# Civitai Model Scraper — AI Image Model Intelligence (`straightforward_hydra/civitai-model-scraper`) Actor Search Civitai for AI image models (checkpoints, LoRAs, embeddings): creator, downloads, ratings, tags, base model, latest version & download URL. Keyless. - **URL**: https://apify.com/straightforward\_hydra/civitai-model-scraper.md - **Developed by:** [Dev D](https://apify.com/straightforward_hydra) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 models This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Civitai Model Scraper — AI Image Model Intelligence 🎨 **Search Civitai — the largest hub for AI image-generation models — and get clean records for checkpoints, LoRAs, embeddings and more: creator, downloads, ratings, tags, base model and latest version (with download URL). No API key.** Civitai hosts the Stable Diffusion / AI-art model ecosystem. This Actor turns its public API into structured data for **model discovery, trend tracking, market research and datasets**. --- ### What you get per model `id` · `name` · `type` · `nsfw` · `description` · `creator` · `tags` · `base_models` · `allow_commercial_use` · `download_count` · `thumbs_up_count` · `thumbs_down_count` · `comment_count` · `tipped_amount` · `version_id` · `version_name` · `version_base_model` · `published_at` · `trained_words` · `download_url` · `file_name` · `file_size_kb` · `preview_image` · `image_count` · `model_url`. ### Examples **Top downloaded models for a term** ```json { "mode": "search", "query": "realistic", "sort": "Most Downloaded", "maxResults": 100 } ```` **Newest SDXL LoRAs** ```json { "mode": "search", "types": ["LORA"], "baseModel": "SDXL 1.0", "sort": "Newest" } ``` **Look up specific models** ```json { "mode": "models", "modelIds": ["4201"] } ``` ### Use cases - **Model discovery & trend tracking** — what's downloaded, rated and trending by type and base model. - **AI-art market research** — creator and ecosystem analytics. - **Datasets** — a clean, structured slice of the Civitai catalog. ### Notes - **No API key.** Uses the public Civitai v1 API. - **NSFW is excluded by default** — set `includeNsfw: true` to include flagged models. - **Types** include Checkpoint, LORA, LoCon, TextualInversion, Hypernetwork, Controlnet, Poses, VAE. - Model files and metadata belong to their creators and are subject to each model's license — check `allow_commercial_use` and use responsibly. *** #### Keywords Civitai, Stable Diffusion, AI art, image models, checkpoints, LoRA, embeddings, AI models, generative AI, model hub, downloads, ratings, base model, SDXL, Flux, no API key. # Actor input Schema ## `mode` (type: `string`): search = query Civitai; models = fetch specific models by id. ## `query` (type: `string`): Free-text query, e.g. "realistic", "anime", "pixel art". Leave empty to list top models. ## `types` (type: `array`): Filter to model types, e.g. Checkpoint, LORA, LoCon, TextualInversion, Hypernetwork, Controlnet, Poses, VAE. ## `sort` (type: `string`): Ordering of results. ## `period` (type: `string`): Stats window for sorting. ## `baseModel` (type: `string`): Filter by base model, e.g. "SDXL 1.0", "SD 1.5", "Flux.1 D", "Pony". ## `includeNsfw` (type: `boolean`): Include models flagged NSFW. Off by default (SFW only). ## `modelIds` (type: `array`): One or more numeric Civitai model ids, e.g. "4201". ## `maxResults` (type: `integer`): Cap on models returned in search mode. ## Actor input object example ```json { "mode": "search", "sort": "Most Downloaded", "period": "AllTime", "includeNsfw": false, "maxResults": 100 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("straightforward_hydra/civitai-model-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("straightforward_hydra/civitai-model-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call straightforward_hydra/civitai-model-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=straightforward_hydra/civitai-model-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Civitai Model Scraper — AI Image Model Intelligence", "description": "Search Civitai for AI image models (checkpoints, LoRAs, embeddings): creator, downloads, ratings, tags, base model, latest version & download URL. Keyless.", "version": "0.1", "x-build-id": "MfHFWvg5arGXV8mFB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/straightforward_hydra~civitai-model-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-straightforward_hydra-civitai-model-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/straightforward_hydra~civitai-model-scraper/runs": { "post": { "operationId": "runs-sync-straightforward_hydra-civitai-model-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/straightforward_hydra~civitai-model-scraper/run-sync": { "post": { "operationId": "run-sync-straightforward_hydra-civitai-model-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Mode", "enum": [ "search", "models" ], "type": "string", "description": "search = query Civitai; models = fetch specific models by id.", "default": "search" }, "query": { "title": "Search text (search mode)", "type": "string", "description": "Free-text query, e.g. \"realistic\", \"anime\", \"pixel art\". Leave empty to list top models." }, "types": { "title": "Model types (search mode)", "type": "array", "description": "Filter to model types, e.g. Checkpoint, LORA, LoCon, TextualInversion, Hypernetwork, Controlnet, Poses, VAE.", "items": { "type": "string" } }, "sort": { "title": "Sort by (search mode)", "enum": [ "Most Downloaded", "Highest Rated", "Most Liked", "Newest" ], "type": "string", "description": "Ordering of results.", "default": "Most Downloaded" }, "period": { "title": "Time period (search mode)", "enum": [ "AllTime", "Year", "Month", "Week", "Day" ], "type": "string", "description": "Stats window for sorting.", "default": "AllTime" }, "baseModel": { "title": "Base model (search mode, optional)", "type": "string", "description": "Filter by base model, e.g. \"SDXL 1.0\", \"SD 1.5\", \"Flux.1 D\", \"Pony\"." }, "includeNsfw": { "title": "Include NSFW", "type": "boolean", "description": "Include models flagged NSFW. Off by default (SFW only).", "default": false }, "modelIds": { "title": "Model ids (models mode)", "type": "array", "description": "One or more numeric Civitai model ids, e.g. \"4201\".", "items": { "type": "string" } }, "maxResults": { "title": "Max results (search mode)", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Cap on models returned in search mode.", "default": 100 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```