218 lines
12 KiB
Python
218 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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# For dataset manager
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class ModelUIName():
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def __init__(self, language='en'):
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if language == 'en':
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self.output_model_block = 'Model Output'
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self.output_model_name = 'Output Model Name'
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self.output_ckpt_name = 'Output Ckpt Name'
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self.test_prompt = 'Test Prompt'
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self.test_prefix = 'Test Prefix'
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self.test_n_prompt = 'Negative Prompt'
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self.sampler = 'Sampler'
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self.num_inference_steps = 'Sampling Step Length'
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self.inference_num = 'Number of Inferences'
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self.generator_seed = 'Sampling Seed'
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self.tuner_method = 'Tuning Method'
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self.inference_resolution = 'Inference Resolution'
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self.output_image = 'Output Result'
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self.display_button = 'Infer'
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self.extra_model_gtxt = 'Extra Model'
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self.extra_model_gbtn = 'Add Model'
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self.refresh_model_gbtn = 'Refresh Model'
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self.go_to_inference = 'Go to inference'
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self.btn_export_log = 'Export Log'
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self.export_file = 'Log File'
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self.log_block = 'Training Log...'
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self.gallery_block = 'Gallery Log...'
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self.eval_gallery = 'Eval Gallery'
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# Error or Warning
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self.inference_err1 = 'Inference failed, please try again.'
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self.inference_err2 = 'Test prompt is empty.'
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self.model_err3 = "Doesn't surpport this base model"
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self.model_err4 = "This model maybe not finish training, because model doesn't exist."
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self.model_err5 = "Model {} doesn't exist."
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self.training_warn1 = 'No log message util now.'
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elif language == 'zh':
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self.output_model_block = '模型产出'
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self.output_model_name = '产出名称'
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self.output_ckpt_name = '产出检查点名称'
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self.test_prompt = '测试提示词'
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self.test_prefix = '测试前缀'
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self.test_n_prompt = '负向提示词'
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self.sampler = '采样器'
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self.num_inference_steps = '采样步长'
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self.inference_num = '推理数'
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self.generator_seed = '采样种子'
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self.tuner_method = '训练方式'
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self.inference_resolution = '推理分辨率'
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self.output_image = '输出结果'
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self.display_button = '推理'
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self.extra_model_gtxt = '额外模型'
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self.extra_model_gbtn = '添加模型'
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self.refresh_model_gbtn = '刷新模型'
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self.btn_export_log = '导出日志'
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self.export_file = '日志文件'
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self.log_block = '训练日志...'
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self.training_button = '开始训练'
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self.gallery_block = '图像日志...'
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self.eval_gallery = '评测图像'
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# Error or Warning
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self.inference_err1 = '推理失败，请重试。'
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self.inference_err2 = '测试提示词为空。'
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self.model_err3 = '不支持的基础模型'
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self.go_to_inference = '使用模型'
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self.model_err4 = '模型可能没有训练完成或者模型不存在'
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self.model_err5 = '模型{}不存在'
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self.training_warn1 = '暂时没有日志文件；任务启动中或失败！'
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class TrainerUIName():
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def __init__(self, language='en'):
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self.task_choices = ['Text2Image', 'Image Editing']
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self.data_task_map = {
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'scepter_txt2img': None,
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'scepter_img2img': 'edit'
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}
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if language == 'en':
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self.user_direction = '''
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### User Guide
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- Data: Data preparation is done through the Data Manager. (zip example: [3D](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip))
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- Parameters: You can try modifying the related parameters.
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- Training: Click on [Start Training].
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- Testing: After completing the training, click [Go to inference].
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- Note: Timeouts may cause the connection to disconnect (an Error may occur).
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After waiting for the time when the training is likely to be almost complete,
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refresh the interface and then click [Refresh Model] at the bottom of the page.
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The trained model should appear in the [Output Model Name] if training was successful;
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if not, the training may be incomplete or have failed.
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- For processing and training with large-scale data, it is recommended to use the command line.
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''' # noqa
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self.data_source_choices = [
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'Dataset zip', 'MaaS Dataset', 'Dataset Management'
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]
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self.data_source_value = 'Dataset zip'
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self.data_source_name = 'Data Source'
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self.data_type_map = {
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'scepter_txt2img': 'Text2Image Generation',
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'scepter_img2img': 'Image Edit Generation'
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}
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self.data_type_choices = list(self.data_type_map.keys())
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self.data_type_value = 'scepter_txt2img'
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self.data_type_name = 'Data Type'
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self.ori_data_name = 'Data Name'
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# Supports MaaS dataset/local/HTTP Zip package
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self.ms_data_name_place_hold = 'Please use Dataset Management.'
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self.ms_data_space = 'ModelScope Space'
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self.ms_data_subname = 'MaaS Dataset - Subset'
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self.task = 'Eval Editing Image'
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self.eval_data = 'Evaluation Data'
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self.train_data = 'Training Data'
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self.eval_prompts = 'Eval Prompts'
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self.eval_image = 'Eval Image'
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self.training_block = '''
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### Training Parameters
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'''
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self.model_param = 'Model Parameters'
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self.base_param = 'Base Parameters'
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self.base_model = 'Base Model'
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self.tuner_name = 'Tuner Method'
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self.base_model_revision = 'Model Version Number'
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self.resolution_height = 'Train Image Height'
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self.resolution_width = 'Train Image Width'
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self.resolution_height_max = 'Resolution Height Max'
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self.resolution_width_max = 'Resolution Width Max'
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self.train_epoch = 'Total Training Epochs'
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self.learning_rate = 'Learning Rate'
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self.save_interval = 'Checkpoint Save Interval (Epochs)'
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self.train_batch_size = 'Training Batch Size'
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self.prompt_prefix = 'Prefix'
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self.replace_keywords = 'Trigger Keywords'
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self.work_name = 'Save Model Name (refresh to get a random value)'
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self.push_to_hub = 'Push to hub'
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self.training_button = 'Start Training'
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self.tuner_param = 'Tuner Parameters'
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self.enable_resolution_bucket = 'Enable Resolution Bucket'
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self.enable_resolution_bucket_ins = 'Automatically Pack Multi-Resolution Batches'
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self.resolution_param = 'Resolution Parameters'
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self.min_bucket_resolution = 'Min Bucket Resolution'
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self.max_bucket_resolution = 'Max Bucket Resolution'
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self.bucket_resolution_steps = 'Bucket Resolution Steps'
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self.bucket_no_upscale = 'Bucket No Upscale'
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self.bucket_no_upscale_ins = 'Disable Automatic Image Upscaling'
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# Error or Warning
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self.training_err1 = 'CUDA is unavailable.'
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self.training_err2 = 'Currently insufficient VRAM, training failed!'
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self.training_err3 = 'You need to prepare training data.'
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self.training_err4 = 'Save model name already exists or is None, please regenerate this name.'
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self.training_err5 = 'Training failed.'
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self.training_err6 = "Can't process training data"
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elif language == 'zh':
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self.user_direction = '''
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### 使用说明
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- 数据: 通过数据管理器进行数据的准备（ZIP样例：[3D](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip)）
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- 参数: 可尝试进行相关参数的修改
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- 训练: 点击【开始训练】
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- 测试: 完成训练后点击【使用模型】
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- 注意：超时可能导致连接断开(出现Error)，可以等差不多可能训完后，刷新界面再点击页面最后的[刷新模型]，即可在[产出模型名称中]出现已经完成训练的模型，若不存在则没有完成训练或训练失败
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- 对于大规模数据的处理和训练，建议使用命令行形式
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''' # noqa
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self.data_source_choices = ['数据集zip', 'MaaS数据集', '数据管理器']
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self.data_source_value = '数据集zip'
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self.data_source_name = '数据集来源'
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self.data_type_map = {
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'scepter_txt2img': '文生图数据',
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'scepter_img2img': '图像编辑（图生图）数据'
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}
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self.data_type_choices = list(self.data_type_map.keys())
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self.data_type_value = 'scepter_txt2img'
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self.data_type_name = '数据类型'
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self.ori_data_name = '数据集名称'
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self.ms_data_name_place_hold = '请使用数据管理器导入' # '支持MaaS数据集/本地/Http Zip包'
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self.ms_data_space = 'ModelScope 空间'
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self.ms_data_subname = 'MaaS数据集-子集'
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self.task = '任务'
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self.eval_data = '评测数据'
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self.train_data = '训练数据'
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self.eval_prompts = '评测文本'
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self.eval_image = '评测图片'
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self.training_block = '''
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### 训练参数
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'''
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self.model_param = '模型参数'
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self.base_param = '基本参数'
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self.base_model = '基础模型'
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self.tuner_name = '微调方法'
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self.base_model_revision = '模型版本号'
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self.resolution_height = '训练图片高度'
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self.resolution_width = '训练图片宽度'
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self.resolution_height_max = '最大训练高度'
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self.resolution_width_max = '最大训练宽度'
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self.train_epoch = '总训练轮数'
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self.learning_rate = '学习率'
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self.save_interval = '中间结果存储间隔（轮数）'
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self.train_batch_size = '每批次数据条数（Batch Size）'
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self.prompt_prefix = '前缀'
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self.replace_keywords = '触发关键词'
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self.work_name = '保存模型名称（刷新获得随机值）'
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self.push_to_hub = '推送魔搭社区'
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self.tuner_param = '微调参数'
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self.enable_resolution_bucket = '开启分辨率分桶'
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self.enable_resolution_bucket_ins = '自动组装多分辨率Batch'
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self.resolution_param = '分辨率参数'
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self.min_bucket_resolution = '最小分桶分辨率'
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self.max_bucket_resolution = '最大分桶分辨率'
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self.bucket_resolution_steps = '分桶分辨率步长'
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self.bucket_no_upscale = '分桶分辨率不做放大'
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self.bucket_no_upscale_ins = '禁止图片分辨率上采样'
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# Error or Warning
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self.training_err1 = 'CUDA不可用.'
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self.training_err2 = '目前显存不足，训练失败！'
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self.training_err3 = '您需要准备训练数据'
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self.training_err4 = '保存模型未生成或名称已经存在，请重新生成名称'
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self.training_err5 = '训练失败'
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self.training_err6 = '无法处理的数据'
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