sd webui api模式使用教程大全

简介：

这是我亲自探索的能跑通的sd webui api模式的python调用记录，我将尽我最大的力量来做好/维护好本教程。

当然啦，有些我也没有探索成功，希望各位有成功经历的小伙伴在评论区告诉我怎么调用哈~

<code>注：未经同意，禁止转载

知识是属于全人类的。

拒绝野鸡收费教程，从我做起。

📋TODO：

文生图 图生图 底模的获取/重载/切换🔥🔥🔥 控制网络(controlnet)🔥🔥🔥 分割(SAM)🔥🔥🔥 获取png_info 扩图(outpainting) 叠图 人像修复(Adetailer) 融图

前排提示：在使用API模式启动好服务后，填你的地址:端口+/docs#/就能找到对应的API文档，例如我的在 http://127.0.0.1:7860/docs#/

文生图示例

注意端口和地址填你自己对应的哈！

<code>from datetime import datetime

import urllib.request

import base64

import json

import time

import os

webui_server_url = 'http://127.0.0.1:7861'

out_dir_t2i = os.path.join('api_out', 'txt2img') #图片保存路径

os.makedirs(out_dir_t2i, exist_ok=True)

def timestamp():

'''时间戳'''

return datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S")

def decode_and_save_base64(base64_str, save_path):

'''base64→图片'''

with open(save_path, "wb") as file:

file.write(base64.b64decode(base64_str))

def call_api(api_endpoint, **payload):

data = json.dumps(payload).encode('utf-8')

request = urllib.request.Request(f'{ webui_server_url}/{ api_endpoint}',headers={ 'Content-Type': 'application/json'},data=data,)

response = urllib.request.urlopen(request)

return json.loads(response.read().decode('utf-8'))

def call_txt2img_api(**payload):

response = call_api('sdapi/v1/txt2img', **payload)

for index, image in enumerate(response.get('images')):

save_path = os.path.join(out_dir_t2i, f'txt2img-{ timestamp()}-{ index}.png')

decode_and_save_base64(image, save_path)

if __name__ == '__main__':

payload = {

"prompt": "masterpiece, (best quality:1.1)",

"negative_prompt": "",

"seed": 1,

"steps": 20,

"width": 512,

"height": 512,

"cfg_scale": 7,

"sampler_name": "DPM++ 2M",

"n_iter": 1,

"batch_size": 1,}

call_txt2img_api(**payload)

图生图示例

from datetime import datetime

import urllib.request

import base64

import json

import time

import os

webui_server_url = 'http://127.0.0.1:7861'

out_dir_i2i = os.path.join('api_out', 'img2img') #图片保存路径

os.makedirs(out_dir_i2i, exist_ok=True)

def timestamp():

'''时间戳'''

return datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S")

def encode_file_to_base64(path):

'''图片→base64'''

with open(path, 'rb') as file:

return base64.b64encode(file.read()).decode('utf-8')

def decode_and_save_base64(base64_str, save_path):

'''base64→图片'''

with open(save_path, "wb") as file:

file.write(base64.b64decode(base64_str))

def call_api(api_endpoint, **payload):

data = json.dumps(payload).encode('utf-8')

request = urllib.request.Request(f'{ webui_server_url}/{ api_endpoint}',headers={ 'Content-Type': 'application/json'},data=data,)

response = urllib.request.urlopen(request)

return json.loads(response.read().decode('utf-8'))

def call_img2img_api(**payload):

response = call_api('sdapi/v1/img2img', **payload)

for index, image in enumerate(response.get('images')):

save_path = os.path.join(out_dir_i2i, f'img2img-{ timestamp()}-{ index}.png')

decode_and_save_base64(image, save_path)

if __name__ == '__main__':

init_images = [encode_file_to_base64(r"api_out/txt2img/0.png"),]

batch_size = 2

payload = {

"prompt": "1girl, blue hair",

"seed": 1,

"steps": 20,

"width": 512,

"height": 512,

"denoising_strength": 0.5,

"n_iter": 1,

"init_images": init_images,

"batch_size": batch_size if len(init_images) == 1 else len(init_images),}

call_img2img_api(**payload)

底模操作

1、底模获取

获取当前服务所用的底模，返回一个字典，可自行打印根据需求查看对应字段。

import base64

import requests

from PIL import Image

from io import BytesIO

url = "http://127.0.0.1:7861/sdapi/v1/sd-models"

res = requests.get(url)

print(res.json())

2、底模重载

import base64

import requests

from PIL import Image

from io import BytesIO

url = "http://127.0.0.1:7861/sdapi/v1/refresh-checkpoints"

res = requests.post(url)

注：我运行完后返回了200，但是时间过的特别快，感觉不像是重载而是那个♻️按钮。

3、底模切换🔥🔥🔥

直接在data字段里加入"override_settings"字段，如下所示：

data = {

"prompt": "a girl",

"negative_prompt": "boy",

"seed": -1, # 随机种子

"sampler_name": "取样器（之间复制webui的名字就行）",

"cfg_scale": 7, # 提示词相关性 越大越接近提示词

"width": 512, # 宽 （注意要被16整除）

"height": 512, # 高 （注意要被16整除）

"override_settings": {

"sd_model_checkpoint": "sd_xl_base_1.0.safetensors [31e35c80fc]", # 指定大模型

"sd_vae": "Automatic", # 指定vae 默认自动

},

"override_settings_restore_afterwards": True # override_settings 是否在之后恢复覆盖的设置 默认是True

}

然后运行，观察后台有没有打印换底模的log，一般情况下都能成功。

假如以上步骤切换失败了，这就涉及到改代码了，建议有代码基础的尝试以下步骤：

①修改modules\api\models.py增加一个"model_name"的key：

StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(

"StableDiffusionProcessingTxt2Img",

StableDiffusionProcessingTxt2Img,

[

{ "key": "sampler_index", "type": str, "default": "Euler"},

{ "key": "script_name", "type": str, "default": None},

{ "key": "script_args", "type": list, "default": []},

{ "key": "send_images", "type": bool, "default": True},

{ "key": "save_images", "type": bool, "default": False},

{ "key": "alwayson_scripts", "type": dict, "default": { }},

{ "key": "model_name", "type": str, "default": None}, # 新增此代码

]

).generate_model()

StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(

"StableDiffusionProcessingImg2Img",

StableDiffusionProcessingImg2Img,

[

{ "key": "sampler_index", "type": str, "default": "Euler"},

{ "key": "init_images", "type": list, "default": None},

{ "key": "denoising_strength", "type": float, "default": 0.75},

{ "key": "mask", "type": str, "default": None},

{ "key": "include_init_images", "type": bool, "default": False, "exclude" : True},

{ "key": "script_name", "type": str, "default": None},

{ "key": "script_args", "type": list, "default": []},

{ "key": "send_images", "type": bool, "default": True},

{ "key": "save_images", "type": bool, "default": False},

{ "key": "alwayson_scripts", "type": dict, "default": { }},

{ "key": "model_name", "type": str, "default": None}, # 新增此代码

]

).generate_model()

②修改modules\processing.py

在StableDiffusionProcessingTxt2Img和StableDiffusionProcessingImg2Img两个类里加上model_name: str = None，假如你的版本比较老，就加到这两个类的def __init__里，否则就直接建一个类的属性。例如我的：

class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

enable_hr: bool = False

denoising_strength: float = 0.75

firstphase_width: int = 0

firstphase_height: int = 0

hr_scale: float = 2.0

hr_upscaler: str = None

hr_second_pass_steps: int = 0

hr_resize_x: int = 0

hr_resize_y: int = 0

hr_checkpoint_name: str = None

hr_sampler_name: str = None

hr_prompt: str = ''

hr_negative_prompt: str = ''

model_name: str = None# 新增此代码

③修改modules\api\api.py text2imgapi和img2imgapi两个函数

def text2imgapi(self,txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):

...

model_name = txt2imgreq.model_name# 新增此代码

with self.queue_lock:

if model_name is not None:

w_info = sd_models.CheckpointInfo(os.path.join('models/Stable-diffusion'.model_name))# 新增此代码

sd_models.reload_model_weights(info=w_info)# 新增此代码

with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:

...

def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):

...

model_name = img2imgreq.model_name# 新增此代码

with self.queue_lock:

if model_name is not None:

w_info = sd_models.CheckpointInfo(os.path.join('models/Stable-diffusion'.model_name))# 新增此代码

sd_models.reload_model_weights(info=w_info)# 新增此代码

with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:

...

④🏃‍♂️调用

在传入的data里加上model_name字段就能换模型了。

payload = {

"prompt": "masterpiece, (best quality:1.1)",

"negative_prompt": "",

"seed": 1,

"steps": 2,

"width": 512,

"height": 512,

"cfg_scale": 7,

"sampler_name": "DPM++ 2M",

"model_name": "sd_xl_base_1.0.safetensors",}# 新增的字段

controlnet

首先，我假设您的webui已经安装好了controlnet并下载好相关权重。

文生图和图生图都是在data字段里加入一个"alwayson_scripts"字段，如下所示：

from datetime import datetime

import urllib.request

import base64

import json

import time

import os

webui_server_url = 'http://127.0.0.1:7861'

out_dir_t2i = os.path.join('api_out', 'txt2img') #图片保存路径

os.makedirs(out_dir_t2i, exist_ok=True)

def timestamp():

'''时间戳'''

return datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S")

def decode_and_save_base64(base64_str, save_path):

'''base64→图片'''

with open(save_path, "wb") as file:

file.write(base64.b64decode(base64_str))

def call_api(api_endpoint, **payload):

data = json.dumps(payload).encode('utf-8')

request = urllib.request.Request(f'{ webui_server_url}/{ api_endpoint}',headers={ 'Content-Type': 'application/json'},data=data,)

response = urllib.request.urlopen(request)

return json.loads(response.read().decode('utf-8'))

def call_txt2img_api(**payload):

response = call_api('sdapi/v1/txt2img', **payload)

for index, image in enumerate(response.get('images')):

save_path = os.path.join(out_dir_t2i, f'txt2img-{ timestamp()}-{ index}.png')

decode_and_save_base64(image, save_path)

if __name__ == '__main__':

payload = {

"prompt": "masterpiece, (best quality:1.1)",

"negative_prompt": "",

"seed": 1,

"steps": 20,

"width": 512,

"height": 512,

"cfg_scale": 7,

"sampler_name": "DPM++ 2M",

"n_iter": 1,

"batch_size": 1,

"alwayson_scripts": {

"controlnet":{

"args": [

{

"enabled": True, # 启用

"control_mode": 0, # 对应webui 的 Control Mode 可以直接填字符串 推荐使用下标 0 1 2

"model": "t2i-adapter_diffusers_xl_lineart [bae0efef]", # 对应webui 的 Model

"module": "lineart_standard (from white bg & black line)", # 对应webui 的 Preprocessor

"weight": 0.45, # 对应webui 的Control Weight

"resize_mode": "Crop and Resize",

"threshold_a": 200, # 阈值a 部分control module会用上

"threshold_b": 245, # 阈值b

"guidance_start": 0, # 什么时候介入 对应webui 的 Starting Control Step

"guidance_end": 0.7, # 什么时候退出 对应webui 的 Ending Control Step

"pixel_perfect": True, # 像素完美

"processor_res": 512, # 预处理器分辨率

"save_detected_map": False, # 因为使用了 controlnet API会返回生成controlnet的效果图，默认是True，如何不需要，改成False

"input_image": "", # 图片 格式为base64

}# 多个controlnet 在复制上面一个字典下来就行

]

}

}

call_txt2img_api(**payload)

SAM

首先，我假设您的webui已经安装好了SAM并下载好相关权重。

import base64

import requests

from PIL import Image

from io import BytesIO

def image_to_base64(img_path: str) -> str:

with open(img_path, "rb") as img_file:

img_base64 = base64.b64encode(img_file.read()).decode()

return img_base64

def save_image(b64_img:str, p_out:str) -> None:

with open(p_out,'wb') as f:

f.write(base64.b64decode(b64_img))

url = "http://127.0.0.1:7861/sam/sam-predict"

data = {

"sam_model_name": "sam_hq_vit_b.pth",

"input_image": image_to_base64("api_out/txt2img/txt2img-20240716-162233-0.png"),

"sam_positive_points": [[100.25,100.25]],

"sam_negative_points": [],

"dino_enabled": False,

"dino_model_name": "GroundingDINO_SwinT_OGC (694MB)",

"dino_text_prompt": "string",

"dino_box_threshold": 0.3,

"dino_preview_checkbox": False,

"dino_preview_boxes_selection": [0]

}

res = requests.post(url, json=data)

save_image(res.json()['masks'][0], 'oup_mask.png')

获取png_info

这是一个非常实用的功能，可以获取png图片的一些信息。

import base64

import requests

def image_to_base64(img_path: str) -> str:

with open(img_path, "rb") as img_file:

img_base64 = base64.b64encode(img_file.read()).decode()

return img_base64

url = "http://127.0.0.1:7861/sdapi/v1/png-info"

data = { "image": image_to_base64("api_out/txt2img/txt2img-20240716-162233-0.png")}#要传入的图片路径

res = requests.post(url, json=data)

print(res.json())

扩图

基于controlnet实现叠图功能，首先按网上教程把controlnet和LAMA装好，然后下载一个扩图的controlnet权重：control_v11p_sd15_inpaint.pth，放入相应的目录后按照以下步骤即可。

import json

import base64

import requests

def submit_post(url,data):

return requests.post(url,data=json.dumps(data))

def save_encoded_image(b64_image: str, output_path: str):

with open(output_path, 'wb') as image_file:

image_file.write(base64.b64decode(b64_image))

def encode_file_to_base64(path):

with open(path, 'rb') as file:

return base64.b64encode(file.read()).decode('utf-8')

if __name__ == '__main__':

img2img_url = r'http://127.0.0.1:1245/sdapi/v1/img2img'

init_images = [encode_file_to_base64(r"IP-Adapter1.png"),]

data = {

"seed": -1,

"steps": 20,

"width": 1024,

"height": 512,

'sampler_index': 'DPM++ 2M Karras',

"denoising_strength": 0.5,

"n_iter": 1,

"init_images": init_images,

# "batch_size": batch_size if len(init_images) == 1 else len(init_images),

"alwayson_scripts":{

"controlnet":

{

"args": [

{

"enabled": True, # 是否启用

"control_mode": 2, # 对应webui 的 Control Mode 可以直接填字符串 推荐使用下标 0 1 2

"model": "control_v11p_sd15_inpaint [ebff9138]", # 对应webui 的 Model

"module": "inpaint_only+lama", # 对应webui 的 Preprocessor

"weight": 1, # 对应webui 的Control Weight

"resize_mode": "Resize and Fill",

"threshold_a": 200, # 阈值a 部分control module会用上

"threshold_b": 245, # 阈值b

"guidance_start": 0, # 什么时候介入 对应webui 的 Starting Control Step

"guidance_end": 0.7, # 什么时候退出 对应webui 的 Ending Control Step

"pixel_perfect": True, # 像素完美

"processor_res": 512, # 预处理器分辨率

"save_detected_map": False, # 因为使用了 controlnet API会返回生成controlnet的效果图，默认是True，如何不需要，改成False

"input_image": encode_file_to_base64(r"IP-Adapter1.png"), # 图片 格式为base64

}

# 多个controlnet 在复制上面一个字典下来就行

]

}

}

}

response = submit_post(img2img_url, data)

save_image_path = r'outpainting.png'

save_encoded_image(response.json()['images'][0], save_image_path)

叠图

基于ipadapter实现叠图功能，先按网上教程把ipadapter装好，按照以下步骤即可实现叠图：

import json

import base64

import requests

def submit_post(url,data):

return requests.post(url,data=json.dumps(data))

def save_encoded_image(b64_image: str, output_path: str):

with open(output_path, 'wb') as image_file:

image_file.write(base64.b64decode(b64_image))

def encode_file_to_base64(path):

with open(path, 'rb') as file:

return base64.b64encode(file.read()).decode('utf-8')

if __name__ == '__main__':

img2img_url = r'http://172.0.0.1:4524/sdapi/v1/img2img'

input_image = encode_file_to_base64(r"/data/zkzou2/wwj/image_generation/stable-diffusion-webui/Canny.jpg")

style_image = encode_file_to_base64(r"/data/zkzou2/wwj/image_generation/stable-diffusion-webui/IP-Adapter1.png")

data = {

"seed": -1,

"steps": 20,

"width": 512,

"height": 512,

'sampler_index': 'DPM++ 2M Karras',

"denoising_strength": 0.5,

"n_iter": 1,

"init_images": [input_image],

# "batch_size": batch_size if len(init_images) == 1 else len(init_images),

"alwayson_scripts":{

"controlnet":

{

"args": [

{

"enabled": True, # 是否启用

"control_mode": 0, # 对应webui 的 Control Mode 可以直接填字符串 推荐使用下标 0 1 2

"model": "ip-adapter_sd15_plus [32cd8f7f]", # 对应webui 的 Model

"module": "ip-adapter_clip_sd15", # 对应webui 的 Preprocessor

"weight": 1, # 对应webui 的Control Weight

"resize_mode": "Crop and Resize",

"threshold_a": 200, # 阈值a 部分control module会用上

"threshold_b": 245, # 阈值b

"guidance_start": 0, # 什么时候介入 对应webui 的 Starting Control Step

"guidance_end": 0.7, # 什么时候退出 对应webui 的 Ending Control Step

"pixel_perfect": True, # 像素完美

"processor_res": 512, # 预处理器分辨率

"save_detected_map": False, # 因为使用了 controlnet API会返回生成controlnet的效果图，默认是True，如何不需要，改成False

"input_image": style_image, # 图片 格式为base64

},

{

"enabled": True, # 是否启用

"control_mode": 0, # 对应webui 的 Control Mode 可以直接填字符串 推荐使用下标 0 1 2

"model": "control_v11p_sd15_canny [d14c016b]", # 对应webui 的 Model, 叠图可以更换 Model 和 Preprocessor

"module": "canny", # 对应webui 的 Preprocessor

"weight": 1, # 对应webui 的Control Weight

"resize_mode": "Crop and Resize",

"threshold_a": 200, # 阈值a 部分control module会用上

"threshold_b": 245, # 阈值b

"guidance_start": 0, # 什么时候介入 对应webui 的 Starting Control Step

"guidance_end": 0.7, # 什么时候退出 对应webui 的 Ending Control Step

"pixel_perfect": True, # 像素完美

"processor_res": 512, # 预处理器分辨率

"save_detected_map": False, # 因为使用了 controlnet API会返回生成controlnet的效果图，默认是True，如何不需要，改成False

"input_image": input_image, # 图片 格式为base64

}

]

}

},

"override_settings": {

"sd_model_checkpoint": "v1-5-pruned-emaonly.safetensors [6ce0161689]", # 指定大模型

"sd_vae": "Automatic", # 指定vae 默认自动

},

"override_settings_restore_afterwards": True # override_settings 是否在之后恢复覆盖的设置 默认是True

}

response = submit_post(img2img_url, data)

save_image_path = r'stacked.png'

save_encoded_image(response.json()['images'][0], save_image_path)

人像修复（Adetailer）

评论区有人问我能不能调Adetailer，那必须安排呀！

首先，你先按网上教程把Adetailer装好，该下的权重下好，参考以下代码调用：

import json

import base64

import requests

import os

def submit_post(url,data):

return requests.post(url,data=json.dumps(data))

def save_encoded_image(b64_image: str, output_path: str):

with open(output_path, 'wb') as image_file:

image_file.write(base64.b64decode(b64_image))

def encode_file_to_base64(path):

with open(path, 'rb') as file:

return base64.b64encode(file.read()).decode('utf-8')

if __name__ == '__main__':

img2img_url = r'http://127.0.0.1:1245/sdapi/v1/img2img'

image = [encode_file_to_base64("/data/zkzou2/wwj/image_generation/stable-diffusion-webui/Ad2.png"),]

data = {

"prompt": "a beautiful girl, full body",

"seed": 816743407,

"steps": 20,

"width": 512,

"height": 512,

'sampler_index': 'DPM++ 2M SDE Karras',

"denoising_strength": 0.55,

"n_iter": 1,

"init_images": image,

"model_name": "v1-5-pruned-emaonly.safetensors",

# "batch_size": batch_size if len(init_images) == 1 else len(init_images),

"alwayson_scripts":{

"ADetailer":{

"args":[

True, # ad_enable

True, # 是否启用skip_img2img

{

"ad_model": "face_yolov8n.pt",

"ad_prompt": "detailed face"

}

# 添加多个ad_model

]

}

}

}

response = submit_post(img2img_url, data)

save_image_path = r"Ad_output.png"

save_encoded_image(response.json()['images'][0], save_image_path)

融图

探索中。。。