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post-img2img-inferences

Generate new images by transforming an existing reference image using AI models. Control details like strength, prompts, and model parameters to modify or enhance visual content.

Instructions

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

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)
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.
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'triggers' a generation, implying a write operation that may consume resources, but doesn't mention costs, rate limits, permissions, or what happens to the reference image. For a complex generative AI tool with 23 parameters, this lack of behavioral context is a significant gap.

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 front-loads the core purpose. It wastes no words and directly states what the tool does, making it easy for an agent to parse quickly. Every word earns its place, with no redundant or vague phrasing.

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 (23 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain the output format, error conditions, or how results are returned. For a generative AI tool that likely produces images or asset IDs, the lack of output information is a major omission, leaving the agent unprepared for what happens after invocation.

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 high at 91%, so the schema already documents most parameters thoroughly. The description adds minimal value beyond the schema, only implying that 'one reference image' is required (mapping to the 'image' parameter) without explaining parameter interactions or dependencies. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate for the remaining 9% gap.

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 image generation in Img2Img mode with one reference image that initializes the generation process.' It specifies the verb ('trigger'), resource ('image generation'), and mode ('Img2Img'), distinguishing it from other inference tools like txt2img or controlnet. However, it doesn't explicitly differentiate from similar siblings like 'post-controlnet-img2img-inferences' or 'post-img2img-ip-adapter-inferences' beyond the basic 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. It mentions 'Img2Img mode' but doesn't explain when this mode is appropriate compared to txt2img, controlnet, or other variants. There are no prerequisites, exclusions, or named alternatives provided, leaving the agent to infer usage from the tool 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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