target="_blank">
        <img src="asset/workflow/sdxl_base_mantra.jpg" width="200">
      </a>
    </td>
    <td>
      <a href="asset/workflow/sdxl_base_mantra_tuner.json" target="_blank">
        <img src="asset/workflow/sdxl_base_mantra_tuner.jpg" width="200">
      </a>
    </td>
    <td>
      <a href="asset/workflow/sdxl_base_mantra_tuner_control.json" target="_blank">
        <img src="asset/workflow/sdxl_base_mantra_tuner_control.jpg" width="200">
      </a>
    </td>
  </tr>
 </tbody>
 </table>
 ## 🛠️ Installation
- Create new environment
+- Create new environment with `conda` command:
 ```shell
 conda env create -f environment.yaml
 conda activate scepter
 ```
- Install SCEPTER by the `pip` command:
+- Install with `pip` command:
 We recommend installing the specific version of PyTorch and accelerate toolbox [xFormers](https://pypi.org/project/xformers/). You can install these recommended version by pip:
 ```shell
 pip install -r requirements/recommended.txt
 pip install scepter
 ```
 - PS: We recommend installing PyTorch follwing [official documentation](https://pytorch.org/get-started/locally/)
-## 🚀 Getting Started
+## 🧩 Generative Framework
-### Dataset
+### Tutorials
-#### Modelscope Format
+| Documentation                                      | Key Features                      |
 |:---------------------------------------------------|:----------------------------------|
 | [Train](docs/en/tutorials/train.md)                | DDP / FSDP / FairScale / Xformers |
 | [Inference](docs/en/tutorials/inference.md)        | Dynamic load/unload               |
 | [Dataset Management](docs/en/tutorials/dataset.md) | Local / Http / OSS / Modelscope   |
 We use a [custom-stylized dataset](https://modelscope.cn/datasets/damo/style_custom_dataset/summary), which included classes 3D, anime, flat illustration, oil painting, sketch, and watercolor, each with 30 image-text pairs.
-```python
+## 📝 Popular Approaches
 # pip install modelscope
 from modelscope.msdatasets import MsDataset
 ms_train_dataset = MsDataset.load('style_custom_dataset', namespace='damo', subset_name='3D', split='train_short')
 print(next(iter(ms_train_dataset)))
 ```
-#### CSV Format
+### Currently supported approaches
-For the data format used by SCEPTER Studio, please refer to [3D_example_csv.zip](https://modelscope.cn/api/v1/models/damo/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip).
+|            Tasks             |                    Methods                     | Links                                                                                                                                                                                                                                                       |
-
+|:----------------------------:|:----------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-#### TXT Format
+|   Text-to-image Generation   |                    SD v1.5                     | [![Hugging Face Repo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Repo-blue)](https://huggingface.co/runwayml/stable-diffusion-v1-5)                                                                                                         |
-
+|   Text-to-image Generation   |                    SD v2.1                     | [![Hugging Face Repo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Repo-blue)](https://huggingface.co/runwayml/stable-diffusion-v1-5)                                                                                                         |
-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)
+|   Text-to-image Generation   |                     SD-XL                      | [![Hugging Face Repo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Repo-blue)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)                                                                                               |
-
+|   Text-to-image Generation   |                      FLUX                      | [![Hugging Face Repo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Repo-blue)](https://huggingface.co/black-forest-labs/FLUX.1-dev)                                                                                               |
-```shell
+|       Efficient Tuning       |                      LoRA                      | [![Arxiv   link](https://img.shields.io/static/v1?label=arXiv&message=LoRA&color=red&logo=arxiv)](https://arxiv.org/abs/2106.09685)                                                                                                                         |
-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
+|       Efficient Tuning       |             Res-Tuning(NeurIPS23)              | [![Arxiv   link](https://img.shields.io/static/v1?label=arXiv&message=Res-Tuing&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/)                    |
-```
+| Controllable Image Synthesis |  [🌟SCEdit(CVPR24)](docs/en/tasks/scedit.md)   | [![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/)                            |
-
+|        Image Editing         |      [🌟LAR-Gen](docs/en/tasks/largen.md)      | [![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/)             |
-### Training
+|        Image Editing         |  [🌟StyleBooth](docs/en/tasks/stylebooth.md)   | [![Arxiv   link](https://img.shields.io/static/v1?label=arXiv&message=StyleBooth&color=red&logo=arxiv)](https://arxiv.org/abs/2404.12154)   [![Page link](https://img.shields.io/badge/Page-StyleBooth-Gree)](https://ali-vilab.github.io/stylebooth-page/) |
-
+| Image Generation and Editing | [🌟ACE](https://ali-vilab.github.io/ace-page/) | [![Arxiv   link](https://img.shields.io/static/v1?label=arXiv&message=ACE&color=red&logo=arxiv)](https://arxiv.org/abs/2410.00086)   [![Page link](https://img.shields.io/badge/Page-ACE-Gree)](https://ali-vilab.github.io/ace-page/) [![Demo link](https://img.shields.io/badge/Demo-ACE-purple)](https://huggingface.co/spaces/scepter-studio/ACE-Chat) <br> [![ModelScope link](https://img.shields.io/badge/ModelScope-Model-blue)](https://www.modelscope.cn/models/iic/ACE-0.6B-512px)  [![HuggingFace link](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-yellow)](https://huggingface.co/scepter-studio/ACE-0.6B-512px) |
 We provide a framework for training and inference, so the script below is just for illustration purposes. To achieve better results, you can modify the corresponding parameters as needed.
 #### Text-to-Image Generation
 - SCEdit
 ```python
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd15_512_sce_t2i.yaml  # SD v1.5
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd21_768_sce_t2i.yaml  # SD v2.1
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sdxl_1024_sce_t2i.yaml  # SD XL
 ```
 - Existing Tuning Strategies
 ```python
 python scepter/tools/run_train.py --cfg scepter/methods/examples/generation/stable_diffusion_1.5_512.yaml  # fully-tuning on SD v1.5
 python scepter/tools/run_train.py --cfg scepter/methods/examples/generation/stable_diffusion_2.1_768_lora.yaml  # lora-tuning on SD v2.1
 ```
 - Data Text Format
 ```python
 # Download the 3D_example_txt.zip as previously mentioned
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sdxl_1024_sce_t2i_datatxt.yaml
 ```
 #### Controllable Image Synthesis
 - SCEdit
 The YAML configuration can be modified to combine different base models and conditions. The following is provided as an example.
 ```python
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd15_512_sce_ctr_hed.yaml  # SD v1.5 + hed
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_canny.yaml  # SD v2.1 + canny
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_pose.yaml  # SD v2.1 + pose
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_depth.yaml  # SD XL + depth
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_color.yaml  # SD XL + color
 ```
 - Data Text Format
 ```python
 # Download the 3D_example_txt.zip as previously mentioned
 python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_color_datatxt.yaml
 ```
 ### Inference
 #### Base Model Inference
 ```python
 python scepter/tools/run_inference.py --cfg scepter/methods/examples/generation/stable_diffusion_1.5_512.yaml --prompt 'a cute dog' --save_folder 'inference'  # generation on SD v1.5
 python scepter/tools/run_inference.py --cfg scepter/methods/examples/generation/stable_diffusion_2.1_768.yaml --prompt 'a cute dog' --save_folder 'inference'  # generation on SD v2.1
 python scepter/tools/run_inference.py --cfg scepter/methods/examples/generation/stable_diffusion_xl_1024.yaml --prompt 'a cute dog' --save_folder 'inference'  # generation on SD XL
 ```
 #### Fine-tuned Model Inference
 ```python
 python scepter/tools/run_inference.py --cfg scepter/methods/scedit/t2i/sd15_512_sce_t2i_swift.yaml --pretrained_model 'cache/save_data/sd15_512_sce_t2i_swift/checkpoints/ldm_step-100.pth' --prompt 'A close up of a small rabbit wearing a hat and scarf' --save_folder 'trained_test_prompt_rabbit'
 ```
 #### Controllable Image Synthesis Inference
 - SCEdit
 ```python
 python scepter/tools/run_inference.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_canny.yaml --num_samples 1 --prompt 'a single flower is shown in front of a tree' --save_folder 'test_flower_canny' --image_size 768 --task control --image 'asset/images/flower.jpg' --control_mode canny --pretrained_model ms://damo/scepter_scedit@controllable_model/SD2.1/canny_control/0_SwiftSCETuning/pytorch_model.bin   # canny
 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
 ```
 ## 🖥️ SCEPTER Studio
@@ -176,50 +255,42 @@ 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. 
+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. 
+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.
 ### Usage Demo
-### Modelscope Studio
+|              [Image Editing](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fimage_editing_20240419.webm)              |                [Training](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Ftraining_20240419.webm)                 |              [Model Sharing](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fmodel_sharing_20240419.webm)               |             [Model Inference](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fmodel_inference_20240419.webm)              |             [Data Management](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fdata_management_20240419.webm)              |
 |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------:|
 | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fimage_editing_20240419.webm" width="240" controls></video> | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Ftraining_20240419.webm" width="240" controls></video> | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fmodel_sharing_20240419.webm" width="240" controls></video>  | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fmodel_inference_20240419.webm" width="240" controls></video>  | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fdata_management_20240419.webm" width="240" controls></video>  |
-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)
+### Modelscope Studio & Huggingface Space
-## ✨ Features
+We deploy a work studio on Modelscope that includes only the inference tab, please refer to [ms_scepter_studio](https://www.modelscope.cn/studios/iic/scepter_studio/summary) and [hf_scepter_studio](https://huggingface.co/spaces/modelscope/scepter_studio)
 ### Text-to-Image Generation
 | **Model** | **SCEdit** | **Full** | **LoRA** |
 |:---------:|:----------:|:--------:|:--------:|
 |   SD 1.5  |      🪄     |     ✅    |     ✅    |
 |   SD 2.1  |      🪄     |     ✅    |     ✅    |
 |   SD XL   |      🪄     |     ✅    |     ✅    |
-### Controllable Image Synthesis
+## ⚙️️ ComfyUI Workflow
 - SCEdit
-| **Model** | **Canny** | **HED** | **Depth** | **Pose** | **Color** |
+We support the use of all models in the ComfyUI Workflow through the following methods:
 |:---------:|:---------:|:-------:|:---------:|:--------:|:---------:|
 |   SD 1.5  |     ✅     |    ✅    |     ✅     |     ✅    |     ✅     |
 |   SD 2.1  |     🪄     |    ✅    |     ✅     |     🪄    |     🪄     |
 |   SD XL   |     ✅     |    ✅    |     ✅     |     ✅    |     ✅     |
-### Model URL
+1) Automatic installation directly via the ComfyUI Manager by searching for the **ComfyUI-Scepter** node.
-
+2) Manually install by moving custom_nodes from Scepter to ComfyUI.
- ✅ indicates support for both training and inference.
+```shell
- 🪄 denotes that the model has been published.
+git clone https://github.com/modelscope/scepter.git
- More models will be released in the future.
+cd path/to/scepter
-
+pip install -e .
-| Model  | URL                                                                                 |
+cp -r path/to/scepter/workflow/ path/to/ComfyUI/custom_nodes/ComfyUI-Scepter
-|--------|-------------------------------------------------------------------------------------|
+cd path/to/ComfyUI
-| SCEdit | [ModelCard](https://modelscope.cn/models/damo/scepter_scedit/summary) |
+python main.py
-
+```
 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.
 **Note**: You can use the nodes by dragging the sample images into ComfyUI. Additionally, our nodes can automatically pull models from ModelScope or HuggingFace by selecting the *model_source* field, or you can place the already downloaded models in a local path.
 ## 🔍 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.
@@ -232,6 +303,21 @@ 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/), [Fooocus](https://github.com/lllyasviel/Fooocus) and [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for their awesome work.
@@ -1,11 +1,18 @@
 albumentations
 beautifulsoup4
 bezier
 einops
-modelscope
+modelscope[framework]<=1.20.1
-ms-swift>=1.5.2
+ms-swift
 numpy
 open_clip_torch
 opencv-python
 opencv_transforms>=0.0.6
 oss2>=2.15.0
 pycocotools
 pyyaml>=5.3.1
-transformers
+scikit-image
-xformers>=0.0.21
+scikit-learn
 sentencepiece
 torchsde
 transformers<=4.46.3
@@ -0,0 +1,5 @@
 git+https://github.com/cocodataset/panopticapi.git
 torch==2.4.1
 torchvision==.19.1
 flash-attn==2.5.8
 xformers==0.0.28
@@ -1,2 +1,7 @@
-gradio>=3.47.1,<4.0.0
+bitsandbytes<=0.44.1
 gradio
 gradio_imageslider
 imagehash
 psutil
 tiktoken
 transformers_stream_generator
@@ -0,0 +1,161 @@
 ENV:
  BACKEND: nccl
  SEED: 2024
 #
 SOLVER:
  NAME: ACESolver
  RESUME_FROM:
  LOAD_MODEL_ONLY: True
  USE_FSDP: False
  SHARDING_STRATEGY:
  USE_AMP: True
  DTYPE: float16
  CHANNELS_LAST: True
  MAX_STEPS: 500
  MAX_EPOCHS: -1
  NUM_FOLDS: 1
  ACCU_STEP: 1
  EVAL_INTERVAL: 50
  RESCALE_LR: False
  #
  WORK_DIR: ./cache/save_data/ace_0.6b_1024
  LOG_FILE: std_log.txt
  #
  FILE_SYSTEM:
    - NAME: "HuggingfaceFs"
      TEMP_DIR: ./cache/cache_data
    - NAME: "LocalFs"
      TEMP_DIR: ./cache/cache_data
    - NAME: "ModelscopeFs"
      TEMP_DIR: ./cache/cache_data
  #
  MODEL:
    NAME: LatentDiffusionACE
    PRETRAINED_MODEL:
    IGNORE_KEYS: [ ]
    SCALE_FACTOR: 0.18215
    SIZE_FACTOR: 8
    DECODER_BIAS: 0.5
    DEFAULT_N_PROMPT:
    USE_EMA: True
    EVAL_EMA: False
    TEXT_IDENTIFIER: [ '{image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
    USE_TEXT_POS_EMBEDDINGS: True
    #
    DIFFUSION:
      NAME: BaseDiffusion
      PREDICTION_TYPE: eps
      MIN_SNR_GAMMA:
      NOISE_SCHEDULER:
        NAME: LinearScheduler
        NUM_TIMESTEPS: 1000
        BETA_MIN: 0.0001
        BETA_MAX: 0.02
    #
    DIFFUSION_MODEL:
      NAME: ACE
      PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/dit/ace_0.6b_1024px.pth
      IGNORE_KEYS: [ ]
      PATCH_SIZE: 2
      IN_CHANNELS: 4
      HIDDEN_SIZE: 1152
      DEPTH: 28
      NUM_HEADS: 16
      MLP_RATIO: 4.0
      PRED_SIGMA: True
      DROP_PATH: 0.0
      WINDOW_DIZE: 0
      Y_CHANNELS: 4096
      MAX_SEQ_LEN: 4096
      QK_NORM: True
      USE_GRAD_CHECKPOINT: True
      ATTENTION_BACKEND: flash_attn
    #
    FIRST_STAGE_MODEL:
      NAME: AutoencoderKL
      EMBED_DIM: 4
      PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/vae/vae.bin
      IGNORE_KEYS: []
      #
      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:
      NAME: T5EmbedderHF
      PRETRAINED_MODEL: ms://iic/ACE-0.6B-1024px@models/text_encoder/t5-v1_1-xxl/
      TOKENIZER_PATH: ms://iic/ACE-0.6B-1024px@models/tokenizer/t5-v1_1-xxl
      LENGTH: 120
      T5_DTYPE: bfloat16
      ADDED_IDENTIFIER: [ '{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
      CLEAN: whitespace
      USE_GRAD: False
    LOSS:
      NAME: ReconstructLoss
      LOSS_TYPE: l2
  #
  SAMPLE_ARGS:
    SAMPLER: ddim
    SAMPLE_STEPS: 20
    GUIDE_SCALE: 4.5
    GUIDE_RESCALE: 0.5
  #
  OPTIMIZER:
    NAME: AdamW
    LEARNING_RATE: 1e-7
    EPS: 1e-10
    WEIGHT_DECAY: 5e-4
  #
  TRAIN_DATA:
    NAME: ImageTextPairMSDatasetForACE
    MODE: train
    MS_DATASET_NAME: cache/datasets/hed_pair
    MS_DATASET_NAMESPACE: ""
    MS_DATASET_SPLIT: "train"
    MS_DATASET_SUBNAME: ""
    PROMPT_PREFIX: ""
    REPLACE_STYLE: False
    MAX_SEQ_LEN: 4096
    PIN_MEMORY: True
    BATCH_SIZE: 1
    NUM_WORKERS: 1
    SAMPLER:
      NAME: LoopSampler
  #
  TRAIN_HOOKS:
    -
      NAME: BackwardHook
      PRIORITY: 0
    -
      NAME: LogHook
      LOG_INTERVAL: 50
    -
      NAME: CheckpointHook
      INTERVAL: 100
    -
      NAME: ProbeDataHook
      PROB_INTERVAL: 100
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
皓童	af5cf1ccc0	modify edit	2024-12-08 00:31:50 +08:00
皓童	0ed54e05e4	modify edit	2024-12-08 00:28:11 +08:00
皓童	27a2b7833f	modify edit	2024-12-08 00:22:43 +08:00
皓童	6c8ce55888	modify edit	2024-12-08 00:16:03 +08:00
皓童	448cdba522	add init file for chatbot	2024-12-05 15:20:00 +08:00
皓童	2a29446d45	modify ace inference and ace yaml	2024-11-25 14:24:54 +08:00
maochaojie	cd33b4ab15	Merge branch 'v1.3.0_dev' of https://github.com/modelscope/scepter into v1.3.0_dev	2024-11-21 15:42:05 +08:00
maochaojie	7d7943fed3	modify yaml and workflow	2024-11-21 15:41:45 +08:00
jiangzeyinzi	d48b2f110f	Merge pull request #63 from yaosheng216/patch-5 Update model_node.py	2024-11-20 13:26:33 +08:00
Great	82486adf38	Update model_node.py	2024-11-20 13:24:28 +08:00
maochaojie	a683061c6f	upgrade from 1.2.0 to 1.3.0	2024-11-19 19:20:02 +08:00
mcj	4ec0492897	Merge pull request #62 from modelscope/v1.2.0_dev update chatbot example	2024-11-07 20:30:51 +08:00
LouieStark	aac85fa94f	update readme	2024-11-05 19:44:54 +08:00
LouieStark	3f267aaea2	update example	2024-11-05 15:49:00 +08:00
LouieStark	53357f95d6	update chatbot example	2024-11-05 14:36:52 +08:00
mcj	02c0ba9757	Merge pull request #61 from modelscope/v1.2.0_dev update instr	2024-11-04 17:14:27 +08:00
LouieStark	cef93bdbfe	update instr	2024-11-04 16:23:19 +08:00
jiangzeyinzi	82132ff3a1	Merge pull request #60 from modelscope/v1.2.0_dev add instruction	2024-11-04 14:38:03 +08:00
LouieStark	b886400e06	add instruction	2024-11-04 14:34:07 +08:00
mcj	73984c4f9e	Merge pull request #59 from modelscope/v1.2.0_dev update readme	2024-11-02 06:48:03 +08:00
LouieStark	f98adabeb3	update readme	2024-11-01 23:32:28 +08:00
jiangzeyinzi	edb46a615c	Merge pull request #58 from modelscope/v1.2.0_dev V1.2.0 dev	2024-11-01 21:30:20 +08:00
LouieStark	fe3e11b49e	update chatbot	2024-11-01 21:13:21 +08:00
LouieStark	e6b43f19f6	update chatbot	2024-11-01 17:10:56 +08:00
jiangzeyinzi	d9b207cf5b	Merge pull request #55 from modelscope/v1.2.0_dev V1.2.0 dev	2024-11-01 16:53:16 +08:00
LouieStark	986349deac	fix chatbot bug	2024-11-01 16:38:46 +08:00
LouieStark	3f047be43c	update readme and yaml	2024-11-01 11:37:53 +08:00
LouieStark	e8d8e63cba	update v1.2.0	2024-11-01 10:15:27 +08:00
jiangzeyinzi	eac03e9856	Merge pull request #52 from modelscope/v1.1.0_dev update v1.1.0	2024-10-23 10:19:29 +08:00
jiangzeyinzi	379b94ab4f	update	2024-10-23 10:15:31 +08:00
jiangzeyinzi	342c6b8a15	Merge branch 'v1.1.0_dev' of https://github.com/modelscope/scepter into v1.1.0_dev	2024-10-21 11:59:55 +08:00
jiangzeyinzi	4ddcb08c7b	update	2024-10-21 11:59:41 +08:00
jiangzeyinzi	b2169a1597	Update readme.md	2024-10-21 11:58:43 +08:00
jiangzeyinzi	cffd54a02a	update	2024-10-21 11:36:02 +08:00
zeyinzi.jzyz	0bba2c319d	update v1.1.0	2024-10-21 00:35:53 +08:00
LouieStark	7d6451efad	update project page url	2024-10-01 23:45:22 +08:00
LouieStark	a5decd17aa	update readme	2024-09-30 14:22:14 +08:00
jiangzeyinzi	8a14866562	Merge pull request #46 from yaosheng216/patch-4 Update ldm_sce.py	2024-09-27 09:49:27 +08:00
jiangzeyinzi	5a94f6c4a2	Merge pull request #45 from yaosheng216/patch-3 Update sd15_512_sce_ctr_hed.yaml	2024-09-27 09:48:43 +08:00
Great	4ad0f6038c	Update ldm_sce.py	2024-09-27 09:39:22 +08:00
Great	4f56623627	Update sd15_512_sce_ctr_hed.yaml	2024-09-27 09:36:27 +08:00
jiangzeyinzi	30afc0a1de	Merge pull request #39 from yaosheng216/patch-2 Update readme.md	2024-07-18 17:51:53 +08:00
Great	aeabef75b4	Update readme.md	2024-07-18 17:40:28 +08:00
jiangzeyinzi	9376149415	Merge pull request #38 from modelscope/v1.0.3_dev v1.0.3	2024-07-18 17:31:44 +08:00
zeyinzi.jzyz	01fd8335af	v1.0.3 update	2024-07-18 14:12:42 +08:00
Zhen Han	7a9f90efb2	Update stylebooth.md	2024-07-14 20:10:10 +08:00
Zhen Han	cbbef4a2da	Update stylebooth.md	2024-07-14 19:53:11 +08:00
hanzhen.hz	b242449b25	v1.0.2	2024-06-05 16:12:05 +08:00
jiangzeyinzi	2fb8fd1872	Merge pull request #34 from modelscope/v1.0.0_dev v1.0.0 update	2024-06-03 17:49:47 +08:00
jiangzeyinzi	ef82a32944	Update process_watcher.py	2024-06-03 17:47:55 +08:00
hanzhn	aa7e959330	v1.0.0 update	2024-05-31 14:05:54 +08:00
hanzhn	edff6352d5	v1.0.0 update	2024-05-29 13:17:03 +08:00
hanzhn	aa8250e5d2	v1.0.0 update	2024-05-29 10:30:04 +08:00
hanzhn	e7255aac25	v1.0.0 update	2024-05-27 17:12:57 +08:00
Zhen Han	b752ff1cbc	Update readme.md	2024-05-27 14:01:28 +08:00
Zhen Han	8b08629bd0	Update readme.md	2024-05-27 13:28:27 +08:00
hanzhn	c70ef0fc47	v1.0.0 update	2024-05-27 13:15:48 +08:00
Zhen Han	8076aae7da	Merge pull request #28 from modelscope/v0.0.5_dev V0.0.5 dev	2024-04-29 16:54:18 +08:00
靖渊	79e4910ce4	Merge branch 'main' into v0.0.5_dev	2024-04-29 16:04:23 +08:00
靖渊	6be496d5ac	update v0.0.5.post1	2024-04-29 16:02:23 +08:00
jiangzeyinzi	b5b2fd9974	Merge pull request #25 from modelscope/v0.0.5_dev fix OOM bug	2024-04-23 17:46:53 +08:00
靖渊	f502f8063d	fix OOM bug	2024-04-23 17:43:51 +08:00
mcj	a3711e1d1d	Merge pull request #24 from modelscope/v0.0.5_dev update readme&OOM	2024-04-22 20:24:57 +08:00
靖渊	8ba0b4c673	update readme&OOM	2024-04-22 16:19:03 +08:00
mcj	4f4e516164	Merge pull request #22 from modelscope/v0.0.5_dev update readme.md	2024-04-20 11:31:55 +08:00
Jingfeng727	88f0244516	update readme.md	2024-04-20 10:40:06 +08:00
hanzhn	5733918aec	readme update	2024-04-19 21:56:35 +08:00
mcj	da88802b68	Merge pull request #21 from modelscope/v0.0.5_dev update v0.0.5	2024-04-19 11:33:41 +08:00
Jingfeng727	16d99a268f	modify req&import format	2024-04-19 10:39:21 +08:00
Jingfeng727	fc46a63ac8	update v0.0.5	2024-04-18 15:53:37 +08:00
mcj	0010d4282a	Merge pull request #18 from modelscope/v0.0.4_dev V0.0.4 dev	2024-04-10 09:41:45 +08:00
LouieStark	8a9b791e3f	fix bug	2024-04-09 14:02:28 +08:00
LouieStark	ee7fe888f2	delete clip.py	2024-04-02 18:27:47 +08:00
LouieStark	36b259b4b4	Merge pull request #14 from modelscope/v0.0.4_dev fix largen default	2024-04-02 16:19:30 +08:00
LouieStark	92c849412e	fix largen default	2024-04-02 16:15:25 +08:00
LouieStark	2a7e026f84	Merge pull request #13 from modelscope/v0.0.4_dev V0.0.4 dev	2024-04-01 12:07:24 +08:00
LouieStark	cdba82baf8	update readme	2024-04-01 12:06:48 +08:00
LouieStark	565c7957d8	fix bug	2024-04-01 11:46:03 +08:00
LouieStark	e00c23d09a	fix error	2024-03-31 19:26:14 +08:00
LouieStark	bf53829530	update v0.0.4	2024-03-31 13:08:41 +08:00
zeyinzi.jzyz	35aada8ce8	Fix required_memory	2024-02-29 20:12:08 +08:00
Zhen Han	d3ce651bf7	Update readme.md	2024-02-07 21:08:16 +08:00
Zhen Han	7a58c91940	Update readme.md	2024-02-07 20:24:19 +08:00
Zhen Han	4e1606af2d	Merge pull request #6 from modelscope/v0.0.3_dev V0.0.3 dev	2024-02-07 20:18:55 +08:00
Zhen Han	09459c11b7	Update readme.md	2024-02-07 20:16:58 +08:00
hanzhn	d9a48268d5	update example cache dir	2024-02-07 20:02:58 +08:00
jiangzeyinzi	9f1847501d	fix ctr null	2024-02-07 19:46:48 +08:00
Zhen Han	8214227098	Update readme.md	2024-02-07 18:49:21 +08:00
Zhen Han	2e69b2b116	Update readme.md	2024-02-07 15:35:12 +08:00
hanzhn	3440ec7c38	update v0.0.3	2024-02-07 14:57:28 +08:00
hanzhn	9999e0e1f9	v0.0.3	2024-02-06 17:58:30 +08:00
mcj	01c03683e8	Merge pull request #5 from eltociear/patch-1 Update readme.md	2024-01-25 09:45:20 +08:00
Ikko Eltociear Ashimine	9adb273e4b	Update readme.md approches -> approaches	2024-01-25 02:52:48 +09:00
jiangzeyinzi	2249ff37c9	Merge pull request #4 from modelscope/v0.0.2_dev V0.0.2 dev	2024-01-19 15:14:27 +08:00
`@@ -1 +1,2 @@`
	`recursive-include scepter *.yaml`	`recursive-include scepter *.yaml`
		`recursive-include scepter *.md`
		`@@ -0,0 +1,2 @@`
							`# -- coding: utf-8 --`
							`# Copyright (c) Alibaba, Inc. and its affiliates.`