z-image/turbo-lora

teks-ke-gambar

TURBO

Z-Image-Turbo LoRA API by Atlas Cloud

z-image/turbo-lora

Turbo-lora

Z-Image Turbo text-to-image generation with user-supplied LoRA weights, custom sizes, and reproducible output.

INPUT

Prompt *

Loras

MAKS: 3

Ukuran

Lebar

Tinggi

1024 × 1024 pxRentang: 256 - 1536

Seed

Num inference steps

Guidance scale

Output format

Civitai links require your API token appended: download_url&token=YOUR_TOKEN. Get it from Civitai Account Settings → API Keys.
Hugging Face links must point to a specific file (e.g. .safetensors), not a repository directory.

OUTPUT

Menunggu

Gambar yang dihasilkan akan muncul di sini

Konfigurasikan pengaturan Anda dan klik Jalankan untuk memulai

Permintaan Anda akan dikenakan biaya $0.01 per eksekusi. Dengan $10 Anda dapat menjalankan model ini sekitar 1000 kali.

Berikut yang dapat Anda lakukan selanjutnya:

Gambar ke Video Gambar ke Gambar

Parameter

Contoh kode
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "z-image/turbo-lora",  # Required. Model name
    "prompt": "A beautiful landscape with mountains and lake",  # Required. The positive prompt for the generation
    "loras": [],  # List of LoRA weights to apply. default: []
    "size": "1024*1024",  # Output image size in width*height format. (max: 1536)
    "seed": -1,  # Seed for reproducible output
    "num_inference_steps": 9,  # Number of inference steps used for generation. (min: 1)
    "guidance_scale": 1,  # Classifier-free guidance scale
    "output_format": "png",  # Image output format. options: png | jpeg | webp
    "enable_sync_mode": False,  # Wait for the result to be generated and uploaded before returning the response
    "enable_base64_output": False,  # Return base64 image data instead of an image URL
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] == "completed":
            print("Generated image:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

image_url = check_status()

Instalasi

Instal paket dependensi yang diperlukan.

pip install requests

Autentikasi

Semua permintaan API memerlukan autentikasi melalui API key. Anda bisa mendapatkan API key dari dasbor Atlas Cloud.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Jaga keamanan API key Anda

Jangan pernah mengekspos API key Anda di kode sisi klien atau repositori publik. Gunakan variabel lingkungan atau proxy backend sebagai gantinya.

Kirim permintaan

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

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

Kirim Permintaan

Kirim permintaan pembuatan asinkron. API mengembalikan prediction ID yang dapat Anda gunakan untuk memeriksa status dan mengambil hasil.

POST/api/v1/model/generateImage

Isi Permintaan

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "z-image/turbo-lora",
    "prompt": "A beautiful landscape with mountains and lake"
}

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

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Respons

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Periksa Status

Polling prediction endpoint untuk memeriksa status permintaan Anda saat ini.

GET/api/v1/model/prediction/{prediction_id}

Contoh Polling

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Nilai Status

processingPermintaan masih diproses.

completedPembuatan selesai. Output tersedia.

succeededPembuatan berhasil. Output tersedia.

failedPembuatan gagal. Periksa field error.

Respons Selesai

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Unggah File

Unggah file ke penyimpanan Atlas Cloud dan dapatkan URL yang dapat Anda gunakan dalam permintaan API Anda. Gunakan multipart/form-data untuk mengunggah.

POST/api/v1/model/uploadMedia

Contoh Unggah

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Respons

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

Parameter berikut diterima di isi permintaan.

Total: 10Wajib: 2Opsional: 8

Civitai links require your API token appended: download_url&token=YOUR_TOKEN. Get it from Civitai Account Settings → API Keys.
Hugging Face links must point to a specific file (e.g. .safetensors), not a repository directory.

modelstringrequired

Model name.

Default: "z-image/turbo-lora"

promptstringrequired

The positive prompt for the generation.

lorasarray[object]

List of LoRA weights to apply.

Default: []Max items: 3

pathstringrequired

URL or path to the LoRA weights.

scalenumber

Scale used before merging the LoRA weights.

Default: 1Min: 0Max: 4

sizestring

Output image size in width*height format.

Default: "1024*1024"Max: 1536

seedinteger

Seed for reproducible output. Use -1 for random.

Default: -1

num_inference_stepsinteger

Number of inference steps used for generation.

Default: 9Min: 1

guidance_scalenumber

Classifier-free guidance scale. The default value follows the Z-Image Turbo path.

Default: 1

output_formatstring

Image output format.

Default: "png"

pngjpegwebp

enable_sync_modeboolean

Wait for the result to be generated and uploaded before returning the response. This property is only available through the API.

Default: false

enable_base64_outputboolean

Return base64 image data instead of an image URL. This property is only available through the API.

Default: false

Contoh Isi Permintaan

{
  "model": "z-image/turbo-lora",
  "prompt": "A beautiful landscape",
  "loras": [],
  "size": "1024*1024",
  "seed": -1,
  "num_inference_steps": 9,
  "guidance_scale": 1,
  "output_format": "png",
  "enable_sync_mode": false,
  "enable_base64_output": false
}

Output Schema

API mengembalikan respons prediction dengan URL output yang dihasilkan.

created_atstring

ISO timestamp when the request was created.

idstring

Unique prediction id.

modelstring

Model id used for the prediction.

outputsarray

Generated image URLs or base64 payloads.

statusstring

Task status.

Contoh Respons

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills mengintegrasikan 400+ model AI langsung ke asisten pengkodean AI Anda. Satu perintah untuk menginstal, lalu gunakan bahasa alami untuk menghasilkan gambar, video, dan mengobrol dengan LLM.

Klien yang Didukung

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ klien yang didukung

Instalasi

npx skills add AtlasCloudAI/atlas-cloud-skills

Atur API Key

Dapatkan API key dari dasbor Atlas Cloud dan atur sebagai variabel lingkungan.

export ATLASCLOUD_API_KEY="your-api-key-here"

Kemampuan

Setelah diinstal, Anda dapat menggunakan bahasa alami di asisten AI Anda untuk mengakses semua model Atlas Cloud.

Pembuatan GambarBuat gambar dengan model seperti Nano Banana 2, Z-Image, dan lainnya.

Pembuatan VideoBuat video dari teks atau gambar dengan Kling, Vidu, Veo, dll.

Obrolan LLMMengobrol dengan Qwen, DeepSeek, dan model bahasa besar lainnya.

Unggah MediaUnggah file lokal untuk pengeditan gambar dan alur kerja gambar-ke-video.

Pelajari lebih lanjut

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server menghubungkan IDE Anda dengan 400+ model AI melalui Model Context Protocol. Berfungsi dengan klien apa pun yang kompatibel dengan MCP.

Klien yang Didukung

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ klien yang didukung

Instalasi

npx -y atlascloud-mcp

Konfigurasi

Tambahkan konfigurasi berikut ke file pengaturan MCP di IDE Anda.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Alat yang Tersedia

atlas_generate_imageBuat gambar dari prompt teks.

atlas_generate_videoBuat video dari teks atau gambar.

atlas_chatMengobrol dengan model bahasa besar.

atlas_list_modelsJelajahi 400+ model AI yang tersedia.

atlas_quick_generatePembuatan konten satu langkah dengan pemilihan model terbaik otomatis.

atlas_upload_mediaUnggah file lokal untuk alur kerja API.

Pelajari lebih lanjut

github.com/AtlasCloudAI/mcp-server

Schema API

{
  "components": {
    "schemas": {
      "Input": {
        "type": "object",
        "required": [
          "model",
          "prompt"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "z-image/turbo-lora"
          },
          "prompt": {
            "type": "string",
            "description": "The positive prompt for the generation."
          },
          "loras": {
            "type": "array",
            "description": "List of LoRA weights to apply.",
            "default": [],
            "maxItems": 3,
            "items": {
              "$ref": "#/components/schemas/LoraWeight"
            }
          },
          "size": {
            "type": "string",
            "description": "Output image size in width*height format.",
            "default": "1024*1024",
            "maximum": 1536
          },
          "seed": {
            "type": "integer",
            "description": "Seed for reproducible output. Use -1 for random.",
            "default": -1
          },
          "num_inference_steps": {
            "type": "integer",
            "description": "Number of inference steps used for generation.",
            "default": 9,
            "minimum": 1
          },
          "guidance_scale": {
            "type": "number",
            "description": "Classifier-free guidance scale. The default value follows the Z-Image Turbo path.",
            "default": 1
          },
          "output_format": {
            "type": "string",
            "description": "Image output format.",
            "default": "png",
            "enum": [
              "png",
              "jpeg",
              "webp"
            ]
          },
          "enable_sync_mode": {
            "type": "boolean",
            "description": "Wait for the result to be generated and uploaded before returning the response. This property is only available through the API.",
            "default": false,
            "disabled": true
          },
          "enable_base64_output": {
            "type": "boolean",
            "description": "Return base64 image data instead of an image URL. This property is only available through the API.",
            "default": false,
            "disabled": true
          }
        },
        "x-order-properties": [
          "model",
          "prompt",
          "loras",
          "size",
          "seed",
          "num_inference_steps",
          "guidance_scale",
          "output_format",
          "enable_sync_mode",
          "enable_base64_output"
        ]
      },
      "LoraWeight": {
        "type": "object",
        "required": [
          "path"
        ],
        "properties": {
          "path": {
            "type": "string",
            "description": "URL or path to the LoRA weights."
          },
          "scale": {
            "type": "number",
            "description": "Scale used before merging the LoRA weights.",
            "default": 1,
            "minimum": 0,
            "maximum": 4
          }
        },
        "x-order-properties": [
          "path",
          "scale"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "created_at": {
            "type": "string",
            "format": "date-time",
            "description": "ISO timestamp when the request was created."
          },
          "has_nsfw_contents": {
            "type": "array",
            "description": "NSFW detection result for each output.",
            "items": {
              "type": "boolean"
            }
          },
          "id": {
            "type": "string",
            "description": "Unique prediction id."
          },
          "model": {
            "type": "string",
            "description": "Model id used for the prediction."
          },
          "outputs": {
            "type": "array",
            "description": "Generated image URLs or base64 payloads.",
            "items": {
              "type": "string"
            }
          },
          "status": {
            "type": "string",
            "description": "Task status."
          },
          "urls": {
            "type": "object",
            "description": "Related API endpoints."
          }
        }
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "type": "apiKey",
        "in": "header",
        "name": "Authorization"
      }
    }
  },
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "The AtlasCloud API."
  },
  "openapi": "3.0.0",
  "paths": {
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "type": "string",
              "description": "Request ID"
            }
          }
        ],
        "responses": {
          "200": {
            "description": "Result of the request.",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            }
          }
        }
      },
      "x-api-name": "model_result"
    },
    "/api/v1/model/generateImage": {
      "post": {
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          }
        },
        "responses": {
          "200": {
            "description": "The request status.",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            }
          }
        }
      },
      "x-api-name": "model_run"
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

Template Prompt untuk LLM

# z-image/turbo-lora

> Z-Image Turbo text-to-image generation with user-supplied LoRA weights, custom sizes, and reproducible output.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `z-image/turbo-lora`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  Model name.
  - Default: `"z-image/turbo-lora"`

- **`prompt`** (`string`, _required_):
  The positive prompt for the generation.

- **`loras`** (`array`, _optional_):
  List of LoRA weights to apply.
  - Default: `[]`
  - Max items: 3

- **`size`** (`string`, _optional_):
  Output image size in width*height format.
  - Default: `"1024*1024"`
  - Max: 1536

- **`seed`** (`integer`, _optional_):
  Seed for reproducible output. Use -1 for random.
  - Default: `-1`

- **`num_inference_steps`** (`integer`, _optional_):
  Number of inference steps used for generation.
  - Default: `9`
  - Min: 1

- **`guidance_scale`** (`number`, _optional_):
  Classifier-free guidance scale. The default value follows the Z-Image Turbo path.
  - Default: `1`

- **`output_format`** (`string`, _optional_):
  Image output format.
  - Default: `"png"`
  - Options: "png", "jpeg", "webp"

- **`enable_sync_mode`** (`boolean`, _optional_):
  Wait for the result to be generated and uploaded before returning the response. This property is only available through the API.
  - Default: `false`

- **`enable_base64_output`** (`boolean`, _optional_):
  Return base64 image data instead of an image URL. This property is only available through the API.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "z-image/turbo-lora",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "z-image/turbo-lora",
  "prompt": "",
  "loras": [],
  "size": "1024*1024",
  "seed": -1,
  "num_inference_steps": 9,
  "guidance_scale": 1,
  "output_format": "png",
  "enable_sync_mode": false,
  "enable_base64_output": false
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp when the request was created.

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  NSFW detection result for each output.

- **`id`** (`string`, _optional_):
  Unique prediction id.

- **`model`** (`string`, _optional_):
  Model id used for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Generated image URLs or base64 payloads.

- **`status`** (`string`, _optional_):
  Task status.

- **`urls`** (`object`, _optional_):
  Related API endpoints.



**Example Response**:

```json
{
  "created_at": "",
  "has_nsfw_contents": [],
  "id": "",
  "model": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "z-image/turbo-lora",
  "prompt": "",
  "loras": [],
  "size": "1024*1024",
  "seed": -1,
  "num_inference_steps": 9,
  "guidance_scale": 1,
  "output_format": "png",
  "enable_sync_mode": false,
  "enable_base64_output": false
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/z-image/turbo-lora)

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Memuat...

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Model 6 Miliar Parameter dari Alibaba TONGYIMAI

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Generasi Ultra Cepat

Hanya 8 langkah inferensi (vs 20-50 untuk kompetitor)
Generasi di bawah satu detik pada GPU H800
1.31-1.41× lebih cepat dari Qwen Image per langkah
Muat dalam VRAM 16GB (RTX 3060/4090)

Kualitas Fotorealistik

Model open source peringkat #1 di AI Arena
Rendering teks bilingual (Inggris & Mandarin)
Kepatuhan instruksi yang kuat
Mengalahkan FLUX.1 [dev] dan Qwen di semua kategori

Portofolio Model Strategis Alibaba

Alibaba menawarkan tiga sistem generasi gambar AI khusus, masing-masing dioptimalkan untuk kasus penggunaan yang berbeda

Juara Kecepatan

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Tim Tongyi-MAI

Best For: Beban kerja produksi yang kritis terhadap kecepatan

⚡ Tercepat: 8 langkah, generasi di bawah satu detik
🏆 Model open source peringkat #1
💰 Paling hemat biaya ($0.005/gambar)
🎯 Dioptimalkan untuk iterasi cepat

Raja Kualitas

Qwen-Image

Tim Qwen

Best For: Render akhir kualitas maksimum

🎨 Fotorealisme & tekstur kulit yang tak tertandingi
💡 Interaksi pencahayaan superior
⏱️ Lebih lambat (20dtk vs 5-10dtk untuk Z-Image)
🎯 Terbaik untuk pekerjaan produksi kelas atas

Pro Serbaguna

Wan 2.5/2.6

Tim Wan

Best For: Keserbagunaan multimedia

🎬 Teks ke Video + Gambar ke Video
📹 Dukungan multi-resolusi (480P-720P)
🔄 Sinkronisasi audio-visual
🎯 Generasi konten lintas modal

Key Insight: Z-Image Turbo 1.31-1.41× lebih cepat dari Qwen-Image per langkah, menjadikannya ideal untuk aplikasi yang memerlukan generasi cepat. Meskipun Qwen-Image menawarkan fotorealisme sedikit lebih baik untuk render akhir, Z-Image Turbo memberikan keseimbangan terbaik antara kecepatan dan kualitas untuk lingkungan produksi.

Sorotan Teknis

Kinerja

Arsitektur S3-DiT

Mengadopsi arsitektur Single-Stream Diffusion Transformer (S3-DiT) yang menyatukan pemrosesan berbagai input kondisional. Desain 6 miliar parameter ini mencapai hasil profesional tanpa overhead komputasi model yang lebih besar sambil mempertahankan kualitas terkini.

Kecepatan

Distilasi Decoupled-DMD

Algoritma distilasi canggih dengan mekanisme CFG Augmentation dan Distribution Matching memungkinkan inferensi 8 langkah (vs 20-50 untuk kompetitor). Mencapai generasi di bawah satu detik pada GPU H800 dan berjalan lancar pada RTX 3060/4090 konsumen dengan VRAM 16GB.

Kualitas

Kinerja Open Source Terdepan

Diperingkat sebagai model open source #1 di Artificial Analysis Image Arena, mengalahkan FLUX.2 [dev], HunyuanImage 3.0, dan Qwen-Image. Unggul dalam rendering teks bilingual (Inggris & Mandarin), generasi fotorealistik, dan mengikuti instruksi yang kuat. Dirilis di bawah lisensi Apache 2.0 untuk penggunaan komersial.

Sempurna Untuk

🎨

Pembuatan Seni Digital

📸

Fotografi Produk

📊

Materi Pemasaran

🎬

Desain Konsep

📱

Konten Media Sosial

🖼️

Fotografi Stok

🎮

Aset Game

✨

Prototipe Kreatif

Mengapa Memilih Z-Image Turbo

⚡

Hasil Instan

Generasi di bawah satu detik dengan latensi cold start nol. Dapatkan gambar Anda segera tanpa menunggu.

💰

Hemat Biaya

Harga terjangkau $0.005 per gambar. Skalakan proyek kreatif Anda tanpa merusak anggaran.

🔌

API Siap Pakai

Integrasi REST API sederhana. Mulai menghasilkan gambar dalam hitungan menit dengan dokumentasi komprehensif kami.

Spesifikasi Teknis

Arsitektur Model6 Miliar Parameter

Langkah Inferensi8 NFEs (Jumlah Evaluasi Fungsi)

Kecepatan GenerasiDi bawah satu detik pada H800, 5-10dtk pada GPU konsumen

Kebutuhan VRAM16GB (kompatibel RTX 3060/4090)

ArsitekturSingle-Stream Diffusion Transformer (S3-DiT)

Metode DistilasiDecoupled-DMD dengan CFG Augmentation

LisensiApache 2.0 (Penggunaan Komersial Diizinkan)

Peringkat#1 Open Source di Artificial Analysis Arena

Harga$0.005 per Gambar

Mulai Berkreasi dengan Z-Image Turbo

Rasakan generasi gambar fotorealistik secepat kilat hari ini. Tanpa pengaturan, cukup panggil API kami dan mulai berkreasi.

Tanpa cold start - generasi instan

Harga terjangkau - $0.005 per gambar

Hasil kualitas profesional

Z-Image-Turbo LoRA — 6B-parameter, ultra-fast text-to-image with custom styles

Z-Image-Turbo LoRA is a personalised version of Tongyi-MAI’s 6B-parameter Z-Image-Turbo model. It keeps the same 8-step, ultra-fast sampler and low VRAM footprint, while letting you plug in up to three LoRA adapters to inject your own styles, characters, or brand identity into each generation.

Ultra-fast generation with LoRA personalisation

Where many diffusion models need dozens of steps, Z-Image-Turbo LoRA stays aggressively optimised around 8 sampling steps. On top of that, it adds LoRA hooks so you can steer the visual style without retraining the base model—perfect for interactive products, dashboards, and large-scale backends that still need a branded look.

Why it looks so good

• Photorealistic output at speed Generates high-fidelity, realistic images suitable for product photos, hero banners, and UI visuals—now with your own LoRA styles layered on top.

• Bilingual prompts and text Understands prompts in English and Chinese, and can render multilingual on-image text, ideal for cross-market campaigns and UI screenshots.

• LoRA-powered customisation Attach up to 3 LoRAs per request to add a specific art style, character look, or brand aesthetics without touching the base weights.

• Low-latency, low-step design Only 8 function evaluations per image deliver extremely low latency, ideal for chatbots, configuration tools, design assistants, and any “type → image” workflow.

• Friendly VRAM footprint Runs well in 16 GB VRAM environments, reducing hardware costs and making local or edge deployments more realistic—even with LoRAs enabled.

• Scales for bulk generation The efficient sampler keeps large jobs—catalogues, continuous feeds, or mass thumbnail generation—practical, even when every image uses one or more LoRAs.

• Reproducible generations A controllable seed parameter lets you recreate previous images or generate small, controlled variations for brand safety and experimentation.

How to use

prompt – natural-language description of the scene, style, and any on-image text (English or Chinese).
size (width / height) – choose the output resolution that fits your use case.
seed – set to -1 for random results, or use a fixed integer to make outputs reproducible.
loras – optional list of up to three LoRA adapters:
path – a LoRA identifier such as <owner>/<model-name> or a direct .safetensors URL.
scale – numeric strength for that LoRA; higher values apply a stronger stylistic effect.

You can click “Add Item” in the loras panel to add 1–3 LoRAs. They are combined during generation, so a single prompt can mix, for example, a character LoRA, a style LoRA, and a brand-colour LoRA.

Pricing

Simple per-image billing:

$0.01 per generated image

Jelajahi Model Serupa

NEW

alat-gambar

Face Swap

Swap a face onto any photo. The facial identity is transferred while pose, clothing and background stay intact.

Photo Cleanup

Restore single images by reducing dust, scratches, noise, and repeated micro-texture artifacts without changing the image content.

Openai GPT Image 2 Text-to-Image

GPT Image 2 text to image is OpenAI's fast, cost-efficient text-to-image generator powered by GPT-5 guidance. Create photorealistic shots, product renders, concept art, and stylized graphics from natural-language prompts (optionally conditioned with an image). Supports custom aspect ratios, seeds, negative prompts, hex color hints, and style presets. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Openai GPT Image 2 Edit

GPT Image 2 Edit is OpenAI's image model for precise, natural-language edits. Add/remove objects, swap backgrounds, retouch faces, adjust colors/lighting, edit text/graphics, crop/resize, and apply hex color control. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

GPT Image 2 Developer Edit

GPT Image 2 Developer Edit applies natural-language instructions to one or more reference images, with common aspect ratios and 1k, 2k, or supported 4k output tiers. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

GPT Image 2 Developer Text-to-Image

GPT Image 2 Developer Text-to-Image generates polished visuals from natural-language prompts, with common aspect ratios and 1k, 2k, or supported 4k output tiers. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Seedream v5.0 Pro Edit

ByteDance flagship next-generation image editing model. Supports up to 10 reference images while preserving identity, lighting, and color tones for professional-quality modifications.

Seedream v5.0 Pro Text-to-Image

ByteDance flagship next-generation image generation model with stronger prompt adherence, refined typography, and photorealistic detail. Single-image output at 1.5K and 2K tiers with JPEG and PNG support.

Nano Banana 2 Lite Edit Developer

Google's fastest and most cost-efficient Nano Banana image model for editing, applying natural-language edits and multi-image composition to up to 14 reference images with low latency.

Nano Banana 2 Lite Text-to-Image Developer

Google's fastest and most cost-efficient Nano Banana image model, turning natural-language text prompts into high-quality 1k images in as little as 4 seconds for rapid, high-volume generation.

Nano Banana 2 Lite Edit

Nano banana lite is the efficiency-focused model in the image generation family. Sub-2 second latency with cost-effective generation and editing, fast multi-turn local edits, and 14 supported aspect ratios.

Nano Banana 2 Lite Text-to-image

MAI-Image-2.5-Flash Text-to-image

Microsoft's fast, cost-optimized text-to-image generation model, creating high-quality images at lower cost using the same diffusion-based architecture as MAI-Image-2.5.

From

$0.03/GAMBAR