xai/grok-imagine-image-quality/text-to-image

teks-ke-gambar

Grok Imagine Image Quality Text-to-Image API by xAI

xai/grok-imagine-image-quality/text-to-image

Text-to-image

xAI Grok Imagine generates polished visuals from natural-language prompts at 1K or 2K resolution, with 14 aspect ratios.

INPUT

Memuat konfigurasi parameter...

OUTPUT

Menunggu

Gambar yang dihasilkan akan muncul di sini

Konfigurasikan pengaturan Anda dan klik Jalankan untuk memulai

Permintaan Anda akan dikenakan biaya $0.055 per eksekusi. Dengan $10 Anda dapat menjalankan model ini sekitar 181 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": "xai/grok-imagine-image-quality/text-to-image",
    "prompt": "A beautiful landscape with mountains and lake",
    "width": 512,
    "height": 512,
    "steps": 20,
    "guidance_scale": 7.5,
}

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 yang diperlukan untuk bahasa pemrograman Anda.

bash

pip install requests

Autentikasi

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

bash

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

HTTP Headers

python

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": "xai/grok-imagine-image-quality/text-to-image",
    "input": {
        "prompt": "A beautiful landscape with mountains and lake"
    }
}

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

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

Respons

{
  "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: 0Wajib: 0Opsional: 0

Tidak ada parameter yang tersedia.

Contoh Isi Permintaan

json

{
  "model": "xai/grok-imagine-image-quality/text-to-image"
}

Output Schema

API mengembalikan respons prediction dengan URL output yang dihasilkan.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for image generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Contoh Respons

json

{
  "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 300+ 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

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40+ klien yang didukung

Instalasi

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Atur API Key

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

bash

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 300+ model AI melalui Model Context Protocol. Berfungsi dengan klien apa pun yang kompatibel dengan MCP.

Klien yang Didukung

Cursor

VS Code

Windsurf

Claude Code

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Gemini CLI

Cline

Roo Code

100+ klien yang didukung

Instalasi

bash

npx -y atlascloud-mcp

Konfigurasi

Tambahkan konfigurasi berikut ke file pengaturan MCP di IDE Anda.

json

{
  "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 300+ model AI yang tersedia.

atlas_quick_generatePembuatan konten satu langkah dengan pemilihan model otomatis.

atlas_upload_mediaUnggah file lokal untuk alur kerja API.

Pelajari lebih lanjut

github.com/AtlasCloudAI/mcp-server

Schema API

Schema tidak tersedia

Tidak ada contoh yang tersedia

Silakan masuk untuk melihat riwayat permintaan

Anda perlu masuk untuk mengakses riwayat permintaan model Anda.

Masuk

1. Introduction

Grok Imagine Image Quality is xAI's flagship image generation and editing system, also known as "Quality Mode," designed to deliver photorealistic imagery, legible in-image typography, and tight prompt adherence across diverse visual styles. This README applies to the following API model identifiers:

xai/grok-imagine-image-quality/text-to-image
xai/grok-imagine-image-quality/edit

Developed by xAI and built on the Aurora foundation—an autoregressive Mixture-of-Experts (MoE) architecture that differentiates it from diffusion-based competitors—Grok Imagine Image Quality targets creators, developers, and enterprises who require high-fidelity static imagery alongside natural-language editing. The consumer version launched on April 3, 2026 via grok.com/imagine and the Grok iOS/Android apps, and the API became publicly available on May 6, 2026 through the official announcement.

The system is exposed through two API variants that share the same underlying model but are optimized for distinct workflows. The xai/grok-imagine-image-quality/text-to-image endpoint produces images from text prompts with approximately 4-second latency, while xai/grok-imagine-image-quality/edit applies prompt-driven modifications to existing images—including multi-image reference composition—with approximately 13-second latency.

2. Key Features & Innovations

Aurora MoE Architecture: Unlike most image generators that rely on diffusion, Grok Imagine Image Quality is powered by Aurora, an autoregressive Mixture-of-Experts model. This approach yields strong facial consistency, accurate textures, and cinematic lighting behavior that reviewers have compared favorably with diffusion competitors on photorealistic sharpness.
High-Fidelity Text Rendering: The model produces legible in-image typography across multiple languages, addressing one of the historically weakest areas of generative image models. While Ideogram and GPT Image 2 still hold the lead in pure text rendering, Quality Mode closes the gap considerably versus prior Grok generations.
Prompt-Driven Editing Without Masks: The xai/grok-imagine-image-quality/edit variant supports object addition, removal, swapping, style transfer, and multi-image reference composition entirely through natural-language prompts. No mask-based inpainting is required, and multi-turn iterative refinement is supported for progressive edits.
Multi-Resolution and Multi-Format Output: Outputs are available at 1K (1024×1024) or 2K (2048×2048) resolution, across 13 aspect ratios ranging from 2:1 to 1:2. JPEG, PNG, and WebP formats are supported, with alpha channel available on PNG and WebP.
Batch Generation: Both variants accept a num_images parameter (1–4) to generate multiple candidates per request, useful for creative exploration and A/B selection in production pipelines.
Broad Stylistic Range: The model demonstrates competent prompt adherence across photorealistic, anime, oil painting, 3D-rendered, and abstract styles, making it suitable for varied creative and commercial briefs from a single endpoint.
Integrated Image-to-Video Pipeline: Grok Imagine Image Quality feeds directly into xAI's image-to-video capabilities, which currently rank #1 on the Artificial Analysis Image-to-Video Arena (Elo 1,336) and Multi-Image-to-Video Arena (Elo 1,342).

3. Model Architecture & Technical Details

Grok Imagine Image Quality uses the Aurora architecture—an autoregressive Mixture-of-Experts design. Rather than iteratively denoising latent representations as diffusion models do, autoregressive image models generate tokens sequentially, which contributes to the system's strong consistency across faces, fine textures, and typography. The MoE routing allows expert specialization across visual domains (portraiture, text, lighting, stylization) while keeping inference latency competitive.

Both API identifiers (xai/grok-imagine-image-quality/text-to-image and xai/grok-imagine-image-quality/edit) are served by the same underlying weights; the distinction lies in the input schema and conditioning path. The editing variant accepts a prompt plus one or more image_urls, enabling single-image edits as well as multi-image composition in which reference imagery informs the generated output.

API specifications:

Parameter	Text-to-Image	Edit
Required inputs	`prompt`	`prompt`, `image_urls`
`num_images`	1–4	1–4
`aspect_ratio`	13 options (2:1 to 1:2)	Defaults to `auto`
`resolution`	1k / 2k	1k / 2k
Typical latency	~4 s	~13 s

The model is positioned within xAI's tiered product line—Speed → Quality → Pro—where Quality Mode represents the balanced tier and Pro Mode adds 2K output with iterative editing workflows.

4. Performance Highlights

On the Artificial Analysis Text-to-Image Arena, Grok Imagine Image Quality sits within the top five models but trails the current leaders. Its strongest competitive results come from the image-to-video pipeline it feeds, where xAI's system ranks first overall.

Text-to-Image Arena (indicative rankings):

Rank	Model	Developer	Elo Score
1	GPT Image 2	OpenAI	1338
2	GPT Image 1.5	OpenAI	1273
3	Nano Banana Pro	Google	1219
Top 5	Grok Imagine Image Quality	xAI	Top-5 tier

Image-to-Video / Multi-Image-to-Video Arena (pipeline context):

Arena	Rank	Elo
Image-to-Video	#1	1,336
Multi-Image-to-Video	#1	1,342

Qualitative strengths:

Photoreal sharpness rated above Nano Banana by independent reviewers
Strong facial consistency and cinematic lighting
Competitive price-performance and fast inference
Permissive content handling with an integrated video pipeline

Known limitations:

In-image text rendering trails Ideogram, GPT Image 2, and FLUX
Editing fidelity trails GPT Image 1.5 on complex structural edits
Artistic stylization trails Midjourney V7 on illustrative aesthetics
Moderation behavior has been reported as inconsistent by some users

5. Intended Use & Applications

Portrait and Character Art: The Aurora architecture's facial consistency and texture accuracy make xai/grok-imagine-image-quality/text-to-image well suited for portrait generation, concept characters, and hero imagery where identity fidelity matters.
Product and Commercial Marketing: Produce product advertisements, UGC-style marketing visuals, and product-film mockups at 2K resolution with cinematic lighting. The fast inference and per-image pricing support high-volume creative iteration.
Prompt-Driven Image Editing: Use xai/grok-imagine-image-quality/edit for object addition, removal, swapping, and style transfer without requiring masks. Multi-turn refinement supports iterative polish workflows typical of design review cycles.
Multi-Image Composition: The editing variant accepts multiple reference images, enabling workflows such as combining a subject with a new background, transferring wardrobe across references, or blending compositional cues from several inputs.
Social and Short-Form Content: Generate social-first imagery and stills that feed into the Grok Imagine image-to-video pipeline—currently ranked #1 on Artificial Analysis's video arenas—for an end-to-end static-to-motion workflow.
Concept Art and Creative Exploration: With batch sizes up to four images and broad stylistic range across photorealistic, anime, oil painting, 3D, and abstract styles, the model serves concept artists and creative directors exploring visual directions quickly.
Enterprise Creative Agencies and Media: The combination of 2K output, permissive content policy, and integrated video pipeline positions Grok Imagine Image Quality for creative agencies, entertainment and media production, and social-first consumer brands.

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Grok Imagine Image Quality Text-to-Image API by xAI

INPUT

OUTPUT

Parameter

Contoh kode

Instalasi

Autentikasi

HTTP Headers

Kirim permintaan

Kirim Permintaan

Isi Permintaan

Respons

Periksa Status

Contoh Polling

Nilai Status

Respons Selesai

Unggah File

Contoh Unggah

Respons

Input Schema

Contoh Isi Permintaan

Output Schema

Contoh Respons

Atlas Cloud Skills

Klien yang Didukung

Instalasi

Atur API Key

Kemampuan

MCP Server

Klien yang Didukung

Instalasi

Konfigurasi

Alat yang Tersedia

Schema API

Silakan masuk untuk melihat riwayat permintaan

1. Introduction

2. Key Features & Innovations

3. Model Architecture & Technical Details

4. Performance Highlights

5. Intended Use & Applications

Jelajahi Model Serupa

Grok Imagine Image Quality Edit

Openai GPT Image 2 Text-to-Image

Openai GPT Image 2 Edit

Baidu ERNIE Image Turbo Text-to-image

Mulai dari 300+ Model,

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