调用说明

本站点大部分模型都可以直接通过OpenAI的 API 进行调用, 您只需要更改模型名称即可。

OpenAI

将 OpenAI 的 API 基础地址https://api.openai.com改为 UniAPI 的接口地址：https://api.uniapi.io，参照 OpenAI 官方文档搭配令牌页面生成的 Key 进行调用即可快速调用。

Python 示例

bash

pip install openai

python

from openai import OpenAI
client = OpenAI(
    base_url="https://api.uniapi.io/v1",
    api_key="sk-xxxxxx"
)

completion = client.chat.completions.create(
    model="gpt-4o-mini",
    max_tokens=16384,
    messages=[
        {"role": "user", "content": "hi"}
    ]
)
print(completion)

Curl 示例

聊天接口

bash

curl --request POST \
    --url https://api.uniapi.io/v1/chat/completions \
    --header 'Authorization: Bearer sk-替换为你的key' \
    -H "Content-Type: application/json" \
    --data '{
      "max_tokens": 1200,
      "model": "gpt-3.5-turbo",
      "temperature": 0.8,
      "top_p": 1,
      "presence_penalty": 1,
      "messages": [
          {
              "role": "system",
              "content": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible."
          },
          {
              "role": "user",
              "content": "hi~"
          }
      ]
  }'

DALL-E

bash

curl  https://api.uniapi.io/v1/images/generations \
    -H 'Authorization: Bearer sk-替换为你的key' \
    -H "Content-Type: application/json" \
    -d '{
      "model": "dall-e-3",
      "prompt": "a white siamese cat",
      "n": 1,
      "size": "1024x1024"
    }'

vision

bash

curl  https://api.uniapi.io/v1/chat/completions \
    -H 'Authorization: Bearer sk-替换为你的key' \
    -H "Content-Type: application/json" \
    -d '{
      "max_tokens": 1200,
      "model": "gpt-4-vision-preview",
      "messages": [
          {
              "role": "system",
              "content": "You are an expert Tailwind developer"
          },
          {
              "role": "user",
              "content": [
                  {
                      "type": "text",
                      "text": "将图片生成网页代码"
                  },
                  {
                      "type": "image_url",
                      "image_url": {
                          "url": "data:image/jpeg;base64,xxxx=图片链接或者图片base64"
                      }
                  }
              ]
          }
      ]
  }'

whisper

bash

curl --request POST \
    --url https://api.uniapi.io/v1/audio/transcriptions \
    --header 'Authorization: Bearer sk-你的key' \
    --header 'Content-Type: multipart/form-data' \
    --form file=@/path/to/file/openai.mp3 \
    --form model=whisper-1

tts

bash

 curl https://api.uniapi.io/v1/audio/speech \
    -H "Authorization: Bearer sk-你的key" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "tts-1",
      "input": "你说点什么 包括中文!",
      "voice": "alloy"
    }' \
    --output speech.mp3

Claude

您使用OpenAI SDK或者使用OpenAI API格式，您只需要将model改成Claude的模型名称即可调用Claude的模型。

如果需要使用claudeAPI, 请在 base_url 中添加/claude后缀，例如：https://api.uniapi.io/claude

⚠️ 注意

Claude 默认渠道请优先使用 OpenAI API 进行请求。稳定渠道无限制。

python 示例

bash

pip install anthropic

python

import anthropic

client = Anthropic(
    api_key="sk-xxxxxxx",
    base_url="https://api.uniapi.io/claude",
)
message = client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ]
)
print(message.content)

Curl 示例

更多示例，请查看Anthropic 官方文档

聊天接口

bash

curl --request POST \
    --url https://api.uniapi.io/claude/v1/messages \
    -H "Content-Type: application/json" \
    -H "x-api-key: sk-替换为你的key" \
    --data '{
  "model": "claude-3-haiku-20240307",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "Hello, world"
    }
  ]
}'

Gemini

使用OpenAI SDK或者使用OpenAI API格式，您只需要将model改成Gemini的模型名称即可调用Gemini的模型。

更多示例，请查看Gemini 官方文档

Python 示例

bash

pip install -q -U google-genai

python

from google import genai
from google.genai import types

prompt = "hi~"
budget = 1024  # You can set this variable to any value between 0 and 24k

client = genai.Client(
    http_options=types.HttpOptions(base_url='https://api.uniapi.io/gemini'),
    api_key="sk-替换为你的key",
    )
response = client.models.generate_content(
    model="gemini-2.5-flash-preview-04-17",
    contents=prompt,
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_budget=budget, include_thoughts=True)
    ),
)

print(response.text)

Curl 示例

bash

curl --request POST \
  --url 'https://api.uniapi.io/gemini/v1alpha/models/gemini-1.5-flash-latest:generateContent?alt=sse' \
  --header 'Content-Type: application/json' \
  --header 'x-goog-api-key: sk-替换为你的key' \
  --data '{
	"contents": [
		{
			"parts": [
				{
					"text": "hi~"
				}
			],
			"role": "user"
		}
	]
}'

OCR

目前接入的是mistral的 OCR 模型。详情请参考OCR 官方文档

Curl 示例

bash

curl --request POST \
    --url https://api.uniapi.io/ocr/v1/ocr \
    --header 'Authorization: Bearer sk-替换为你的key' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "mistral-ocr-latest",
    "document": {
        "type": "document_url",
        "document_url": "https://assets.anthropic.com/m/1cd9d098ac3e6467/original/Claude-3-Model-Card-October-Addendum.pdf"
    },
    "include_image_base64": true
  }'

Midjourney

支持 midjourney-proxy-plus 接口协议，接口地址使用：https://hk.uniapi.io(境内网路)或https://api.uniapi.io(境外网路)
Relax 模式路径：/mj-relax/mj Fast 模式路径：/mj或/mj-fast/mj Turbo 模式路径：/mj-turbo/mj

详见接口说明。

python 示例

python

import requests
import time
import json

# 配置信息
# Relax模式请求路径：/mj-relax
# Fast模式请求路径：/mj-fast
# Turbo模式请求路径：/mj-turbo
# 如果不填写，默认是Fast模式
BASE_URL = "https://api.uniapi.io/mj-relax"
BEARER_TOKEN = "your_token_here"
HEADERS = {
    "mj-api-secret:": f"{BEARER_TOKEN}",
    "Content-Type": "application/json"
}

class MJImageGenerator:
    def __init__(self):
        self.base_url = BASE_URL
        self.headers = HEADERS

    def submit_task(self, prompt):
        """提交图片生成任务"""
        url = f"{self.base_url}/mj/submit/imagine"
        payload = {
            "base64Array": [], # 这个是可选的，如果垫图，需要传入垫图的base64， 可以使用多张垫图 最多5张， 单张图片不超过1M
            "prompt": prompt, # 这个是必填的，prompt 是必填的
            "botType": "MID_JOURNEY" # 这个是必填的，botType 是必填的，MID_JOURNEY 表示 Midjourney 模型,NIJI_JOURNEY 表示 Niji 模型， 详细参考 https://docs.midjourney.com/docs/models
        }

        response = requests.post(url, headers=self.headers, json=payload)
        data = response.json()

        if data["code"] not in [1, 22]:
            raise Exception(f"提交任务失败: {data['description']}")

        return data["result"]
    def action(self, customId, taskId):
        """执行操作"""
        url = f"{self.base_url}/mj/submit/action"
        payload = {
            "customId": customId,
            "taskId": taskId
        }
        response = requests.post(url, headers=self.headers, json=payload)
        data = response.json()

        if data["code"] not in [1, 22]:
            raise Exception(f"提交任务失败: {data['description']}")

        return data["result"]
    def modal(self, taskId, prompt):
        """执行modal操作"""
        url = f"{self.base_url}/mj/submit/modal"
        payload = {
            "prompt": prompt,
            "taskId": taskId
        }
        response = requests.post(url, headers=self.headers, json=payload)
        data = response.json()

        if data["code"] not in [1, 22]:
            raise Exception(f"提交任务失败: {data['description']}")

        return data["result"]
    def describe(self, base64, botType):
        """执行图生文操作"""
        url = f"{self.base_url}/mj/submit/describe"
        payload = {
            "base64": base64,
            "botType": botType
        }
        response = requests.post(url, headers=self.headers, json=payload)
        data = response.json()

        if data["code"] not in [1, 22]:
            raise Exception(f"提交任务失败: {data['description']}")

        return data["result"]
    def shorten(self, prompt, botType):
        """执行缩短提示词的操作"""
        url = f"{self.base_url}/mj/submit/shorten"
        payload = {
            "prompt": prompt,
            "botType": botType
        }
        response = requests.post(url, headers=self.headers, json=payload)
        data = response.json()

        if data["code"] not in [1, 22]:
            raise Exception(f"提交任务失败: {data['description']}")

        return data["result"]

    def fetch_task_status(self, task_id):
        """获取任务状态"""
        url = f"{self.base_url}/mj/task/{task_id}/fetch"
        response = requests.get(url, headers=self.headers)
        return response.json()

    def wait_for_completion(self, task_id):
        """轮询等待任务完成"""
        while True:
            data = self.fetch_task_status(task_id)
            status = data["status"]
            progress = data["progress"]
            print(f"当前进度: {progress}")

            if status == "SUCCESS":
                print("任务完成!")
                print(f"图片链接: {data['imageUrl']}")
                print(f"提示词: {data['prompt']}")
                print(f"提示词EN: {data['promptEn']}")
                print("\n可用的操作按钮:")
                print("\按钮说明， 顺序，从左往右，从上往下。 Ux:表示你想要操作哪张图片。 🔄 表示重新绘制。 Vx:表示你想要变化哪一张图片 ")
                # 参考
                # https://docs.midjourney.com/docs/midjourney-discord
                # https://docs.midjourney.com/docs/variations
                for button in data["buttons"]:
                    label = button['label'] if button['label'] != "" else button['emoji']
                    print(f"{label}: {button['customId']}")
                return data
            elif status == "FAILURE":
                raise Exception(f"任务失败: {data['failReason']}")

            time.sleep(10)  # 每10秒轮询一次

def main():
    try:
        generator = MJImageGenerator()

        # 示例prompt
        # Cyberpunk 风格， 可以不填，可选值: 賽博朋克: Cyberpunk,星際: Warframe,動漫: ACGN,日本漫畫: Japanese comics/manga,水墨畫風格: Ink Wash Painting Style,原創: Original,風景畫: landscape,插畫: illustration,漫畫: Manga,現代自然: modern organic,創世紀: Genesis,海報風格: posterstyle,超現實主義: surrealism,素描: sketch,寫實: realism,水彩畫: Watercolor painting,立體主義: Cubism,黑白: black and white,膠片攝影風格: fm photography,電影化: cinematic,清晰的面部特徵: dlear facial features
        # Wide view 宽视角， 可以不填, 可选值： 寬視角: Wide view,鳥瞰視角: Aerial view,頂視角: Top view,仰視角: Upview,正面視角: Front view,頭部特寫: Headshot,超廣角視角: Ultrawideshot,中景: Medium Shot(MS),遠景: Long Shot(LS),景深: depth offield(dof)
        # Face Shot (VCU) 人物特写， 可以不填， 可选值： 臉部特寫: Face Shot (VCU),大特寫: Big Close-Up(BCU),特寫: Close-Up(CU),腰部以上: Waist Shot(WS),膝蓋以上: KneeShot(KS),全身照: Full Length Shot(FLS),極遠景: Extra Long Shot(ELS)
        # Cold light 灯光， 可以不填，可选值： 冷光: Cold light,暖光: Warm light,硬光: hard lighting,戲劇性光線: Dramatic light,反射光: reflection light,薄霧: Misty foggy,自然光: Natural light,陽光: Sun light,情緒化: moody
        # 可爱的猫， 描述你想要生成的图片
        # --q 2 表示质量， 可选值： 0.25,0.5,1,2， 参考 https://docs.midjourney.com/docs/quality
        # --s 50 表示艺术程度，可选值： 0-1000，参考 https://docs.midjourney.com/docs/stylize
        # --v 6.1 表示版本， 可选值：1、2、3、4、5、5.0、5.1、5.2、6 和 6.1， 如果使用niji模型，则需要改为 --niji 版本号，  参考 https://docs.midjourney.com/docs/models
        # --ar 4:3 表示宽高比
        # 更多请参考 https://docs.midjourney.com/docs/parameter-list
        prompt = "Cyberpunk,Wide view,Face Shot (VCU),Cold light,可爱的猫 --q 2 --s 50 --v 6.1 --ar 4:3"

        print("提交任务中...")
        task_id = generator.submit_task(prompt)
        print(f"任务ID: {task_id}")

        print("等待任务完成...")
        generator.wait_for_completion(task_id)

        # 按钮操作, 每次有按钮，都按照这个操作执行。
        # action_id = generator.action("MJ::JOB::upsample::1::4f9c53b7-dc2a-441f-a7e0-e8b65dd2ce6d", task_id)
        # print("等待任务完成...")
        # generator.wait_for_completion(action_id)

        # ------------------------------------------------------------

        #  特殊操作， CustomZoom， 先 执行 按钮操作， 然后 执行 modal 操作
        # 执行按钮操作
        # action_id = generator.action("MJ::CustomZoom::ec92d09b-e6c4-458c-952f-d34e87b090a8", task_id)
        # 执行 modal 操作
        # 填写变焦值， 例如 1.8
        # prompt += " --zoom 1.8"
        # modal_id = generator.modal(prompt, action_id)
        # print("等待任务完成...")
        # generator.wait_for_completion(modal_id)


        # 执行图生文操作
        # task_id = generator.describe("data:image/jpeg;base64,xxxxx", "MID_JOURNEY")
        # print(f"任务ID: {task_id}")

        # print("等待任务完成...")
        # generator.wait_for_completion(task_id)

        # 执行缩短提示词操作
        # task_id = generator.shorten("Please create a whimsical majestic tower of donuts, intricately crafted and adorned with a mesmerizing array of colorful sprinkles. Bring this sugary masterpiece to life, ensuring every detail is rendered in stunning magical realism. Thank you!", "MID_JOURNEY")
        # print(f"任务ID: {task_id}")

        # print("等待任务完成...")
        # generator.wait_for_completion(task_id)



    except Exception as e:
        print(f"发生错误: {str(e)}")

if __name__ == "__main__":
    main()

Suno

Python 示例

python

import requests
import time
from typing import Dict, Any

key = "sk-xxxxx"  # 替换为你的key
BASE_URL = "https://api.uniapi.io/suno"


def get_headers() -> Dict[str, str]:
    return {
        "Authorization": f"Bearer {key}",
        "Content-Type": "application/json"
    }

def handle_response(response: requests.Response) -> Dict[str, Any]:
    response_data = response.json()
    if response.status_code != 200:
        raise Exception(f"请求失败，状态码: {response.status_code}，响应信息: {response_data}")
    if response_data.get("code") != "success":
        raise Exception(f"操作失败，响应信息: {response_data}")
    return response_data["data"]

def submit_lyrics(prompt: str) -> str:
    url = f"{BASE_URL}/submit/lyrics"
    data = {"prompt": prompt}
    response = requests.post(url, headers=get_headers(), json=data)
    return handle_response(response)

def fetch(task_id: str) -> Dict[str, Any]:
    url = f"{BASE_URL}/fetch/{task_id}"
    response = requests.get(url, headers=get_headers())
    return handle_response(response)

def submit_song(payload: Dict[str, Any]) -> str:
    url = f"{BASE_URL}/submit/music"
    response = requests.post(url, headers=get_headers(), json=payload)
    return handle_response(response)


def main():
    prompt = "愉快的，摇滚的，学猫叫"
    try:
        lyrics_task_id = submit_lyrics(prompt)
        lyrics_text = ""

        while True:
            lyrics_fetch = fetch(lyrics_task_id)
            task_status = lyrics_fetch["status"]
            if task_status == "FAILURE":
                raise Exception("歌词生成失败")
            if task_status == "SUCCESS":
                lyrics_text = lyrics_fetch['data']['text']
                break
            print(f"歌词生成状态： {task_status}，请等待2s...")
            time.sleep(2)

        print("歌词内容:" + lyrics_text)

        if not lyrics_text:
            raise Exception("歌词为空终止调用")

        # 组装歌曲生成请求
        payload = {
            "prompt": lyrics_text,
            "tags": "emotional punk",
            "mv": "chirp-v3-5",
            "title": "学猫叫"
        }

        # 提交歌曲生成请求
        song_task_id = submit_song(payload)
        print("歌曲任务ID:" + song_task_id)

        isStream = False
        # 轮询查询歌曲生成状态
        while True:
            task_data = fetch(song_task_id)
            task_status = task_data["status"]

            if task_status == "FAILURE":
                raise Exception("歌曲生成失败")

            if task_status == "SUCCESS":
                break

            if task_status == "IN_PROGRESS" and isStream == False:
                isStream = True
                print(f"歌曲已经可以通过流播放，但不可下载")
                for song in task_data["data"]:
                    print(f"歌曲名称: {song['title']}")
                    print(f"歌曲封面: {song['image_url']}")
                    print(f"音频地址: {song['audio_url']}")
                    print("-" * 40)

            print(f"歌曲生成状态： {task_status}，请等待15s...")

            time.sleep(15)
        # 打印歌曲信息
        for song in task_data["data"]:
            print(f"歌曲名称: {song['title']}")
            print(f"歌曲封面: {song['image_url']}")
            print(f"音频地址: {song['audio_url']}")
            print(f"视频地址: {song['video_url']}")
            print("-" * 40)

    except Exception as e:
        print(f"发生错误: {e}")

if __name__ == "__main__":
    main()

Udio

Python 示例

python

import requests
import time
from typing import Dict, Any

key = "sk-xxxxx"  # 替换为你的key
BASE_URL = "https://api.uniapi.io/udio"


def get_headers() -> Dict[str, str]:
    return {
        "Authorization": f"Bearer {key}",
        "Content-Type": "application/json"
    }

def handle_response(response: requests.Response) -> Dict[str, Any]:
    response_data = response.json()
    if response.status_code != 200:
        raise Exception(f"请求失败，状态码: {response.status_code}，响应信息: {response_data}")
    if response_data.get("code") != "success":
        raise Exception(f"操作失败，响应信息: {response_data}")
    return response_data["data"]

def fetch(task_id: str) -> Dict[str, Any]:
    url = f"{BASE_URL}/fetch/{task_id}"
    response = requests.get(url, headers=get_headers())
    return handle_response(response)

def submit_song(payload: Dict[str, Any]) -> str:
    url = f"{BASE_URL}/submit/music"
    response = requests.post(url, headers=get_headers(), json=payload)
    return handle_response(response)


def main():
    try:
        # 组装歌曲生成请求
        # 自定义歌词示例：
        payload = {
            "gen_params": {
                "lyrics": "[Verse]\n城中古庙尘封梦乡\n梵蒂冈街头雾漫黄昏光\n信徒静踏圣殿的星芒\n祈求灵感心中起波浪\n\n[Verse 2]\n香火缭绕心愿飞扬\n青石阶边影子偷偷藏\n钟声长鸣驱散迷茫\n老君庙中祷告声悠长\n\n[Chorus]\n去梵蒂冈上一柱老君庙请的香\n滚滚红尘中找寻方向\n去追梦像是风一样轻荡\n心灵在这里找到新希望\n\n[Verse 3]\n巷弄深处古老神像\n祈愿的心灵火焰炙热燃\n古文明与现代相辉映\n圣光普照人间暖心肠\n\n[Bridge]\n万里之外的梦想启航\n老君庙中眺望远方\n香火明灭如星光闪\n心愿在心中永存不忘\n\n[Chorus]\n去梵蒂冈上一柱老君庙请的香\n滚滚红尘中找寻方向\n去追梦像是风一样轻荡\n心灵在这里找到新希望",
                # user 使用用户提供的歌词生成音乐，此时 prompt 的作用类似于 tags，填写风格
                # instrumental 用于创建纯音乐，prompt 用于相关提示词
                # generate 表示自动生成歌词，prompt 用于描述需要生成什么样的歌曲。
                "lyrics_type": "user",
                "prompt": "周杰伦",
                "bypass_prompt_optimization": False,
                "seed": -1,
                # 用于指定在完整歌曲中生成片段的起始和结束位置。0% (0) 对应歌曲开头，50% (0.5) 对应歌曲中间，100% (1) 对应歌曲结尾。点击数值指示器可切换为自动模式，让模型自行决定最佳位置。这个功能在使用歌曲扩展特性时特别有用。
	            # 歌曲片段开始位置	范围 0-1, 默认 0
                "song_section_start": 0.4,
                # 控制提示词对生成内容的影响程度。较高的值可以提高对提示词的遵循度，但可能会导致生成的声音不够自然。
                # 提示词强度	范围 0-1, 默认 0.5
                "prompt_strength": 0.5,
                # 控制模型在生成音频时对清晰度和乐器分离度的优先级。较高的值可能会产生更清晰的音频，但声音可能不够自然。
                # 清晰度强度	范围 0-1，默认 0.25
                "clarity_strength": 0.25,
                # 控制歌词对生成内容的影响程度。较低的值可能会产生更自然的声音，但可能会忽略部分歌词内容。
                # 歌词强度	范围 0-1，默认 0.5
                "lyrics_strength": 0.5,
                # 实验性功能。用于在生成质量和生成速度之间进行权衡。
                # 生成质量	可选 0.25, 0.5, 0.75, 1。默认 0.75，数字越大，质量越高，速度越慢。
                "generation_quality": 0.75,
                "model_type": "udio130-v1.5", # udio32-v1.5 (30s的歌曲) / udio130-v1.5 (130s的歌曲)
                "config": {
                    "mode": "regular" # 生成歌曲
                }
            }
        }

        # 自动生成歌词示例
        payload2 = {
            "gen_params": {
                "prompt": "愉快的，摇滚的，学猫叫",
                "lyrics": "",
                "lyrics_type": "generate",
                "bypass_prompt_optimization": False,
                "seed": -1,
                "song_section_start": 0.4,
                "prompt_strength": 0.5,
                "clarity_strength": 0.25,
                "lyrics_strength": 0.5,
                "generation_quality": 0.75,
                "model_type": "udio130-v1.5",
                "config": {
                    "mode": "regular"
                }
            }
        }

        # 提交歌曲生成请求
        song_task_id = submit_song(payload)
        print("歌曲任务ID:" + song_task_id)

        isStream = False
        # 轮询查询歌曲生成状态
        while True:
            task_data = fetch(song_task_id)
            task_status = task_data["status"]

            if task_status == "FAILURE":
                raise Exception("歌曲生成失败")

            if task_status == "SUCCESS":
                break

            print(f"歌曲生成状态： {task_status}，请等待15s...")

            time.sleep(15)
        # 打印歌曲信息
        for song in task_data["data"]['songs']:
            print(f"歌曲名称: {song['title']}")
            print(f"歌曲封面: {song['image_path']}")
            print(f"音频地址: {song['song_path']}")
            print("-" * 40)

    except Exception as e:
        print(f"发生错误: {e}")

if __name__ == "__main__":
    main()

FalAI

视频和图像调用方法一致。

⚠️ 注意

根据 FalAI 官方文档规则，如因为您输入参数错误导致的请求失败，积分不予退还。详情见FalAI 官方文档

Python 示例

python


import requests
import time
from typing import Dict, Any

key = "sk-xxx"  # 替换为实际的 Bearer Token
BASE_URL = "https://us.uniapi.io/fal-ai"


def get_headers() -> Dict[str, str]:
    return {
        "Authorization": f"Key {key}",
        "Content-Type": "application/json"
    }

def handle_response(response: requests.Response) -> Dict[str, Any]:
    response_data = response.json()
    if response.status_code != 200:
        raise Exception(f"请求失败，状态码: {response.status_code}，响应信息: {response_data}")
    if response_data.get("status") == "FAILURE":
        raise Exception(f"操作失败，响应信息: {response_data}")
    return response_data

def submit(model: str, data: Dict[str, Any]) -> str:
    url = f"{BASE_URL}/{model}"
    response = requests.post(url, headers=get_headers(), json=data)
    return handle_response(response)

def fetch(url: str) -> Dict[str, Any]:
    response = requests.get(url, headers=get_headers())
    return handle_response(response)




def main():
    model = "flux-pro/kontext/text-to-image"
    data = {
  "prompt": "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture.",
  "guidance_scale": 1,
  "num_images": 1,
  "safety_tolerance": "2",
  "output_format": "jpeg",
  "aspect_ratio": "9:21"
}
    try:
        task = submit(model, data)
        status_url = task['status_url']
        response_url = task['response_url']

        while True:
            status_fetch = fetch(status_url)
            task_status = status_fetch["status"]
            if task_status != "COMPLETED":
                print(f"视频生成状态： {task_status}，请等待2s...")
                time.sleep(2)
                continue

            response_fetch = fetch(response_url)
            print("响应:")
            print(response_fetch)
            break

    except Exception as e:
        print(f"发生错误: {e}")

if __name__ == "__main__":
    main()

图像模型调用

可以直接通过 OpenAI 的 image generation 接口调用.

curl

curl --request POST \
  --url https://us.uniapi.io/v1/images/generations \
  --header 'Authorization: Bearer sk-xxx' \
  --header 'Content-Type: application/json' \
  --data '{
"model": "flux-pro/kontext/text-to-image",
  "prompt": "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture.",
  "guidance_scale": 1,
  "n": 1,
  "num_images": 2,
  "safety_tolerance": "2",
  "output_format": "jpeg",
  "aspect_ratio": "1:1"
}
'

调用说明 ​

OpenAI ​

Python 示例 ​

Curl 示例 ​

聊天接口 ​

DALL-E ​

vision ​

whisper ​

tts ​

Claude ​

python 示例 ​

Curl 示例 ​

聊天接口 ​

Gemini ​

Python 示例 ​

Curl 示例 ​

OCR ​

Curl 示例 ​

Midjourney ​

python 示例 ​

Suno ​

Python 示例 ​

Udio ​

Python 示例 ​

FalAI ​

Python 示例 ​

图像模型调用 ​

调用说明

OpenAI

Python 示例

Curl 示例

聊天接口

DALL-E

vision

whisper

tts

Claude

python 示例

Curl 示例

聊天接口

Gemini

Python 示例

Curl 示例

OCR

Curl 示例

Midjourney

python 示例

Suno

Python 示例

Udio

Python 示例

FalAI

Python 示例

图像模型调用