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post-txt2img-inferences

Generate AI images from text prompts using Scenario.com's models, with customizable parameters for control, quality, and creative output.

Instructions

Trigger a new 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)
aspectRatioNoThe aspect ratio of the generated images. Only used for the model flux.1.1-pro-ultra. The aspect ratio is a string formatted as "width:height" (example: "16:9").
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?

No annotations are provided, so the description carries full burden. It mentions 'trigger a new image generation' which implies a write operation that consumes resources, but doesn't disclose critical behavioral traits like whether this is an asynchronous job, rate limits, authentication requirements, cost implications, or what happens on failure. For a complex generative AI tool with 19 parameters, this minimal description is insufficient.

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 extremely concise with a single sentence that gets straight to the point: 'Trigger a new image generation in Txt2Img mode.' There's no wasted words or unnecessary elaboration, making it easy to parse quickly. Every word earns its place by specifying both the action and the 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 (19 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain what the tool returns (images, job IDs, error formats), doesn't mention resource consumption or performance characteristics, and provides no guidance on parameter interactions. For a generative AI inference tool with many configuration options, this leaves too many gaps for effective agent use.

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 89%, so most parameters are well-documented in the schema itself. The description adds no parameter-specific information beyond what's in the schema, not even mentioning key required parameters like 'prompt' or 'modelId'. However, the high schema coverage means the baseline is 3, as the schema does the heavy lifting for parameter documentation.

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 image generation') and specifies the mode ('Txt2Img'), which distinguishes it from other image generation tools like 'post-img2img-inferences' or 'post-controlnet-inferences'. However, it doesn't explicitly differentiate from other txt2img variants like 'post-txt2img-ip-adapter-inferences' or 'post-txt2img-texture-inferences', which would require more specific context about what makes this base version unique.

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. With many sibling tools for different inference types (e.g., img2img, controlnet, ip-adapter variants), there's no indication of prerequisites, typical use cases, or comparisons to help an agent choose appropriately. The lack of context leaves 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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