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text_to_image

Generate images from a text description. Customize resolution, sampling steps, and more to get the exact image you need.

Instructions

Generate images from a text description.

All parameters except prompt are optional with sensible defaults. Change any parameter to customize the generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cfgNoClassifier-free guidance scale. Default: 8.0. Higher = closer to prompt. Typical: 1.0-20.0.
seedNoRandom seed for reproducibility. Default: -1 (random).
stepsNoSampling steps. Default: 20. Higher = better quality, slower. Typical: 15-50.
widthNoImage width in pixels. Default: 512. Must be multiple of 8.
heightNoImage height in pixels. Default: 512. Must be multiple of 8.
promptYesText description of the desired image.
samplerNoSampler algorithm. Default: "euler". Valid: "euler", "dpmpp_2m", "dpmpp_sde", "ddim", "uni_pc", etc. Use list_samplers_and_schedulers() to see all.euler
lora_nameNoLoRA model to apply. Default: "" (none). Use list_models(folder="loras") to see options.
schedulerNoNoise scheduler. Default: "normal". Valid: "normal", "karras", "sgm_uniform", "exponential", etc. Use list_samplers_and_schedulers() to see all.normal
batch_sizeNoNumber of images to generate. Default: 1.
checkpointNoCheckpoint model name. Default: "" (auto-selects first available). Use list_models(folder="checkpoints") to see options.
lora_strengthNoLoRA influence. Default: 1.0. Range: 0.0-2.0.
negative_promptNoThings to avoid. Default: "" (none).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are present, so the description carries the full burden. It only says 'generate images' and mentions parameter optionality. It does not disclose that generation may be asynchronous, require job tracking, or have resource implications. The description is minimal and lacks behavioral context.

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

Conciseness4/5

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

The description is concise (two sentences) and front-loaded with the core purpose. The second sentence about customization is somewhat vague but serves as a useful expectation-setter. It is efficient without unnecessary detail.

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

Completeness2/5

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

Despite having a rich schema and output schema, the description misses critical context about how the tool integrates with other tools, such as job submission, result retrieval, or asynchronous behavior. For a complex generation tool with sibling workflow tools, this is a significant completeness gap.

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

Parameters3/5

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

Schema description coverage is 100%, with every parameter already detailed in the input schema. The description only adds the generic note that parameters are optional with defaults, which is redundant with schema default values. It does not enrich parameter understanding beyond what the schema provides.

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 clearly states the tool's function: 'Generate images from a text description.' This is a specific verb+resource combination that distinguishes it from image-to-image or video generation. However, it does not differentiate among sibling text-to-image variants like flux_text_to_image or dalle3_image, making the choice among them ambiguous.

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

Usage Guidelines3/5

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

The description provides basic guidance: all parameters except prompt are optional with sensible defaults, implying users can customize. It does not state when to use this tool versus alternatives, nor any prerequisites or exclusions. The guidance is implied rather than explicit.

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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