import cv2 import numpy as np import diffusers from diffusers.models import ControlNetModel from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel from diffusers.utils import load_image import torch import torch.nn.functional as F from torchvision.transforms import Compose from style_template import styles from PIL import Image from depth_anything.dpt import DepthAnything from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet from insightface.app import FaceAnalysis from pipeline_stable_diffusion_xl_instantid_full import StableDiffusionXLInstantIDPipeline, draw_kps from controlnet_aux import OpenposeDetector STYLE_NAMES = list(styles.keys()) DEFAULT_STYLE_NAME = "Mars" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device.type != 'cuda': raise ValueError("Se requiere ejecutar en GPU") dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32 class EndpointHandler(): def __init__(self, model_dir): print("Loading FaceAnalysis", model_dir) # self.app = FaceAnalysis( # name="antelopev2", # root=f"./antelopev2", # providers=["CPUExecutionProvider"], # ) self.app = FaceAnalysis( name="buffalo_l", root="./", providers=["CPUExecutionProvider"], ) self.app.prepare(ctx_id=0, det_size=(640, 640)) openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(device).eval() transform = Compose([ Resize( width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) face_adapter = f"/repository/checkpoints/ip-adapter.bin" controlnet_path = f"/repository/checkpoints/ControlNetModel" self.controlnet_identitynet = ControlNetModel.from_pretrained( controlnet_path, torch_dtype=dtype ) controlnet_pose_model = "thibaud/controlnet-openpose-sdxl-1.0" controlnet_canny_model = "diffusers/controlnet-canny-sdxl-1.0" controlnet_depth_model = "diffusers/controlnet-depth-sdxl-1.0-small" controlnet_pose = ControlNetModel.from_pretrained( controlnet_pose_model, torch_dtype=dtype ).to(device) controlnet_canny = ControlNetModel.from_pretrained( controlnet_canny_model, torch_dtype=dtype ).to(device) controlnet_depth = ControlNetModel.from_pretrained( controlnet_depth_model, torch_dtype=dtype ).to(device) def get_depth_map(image): image = np.array(image) / 255.0 h, w = image.shape[:2] image = transform({'image': image})['image'] image = torch.from_numpy(image).unsqueeze(0).to("cuda") with torch.no_grad(): depth = depth_anything(image) depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0] depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 depth = depth.cpu().numpy().astype(np.uint8) depth_image = Image.fromarray(depth) return depth_image def get_canny_image(image, t1=100, t2=200): image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) edges = cv2.Canny(image, t1, t2) return Image.fromarray(edges, "L") self.controlnet_map = { "pose": controlnet_pose, "canny": controlnet_canny, "depth": controlnet_depth, } self.controlnet_map_fn = { "pose": openpose, "canny": get_canny_image, "depth": get_depth_map, } pretrained_model_name_or_path = "wangqixun/YamerMIX_v8" self.pipe = StableDiffusionXLInstantIDPipeline.from_pretrained( pretrained_model_name_or_path, controlnet=[self.controlnet_identitynet], torch_dtype=dtype, safety_checker=None, feature_extractor=None, ).to(device) self.pipe.scheduler = diffusers.EulerDiscreteScheduler.from_config( self.pipe.scheduler.config ) # load and disable LCM self.pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") self.pipe.disable_lora() self.pipe.cuda() self.pipe.load_ip_adapter_instantid(face_adapter) self.pipe.image_proj_model.to("cuda") self.pipe.unet.to("cuda") # if we need more parameters scheduler_class_name = "EulerDiscreteScheduler" add_kwargs = {} scheduler = getattr(diffusers, scheduler_class_name) self.pipe.scheduler = scheduler.from_config(self.pipe.scheduler.config, **add_kwargs) identitynet_strength_ratio = 0.8 pose_strength = 0.5 canny_strength = 0.3 depth_strength = 0.5 self.my_controlnet_selection = ["pose", "canny"] controlnet_scales = { "pose": pose_strength, "canny": canny_strength, "depth": depth_strength, } self.pipe.controlnet = MultiControlNetModel( [self.controlnet_identitynet] + [self.controlnet_map[s] for s in self.my_controlnet_selection] ) self.control_scales = [float(identitynet_strength_ratio)] + [ controlnet_scales[s] for s in self.my_controlnet_selection ] def __call__(self, data): def apply_style(style_name: str, positive: str) -> str: p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) return p.replace("{prompt}", positive) default_negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" # hyperparamters face_image_path = data.pop("face_image_path", "https://i.ibb.co/GQzm527/examples-musk-resize.jpg") pose_image_path = data.pop("pose_image_path", "https://i.ibb.co/TRCK4MS/examples-poses-pose2.jpg") prompt_input = data.pop("inputs", "a man flying in the sky in Mars") num_inference_steps = data.pop("num_inference_steps", 20) guidance_scale = data.pop("guidance_scale", 5.0) negative_prompt = data.pop("negative_prompt", default_negative_prompt) style_name = data.pop("style_name", DEFAULT_STYLE_NAME) prompt = apply_style(style_name, prompt_input) adapter_strength_ratio = 0.8 def convert_from_cv2_to_image(img: np.ndarray) -> Image: return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) def convert_from_image_to_cv2(img: Image) -> np.ndarray: return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) def resize_img( input_image, max_side=1280, min_side=1024, size=None, pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64, ): if size is not None: w_resize_new, h_resize_new = size else: w, h = input_image.size # Calcular el redimensionamiento con un solo paso ratio_min = min_side / min(w, h) w_min, h_min = round(ratio_min * w), round(ratio_min * h) ratio_max = max_side / max(w_min, h_min) # Aplicar la menor de las dos ratios para asegurar que cumple ambas condiciones final_ratio = min(ratio_min, ratio_max) w_final, h_final = round(final_ratio * w), round(final_ratio * h) # Ajustar al número base de píxeles más cercano w_resize_new = (w_final // base_pixel_number) * base_pixel_number h_resize_new = (h_final // base_pixel_number) * base_pixel_number # Redimensionar una sola vez input_image = input_image.resize([w_resize_new, h_resize_new], mode) if pad_to_max_side: # Optimizar la creación del fondo res = Image.new("RGB", (max_side, max_side), (255, 255, 255)) offset_x = (max_side - w_resize_new) // 2 offset_y = (max_side - h_resize_new) // 2 res.paste(input_image, (offset_x, offset_y)) return res return input_image face_image = load_image(face_image_path) face_image = resize_img(face_image, max_side=1024) face_image_cv2 = convert_from_image_to_cv2(face_image) height, width, _ = face_image_cv2.shape # Extract face features face_info = self.app.get(face_image_cv2) # if len(face_info) == 0: # raise gr.Error( # f"Unable to detect a face in the image. Please upload a different photo with a clear face." # ) face_info = sorted( face_info, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * x["bbox"][3] - x["bbox"][1], )[ -1 ] face_emb = face_info["embedding"] face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info["kps"]) img_controlnet = face_image pose_image = load_image(pose_image_path) pose_image = resize_img(pose_image, max_side=1024) img_controlnet = pose_image pose_image_cv2 = convert_from_image_to_cv2(pose_image) face_info = self.app.get(pose_image_cv2) # get error if no face is detected # if len(face_info) == 0: # raise gr.Error( # f"Cannot find any face in the reference image! Please upload another person image" # ) face_info = face_info[-1] face_kps = draw_kps(pose_image, face_info["kps"]) width, height = face_kps.size control_mask = np.zeros([height, width, 3]) x1, y1, x2, y2 = face_info["bbox"] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) control_mask[y1:y2, x1:x2] = 255 control_mask = Image.fromarray(control_mask.astype(np.uint8)) control_images = [face_kps] + [ self.controlnet_map_fn[s](img_controlnet).resize((width, height)) for s in self.my_controlnet_selection ] print("Start inference...") self.generator = torch.Generator(device=device).manual_seed(42) self.pipe.set_ip_adapter_scale(adapter_strength_ratio) images = self.pipe( prompt=prompt, negative_prompt=negative_prompt, image_embeds=face_emb, image=control_images, control_mask=control_mask, controlnet_conditioning_scale=self.control_scales, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, height=height, width=width, generator=self.generator, ).images return images[0]