revise multi-user support

launch.py

@@ -149,6 +149,7 @@ def prepare_enviroment():
     sys.argv += shlex.split(commandline_args)
     test_argv = [x for x in sys.argv if x != '--tests']
 
+    sys.argv, skip_torch_cuda = extract_arg(sys.argv, '--skip-torch-cuda')
     sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
     sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
     sys.argv, update_check = extract_arg(sys.argv, '--update-check')
@@ -164,11 +165,11 @@ def prepare_enviroment():
 
     print(f"Python {sys.version}")
     print(f"Commit hash: {commit}")
-    
-    if not is_installed("torch") or not is_installed("torchvision"):
+
+    if not skip_torch_cuda and (not is_installed("torch") or not is_installed("torchvision")):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
 
-    if not skip_torch_cuda_test:
+    if not skip_torch_cuda and not skip_torch_cuda_test:
         run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
 
     if not is_installed("gfpgan"):
@@ -206,7 +207,7 @@ def prepare_enviroment():
     if not is_installed("lpips"):
         run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 
-    run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+    #run_pip(f"install -r {requirements_file}", "requirements for Web UI")
 
     run_extensions_installers()
 

localizations/zh_CN.json

@@ -618,7 +618,35 @@
     "favorites": "收藏夹(已保存)",
     "others": "其他",
     "Collect": "收藏(保存)",
-
+    "Create & Train Embedding": "创建并训练 Embedding",
+    "Train an embedding; you must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
+    "Embedding settings": "Embedding 设置",
+    "Image preprocess settings": "图像预处理设置",
+    "Train settings": "训练设置",
+    "Create & Train Hypernetwork": "创建并训练 Hypernetwork",
+    "Train an hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
+    "Hypernetwork settings": "Hypernetwork 设置",
+    "Sign Options": "登陆选项",
+    "Sign In": "登入",
+    "Sign Up": "注册",
+    "Sign Out": "登出",
+    "Username": "用户名",
+    "Password": "密码",
+    "Email": "电子邮箱",
+    "Update": "更新",
+    "Delete": "删除",
+    "Mismatched username/password or not existed username": "用户名/密码不匹配或用户不存在",
+    "Signup failed, please check and retry again": "注册失败，请检查后并重试",
+    "Update failed, please check and retry again": "更新失败，请检查后并重试",
+    "Output": "输出",
+    "Images S3 URI": "图像 S3 位置",
+    "Models S3 URI": "模型 S3 位置",
+    "Instance type": "实例类型",
+    "Instance count": "实例数量",
+    "Submit training job sucessful": "训练任务提交成功",
+    "Settings saved failed": "设置保存错误",
+    "SageMaker endpoint": "SageMaker 端点",
+    "User": "用户",
 
     "--------": "--------"
 }

modules/api/api.py

@@ -18,7 +18,7 @@
 import json
 import os
 import boto3
-from modules import sd_hijack, hypernetworks
+from modules import sd_hijack, hypernetworks, sd_models
 from typing import Union
 import traceback
 import requests
@@ -359,6 +359,7 @@ def invocations(self, req: InvocationsRequest):
                 response = requests.post(url=f'{api_endpoint}/sd/user', json=inputs)
                 if response.status_code == 200 and response.text != '':
                     shared.opts.data = json.loads(response.text)
+                    sd_models.reload_model_weights()
 
                 self.download_s3files(hypernetwork_s3uri, os.path.join(script_path, shared.cmd_opts.hypernetwork_dir))
                 hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)

modules/sd_models.py

@@ -311,6 +311,8 @@ def reload_model_weights(sd_model=None, info=None):
     if not sd_model:
         sd_model = shared.sd_model
 
+    print('Origin checkpoint: ', sd_model.sd_model_checkpoint)
+    print('Current checkpoint: ', checkpoint_info.filename)
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 

modules/shared.py

@@ -136,8 +136,6 @@
     username = ''
     api_endpoint = os.environ['api_endpoint']
     industrial_model = ''
-    endpoint_name = ''
-    endpoint_names = []
     default_options = {}
 
 def reload_hypernetworks():
@@ -268,7 +266,49 @@ def options_section(section_identifier, options_dict):
 
 options_templates = {}
 
+def refresh_sagemaker_endpoints():
+    global industrial_model, api_endpoint, default_options
+
+    if industrial_model == '':
+        response = requests.get(url=f'{api_endpoint}/sd/industrialmodel')
+        if response.status_code == 200:
+            industrial_model = response.text
+        else:
+            model_name = 'stable-diffusion-webui'
+            model_description = model_name
+            inputs = {
+                'model_algorithm': 'stable-diffusion-webui',
+                'model_name': model_name,
+                'model_description': model_description,
+                'model_extra': '{"visible": "false"}',
+                'model_samples': '',
+                'file_content': {
+                        'data': [(lambda x: int(x))(x) for x in open(os.path.join(script_path, 'logo.ico'), 'rb').read()]
+                }
+            }
+
+            response = requests.post(url=f'{api_endpoint}/industrialmodel', json = inputs)
+            if response.status_code == 200:
+                body = json.loads(response.text)
+                industrial_model = body['id']
+
+            default_options = self.data
+
+    sagemaker_endpoints = []
+
+    if industrial_model != '':
+        params = {
+            'industrial_model': industrial_model
+        }
+        response = requests.get(url=f'{api_endpoint}/endpoint', params=params)
+        if response.status_code == 200:
+            for endpoint_item in json.loads(response.text):
+                sagemaker_endpoints.append(endpoint_item['EndpointName'])
+
+    return sagemaker_endpoints
+
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
+    "sagemaker_endpoint": OptionInfo(None, "SaegMaker endpoint", gr.Dropdown, lambda: {"choices": refresh_sagemaker_endpoints()}, refresh=refresh_sagemaker_endpoints),
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
@@ -393,7 +433,7 @@ def options_section(section_identifier, options_dict):
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    'quicksettings': OptionInfo("sagemaker_endpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
 
@@ -480,34 +520,6 @@ def load(self, filename):
         if bad_settings > 0:
             print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
 
-        if cmd_opts.pureui:
-            global api_endpoint, industrial_model, default_options
-
-            #opts.show_progressbar = False
-            response = requests.get(url=f'{api_endpoint}/sd/industrialmodel')
-            if response.status_code == 200:
-                industrial_model = response.text            
-            else:
-                model_name = 'stable-diffusion-webui'
-                model_description = model_name
-                inputs = {
-                    'model_algorithm': 'stable-diffusion-webui',
-                    'model_name': model_name,
-                    'model_description': model_description,
-                    'model_extra': '{"visible": "false"}',
-                    'model_samples': '',
-                    'file_content': {
-                        'data': [(lambda x: int(x))(x) for x in open(os.path.join(script_path, 'logo.ico'), 'rb').read()]
-                    }
-                }
-
-                response = requests.post(url=f'{api_endpoint}/industrialmodel', json = inputs)
-                if response.status_code == 200:
-                    body = json.loads(response.text)
-                    industrial_model = body['id']
-
-            default_options = self.data
-
     def onchange(self, key, func, call=True):
         item = self.data_labels.get(key)
         item.onchange = func
@@ -587,18 +599,3 @@ def clear(self):
 def listfiles(dirname):
     filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
     return [file for file in filenames if os.path.isfile(file)]
-
-if cmd_opts.pureui:
-    def init_endpoints():    
-        global endpoint_name, endpoint_names, industrial_model, api_endpoint
-
-        endpoints = []
-        params = {
-            'industrial_model': industrial_model
-        }
-        response = requests.get(url=f'{api_endpoint}/endpoint', params=params)
-        if response.status_code == 200:
-            for endpoint_item in json.loads(response.text):
-                endpoints.append(endpoint_item['EndpointName'])
-            endpoint_name = endpoints[0] if len(endpoints) > 0 else ''
-            endpoint_names = endpoints

modules/ui.py

@@ -2331,36 +2331,6 @@ def user_delete(login_username, login_password, login_email):
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
-        if cmd_opts.pureui:
-            shared.init_endpoints()
-
-            with gr.Row():
-                with gr.Column(scale=9):
-                    endpoint_names = gr.Dropdown(label='SageMaker endpoint', value=shared.endpoint_name, choices=shared.endpoint_names)
-                with gr.Column(scale=1):
-                    endpoint_refresh = gr.Button(refresh_symbol)
-
-            def refresh_endpoint():
-                shared.init_endpoints()
-                return {
-                    endpoint_names: gr.update(value=shared.endpoint_name, choices=shared.endpoint_names)
-                }
-
-            def change_endpoint(endpoint_names):
-                shared.endpoint_name = endpoint_names
-
-            endpoint_names.change(
-                fn=change_endpoint,
-                inputs=[endpoint_names],
-                outputs=[]
-            )
-
-            endpoint_refresh.click(
-                fn=refresh_endpoint,
-                inputs=[],
-                outputs=[endpoint_names]
-            )
-
         parameters_copypaste.integrate_settings_paste_fields(component_dict)
         parameters_copypaste.run_bind()
 

style.css

@@ -501,7 +501,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
+#refresh_sagemaker_endpoint, #refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;

webui.py

@@ -286,7 +286,7 @@ def sagemaker_inference(task, infer, *args, **kwargs):
                 }
 
             params = {
-                'endpoint_name': shared.endpoint_name
+                'endpoint_name': shared.opts.sagemaker_endpoint
             }
 
             response = requests.post(url=f'{shared.api_endpoint}/inference', params=params, json=inputs)
@@ -384,7 +384,7 @@ def sagemaker_inference(task, infer, *args, **kwargs):
                 }
 
             params = {
-                'endpoint_name': shared.endpoint_name
+                'endpoint_name': shared.opts.sagemaker_endpoint
             }
             response = requests.post(url=f'{shared.api_endpoint}/inference', params=params, json=inputs)
             if infer == 'async':
@@ -440,6 +440,7 @@ def initialize():
     modules.scripts.load_scripts()
 
     modules.sd_vae.refresh_vae_list()
+
     shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
 
     if not cmd_opts.pureui:
@@ -593,10 +594,7 @@ def train():
         response = requests.post(url=f'{api_endpoint}/sd/user', json=inputs)
         if response.status_code == 200 and response.text != '':
             opts.data = json.loads(response.text)
-            for key in modules.sd_models.checkpoints_list:
-                if modules.sd_models.checkpoints_list[key].title == opts.data['sd_model_checkpoint']:
-                    shared.sd_model.sd_model_name = modules.sd_models.checkpoints_list[key].model_name
-                    break
+            modules.sd_models.load_model()
 
     if train_task == 'embedding':
         name = train_args['embedding_settings']['name']