[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-runway-rw-integrate-uploads":3,"mdc--h13isg-key":32,"related-org-runway-rw-integrate-uploads":4514,"related-repo-runway-rw-integrate-uploads":4684},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":21,"repoUrl":22,"updatedAt":23,"license":24,"forks":25,"topics":26,"repo":27,"sourceUrl":30,"mdContent":31},"rw-integrate-uploads","upload local files to Runway","Help users upload local files to Runway for use as inputs to generation models",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"runway","Runway","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Frunway.png","runwayml",[13,17,18],{"name":14,"slug":15,"type":16},"File Uploads","file-uploads","tag",{"name":9,"slug":8,"type":16},{"name":19,"slug":20,"type":16},"API Development","api-development",57,"https:\u002F\u002Fgithub.com\u002Frunwayml\u002Fskills","2026-04-08T04:42:03.732618",null,15,[],{"repoUrl":22,"stars":21,"forks":25,"topics":28,"description":29},[],"for Runway coding agent skills","https:\u002F\u002Fgithub.com\u002Frunwayml\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Frw-integrate-uploads","---\nname: rw-integrate-uploads\ndescription: \"Help users upload local files to Runway for use as inputs to generation models\"\nuser-invocable: false\nallowed-tools: Read, Grep, Glob, Edit, Write\n---\n\n# Integrate Uploads\n\n> **PREREQUISITE:** Run `+rw-check-compatibility` first. Run `+rw-fetch-api-reference` to load the latest API reference before integrating. Requires `+rw-setup-api-key` for API credentials.\n\nHelp users upload local files (images, videos, audio) to Runway's ephemeral storage for use as inputs to generation models.\n\n## When to Use Uploads\n\nUse the Uploads API when:\n- The user has a **local file** (not a public URL) they want to use as input\n- The file exceeds **data URI size limits** (5 MB for images, 16 MB for video\u002Faudio)\n- The file's URL doesn't meet Runway's **URL requirements** (HTTPS, proper headers, no redirects)\n\n**You do NOT need uploads when:**\n- The asset is already at a public HTTPS URL with proper headers\n- The asset is small enough for a data URI (\u003C 5 MB image, \u003C 16 MB video)\n\n## How It Works\n\n1. Request an ephemeral upload slot → get a presigned upload URL and form fields\n2. Upload the file to the presigned URL\n3. Use the returned `runway:\u002F\u002F` URI as input to any generation endpoint\n\n**`runway:\u002F\u002F` URIs are valid for 24 hours.**\n\n## SDK Upload (Recommended)\n\n### Node.js\n\n```javascript\nimport RunwayML from '@runwayml\u002Fsdk';\nimport fs from 'fs';\n\nconst client = new RunwayML();\n\n\u002F\u002F Upload from a file stream\nconst upload = await client.uploads.createEphemeral(\n fs.createReadStream('\u002Fpath\u002Fto\u002Fimage.jpg')\n);\n\n\u002F\u002F Use the runway:\u002F\u002F URI in any generation call\nconst task = await client.imageToVideo.create({\n model: 'gen4.5',\n promptImage: upload.runwayUri,\n promptText: 'The scene comes to life',\n ratio: '1280:720',\n duration: 5\n}).waitForTaskOutput();\n```\n\nThe Node.js SDK accepts:\n- `fs.ReadStream` — file streams\n- `File` objects — from web APIs\n- `Blob` objects\n- `Buffer` \u002F `ArrayBuffer` \u002F typed arrays\n- `Response` objects — from `fetch()`\n- Async iterables\n\n### Python\n\n```python\nfrom runwayml import RunwayML\nfrom pathlib import Path\n\nclient = RunwayML()\n\n# Upload from a file path\nupload = client.uploads.create_ephemeral(\n Path('\u002Fpath\u002Fto\u002Fimage.jpg')\n)\n\n# Use the runway:\u002F\u002F URI\ntask = client.image_to_video.create(\n model='gen4.5',\n prompt_image=upload.runway_uri,\n prompt_text='The scene comes to life',\n ratio='1280:720',\n duration=5\n).wait_for_task_output()\n```\n\nThe Python SDK accepts:\n- `pathlib.Path` objects\n- `IOBase` objects (file-like objects)\n- Two-tuples of `(filename, content)`\n\n## REST API Upload (Manual)\n\nIf not using the SDK, the upload flow has three steps:\n\n### Step 1: Create an upload slot\n\n```javascript\nconst response = await fetch('https:\u002F\u002Fapi.dev.runwayml.com\u002Fv1\u002Fuploads', {\n method: 'POST',\n headers: {\n 'Authorization': `Bearer ${process.env.RUNWAYML_API_SECRET}`,\n 'X-Runway-Version': '2024-11-06',\n 'Content-Type': 'application\u002Fjson'\n },\n body: JSON.stringify({\n filename: 'image.jpg',\n type: 'ephemeral'\n })\n});\n\nconst { uploadUrl, fields, runwayUri } = await response.json();\n```\n\n### Step 2: Upload the file using the presigned URL\n\n```javascript\nconst formData = new FormData();\n\n\u002F\u002F Add all presigned form fields first\nfor (const [key, value] of Object.entries(fields)) {\n formData.append(key, value);\n}\n\n\u002F\u002F Add the file last\nformData.append('file', fileBuffer, 'image.jpg');\n\nawait fetch(uploadUrl, {\n method: 'POST',\n body: formData\n});\n```\n\n### Step 3: Use the `runway:\u002F\u002F` URI\n\n```javascript\nconst task = await fetch('https:\u002F\u002Fapi.dev.runwayml.com\u002Fv1\u002Fimage_to_video', {\n method: 'POST',\n headers: {\n 'Authorization': `Bearer ${process.env.RUNWAYML_API_SECRET}`,\n 'X-Runway-Version': '2024-11-06',\n 'Content-Type': 'application\u002Fjson'\n },\n body: JSON.stringify({\n model: 'gen4.5',\n promptImage: runwayUri,\n promptText: 'Animate this scene',\n ratio: '1280:720',\n duration: 5\n })\n});\n```\n\n## Upload Constraints\n\n| Constraint | Value |\n|-----------|-------|\n| Minimum file size | 512 bytes |\n| Maximum file size | 200 MB |\n| URI validity | 24 hours |\n| Requires credits | Yes (must have purchased credits) |\n\n## Integration Pattern\n\n### Express.js — Upload Endpoint with File Generation\n\n```javascript\nimport RunwayML from '@runwayml\u002Fsdk';\nimport express from 'express';\nimport multer from 'multer';\n\nconst client = new RunwayML();\nconst app = express();\nconst upload = multer({ storage: multer.memoryStorage() });\n\napp.post('\u002Fapi\u002Fimage-to-video', upload.single('image'), async (req, res) => {\n try {\n \u002F\u002F Upload the user's file to Runway\n const runwayUpload = await client.uploads.createEphemeral(req.file.buffer);\n\n \u002F\u002F Use the uploaded file for video generation\n const task = await client.imageToVideo.create({\n model: 'gen4.5',\n promptImage: runwayUpload.runwayUri,\n promptText: req.body.prompt || 'Animate this image',\n ratio: '1280:720',\n duration: 5\n }).waitForTaskOutput();\n\n res.json({ videoUrl: task.output[0] });\n } catch (error) {\n console.error('Generation failed:', error);\n res.status(500).json({ error: error.message });\n }\n});\n```\n\n### Next.js — Upload + Generate\n\n```typescript\n\u002F\u002F app\u002Fapi\u002Fimage-to-video\u002Froute.ts\nimport RunwayML from '@runwayml\u002Fsdk';\nimport { NextRequest, NextResponse } from 'next\u002Fserver';\n\nconst client = new RunwayML();\n\nexport async function POST(request: NextRequest) {\n const formData = await request.formData();\n const imageFile = formData.get('image') as File;\n const prompt = formData.get('prompt') as string;\n\n try {\n \u002F\u002F Upload file to Runway\n const upload = await client.uploads.createEphemeral(imageFile);\n\n \u002F\u002F Generate video from the uploaded image\n const task = await client.imageToVideo.create({\n model: 'gen4.5',\n promptImage: upload.runwayUri,\n promptText: prompt || 'Animate this image',\n ratio: '1280:720',\n duration: 5\n }).waitForTaskOutput();\n\n return NextResponse.json({ videoUrl: task.output[0] });\n } catch (error) {\n return NextResponse.json(\n { error: error instanceof Error ? error.message : 'Failed' },\n { status: 500 }\n );\n }\n}\n```\n\n### FastAPI — Upload + Generate\n\n```python\nfrom fastapi import FastAPI, UploadFile, Form, HTTPException\nfrom runwayml import RunwayML\n\napp = FastAPI()\nclient = RunwayML()\n\n@app.post(\"\u002Fapi\u002Fimage-to-video\")\nasync def image_to_video(image: UploadFile, prompt: str = Form(\"Animate this image\")):\n try:\n # Upload to Runway\n content = await image.read()\n upload = client.uploads.create_ephemeral((image.filename, content))\n\n # Generate video\n task = client.image_to_video.create(\n model=\"gen4.5\",\n prompt_image=upload.runway_uri,\n prompt_text=prompt,\n ratio=\"1280:720\",\n duration=5\n ).wait_for_task_output()\n\n return {\"video_url\": task.output[0]}\n except Exception as e:\n raise HTTPException(status_code=500, detail=str(e))\n```\n\n## Tips\n\n- **Always upload local files** before passing them to generation endpoints. Don't try to pass local file paths — they won't work.\n- **`runway:\u002F\u002F` URIs expire after 24 hours.** If you need to re-use an asset, upload it again.\n- **The SDK handles the presigned URL flow automatically** — prefer the SDK over manual REST calls.\n- **For models requiring image\u002Fvideo input** (image-to-video, video-to-video, character performance), upload the asset first, then pass the `runway:\u002F\u002F` URI.\n- **Maximum 200 MB per file** via uploads — larger than URL (16 MB) or data URI (5 MB) limits.\n",{"data":33,"body":36},{"name":4,"description":6,"user-invocable":34,"allowed-tools":35},false,"Read, Grep, Glob, Edit, Write",{"type":37,"children":38},"root",[39,48,89,94,101,106,147,155,168,174,201,214,220,227,731,736,813,819,967,972,1007,1013,1018,1024,1467,1473,1817,1830,2238,2244,2324,2330,2336,3309,3315,4230,4236,4437,4443,4508],{"type":40,"tag":41,"props":42,"children":44},"element","h1",{"id":43},"integrate-uploads",[45],{"type":46,"value":47},"text","Integrate Uploads",{"type":40,"tag":49,"props":50,"children":51},"blockquote",{},[52],{"type":40,"tag":53,"props":54,"children":55},"p",{},[56,62,64,71,73,79,81,87],{"type":40,"tag":57,"props":58,"children":59},"strong",{},[60],{"type":46,"value":61},"PREREQUISITE:",{"type":46,"value":63}," Run ",{"type":40,"tag":65,"props":66,"children":68},"code",{"className":67},[],[69],{"type":46,"value":70},"+rw-check-compatibility",{"type":46,"value":72}," first. 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