多重controlnet控制（使用huggingface提供的API）_controlnet api

huggingface相关diffusers等库的下载暂不提供，可以轻易找到。

直接放代码。

import torch
import datetime
import cv2
import numpy as np
from PIL import Image
import PIL.Image
import PIL.ImageOps
from controlnet_aux import OpenposeDetector
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler, \
    StableDiffusionPipeline
 
 
def canny_img_process(imagepath, low_threshold, high_threshold):
    img = cv2.imread(imagepath)
    image = cv2.Canny(img, low_threshold, high_threshold)
    
    # zero_start = image.shape[1] // 4
    # zero_end = zero_start + image.shape[1] // 2
    # image[:, zero_start:zero_end] = 0
 
    image = image[:, :, None]
    image = np.concatenate([image, image, image], axis=2)
    image = Image.fromarray(image)
    return image
 
 
def openpose_img_process(imagepath):
    image = PIL.Image.open(imagepath)
    openpose = OpenposeDetector.from_pretrained(
        # 'lllyasviel/ControlNet'
        './ControlNet_Cache/',
        filename="body_pose_model.pth",
        hand_filename="hand_pose_model.pth",
        face_filename="facenet.pth"
    )
    image = PIL.ImageOps.exif_transpose(image)
    image = image.convert("RGB")
    image = openpose(image)
    return image
 
 
def multi_control_sd(prompt, control_mode=None, images_PATH=None, low_threshold=100,
                     high_threshold=200, negative_prompt=None, GenNum=20, num_inference_steps=50, sketch_mode=True,
                     controlnet_conditioning_scale=None):
    """
    :param control_mode: only support canny openpose
    """
 
    controlnets = []
    inference_img = []
    for mode, image_path in zip(control_mode, images_PATH):
        if mode == "canny":
            controlnets.append(ControlNetModel.from_pretrained("./Control/canny/", torch_dtype=torch.float16))
            cur_img = canny_img_process(image_path, low_threshold, high_threshold)
            inference_img.append(cur_img)
        if mode == "openpose":
            if sketch_mode:
                controlnets.append(ControlNetModel.from_pretrained("./Control/openpose/", torch_dtype=torch.float16))
                image = PIL.Image.open(image_path)
                inference_img.append(image)
            else:
                controlnets.append(ControlNetModel.from_pretrained("./Control/openpose/", torch_dtype=torch.float16))
                cur_img = openpose_img_process(image_path)
                inference_img.append(cur_img)
 
    pipe = StableDiffusionControlNetPipeline.from_pretrained("./stable-diffusion-v1-5/", controlnet=controlnets,
                                                             torch_dtype=torch.float16)
    pipe = pipe.to("cuda")
    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
    pipe.enable_model_cpu_offload()
 
    for index in range(GenNum):
        print("第{}次生成".format(index + 1))
        # random seed
        seed_id = int(datetime.datetime.now().timestamp())
        generator = torch.Generator("cuda").manual_seed(seed_id)
 
        final_image = \
            pipe(prompt, inference_img, num_inference_steps=num_inference_steps, negative_prompt=negative_prompt,
                 generator=generator, controlnet_conditioning_scale=controlnet_conditioning_scale).images[0]  # PIL格式 (https://pillow.readthedocs.io/en/stable/)
        time = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M')
        name = time + "_" + str(seed_id)
        final_image.save("./control_generate_image/{}.png".format(name))
 
 
if __name__ == '__main__':
 
    prompt = "a giant standing in a fantasy landscape, best quality"
    #prompt = "a boy is watching a cute cat"
    negative_prompt = "nsfw,disfigured,long neck, deformed,ugly, malformed hands,floating limbs"
    control_mode = ["openpose", "canny"]
    images_path = ["./person_pose.png", "./landscape.png"]
    control_scale = [1.0, 0.6]
    multi_control_sd(prompt, control_mode, images_path, negative_prompt=negative_prompt, num_inference_steps=399,
                     controlnet_conditioning_scale=control_scale)
 
 
    # control_stable_diffusion(prompt, canny_mode=False, openpose_mode=True, imagepath="./ceshi.png",
    #                          GenNum=10, num_inference_steps=50,sketch_mode=False)
    # simple_stable_diffusion(prompt,negative_prompt)

以上设置都下载了相关权重文件，所以可以本地使用。在openpose处理部分需要修改源码才能实现本地部署，不然的话会连接huggingface官方，离线就不能运行了。相关操作如下：

首先进入 from_pretrained的源码，然后做如下修改：

 def from_pretrained(cls, pretrained_model_or_path, filename=None, hand_filename=None, face_filename=None, cache_dir=None):
 
        if pretrained_model_or_path == "lllyasviel/ControlNet":
            filename = filename or "annotator/ckpts/body_pose_model.pth"
            hand_filename = hand_filename or "annotator/ckpts/hand_pose_model.pth"
            face_filename = face_filename or "facenet.pth"
 
            face_pretrained_model_or_path = "lllyasviel/Annotators"
        else:
            filename = filename or "body_pose_model.pth"
            hand_filename = hand_filename or "hand_pose_model.pth"
            face_filename = face_filename or "facenet.pth"
 
            face_pretrained_model_or_path = pretrained_model_or_path
 
        # body_model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir)
        # hand_model_path = hf_hub_download(pretrained_model_or_path, hand_filename, cache_dir=cache_dir)
        # face_model_path = hf_hub_download(face_pretrained_model_or_path, face_filename, cache_dir=cache_dir)
 
 
        body_model_path = pretrained_model_or_path + filename
        hand_model_path = pretrained_model_or_path + hand_filename
        face_model_path = pretrained_model_or_path + face_filename
 
        body_estimation = Body(body_model_path)
        hand_estimation = Hand(hand_model_path)
        face_estimation = Face(face_model_path)
 
        return cls(body_estimation, hand_estimation, face_estimation)

该部分是用来处理人体姿态的，有很多其他的方法，也可以不使用他们huggingface提供的一个方法。这样就省了这个操作。