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post-txt2img-texture-inferences

Generate seamless texture images from text prompts using AI models for design and creative projects.

Instructions

Trigger a new seamless texture image generation in Txt2Img mode.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
dryRunNo
seedNoUsed to reproduce previous results. Default: randomly generated number.
modelIdYesThe model id to use for the inference
modelEpochNoThe epoch of the model to use for the inference. Only available for Flux Lora Trained models.
hideResultsNoIf set, generated assets will be hidden and not returned in the list of images of the inference or when listing assets (default: false)
negativePromptNoThe prompt not to guide the image generation, ignored when guidance < 1 (example: "((ugly face))") For Flux based model (not Fast-Flux): requires negativePromptStrength > 0 and active only for inference types txt2img / img2img / controlnet.
schedulerNoThe scheduler to use to override the default configured for the model. See detailed documentation for more details.
intermediateImagesNoEnable or disable the intermediate images generation (default: false)
conceptsNo
guidanceNoControls how closely the generated image follows the prompt. Higher values result in stronger adherence to the prompt. Default and allowed values depend on the model type: - For Flux dev models, the default is 3.5 and allowed values are within [0, 10] - For Flux pro models, the default is 3 and allowed values are within [2, 5] - For SDXL models, the default is 6 and allowed values are within [0, 20] - For SD1.5 models, the default is 7.5 and allowed values are within [0, 20]
numInferenceStepsNoThe number of denoising steps for each image generation (within [1, 150], default: 30)
numSamplesNoThe number of images to generate (within [1, 128], default: 4)
widthNoThe width of the generated images, must be a 8 multiple (within [64, 2048], default: 512) If model.type is `sd-xl`, `sd-xl-lora`, `sd-xl-composition` the width must be within [512, 2048] If model.type is `sd-1_5`, the width must be within [64, 1024] If model.type is `flux.1.1-pro-ultra`, you can use the aspectRatio parameter instead
negativePromptStrengthNoOnly applicable for flux-dev based models for `txt2img`, `img2img`, and `controlnet` inference types. Controls the influence of the negative prompt. Default 0 means the negative prompt has no effect. Higher values increase negative prompt influence. Must be > 0 if negativePrompt is provided.
baseModelIdNoThe base model to use for the inference. Only Flux LoRA models can use this parameter. Allowed values are available in the model's attribute: `compliantModelIds`
promptYesFull text prompt including the model placeholder. (example: "an illustration of phoenix in a fantasy world, flying over a mountain, 8k, bokeh effect")
heightNoThe height of the generated images, must be a 8 multiple (within [64, 2048], default: 512) If model.type is `sd-xl`, `sd-xl-lora`, `sd-xl-composition` the height must be within [512, 2048] If model.type is `sd-1_5`, the height must be within [64, 1024] If model.type is `flux.1.1-pro-ultra`, you can use the aspectRatio parameter instead
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Trigger a new... generation,' implying a write operation that creates assets, but fails to mention critical aspects like whether this is an asynchronous job, potential costs, rate limits, authentication requirements, or what happens on failure. For a complex generative tool with 18 parameters, this is a significant gap in transparency.

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 is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every word earns its place by specifying the action and context.

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?

Given the tool's complexity (18 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain the return values, error handling, or behavioral traits like job queuing or asset management. For a generative AI tool with many tunable parameters, more context is needed to guide effective usage beyond the basic purpose.

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?

The schema description coverage is high (89%), so the schema already documents most parameters well. The description adds no additional parameter semantics beyond implying 'seamless texture' generation, which might hint at use cases but doesn't clarify specific parameters like 'originalAssets' or 'concepts.' With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.

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 action ('Trigger a new seamless texture image generation') and the mode ('in Txt2Img mode'), which specifies the verb and resource. It distinguishes from general 'post-txt2img-inferences' by focusing on 'seamless texture,' but doesn't explicitly differentiate from other texture-related siblings like 'post-texture-inferences' or 'post-img2img-texture-inferences' beyond the mode.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, such as other texture generation tools (e.g., 'post-texture-inferences' or 'post-controlnet-texture-inferences') or general Txt2Img tools. It lacks context about prerequisites, ideal scenarios, or exclusions, leaving the agent to infer usage from the name alone.

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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