import os import os.path as osp import cv2 import numpy as np import torch from basicsr.utils import img2tensor, tensor2img from pytorch_lightning import seed_everything from ldm.models.diffusion.plms import PLMSSampler from ldm.modules.encoders.adapter import Adapter from ldm.util import instantiate_from_config from model_edge import pidinet import gradio as gr from omegaconf import OmegaConf import pathlib import random import shlex import subprocess import sys import mmcv from mmdet.apis import inference_detector, init_detector from mmpose.apis import (inference_top_down_pose_model, init_pose_model, process_mmdet_results, vis_pose_result) skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9], [8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6]] pose_kpt_color = [[51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0]] pose_link_color = [[0, 255, 0], [0, 255, 0], [255, 128, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255]] sys.path.append('T2I-Adapter') config_path = 'https://github.com/TencentARC/T2I-Adapter/raw/main/configs/stable-diffusion/' model_path = 'https://github.com/TencentARC/T2I-Adapter/raw/main/models/' def imshow_keypoints(img, pose_result, skeleton=None, kpt_score_thr=0.1, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1): """Draw keypoints and links on an image. Args: img (ndarry): The image to draw poses on. pose_result (list[kpts]): The poses to draw. Each element kpts is a set of K keypoints as an Kx3 numpy.ndarray, where each keypoint is represented as x, y, score. kpt_score_thr (float, optional): Minimum score of keypoints to be shown. Default: 0.3. pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None, the keypoint will not be drawn. pose_link_color (np.array[Mx3]): Color of M links. If None, the links will not be drawn. thickness (int): Thickness of lines. """ img_h, img_w, _ = img.shape img = np.zeros(img.shape) for idx, kpts in enumerate(pose_result): if idx > 1: continue kpts = kpts['keypoints'] # print(kpts) kpts = np.array(kpts, copy=False) # draw each point on image if pose_kpt_color is not None: assert len(pose_kpt_color) == len(kpts) for kid, kpt in enumerate(kpts): x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2] if kpt_score < kpt_score_thr or pose_kpt_color[kid] is None: # skip the point that should not be drawn continue color = tuple(int(c) for c in pose_kpt_color[kid]) cv2.circle(img, (int(x_coord), int(y_coord)), radius, color, -1) # draw links if skeleton is not None and pose_link_color is not None: assert len(pose_link_color) == len(skeleton) for sk_id, sk in enumerate(skeleton): pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1])) pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1])) if (pos1[0] <= 0 or pos1[0] >= img_w or pos1[1] <= 0 or pos1[1] >= img_h or pos2[0] <= 0 or pos2[0] >= img_w or pos2[1] <= 0 or pos2[1] >= img_h or kpts[sk[0], 2] < kpt_score_thr or kpts[sk[1], 2] < kpt_score_thr or pose_link_color[sk_id] is None): # skip the link that should not be drawn continue color = tuple(int(c) for c in pose_link_color[sk_id]) cv2.line(img, pos1, pos2, color, thickness=thickness) return img def load_model_from_config(config, ckpt, verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) # if len(m) > 0 and verbose: # print("missing keys:") # print(m) # if len(u) > 0 and verbose: # print("unexpected keys:") # print(u) model.cuda() model.eval() return model class Model: def __init__(self, model_config_path: str = 'ControlNet/models/cldm_v15.yaml', model_dir: str = 'models', use_lightweight: bool = True): self.device = torch.device( 'cuda:0' if torch.cuda.is_available() else 'cpu') self.model_dir = pathlib.Path(model_dir) self.model_dir.mkdir(exist_ok=True, parents=True) self.download_pose_models() self.download_models() def download_pose_models(self) -> None: ## mmpose device = "cuda" det_config_file = model_path+"faster_rcnn_r50_fpn_coco.py" subprocess.run(shlex.split(f'wget {det_config_file} -O models/faster_rcnn_r50_fpn_coco.py')) det_config = 'models/faster_rcnn_r50_fpn_coco.py' det_checkpoint_file = "https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth" subprocess.run(shlex.split(f'wget {det_checkpoint_file} -O models/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth')) det_checkpoint = 'models/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' pose_config_file = model_path+"hrnet_w48_coco_256x192.py" subprocess.run(shlex.split(f'wget {pose_config_file} -O models/hrnet_w48_coco_256x192.py')) pose_config = 'models/hrnet_w48_coco_256x192.py' pose_checkpoint_file = "https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth" subprocess.run(shlex.split(f'wget {pose_checkpoint_file} -O models/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth')) pose_checkpoint = 'models/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth' ## detector det_config_mmcv = mmcv.Config.fromfile(det_config) self.det_model = init_detector(det_config_mmcv, det_checkpoint, device=device) pose_config_mmcv = mmcv.Config.fromfile(pose_config) self.pose_model = init_pose_model(pose_config_mmcv, pose_checkpoint, device=device) def download_models(self) -> None: device = 'cuda' config = OmegaConf.load("configs/stable-diffusion/test_sketch.yaml") config.model.params.cond_stage_config.params.device = device base_model_file = "https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt" base_model_file_anything = "https://huggingface.co/andite/anything-v4.0/resolve/main/anything-v4.0-pruned.ckpt" sketch_adapter_file = "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth" pose_adapter_file = "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth" pidinet_file = model_path+"table5_pidinet.pth" clip_file = "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/*" subprocess.run(shlex.split(f'wget {base_model_file} -O models/sd-v1-4.ckpt')) subprocess.run(shlex.split(f'wget {base_model_file_anything} -O models/anything-v4.0-pruned.ckpt')) subprocess.run(shlex.split(f'wget {sketch_adapter_file} -O models/t2iadapter_sketch_sd14v1.pth')) subprocess.run(shlex.split(f'wget {pose_adapter_file} -O models/t2iadapter_keypose_sd14v1.pth')) subprocess.run(shlex.split(f'wget {pidinet_file} -O models/table5_pidinet.pth')) self.model = load_model_from_config(config, "models/sd-v1-4.ckpt").to(device) self.model_anything = load_model_from_config(config, "models/anything-v4.0-pruned.ckpt").to(device) current_base = 'sd-v1-4.ckpt' self.model_ad_sketch = Adapter(channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) self.model_ad_sketch.load_state_dict(torch.load("models/t2iadapter_sketch_sd14v1.pth")) net_G = pidinet() ckp = torch.load('models/table5_pidinet.pth', map_location='cpu')['state_dict'] net_G.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()}) net_G.to(device) self.sampler= PLMSSampler(self.model) self.sampler_anything= PLMSSampler(self.model_anything) save_memory=True self.model_ad_pose = Adapter(cin=int(3*64),channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) self.model_ad_pose.load_state_dict(torch.load("models/t2iadapter_keypose_sd14v1.pth")) @torch.inference_mode() def process_sketch(self, input_img, type_in, color_back, prompt, neg_prompt, fix_sample, scale, con_strength, base_model): global current_base device = 'cuda' if base_model == 'sd-v1-4.ckpt': model = self.model sampler = self.sampler else: model = self.model_anything sampler = self.sampler_anything # if current_base != base_model: # ckpt = os.path.join("models", base_model) # pl_sd = torch.load(ckpt, map_location="cpu") # if "state_dict" in pl_sd: # sd = pl_sd["state_dict"] # else: # sd = pl_sd # model.load_state_dict(sd, strict=False) #load_model_from_config(config, os.path.join("models", base_model)).to(device) # current_base = base_model con_strength = int((1-con_strength)*50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img,(512,512)) if type_in == 'Sketch': # net_G = net_G.cpu() if color_back == 'White': im = 255-im im_edge = im.copy() im = img2tensor(im)[0].unsqueeze(0).unsqueeze(0)/255. # edge = 1-edge # for white background im = im>0.5 im = im.float() elif type_in == 'Image': im = img2tensor(im).unsqueeze(0)/255. im = net_G(im.to(device))[-1] im = im>0.5 im = im.float() im_edge = tensor2img(im) c = model.get_learned_conditioning([prompt]) nc = model.get_learned_conditioning([neg_prompt]) with torch.no_grad(): # extract condition features features_adapter = self.model_ad_sketch(im.to(device)) shape = [4, 64, 64] # sampling samples_ddim, _ = sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode = 'sketch', con_strength = con_strength) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).cpu().numpy()[0] x_samples_ddim = 255.*x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_edge, x_samples_ddim] @torch.inference_mode() def process_pose(self, input_img, prompt, neg_prompt, fix_sample, scale, con_strength, base_model): global current_base det_cat_id = 1 bbox_thr = 0.2 device = 'cuda' if base_model == 'sd-v1-4.ckpt': model = self.model sampler = self.sampler else: model = self.model_anything sampler = self.sampler_anything # if current_base != base_model: # ckpt = os.path.join("models", base_model) # pl_sd = torch.load(ckpt, map_location="cpu") # if "state_dict" in pl_sd: # sd = pl_sd["state_dict"] # else: # sd = pl_sd # model.load_state_dict(sd, strict=False) #load_model_from_config(config, os.path.join("models", base_model)).to(device) # current_base = base_model con_strength = int((1-con_strength)*50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img,(512,512)) image = im.copy() im = img2tensor(im).unsqueeze(0)/255. mmdet_results = inference_detector(self.det_model, image) # keep the person class bounding boxes. person_results = process_mmdet_results(mmdet_results, det_cat_id) # optional return_heatmap = False dataset = self.pose_model.cfg.data['test']['type'] # e.g. use ('backbone', ) to return backbone feature output_layer_names = None pose_results, returned_outputs = inference_top_down_pose_model( self.pose_model, image, person_results, bbox_thr=bbox_thr, format='xyxy', dataset=dataset, dataset_info=None, return_heatmap=return_heatmap, outputs=output_layer_names) # show the results im_pose = imshow_keypoints( image, pose_results, skeleton=skeleton, pose_kpt_color=pose_kpt_color, pose_link_color=pose_link_color, radius=2, thickness=2) im_pose = cv2.resize(im_pose,(512,512)) c = model.get_learned_conditioning([prompt]) nc = model.get_learned_conditioning([neg_prompt]) with torch.no_grad(): # extract condition features pose = img2tensor(im_pose, bgr2rgb=True, float32=True)/255. pose = pose.unsqueeze(0) features_adapter = self.model_ad_pose(pose.to(device)) shape = [4, 64, 64] # sampling samples_ddim, _ = sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode = 'sketch', con_strength = con_strength) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).cpu().numpy()[0] x_samples_ddim = 255.*x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_pose[:,:,::-1].astype(np.uint8), x_samples_ddim]