Minimax Image 01 Image To Image

Minimax Image 01 Image To Image

Playground

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MiniMax Image-01 image-to-image model transforms existing images using text prompts. Generate variations, apply style transfers, or modify images with character references. Supports multiple aspect ratios and custom dimensions. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

MiniMax Image-01 Image-to-Image is an advanced AI model that transforms existing images using text prompts. Part of the MiniMax image-01 family, this model enables you to generate variations, apply style transfers, modify compositions, and create character-consistent images from reference photos. Perfect for creative workflows, product visualization, and content creation.


Key Features

  • Image-Based Generation Generate new images based on an existing image input combined with text prompts. The model intelligently understands the reference image and applies your text description to create variations.

  • Character Reference Support Use portrait photos as character references to maintain consistent character appearance across generated images. Ideal for creating character variations, different poses, or placing characters in new scenes.

  • Flexible Image Dimensions Specify custom dimensions from 512×512 to 2048×2048 pixels (must be divisible by 8) for precise control over output size. Common sizes include 1024×1024, 1280×720, 1152×864, and more.

  • Prompt Optimization Built-in prompt optimizer automatically enhances your text descriptions for better generation results, helping you achieve the desired output even with simple prompts.

  • Batch Generation Generate up to 9 images in a single request, perfect for exploring variations and selecting the best result.

  • Reproducible Results Use seed values to generate consistent results across multiple runs, essential for iterative refinement and production workflows.


Use Cases

  • Product Visualization: Transform product photos into different contexts, backgrounds, or styles
  • Character Art: Create consistent character variations with different poses, outfits, or environments
  • Style Transfer: Apply artistic styles or visual treatments to existing images
  • Image Editing: Modify specific aspects of an image through natural language descriptions
  • Creative Exploration: Generate multiple variations of a concept for selection and refinement
  • Content Creation: Quickly produce social media content, marketing materials, or creative assets

Supported Formats & Dimensions

Input Image Formats:

  • JPG, JPEG, PNG
  • Maximum file size: 10MB
  • Accepts public URLs or Base64-encoded Data URLs

Output Dimensions:

  • Width/Height range: 512 to 2048 pixels
  • Must be divisible by 8
  • Common sizes: 1024×1024 (square), 1280×720 (widescreen), 1152×864 (standard), 1248×832 (photo), 832×1248 (portrait photo), 864×1152 (portrait), 720×1280 (mobile/vertical), 1344×576 (ultra-wide)

How to Use

Basic Image-to-Image Generation

  1. Upload Reference Image
  • Provide a public URL or Base64-encoded image in the image field
  • Ensure the image is in JPG, JPEG, or PNG format and under 10MB
  1. Write Your Prompt
  • Describe the desired output in the prompt field (max 1500 characters)
  • Example: “Transform this photo into a watercolor painting style with soft pastel colors”
  1. Select Image Size
  • Specify dimensions using the size parameter like “10241024” or “1280720”
  • Choose dimensions that match your use case (square for social media, widescreen for presentations, etc.)
  1. Configure Options
  • num_images: Set 1-9 to generate multiple variations
  • prompt_optimizer: Enable for automatic prompt enhancement
  • seed: Use for reproducible results
  1. Generate
  • Submit your request and receive generated images as URLs or Base64 strings

Character Reference Generation

For consistent character appearance:

  1. Prepare Portrait Photo
  • Use a clear, front-facing portrait with good lighting
  • Single person in frame works best
  1. Configure Character Reference
  • Use the subject_reference parameter with type “character”
  • Provide the portrait image URL or Base64 data
  1. Describe the Scene
  • Write a prompt describing the desired scene, pose, or context
  • Example: “The character standing on a mountain peak at sunset”

API Parameters

  • prompt (required): Text description of desired output (max 1500 chars)
  • image (required): Reference image as URL or Base64 string
  • size: Image dimensions (e.g., “1024 * 1024”, “1280 * 720”)
  • num_images: Number of images to generate (1-9, default: 1)
  • seed: Random seed for reproducible results
  • prompt_optimizer: Enable automatic prompt enhancement (boolean)
  • enable_base64_output: Return Base64 instead of URLs (boolean)
  • enable_sync_mode: Wait for generation to complete before returning (boolean)
  • subject_reference: Array of character reference images (optional)

Pricing

  • $0.0035 per image
  • Generate multiple images in one request for efficient batch processing
  • Total cost = $0.0035 × number of images generated

Output Format

Generations return as:

  • URLs (default): Direct links to generated images hosted on WaveSpeedAI
  • Base64 (optional): Encoded image data for direct embedding

Response includes:

  • Unique request ID for tracking
  • Generation status (created, processing, completed, failed)
  • Output array with generated image URLs or Base64 data
  • NSFW content detection flags
  • Creation timestamp

Best Practices

  • Use clear, well-lit reference images for best results
  • For character references, front-facing portraits work best
  • Enable prompt optimizer if you’re new to prompt writing
  • Use seed values when iterating on a specific result
  • Generate multiple variations (num_images > 1) to select the best output
  • Keep prompts descriptive but concise for optimal results

Also available on WaveSpeedAI:

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "size": "1024*1024",
  "num_images": 1,
  "prompt_optimizer": false
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/minimax/image-01/image-to-image" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "${TASK}" | jq -r '.urls.get // empty')
if [ -z "${RESULT_URL}" ]; then RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"; fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}")
  RESULT=$(printf '%s' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "${STATUS}" >&2; exit 1 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-Text description of the image, max length 1500 characters.
imagestringNo-Reference image file. Supports public URLs or Base64-encoded
sizestringNo1024*1024512 ~ 2048Specify the width and height pixel values of the generated image.
num_imagesintegerNo11 ~ 9The number of images to generate.
prompt_optimizerbooleanNofalse-Enable automatic optimization of prompt.
enable_sync_modebooleanNofalse-If set to `true`, the request attempts to wait for the generated result and return outputs in the same response. If the result is not ready within the sync wait window, the API can return a timeout body while the task continues processing. This option is only available via the API and is supported only by some models.
enable_base64_outputbooleanNofalse-If set to `true`, the prediction's `output` strings are returned as **naked base64** (no `data:<mime>;base64,` prefix). When `false` (default), outputs are returned as URLs pointing to our CDN.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayOutput values, usually URL strings; some models return text strings or structured result objects (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction
data.modelstringModel ID used for the prediction
data.outputsarray<string | object>Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model.
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to poll for the prediction result
data.statusstringStatus: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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