Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860", "--reload"]

README.md

@@ -1,12 +1,13 @@
 ---
-title: Testlcm
-emoji: 🏢
-colorFrom: pink
-colorTo: purple
+title: Fastlcm
+emoji: 🏆
+colorFrom: gray
+colorTo: yellow
 sdk: gradio
-sdk_version: 4.7.1
+sdk_version: 4.8.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -7,6 +7,7 @@ from constants import APP_VERSION, LCM_DEFAULT_MODEL_OPENVINO
 from models.interface_types import InterfaceType
 from constants import DEVICE
 from state import get_settings
+import traceback
 
 
 from fastapi import FastAPI,Body
@@ -22,15 +23,12 @@ import base64
 import io
 from datetime import datetime
 
-app = FastAPI(name="mutilParam")
-
 from typing import Any
 from backend.models.lcmdiffusion_setting import DiffusionTask
 
 from frontend.utils import is_reshape_required
 from concurrent.futures import ThreadPoolExecutor
 
-app_settings = get_settings()
 
 context = Context(InterfaceType.WEBUI)
 previous_width = 0
@@ -38,136 +36,136 @@ previous_height = 0
 previous_model_id = ""
 previous_num_of_images = 0
 
-parser = ArgumentParser(description=f"FAST SD CPU {constants.APP_VERSION}")
-parser.add_argument(
-    "-s",
-    "--share",
-    action="store_true",
-    help="Create sharable link(Web UI)",
-    required=False,
-)
-group = parser.add_mutually_exclusive_group(required=False)
-group.add_argument(
-    "-g",
-    "--gui",
-    action="store_true",
-    help="Start desktop GUI",
-)
-group.add_argument(
-    "-w",
-    "--webui",
-    action="store_true",
-    help="Start Web UI",
-)
-group.add_argument(
-    "-r",
-    "--realtime",
-    action="store_true",
-    help="Start realtime inference UI(experimental)",
-)
-group.add_argument(
-    "-v",
-    "--version",
-    action="store_true",
-    help="Version",
-)
-parser.add_argument(
-    "--lcm_model_id",
-    type=str,
-    help="Model ID or path,Default SimianLuo/LCM_Dreamshaper_v7",
-    default="SimianLuo/LCM_Dreamshaper_v7",
-)
-parser.add_argument(
-    "--prompt",
-    type=str,
-    help="Describe the image you want to generate",
-)
-parser.add_argument(
-    "--image_height",
-    type=int,
-    help="Height of the image",
-    default=512,
-)
-parser.add_argument(
-    "--image_width",
-    type=int,
-    help="Width of the image",
-    default=512,
-)
-parser.add_argument(
-    "--inference_steps",
-    type=int,
-    help="Number of steps,default : 4",
-    default=4,
-)
-parser.add_argument(
-    "--guidance_scale",
-    type=int,
-    help="Guidance scale,default : 1.0",
-    default=1.0,
-)
+# parser = ArgumentParser(description=f"FAST SD CPU {constants.APP_VERSION}")
+# parser.add_argument(
+#     "-s",
+#     "--share",
+#     action="store_true",
+#     help="Create sharable link(Web UI)",
+#     required=False,
+# )
+# group = parser.add_mutually_exclusive_group(required=False)
+# group.add_argument(
+#     "-g",
+#     "--gui",
+#     action="store_true",
+#     help="Start desktop GUI",
+# )
+# group.add_argument(
+#     "-w",
+#     "--webui",
+#     action="store_true",
+#     help="Start Web UI",
+# )
+# group.add_argument(
+#     "-r",
+#     "--realtime",
+#     action="store_true",
+#     help="Start realtime inference UI(experimental)",
+# )
+# group.add_argument(
+#     "-v",
+#     "--version",
+#     action="store_true",
+#     help="Version",
+# )
+# parser.add_argument(
+#     "--lcm_model_id",
+#     type=str,
+#     help="Model ID or path,Default SimianLuo/LCM_Dreamshaper_v7",
+#     default="SimianLuo/LCM_Dreamshaper_v7",
+# )
+# parser.add_argument(
+#     "--prompt",
+#     type=str,
+#     help="Describe the image you want to generate",
+# )
+# parser.add_argument(
+#     "--image_height",
+#     type=int,
+#     help="Height of the image",
+#     default=512,
+# )
+# parser.add_argument(
+#     "--image_width",
+#     type=int,
+#     help="Width of the image",
+#     default=512,
+# )
+# parser.add_argument(
+#     "--inference_steps",
+#     type=int,
+#     help="Number of steps,default : 4",
+#     default=4,
+# )
+# parser.add_argument(
+#     "--guidance_scale",
+#     type=int,
+#     help="Guidance scale,default : 1.0",
+#     default=1.0,
+# )
 
-parser.add_argument(
-    "--number_of_images",
-    type=int,
-    help="Number of images to generate ,default : 1",
-    default=1,
-)
-parser.add_argument(
-    "--seed",
-    type=int,
-    help="Seed,default : -1 (disabled) ",
-    default=-1,
-)
-parser.add_argument(
-    "--use_openvino",
-    action="store_true",
-    help="Use OpenVINO model",
-)
+# parser.add_argument(
+#     "--number_of_images",
+#     type=int,
+#     help="Number of images to generate ,default : 1",
+#     default=1,
+# )
+# parser.add_argument(
+#     "--seed",
+#     type=int,
+#     help="Seed,default : -1 (disabled) ",
+#     default=-1,
+# )
+# parser.add_argument(
+#     "--use_openvino",
+#     action="store_true",
+#     help="Use OpenVINO model",
+# )
 
-parser.add_argument(
-    "--use_offline_model",
-    action="store_true",
-    help="Use offline model",
-)
-parser.add_argument(
-    "--use_safety_checker",
-    action="store_false",
-    help="Use safety checker",
-)
-parser.add_argument(
-    "--use_lcm_lora",
-    action="store_true",
-    help="Use LCM-LoRA",
-)
-parser.add_argument(
-    "--base_model_id",
-    type=str,
-    help="LCM LoRA base model ID,Default Lykon/dreamshaper-8",
-    default="Lykon/dreamshaper-8",
-)
-parser.add_argument(
-    "--lcm_lora_id",
-    type=str,
-    help="LCM LoRA model ID,Default latent-consistency/lcm-lora-sdv1-5",
-    default="latent-consistency/lcm-lora-sdv1-5",
-)
-parser.add_argument(
-    "-i",
-    "--interactive",
-    action="store_true",
-    help="Interactive CLI mode",
-)
-parser.add_argument(
-    "--use_tiny_auto_encoder",
-    action="store_true",
-    help="Use tiny auto encoder for SD (TAESD)",
-)
-args = parser.parse_args()
+# parser.add_argument(
+#     "--use_offline_model",
+#     action="store_true",
+#     help="Use offline model",
+# )
+# parser.add_argument(
+#     "--use_safety_checker",
+#     action="store_false",
+#     help="Use safety checker",
+# )
+# parser.add_argument(
+#     "--use_lcm_lora",
+#     action="store_true",
+#     help="Use LCM-LoRA",
+# )
+# parser.add_argument(
+#     "--base_model_id",
+#     type=str,
+#     help="LCM LoRA base model ID,Default Lykon/dreamshaper-8",
+#     default="Lykon/dreamshaper-8",
+# )
+# parser.add_argument(
+#     "--lcm_lora_id",
+#     type=str,
+#     help="LCM LoRA model ID,Default latent-consistency/lcm-lora-sdv1-5",
+#     default="latent-consistency/lcm-lora-sdv1-5",
+# )
+# parser.add_argument(
+#     "-i",
+#     "--interactive",
+#     action="store_true",
+#     help="Interactive CLI mode",
+# )
+# parser.add_argument(
+#     "--use_tiny_auto_encoder",
+#     action="store_true",
+#     help="Use tiny auto encoder for SD (TAESD)",
+# )
+# args = parser.parse_args()
 
-if args.version:
-    print(APP_VERSION)
-    exit()
+# if args.version:
+#     print(APP_VERSION)
+#     exit()
 
 # parser.print_help()
 show_system_info()
@@ -184,7 +182,7 @@ print(
 print(
     f"Found {len(app_settings.openvino_lcm_models)} OpenVINO LCM models in config/openvino-lcm-models.txt"
 )
-
+app_settings.settings.lcm_diffusion_setting.use_openvino = True
 from frontend.webui.ui import start_webui
 
 print("Starting web UI mode")
@@ -192,91 +190,85 @@ start_webui(
     args.share,
 )
 
-app.get("/")
-def root():
-    return {"API": "hello"}
-    
-@app.post("/img2img")
-async def predict(prompt=Body(...),imgbase64data=Body(...),negative_prompt=Body(None),userId=Body(None)):
-    pipeline = get_pipeline()
-    MAX_QUEUE_SIZE = 4
-    start = time.time()
-    print("参数",imgbase64data,prompt)
-    image_data = base64.b64decode(imgbase64data)
-    image1 = Image.open(io.BytesIO(image_data))
-    w, h = image1.size
-    newW = 512
-    newH = int(h * newW / w)
-    img = image1.resize((newW, newH))  
-    end1 = time.time()
-    now = datetime.now()
-    print(now)
-    print("图像:", img.size)
-    print("加载管道:", end1 - start)
-    global previous_height, previous_width, previous_model_id, previous_num_of_images, app_settings
+# app = FastAPI(name="mutilParam")
+# print("我执行了")
+# @app.get("/")
+# def root():
+#     return {"API": "hello"}
+
+# @app.post("/img2img")
+# async def predict(prompt=Body(...),imgbase64data=Body(...),negative_prompt=Body(None),userId=Body(None)):
+#     MAX_QUEUE_SIZE = 4
+#     start = time.time()
+#     print("参数",imgbase64data,prompt)
+#     image_data = base64.b64decode(imgbase64data)
+#     image1 = Image.open(io.BytesIO(image_data))
+#     w, h = image1.size
+#     newW = 512
+#     newH = int(h * newW / w)
+#     img = image1.resize((newW, newH))  
+#     end1 = time.time()
+#     now = datetime.now()
+#     print(now)
+#     print("图像:", img.size)
+#     print("加载管道:", end1 - start)
+#     global previous_height, previous_width, previous_model_id, previous_num_of_images, app_settings
     
-    app_settings.settings.lcm_diffusion_setting.prompt = prompt
-    app_settings.settings.lcm_diffusion_setting.negative_prompt = negative_prompt
-    app_settings.settings.lcm_diffusion_setting.init_image = img
-    app_settings.settings.lcm_diffusion_setting.strength = 0.6
+#     app_settings.settings.lcm_diffusion_setting.prompt = prompt
+#     app_settings.settings.lcm_diffusion_setting.negative_prompt = negative_prompt
+#     app_settings.settings.lcm_diffusion_setting.init_image = image1
+#     app_settings.settings.lcm_diffusion_setting.strength = 0.6
    
-    app_settings.settings.lcm_diffusion_setting.diffusion_task = (
-        DiffusionTask.image_to_image.value
-    )
-    model_id = app_settings.settings.lcm_diffusion_setting.openvino_lcm_model_id
-    reshape = False
-    app_settings.settings.lcm_diffusion_setting.image_height=newH
-    image_width = app_settings.settings.lcm_diffusion_setting.image_width
-    image_height = app_settings.settings.lcm_diffusion_setting.image_height
-    num_images = app_settings.settings.lcm_diffusion_setting.number_of_images
-    reshape = is_reshape_required(
-        previous_width,
-        image_width,
-        previous_height,
-        image_height,
-        previous_model_id,
-        model_id,
-        previous_num_of_images,
-        num_images,
-    )
+#     app_settings.settings.lcm_diffusion_setting.diffusion_task = (
+#         DiffusionTask.image_to_image.value
+#     )
+#     model_id = app_settings.settings.lcm_diffusion_setting.openvino_lcm_model_id
+#     reshape = False
+#     app_settings.settings.lcm_diffusion_setting.image_height=newH
+#     image_width = app_settings.settings.lcm_diffusion_setting.image_width
+#     image_height = app_settings.settings.lcm_diffusion_setting.image_height
+#     num_images = app_settings.settings.lcm_diffusion_setting.number_of_images
+#     reshape = is_reshape_required(
+#         previous_width,
+#         image_width,
+#         previous_height,
+#         image_height,
+#         previous_model_id,
+#         model_id,
+#         previous_num_of_images,
+#         num_images,
+#     )
 
 
-    with ThreadPoolExecutor(max_workers=1) as executor:
-        future = executor.submit(
-            context.generate_text_to_image,
-            app_settings.settings,
-            reshape,
-            DEVICE,
-        )
-        images = future.result()
-    # images = context.generate_text_to_image(
-    #     app_settings.settings,
-    #     reshape,
-    #     DEVICE,
-    # )
-    previous_width = image_width
-    previous_height = image_height
-    previous_model_id = model_id
-    previous_num_of_images = num_images
-    output_image = images[0]
-    end2 = time.time()
-    print("测试",output_image)
-    print("s生成完成:", end2 - end1)    
-    # 将图片对象转换为bytes
-    image_data = io.BytesIO()
+#     with ThreadPoolExecutor(max_workers=1) as executor:
+#         future = executor.submit(
+#             context.generate_text_to_image,
+#             app_settings.settings,
+#             reshape,
+#             DEVICE,
+#         )
+#         images = future.result()
+#     previous_width = image_width
+#     previous_height = image_height
+#     previous_model_id = model_id
+#     previous_num_of_images = num_images
+#     output_image = images[0]
+#     end2 = time.time()
+#     print("测试",output_image)
+#     print("s生成完成:", end2 - end1)    
+#     # 将图片对象转换为bytes
+#     image_data = io.BytesIO()
 
-    # 将图像保存到BytesIO对象中，格式为JPEG
-    output_image.save(image_data, format='JPEG')
+#     # 将图像保存到BytesIO对象中，格式为JPEG
+#     output_image.save(image_data, format='JPEG')
     
-    # 将BytesIO对象的内容转换为字节串
-    image_data_bytes = image_data.getvalue()
-    output_image_base64 = base64.b64encode(image_data_bytes).decode('utf-8')
-    print("完成的图片:", output_image_base64)
-    logger = logging.getLogger('')
-    logger.info(output_image_base64)
-    return output_image_base64
+#     # 将BytesIO对象的内容转换为字节串
+#     image_data_bytes = image_data.getvalue()
+#     output_image_base64 = base64.b64encode(image_data_bytes).decode('utf-8')
+#     print("完成的图片:", output_image_base64)
+#     return output_image_base64
         
     
-@app.post("/predict")
-async def predict(prompt=Body(...)):
-  return f"您好,{prompt}"
+# @app.post("/predict")
+# async def predict(prompt=Body(...)):
+#   return f"您好,{prompt}"

app_settings.py

@@ -15,9 +15,7 @@ from copy import deepcopy
 class AppSettings:
     def __init__(self):
         self.config_path = FastStableDiffusionPaths().get_app_settings_path()
-        print("打印模型地址",self.config_path)
-        base_url='/app/'
-        self._stable_diffsuion_models = get_models_from_text_file (
+        self._stable_diffsuion_models = get_models_from_text_file(
             FastStableDiffusionPaths().get_models_config_path(SD_MODELS_FILE)
         )
         self._lcm_lora_models = get_models_from_text_file(

backend/lcm_text_to_image.py

@@ -38,6 +38,7 @@ class LCMTextToImage:
         self.previous_use_openvino = False
         self.img_to_img_pipeline = None
         self.is_openvino_init = False
+        self.task_type = DiffusionTask.text_to_image
         self.torch_data_type = (
             torch.float32 if is_openvino_device() or DEVICE == "mps" else torch.float16
         )
@@ -93,12 +94,18 @@ class LCMTextToImage:
         lcm_lora: LCMLora = lcm_diffusion_setting.lcm_lora
         ov_model_id = lcm_diffusion_setting.openvino_lcm_model_id
 
-        # if lcm_diffusion_setting.diffusion_task == DiffusionTask.image_to_image.value:
-        #     lcm_diffusion_setting.init_image = resize_pil_image(
-        #         lcm_diffusion_setting.init_image,
-        #         lcm_diffusion_setting.image_width,
-        #         lcm_diffusion_setting.image_height,
-        #     )
+        if lcm_diffusion_setting.diffusion_task == DiffusionTask.image_to_image.value:
+            w, h = lcm_diffusion_setting.init_image.size
+            newW = lcm_diffusion_setting.image_width
+            newH = int(h * newW / w)
+            img = lcm_diffusion_setting.init_image.resize((newW, newH)) 
+            print("新图",newH,newW, lcm_diffusion_setting.image_height)
+            lcm_diffusion_setting.init_image = resize_pil_image(
+                img,
+                lcm_diffusion_setting.image_width,
+                lcm_diffusion_setting.image_height,
+            )
+            print("图片大小",lcm_diffusion_setting.init_image)
 
         if (
             self.pipeline is None
@@ -110,6 +117,7 @@ class LCMTextToImage:
             or self.previous_ov_model_id != ov_model_id
             or self.previous_safety_checker != lcm_diffusion_setting.use_safety_checker
             or self.previous_use_openvino != lcm_diffusion_setting.use_openvino
+            or self.previous_task_type != lcm_diffusion_setting.diffusion_task
         ):
             if self.use_openvino and is_openvino_device():
                 if self.pipeline:
@@ -218,6 +226,7 @@ class LCMTextToImage:
             self.previous_use_lcm_lora = use_lora
             self.previous_safety_checker = lcm_diffusion_setting.use_safety_checker
             self.previous_use_openvino = lcm_diffusion_setting.use_openvino
+            self.previous_task_type = lcm_diffusion_setting.diffusion_task
             if (
                 lcm_diffusion_setting.diffusion_task
                 == DiffusionTask.text_to_image.value
@@ -238,12 +247,6 @@ class LCMTextToImage:
         reshape: bool = False,
     ) -> Any:
         guidance_scale = lcm_diffusion_setting.guidance_scale
-        # w, h = lcm_diffusion_setting.init_image.size
-        # newW = lcm_diffusion_setting.image_width
-        # newH = int(h * newW / w)
-        # lcm_diffusion_setting.image_height=newH
-        # lcm_diffusion_setting.init_image = lcm_diffusion_setting.init_image.resize((newW, newH)) 
-        print("修改图像尺寸了",lcm_diffusion_setting.image_height,lcm_diffusion_setting.image_width)
         img_to_img_inference_steps = lcm_diffusion_setting.inference_steps
         check_step_value = int(
             lcm_diffusion_setting.inference_steps * lcm_diffusion_setting.strength
@@ -268,7 +271,7 @@ class LCMTextToImage:
         if is_openvino_pipe:
             print("Using OpenVINO")
             if reshape and not self.is_openvino_init:
-                print("Reshape and compile，调整尺寸")
+                print("Reshape and compile")
                 self.pipeline.reshape(
                     batch_size=-1,
                     height=lcm_diffusion_setting.image_height,

configs/lcm-models.txt

@@ -1,5 +1,5 @@
+SimianLuo/LCM_Dreamshaper_v7
 stabilityai/sd-turbo
 stabilityai/sdxl-turbo
-SimianLuo/LCM_Dreamshaper_v7
 latent-consistency/lcm-sdxl
 latent-consistency/lcm-ssd-1b

configs/openvino-lcm-models.txt

@@ -1,4 +1,4 @@
+rupeshs/LCM-dreamshaper-v7-openvino
 rupeshs/sd-turbo-openvino
 rupeshs/sdxl-turbo-openvino-int8
-rupeshs/LCM-dreamshaper-v7-openvino
 Disty0/LCM_SoteMix

constants.py

@@ -1,6 +1,6 @@
 from os import environ
 
-APP_VERSION = "v1.0.0 beta 22"
+APP_VERSION = "v1.0.0 beta 23"
 LCM_DEFAULT_MODEL = "SimianLuo/LCM_Dreamshaper_v7"
 LCM_DEFAULT_MODEL_OPENVINO = "rupeshs/LCM-dreamshaper-v7-openvino"
 APP_NAME = "FastSD CPU"

context.py

@@ -5,7 +5,6 @@ from backend.lcm_text_to_image import LCMTextToImage
 from time import perf_counter
 from backend.image_saver import ImageSaver
 from pprint import pprint
-from state import get_settings
 
 
 class Context:
@@ -23,8 +22,10 @@ class Context:
         reshape: bool = False,
         device: str = "cpu",
     ) -> Any:
-        get_settings().save()
         tick = perf_counter()
+        from state import get_settings
+
+        get_settings().save()
         pprint(settings.lcm_diffusion_setting.model_dump())
         if not settings.lcm_diffusion_setting.lcm_lora:
             return None

frontend/webui/generation_settings_ui.py

@@ -137,4 +137,4 @@ def get_generation_settings_ui() -> None:
         tiny_auto_encoder_checkbox.change(
             on_change_tiny_auto_encoder_checkbox, tiny_auto_encoder_checkbox
         )
-        offline_checkbox.change(on_change_tiny_auto_encoder_checkbox, offline_checkbox)
+        offline_checkbox.change(on_offline_checkbox, offline_checkbox)

frontend/webui/image_to_image_ui.py

@@ -1,16 +1,15 @@
 from typing import Any
 import gradio as gr
 from backend.models.lcmdiffusion_setting import DiffusionTask
-from context import Context
 from models.interface_types import InterfaceType
 from frontend.utils import is_reshape_required
 from constants import DEVICE
-from state import get_settings
+from state import get_settings, get_context
 from concurrent.futures import ThreadPoolExecutor
 
 app_settings = get_settings()
 
-context = Context(InterfaceType.WEBUI)
+context = get_context(InterfaceType.WEBUI)
 previous_width = 0
 previous_height = 0
 previous_model_id = ""
@@ -24,22 +23,17 @@ def generate_image_to_image(
     strength,
 ) -> Any:
     global previous_height, previous_width, previous_model_id, previous_num_of_images, app_settings
-    w, h = init_image.size
-    newW = app_settings.settings.lcm_diffusion_setting.image_width
-    newH = int(h * newW / w)
-    img = init_image.resize((newW, newH)) 
-    print("新图",newH,newW,app_settings.settings.lcm_diffusion_setting.image_height)
+
     app_settings.settings.lcm_diffusion_setting.prompt = prompt
     app_settings.settings.lcm_diffusion_setting.negative_prompt = negative_prompt
-    app_settings.settings.lcm_diffusion_setting.init_image = img
+    app_settings.settings.lcm_diffusion_setting.init_image = init_image
     app_settings.settings.lcm_diffusion_setting.strength = strength
-   
+
     app_settings.settings.lcm_diffusion_setting.diffusion_task = (
         DiffusionTask.image_to_image.value
     )
     model_id = app_settings.settings.lcm_diffusion_setting.openvino_lcm_model_id
     reshape = False
-    # app_settings.settings.lcm_diffusion_setting.image_height=newH
     image_width = app_settings.settings.lcm_diffusion_setting.image_width
     image_height = app_settings.settings.lcm_diffusion_setting.image_height
     num_images = app_settings.settings.lcm_diffusion_setting.number_of_images
@@ -55,7 +49,6 @@ def generate_image_to_image(
             num_images,
         )
 
-
     with ThreadPoolExecutor(max_workers=1) as executor:
         future = executor.submit(
             context.generate_text_to_image,

frontend/webui/image_variations_ui.py

@@ -0,0 +1,105 @@
+from typing import Any
+import gradio as gr
+from backend.models.lcmdiffusion_setting import DiffusionTask
+from context import Context
+from models.interface_types import InterfaceType
+from frontend.utils import is_reshape_required
+from constants import DEVICE
+from state import get_settings, get_context
+from concurrent.futures import ThreadPoolExecutor
+
+app_settings = get_settings()
+
+context = get_context(InterfaceType.WEBUI)
+previous_width = 0
+previous_height = 0
+previous_model_id = ""
+previous_num_of_images = 0
+
+
+def generate_image_variations(
+    init_image,
+    variation_strength,
+) -> Any:
+    global previous_height, previous_width, previous_model_id, previous_num_of_images, app_settings
+
+    app_settings.settings.lcm_diffusion_setting.init_image = init_image
+    app_settings.settings.lcm_diffusion_setting.strength = variation_strength
+    app_settings.settings.lcm_diffusion_setting.prompt = ""
+    app_settings.settings.lcm_diffusion_setting.negative_prompt = ""
+
+    app_settings.settings.lcm_diffusion_setting.diffusion_task = (
+        DiffusionTask.image_to_image.value
+    )
+    model_id = app_settings.settings.lcm_diffusion_setting.openvino_lcm_model_id
+    reshape = False
+    image_width = app_settings.settings.lcm_diffusion_setting.image_width
+    image_height = app_settings.settings.lcm_diffusion_setting.image_height
+    num_images = app_settings.settings.lcm_diffusion_setting.number_of_images
+    if app_settings.settings.lcm_diffusion_setting.use_openvino:
+        reshape = is_reshape_required(
+            previous_width,
+            image_width,
+            previous_height,
+            image_height,
+            previous_model_id,
+            model_id,
+            previous_num_of_images,
+            num_images,
+        )
+
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        future = executor.submit(
+            context.generate_text_to_image,
+            app_settings.settings,
+            reshape,
+            DEVICE,
+        )
+        images = future.result()
+
+    previous_width = image_width
+    previous_height = image_height
+    previous_model_id = model_id
+    previous_num_of_images = num_images
+    return images
+
+
+def get_image_variations_ui() -> None:
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(label="Init image", type="pil")
+                with gr.Row():
+                    generate_btn = gr.Button(
+                        "Generate",
+                        elem_id="generate_button",
+                        scale=0,
+                    )
+
+                variation_strength = gr.Slider(
+                    0.1,
+                    1,
+                    value=0.4,
+                    step=0.01,
+                    label="Variations Strength",
+                )
+
+                input_params = [
+                    input_image,
+                    variation_strength,
+                ]
+
+            with gr.Column():
+                output = gr.Gallery(
+                    label="Generated images",
+                    show_label=True,
+                    elem_id="gallery",
+                    columns=2,
+                    height=512,
+                )
+
+    generate_btn.click(
+        fn=generate_image_variations,
+        inputs=input_params,
+        outputs=output,
+    )

frontend/webui/models_ui.py

@@ -71,7 +71,6 @@ def get_models_ui() -> None:
                 value=get_valid_model_id(
                     app_settings.openvino_lcm_models,
                     app_settings.settings.lcm_diffusion_setting.openvino_lcm_model_id,
-                    LCM_DEFAULT_MODEL_OPENVINO
                 ),
                 interactive=True,
             )

frontend/webui/text_to_image_ui.py

@@ -1,16 +1,15 @@
 import gradio as gr
 from typing import Any
 from backend.models.lcmdiffusion_setting import DiffusionTask
-from context import Context
 from models.interface_types import InterfaceType
 from constants import DEVICE
-from state import get_settings
+from state import get_settings, get_context
 from frontend.utils import is_reshape_required
 from concurrent.futures import ThreadPoolExecutor
 from pprint import pprint
 
 app_settings = get_settings()
-context = Context(InterfaceType.WEBUI)
+context = get_context(InterfaceType.WEBUI)
 previous_width = 0
 previous_height = 0
 previous_model_id = ""

frontend/webui/ui.py

@@ -4,6 +4,7 @@ from frontend.webui.text_to_image_ui import get_text_to_image_ui
 from frontend.webui.image_to_image_ui import get_image_to_image_ui
 from frontend.webui.generation_settings_ui import get_generation_settings_ui
 from frontend.webui.models_ui import get_models_ui
+from frontend.webui.image_variations_ui import get_image_variations_ui
 from paths import FastStableDiffusionPaths
 from state import get_settings
 
@@ -53,6 +54,8 @@ def get_web_ui() -> gr.Blocks:
                 get_text_to_image_ui()
             with gr.TabItem("Image to Image"):
                 get_image_to_image_ui()
+            with gr.TabItem("Image Variations"):
+                get_image_variations_ui()
             with gr.TabItem("Generation Settings"):
                 get_generation_settings_ui()
             with gr.TabItem("Models"):

image_ops.py

@@ -6,9 +6,6 @@ def resize_pil_image(
     image_width,
     image_height,
 ):
-    w, h = pil_image.size
-    newW = image_width
-    newH = int(h * newW / w)
     return pil_image.convert("RGB").resize(
         (
             image_width,

paths.py

@@ -11,8 +11,7 @@ def join_paths(
 
 def get_app_path() -> str:
     app_dir = os.path.dirname(__file__)
-    work_dir = os.path.dirname(app_dir)+'/app'
-    print("基础地址",work_dir)
+    work_dir = os.path.dirname(app_dir)+'/app/'
     return work_dir
 
 
@@ -51,7 +50,6 @@ class FastStableDiffusionPaths:
     @staticmethod
     def get_models_config_path(model_config_file: str) -> str:
         configs_path = get_configs_path()
-        
         models_path = join_paths(
             configs_path,
             model_config_file,

state.py

@@ -1,10 +1,14 @@
 from app_settings import AppSettings
 from typing import Optional
 
+from context import Context
+from models.interface_types import InterfaceType
+
 
 class _AppState:
     _instance: Optional["_AppState"] = None
     settings: Optional[AppSettings] = None
+    context: Optional[Context] = None
 
 
 def get_state() -> _AppState:
@@ -19,3 +23,10 @@ def get_settings(skip_file: bool = False) -> AppSettings:
         state.settings = AppSettings()
         state.settings.load(skip_file)
     return state.settings
+
+
+def get_context(interface_type: InterfaceType) -> Context:
+    state = get_state()
+    if state.context is None:
+        state.context = Context(interface_type)
+    return state.context