# from typing import List, Any # import torch # from diffusers import StableCascadePriorPipeline, StableCascadeDecoderPipeline # # Configurar el dispositivo para ejecutar el modelo # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # if device.type != 'cuda': # raise ValueError("Se requiere ejecutar en GPU") # # Configurar el tipo de dato mixto basado en la capacidad de la GPU # dtype = torch.bfloat16 if torch.cuda.get_device_capability(device.index)[0] >= 8 else torch.float16 # start_test 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 import PIL 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 from huggingface_hub import hf_hub_download # end_test 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): hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints") hf_hub_download( repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints", ) hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints") print("Model dir: ", model_dir) face_adapter = f"./checkpoints/ip-adapter.bin" controlnet_path = f"./checkpoints/ControlNetModel" # 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(), # ]) self.controlnet_identitynet = ControlNetModel.from_pretrained( controlnet_path, torch_dtype=dtype ) 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") # controlnet-pose/canny/depth 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_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") # 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 self.controlnet_map = { "pose": controlnet_pose, # "canny": controlnet_canny, # "depth": controlnet_depth, } openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") # depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(device).eval() self.controlnet_map_fn = { "pose": openpose, # "canny": get_canny_image, # "depth": get_depth_map, } self.app = FaceAnalysis(name="buffalo_l", root="./", providers=["CPUExecutionProvider"]) self.app.prepare(ctx_id=0, det_size=(640, 640)) def __call__(self, param): self.pipe.scheduler = diffusers.LCMScheduler.from_config(self.pipe.scheduler.config) self.pipe.enable_lora() adapter_strength_ratio = 0.8 identitynet_strength_ratio = 0.8 pose_strength = 0.4 # canny_strength = 0.3 # depth_strength = 0.5 controlnet_selection = ["pose"] # controlnet_selection = ["pose", "canny", "depth"] face_image_path = "https://i.ibb.co/SKg69dD/kaifu-resize.png" pose_image_path = "https://i.ibb.co/ZSrQ8ZJ/pose.jpg" 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=PIL.Image.BILINEAR, base_pixel_number=64, ): w, h = input_image.size if size is not None: w_resize_new, h_resize_new = size else: ratio = min_side / min(h, w) w, h = round(ratio * w), round(ratio * h) ratio = max_side / max(h, w) input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode) w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number input_image = input_image.resize([w_resize_new, h_resize_new], mode) if pad_to_max_side: res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255 offset_x = (max_side - w_resize_new) // 2 offset_y = (max_side - h_resize_new) // 2 res[ offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new ] = np.array(input_image) input_image = Image.fromarray(res) return input_image # check if the input is valid # if face_image_path is None: # raise gr.Error( # f"Cannot find any input face image! Please upload the face image" # ) # check the prompt # if prompt is None: prompt = "a person" negative_prompt="" # apply the style template # prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) 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) print(len(face_info)) print("error si no hay face") # 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 ] # only use the maximum face face_emb = face_info["embedding"] face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info["kps"]) img_controlnet = face_image if pose_image_path is not None: 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)) if len(controlnet_selection) > 0: 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 controlnet_selection] ) control_scales = [float(identitynet_strength_ratio)] + [ controlnet_scales[s] for s in controlnet_selection ] control_images = [face_kps] + [ self.controlnet_map_fn[s](img_controlnet).resize((width, height)) for s in controlnet_selection ] else: self.pipe.controlnet = self.controlnet_identitynet control_scales = float(identitynet_strength_ratio) control_images = face_kps generator = torch.Generator(device=device.type).manual_seed(3) print("Start inference...") 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=control_scales, num_inference_steps=30, guidance_scale=7.5, height=height, width=width, generator=generator, ).images print("Inference done!") return images[0]