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app.py

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+import os
+import glob
+import gradio as gr
+import base64
+import cv2
+import numpy as np
+import oss2
+import time
+
+from ai_service_python_sdk.client.api.ai_service_aigc_images_api import AIGCImagesApi
+from ai_service_python_sdk.client.api.ai_service_job_api import AiServiceJobApi
+from ai_service_python_sdk.client.api_client import ApiClient
+from ai_service_python_sdk.test import appId, host, token
+
+
+host = os.getenv("PAI_REC_HOST")
+appId = os.getenv("PAI_REC_APP_ID")
+token = os.getenv("PAI_REC_TOKEN")
+access_key_id = os.getenv('OSS_ACCESS_KEY_ID')  
+access_key_secret = os.getenv('OSS_ACCESS_KEY_SECRET')  
+bucket_name = os.getenv('OSS_BUCKET')  
+endpoint = os.getenv('OSS_ENDPOINT')  
+
+
+def upload_file(files, current_files):
+    file_paths = [file_d['name'] for file_d in current_files] + [file.name for file in files]
+    return file_paths
+
+
+def decode_image_from_base64jpeg(base64_image):
+    image_bytes = base64.b64decode(base64_image)
+    np_arr = np.frombuffer(image_bytes, np.uint8)
+    image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image
+
+
+def upload(image_path, number):
+    bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name)
+    file_name = image_path.split('/')[-1]
+    ext = file_name.split('.')[-1]
+    file_name = str(number) + '.' + ext
+    timestamp = str(time.time()).split('.')[0]
+    bucket_folder = 'aigc-data/easyphoto_demo_data/' + timestamp + '_user_lora/'
+    oss_file_path = bucket_folder + file_name
+    bucket.put_object_from_file(oss_file_path, image_path)    
+    file_url = 'https://' + bucket_name + '.' + endpoint + '/' + bucket_folder + file_name
+    return file_url
+
+
+def upload_template(image_path):
+    bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name)
+    file_name = image_path.split('/')[-1]
+    timestamp = str(time.time()).split('.')[0]
+    bucket_folder = 'aigc-data/easyphoto_demo_data/' + timestamp + '_user_template/'
+    oss_file_path = bucket_folder + file_name
+    bucket.put_object_from_file(oss_file_path, image_path)    
+    file_url = 'https://' + bucket_name + '.' + endpoint + '/' + bucket_folder + file_name
+    return file_url
+
+
+def easyphoto_train(instance_images):
+    images = []
+    if instance_images is None or len(instance_images)==0:
+        output = 'Status: no image updated! 没有上传照片'
+        return output, [], []
+    for number, image in enumerate(instance_images):
+        image_path = image['name']
+        image_url = upload(image_path, number)
+        images.append(image_url)
+    client = ApiClient(host, appId, token)
+    api = AIGCImagesApi(client)
+    response = api.aigc_images_train(images, '', None)
+    message = response.message
+    model_id = response.data['model_id']
+    job_id = response.data['job_id']
+    if message == 'success':
+        state = 'training job submitted. 提交训练任务成功'
+        output = 'Status: ' + state
+        print("job id: " + str(job_id))
+        print("model id: " + str(model_id))
+        return output, job_id, model_id
+    else:
+        output = 'Status: submitting training job failed! 提交训练任务失败'
+        return output, [], []
+
+    
+def easyphoto_check(job_id):
+    client = ApiClient(host, appId, token)
+    api = AiServiceJobApi(client)
+    if job_id is None:
+        output = 'Status: checking training status failed! No job id. 状态检查失败'
+    else:
+        try:
+            job_id = int(str(job_id).strip())
+            response = api.get_async_job_with_id(job_id)
+            message = response.data['job']['message']
+            output = 'Status: ' + message
+        except:
+            output = 'Status: checking training status failed! 状态检查失败'
+    return output
+
+
+def easyphoto_infer(model_id, selected_template_images, additional_prompt, seed, before_face_fusion_ratio, after_face_fusion_ratio, first_diffusion_steps, first_denoising_strength, second_diffusion_steps, second_denoising_strength, crop_face_preprocess, apply_face_fusion_before, apply_face_fusion_after, color_shift_middle, color_shift_last, background_restore):
+    image_urls = []
+    if len(selected_template_images) == 0:
+        output_info = 'Status: no templete selected! 需要选择模版'
+        return output_info, []
+    selected_template_images = eval(selected_template_images)
+    for image in selected_template_images:
+        image_url = upload_template(image)
+        image_urls.append(image_url)
+
+    client = ApiClient(host, appId, token)
+    api = AIGCImagesApi(client)
+    outputs = []
+    output_info = None
+
+    if model_id is None:
+        output_info = 'Status: no model id provided! 需要提供模型id'
+        return output_info, []
+    
+    model_id = str(model_id).strip()
+    print('model id: ' + model_id)
+
+    for image_url in image_urls:
+        try:
+            params = {
+                "additional_prompt": additional_prompt,
+                "seed": seed,
+                "before_face_fusion_ratio": before_face_fusion_ratio,
+                "after_face_fusion_ratio": after_face_fusion_ratio,
+                "first_diffusion_steps": first_diffusion_steps,
+                "first_denoising_strength": first_denoising_strength,
+                "second_diffusion_steps": second_diffusion_steps,
+                "second_denoising_strength": second_denoising_strength,
+                "crop_face_preprocess": crop_face_preprocess,
+                "apply_face_fusion_before": apply_face_fusion_before,
+                "apply_face_fusion_after": apply_face_fusion_after,
+                "color_shift_middle": color_shift_middle,
+                "color_shift_last": color_shift_last,
+                "background_restore": background_restore
+            }
+            response = api.aigc_images_create(model_id, image_url, 'photog_infer_with_webui_pmml', params)
+        except:
+            output_info = 'Status: calling eas service failed!'
+            return output_info, []
+    
+        data = response.data
+        message = response.message
+        if message == 'success':
+            image = data['image']
+            image = decode_image_from_base64jpeg(image)
+            outputs.append(image)
+            output_info = 'Status: generating image succesfully! 图像生成成功'
+        else:
+            output_info = 'Status: generating image failed! 图像生成失败'
+            return output_info, []
+    return output_info, outputs
+
+
+with gr.Blocks() as easyphoto_demo:
+    model_id = gr.Textbox(visible=False)
+    with gr.TabItem('Training 训练'):
+        with gr.Blocks():
+            with gr.Row():
+                with gr.Column():
+                    instance_images = gr.Gallery().style(columns=[4], rows=[2], object_fit="contain", height="auto")
+                    with gr.Row():
+                        upload_button = gr.UploadButton(
+                            "Upload Photos 上传照片", file_types=["image"], file_count="multiple"
+                        )
+                        clear_button = gr.Button("Clear Photos 清除照片")
+                    clear_button.click(fn=lambda: [], inputs=None, outputs=instance_images)
+                    upload_button.upload(upload_file, inputs=[upload_button, instance_images], outputs=instance_images, queue=False)
+                    gr.Markdown(
+                        '''
+                        训练步骤：
+
+                        1.请上传5-20张半身照片或头肩照片，请确保面部比例不要太小。
+
+                        2.点击下方的训练按钮，提交训练任务，大约需要15分钟，您可以检查您的训练任务状态。请不要重复点击提交训练任务的按钮！
+
+                        3.当模型训练完成后，任务状态会显示success，切换到推理模式，并根据模板生成照片。
+
+                        4.如果在上传时遇到卡顿，请修改上传图片的大小，尽量限制在1.5MB以内。
+                        
+                        5.在训练或推理过程中，请不要刷新或关闭窗口。
+                        '''
+                    )
+
+            job_id = gr.Textbox(visible=False)
+            with gr.Row():
+                run_button = gr.Button('Submit My Training Job 提交训练任务')
+                check_button = gr.Button('Check My Training Job Status 检查我的训练任务状态')
+            output_message = gr.Textbox(value="", label="Status 状态", interactive=False)
+
+            run_button.click(fn=easyphoto_train,
+                            inputs=[instance_images],
+                            outputs=[output_message, job_id, model_id])
+            
+            check_button.click(fn=easyphoto_check,
+                            inputs=[job_id],
+                            outputs=[output_message])
+
+            
+    with gr.TabItem('Inference 推理'):
+        templates = glob.glob(r'./*.jpg')
+        preset_template = list(templates)
+
+        with gr.Blocks() as demo:
+            with gr.Row():
+                with gr.Column():
+                    template_gallery_list = [(i, i) for i in preset_template]
+                    gallery = gr.Gallery(template_gallery_list).style(columns=[4], rows=[2], object_fit="contain", height="auto")
+
+                    def select_function(evt: gr.SelectData):
+                        return [preset_template[evt.index]]
+
+                    selected_template_images = gr.Text(show_label=False, visible=False, placeholder="Selected")
+                    gallery.select(select_function, None, selected_template_images)
+                    
+                    with gr.Accordion("Advanced Options 参数设置", open=False):
+                        additional_prompt = gr.Textbox(
+                                label="Additional Prompt",
+                                lines=3,
+                                value='masterpiece, beauty',
+                                interactive=True
+                        )
+                        seed = gr.Textbox(
+                                label="Seed", 
+                                value=12345,
+                        )
+                        with gr.Row():
+                            before_face_fusion_ratio = gr.Slider(
+                                    minimum=0.2, maximum=0.8, value=0.50,
+                                    step=0.05, label='Face Fusion Ratio Before'
+                            )
+                            after_face_fusion_ratio = gr.Slider(
+                                    minimum=0.2, maximum=0.8, value=0.50,
+                                    step=0.05, label='Face Fusion Ratio After'
+                            )
+
+                        with gr.Row():
+                            first_diffusion_steps = gr.Slider(
+                                    minimum=15, maximum=50, value=50,
+                                    step=1, label='First Diffusion steps'
+                            )
+                            first_denoising_strength = gr.Slider(
+                                    minimum=0.30, maximum=0.60, value=0.45,
+                                    step=0.05, label='First Diffusion denoising strength'
+                            )
+                        with gr.Row():
+                            second_diffusion_steps = gr.Slider(
+                                    minimum=15, maximum=50, value=20,
+                                    step=1, label='Second Diffusion steps'
+                            )
+                            second_denoising_strength = gr.Slider(
+                                    minimum=0.20, maximum=0.40, value=0.30,
+                                    step=0.05, label='Second Diffusion denoising strength'
+                            )
+                        with gr.Row():
+                            crop_face_preprocess = gr.Checkbox(
+                                    label="Crop Face Preprocess",  
+                                    value=True
+                            )
+                            apply_face_fusion_before = gr.Checkbox(
+                                    label="Apply Face Fusion Before", 
+                                    value=True
+                            )
+                            apply_face_fusion_after = gr.Checkbox(
+                                    label="Apply Face Fusion After",  
+                                    value=True
+                            )
+                        with gr.Row():
+                            color_shift_middle = gr.Checkbox(
+                                    label="Apply color shift first",  
+                                    value=True
+                            )
+                            color_shift_last = gr.Checkbox(
+                                    label="Apply color shift last",  
+                                    value=True
+                            )
+                            background_restore = gr.Checkbox(
+                                    label="Background Restore",  
+                                    value=False
+                            )
+
+                        with gr.Box():
+                            gr.Markdown(
+                                    '''
+                                    Parameters:
+                                    1. **Face Fusion Ratio Before** represents the proportion of the first facial fusion, which is higher and more similar to the training object.  
+                                    2. **Face Fusion Ratio After** represents the proportion of the second facial fusion, which is higher and more similar to the training object.  
+                                    3. **Crop Face Preprocess** represents whether to crop the image before generation, which can adapt to images with smaller faces.  
+                                    4. **Apply Face Fusion Before** represents whether to perform the first facial fusion.  
+                                    5. **Apply Face Fusion After** represents whether to perform the second facial fusion.  
+
+                                    参数：
+
+                                    1.**Face Fusion Ratio Before**表示第一次面部融合的比例，更高且更接近训练对象。
+                                    
+                                    2.**Face Fusion Ratio After**表示第二次面部融合的比例，更高且更接近训练对象。
+                                    
+                                    3.**Crop Face Preprocess**表示是否在生成之前裁剪图像，以适应面部较小的图像。
+                                    
+                                    4.**Apply Face Fusion Before**表示是否执行第一次面部融合。
+                                    
+                                    5.**Apply Face Fusion After**表示是否执行第二次面部融合。    
+                                    '''
+                            )
+
+                with gr.Column():
+                    gr.Markdown('Generated Results 生成结果')
+                    output_images = gr.Gallery(
+                            label='Output',
+                            show_label=False
+                    ).style(columns=[4], rows=[2], object_fit="contain", height="auto")
+                    display_button = gr.Button('Start Generation 开始生成')
+                    infer_progress = gr.Textbox(
+                            label="Generation Progress 生成进度",
+                            value="",
+                            interactive=False
+                    )
+
+            display_button.click(
+                fn=easyphoto_infer,
+                inputs=[model_id, selected_template_images, additional_prompt, seed, before_face_fusion_ratio, after_face_fusion_ratio, first_diffusion_steps, first_denoising_strength, second_diffusion_steps, second_denoising_strength, crop_face_preprocess, apply_face_fusion_before, apply_face_fusion_after, color_shift_middle, color_shift_last, background_restore],
+                outputs=[infer_progress, output_images]
+            )
+    
+    gr.Markdown(
+    """
+    参考链接
+    
+    EasyPhoto GitHub：https://github.com/aigc-apps/sd-webui-EasyPhoto
+    
+    阿里云Freetier：https://help.aliyun.com/document_detail/2567864.html
+    
+    智码实验室：https://gallery.pai-ml.com/#/preview/deepLearning/cv/stable_diffusion_easyphoto
+    """)
+    
+easyphoto_demo.launch(share=True).queue()

requirements.txt

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+ai_service_python_sdk-1.1.1-py3-none-any.whl