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syh52
by syh52

Blend Multiple Images

seedream_blend

Combine elements from 2-14 reference images by describing what to take from each, enabling virtual try-on, face swaps, style transfer, and product placement.

Instructions

Combine elements from multiple images using SeeDream 4.5.

⚠️ CLAUDE.AI USERS: This tool WILL TIMEOUT on Claude.ai (takes 30-60 seconds). Use "seedream_submit" instead with mode="multi" for Claude.ai compatibility.

Use this tool to blend 2-14 reference images together. Perfect for:

  • Virtual try-on: Dress person from image 1 in clothes from image 2

  • Face/Subject swap: Put face/subject from image 1 onto body/scene in image 2

  • Style transfer: Apply style of image 2 to content of image 1

  • Product visualization: Put product from image 1 into scene from image 2

  • Character consistency: Combine character traits from multiple references

Args:

  • prompt (string, required): Blending instruction referencing images by number

  • images (string[], required): Array of 2-14 image URLs or local paths

  • size (string): Output size (default: '2K')

  • strength (number): Reference strength 0-1 (default: 0.7)

  • watermark (boolean): Add watermark (default: false)

  • download (boolean): Save locally (default: true)

  • download_dir (string): Local save directory

Returns: Blended image URL, local path if downloaded, and performance timing.

Prompt Structure (per official docs):

  • Reference Target: Clearly specify what to extract from each reference image

  • Scene Description: Describe the output scene layout and composition

  • Always reference images by number: "image 1", "image 2", etc.

Common Use Cases:

  • Virtual try-on: "Dress the character in Image 1 with the outfit from Image 2"

  • Subject replacement: "Replace the subject in Image 1 with the subject from Image 2"

  • Style transfer: "Apply the style of Image 2 to Image 1"

  • Product placement: "Place the product from Image 1 on the table in Image 2"

Examples:

  • "Replace the clothing in image 1 with the outfit from image 2"

  • "Put the face from image 1 onto the body in image 2, maintaining natural lighting"

  • "Apply the linear minimalist style of image 2 to design icons based on image 1"

  • "Generate four tops in different materials, based on the clothing style worn by the girl in the reference images"

Tips:

  • Be specific about which elements to take from each image

  • For best results, use similar aspect ratios for inputs

  • Higher strength preserves more details from reference images

  • Using 10+ reference images significantly impacts generation speed

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoOutput image size2K
imagesYesArray of image URLs or local file paths to blend
promptYesInstruction for combining images. Reference images by number: 'Dress the person in image 1 with outfit from image 2'
downloadNoWhether to download generated images to local directory
strengthNoReference strength (0-1). Higher values preserve more from reference images.
watermarkNoWhether to add 'AI generated' watermark
download_dirNoLocal directory to save images (defaults to ./generated_images)
response_formatNoOutput format: 'markdown' for human-readable or 'json' for structured datamarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
usageNoAPI usage metrics
imagesYesArray of generated images
promptYesThe prompt used for generation
timingNoPerformance timing metrics
successYesWhether the generation was successful
strengthYesThe strength value used for blending
image_countYesNumber of images blended
source_imagesYesArray of source images used
Install Server

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already signal readOnlyHint=false and destructiveHint=false, so the description does not need to restate those. It adds valuable behavioral context beyond the schema: the tool can take 30-60 seconds, will time out on Claude.ai, and using 10+ reference images significantly impacts generation speed. It also discloses return details such as URL, local path, and timing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with clear sections and front-loads the critical timeout warning. However, it is long and contains redundancy: the 'Perfect for' bullet list near the top substantially overlaps with the later 'Common Use Cases' section. It earns above-minimal credit for structure but loses points for repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the warning, use cases, parameter guidance, return behavior, prompt structure, examples, and practical tips. Given the output schema exists and annotations are present, an agent has everything needed to decide whether to call this tool and how to construct a valid multi-image blending request.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters, establishing a baseline of 3. The description adds meaningful extra guidance: how to structure prompts around numbered reference images, example prompts for different use cases, and an explanation that higher strength preserves more reference detail. It slightly over-relies on the schema for response_format, but overall it enhances parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: 'Combine elements from multiple images using SeeDream 4.5.' It reinforces the purpose with concrete use cases such as virtual try-on, face swap, and style transfer, and the '2-14 reference images' constraint distinguishes it from sibling generation/edit/variation tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this tool to blend 2-14 reference images together' and enumerates scenarios where it is appropriate. It also names an alternative tool and condition: Claude.ai users should use seedream_submit with mode='multi' instead due to timeout risk. This gives an agent clear routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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