Update app.py

app.py

@@ -4,603 +4,12 @@ import os
 
 
 
-# os.system(f"git clone https://github.com/Curt-Park/yolo-world-with-efficientvit-sam.git")
-# cwd0 = os.getcwd()
-# cwd1 = os.path.join(cwd0, "yolo-world-with-efficientvit-sam")
-# os.chdir(cwd1)
-# os.system("make setup")
-# os.system(f"cd /home/user/app")
+os.system(f"git clone https://github.com/Curt-Park/yolo-world-with-efficientvit-sam.git")
+cwd0 = os.getcwd()
+cwd1 = os.path.join(cwd0, "yolo-world-with-efficientvit-sam")
+os.chdir(cwd1)
+os.system("make setup")
+os.system(f"cd /home/user/app")
+os.system("python app2.py")
 
-# sys.path.append('./')
-import gradio as gr
-import random
-import numpy as np
-from gradio_demo.character_template import character_man, lorapath_man
-from gradio_demo.character_template import character_woman, lorapath_woman
-from gradio_demo.character_template import styles, lorapath_styles
-import torch
-import os
-from typing import Tuple, List
-import copy
-import argparse
-from diffusers.utils import load_image
-import cv2
-from PIL import Image, ImageOps
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
-# from controlnet_aux import OpenposeDetector
-# from controlnet_aux.open_pose.body import Body
-
-# try:
-from inference.models.yolo_world import YOLOWorld
-from src.efficientvit.models.efficientvit.sam import EfficientViTSamPredictor
-from src.efficientvit.sam_model_zoo import create_sam_model
-import supervision as sv
-# except:
-#     print("YoloWorld can not be load")
-
-try:
-    from groundingdino.models import build_model
-    from groundingdino.util import box_ops
-    from groundingdino.util.slconfig import SLConfig
-    from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
-    from groundingdino.util.inference import annotate, predict
-    from segment_anything import build_sam, SamPredictor
-    import groundingdino.datasets.transforms as T
-except:
-    print("groundingdino can not be load")
-
-from src.pipelines.lora_pipeline import LoraMultiConceptPipeline
-from src.prompt_attention.p2p_attention import AttentionReplace
-from diffusers import ControlNetModel, StableDiffusionXLPipeline
-from src.pipelines.lora_pipeline import revise_regionally_controlnet_forward
-
-from download import OMG_download
-
-CHARACTER_MAN_NAMES = list(character_man.keys())
-CHARACTER_WOMAN_NAMES = list(character_woman.keys())
-STYLE_NAMES = list(styles.keys())
-MAX_SEED = np.iinfo(np.int32).max
-
-### Description
-title = r"""
-<h1 align="center">OMG: Occlusion-friendly Personalized Multi-concept Generation In Diffusion Models</h1>
-"""
-
-description = r"""
-<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/' target='_blank'><b>OMG: Occlusion-friendly Personalized Multi-concept Generation In Diffusion Models</b></a>.<br>
-How to use:<br>
-1. Select two characters.
-2. Enter a text prompt as done in normal text-to-image models.
-3. Click the <b>Submit</b> button to start customizing.
-4. Enjoy the generated image😊!
-"""
-
-article = r"""
----
-📝 **Citation**
-<br>
-If our work is helpful for your research or applications, please cite us via:
-```bibtex
-@article{,
-title={OMG: Occlusion-friendly Personalized Multi-concept Generation In Diffusion Models},
-author={},
-journal={},
-year={}
-}
-```
-"""
-
-tips = r"""
-### Usage tips of OMG
-1. Input text prompts to describe a man and a woman
-"""
-
-css = '''
-.gradio-container {width: 85% !important}
-'''
-
-def sample_image(pipe,
-    input_prompt,
-    input_neg_prompt=None,
-    generator=None,
-    concept_models=None,
-    num_inference_steps=50,
-    guidance_scale=7.5,
-    controller=None,
-    stage=None,
-    region_masks=None,
-    lora_list = None,
-    styleL=None,
-    **extra_kargs
-):
-
-    spatial_condition = extra_kargs.pop('spatial_condition')
-    if spatial_condition is not None:
-        spatial_condition_input = [spatial_condition] * len(input_prompt)
-    else:
-        spatial_condition_input = None
-
-    images = pipe(
-        prompt=input_prompt,
-        concept_models=concept_models,
-        negative_prompt=input_neg_prompt,
-        generator=generator,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        cross_attention_kwargs={"scale": 0.8},
-        controller=controller,
-        stage=stage,
-        region_masks=region_masks,
-        lora_list=lora_list,
-        styleL=styleL,
-        image=spatial_condition_input,
-        **extra_kargs).images
-
-    return images
-
-def load_image_yoloworld(image_source) -> Tuple[np.array, torch.Tensor]:
-    image = np.asarray(image_source)
-    return image
-
-def load_image_dino(image_source) -> Tuple[np.array, torch.Tensor]:
-    transform = T.Compose(
-        [
-            T.RandomResize([800], max_size=1333),
-            T.ToTensor(),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-        ]
-    )
-    image = np.asarray(image_source)
-    image_transformed, _ = transform(image_source, None)
-    return image, image_transformed
-
-def predict_mask(segmentmodel, sam, image, TEXT_PROMPT, segmentType, confidence = 0.2, threshold = 0.5):
-    if segmentType=='GroundingDINO':
-        image_source, image = load_image_dino(image)
-        boxes, logits, phrases = predict(
-            model=segmentmodel,
-            image=image,
-            caption=TEXT_PROMPT,
-            box_threshold=0.3,
-            text_threshold=0.25
-        )
-        sam.set_image(image_source)
-        H, W, _ = image_source.shape
-        boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H])
-
-        transformed_boxes = sam.transform.apply_boxes_torch(boxes_xyxy, image_source.shape[:2]).cuda()
-        masks, _, _ = sam.predict_torch(
-            point_coords=None,
-            point_labels=None,
-            boxes=transformed_boxes,
-            multimask_output=False,
-        )
-        masks=masks[0].squeeze(0)
-    else:
-        image_source = load_image_yoloworld(image)
-        segmentmodel.set_classes([TEXT_PROMPT])
-        results = segmentmodel.infer(image_source, confidence=confidence)
-        detections = sv.Detections.from_inference(results).with_nms(
-            class_agnostic=True, threshold=threshold
-        )
-        masks = None
-        if len(detections) != 0:
-            print(TEXT_PROMPT + " detected!")
-            sam.set_image(image_source, image_format="RGB")
-            masks, _, _ = sam.predict(box=detections.xyxy[0], multimask_output=False)
-            masks = torch.from_numpy(masks.squeeze())
-
-    return masks
-
-def prepare_text(prompt, region_prompts):
-    '''
-    Args:
-        prompt_entity: [subject1]-*-[attribute1]-*-[Location1]|[subject2]-*-[attribute2]-*-[Location2]|[global text]
-    Returns:
-        full_prompt: subject1, attribute1 and subject2, attribute2, global text
-        context_prompt: subject1 and subject2, global text
-        entity_collection: [(subject1, attribute1), Location1]
-    '''
-    region_collection = []
-
-    regions = region_prompts.split('|')
-
-    for region in regions:
-        if region == '':
-            break
-        prompt_region, neg_prompt_region = region.split('-*-')
-        prompt_region = prompt_region.replace('[', '').replace(']', '')
-        neg_prompt_region = neg_prompt_region.replace('[', '').replace(']', '')
-
-        region_collection.append((prompt_region, neg_prompt_region))
-    return (prompt, region_collection)
-
-
-def build_model_sd(pretrained_model, controlnet_path, device, prompts):
-    controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16).to(device)
-    pipe = LoraMultiConceptPipeline.from_pretrained(
-        pretrained_model, controlnet=controlnet, torch_dtype=torch.float16, variant="fp16").to(device)
-    controller = AttentionReplace(prompts, 50, cross_replace_steps={"default_": 1.}, self_replace_steps=0.4, tokenizer=pipe.tokenizer, device=device, dtype=torch.float16, width=1024//32, height=1024//32)
-    revise_regionally_controlnet_forward(pipe.unet, controller)
-    pipe_concept = StableDiffusionXLPipeline.from_pretrained(pretrained_model, torch_dtype=torch.float16,
-                                                             variant="fp16").to(device)
-    return pipe, controller, pipe_concept
-
-def build_model_lora(pipe_concept, lora_paths, style_path, condition, args, pipe):
-    pipe_list = []
-    if condition == "Human pose":
-        controlnet = ControlNetModel.from_pretrained(args.openpose_checkpoint, torch_dtype=torch.float16).to(device)
-        pipe.controlnet = controlnet
-    elif condition == "Canny Edge":
-        controlnet = ControlNetModel.from_pretrained(args.canny_checkpoint, torch_dtype=torch.float16, variant="fp16").to(device)
-        pipe.controlnet = controlnet
-    elif condition == "Depth":
-        controlnet = ControlNetModel.from_pretrained(args.depth_checkpoint, torch_dtype=torch.float16).to(device)
-        pipe.controlnet = controlnet
-
-    if style_path is not None and os.path.exists(style_path):
-        pipe_concept.load_lora_weights(style_path, weight_name="pytorch_lora_weights.safetensors", adapter_name='style')
-        pipe.load_lora_weights(style_path, weight_name="pytorch_lora_weights.safetensors", adapter_name='style')
-
-    for lora_path in lora_paths.split('|'):
-        adapter_name = lora_path.split('/')[-1].split('.')[0]
-        pipe_concept.load_lora_weights(lora_path, weight_name="pytorch_lora_weights.safetensors", adapter_name=adapter_name)
-        pipe_concept.enable_xformers_memory_efficient_attention()
-        pipe_list.append(adapter_name)
-    return pipe_list
-
-def build_yolo_segment_model(sam_path, device):
-    yolo_world = YOLOWorld(model_id="yolo_world/l")
-    sam = EfficientViTSamPredictor(
-        create_sam_model(name="xl1", weight_url=sam_path).to(device).eval()
-    )
-    return yolo_world, sam
-
-def load_model_hf(repo_id, filename, ckpt_config_filename, device='cpu'):
-    args = SLConfig.fromfile(ckpt_config_filename)
-    model = build_model(args)
-    args.device = device
-
-    checkpoint = torch.load(os.path.join(repo_id, filename), map_location='cpu')
-    log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)
-    print("Model loaded from {} \n => {}".format(filename, log))
-    _ = model.eval()
-    return model
-
-def build_dino_segment_model(ckpt_repo_id, sam_checkpoint):
-    ckpt_filenmae = "groundingdino_swinb_cogcoor.pth"
-    ckpt_config_filename = os.path.join(ckpt_repo_id, "GroundingDINO_SwinB.cfg.py")
-    groundingdino_model = load_model_hf(ckpt_repo_id, ckpt_filenmae, ckpt_config_filename)
-    sam = build_sam(checkpoint=sam_checkpoint)
-    sam.cuda()
-    sam_predictor = SamPredictor(sam)
-    return groundingdino_model, sam_predictor
-
-def resize_and_center_crop(image, output_size=(1024, 576)):
-    width, height = image.size
-    aspect_ratio = width / height
-    new_height = output_size[1]
-    new_width = int(aspect_ratio * new_height)
-
-    resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-
-    if new_width < output_size[0] or new_height < output_size[1]:
-        padding_color = "gray"
-        resized_image = ImageOps.expand(resized_image,
-                                        ((output_size[0] - new_width) // 2,
-                                         (output_size[1] - new_height) // 2,
-                                         (output_size[0] - new_width + 1) // 2,
-                                         (output_size[1] - new_height + 1) // 2),
-                                        fill=padding_color)
-
-    left = (resized_image.width - output_size[0]) / 2
-    top = (resized_image.height - output_size[1]) / 2
-    right = (resized_image.width + output_size[0]) / 2
-    bottom = (resized_image.height + output_size[1]) / 2
-
-    cropped_image = resized_image.crop((left, top, right, bottom))
-
-    return cropped_image
-
-def main(device, segment_type):
-    pipe, controller, pipe_concept = build_model_sd(args.pretrained_sdxl_model, args.openpose_checkpoint, device, prompts_tmp)
-
-    # if segment_type == 'GroundingDINO':
-    #     detect_model, sam = build_dino_segment_model(args.dino_checkpoint, args.sam_checkpoint)
-    # else:
-    #     detect_model, sam = build_yolo_segment_model(args.efficientViT_checkpoint, device)
-
-    resolution_list = ["1440*728",
-                       "1344*768",
-                       "1216*832",
-                       "1152*896",
-                       "1024*1024",
-                       "896*1152",
-                       "832*1216",
-                       "768*1344",
-                       "728*1440"]
-    ratio_list = [1440 / 728, 1344 / 768, 1216 / 832, 1152 / 896, 1024 / 1024, 896 / 1152, 832 / 1216, 768 / 1344,
-                  728 / 1440]
-    condition_list = ["None",
-                      "Human pose",
-                      "Canny Edge",
-                      "Depth"]
-
-    depth_estimator = DPTForDepthEstimation.from_pretrained(args.dpt_checkpoint).to("cuda")
-    feature_extractor = DPTFeatureExtractor.from_pretrained(args.dpt_checkpoint)
-    # body_model = Body(args.pose_detector_checkpoint)
-    # openpose = OpenposeDetector(body_model)
-
-    def remove_tips():
-        return gr.update(visible=False)
-
-    def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-        return seed
-
-    def get_humanpose(img):
-        openpose_image = openpose(img)
-        return openpose_image
-
-    def get_cannyedge(image):
-        image = np.array(image)
-        image = cv2.Canny(image, 100, 200)
-        image = image[:, :, None]
-        image = np.concatenate([image, image, image], axis=2)
-        canny_image = Image.fromarray(image)
-        return canny_image
-
-    def get_depth(image):
-        image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
-        with torch.no_grad(), torch.autocast("cuda"):
-            depth_map = depth_estimator(image).predicted_depth
-
-        depth_map = torch.nn.functional.interpolate(
-            depth_map.unsqueeze(1),
-            size=(1024, 1024),
-            mode="bicubic",
-            align_corners=False,
-        )
-        depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
-        depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
-        depth_map = (depth_map - depth_min) / (depth_max - depth_min)
-        image = torch.cat([depth_map] * 3, dim=1)
-        image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
-        image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
-        return image
-
-    @spaces.GPU
-    def generate_image(prompt1, negative_prompt, man, woman, resolution, local_prompt1, local_prompt2, seed, condition, condition_img1, style):
-        # try:
-        path1 = lorapath_man[man]
-        path2 = lorapath_woman[woman]
-        pipe_concept.unload_lora_weights()
-        pipe.unload_lora_weights()
-        pipe_list = build_model_lora(pipe_concept, path1 + "|" + path2, lorapath_styles[style], condition, args, pipe)
-
-        if lorapath_styles[style] is not None and os.path.exists(lorapath_styles[style]):
-            styleL = True
-        else:
-            styleL = False
-
-        input_list = [prompt1]
-        condition_list = [condition_img1]
-        output_list = []
-
-        width, height = int(resolution.split("*")[0]), int(resolution.split("*")[1])
-
-        kwargs = {
-            'height': height,
-            'width': width,
-        }
-
-        for prompt, condition_img in zip(input_list, condition_list):
-            if prompt!='':
-                input_prompt = []
-                p = '{prompt}, 35mm photograph, film, professional, 4k, highly detailed.'
-                if styleL:
-                    p = styles[style] + p
-                input_prompt.append([p.replace("{prompt}", prompt), p.replace("{prompt}", prompt)])
-                if styleL:
-                    input_prompt.append([(styles[style] + local_prompt1, character_man.get(man)[1]),
-                                         (styles[style] + local_prompt2, character_woman.get(woman)[1])])
-                else:
-                    input_prompt.append([(local_prompt1, character_man.get(man)[1]),
-                                         (local_prompt2, character_woman.get(woman)[1])])
-
-                if condition == 'Human pose' and condition_img is not None:
-                    index = ratio_list.index(
-                        min(ratio_list, key=lambda x: abs(x - condition_img.shape[1] / condition_img.shape[0])))
-                    resolution = resolution_list[index]
-                    width, height = int(resolution.split("*")[0]), int(resolution.split("*")[1])
-                    kwargs['height'] = height
-                    kwargs['width'] = width
-                    condition_img = resize_and_center_crop(Image.fromarray(condition_img), (width, height))
-                    spatial_condition = get_humanpose(condition_img)
-                elif condition == 'Canny Edge' and condition_img is not None:
-                    index = ratio_list.index(
-                        min(ratio_list, key=lambda x: abs(x - condition_img.shape[1] / condition_img.shape[0])))
-                    resolution = resolution_list[index]
-                    width, height = int(resolution.split("*")[0]), int(resolution.split("*")[1])
-                    kwargs['height'] = height
-                    kwargs['width'] = width
-                    condition_img = resize_and_center_crop(Image.fromarray(condition_img), (width, height))
-                    spatial_condition = get_cannyedge(condition_img)
-                elif condition == 'Depth' and condition_img is not None:
-                    index = ratio_list.index(
-                        min(ratio_list, key=lambda x: abs(x - condition_img.shape[1] / condition_img.shape[0])))
-                    resolution = resolution_list[index]
-                    width, height = int(resolution.split("*")[0]), int(resolution.split("*")[1])
-                    kwargs['height'] = height
-                    kwargs['width'] = width
-                    condition_img = resize_and_center_crop(Image.fromarray(condition_img), (width, height))
-                    spatial_condition = get_depth(condition_img)
-                else:
-                    spatial_condition = None
-
-                kwargs['spatial_condition'] = spatial_condition
-                controller.reset()
-                image = sample_image(
-                    pipe,
-                    input_prompt=input_prompt,
-                    concept_models=pipe_concept,
-                    input_neg_prompt=[negative_prompt] * len(input_prompt),
-                    generator=torch.Generator(device).manual_seed(seed),
-                    controller=controller,
-                    stage=1,
-                    lora_list=pipe_list,
-                    styleL=styleL,
-                    **kwargs)
-
-                controller.reset()
-                if pipe.tokenizer("man")["input_ids"][1] in pipe.tokenizer(args.prompt)["input_ids"][1:-1]:
-                    mask1 = predict_mask(detect_model, sam, image[0], 'man', args.segment_type, confidence=0.15,
-                                         threshold=0.5)
-                else:
-                    mask1 = None
-
-                if pipe.tokenizer("woman")["input_ids"][1] in pipe.tokenizer(args.prompt)["input_ids"][1:-1]:
-                    mask2 = predict_mask(detect_model, sam, image[0], 'woman', args.segment_type, confidence=0.15,
-                                         threshold=0.5)
-                else:
-                    mask2 = None
-
-                if mask1 is None and mask2 is None:
-                    output_list.append(image[1])
-                else:
-                    image = sample_image(
-                        pipe,
-                        input_prompt=input_prompt,
-                        concept_models=pipe_concept,
-                        input_neg_prompt=[negative_prompt] * len(input_prompt),
-                        generator=torch.Generator(device).manual_seed(seed),
-                        controller=controller,
-                        stage=2,
-                        region_masks=[mask1, mask2],
-                        lora_list=pipe_list,
-                        styleL=styleL,
-                        **kwargs)
-                    output_list.append(image[1])
-            else:
-                output_list.append(None)
-        output_list.append(spatial_condition)
-        return output_list
-        # except:
-        #     print("error")
-        #     return
-
-    def get_local_value_man(input):
-        return character_man[input][0]
-
-    def get_local_value_woman(input):
-        return character_woman[input][0]
-
-    @spaces.GPU
-    def generate(prompt):
-        res = (os.system(prompt))
-        return res
-
-    gr.Interface(
-        fn=generate,
-        inputs=gr.Text(),
-        outputs=gr.Text(),
-    ).launch()
-
-    # with gr.Blocks(css=css) as demo:
-    #     # description
-    #     gr.Markdown(title)
-    #     gr.Markdown(description)
-    
-    #     with gr.Row():
-    #         gallery = gr.Image(label="Generated Images", height=512, width=512)
-    #         gen_condition = gr.Image(label="Spatial Condition", height=512, width=512)
-    #         usage_tips = gr.Markdown(label="Usage tips of OMG", value=tips, visible=False)
-    
-    #     with gr.Row():
-    #         condition_img1 = gr.Image(label="Input an RGB image for condition", height=128, width=128)
-    
-    #     # character choose
-    #     with gr.Row():
-    #         man = gr.Dropdown(label="Character 1 selection", choices=CHARACTER_MAN_NAMES, value="Chris Evans (identifier: Chris Evans)")
-    #         woman = gr.Dropdown(label="Character 2 selection", choices=CHARACTER_WOMAN_NAMES, value="Taylor Swift (identifier: TaylorSwift)")
-    #         resolution = gr.Dropdown(label="Image Resolution (width*height)", choices=resolution_list, value="1024*1024")
-    #         condition = gr.Dropdown(label="Input condition type", choices=condition_list, value="None")
-    #         style = gr.Dropdown(label="style", choices=STYLE_NAMES, value="None")
-    
-    #     with gr.Row():
-    #         local_prompt1 = gr.Textbox(label="Character1_prompt",
-    #                             info="Describe the Character 1, this prompt should include the identifier of character 1",
-    #                             value="Close-up photo of the Chris Evans, 35mm photograph, film, professional, 4k, highly detailed.")
-    #         local_prompt2 = gr.Textbox(label="Character2_prompt",
-    #                                    info="Describe the Character 2, this prompt should include the identifier of character2",
-    #                                    value="Close-up photo of the TaylorSwift, 35mm photograph, film, professional, 4k, highly detailed.")
-    
-    #     man.change(get_local_value_man, man, local_prompt1)
-    #     woman.change(get_local_value_woman, woman, local_prompt2)
-    
-    #     # prompt
-    #     with gr.Column():
-    #         prompt = gr.Textbox(label="Prompt 1",
-    #                             info="Give a simple prompt to describe the first image content",
-    #                             placeholder="Required",
-    #                             value="close-up shot, photography, a man and a woman on the street, facing the camera smiling")
-    
-    
-    #     with gr.Accordion(open=False, label="Advanced Options"):
-    #         seed = gr.Slider(
-    #             label="Seed",
-    #             minimum=0,
-    #             maximum=MAX_SEED,
-    #             step=1,
-    #             value=42,
-    #         )
-    #         negative_prompt = gr.Textbox(label="Negative Prompt",
-    #                             placeholder="noisy, blurry, soft, deformed, ugly",
-    #                             value="noisy, blurry, soft, deformed, ugly")
-    #         randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-    
-    #     submit = gr.Button("Submit", variant="primary")
-    
-    #     submit.click(
-    #         fn=remove_tips,
-    #         outputs=usage_tips,
-    #     ).then(
-    #         fn=randomize_seed_fn,
-    #         inputs=[seed, randomize_seed],
-    #         outputs=seed,
-    #         queue=False,
-    #         api_name=False,
-    #     ).then(
-    #         fn=generate_image,
-    #         inputs=[prompt, negative_prompt, man, woman, resolution, local_prompt1, local_prompt2, seed, condition, condition_img1, style],
-    #         outputs=[gallery, gen_condition]
-    #     )
-    # demo.launch(share=True)
-
-def parse_args():
-    parser = argparse.ArgumentParser('', add_help=False)
-    parser.add_argument('--pretrained_sdxl_model', default='Fucius/stable-diffusion-xl-base-1.0', type=str)
-    parser.add_argument('--openpose_checkpoint', default='thibaud/controlnet-openpose-sdxl-1.0', type=str)
-    parser.add_argument('--canny_checkpoint', default='diffusers/controlnet-canny-sdxl-1.0', type=str)
-    parser.add_argument('--depth_checkpoint', default='diffusers/controlnet-depth-sdxl-1.0', type=str)
-    parser.add_argument('--efficientViT_checkpoint', default='../checkpoint/sam/xl1.pt', type=str)
-    parser.add_argument('--dino_checkpoint', default='./checkpoint/GroundingDINO', type=str)
-    parser.add_argument('--sam_checkpoint', default='./checkpoint/sam/sam_vit_h_4b8939.pth', type=str)
-    parser.add_argument('--dpt_checkpoint', default='Intel/dpt-hybrid-midas', type=str)
-    parser.add_argument('--pose_detector_checkpoint', default='../checkpoint/ControlNet/annotator/ckpts/body_pose_model.pth', type=str)
-    parser.add_argument('--prompt', default='Close-up photo of the cool man and beautiful woman in surprised expressions as they accidentally discover a mysterious island while on vacation by the sea, 35mm photograph, film, professional, 4k, highly detailed.', type=str)
-    parser.add_argument('--negative_prompt', default='noisy, blurry, soft, deformed, ugly', type=str)
-    parser.add_argument('--seed', default=22, type=int)
-    parser.add_argument('--suffix', default='', type=str)
-    parser.add_argument('--segment_type', default='yoloworld', help='GroundingDINO or yoloworld', type=str)
-    return parser.parse_args()
-
-if __name__ == '__main__':
-    args = parse_args()
 
-    prompts = [args.prompt]*2
-    prompts_tmp = copy.deepcopy(prompts)
-    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-    download = OMG_download()
-    main(device, args.segment_type)