Skip to main content
Glama

generate_with_controlnet

Condition image generation on a ControlNet preprocessed image (pose, depth, canny) plus a text prompt. Upload control image first, then pass filename and prompt.

Instructions

Generate an image conditioned by a ControlNet preprocessed image (pose skeleton, depth, canny, normal, etc.) plus a text prompt. Upload the control image first with upload_image, then pass its filename as control_image. Unspecified params fall back to your defaults; checkpoint and controlnet_model auto-resolve from local models. Returns prompt_id immediately; asset_id arrives in the completion notification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cfgNo
seedNo
stepsNo
widthNo
heightNo
promptYesPositive text prompt
samplerNo
strengthNoControlNet conditioning strength (default 1.0)
schedulerNo
checkpointNoCheckpoint filename; auto-selected if omitted
control_imageYesFilename of the (already-uploaded) control image in ComfyUI's input dir
negative_promptNo
controlnet_modelNoControlNet model file (in models/controlnet/); auto-selected if omitted
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that returns are asynchronous (prompt_id immediately, asset_id later), and mentions fallback defaults and auto-resolution for checkpoint and controlnet_model. Missing details on potential errors or rate limits.

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?

Three sentences, front-loaded with purpose, no redundancy. Each sentence earns its place: purpose, prerequisite, and return behavior.

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

Completeness3/5

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

Given 13 parameters and no output schema, the description covers the workflow outline but lacks parameter ranges, defaults, and output format details. It is adequate but not complete.

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 coverage is low (38%). The description adds context by explaining auto-resolve and fallback behavior, but it does not describe most parameters (e.g., cfg, seed, steps). It partially compensates but is insufficient given the low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: generating images conditioned by a ControlNet preprocessed image plus a text prompt. It distinguishes itself from siblings like generate_image and generate_with_ip_adapter by specifying the ControlNet conditioning.

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

Usage Guidelines4/5

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

The description provides clear context on when to use the tool (with a ControlNet preprocessed image) and gives a prerequisite step (upload control image first with upload_image). However, it does not explicitly list alternatives or scenarios where other tools are better.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sandyup/comfyui-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server