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download_model

Download model files from URLs into the correct ComfyUI models subfolder, supporting HuggingFace, HTTP, S3, and Azure Blob with optional authentication.

Instructions

Download a model file to the connected ComfyUI's models directory from a URL (HuggingFace, direct HTTP(S), s3://, or Azure Blob). PREFER this over a raw shell download (curl/wget) for model weights: it lands the file in the right models/ subfolder. LOCAL ComfyUI: streams to disk and surfaces live progress in the panel download tray. REMOTE ComfyUI: dispatches the fetch to the ComfyUI host via the ComfyUI-Manager install-model HTTP API (downloaded server-side; a per-request auth header can't be forwarded). This requires the host's Manager to run with network_mode=personal_cloud (or loopback) and a permissive security level — a stricter gate silently rejects the download, and Manager reports the queue task 'done' even on failure, so a remote dispatch does not guarantee the file landed. target_subfolder accepts any relative subfolder (incl. nested, e.g. 'loras/').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesDirect download URL for the model file
authNoOptional per-request authentication for private/gated model URLs. When provided it overrides built-in HuggingFace/CivitAI token handling.
filenameNoOverride filename (auto-detected from URL if omitted)
target_subfolderYesTarget subfolder under ComfyUI models/. Standard names: checkpoints, loras, vae, upscale_models, controlnet, embeddings, clip, diffusers, diffusion_models, gligen, hypernetworks, photomaker, style_models, text_encoders, unet. Any other relative subfolder (incl. nested like 'loras/<subdir>') is allowed; absolute paths and '..' escapes are rejected.
Behavior4/5

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

With no annotations, the description carries full burden. Discloses local streaming vs remote API dispatch, progress reporting, auth forwarding limitation, and remote failure conditions (silent success on failure). Could mention return value or status but otherwise thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single dense paragraph, front-loaded with purpose but remote details are lengthy. Could benefit from bullet points or segmentation for readability. Not overly verbose but structure could be improved.

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?

Covers behavior well for both local and remote, but lacks description of return value/output (no output schema). Given 4 params and 100% schema coverage, the description is mostly complete except for expected result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds value: explains why auth can't be forwarded, filename auto-detection, nested subfolder acceptance, and URL types. Schema provides basics; description enriches understanding.

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 downloads a model file to ComfyUI's models directory from URLs (HuggingFace, HTTP, S3, Azure). It contrasts with shell download, specifying the benefit of landing in the correct subfolder. This distinguishes it from siblings like download_civitai_model.

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?

Explicitly advises preferring this over curl/wget for model weights. Covers local vs remote behavior and constraints (auth forwarding, network_mode, silent failure). Lacks explicit when-not scenarios but provides sufficient context for decision-making.

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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