Image to Image

Action ID: image_to_image

Description

Use AI to generate images from your imagination. Simply describe what you want to see, and watch the AI bring it to life.

Connection

Name	Description	Required	Category
PixelML Connection	The PixelML connection to call PixelML API.	True	pixelml

Input Schema

{
  "description": "Image to image node input.",
  "properties": {
    "source_image": {
      "description": "The image to use as the source for the image to image generation.",
      "title": "Source Image",
      "type": "string"
    },
    "prompt": {
      "description": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.",
      "title": "Prompt",
      "type": "string"
    },
    "prompt_2": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "A second prompt to guide the image generation.",
      "title": "Prompt 2"
    },
    "negative_prompt": {
      "description": "A blurb of text describing what you do not wish to see in the output image.",
      "title": "Negative Prompt",
      "type": "string"
    },
    "guidance_scale": {
      "default": 7.5,
      "description": "The guidance scale of the generated image.",
      "exclusiveMinimum": 0,
      "maximum": 15,
      "title": "Guidance scale",
      "type": "number"
    },
    "image_guidance_scale": {
      "default": 1.5,
      "description": "The guidance scale of the generated image.",
      "exclusiveMinimum": 0,
      "maximum": 15,
      "title": "Image Guidance Scale",
      "type": "number"
    },
    "strength": {
      "default": 0.8,
      "description": "The strength of the source image.",
      "exclusiveMinimum": 0,
      "maximum": 1,
      "title": "Strength",
      "type": "number"
    },
    "steps": {
      "default": 20,
      "description": "The number of steps to take in the generated image.",
      "exclusiveMinimum": 0,
      "maximum": 50,
      "title": "Steps",
      "type": "integer"
    },
    "batch_size": {
      "default": 1,
      "description": "The number of images to generate in a batch.",
      "exclusiveMinimum": 0,
      "maximum": 8,
      "title": "Batch size",
      "type": "integer"
    },
    "seed": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "The seed to use for the generated image.",
      "title": "Seed"
    },
    "width": {
      "default": 0,
      "description": "The width of the generated image.",
      "title": "Width",
      "type": "integer"
    },
    "height": {
      "default": 0,
      "description": "The height of the generated image.",
      "title": "Height",
      "type": "integer"
    }
  },
  "required": [
    "source_image",
    "prompt",
    "negative_prompt"
  ],
  "title": "ImageToImageNodeInput",
  "type": "object"
}

Output Schema

{
  "description": "Image to image node output.",
  "properties": {
    "images": {
      "items": {
        "type": "string"
      },
      "title": "Image output",
      "type": "array"
    }
  },
  "required": [
    "images"
  ],
  "title": "ImageToImageNodeOutput",
  "type": "object"
}

How It Works

This node transforms an existing image into a new variation using AI image generation. The source image serves as a visual reference, and your prompt guides the AI to modify, enhance, or completely reimagine it while maintaining compositional elements. The strength parameter controls how much the AI deviates from the source image (lower values preserve more of the original, higher values allow more creative freedom). The guidance_scale and image_guidance_scale parameters balance adherence to your text prompt versus the source image structure. You can generate multiple variations in a single request using batch_size.

Usage Examples

Example 1: Style Transfer on Product Photo

Input:

source_image: "https://example.com/products/chair-photo.jpg"
prompt: "modern minimalist chair, white background, professional product photography, studio lighting"
negative_prompt: "blurry, distorted, low quality, shadows"
strength: 0.6
guidance_scale: 7.5
batch_size: 1

Output:

images: ["https://storage.pixelml.com/img2img/styled-chair-001.jpg"]

Example 2: Artistic Transformation

Input:

source_image: "https://example.com/photos/landscape.jpg"
prompt: "oil painting style, impressionist art, vibrant colors, thick brush strokes, masterpiece"
negative_prompt: "photorealistic, digital, modern, pixelated"
strength: 0.8
guidance_scale: 9.0
image_guidance_scale: 1.5
steps: 30
batch_size: 3

Output:

images: [
  "https://storage.pixelml.com/img2img/painting-var1.jpg",
  "https://storage.pixelml.com/img2img/painting-var2.jpg",
  "https://storage.pixelml.com/img2img/painting-var3.jpg"
]

Example 3: Image Enhancement with Consistency

Input:

source_image: "https://example.com/sketches/character-sketch.jpg"
prompt: "detailed character illustration, anime style, colorful, high quality, professional digital art"
prompt_2: "full color rendering, cel shading, vibrant palette"
negative_prompt: "sketch, monochrome, unfinished, rough"
strength: 0.7
guidance_scale: 8.0
steps: 25
seed: 42
width: 1024
height: 1024

Output:

images: ["https://storage.pixelml.com/img2img/character-colored.jpg"]

Common Use Cases

• Product Variations: Generate multiple styled versions of product photos for A/B testing or different marketing channels

• Artistic Filters: Transform photos into different artistic styles (oil painting, watercolor, sketch, cartoon, etc.)

• Image Enhancement: Upscale and enhance low-quality images with AI while maintaining the original composition

• Background Replacement: Keep the main subject while changing backgrounds, lighting, or environmental elements

• Character Redesign: Modify character illustrations, outfits, poses, or art styles while preserving core identity

• Architectural Rendering: Transform architectural sketches into photorealistic renderings or different design styles

• Photo Restoration: Enhance old or damaged photos by generating improved versions based on the original

Error Handling

Error Type	Cause	Solution
Invalid Connection	PixelML connection not configured or expired	Verify your PixelML connection credentials and ensure the API key is valid
Source Image Error	source_image URL is invalid or inaccessible	Provide a valid, publicly accessible image URL in a supported format
Empty Prompt	No prompt provided for image generation	Provide a descriptive prompt that defines the desired output style and elements
Invalid Strength	Strength value outside 0-1 range	Set strength between 0.01 and 1.0 (lower preserves more of source image)
Invalid Guidance	guidance_scale or image_guidance_scale out of range	Keep guidance_scale and image_guidance_scale between 0.1 and 15
Invalid Steps	steps parameter exceeds maximum of 50	Set steps between 1 and 50 (20-30 is typically sufficient)
Batch Size Exceeded	batch_size greater than 8	Reduce batch_size to 8 or fewer images per generation
Generation Failed	AI model failed to generate image	Simplify your prompt, adjust strength/guidance parameters, or try different source image

Notes

• Strength Parameter: Controls transformation intensity. Use 0.3-0.5 for subtle changes, 0.6-0.7 for moderate transformation, 0.8-1.0 for major creative reimagining

• Guidance Scale: Higher values (8-12) follow your prompt more strictly, lower values (5-7) allow more AI creativity. Default 7.5 works well for most cases

• Image Guidance: Controls how much the composition follows the source image. Higher values preserve layout better, lower values allow more structural changes

• Steps: More steps (30-50) produce higher quality but take longer. 20-30 steps offer a good quality/speed balance for most uses

• Dual Prompts: Use prompt_2 to provide additional guidance or emphasize specific aspects not covered in the main prompt

• Negative Prompts: Crucial for quality - always specify what you don't want (blurry, distorted, low quality, artifacts, etc.)

• Seed Consistency: Use the same seed value to generate reproducible results across multiple runs with same parameters

• Batch Generation: Generate multiple variations (batch_size 2-8) to choose the best result. Each uses the same parameters but produces different outputs