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You can install it with:\n\n```bash\nnpm i @decartai/ai-sdk-provider\n```\n\n## Provider Instance\n\nYou can import the default provider instance `decart` from `@decartai/ai-sdk-provider`:\n\n```ts\nimport { decart } from '@decartai/ai-sdk-provider';\n```\n\nIf you need a customized setup, you can import `createDecart` and create a provider instance with your settings:\n\n```ts\nimport { createDecart } from '@decartai/ai-sdk-provider';\n\nconst decart = createDecart({\n  apiKey: 'your-api-key', // optional, defaults to DECART_API_KEY environment variable\n  baseURL: 'custom-url', // optional\n  headers: {\n    /* custom headers */\n  }, // optional\n});\n```\n\nYou can use the following optional settings to customize the Decart provider instance:\n\n- **baseURL** _string_\n\n  Use a different URL prefix for API calls, e.g. to use proxy servers.\n  The default prefix is `https://api.decart.ai`.\n\n- **apiKey** _string_\n\n  API key that is being sent using the `X-API-KEY` header.\n  It defaults to the `DECART_API_KEY` environment variable.\n\n- **headers** _Record&lt;string,string&gt;_\n\n  Custom headers to include in the requests.\n\n- **fetch** _(input: RequestInfo, init?: RequestInit) => Promise&lt;Response&gt;_\n\n  Custom [fetch](https://developer.mozilla.org/en-US/docs/Web/API/fetch) implementation.\n  You can use it as a middleware to intercept requests,\n  or to provide a custom fetch implementation for e.g. testing.\n\n## Image Models\n\nYou can create Decart image models using the `.image()` factory method.\nFor more on image generation with the AI SDK see [generateImage()](https://ai-sdk.dev/docs/reference/ai-sdk-core/generate-image).\n\n### Basic Usage\n\n```ts\nimport { decart } from '@decartai/ai-sdk-provider';\nimport { generateImage } from 'ai';\nimport fs from 'fs';\n\nconst { image } = await generateImage({\n  model: decart.image('lucy-pro-t2i'),\n  prompt: 'Three dogs playing in the snow',\n});\n\nconst filename = `image-${Date.now()}.png`;\nfs.writeFileSync(filename, image.uint8Array);\nconsole.log(`Image saved to ${filename}`);\n```\n\n### Model Capabilities\n\nDecart currently offers:\n\n| Model          | Description                                 |\n| -------------- | ------------------------------------------- |\n| `lucy-pro-t2i` | High-quality text-to-image generation model |\n\nThe model supports the following aspect ratios:\n\n- 16:9 (landscape)\n- 9:16 (portrait)\n\n<Note>\n  Other aspect ratios will generate a warning and fall back to the default\n  behavior.\n</Note>\n\n### Image Model Settings\n\nYou can customize the generation behavior with optional settings:\n\n```ts\nconst { image } = await generateImage({\n  model: decart.image('lucy-pro-t2i'),\n  prompt: 'Three dogs playing in the snow',\n  aspectRatio: '16:9',\n  seed: 42,\n});\n```\n\nSupported settings:\n\n- **aspectRatio** _string_\n\n  Control the aspect ratio of the generated image. Supported values: `16:9` (landscape) and `9:16` (portrait).\n\n- **seed** _number_\n\n  Set a seed value for reproducible results.\n\n## Video Models\n\nYou can create Decart video models using the `.video()` factory method.\nFor more on video generation with the AI SDK see [experimental_generateVideo()](https://ai-sdk.dev/docs/reference/ai-sdk-core/generate-video).\n\n### Text-to-Video\n\n```ts\nimport { decart } from '@decartai/ai-sdk-provider';\nimport { experimental_generateVideo as generateVideo } from 'ai';\nimport fs from 'fs';\n\nconst { videos } = await generateVideo({\n  model: decart.video('lucy-pro-t2v'),\n  prompt: 'A man is riding a horse in a field',\n});\n\nfs.writeFileSync('video.mp4', videos[0].uint8Array);\n```\n\n### Image-to-Video\n\nPass an image to animate it into a video:\n\n```ts\nconst { videos } = await generateVideo({\n  model: decart.video('lucy-pro-i2v'),\n  prompt: {\n    image: imageData,\n    text: 'The subject begins to walk forward slowly',\n  },\n});\n```\n\n### Motion Control\n\nUse `lucy-motion` with a trajectory to control camera or subject movement:\n\n```ts\nconst { videos } = await generateVideo({\n  model: decart.video('lucy-motion'),\n  prompt: {\n    image: imageData,\n    text: 'The subject moves along the specified path',\n  },\n  providerOptions: {\n    decart: {\n      trajectory: [\n        { frame: 0, x: 0.5, y: 0.5 },\n        { frame: 12, x: 0.7, y: 0.9 },\n        { frame: 25, x: 0.3, y: 0.1 },\n      ],\n    },\n  },\n});\n```\n\n### Model Capabilities\n\n| Model          | Description                                          |\n| -------------- | ---------------------------------------------------- |\n| `lucy-pro-t2v` | Text-to-video generation                             |\n| `lucy-pro-i2v` | Image-to-video generation                            |\n| `lucy-dev-i2v` | Image-to-video generation (dev)                      |\n| `lucy-motion`  | Image-to-video with trajectory-based motion control  |\n\n### Video Model Settings\n\n```ts\nconst { videos } = await generateVideo({\n  model: decart.video('lucy-pro-t2v'),\n  prompt: 'A sunset over the ocean',\n  aspectRatio: '16:9',\n  seed: 42,\n});\n```\n\nSupported settings:\n\n- **aspectRatio** _string_\n\n  Control the aspect ratio. Supported values: `16:9` (landscape) and `9:16` (portrait).\n\n- **seed** _number_\n\n  Set a seed value for reproducible results.\n\n- **resolution** _string_\n\n  Video resolution. Supported values: `1280x720` (720p) and `854x480` (480p).\n\n### Provider Options\n\nPass Decart-specific options via `providerOptions.decart`:\n\n- **trajectory** _Array<{ frame: number; x: number; y: number }>_\n\n  Motion path for `lucy-motion`. Each point specifies a frame number and normalized x,y coordinates.\n\n- **orientation** _\"landscape\" | \"portrait\"_\n\n  Override orientation directly instead of deriving from `aspectRatio`.\n\n## Learn More\n\nFor more details about Decart's capabilities and features, visit [Decart AI](https://decart.ai).\n","readmeFilename":"README.md"}