Generate & Edit

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Generate the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": "Generate an image of a modern building as a cyberpunk-style futuristic structure with neon lights and holographic elements"
        }
    ]
)

print(response.choices[0].message.content)
# >>> {"url": "https://.../image.jpg"}

from vlmrun.client import VLMRun
from pydantic import BaseModel, Field

# Define the response schema
class ImageGenerationResponse(BaseModel):
  url: str = Field(..., description="The URL of the generated image")

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Generate the image with structured output
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": "Generate an image of a modern building as a cyberpunk-style futuristic structure with neon lights and holographic elements"
        }
    ],
    response_format={"type": "json_schema", "schema": ImageGenerationResponse.model_json_schema()}
)

# Validate the response
result = ImageGenerationResponse.model_validate_json(response.choices[0].message.content)
# >>> ImageGenerationResponse(url="https://.../image.jpg")

import { VlmRun } from "vlmrun";

const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

const response = await client.agent.completions.create({
  model: "vlmrun-orion-1:auto",
  messages: [
    {
      role: "user",
      content: "Generate an image of a modern building as a cyberpunk-style futuristic structure with neon lights and holographic elements"
    }
  ]
});

console.log(response.choices[0].message.content);

import { VlmRun } from "vlmrun";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

// Define the response schema with Zod
const ImageGenerationResponseSchema = z.object({
  url: z.string().describe("The URL of the generated image")
});

// Initialize the VLMRun client
const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

// Generate the image with structured output
const response = await client.agent.completions.create({
  model: "vlmrun-orion-1:auto",
  messages: [
    {
      role: "user",
      content: "Generate an image of a modern building as a cyberpunk-style futuristic structure with neon lights and holographic elements"
    }
  ],
  response_format: {
    type: "json_schema",
    schema: zodToJsonSchema(ImageGenerationResponseSchema)
  }
});

const result = ImageGenerationResponseSchema.parse(JSON.parse(response.choices[0].message.content));

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Generate the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Transform the image of my dog on the right into a flying dog with superman cape, with majestic background"},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dog-cat.jpg", "detail": "auto"}}
            ]
        }
    ]
)

print(response.choices[0].message.content)
# >>> {"url": "https://.../image.jpg"}

from vlmrun.client import VLMRun
from pydantic import BaseModel, Field

# Define the response schema
class ImageGenerationResponse(BaseModel):
  url: str = Field(..., description="The URL of the generated image")

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Transform the image with structured output
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Transform the image of my dog on the right into a flying dog with superman cape, with majestic background"},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dog-cat.jpg", "detail": "auto"}}
            ]
        }
    ],
    response_format={"type": "json_schema", "schema": ImageGenerationResponse.model_json_schema()}
)

# Validate the response
result = ImageGenerationResponse.model_validate_json(response.choices[0].message.content)

import { VlmRun } from "vlmrun";

const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

const response = await client.agent.completions.create({
  model: "vlmrun-orion-1:auto",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Transform the image of my dog on the right into a flying dog with superman cape, with majestic background" },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dog-cat.jpg", detail: "auto" } }
      ]
    }
  ]
});

console.log(response.choices[0].message.content);

import { VlmRun } from "vlmrun";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

// Define the response schema with Zod
const ImageGenerationResponseSchema = z.object({
  url: z.string().describe("The URL of the generated image")
});

// Initialize the VLMRun client
const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

// Transform the image with structured output
const response = await client.agent.completions.create({
  model: "vlmrun-orion-1:auto",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Transform the image of my dog on the right into a flying dog with superman cape, with majestic background" },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dog-cat.jpg", detail: "auto" } }
      ]
    }
  ],
  response_format: {
    type: "json_schema",
    schema: zodToJsonSchema(ImageGenerationResponseSchema)
  }
});

const result = ImageGenerationResponseSchema.parse(JSON.parse(response.choices[0].message.content));

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Generate the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Given two images: the first with a dog in front holding a yellow ball, inpaint her driving the car shown in the second image."},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dogs.jpg", "detail": "auto"}},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg", "detail": "auto"}}
            ]
        }
    ]
)

print(response.choices[0].message.content)
# >>> {"url": "https://.../image.jpg"}

from vlmrun.client import VLMRun
from pydantic import BaseModel, Field

# Define the response schema
class ImageGenerationResponse(BaseModel):
  url: str = Field(..., description="The URL of the generated image")

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Inpaint the image with structured output
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Given two images: the first with a dog in front holding a yellow ball, inpaint her driving the car shown in the second image."},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dogs.jpg", "detail": "auto"}},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg", "detail": "auto"}}
            ]
        }
    ],
    response_format={"type": "json_schema", "schema": ImageGenerationResponse.model_json_schema()}
)

# Validate the response
result = ImageGenerationResponse.model_validate_json(response.choices[0].message.content)

import { VlmRun } from "vlmrun";

const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

const response = await client.agent.completions.create({
  model: "vlmrun-orion-1:auto",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Given two images: the first with a dog in front holding a yellow ball, inpaint her driving the car shown in the second image." },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dogs.jpg", detail: "auto" } },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg", detail: "auto" } }
      ]
    }
  ]
});

console.log(response.choices[0].message.content);

import { VlmRun } from "vlmrun";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

// Define the response schema with Zod
const ImageGenerationResponseSchema = z.object({
  url: z.string().describe("The URL of the generated image")
});

// Initialize the VLMRun client
const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

// Inpaint the image with structured output
const response = await client.agent.completions.create({
  model: "vlmrun-orion-1:auto",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Given two images: the first with a dog in front holding a yellow ball, inpaint her driving the car shown in the second image." },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dogs.jpg", detail: "auto" } },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg", detail: "auto" } }
      ]
    }
  ],
  response_format: {
    type: "json_schema",
    schema: zodToJsonSchema(ImageGenerationResponseSchema)
  }
});

const result = ImageGenerationResponseSchema.parse(JSON.parse(response.choices[0].message.content));

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Generate the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Given two images: the first with a Van Gogh painting, and the second with a city skyline at night, apply the Van Gogh painting style to the city skyline."},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/starry-night.jpg", "detail": "auto"}},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/sf-golden-gate.jpg", "detail": "auto"}}
            ]
        }
    ]
)

print(response.choices[0].message.content)
# >>> {"url": "https://.../image.jpg"}

from vlmrun.client import VLMRun
from pydantic import BaseModel, Field

# Define the response schema
class ImageGenerationResponse(BaseModel):
  url: str = Field(..., description="The URL of the generated image")

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Apply style transfer with structured output
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Given two images: the first with a Van Gogh painting, and the second with a city skyline at night, apply the Van Gogh painting style to the city skyline."},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/starry-night.jpg", "detail": "auto"}},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/sf-golden-gate.jpg", "detail": "auto"}}
            ]
        }
    ],
    response_format={"type": "json_schema", "schema": ImageGenerationResponse.model_json_schema()}
)

# Validate the response
result = ImageGenerationResponse.model_validate_json(response.choices[0].message.content)

import { VlmRun } from "vlmrun";

const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

const response = await client.agent.completions.create({
  model: "vlmrun-orion-1:auto",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Given two images: the first with a Van Gogh painting, and the second with a city skyline at night, apply the Van Gogh painting style to the city skyline." },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/starry-night.jpg", detail: "auto" } },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/sf-golden-gate.jpg", detail: "auto" } }
      ]
    }
  ]
});

console.log(response.choices[0].message.content);

import { VlmRun } from "vlmrun";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

// Define the response schema with Zod
const ImageGenerationResponseSchema = z.object({
  url: z.string().describe("The URL of the generated image")
});

// Initialize the VLMRun client
const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

// Apply style transfer with structured output
const response = await client.agent.completions.create({
  model: "vlmrun-orion-1:auto",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Given two images: the first with a Van Gogh painting, and the second with a city skyline at night, apply the Van Gogh painting style to the city skyline." },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/starry-night.jpg", detail: "auto" } },
        { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/sf-golden-gate.jpg", detail: "auto" } }
      ]
    }
  ],
  response_format: {
    type: "json_schema",
    schema: zodToJsonSchema(ImageGenerationResponseSchema)
  }
});

const result = ImageGenerationResponseSchema.parse(JSON.parse(response.choices[0].message.content));