nvidia/cosmos-3-super/text-to-image

tekst-do-obrazu

Nvidia Cosmos 3 Super Text-to-Image API

nvidia/cosmos-3-super/text-to-image

Text-to-image

Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs.

WEJŚCIE

Prompt *

Prompt Negatywny

Num inference steps

Guidance scale

Output format

Seed

WYJŚCIE

Bezczynny

Twoje wygenerowane obrazy pojawią się tutaj

Skonfiguruj ustawienia i kliknij Uruchom, aby rozpocząć

Każde uruchomienie będzie kosztować $0.044. Za $10 możesz uruchomić ten model około 227 razy.

Co możesz zrobić dalej:

Obraz do Wideo Obraz do Obrazu

Parametry

Przykład kodu
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": "nvidia/cosmos-3-super/text-to-image",  # Required. model name
    "prompt": "A beautiful landscape with mountains and lake",  # Required. The text prompt describing the image to generate
    "negative_prompt": "",  # Text describing content to exclude from the generated image
    "image_size": "landscape_16_9",  # Output image resolution
    "num_inference_steps": 28,  # Number of denoising steps. (min: 1, max: 50)
    "guidance_scale": 4,  # Classifier-free guidance scale. (min: 1, max: 20)
    "output_format": "jpeg",  # Output image format. options: jpeg | png
    "seed": 0,  # Random seed for reproducibility. (min: 0)
    "enable_safety_checker": False,  # Enable or disable the safety checker
}

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()

Instalacja

Zainstaluj wymagany pakiet dla swojego języka programowania.

pip install requests

Uwierzytelnianie

Wszystkie żądania API wymagają uwierzytelnienia za pomocą klucza API. Klucz API możesz uzyskać z panelu Atlas Cloud.

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

Nagłówki HTTP

import os

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

Chroń swój klucz API

Nigdy nie ujawniaj swojego klucza API w kodzie po stronie klienta ani w publicznych repozytoriach. Zamiast tego użyj zmiennych środowiskowych lub proxy backendowego.

Wyślij żądanie

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())

Wyślij żądanie

Wyślij asynchroniczne żądanie generowania. API zwróci identyfikator predykcji, którego możesz użyć do sprawdzania statusu i pobierania wyniku.

POST/api/v1/model/generateImage

Treść żądania

import requests

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

data = {
    "model": "nvidia/cosmos-3-super/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']}")

Odpowiedź

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

Sprawdź status

Odpytuj endpoint predykcji, aby sprawdzić bieżący status żądania.

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

Przykład odpytywania

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)

Wartości statusu

processingŻądanie jest wciąż przetwarzane.

completedGenerowanie zakończone. Wyniki są dostępne.

succeededGenerowanie powiodło się. Wyniki są dostępne.

failedGenerowanie nie powiodło się. Sprawdź pole błędu.

Odpowiedź ukończona

{
  "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"
  }
}

Prześlij pliki

Prześlij pliki do magazynu Atlas Cloud i uzyskaj URL, którego możesz użyć w swoich żądaniach API. Użyj multipart/form-data do przesyłania.

POST/api/v1/model/uploadMedia

Przykład przesyłania

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}")

Odpowiedź

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

Schema wejściowy

Następujące parametry są akceptowane w treści żądania.

Łącznie: 9Wymagane: 2Opcjonalne: 7

modelstringrequired

model name

Default: "nvidia/cosmos-3-super/text-to-image"

promptstringrequired

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

negative_promptstring

Text describing content to exclude from the generated image.

Default: ""

image_sizeunknown

Output image resolution. Use a preset size or specify custom width and height via an ImageSize object.

Default: "landscape_16_9"

num_inference_stepsinteger

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

Default: 28Min: 1Max: 50

guidance_scalenumber

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

Default: 4Min: 1Max: 20

output_formatstring

Output image format.

Default: "jpeg"

jpegpng

seedinteger

Random seed for reproducibility. Use the same seed and prompt to reproduce results.

Min: 0

enable_safety_checkerboolean

Enable or disable the safety checker. Disabling the safety checker may result in NSFW content.

Default: false

Przykładowa treść żądania

{
  "model": "nvidia/cosmos-3-super/text-to-image",
  "prompt": "A beautiful landscape",
  "negative_prompt": "",
  "image_size": "landscape_16_9",
  "num_inference_steps": 28,
  "guidance_scale": 4,
  "output_format": "jpeg",
  "enable_safety_checker": false
}

Schema wyjściowy

API zwraca odpowiedź predykcji z URL-ami wygenerowanych wyników.

modelstring

model name

Default: "nvidia/cosmos-3-super/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.

Przykładowa odpowiedź

{
  "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 integruje ponad 400 modeli AI bezpośrednio z Twoim asystentem kodowania AI. Jedno polecenie do instalacji, a następnie używaj języka naturalnego do generowania obrazów, filmów i rozmów z LLM.

Obsługiwani klienci

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ obsługiwani klienci

Instalacja

npx skills add AtlasCloudAI/atlas-cloud-skills

Skonfiguruj klucz API

Uzyskaj klucz API z panelu Atlas Cloud i ustaw go jako zmienną środowiskową.

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

Możliwości

Po zainstalowaniu możesz używać języka naturalnego w swoim asystencie AI, aby uzyskać dostęp do wszystkich modeli Atlas Cloud.

Generowanie obrazówGeneruj obrazy za pomocą modeli takich jak Nano Banana 2, Z-Image i inne.

Tworzenie wideoTwórz filmy z tekstu lub obrazów za pomocą Kling, Vidu, Veo itp.

Chat LLMRozmawiaj z Qwen, DeepSeek i innymi dużymi modelami językowymi.

Przesyłanie mediówPrześlij lokalne pliki do edycji obrazów i przepływów pracy obraz-do-wideo.

Dowiedz się więcej

github.com/AtlasCloudAI/atlas-cloud-skills

Serwer MCP

Serwer MCP Atlas Cloud łączy Twoje IDE z ponad 400 modelami AI za pośrednictwem Model Context Protocol. Działa z każdym klientem kompatybilnym z MCP.

Obsługiwani klienci

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ obsługiwani klienci

Instalacja

npx -y atlascloud-mcp

Konfiguracja

Dodaj następującą konfigurację do pliku ustawień MCP w swoim IDE.

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

Dostępne narzędzia

atlas_generate_imageGeneruj obrazy z promptów tekstowych.

atlas_generate_videoTwórz filmy z tekstu lub obrazów.

atlas_chatRozmawiaj z dużymi modelami językowymi.

atlas_list_modelsPrzeglądaj ponad 400 dostępnych modeli AI.

atlas_quick_generateTworzenie treści w jednym kroku z automatycznym wyborem modelu.

atlas_upload_mediaPrześlij lokalne pliki do przepływów pracy API.

Dowiedz się więcej

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": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "nvidia/cosmos-3-super/text-to-image"
          },
          "prompt": {
            "description": "The text prompt describing the image to generate. Maximum 2,500 characters.",
            "type": "string",
            "minLength": 1,
            "maxLength": 2500
          },
          "negative_prompt": {
            "description": "Text describing content to exclude from the generated image.",
            "type": "string",
            "default": ""
          },
          "image_size": {
            "anyOf": [
              {
                "$ref": "#/components/schemas/ImageSize"
              },
              {
                "type": "string",
                "enum": [
                  "square_hd",
                  "square",
                  "portrait_4_3",
                  "portrait_16_9",
                  "landscape_4_3",
                  "landscape_16_9"
                ]
              }
            ],
            "description": "Output image resolution. Use a preset size or specify custom width and height via an ImageSize object.",
            "title": "Resolution",
            "default": "landscape_16_9",
            "x-ui-component": "select",
            "x-enum-labels": {
              "square_hd": "Square HD (1024×1024)",
              "square": "Square (512×512)",
              "portrait_4_3": "Portrait 4:3",
              "portrait_16_9": "Portrait 16:9",
              "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": 28,
            "minimum": 1,
            "maximum": 50
          },
          "guidance_scale": {
            "description": "Classifier-free guidance scale. Higher values make the output follow the prompt more closely.",
            "type": "number",
            "default": 4,
            "minimum": 1,
            "maximum": 20
          },
          "output_format": {
            "description": "Output image format.",
            "type": "string",
            "default": "jpeg",
            "enum": [
              "jpeg",
              "png"
            ],
            "x-ui-component": "select"
          },
          "seed": {
            "description": "Random seed for reproducibility. Use the same seed and prompt to reproduce results.",
            "type": "integer",
            "minimum": 0
          },
          "enable_safety_checker": {
            "description": "Enable or disable the safety checker. Disabling the safety checker may result in NSFW content.",
            "type": "boolean",
            "default": false
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "negative_prompt",
          "image_size",
          "num_inference_steps",
          "guidance_scale",
          "output_format",
          "seed"
        ]
      },
      "ImageSize": {
        "properties": {
          "width": {
            "type": "integer",
            "title": "Width",
            "description": "The width of the generated image.",
            "exclusiveMinimum": 0,
            "maximum": 14142,
            "default": 512
          },
          "height": {
            "type": "integer",
            "title": "Height",
            "description": "The height of the generated image.",
            "exclusiveMinimum": 0,
            "maximum": 14142,
            "default": 512
          }
        },
        "title": "ImageSize",
        "type": "object",
        "x-order-properties": [
          "width",
          "height"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "nvidia/cosmos-3-super/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"
      }
    }
  }
}

Szablon Prompt dla LLM

# nvidia/cosmos-3-super/text-to-image

> Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs.


## 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**: `nvidia/cosmos-3-super/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: `"nvidia/cosmos-3-super/text-to-image"`

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

- **`negative_prompt`** (`string`, _optional_):
  Text describing content to exclude from the generated image.
  - Default: `""`

- **`image_size`** (`string`, _optional_):
  Output image resolution. Use a preset size or specify custom width and height via an ImageSize object.
  - Default: `"landscape_16_9"`

- **`num_inference_steps`** (`integer`, _optional_):
  Number of denoising steps. Higher values improve quality but increase generation time.
  - Default: `28`
  - Min: 1
  - Max: 50

- **`guidance_scale`** (`number`, _optional_):
  Classifier-free guidance scale. Higher values make the output follow the prompt more closely.
  - Default: `4`
  - Min: 1
  - Max: 20

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

- **`seed`** (`integer`, _optional_):
  Random seed for reproducibility. Use the same seed and prompt to reproduce results.
  - Min: 0



**Required Parameters Example**:

```json
{
  "model": "nvidia/cosmos-3-super/text-to-image",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "nvidia/cosmos-3-super/text-to-image",
  "prompt": "",
  "negative_prompt": "",
  "image_size": "landscape_16_9",
  "num_inference_steps": 28,
  "guidance_scale": 4,
  "output_format": "jpeg",
  "seed": 0
}
```


### Output Schema

The API returns the following output format:


- **`model`** (`string`, _optional_):
  model name
  - Default: `"nvidia/cosmos-3-super/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": "nvidia/cosmos-3-super/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": "nvidia/cosmos-3-super/text-to-image",
  "prompt": "",
  "negative_prompt": "",
  "image_size": "landscape_16_9",
  "num_inference_steps": 28,
  "guidance_scale": 4,
  "output_format": "jpeg",
  "seed": 0
}'

# 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/nvidia/cosmos-3-super/text-to-image)

A symphony orchestra performing inside a grand European train station after the last train has departed. Empty platforms stretch into darkness while warm lantern light illuminates the musicians. Steam drifts through the air, creating a dreamlike cinematic atmosphere. Emotional storytelling, movie still, ultra realistic.

Ładowanie...

NVIDIA Cosmos 3 Super Text-to-Image

NVIDIA Cosmos 3 Super Text-to-Image is NVIDIA's flagship text-to-image model built on the Cosmos 3 architecture. Designed for high-fidelity physical world simulation, it delivers photorealistic images with strong prompt adherence, precise spatial reasoning, and support for negative prompting — making it well suited for both creative and technical generation tasks.

🌟 Key Features

🎨 Photorealistic Generation

Produces high-quality images grounded in physical realism, excelling at scenes with natural lighting, accurate materials, and spatial coherence.

📝 Strong Prompt Fidelity

Understands complex, multi-element prompts and accurately reflects subject, environment, style, and compositional instructions in the output.

🚫 Negative Prompting

Accepts a negative_prompt to explicitly exclude unwanted content, artifacts, or styles from the generated image.

📐 Flexible Output Sizes

Choose from six standard presets covering square, portrait, and landscape orientations to match any platform or use case.

🔁 Reproducible Results

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

⚙️ Parameters

Parameter	Required	Description
`prompt`	✅	Text description of the image to generate. Maximum 2,500 characters.
`negative_prompt`	❌	Content to exclude from the output (default: empty).
`image_size`	❌	Output size preset: `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` (default).
`num_inference_steps`	❌	Denoising steps (1–100, default: 28). Higher values improve quality but increase time.
`guidance_scale`	❌	Prompt adherence strength (1.0–20.0, default: 4.0). Higher values follow the prompt more strictly.
`output_format`	❌	Output format: `jpeg` (default) or `png`.
`seed`	❌	Random seed for reproducibility. Omit for random results.

💲 Pricing

Billing Standard	Price
Per image	$0.044

Billing is based on the number of images generated.

💡 Prompt Tips

A photorealistic close-up of hands shaping clay on a pottery wheel, warm studio lighting, shallow depth of field, 8K resolution.

Describe subject, environment, lighting, and camera perspective in a single coherent prompt.
Use negative_prompt to remove common artifacts: blurry, low quality, distorted, watermark, cartoon.
Keep guidance_scale between 3–5 for natural-looking outputs; increase to 7–10 for strict prompt adherence.
Use landscape_16_9 for cinematic or widescreen compositions.
Fix seed when iterating on prompt wording to isolate the effect of each change.

🎯 Use Cases

Creative Illustration — Generate concept art, characters, and environments from descriptive text prompts.
Product Visualization — Produce photorealistic product renders and lifestyle imagery from written descriptions.
Scene Prototyping — Rapidly visualize environment and set designs before committing to production.
Social Media Content — Create scroll-stopping visuals in any aspect ratio for any platform.
Research & Simulation — Leverage Cosmos's physical world grounding for technically accurate scene generation.
Rapid Iteration — Generate multiple candidates with varied seeds and prompts to explore creative directions quickly.

📝 Notes

prompt is the only required field (besides model).
output_format supports jpeg (smaller file size) and png (lossless).
Task status values: created, processing, completed, failed.
Generated image URLs are returned in outputs once status is completed.

NVIDIA Cosmos 3 Super Image-to-Video — Animate a reference image into a video clip with a text prompt.

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