Open and Advanced Large-Scale Image Generative Models.

Open and Advanced Large-Scale Image Generative Models.
Cada execução custará $0.023. Com $10 você pode executar aproximadamente 434 vezes.
Você pode continuar com:
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": "google/nano-banana/text-to-image-developer",
"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()Instale o pacote necessário para a sua linguagem de programação.
pip install requestsTodas as solicitações de API requerem autenticação por meio de uma chave de API. Você pode obter sua chave de API no painel do 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}"
}Nunca exponha sua chave de API em código do lado do cliente ou repositórios públicos. Use variáveis de ambiente ou um proxy de 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())Envie uma solicitação de geração assíncrona. A API retorna um ID de predição que você pode usar para verificar o status e obter o resultado.
/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": "google/nano-banana/text-to-image-developer",
"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']}"){
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Consulte o endpoint de predição para verificar o status atual da sua solicitação.
/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)processingA solicitação ainda está sendo processada.completedA geração está completa. As saídas estão disponíveis.succeededA geração foi bem-sucedida. As saídas estão disponíveis.failedA geração falhou. Verifique o campo de erro.{
"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"
}
}Envie arquivos para o armazenamento do Atlas Cloud e obtenha uma URL que pode ser usada nas suas solicitações de API. Use multipart/form-data para enviar.
/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
}
}Os seguintes parâmetros são aceitos no corpo da solicitação.
Nenhum parâmetro disponível.
{
"model": "google/nano-banana/text-to-image-developer"
}A API retorna uma resposta de predição com as URL de saída geradas.
{
"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"
}O Atlas Cloud Skills integra mais de 300 modelos de IA diretamente no seu assistente de codificação com IA. Um comando para instalar e depois use linguagem natural para gerar imagens, vídeos e conversar com LLM.
npx skills add AtlasCloudAI/atlas-cloud-skillsObtenha sua chave de API no painel do Atlas Cloud e defina-a como variável de ambiente.
export ATLASCLOUD_API_KEY="your-api-key-here"Após a instalação, você pode usar linguagem natural no seu assistente de IA para acessar todos os modelos do Atlas Cloud.
O Atlas Cloud MCP Server conecta seu IDE com mais de 300 modelos de IA através do Model Context Protocol. Funciona com qualquer cliente compatível com MCP.
npx -y atlascloud-mcpAdicione a seguinte configuração ao arquivo de configuração de MCP do seu IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Schema não disponívelVocê precisa fazer login para acessar o histórico de solicitações do modelo.
Fazer LoginSom e Imagem, Tudo em Uma Única Tomada
O modelo de IA revolucionário da ByteDance que gera áudio e vídeo perfeitamente sincronizados simultaneamente a partir de um único processo unificado. Experimente a verdadeira geração nativa áudio-visual com sincronização labial de precisão milimétrica em mais de 8 idiomas.
Explore curated prompt templates to unlock the full potential of Nano Banana AI. Click to copy any prompt and start creating immediately.

turn this photo into a character figure. Behind it, place a box with the character's image printed on it, and a computer showing the Blender modeling process on its screen. In front of the box, add a round plastic base with the character figure standing on it. set the scene indoors if possible

Generate a highly detailed photo of a girl cosplaying this illustration, at Comiket. Exactly replicate the same pose, body posture, hand gestures, facial expression, and camera framing as in the original illustration. Keep the same angle, perspective, and composition, without any deviation

Transform the the person in the photo into an action figure, styled after [CHARACTER_NAME] from [SOURCE / CONTEXT]. Next to the figure, display the accessories including [ITEM_1], [ITEM_2], and [ITEM_3]. On the top of the toy box, write "[BOX_LABEL_TOP]", and underneath it, "[BOX_LABEL_BOTTOM]". Place the box in a [BACKGROUND_SETTING] environment. Visualize this in a highly realistic way with attention to fine details.

Transform the person in the photo into the style of a Funko Pop figure packaging box, presented in an isometric perspective. Label the packaging with the title 'ZHOGUE'. Inside the box, showcase the figure based on the person in the photo, accompanied by their essential items (such as cosmetics, bags, or others). Next to the box, also display the actual figure itself outside of the packaging, rendered in a realistic and lifelike style.

turn this illustration of a perfume into a realistic version, Frosted glass bottle with a marble cap

Transform the person from image 1 into a Q-version character design based on the face shape from image 2

convert this photo into a architecture model. Behind the model, there should be a cardboard box with an image of the architecture from the photo on it. There should also be a computer, with the content on the computer screen showing the Blender modeling process of the figurine. In front of the cardboard box, place a cardstock and put the architecture model from the photo I provided on it. I hope the PVC material can be clearly presented. It would be even better if the background is indoors.
Optimized for speed with generation times under 2 seconds for most tasks, making it perfect for real-time applications and rapid prototyping workflows.
Leveraging Google's advanced AI architecture to produce highly detailed, photorealistic images with accurate lighting, textures, and compositions.
Revolutionary 2D-to-3D conversion capabilities enabling creation of multiple viewpoints from a single image, opening new possibilities for content creation.
Junte-se a cineastas, anunciantes e criadores de todo o mundo que estão revolucionando a criação de conteúdo de vídeo com a tecnologia inovadora do Seedance 1.5 Pro.
Nano Banana Text-to-Image is Google’s lightweight yet powerful AI image generation model, built for creators who need fast, high-quality visuals from simple text prompts. It transforms words into expressive, realistic images with remarkable clarity, composition, and style diversity — all within seconds.
Difference to Nano Banana Text-to-Image: This model is cheaper and less stable than the version of Nano Banana Text-to-Image.
Try the New Version of Nano Banana!
Instant Image Creation Generate beautiful, coherent visuals from just a short text prompt — no design skills required.
Versatile Visual Styles Supports realistic, illustrative, anime, and painterly outputs, adapting naturally to your creative intent.
Accurate Text-to-Scene Understanding Accurately interprets subjects, backgrounds, and object relationships to create contextually correct compositions.
Fast & Efficient Optimized for quick turnaround and minimal compute cost, perfect for rapid prototyping and social content.
Clean, Balanced Lighting Produces visually appealing results without overexposure or unnatural shadows — ideal for portraits, landscapes, and product imagery.
Input: text prompt
Output: high-quality image (JPEG/PNG/WEBP)
Supports multiple aspect ratios and output format
Compatible with descriptive prompts such as:
Please ensure your prompts comply with Google’s Safety Guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.
Join the Discord community for the latest model updates, prompts, and support.