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# 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 PIL
# from PIL import Image
# import diffusers
# from diffusers.models import ControlNetModel
# from depth_anything.dpt import DepthAnything
# from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
# from diffusers.utils import load_image
# from insightface.app import FaceAnalysis
# import torch
# from pipeline_stable_diffusion_xl_instantid_full import StableDiffusionXLInstantIDPipeline, draw_kps
# from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
# from controlnet_aux import OpenposeDetector
# from depth_anything.dpt import DepthAnything
# import torch.nn.functional as F
# from torchvision.transforms import Compose
# from huggingface_hub import hf_hub_download
# end_test
# 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")
# 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("Model dir: ", model_dir)
pass
# 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": get_canny_image,
# "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="antelopev2",
# root="./",
# providers=["CPUExecutionProvider"],
# )
# self.app.prepare(ctx_id=0, det_size=(640, 640))
def __call__(self):
return None
# 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", "canny", "depth"]
# face_image_path = "./kaifu_resize.png"
# pose_image_path = "./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)
# # 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("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
# 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
# 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
# return images[0] |