hidream-o1-1.5/text-to-image

Text-zu-Bild

HiDream O1 1.5 Text-to-Image API

hidream-o1-1.5/text-to-image

Text-to-image

Eingabe

Prompt *

Image size

Num inference steps

Guidance scale

Output format

Ausgabe

Inaktiv

Ihre generierten Bilder erscheinen hier

Konfigurieren Sie Parameter und klicken Sie auf Ausführen, um mit der Generierung zu beginnen

Jede Ausführung kostet $0.044. Für $10 können Sie ca. 227 Mal ausführen.

Sie können fortfahren mit:

Bild zu Video Bild zu Bild

Parameter

Codebeispiel
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": "hidream-o1-1.5/text-to-image",  # Required. model name
    "prompt": "A beautiful landscape with mountains and lake",  # Required. The text prompt describing the image to generate
    "reference_image_urls": [],  # Reference image URLs for image-to-image generation
    "image_size": "landscape_4_3",  # Output image size. options: square_hd | square | portrait_3_4 | portrait_9_16 | landscape_4_3 | landscape_16_9
    "num_inference_steps": 50,  # Number of denoising steps. (min: 1, max: 100)
    "guidance_scale": 5,  # Classifier-free guidance scale. (min: 1, max: 20)
    "output_format": "png",  # Output image format. options: png | jpeg | webp
}

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()

Installieren

Installieren Sie das erforderliche Paket für Ihre Programmiersprache.

pip install requests

Authentifizierung

Alle API-Anfragen erfordern eine Authentifizierung über einen API-Schlüssel. Sie können Ihren API-Schlüssel über das Atlas Cloud Dashboard erhalten.

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

HTTP-Header

import os

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

Schützen Sie Ihren API-Schlüssel

Geben Sie Ihren API-Schlüssel niemals in clientseitigem Code oder öffentlichen Repositories preis. Verwenden Sie stattdessen Umgebungsvariablen oder einen Backend-Proxy.

Anfrage senden

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())

Anfrage senden

Senden Sie eine asynchrone Generierungsanfrage. Die API gibt eine Vorhersage-ID zurück, mit der Sie den Status prüfen und das Ergebnis abrufen können.

POST/api/v1/model/generateImage

Anfragekörper

import requests

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

data = {
    "model": "hidream-o1-1.5/text-to-image",
    "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']}")

Antwort

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

Status prüfen

Fragen Sie den Vorhersage-Endpunkt ab, um den aktuellen Status Ihrer Anfrage zu überprüfen.

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

Abfrage-Beispiel

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)

Statuswerte

processingDie Anfrage wird noch verarbeitet.

completedDie Generierung ist abgeschlossen. Ergebnisse sind verfügbar.

succeededDie Generierung war erfolgreich. Ergebnisse sind verfügbar.

failedDie Generierung ist fehlgeschlagen. Überprüfen Sie das Fehlerfeld.

Abgeschlossene Antwort

{
  "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"
  }
}

Dateien hochladen

Laden Sie Dateien in den Atlas Cloud Speicher hoch und erhalten Sie eine URL, die Sie in Ihren API-Anfragen verwenden können. Verwenden Sie multipart/form-data zum Hochladen.

POST/api/v1/model/uploadMedia

Upload-Beispiel

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}")

Antwort

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

Eingabe-Schema

Die folgenden Parameter werden im Anfragekörper akzeptiert.

Gesamt: 7Erforderlich: 2Optional: 5

modelstringrequired

model name

Default: "hidream-o1-1.5/text-to-image"

promptstringrequired

The text prompt describing the image to generate. Maximum 2,500 characters.

reference_image_urlsarray[string]

Reference image URLs for image-to-image generation. Use none for text-to-image, one URL for image editing, or multiple URLs for subject-driven personalization. Accepts public URLs.

image_sizestring

Output image size. Choose a preset or specify custom dimensions.

Default: "landscape_4_3"

square_hdsquareportrait_3_4portrait_9_16landscape_4_3landscape_16_9

num_inference_stepsinteger

Number of denoising steps. Higher values improve quality but increase generation time.

Default: 50Min: 1Max: 100

guidance_scalenumber

Classifier-free guidance scale. Higher values make the output follow the prompt more closely.

Default: 5Min: 1Max: 20

output_formatstring

Output image format.

Default: "png"

pngjpegwebp

Beispiel-Anfragekörper

{
  "model": "hidream-o1-1.5/text-to-image",
  "prompt": "A beautiful landscape",
  "image_size": "landscape_4_3",
  "num_inference_steps": 50,
  "guidance_scale": 5,
  "output_format": "png"
}

Ausgabe-Schema

Die API gibt eine Vorhersage-Antwort mit den generierten Ausgabe-URLs zurück.

modelstring

model name

Default: "hidream-o1-1.5/text-to-image"

created_atstring

ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

idstring

Unique identifier for the prediction.

outputsarray

Array of URLs to the generated images. Null when status is not completed.

statusstring

Status of the task: created, processing, completed, or failed.

Beispielantwort

{
  "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 integriert über 400 KI-Modelle direkt in Ihren KI-Programmierassistenten. Ein Befehl zur Installation, dann generieren Sie per natürlicher Sprache Bilder und Videos und chatten mit LLMs.

Unterstützte Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ unterstützte clients

Installieren

npx skills add AtlasCloudAI/atlas-cloud-skills

API-Schlüssel einrichten

Erhalten Sie Ihren API-Schlüssel über das Atlas Cloud Dashboard und setzen Sie ihn als Umgebungsvariable.

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

Funktionen

Nach der Installation können Sie natürliche Sprache in Ihrem KI-Assistenten verwenden, um auf alle Atlas Cloud Modelle zuzugreifen.

BildgenerierungGenerieren Sie Bilder mit Modellen wie Nano Banana 2, Z-Image und mehr.

VideoerstellungErstellen Sie Videos aus Text oder Bildern mit Kling, Vidu, Veo usw.

LLM-ChatChatten Sie mit Qwen, DeepSeek und anderen großen Sprachmodellen.

Medien-UploadLaden Sie lokale Dateien für Bildbearbeitung und Bild-zu-Video-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/atlas-cloud-skills

MCP-Server

Der Atlas Cloud MCP-Server verbindet Ihre IDE mit über 400 KI-Modellen über das Model Context Protocol. Funktioniert mit jedem MCP-kompatiblen Client.

Unterstützte Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ unterstützte clients

Installieren

npx -y atlascloud-mcp

Konfiguration

Fügen Sie die folgende Konfiguration zur MCP-Einstellungsdatei Ihrer IDE hinzu.

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

Verfügbare Werkzeuge

atlas_generate_imageGenerieren Sie Bilder aus Textbeschreibungen.

atlas_generate_videoErstellen Sie Videos aus Text oder Bildern.

atlas_chatChatten Sie mit großen Sprachmodellen.

atlas_list_modelsDurchsuchen Sie über 400 verfügbare KI-Modelle.

atlas_quick_generateInhaltserstellung in einem Schritt mit automatischer Modellauswahl.

atlas_upload_mediaLaden Sie lokale Dateien für API-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/mcp-server

API-Schema

{
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        },
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "hidream-o1-1.5/text-to-image"
          },
          "prompt": {
            "description": "The text prompt describing the image to generate. Maximum 2,500 characters.",
            "type": "string",
            "minLength": 1,
            "maxLength": 2500
          },
          "reference_image_urls": {
            "description": "Reference image URLs for image-to-image generation. Use none for text-to-image, one URL for image editing, or multiple URLs for subject-driven personalization. Accepts public URLs.",
            "type": "array",
            "items": {
              "type": "string"
            },
            "x-ui-component": "uploaders"
          },
          "image_size": {
            "description": "Output image size. Choose a preset or specify custom dimensions.",
            "type": "string",
            "default": "landscape_4_3",
            "enum": [
              "square_hd",
              "square",
              "portrait_3_4",
              "portrait_9_16",
              "landscape_4_3",
              "landscape_16_9"
            ],
            "x-ui-component": "select",
            "x-enum-labels": {
              "square_hd": "Square HD (1024×1024)",
              "square": "Square (512×512)",
              "portrait_3_4": "Portrait 3:4",
              "portrait_9_16": "Portrait 9:16",
              "landscape_4_3": "Landscape 4:3",
              "landscape_16_9": "Landscape 16:9"
            }
          },
          "num_inference_steps": {
            "description": "Number of denoising steps. Higher values improve quality but increase generation time.",
            "type": "integer",
            "default": 50,
            "minimum": 1,
            "maximum": 100
          },
          "guidance_scale": {
            "description": "Classifier-free guidance scale. Higher values make the output follow the prompt more closely.",
            "type": "number",
            "default": 5,
            "minimum": 1,
            "maximum": 20
          },
          "output_format": {
            "description": "Output image format.",
            "type": "string",
            "default": "png",
            "enum": [
              "png",
              "jpeg",
              "webp"
            ],
            "x-ui-component": "select"
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "image_size",
          "num_inference_steps",
          "guidance_scale",
          "output_format"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "hidream-o1-1.5/text-to-image"
          },
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., \"2023-04-01T12:34:56.789Z\").",
            "format": "date-time",
            "type": "string"
          },
          "has_nsfw_contents": {
            "description": "Array of boolean values indicating NSFW detection for each output.",
            "items": {
              "type": "boolean"
            },
            "type": "array"
          },
          "id": {
            "description": "Unique identifier for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated images. Null when status is not completed.",
            "items": {
              "type": "string"
            },
            "nullable": true,
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  }
}

LLM-freundliche Prompt-Vorlage

# hidream-o1-1.5/text-to-image

## 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**: `hidream-o1-1.5/text-to-image`


## 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: `"hidream-o1-1.5/text-to-image"`

- **`prompt`** (`string`, _required_):
  The text prompt describing the image to generate. Maximum 2,500 characters.

- **`image_size`** (`string`, _optional_):
  Output image size. Choose a preset or specify custom dimensions.
  - Default: `"landscape_4_3"`
  - Options: "square_hd", "square", "portrait_3_4", "portrait_9_16", "landscape_4_3", "landscape_16_9"

- **`num_inference_steps`** (`integer`, _optional_):
  Number of denoising steps. Higher values improve quality but increase generation time.
  - Default: `50`
  - Min: 1
  - Max: 100

- **`guidance_scale`** (`number`, _optional_):
  Classifier-free guidance scale. Higher values make the output follow the prompt more closely.
  - Default: `5`
  - Min: 1
  - Max: 20

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



**Required Parameters Example**:

```json
{
  "model": "hidream-o1-1.5/text-to-image",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "hidream-o1-1.5/text-to-image",
  "prompt": "",
  "image_size": "landscape_4_3",
  "num_inference_steps": 50,
  "guidance_scale": 5,
  "output_format": "png"
}
```


### Output Schema

The API returns the following output format:


- **`model`** (`string`, _optional_):
  model name
  - Default: `"hidream-o1-1.5/text-to-image"`

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.

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

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated images. Null when status is not completed.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "model": "hidream-o1-1.5/text-to-image",
  "created_at": "",
  "has_nsfw_contents": [],
  "id": "",
  "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": "hidream-o1-1.5/text-to-image",
  "prompt": "",
  "image_size": "landscape_4_3",
  "num_inference_steps": 50,
  "guidance_scale": 5,
  "output_format": "png"
}'

# 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/hidream-o1-1.5/text-to-image)

A small bird carrying a banana flies through the ruins of an ancient stone bridge suspended over a deep canyon, vines and mist swallowing its broken arches. The journey feels like a forgotten legend being rewritten in motion. Cinematic storytelling, epic scale, moody atmosphere, volumetric fog, ultra-detailed, film still, 35mm anamorphic lens.

Wird geladen...

HiDream O1 Image

HiDream O1 Image is a state-of-the-art image generation model by HiDream AI, supporting text-to-image, image editing, and subject-driven personalization in a single unified interface. With strong prompt fidelity, flexible aspect ratio control, and multi-reference personalization, HiDream O1 delivers high-quality creative output for professional and consumer use cases alike.

🌟 Key Features

✏️ Text-to-Image

Generate high-resolution images from detailed text prompts with strong semantic understanding and photorealistic quality.

🖼 Image Editing

Provide a single reference image to edit, transform, or stylize it while preserving the original composition and subject identity.

🎭 Subject-Driven Personalization

Supply multiple reference images to drive subject-consistent generation across varied scenes, styles, and contexts.

📐 Flexible Output Sizes

Choose from six standard presets — square HD, portrait, landscape — or match the reference image's original aspect ratio automatically.

🔁 Reproducible Results

Pin a seed value to reproduce or iterate on a specific generation, enabling systematic prompt refinement.

⚙️ Parameters

Parameter	Required	Description
`prompt`	✅	Text description of the image to generate. Maximum 5,000 characters.
`image_size`	❌	Output size preset: `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3` (default), `landscape_16_9`.
`num_inference_steps`	❌	Denoising steps (1–100, default: 50). Higher values improve quality but increase time.
`guidance_scale`	❌	Prompt adherence strength (1.0–20.0, default: 5.0). Higher values follow the prompt more strictly.
`output_format`	❌	Output format: `png` (default), `jpeg`, or `webp`.

💲 Pricing

Billing Standard	Price
Per image	$0.044

💡 Prompt Tips

A lone astronaut standing on a rust-colored alien desert, vast canyon in the background, golden hour lighting, cinematic composition, photorealistic.

Be specific about subject, environment, lighting, and camera angle for best results.
For image editing, describe what you want changed while referencing the original subject.
For personalization, provide 2–4 reference images from different angles or contexts.
Use guidance_scale between 4–7 for balanced creativity; go higher (8–12) when strict prompt adherence is needed.
Fix seed when iterating on a prompt to isolate the effect of each change.

🎯 Use Cases

Creative Illustration — Generate concept art, characters, and environments from descriptive text prompts.
Product Visualization — Transform product photos into styled marketing visuals with image editing mode.
Brand Personalization — Use multi-reference personalization to generate consistent subject appearances across campaigns.
Social Media Content — Quickly produce scroll-stopping visuals in portrait or square formats for any platform.
Rapid Prototyping — Iterate on creative directions at scale before committing to production assets.
Style Transfer — Apply artistic styles to existing images while preserving key compositional elements.

🔄 Generation Modes

Mode	`reference_image_urls`	Description
Text-to-Image	Not provided	Pure text-driven image generation
Image Editing	1 URL	Edit or stylize a single reference image
Personalization	2+ URLs	Subject-consistent generation across scenes

📝 Notes

prompt is the only required field (besides model).
reference_image_urls accepts publicly accessible image URLs.
keep_original_aspect only applies when reference_image_urls contains exactly one image.
Task status values: created, processing, completed, failed.
Generated image URLs are returned in outputs once status is completed.

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.

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.

MAI-Image-2.5 Edit

Microsoft's flagship image-to-image editing model, enabling precise, controllable edits to existing images through natural language instructions.

MAI-Image-2.5 Text-to-image

Microsoft's flagship text-to-image generation model, designed to create high-quality, visually rich images from natural language prompts.

Youchuan V8.1 Remove Background

Youchuan automatically removes the background from an input image, returning one transparent-background result.

Youchuan V8.1 Style Transfer

Youchuan retexture changes the artistic style of an input image while preserving its composition, returning four restyled results.

Youchuan V8.1 Blend

Youchuan V8.1 blends two to five input images into four fused results, with an optional guiding prompt and native 2K HD.

Youchuan V8.1 Image-to-Image

Youchuan V8.1 re-imagines an input image guided by a text prompt, returning four variations. Supports native 2K HD, style reference, and aspect-ratio / stylize / chaos / weird controls.

Seed3D 2.0 Image-to-3D

ByteDance Seed3D 2.0 — generates a textured, PBR-shaded 3D model (glb/obj/usd/usdz) from a single input image. Returns a downloadable .zip archive containing the 3D file.

IMAGE-TO-3D

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Eine API für alle Media-KI.

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HiDream O1 1.5 Text-to-Image API

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HiDream O1 Image

🌟 Key Features

✏️ Text-to-Image

🖼 Image Editing

🎭 Subject-Driven Personalization

📐 Flexible Output Sizes

🔁 Reproducible Results

⚙️ Parameters

💲 Pricing

💡 Prompt Tips

🎯 Use Cases

🔄 Generation Modes

📝 Notes

Ähnliche Modelle Erkunden

Openai GPT Image 2 Text-to-Image

Openai GPT Image 2 Edit

GPT Image 2 Developer Edit

GPT Image 2 Developer Text-to-Image

Nano Banana 2 Lite Edit Developer

Nano Banana 2 Lite Text-to-Image Developer

Nano Banana 2 Lite Edit

Nano Banana 2 Lite Text-to-image

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

MAI-Image-2.5 Edit

MAI-Image-2.5 Text-to-image

Youchuan V8.1 Remove Background

Youchuan V8.1 Style Transfer

Youchuan V8.1 Blend

Youchuan V8.1 Image-to-Image

Seed3D 2.0 Image-to-3D

Eine API für alle Media-KI.

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Eingabe

Ausgabe

Parameter

Codebeispiel

Installieren

Authentifizierung

HTTP-Header

Anfrage senden

Anfrage senden

Anfragekörper

Antwort

Status prüfen

Abfrage-Beispiel