Use the Phaya SaaS backend to generate images, videos, audio, music, and run LLM chat completions via simple REST API calls. Use when the user wants to generate media, call AI models, or use the Phaya API for image/video/audio/text generation.
Phaya is a FastAPI backend that brokers AI media generation across KIE.ai (Sora 2, Veo 3.1, Seedance, Kling, Seedream, Suno), Google Gemini TTS, and OpenRouter LLMs.
All endpoints require a Bearer token or API key:
Authorization: Bearer <your_api_key>
Set these environment variables before using this skill:
export PHAYA_API_KEY="your_api_key_here" # required — all endpoints
export PHAYA_BASE="https://your-api-host/api/v1" # required — your Phaya instance URL
Get your profile and credit balance:
GET /api/v1/user/profile — full profileGET /api/v1/user/credits → { "credits_balance": 84.90, ... }Rate limit: 60 requests/minute per API key.
This skill calls a paid credit system. Every generation deducts real credits from your account. Video generation can cost 8–50 credits per job. Start with cheap endpoints (text-to-image at 1 credit) to verify connectivity before running expensive jobs. Credits are auto-refunded on failure, but not on successful jobs you don't use.
Recommendation: create a scoped API key with a small credit balance for initial testing.
Every generation costs credits deducted on job creation; auto-refunded on failure.
| Credits | Service |
|---|---|
| 0.5 | image-to-video (FFmpeg local), Sora 2 character creation |
| 1.0 | text-to-image (Z-Image) |
| 1.5 | Seedream 5.0 |
| 2–4 | Nano Banana 2 (1K/2K/4K resolution) |
| 3.0 | Text-to-music (Suno) |
| 2–35 | Seedance 1.5 Pro (resolution × duration × audio) |
| 8.0 | Sora 2 video |
| 1.21–1.82/sec | Kling 2.6 motion control (720p/1080p) |
| 15.0 | Veo 3.1 fast (veo3_fast) |
| 50.0 | Veo 3.1 quality (veo3) |
Every generation is async. Create endpoints return job_id immediately; poll the status endpoint.
POST /api/v1/<service>/create → { "job_id": "uuid" }
GET /api/v1/<service>/status/{job_id} → { "status": "...", "<media>_url": "..." }
Status values:
PENDING, QUEUED, PROCESSING, COMPLETED, FAILEDPENDING, PROCESSING, COMPLETED, FAILEDprocessing, completed, failed, cancelledResponse URL field by media type:
| Media type | Response field |
|---|---|
| Images | image_url |
| Videos | video_url |
| Audio / music | audio_url (music also returns audio_urls[]) |
| Sora 2 character | character_id (a string ID, not a URL) |
Poll every 3–5 seconds until the terminal status is reached.
import httpx, time
BASE = "https://your-api-host/api/v1"
HEADERS = {"Authorization": "Bearer YOUR_API_KEY"}
r = httpx.post(f"{BASE}/text-to-image/generate", headers=HEADERS, json={
"prompt": "A futuristic city at sunset, ultra-detailed",
"aspect_ratio": "16:9"
})
job_id = r.json()["job_id"]
while True:
s = httpx.get(f"{BASE}/text-to-image/status/{job_id}", headers=HEADERS).json()
if s["status"] == "COMPLETED":
print("Image URL:", s["image_url"])
break
if s["status"] == "FAILED":
raise RuntimeError("Job failed")
time.sleep(4)
r = httpx.post(f"{BASE}/sora2-text-to-video/create", headers=HEADERS, json={
"prompt": "A dragon flying over mountains at dawn",
"aspect_ratio": "landscape",
"n_frames": "10" # "10" or "15" as a string
})
job_id = r.json()["job_id"]
# Poll /sora2-text-to-video/status/{job_id} → s["video_url"]
r = httpx.post(f"{BASE}/phaya-gpt/chat/completions", headers=HEADERS, json={
"messages": [{"role": "user", "content": "Hello, what can you do?"}],
"stream": False
})
print(r.json()["message"]["content"]) # flat dict — NOT choices[0].message.content
Most features only need python3 (or curl) and your API key — all AI processing is remote.
Two optional features invoke local binaries on your machine:
POST /image-to-video/create (FFmpeg local) — requires ffmpeg installed locallyPOST /video-download/create (yt-dlp) — the download runs server-side via yt-dlp on the Phaya host, not locallyNo local AI models, GPU, or disk-intensive operations are required by this skill.