Update app.py

app.py

@@ -1,8 +1,537 @@
+import spaces
+import sys
 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("python app.py")
+
+
+# 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")
+
+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 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):
+        print(os.system(prompt))
+        return prompt
+
+    gr.Interface(
+        fn=generate,
+        inputs=gr.Text(),
+        outputs=gr.Gallery(),
+    ).launch()
+
+
+
+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)