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compare_models

Compare Stable Diffusion models by generating images from the same prompt across multiple checkpoints. Obtain side-by-side results to evaluate model outputs.

Instructions

Generate images from multiple models using the same prompt for comparison.

Args:
    pipeline: Pipeline to use — "sd15", "sdxl", or "illustrious"
    positive_prompt: Text prompt for all models
    models: List of model checkpoint filenames to compare (2-6)
    negative_prompt: Negative prompt (things to avoid)
    width: Image width in pixels (256-2048)
    height: Image height in pixels (256-2048)
    steps: Number of inference steps (1-150)
    cfg_scale: Classifier-free guidance scale (1-30)
    seed: Fixed seed for fair comparison (default 42)
    scheduler: Noise scheduler name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
stepsNo
widthNo
heightNo
modelsYes
pipelineYes
cfg_scaleNo
schedulerNoDPM++ 2M
negative_promptNo
positive_promptYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

There are no annotations, so the description carries the behavioral disclosure burden. It explains the core behavior (same prompt across multiple models) and useful constraints (model count 2-6, fixed seed for fair comparison), but it does not mention execution semantics such as whether the call is synchronous, returns a job, or has notable side effects. Adequate but not deeply transparent.

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

Conciseness5/5

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

The description opens with a concise one-line purpose statement followed by a compact, complete parameter list. Every line carries useful information, with no filler or redundant restatement of the tool name.

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

Completeness4/5

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

Given the tool has 10 parameters and no annotations, the description covers all parameters with constraints and defaults, which is sufficient for an agent to invoke it correctly. An output schema is present, so explaining the return format is not required; the main omission is explicit guidance on job/execution behavior, but this is a minor gap.

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

Parameters5/5

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

Schema description coverage is 0%, and the description compensates exceptionally well by documenting all 10 parameters with meaningful details: allowed pipeline values, model count range, numeric bounds for width/height/steps/cfg_scale, seed default and purpose, and scheduler meaning. This adds real value beyond the bare input schema.

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

Purpose4/5

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

The description states a specific action ('Generate images') and a specific resource/scope ('from multiple models using the same prompt for comparison'), which clearly conveys the tool's purpose. It is distinguishable from siblings like generate_image by the 'multiple models' comparison angle, though it does not explicitly name any sibling alternative.

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

Usage Guidelines4/5

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

The phrase 'for comparison' gives clear context for when to use this tool relative to single-image or batch tools. It does not explicitly mention when not to use it or name an alternative, so it stops short of full 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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