youchuan/v8.1/blend

Imagen a Imagen

Youchuan V8.1 Blend API by MIDJOURNEY

youchuan/v8.1/blend

Blend

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

Entrada

Cargando configuración de parámetros...

Salida

Inactivo

Las imágenes generadas se mostrarán aquí

Configura los parámetros y haz clic en ejecutar para comenzar a generar

Cada ejecución costará $0.086. Con $10 puedes ejecutar aproximadamente 116 veces.

Puedes continuar con:

Imagen a Video Imagen a Imagen

Parámetros

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": "youchuan/v8.1/blend",
    "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 de dependencias necesario.

pip install requests

Autenticación

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}"
}

Mantén tu clave de API segura

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.

POST/api/v1/model/generateImage

Cuerpo 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": "youchuan/v8.1/blend",
    "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']}")

Respuesta

{
  "code": 200,
  "data": {
    "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.

GET/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.

POST/api/v1/model/uploadMedia

Ejemplo 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.

Total: 0Obligatorio: 0Opcional: 0

No hay parámetros disponibles.

Ejemplo de cuerpo de solicitud

{
  "model": "youchuan/v8.1/blend"
}

Schema de salida

La API devuelve una respuesta de predicción con las URL de salida generadas.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for image generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

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

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ clientes compatibles

Instalar

npx skills add AtlasCloudAI/atlas-cloud-skills

Configurar 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.

Generación de imágenesGenera imágenes con modelos como Nano Banana 2, Z-Image y más.

Creación de videosCrea videos a partir de texto o imágenes con Kling, Vidu, Veo, etc.

Chat con LLMChatea con Qwen, DeepSeek y otros modelos de lenguaje de gran escala.

Carga de mediosSube archivos locales para flujos de trabajo de edición de imágenes e imagen a video.

Más información

github.com/AtlasCloudAI/atlas-cloud-skills

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

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ clientes compatibles

Instalar

npx -y atlascloud-mcp

Configuración

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

atlas_generate_imageGenera imágenes a partir de indicaciones de texto.

atlas_generate_videoCrea videos a partir de texto o imágenes.

atlas_chatChatea con modelos de lenguaje de gran escala.

atlas_list_modelsExplora más de 300 modelos de IA disponibles.

atlas_quick_generateCreación de contenido en un solo paso con selección automática de modelo.

atlas_upload_mediaSube archivos locales para flujos de trabajo de API.

Más información

github.com/AtlasCloudAI/mcp-server

API Schema

Schema no disponible

Sin ejemplos disponibles

Cargando...

1. Introduction

About Youchuan (悠船): "Youchuan V8.1" is Midjourney's V8.1 model delivered through Youchuan (悠船), Midjourney's officially authorized platform in China — self-branded "Midjourney 中国实验室" (Midjourney China Lab) and operated by Xiaochuan Creative (Shanghai) Network Technology Co., Ltd. (小船创意). Youchuan is a legitimate, licensed distributor of Midjourney's models (not a third-party clone): it offers the same Midjourney technology, localized for the Chinese market with priority access to new model versions.

Youchuan V8.1 Blend (youchuan/v8.1/blend) merges two to five input images into four new images that fuse the concepts, subjects, and styles of the originals. It is Midjourney's tool for combining multiple visual ideas into a single coherent result — for example, fusing a character with an environment, or mixing the palette of one image with the composition of another.

It is part of the Youchuan V8.1 family exposed through this API:

youchuan/v8.1/text-to-image — generate from a text prompt
youchuan/v8.1/image-to-image — generate guided by one input image
youchuan/v8.1/blend — fuse 2–5 images (this model)
youchuan/v8.1/style-transfer — restyle an image, preserving composition
youchuan/v8.1/remove-background — isolate the subject on transparency
youchuan/v8.1/image-to-video — animate an image into a short clip

Youchuan V8.1 is built by Midjourney, Inc., an independent, self-funded San Francisco research lab founded in August 2021 by David Holz. V8.1 is the company's fastest model to date and produces high-aesthetic imagery at native 2K resolution.

2. Key Features & Innovations

Multi-image fusion: Combine 2–5 images so their concepts and styles merge into a new creative expression.
Optional guiding prompt: Midjourney's native /blend command is image-only, but this endpoint runs through the image-prompt pathway, so you may add an optional text prompt to nudge the fusion toward a theme. Omit it for a pure visual blend.
Native 2K HD: With hd enabled, results render directly at 2048px without a separate upscaling pass.
~4–5× faster generation than earlier Midjourney versions (Midjourney-stated).
Aesthetic controls: stylize, chaos, and weird tune how strongly the house aesthetic, variety, and unconventionality are applied to the blend.
Four results per request: Each task returns a 4-image grid of fused candidates.

3. Parameters & Usage

Parameter	Description
`images` (required)	Array of 2 to 5 input image URLs (or uploads) to blend. Publicly reachable HTTPS URLs.
`prompt`	Optional text to steer the blend. Max 1024 characters. (Native Midjourney `/blend` takes no text; this endpoint accepts one via the image-prompt path.)
`aspect_ratio`	Output aspect ratio (e.g. `1:1`, `16:9`, `9:16`).
`hd`	Enable native 2K (2048px) generation.
`stylize`	Strength of Midjourney's default aesthetic (0–1000).
`chaos`	Variation/unpredictability across the four results (0–100).
`weird`	Unconventionality of the output (0–3000).
`quality`	Detail level; V8.1 supports `1` (default) or `4` (more detail, same price).
`seed`	Fixed seed for reproducible results.

Tips: For the most predictable results, use input images that share the aspect ratio you want in the output. Two or three images tend to blend more legibly than five. Each image contributes both subject and style, so balance your inputs accordingly.

4. Model Architecture & Technical Details

Youchuan V8.1 is a complete from-scratch rewrite of the company's image model, built on a GPU-native PyTorch stack (migrated from the earlier TPU infrastructure). The underlying generative approach is understood to be latent diffusion; Midjourney has not published a technical paper or model card, so the backbone, parameter count, and training data remain undisclosed. The defining methodology is a human-preference (RLHF-style) aesthetic tuning loop combined with per-user personalization. V8.1 was released on midjourney.com on April 30, 2026 and became the default Midjourney model on June 10, 2026.

Blend is a long-standing, model-version-independent Midjourney capability (the 2–5 image limit has been stable since the feature's introduction). The training dataset has never been disclosed and is the subject of active, unresolved copyright litigation — Disney Enterprises, Inc. v. Midjourney, Inc. (No. 2:25-cv-05275, C.D. Cal.), filed June 11, 2025 by a coalition of major studios including Disney, Marvel, Lucasfilm, Twentieth Century, Universal, and DreamWorks. Those infringement claims are allegations in pending litigation and have not been adjudicated.

5. Intended Use & Applications

Concept fusion: Merge a character, object, and setting into a single unified scene.
Style mashups: Combine the aesthetic of one image with the subject of another.
Mood & direction exploration: Blend several references to discover a combined look for a project.
Product in context: Fuse a product image with an environment or lifestyle reference.

To combine images while keeping precise control over a single reference, use youchuan/v8.1/image-to-image; to merge more than five images, run multiple blends or use image-to-image with a curated reference.

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Youchuan V8.1 Blend API by MIDJOURNEY

Entrada

Salida

Parámetros

Ejemplo de código

Instalar

Autenticación

Encabezados HTTP

Enviar una solicitud

Enviar una solicitud

Cuerpo de la solicitud

Respuesta

Verificar estado

Ejemplo de polling

Valores de estado

Respuesta completada

Subir archivos

Ejemplo de carga

Respuesta

Schema de entrada

Ejemplo de cuerpo de solicitud

Schema de salida

Ejemplo de respuesta

Atlas Cloud Skills

Clientes compatibles

Instalar

Configurar clave de API

Funcionalidades

MCP Server

Clientes compatibles

Instalar

Configuración

Herramientas disponibles

API Schema

1. Introduction

2. Key Features & Innovations

3. Parameters & Usage

4. Model Architecture & Technical Details

5. Intended Use & Applications

Explorar Modelos Similares

Youchuan V8.1 Remove Background

Youchuan V8.1 Style Transfer

Youchuan V8.1 Image-to-Image

Youchuan V8.1 Text-to-Image

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Openai GPT Image 2 Edit

GPT Image 2 Developer Edit

GPT Image 2 Developer Text-to-Image

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

MAI-Image-2.5 Edit

MAI-Image-2.5 Text-to-image

Seed3D 2.0 Image-to-3D

Una sola API para toda la IA multimedia.

Join our Discord community

Entrada

Salida

Parámetros

Ejemplo de código

Instalar

Autenticación

Encabezados HTTP

Enviar una solicitud

Enviar una solicitud

Cuerpo de la solicitud

Respuesta

Verificar estado

Ejemplo de polling

Valores de estado

Respuesta completada

Subir archivos

Ejemplo de carga

Respuesta

Schema de entrada

Ejemplo de cuerpo de solicitud

Schema de salida

Ejemplo de respuesta

Atlas Cloud Skills

Clientes compatibles

Instalar

Configurar clave de API