Grok Imagine Image Quality Edit API by xAI
xAI Grok Imagine edits one or more reference images with natural-language instructions at 1K or 2K resolution. Supports single image and multi-image (<IMAGE_0>, <IMAGE_1>) reference editing.
Entrada
Salida
InactivoCada ejecución costará $0.055. Con $10 puedes ejecutar aproximadamente 181 veces.
Puedes continuar con:
Ejemplo de código
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/edit",
"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()Instalar
Instala el paquete necesario para tu lenguaje de programación.
pip install requestsAutenticación
Todas las solicitudes de API requieren autenticación mediante una clave de API. Puedes obtener tu clave de API desde el panel de Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"Encabezados HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Nunca expongas tu clave de API en código del lado del cliente ni en repositorios públicos. Usa variables de entorno o un proxy de backend en su lugar.
Enviar una solicitud
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())Enviar una solicitud
Envía una solicitud de generación asíncrona. La API devuelve un ID de predicción que puedes usar para verificar el estado y obtener el resultado.
/api/v1/model/generateImageCuerpo de la solicitud
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/edit",
"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']}")Respuesta
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Verificar estado
Consulta el endpoint de predicción para verificar el estado actual de tu solicitud.
/api/v1/model/prediction/{prediction_id}Ejemplo de 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)Valores de estado
processingLa solicitud aún se está procesando.completedLa generación está completa. Las salidas están disponibles.succeededLa generación fue exitosa. Las salidas están disponibles.failedLa generación falló. Verifica el campo de error.Respuesta completada
{
"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"
}
}Subir archivos
Sube archivos al almacenamiento de Atlas Cloud y obtén una URL que puedes usar en tus solicitudes de API. Usa multipart/form-data para subir.
/api/v1/model/uploadMediaEjemplo de carga
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}")Respuesta
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Schema de entrada
Los siguientes parámetros se aceptan en el cuerpo de la solicitud.
No hay parámetros disponibles.
Ejemplo de cuerpo de solicitud
{
"model": "xai/grok-imagine-image-quality/edit"
}Schema de salida
La API devuelve una respuesta de predicción con las URL de salida generadas.
Ejemplo de respuesta
{
"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 integra más de 300 modelos de IA directamente en tu asistente de codificación con IA. Un solo comando para instalar y luego usa lenguaje natural para generar imágenes, videos y chatear con LLM.
Clientes compatibles
Instalar
npx skills add AtlasCloudAI/atlas-cloud-skillsConfigurar clave de API
Obtén tu clave de API desde el panel de Atlas Cloud y configúrala como variable de entorno.
export ATLASCLOUD_API_KEY="your-api-key-here"Funcionalidades
Una vez instalado, puedes usar lenguaje natural en tu asistente de IA para acceder a todos los modelos de Atlas Cloud.
MCP Server
Atlas Cloud MCP Server conecta tu IDE con más de 300 modelos de IA a través del Model Context Protocol. Funciona con cualquier cliente compatible con MCP.
Clientes compatibles
Instalar
npx -y atlascloud-mcpConfiguración
Agrega la siguiente configuración al archivo de configuración de MCP de tu IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Herramientas disponibles
API Schema
Schema no disponibleSin ejemplos disponibles
Por favor inicia sesión para ver el historial de solicitudes
Necesitas iniciar sesión para acceder al historial de solicitudes del modelo.
Iniciar Sesión1. 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-imagexai/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/editvariant 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_imagesparameter (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 | 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-imagewell 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/editfor 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.
