z-image/turbo-lora

text-till-bild

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.

Inmatning

Prompt *

Loras

MAX: 3

Storlek

Bredd

Höjd

1024 × 1024 pxOmråde: 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.

Utmatning

Vilande

Dina genererade bilder visas här

Konfigurera parametrar och klicka på Kör för att börja generera

Varje körning kostar $0.01. För $10 kan du köra cirka 1000 gånger.

Du kan fortsätta med:

Bild till video Bild till bild

Parametrar

Kodexempel
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()

Installera

Installera det nödvändiga paketet för ditt programmeringsspråk.

pip install requests

Autentisering

Alla API-förfrågningar kräver autentisering via en API key. Du kan hämta din API key från Atlas Cloud-instrumentpanelen.

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

HTTP-huvuden

import os

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

Håll din API key säker

Exponera aldrig din API key i klientkod eller publika arkiv. Använd miljövariabler eller en backend-proxy istället.

Skicka en förfrågan

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())

Skicka en förfrågan

Skicka en asynkron genereringsförfrågan. API:et returnerar ett prediction ID som du kan använda för att kontrollera statusen och hämta resultatet.

POST/api/v1/model/generateImage

Förfrågningsinnehåll

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']}")

Svar

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

Kontrollera status

Polla prediction-endpointen för att kontrollera den aktuella statusen för din förfrågan.

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

Polling-exempel

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)

Statusvärden

processingFörfrågan bearbetas fortfarande.

completedGenereringen är klar. Utdata är tillgängliga.

succeededGenereringen lyckades. Utdata är tillgängliga.

failedGenereringen misslyckades. Kontrollera error-fältet.

Slutfört svar

{
  "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"
  }
}

Ladda upp filer

Ladda upp filer till Atlas Cloud-lagring och få en URL som du kan använda i dina API-förfrågningar. Använd multipart/form-data för uppladdning.

POST/api/v1/model/uploadMedia

Uppladdningsexempel

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}")

Svar

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

Input Schema

Följande parametrar accepteras i förfrågningsinnehållet.

Totalt: 10Obligatorisk: 2Valfri: 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

Exempel på förfrågningsinnehåll

{
  "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:et returnerar ett prediction-svar med de genererade utdata-URL:erna.

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.

Exempelsvar

{
  "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 integrerar 400+ AI-modeller direkt i din AI-kodassistent. Ett kommando för att installera, sedan använd naturligt språk för att generera bilder, videor och chatta med LLM.

Stödda klienter

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ stödda klienter

Installera

npx skills add AtlasCloudAI/atlas-cloud-skills

Konfigurera API Key

Hämta din API key från Atlas Cloud-instrumentpanelen och ställ in den som en miljövariabel.

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

Funktioner

När det är installerat kan du använda naturligt språk i din AI-assistent för att komma åt alla Atlas Cloud-modeller.

BildgenereringGenerera bilder med modeller som Nano Banana 2, Z-Image och fler.

VideoskapandeSkapa videor från text eller bilder med Kling, Vidu, Veo m.fl.

LLM-chattChatta med Qwen, DeepSeek och andra stora språkmodeller.

MediauppladdningLadda upp lokala filer för bildredigering och bild-till-video-arbetsflöden.

Läs mer

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server ansluter din IDE med 400+ AI-modeller via Model Context Protocol. Fungerar med alla MCP-kompatibla klienter.

Stödda klienter

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ stödda klienter

Installera

npx -y atlascloud-mcp

Konfiguration

Lägg till följande konfiguration i din IDE:s MCP-inställningsfil.

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

Tillgängliga verktyg

atlas_generate_imageGenerera bilder från textpromptar.

atlas_generate_videoSkapa videor från text eller bilder.

atlas_chatChatta med stora språkmodeller.

atlas_list_modelsBläddra bland 400+ tillgängliga AI-modeller.

atlas_quick_generateInnehållsskapande i ett steg med automatiskt modellval.

atlas_upload_mediaLadda upp lokala filer för API-arbetsflöden.

Läs mer

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "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"
    }
  ]
}

LLM-vänlig Promptmall

# 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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A hyper-realistic close-up of a giant ramen bowl with a tiny glowing UFO slowly descending onto the noodles, steam rising, rich broth reflections, surreal but delicious-looking detail

An ultra-detailed robot sitting in a quiet garden carefully repairing a broken flower stem with tiny mechanical tools, soft sunlight, beautiful depth of field, metallic and organic textures blending naturally

A photorealistic macro shot of a fragile soap bubble floating in the air, but inside it is a vivid swirling galaxy with stars and nebulae, high detail, cosmic glow, razor-sharp reflections on bubble surface

a gigantic neon-colored snake coiled around a disco stage, mirror-ball scales reflecting rainbow lights, people dancing below, hyper-realistic glitter particles floating through the air

Laddar...

Z-Image Turbo - Blixtsnabb Text-till-Bild-Generering

NYTT

6 miljarders parametermodell från Alibabas Tongyi-MAI-team

Z-Image Turbo är den #1-rankade öppen källkod text-till-bild-modellen som överträffar FLUX.2 [dev], HunyuanImage 3.0 och Qwen-Image i Artificial Analysis Image Arena. Byggd av Alibabas Tongyi-MAI-team (en separat avdelning från Qwen/Wan) uppnår denna 6 miljarders parametermodell generering under en sekund genom avancerad Decoupled-DMD-destillation medan den bibehåller fotorealistisk kvalitet. Med endast 8 inferenssteg ryms den i 16GB VRAM och levererar professionella resultat optimerade för hastighetskritiska produktionsmiljöer.

Ultrasnabb Generering

Endast 8 inferenssteg (vs 20-50 för konkurrenter)
Generering under en sekund på H800 GPU:er
1.31-1.41× snabbare än Qwen Image per steg
Ryms i 16GB VRAM (RTX 3060/4090)

Fotorealistisk Kvalitet

#1-rankad öppen källkod-modell på AI Arena
Tvåspråkig textrendering (engelska & kinesiska)
Robust instruktionsefterlevnad
Slår FLUX.1 [dev] och Qwen i alla kategorier

Alibabas Strategiska Modellportfölj

Alibaba erbjuder tre specialiserade AI-bildgenereringssystem, var och en optimerad för olika användningsfall

Hastighetsmästare

Z-Image Turbo

Tongyi-MAI Team

Best For: Hastighetskritiska produktionsarbetsbelastningar

⚡ Snabbast: 8 steg, generering under en sekund
🏆 #1-rankad öppen källkod-modell
💰 Mest kostnadseffektiv ($0.005/bild)
🎯 Optimerad för snabb iteration

Kvalitetskung

Qwen-Image

Qwen Team

Best For: Slutliga renderingar av maximal kvalitet

🎨 Oöverträffad fotorealism & hudtexturer
💡 Överlägsna ljusinteraktioner
⏱️ Långsammare (20s vs 5-10s för Z-Image)
🎯 Bäst för högklassigt produktionsarbete

Mångsidighetsprofi

Wan 2.5/2.6

Wan Team

Best For: Multimedia-mångsidighet

🎬 Text-till-Video + Bild-till-Video
📹 Stöd för flera upplösningar (480P-720P)
🔄 Audio-visuell synkronisering
🎯 Korsmodal innehållsgenerering

Key Insight: Z-Image Turbo är 1.31-1.41× snabbare än Qwen-Image per steg, vilket gör den idealisk för applikationer som kräver snabb generering. Medan Qwen-Image erbjuder något bättre fotorealism för slutliga renderingar, ger Z-Image Turbo den bästa balansen mellan hastighet och kvalitet för produktionsmiljöer.

Tekniska Höjdpunkter

Prestanda

S3-DiT-Arkitektur

Anammar Single-Stream Diffusion Transformer (S3-DiT)-arkitekturen som förenar bearbetningen av olika villkorade ingångar. Denna 6 miljarders parameterdesign uppnår professionella resultat utan beräkningsoverheaden hos större modeller samtidigt som den bibehåller toppmodern kvalitet.

Hastighet

Decoupled-DMD-Destillation

Avancerad destillationsalgoritm med CFG Augmentation och Distribution Matching-mekanismer möjliggör 8-stegs inferens (vs 20-50 för konkurrenter). Uppnår generering under en sekund på H800 GPU:er och körs smidigt på konsument-RTX 3060/4090 med 16GB VRAM.

Kvalitet

Ledande Öppen Källkod-Prestanda

Rankad som #1 öppen källkod-modell i Artificial Analysis Image Arena och slår FLUX.2 [dev], HunyuanImage 3.0 och Qwen-Image. Utmärker sig i tvåspråkig textrendering (engelska & kinesiska), fotorealistisk generering och robust instruktionsföljning. Släppt under Apache 2.0-licens för kommersiellt bruk.

Perfekt För

🎨

Digital Konstskapande

📸

Produktfotografering

📊

Marknadsföringsmaterial

🎬

Konceptkonst

📱

Innehåll för Sociala Medier

🖼️

Stockfotografering

🎮

Speltillgångar

✨

Kreativ Prototypframställning

Varför Välja Z-Image Turbo

⚡

Omedelbara Resultat

Generering under en sekund med noll kallstartslatens. Få dina bilder omedelbart utan väntan.

💰

Kostnadseffektiv

Överkomligt pris på $0.005 per bild. Skala dina kreativa projekt utan att spränga budgeten.

🔌

Färdigt API

Enkel REST API-integration. Börja generera bilder på några minuter med vår omfattande dokumentation.

Tekniska Specifikationer

Modellarkitektur6 Miljarder Parametrar

Inferenssteg8 NFEs (Antal Funktionsutvärderingar)

GenereringshastighetUnder en sekund på H800, 5-10s på konsument-GPU:er

VRAM-Krav16GB (RTX 3060/4090-kompatibel)

ArkitekturSingle-Stream Diffusion Transformer (S3-DiT)

DestillationsmetodDecoupled-DMD med CFG Augmentation

LicensApache 2.0 (Kommersiellt Bruk Tillåtet)

Rankning#1 Öppen Källkod på Artificial Analysis Arena

Prissättning$0.005 per Bild

Börja Skapa med Z-Image Turbo

Upplev blixtsnabb, fotorealistisk bildgenerering idag. Ingen installation krävs, ring bara vårt API och börja skapa.

Inga kallstarter - omedelbar generering

Överkomligt pris - $0.005 per bild

Professionella kvalitetsresultat

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

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From

$0.03/BILD