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post-img2img-texture-inferences

Generate seamless texture images from a reference image using img2img AI models. Transform input images into new textures with controlled parameters for creative and design applications.

Instructions

Trigger a new seamless texture image generation in Img2Img mode with one reference image that initializes the generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
dryRunNo
imageNoThe input image as a data URL (example: "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVQYV2NgYAAAAAMAAWgmWQ0AAAAASUVORK5CYII=") or the asset ID (example: "asset_GTrL3mq4SXWyMxkOHRxlpw")
imageIdNoDeprecated: The input image as an AssetId. Prefer to use image with the asset ID instead.
seedNoUsed to reproduce previous results. Default: randomly generated number.
strengthNoControls the noise intensity introduced to the input image, where a value of 1.0 completely erases the original image's details. Available for img2img and inpainting. (within [0.01, 1.0], default: 0.75)
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)
maskIdNoThe mask as an AssetId. Will be ignored if the `image` parameter is provided
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
imageParentIdNoSpecifies the parent asset Id for the image when provided as a dataurl.
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
imageHideNoToggles the hidden status of the image when provided as a dataurl.
maskNoThe mask as a data URL, used to determine the area of change. The mask is a binary mask made out of white and black pixels. The white area is the one that will be replaced. (example: "data:image/png;base64,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")
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 mentions triggering a 'new seamless texture image generation,' implying a write operation that likely consumes resources, but does not address critical aspects like authentication requirements, rate limits, cost implications, or whether the operation is asynchronous. For a complex tool with 25 parameters and no annotations, this is insufficient, warranting a score of 2.

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 details. It is front-loaded and wastes no words, making it easy for an agent to quickly grasp the core functionality. This exemplifies conciseness, earning a score of 5.

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 (25 parameters, no annotations, no output schema), the description is inadequate. It lacks information on behavioral traits, output format, error handling, and usage context. While the schema covers most parameters, the description fails to provide the necessary contextual completeness for a tool of this nature, resulting in a score of 2.

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 description adds minimal parameter semantics beyond the input schema, which has high coverage (92%). It implies the use of a 'reference image' (mapping to the 'image' parameter) but does not explain key parameters like 'prompt' or 'modelId' beyond what the schema provides. Given the high schema coverage, the baseline is 3, as the description does not significantly enhance parameter understanding.

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 purpose: 'Trigger a new seamless texture image generation in Img2Img mode with one reference image that initializes the generation.' It specifies the action (trigger generation), resource (seamless texture image), and mode (Img2Img). However, it does not explicitly differentiate from sibling tools like 'post-texture-inferences' or 'post-txt2img-texture-inferences', which limits the score to 4.

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. It does not mention any prerequisites, exclusions, or comparisons to sibling tools such as 'post-texture-inferences' or 'post-img2img-inferences', leaving the agent without context for selection. This lack of usage guidance results in a score of 2.

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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