civitai

Civitai + comfyui-mcp

comfyui-mcp has native Civitai search built in (download_model action:"search_civitai") plus the local half of the loop: download, wire, queue, tag. The full flow (find, install, generate) needs no other server and no API key, and works on every backend, including small local models behind the compact router.

The built-in flow (default path)

download_model({ action: "search_civitai", query, types: ["LORA"], base_models: ["Flux.1 D"] })
        │                    each hit: model_id · model_version_id · trigger words
        ▼
download_model({ action: "download_civitai", model_version_id, target_subfolder: "loras" })
        │
        ▼
list_local_models({ action: "list" })  →  panel_add_node loader / generate_image   # use it

• ALWAYS filter base_models when the user's checkpoint family is known. A Flux LoRA will not load on an SDXL checkpoint (see model-compatibility). CivitAI labels: Flux.1 D, SDXL 1.0, SD 1.5, Pony, Illustrious, Wan Video.
• target_subfolder must match the model type: checkpoints, loras, vae, controlnet, embeddings, upscale_models, and so on.
• Results are SFW-only by default (nsfw: true to opt in).
• Each hit carries trigger words. Put them in the prompt after installing.
• Prefer model_version_id over model_id. A Civitai page can list several versions and the user usually means a specific one.

Search needs no API key. CIVITAI_API_TOKEN (from civitai.com/user/account) unlocks gated/early-access downloads and gated search results. Set it once, in panel Settings under "Set CivitAI token…" or in env.

Recipes

"Find me a good anime LoRA for Flux and install it"

1. download_model({ action: "search_civitai", query: "anime style", types: ["LORA"], base_models: ["Flux.1 D"] }).
2. Present the top 3 to 5 with name, creator, base model, downloads, and the version id. Let the user pick.
3. download_model({ action: "download_civitai", model_version_id, target_subfolder: "loras" }).
4. In the panel, panel_add_node a LoraLoader, panel_set_widget the lora_name, wire it between checkpoint and sampler, and use the hit's trigger words in the prompt. Headless: generate_image(action="image") / build the workflow.