跳到主要内容Python 调用 Stable Diffusion API 实战指南 | 极客日志PythonAI算法
Python 调用 Stable Diffusion API 实战指南
Stable Diffusion API 集成涉及本地部署与云端调用，涵盖文生图、图生图及后处理流程。通过 Python 封装客户端，可灵活控制采样参数与模型切换，配合 Prompt 工程优化生成质量。支持批量生成与超分辨率放大，适用于自动化工作流构建。
从零开始学习使用 Python 调用 Stable Diffusion API 生成图像，涵盖本地部署、API 封装、ControlNet 扩展及后处理流程。
技术架构概览
整个方案主要围绕 Python 客户端与 Stable Diffusion 模型交互展开。根据部署环境不同，可分为本地部署（如 SD WebUI/ComfyUI）和云端 API（如 Replicate/Stability AI）。核心功能包括文生图、图生图、局部重绘以及超分辨率放大。生成的图像最终通过本地存储或 OSS 进行持久化。
环境准备
1. 本地部署 Stable Diffusion WebUI
首先需要克隆官方仓库并启动服务，务必开启 API 模式以便外部调用。
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
./webui.sh --api --listen
webui.bat --api --listen
2. 安装依赖包
项目需要 requests 处理 HTTP 请求，Pillow 处理图片，以及 base64 进行编码转换。
pip install requests Pillow io base64
核心代码实现
1. SD API 客户端封装
为了简化调用逻辑，我们封装了一个 StableDiffusionClient 类。这里定义了一个配置数据类 GenerationConfig，集中管理提示词、采样器、步数等参数。实际开发中，这样能避免每次调用都重复填写相同的默认值。
import requests
import base64
import io
import json
import time
from pathlib import Path
from PIL import Image
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class GenerationConfig:
"""图像生成配置"""
prompt: str = ""
negative_prompt: str = "low quality, blurry, deformed"
width: =
height: =
steps: =
cfg_scale: =
sampler_name: =
seed: = -
batch_size: =
n_iter: =
model: [] =
:
():
.base_url = base_url
.api_url =
() -> :
img_data = base64.b64decode(b64_str)
img = Image.(io.BytesIO(img_data))
img.save(output_path)
output_path
() -> []:
payload = {
: config.prompt,
: config.negative_prompt,
: config.width,
: config.height,
: config.steps,
: config.cfg_scale,
: config.sampler_name,
: config.seed,
: config.batch_size,
: config.n_iter,
}
config.model:
._switch_model(config.model)
response = requests.post(, json=payload)
response.raise_for_status()
data = response.json()
Path(output_dir).mkdir(exist_ok=)
saved_paths = []
i, img_b64 (data[]):
path =
._save_base64_image(img_b64, path)
saved_paths.append(path)
()
saved_paths
() -> []:
config = config GenerationConfig()
(init_image_path, ) f:
init_images = [base64.b64encode(f.read()).decode()]
payload = {
: init_images,
: prompt,
: config.negative_prompt,
: config.width,
: config.height,
: config.steps,
: config.cfg_scale,
: config.sampler_name,
: denoising_strength,
: config.seed,
}
response = requests.post(, json=payload)
response.raise_for_status()
data = response.json()
Path(output_dir).mkdir(exist_ok=)
saved_paths = []
i, img_b64 (data[]):
path =
._save_base64_image(img_b64, path)
saved_paths.append(path)
()
saved_paths
() -> []:
(init_image_path, ) f:
init_images = [base64.b64encode(f.read()).decode()]
(mask_image_path, ) f:
mask = base64.b64encode(f.read()).decode()
payload = {
: init_images,
: mask,
: prompt,
: ,
: denoising_strength,
: ,
: ,
: ,
: ,
: ,
: ,
: ,
}
response = requests.post(, json=payload)
response.raise_for_status()
data = response.json()
Path(output_dir).mkdir(exist_ok=)
saved_paths = []
i, img_b64 (data[]):
path =
._save_base64_image(img_b64, path)
saved_paths.append(path)
saved_paths
() -> :
(image_path, ) f:
img_b64 = base64.b64encode(f.read()).decode()
payload = {
: img_b64,
: ,
: scale,
}
response = requests.post(, json=payload)
response.raise_for_status()
data = response.json()
Path(output_dir).mkdir(exist_ok=)
path =
._save_base64_image(data[], path)
()
path
():
response = requests.post(, json={: model_name})
response.raise_for_status()
time.sleep()
() -> []:
response = requests.get()
[m[] m response.json()]
() -> []:
response = requests.get()
[s[] s response.json()]
int
512
int
512
int
30
float
7.0
str
"DPM++ 2M Karras"
int
1
int
1
int
1
Optional
str
None
class
StableDiffusionClient
"""Stable Diffusion API 客户端"""
def
__init__
self, base_url: str = "http://127.0.0.1:7860"
self
self
f"{base_url}/sdapi/v1"
def
_save_base64_image
self, b64_str: str, output_path: str
str
"""将 base64 图片保存到文件"""
open
return
def
txt2img
self, config: GenerationConfig, output_dir: str = "./output"
list
str
"""文生图：从文本描述生成图像"""
"prompt"
"negative_prompt"
"width"
"height"
"steps"
"cfg_scale"
"sampler_name"
"seed"
"batch_size"
"n_iter"
if
self
f"{self.api_url}/txt2img"
True
for
in
enumerate
"images"
f"{output_dir}/txt2img_{int(time.time())}_{i}.png"
self
print
f"已保存：{path}"
return
def
img2img
self, init_image_path: str, prompt: str, denoising_strength: float = 0.75, config: GenerationConfig = None, output_dir: str = "./output"
list
str
"""图生图：基于参考图 + 提示词生成新图"""
or
with
open
"rb"
as
"init_images"
"prompt"
"negative_prompt"
"width"
"height"
"steps"
"cfg_scale"
"sampler_name"
"denoising_strength"
"seed"
f"{self.api_url}/img2img"
True
for
in
enumerate
"images"
f"{output_dir}/img2img_{int(time.time())}_{i}.png"
self
print
f"已保存：{path}"
return
def
inpaint
self, init_image_path: str, mask_image_path: str, prompt: str, denoising_strength: float = 0.85, output_dir: str = "./output"
list
str
"""局部重绘：只修改 mask 区域"""
with
open
"rb"
as
with
open
"rb"
as
"init_images"
"mask"
"prompt"
"negative_prompt"
"low quality, blurry"
"denoising_strength"
"inpainting_fill"
1
"inpaint_full_res"
True
"steps"
30
"cfg_scale"
7.0
"sampler_name"
"DPM++ 2M Karras"
"width"
512
"height"
512
f"{self.api_url}/img2img"
True
for
in
enumerate
"images"
f"{output_dir}/inpaint_{int(time.time())}_{i}.png"
self
return
def
upscale
self, image_path: str, scale: int = 2, output_dir: str = "./output"
str
"""使用 ESRGAN 进行超分辨率放大"""
with
open
"rb"
as
"image"
"upscaler_1"
"R-ESRGAN 4x+"
"upscaling_resize"
f"{self.api_url}/extra-single-image"
True
f"{output_dir}/upscaled_{int(time.time())}.png"
self
"image"
print
f"超分辨率完成：{path}"
return
def
_switch_model
self, model_name: str
"""切换模型"""
f"{self.api_url}/options"
"sd_model_checkpoint"
3
def
list_models
self
list
str
"""列出可用模型"""
f"{self.api_url}/sd-models"
return
"title"
for
in
def
list_samplers
self
list
str
"""列出可用采样器"""
f"{self.api_url}/samplers"
return
"name"
for
in
2. 批量生成示例
有了客户端之后，批量任务就很简单了。下面这个脚本展示了如何遍历不同的风格关键词，自动产出多组肖像图。
from sd_client import StableDiffusionClient, GenerationConfig
def batch_generate_portraits():
"""批量生成人物肖像"""
sd = StableDiffusionClient()
print("可用模型:", sd.list_models()[:5])
print("可用采样器:", sd.list_samplers())
styles = ["cyberpunk neon city", "watercolor painting", "oil painting renaissance", "anime style", "photorealistic 8k"]
base_prompt = ("portrait of a young woman, detailed face, beautiful eyes, "
"dramatic lighting, masterpiece, best quality")
for style in styles:
config = GenerationConfig(
prompt=f"{base_prompt}, {style}",
negative_prompt="lowres, bad anatomy, bad hands, text, error",
width=512,
height=768,
steps=30,
cfg_scale=7.5,
)
paths = sd.txt2img(config, output_dir=f"./output/{style.replace(' ', '_')}")
print(f"风格 [{style}] -> {paths}")
if __name__ == "__main__":
batch_generate_portraits()
3. 调用 Stability AI 云端 API
如果没有本地 GPU，可以直接调用官方云端接口。注意这里需要替换为你的 API Key。
import requests
import base64
from pathlib import Path
from PIL import Image
from io import BytesIO
class StabilityAIClient:
"""Stability AI 官方云端 API"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.stability.ai/v2beta"
def generate(self, prompt: str, aspect_ratio: str = "1:1", style: str = "photographic", output_path: str = "output.png") -> str:
"""调用 Stable Diffusion 3 生成图像"""
response = requests.post(
f"{self.base_url}/stable-image/generate/sd3",
headers={"Authorization": f"Bearer {self.api_key}", "Accept": "image/*"},
files={"none": ""},
data={"prompt": prompt, "aspect_ratio": aspect_ratio, "style_preset": style, "output_format": "png"},
)
if response.status_code != 200:
raise Exception(f"API 错误：{response.status_code} - {response.text}")
with open(output_path, "wb") as f:
f.write(response.content)
print(f"已生成：{output_path}")
return output_path
if __name__ == "__main__":
client = StabilityAIClient(api_key="sk-your-api-key")
client.generate(
prompt="A majestic dragon flying over a neon-lit cyberpunk city at night, highly detailed, cinematic lighting, 8k",
aspect_ratio="16:9",
style="cinematic",
output_path="dragon_city.png",
)
4. 图像后处理管道
生成后的图片往往还需要调整。这里用 PIL 实现了基础的增强、水印和网格拼图功能。
from PIL import Image, ImageEnhance, ImageFilter
from pathlib import Path
class ImagePostProcessor:
"""图像后处理：调整色彩、锐化、添加水印"""
@staticmethod
def enhance(image_path: str, brightness: float = 1.1, contrast: float = 1.15, sharpness: float = 1.3, output_path: str = None) -> str:
"""综合增强"""
img = Image.open(image_path)
img = ImageEnhance.Brightness(img).enhance(brightness)
img = ImageEnhance.Contrast(img).enhance(contrast)
img = ImageEnhance.Sharpness(img).enhance(sharpness)
output_path = output_path or image_path.replace(".", "_enhanced.")
img.save(output_path, quality=95)
return output_path
@staticmethod
def add_watermark(image_path: str, text: str = "AI Generated", output_path: str = None) -> str:
"""添加水印"""
from PIL import ImageDraw, ImageFont
img = Image.open(image_path).convert("RGBA")
overlay = Image.new("RGBA", img.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
draw.text((img.width - 200, img.height - 40), text, fill=(255, 255, 255, 128))
img = Image.alpha_composite(img, overlay).convert("RGB")
output_path = output_path or image_path.replace(".", "_wm.")
img.save(output_path, quality=95)
return output_path
@staticmethod
def create_grid(image_paths: list[str], cols: int = 3, output_path: str = "grid.png") -> str:
"""将多张图片拼成网格"""
images = [Image.open(p) for p in image_paths]
w, h = images[0].size
rows = (len(images) + cols - 1) // cols
grid = Image.new("RGB", (w * cols, h * rows), "white")
for i, img in enumerate(images):
row, col = divmod(i, cols)
grid.paste(img, (col * w, row * h))
grid.save(output_path, quality=95)
print(f"网格图已保存：{output_path}")
return output_path
Prompt 工程技巧
好的提示词是生成高质量图像的关键。建议采用结构化写法：主体描述 + 风格关键词 + 质量修饰词 + 负面提示词。
PROMPT_TEMPLATES = {
"人物肖像": ("{subject}, {style}, detailed face, expressive eyes, dramatic lighting, masterpiece, best quality, ultra detailed"),
"风景": ("{scene}, {mood}, volumetric lighting, god rays, landscape photography, 8k uhd, cinematic composition"),
"产品设计": ("{product}, minimalist design, studio lighting, white background, product photography, professional, 4k"),
"动漫": ("{character}, anime style, vibrant colors, detailed illustration, cel shading, masterpiece"),
}
NEGATIVE_PROMPTS = {
"通用": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, cropped, worst quality, low quality, blurry",
"写实": "illustration, painting, drawing, art, sketch, anime, cartoon, CG, render, 3D, watermark, text, font, signature",
"动漫": "photo, realistic, 3d, western, ugly, duplicate, morbid, deformed, bad anatomy, blurry",
}
关键参数影响
| 参数 | 推荐值 | 说明 |
|---|
steps | 25-35 | 步数越多细节越好，但边际递减且更慢 |
cfg_scale | 7-12 | 越高越遵循 prompt，过高会过饱和 |
sampler | DPM++ 2M Karras | 兼顾速度与质量 |
denoising_strength | 0.5-0.8 | 图生图降噪强度，越高变化越大 |
seed | -1 | 随机种子，固定可复现 |
完整使用流程
最后整合一下所有模块，展示一个完整的自动化工作流。
from sd_client import StableDiffusionClient, GenerationConfig
from stability_cloud import StabilityAIClient
from postprocess import ImagePostProcessor
def main():
sd = StableDiffusionClient("http://127.0.0.1:7860")
config = GenerationConfig(
prompt="A serene Japanese garden with cherry blossoms, koi pond, stone bridge, golden hour, cinematic, 8k",
negative_prompt="lowres, blurry, text, watermark",
width=768,
height=512,
steps=30,
cfg_scale=7.5,
)
paths = sd.txt2img(config)
print(f"生成完成：{paths}")
if paths:
new_paths = sd.img2img(
init_image_path=paths[0],
prompt="same scene but in autumn, orange and red leaves, snow",
denoising_strength=0.6,
)
print(f"图生图完成：{new_paths}")
if paths:
upscaled = sd.upscale(paths[0], scale=2)
print(f"超分辨率完成：{upscaled}")
pp = ImagePostProcessor()
if paths:
enhanced = pp.enhance(paths[0])
watermarked = pp.add_watermark(enhanced, text="AI Art")
print(f"后处理完成：{watermarked}")
if __name__ == "__main__":
main()
总结
本文覆盖了 Stable Diffusion 图像生成的完整链路：本地部署 SD WebUI 并开启 API 模式；封装 Python 客户端支持文生图、图生图、局部重绘、超分辨率；云端 API 作为无 GPU 环境的替代方案；配合 Prompt 工程模板化的提示词编写技巧；以及后处理管道增强色彩、添加水印、拼图网格。生成速度参考：RTX 4090 生成 512x512 约 3-5 秒，512x768 约 5-8 秒。云端 API 约 10-20 秒。
相关免费在线工具
- 加密/解密文本
使用加密算法（如AES、TripleDES、Rabbit或RC4）加密和解密文本明文。 在线工具，加密/解密文本在线工具，online
- RSA密钥对生成器
生成新的随机RSA私钥和公钥pem证书。 在线工具，RSA密钥对生成器在线工具，online
- Mermaid 预览与可视化编辑
基于 Mermaid.js 实时预览流程图、时序图等图表，支持源码编辑与即时渲染。 在线工具，Mermaid 预览与可视化编辑在线工具，online
- 随机西班牙地址生成器
随机生成西班牙地址（支持马德里、加泰罗尼亚、安达卢西亚、瓦伦西亚筛选），支持数量快捷选择、显示全部与下载。 在线工具，随机西班牙地址生成器在线工具，online
- Gemini 图片去水印
基于开源反向 Alpha 混合算法去除 Gemini/Nano Banana 图片水印，支持批量处理与下载。 在线工具，Gemini 图片去水印在线工具，online
- curl 转代码
解析常见 curl 参数并生成 fetch、axios、PHP curl 或 Python requests 示例代码。 在线工具，curl 转代码在线工具，online
