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.
La tua richiesta costerà $0.02 per esecuzione. Con $10 puoi eseguire questo modello circa 500 volte.
Ecco cosa puoi fare dopo:
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/edit", # Required. Model name
"prompt": "Render this as a pencil sketch with detailed shading.", # Required. Editing instruction
"image_urls": [], # Required. Source images
"num_images": 1, # Number of edited images to generate. options: 1 | 2 | 3 | 4
"aspect_ratio": "auto", # Aspect ratio of the edited image
"resolution": "1k", # Output resolution. options: 1k | 2k
"enable_base64_output": False, # If enabled, the output will be encoded into a BASE64 string instead of a 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()Installa il pacchetto di dipendenze richiesto.
pip install requestsTutte le richieste API richiedono l'autenticazione tramite una chiave API. Puoi ottenere la tua chiave API dalla dashboard di Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Non esporre mai la tua chiave API nel codice lato client o nei repository pubblici. Utilizza invece variabili d'ambiente o un proxy backend.
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())Invia una richiesta di generazione asincrona. L'API restituisce un ID di previsione che puoi usare per controllare lo stato e recuperare il risultato.
/api/v1/model/generateImageimport 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/edit",
"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']}"){
"code": 200,
"data": {
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}
}Interroga l'endpoint di previsione per verificare lo stato attuale della tua richiesta.
/api/v1/model/prediction/{prediction_id}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)processingLa richiesta è ancora in fase di elaborazione.completedGenerazione completata. Gli output sono disponibili.succeededGenerazione riuscita. Gli output sono disponibili.failedLa generazione è fallita. Controlla il campo errore.{
"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"
}
}Carica file nello storage Atlas Cloud e ottieni un URL utilizzabile nelle tue richieste API. Usa multipart/form-data per il caricamento.
/api/v1/model/uploadMediaimport 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}"){
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}I seguenti parametri sono accettati nel corpo della richiesta.
{
"model": "xai/grok-imagine-image/edit",
"prompt": "Render this as a pencil sketch with detailed shading.",
"image_urls": [
"https://example.com/file.jpg"
],
"num_images": 1,
"aspect_ratio": "auto",
"resolution": "1k",
"enable_base64_output": false
}L'API restituisce una risposta di previsione con gli URL degli output generati.
{
"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 integra oltre 400 modelli di IA direttamente nel tuo assistente di codifica IA. Un comando per installare, poi usa il linguaggio naturale per generare immagini, video e chattare con LLM.
npx skills add AtlasCloudAI/atlas-cloud-skillsOttieni la tua chiave API dalla dashboard di Atlas Cloud e impostala come variabile d'ambiente.
export ATLASCLOUD_API_KEY="your-api-key-here"Una volta installato, puoi usare il linguaggio naturale nel tuo assistente IA per accedere a tutti i modelli Atlas Cloud.
Il server MCP di Atlas Cloud collega il tuo IDE con oltre 400 modelli di IA tramite il Model Context Protocol. Funziona con qualsiasi client compatibile MCP.
npx -y atlascloud-mcpAggiungi la seguente configurazione al file delle impostazioni MCP del tuo IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}{
"info": {
"title": "AtlasCloud API",
"version": "1.0.0",
"description": "The AtlasCloud API."
},
"paths": {
"/api/v1/model/prediction/{request_id}": {
"get": {
"parameters": [
{
"in": "path",
"name": "request_id",
"required": true,
"schema": {
"description": "Request ID",
"type": "string"
}
}
],
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PredictionResponse"
}
}
},
"description": "Result of the request."
}
}
},
"x-api-name": "model_result"
},
"/api/v1/model/generateImage": {
"post": {
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Input"
}
}
},
"required": true
},
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PredictionResponse"
}
}
},
"description": "The request status."
}
}
},
"x-api-name": "model_run"
}
},
"openapi": "3.0.0",
"servers": [
{
"url": "https://api.atlascloud.ai"
}
],
"components": {
"schemas": {
"Input": {
"type": "object",
"required": [
"model",
"prompt",
"image_urls"
],
"properties": {
"model": {
"type": "string",
"description": "Model name.",
"default": "xai/grok-imagine-image/edit"
},
"prompt": {
"type": "string",
"default": "Render this as a pencil sketch with detailed shading.",
"description": "Editing instruction. For multi-image references, cite each input as <IMAGE_0>, <IMAGE_1>, ..."
},
"image_urls": {
"type": "array",
"description": "Source images. Each entry is either a public URL or a base64-encoded data URI (e.g. `data:image/png;base64,...`). At least one image is required.",
"items": {
"type": "string"
},
"minItems": 1,
"maxItems": 8
},
"num_images": {
"type": "integer",
"default": 1,
"enum": [
1,
2,
3,
4
],
"description": "Number of edited images to generate. Each output image is billed separately."
},
"aspect_ratio": {
"type": "string",
"default": "auto",
"enum": [
"auto",
"1:1",
"3:4",
"4:3",
"9:16",
"16:9",
"2:3",
"3:2",
"9:19.5",
"19.5:9",
"9:20",
"20:9",
"1:2",
"2:1"
],
"description": "Aspect ratio of the edited image. With a single source image, the output respects the input's aspect ratio regardless of this value. `auto` lets the model pick."
},
"resolution": {
"type": "string",
"default": "1k",
"enum": [
"1k",
"2k"
],
"description": "Output resolution. 1k = 1024x1024, 2k = 2048x2048."
},
"enable_base64_output": {
"type": "boolean",
"title": "Enable Output base64",
"default": false,
"disabled": true,
"description": "If enabled, the output will be encoded into a BASE64 string instead of a URL."
}
},
"x-order-properties": [
"model",
"prompt",
"image_urls",
"num_images",
"aspect_ratio",
"resolution",
"enable_base64_output"
]
},
"PredictionResponse": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique identifier for the prediction, the ID of the prediction to get."
},
"urls": {
"type": "object",
"description": "Object containing related API endpoints."
},
"model": {
"type": "string",
"description": "Model ID used for the prediction."
},
"status": {
"type": "string",
"description": "Status of the task: created, processing, completed, or failed."
},
"outputs": {
"type": "array",
"items": {
"type": "string"
},
"description": "Array of URLs to the edited images (empty when status is not completed)."
},
"created_at": {
"type": "string",
"format": "date-time",
"description": "ISO timestamp of when the request was created."
}
}
}
},
"securitySchemes": {
"apiKeyAuth": {
"in": "header",
"name": "Authorization",
"type": "apiKey"
}
}
}
}# xai/grok-imagine-image/edit
> 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.
## 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**: `xai/grok-imagine-image/edit`
## 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: `"xai/grok-imagine-image/edit"`
- **`prompt`** (`string`, _required_):
Editing instruction. For multi-image references, cite each input as <IMAGE_0>, <IMAGE_1>, ...
- Default: `"Render this as a pencil sketch with detailed shading."`
- **`image_urls`** (`array[string]`, _required_):
Source images. Each entry is either a public URL or a base64-encoded data URI (e.g. `data:image/png;base64,...`). At least one image is required.
- Min items: 1
- Max items: 8
- **`num_images`** (`integer`, _optional_):
Number of edited images to generate. Each output image is billed separately.
- Default: `1`
- Options: 1, 2, 3, 4
- **`aspect_ratio`** (`string`, _optional_):
Aspect ratio of the edited image. With a single source image, the output respects the input's aspect ratio regardless of this value. `auto` lets the model pick.
- Default: `"auto"`
- Options: "auto", "1:1", "3:4", "4:3", "9:16", "16:9", "2:3", "3:2", "9:19.5", "19.5:9", "9:20", "20:9", "1:2", "2:1"
- **`resolution`** (`string`, _optional_):
Output resolution. 1k = 1024x1024, 2k = 2048x2048.
- Default: `"1k"`
- Options: "1k", "2k"
- **`enable_base64_output`** (`boolean`, _optional_):
If enabled, the output will be encoded into a BASE64 string instead of a URL.
- Default: `false`
**Required Parameters Example**:
```json
{
"model": "xai/grok-imagine-image/edit",
"prompt": "Render this as a pencil sketch with detailed shading.",
"image_urls": [
""
]
}
```
**Full Example**:
```json
{
"model": "xai/grok-imagine-image/edit",
"prompt": "Render this as a pencil sketch with detailed shading.",
"image_urls": [
""
],
"num_images": 1,
"aspect_ratio": "auto",
"resolution": "1k",
"enable_base64_output": false
}
```
### Output Schema
The API returns the following output format:
- **`id`** (`string`, _optional_):
Unique identifier for the prediction, the ID of the prediction to get.
- **`urls`** (`object`, _optional_):
Object containing related API endpoints.
- **`model`** (`string`, _optional_):
Model ID used for the prediction.
- **`status`** (`string`, _optional_):
Status of the task: created, processing, completed, or failed.
- **`outputs`** (`array[string]`, _optional_):
Array of URLs to the edited images (empty when status is not completed).
- **`created_at`** (`string`, _optional_):
ISO timestamp of when the request was created.
**Example Response**:
```json
{
"id": "",
"urls": {},
"model": "",
"status": "",
"outputs": [
""
],
"created_at": ""
}
```
## 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": "xai/grok-imagine-image/edit",
"prompt": "Render this as a pencil sketch with detailed shading.",
"image_urls": [
""
],
"num_images": 1,
"aspect_ratio": "auto",
"resolution": "1k",
"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/xai/grok-imagine-image/edit)
Complete the entire arm, matching the style of the image.
Complete the entire arm, matching the style of the image.