fotohub 1.9.2

Official Python SDK for the FOTOhub AI Platform

Generate images, videos, music, and chat with LLMs — all through a single, unified Python client.
Supports 80+ AI models from 10+ providers with built-in credit management.

---

Features

• Image Generation — 25+ models including SeedDream 5.0, Flux, Imagen, Gemini, GPT Image, and more
• IDA Q 1.0 — FOTOhub's proprietary self-hosted image model, submitted and polled for you
• Video Generation — Veo, Seedance and more; the call blocks and returns the finished URL
• Music, SFX and Speech — AI-generated music, sound effects and TTS
• Virtual Try-On — dress a person photo in a garment, or a full top + bottom outfit in one call
• Chat / LLM — OpenAI-shaped chat completions, plus premium Claude-class models
• Gabriel AI — routes a natural-language request to the right feature and model
• 3D Generation — image-to-mesh and text-to-mesh jobs with polling helpers
• Stability Tools — upscale, erase, inpaint, outpaint, recolor, style transfer
• Billing — balance, credits, pricing, top-ups, transactions and invoices in USD
• Webhooks — register, test and inspect delivery logs
• Translation — multi-language translation
• Sync + Async — both synchronous and asynchronous clients included
• Automatic Retries — exponential backoff with configurable retry logic
• Fully Typed — complete type annotations and a py.typed marker

Installation

pip install fotohub

Requires Python 3.9 or higher.

Quick Start

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

# Generate an image
result = client.generate_image(prompt="A mountain landscape at golden hour")
print(result["images"][0])

Every method returns the API's JSON as a plain dict — index it, don't use attribute access.

Authentication

Get your API key from fotohub.app/settings/api.

from fotohub import FotoHub

# Option 1: Pass directly
client = FotoHub(api_key="fh_live_...")

# Option 2: Environment variable
# export FOTOHUB_API_KEY=fh_live_...
client = FotoHub()

The SDK authenticates via both Authorization: Bearer and x-api-key headers.

Usage Examples

Image Generation

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

# Basic generation (default: seedream-5-0-260128)
result = client.generate_image(
    prompt="A serene Japanese garden with cherry blossoms",
)
print(result["images"][0])

# Advanced options
result = client.generate_image(
    prompt="Cyberpunk cityscape at night, neon reflections on wet streets",
    model="flux-2-pro",
    width=1280,
    height=720,
    num_images=2,
    negative_prompt="blurry, low quality",
    seed=42,
)

for url in result["images"]:
    print(url)
print(f"{result['credits_used']} credits, ${result['billing']['usd_charged']}")

guidance_scale and steps are not accepted by this endpoint — passing them raises TypeError in the SDK. Prefer aspect_ratio over manual width/height; the API picks dimensions the chosen model actually supports.

IDA Q 1.0 (FOTOhub's own model) runs on a single-GPU queue, so it has its own method that submits and polls for you — 30 s at 1K, up to ~3.5 min at 2K:

result = client.generate_ida_q(
    prompt="Portret kobiety w świetle porannym",   # any language
    aspect_ratio="4:3",
    image_size="1.5K",
)
print(result["images"][0])

Video Generation

Video generation is synchronous: the request stays open until the render finishes, so the returned dict already carries the finished video_url. There is no job to poll. Raise timeout on the client if your model is a slow one.

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...", timeout=600.0)

result = client.generate_video(
    prompt="A drone shot flying over a tropical beach at sunrise",
    model="veo-3.1-generate-001",
    duration=5,
    aspect_ratio="16:9",
)
print(result["video_url"])
print(f"{result['credits_used']} credits")

wait_for_video() still exists but is deprecated — it emits a DeprecationWarning and returns the dict you pass it unchanged.

Image-to-Video:

result = client.generate_video(
    prompt="Camera slowly zooms in, subtle parallax motion",
    image_url="https://example.com/photo.jpg",
)

Seedance — long clips and video editing

Seedance is the one video family that runs asynchronously: the API answers 202 with a job_id instead of a finished video, so generate_video() cannot consume it. Use generate_seedance(), which submits the job and then polls until it finishes.

seedance-2-5 renders 4–30 seconds in a single clip — the longest we offer — and its audio track costs nothing extra. The trade-off is resolution: 480p and 720p only, so if you need 1080p or 4K stay on seedance-2-0-pro (4–15s).

result = client.generate_seedance(
    prompt="A lone hiker crossing a snowfield, wind picking up, wide drone shot",
    duration=30,
    resolution="720p",
    generate_audio=True,        # free
)
print(result["video_url"])
print(f"{result['credits_used']} credits")   # ~435 for 30s @ 720p

Rates are per second: 14.5 credits/s at 720p, 6.4 at 480p. Draft at 480p (a 5s test costs 32 credits) and re-render the take you like at 720p.

Edit or extend an existing video — pass reference_videos and duration=-1 to keep the source length:

edited = client.generate_seedance(
    prompt="Make it golden hour, warmer light on the subject's face",
    reference_videos=["https://example.com/clip.mp4"],
    duration=-1,
)

A video reference bills at the higher 17.6 credits/s (720p) because the source frames are charged as input.

Face consistency — register a portrait once, then reuse the asset id:

asset = client.register_video_asset("https://example.com/face.jpg")
result = client.generate_seedance(
    prompt="The same woman walking through a night market, neon reflections",
    asset_ids=[asset["asset_id"]],
    duration=10,
)

generate_seedance() raises fotohub.TimeoutError if the job is still running when timeout (default 1800s) expires — the message carries the job_id so you can keep polling — and FotoHubError if the job fails. Note that fotohub.TimeoutError is a FotoHubError subclass, not Python's built-in TimeoutError; import it as from fotohub.exceptions import TimeoutError as FotoHubTimeoutError if the distinction matters. AsyncFotoHub exposes the same two methods with await.

Music, SFX and Speech

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

track = client.generate_music(
    prompt="Upbeat electronic track with heavy bass and synth arpeggios",
    duration=30,          # integer seconds, capped at 300
    genre="electronic",
    instrumental=True,
)
print(track["audio_url"], track["duration"], track["credits_used"])

sfx = client.generate_sfx(prompt="Heavy wooden door slamming shut", duration=5)
print(sfx["audio_url"])

speech = client.generate_speech(text="Dzień dobry!", language="pl", speed=1.0)
print(speech["audio_url"])

Music costs 5 credits up to 30 s, 10 up to 60 s, 25 beyond that.

Virtual Try-On

A try-on is a job, not a blocking call: a render takes about 11 seconds, so you submit and then wait.

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

job = client.tryon(
    person_image_url="https://example.com/person.jpg",
    garment_image_url="https://example.com/shirt.png",
    category="tops",              # "tops" | "bottoms" | "one-pieces"
    garment_photo_type="flat-lay",
)
result = client.wait_for_tryon(job["job_id"])
print(result["images"][0])

Pass garments to dress a top and a bottom in one job. The API applies the top first, feeds that render into the second pass, and charges 3 credits instead of 4:

job = client.tryon(
    person_image_url="https://example.com/person.jpg",
    garments=[
        {"garment_image_url": "https://example.com/tee.png", "category": "tops"},
        {"garment_id": "0f1e2d3c-...", "category": "bottoms"},   # or a catalogue id
    ],
)
result = client.wait_for_tryon(job["job_id"], timeout=60)

# If the second pass failed, the top-only render still comes back and one credit
# is refunded — so check before calling it a finished outfit.
partial = (result.get("metadata") or {}).get("partial_failure")
if partial:
    print(f"The {partial['slot']} is missing:", result["images"][0])

Exactly one top plus one bottom is required — two tops, three garments, or a one-pieces in the array are rejected with 400. Hats and shoes are not supported by the model at all.

Chat Completions (OpenAI-Compatible)

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

response = client.chat(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."},
    ],
    model="gpt-4o",
    temperature=0.7,
    max_tokens=1000,
)
print(response["choices"][0]["message"]["content"])
print(response["credits_used"])

chat() accepts exactly four model IDs — gemini-flash (default), gemini-pro, gpt-4o, claude-sonnet. Anything else is rejected with 400. For premium Claude-class models use chat_claude() or chat_bedrock(), which take a full model ID and a system= prompt.

credits_used is the authoritative charge; usage is passed through from the provider and can be {}.

Streaming Chat

chat(stream=True) is not supported and raises ValueError. /v1/ai/chat/completions accepts the flag for OpenAI compatibility and then ignores it, returning one complete JSON body. The stream iterator would find no SSE frames in that body and yield nothing while the request was still billed, so the SDK refuses before sending.

The one streaming endpoint on the platform is POST /v1/ai/agent/stream. It has no SDK wrapper yet — call it over plain HTTP. Frames carry a type (text_delta, tool_use, done, error) and the stream ends at data: [DONE]:

import json
import os

import requests

resp = requests.post(
    "https://apis.fotohub.app/v1/ai/agent/stream",
    headers={"Authorization": f"Bearer {os.environ['FOTOHUB_API_KEY']}"},
    json={
        "model": "claude-sonnet-4.6",
        "messages": [{"role": "user", "content": "Write a short poem about the sea."}],
    },
    stream=True,
)
resp.raise_for_status()

for line in resp.iter_lines():
    if not line:
        continue
    payload = line.decode("utf-8")
    if not payload.startswith("data: "):
        continue
    data = payload[6:]
    if data == "[DONE]":          # the only reliable terminator
        break
    frame = json.loads(data)
    if frame["type"] == "text_delta":
        print(frame["text"], end="", flush=True)
    elif frame["type"] == "error":
        raise RuntimeError(frame["message"])
print()

Two things to know about that endpoint: the done frame is optional (it is omitted when the turn produced no tokens, and replaced by error when generation succeeded but settlement failed), and abandoning the stream still bills you — the server settles the tokens it already generated.

Gabriel AI (Model Routing)

Gabriel returns a routing decision, not a completion: it tells you which feature and model to use, and you make that call yourself. An API key is required.

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

decision = client.gabriel_classify(
    "I want to create a logo for my coffee shop",
    language="en",
    context={"user_tier": "pro"},
)
print(decision["action"])             # route | answer | workflow | error
print(decision.get("target"))         # the feature to send the user to
print(decision.get("model_selected"))
print(decision.get("credits_estimated"))

# Autocomplete as the user types, and idle suggestions for a dashboard:
print(client.gabriel_suggest("make me a log", tab="image"))
print(client.gabriel_recommend(credits_remaining=40))

Storage & S3 Buckets

Dedicated S3 buckets live under /v1/storage/s3/* and are not wrapped by this SDK — call them over plain HTTP for now:

import httpx

api = httpx.Client(
    base_url="https://apis.fotohub.app",
    headers={"Authorization": "Bearer fh_live_..."},
)

buckets = api.get("/v1/storage/s3/buckets").json()

upload = api.post(
    f"/v1/storage/s3/buckets/{buckets[0]['id']}/objects/presign-upload",
    json={"key": "images/photo.jpg", "content_type": "image/jpeg", "expires_in": 3600},
).json()
# PUT your bytes to upload["url"]

Translation

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

result = client.translate("Hello, how are you?", "pl")
print(result["translated_text"])   # "Cześć, jak się masz?"
print(result["source_language"])   # "auto"

text and target_language are positional. On a provider timeout the endpoint returns your input text unchanged rather than an error — compare against the input if that matters to you.

Billing & Usage

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

balance = client.get_balance()
print(balance["credits"]["remaining_4h"], "credits left in this 4h window")
print(balance["wallet"]["balance"], balance["wallet"]["currency"])   # USD

print(client.get_transactions(page=1, page_size=20))
print(client.get_invoices())
print(client.estimate_cost([{"type": "generate_image", "model": "flux-2-pro", "count": 10}]))

Per-endpoint analytics live at GET /v1/usage (JWT auth, fixed 30-day window) and have no SDK helper — see the Usage & Analytics docs.

Topping up. Either take a fixed package or name your own amount:

for pkg in client.get_topup_packages():
    # The slugs are historical (topup-50 is now the $15 package) — read
    # amount_usd, never the number in the slug.
    print(pkg["slug"], pkg["amount_usd"], f"+{pkg['bonus_pct']}% bonus credits")

session = client.create_topup("topup-100")       # a package — this one is $25
print(session["checkout_url"])

session = client.topup_wallet(40.0)              # an arbitrary amount, $10–$15000
print(session["checkout_url"])

The wallet is denominated in USD. A customer in Poland can still pay in złoty — pass pay_currency="pln" and Stripe offers BLIK, card and bank transfer while the wallet is credited the USD amount you asked for:

session = client.topup_wallet(40.0, pay_currency="pln")

Async Client

Every method is available as an async variant via AsyncFotoHub:

import asyncio
from fotohub import AsyncFotoHub

async def main():
    async with AsyncFotoHub(api_key="fh_live_...") as client:
        # Generate images concurrently
        results = await asyncio.gather(
            client.generate_image(prompt="A sunset over mountains"),
            client.generate_image(prompt="A forest in morning mist"),
            client.generate_image(prompt="An ocean wave at golden hour"),
            return_exceptions=True,
        )
        for r in results:
            if isinstance(r, Exception):
                print(f"failed: {r}")
            else:
                print(r["images"][0])

asyncio.run(main())

return_exceptions=True matters here: without it, one failed generation cancels the gather while the others keep running server-side — and you are still billed for them.

await client.chat(..., stream=True) raises ValueError on the async client too, for the same reason as the sync one.

Error Handling

The SDK raises typed exceptions for all error conditions:

from fotohub import (
    FotoHub,
    AuthError,
    InsufficientCreditsError,
    RateLimitError,
    ValidationError,
    ServerError,
    TimeoutError,
    VideoJobTimeoutError,
)

client = FotoHub(api_key="fh_live_...")

try:
    result = client.generate_image(prompt="test")
except AuthError as e:
    # Invalid or missing API key (HTTP 401/403)
    print(f"Authentication failed: {e}")
except InsufficientCreditsError as e:
    # Not enough credits (HTTP 402)
    print(f"Out of credits: {e}")
except RateLimitError as e:
    # Too many requests (HTTP 429)
    print(f"Rate limited. Retry after {e.retry_after}s")
except ValidationError as e:
    # Invalid parameters (HTTP 400/422)
    print(f"Invalid request: {e.errors}")
except ServerError as e:
    # Server error (HTTP 5xx)
    print(f"Server error: {e}")
except TimeoutError as e:
    # Request timed out
    print(f"Timed out: {e}")
except VideoJobTimeoutError as e:
    # Video polling exceeded max_wait
    print(f"Video job {e.job_id} timed out")

Exception Hierarchy

Exception	HTTP Status	Description
FotoHubError	Any	Base exception for all SDK errors
AuthError	401, 403	Invalid or missing API key
InsufficientCreditsError	402	Account lacks sufficient credits
RateLimitError	429	Rate limit exceeded
ValidationError	400, 422	Invalid request parameters
ServerError	5xx	Server-side error
TimeoutError	—	Request timed out or connection failed
VideoJobTimeoutError	—	Video polling exceeded max_wait (legacy — video is synchronous now)

All exceptions include status_code and response_body attributes for debugging.

InsufficientCreditsError.credits_required / .credits_available and ValidationError.errors are populated only when the response carries those keys. The API returns FastAPI's {"detail": "..."} envelope, so in practice they are None / [] — read str(e) or e.response_body for the real reason.

Two failure modes that are not exceptions and need an explicit check:

• chat(stream=True) raises ValueError before sending — see Streaming Chat.
• A partially failed try-on outfit completes rather than fails. The top-only render comes back, one credit is refunded, and result["metadata"]["partial_failure"] says which slot is missing.

Configuration

from fotohub import FotoHub

client = FotoHub(
    api_key="fh_live_...",
    base_url="https://apis.fotohub.app",  # Custom API endpoint
    timeout=120.0,                         # Request timeout (seconds)
    max_retries=3,                         # Max retry attempts
)

Environment Variables

Variable	Description	Default
FOTOHUB_API_KEY	API key for authentication	—
FOTOHUB_BASE_URL	Override API base URL	https://apis.fotohub.app

Retry Behavior

The SDK automatically retries on transient failures:

• HTTP 429 — Rate limit (respects Retry-After header)
• HTTP 500, 502, 503, 504 — Server errors
• Connection timeouts — Network failures

Backoff schedule: 0.5s → 1s → 2s → 4s → ... (capped at 30s).

Context Managers

Both clients support context managers for automatic resource cleanup:

# Sync
with FotoHub(api_key="fh_live_...") as client:
    result = client.generate_image(prompt="test")

# Async
async with AsyncFotoHub(api_key="fh_live_...") as client:
    result = await client.generate_image(prompt="test")

API Reference

FotoHub / AsyncFotoHub

On AsyncFotoHub every method below is a coroutine. The two classes are otherwise identical, with one exception: gabriel_stream() is sync-only.

Generation

Method	Description
generate_image(prompt, *, model, width, height, aspect_ratio, num_images, negative_prompt, style, seed)	Generate images from text
generate_ida_q(prompt, *, aspect_ratio, image_size, num_images, seed, poll_interval, timeout)	IDA Q 1.0 — submits and polls to completion
edit_image(image_url, prompt, *, mode, mask_url, model)	Inpaint, remove background, upscale
remove_background(image_url) / upscale_image(image_url, *, scale)	edit_image convenience wrappers
generate_video(prompt, *, model, duration, aspect_ratio, image_url, resolution)	Generate a video — blocks, returns video_url. Not for Seedance
generate_seedance(prompt, *, model, duration, resolution, generate_audio, reference_videos, asset_ids, ...)	Seedance 4–30s — submits and polls to completion
register_video_asset(image_url)	Register a face for Seedance asset_ids — free
generate_music(prompt, *, model, duration, genre, mood, tempo, instrumental)	Generate music from text
generate_sfx(prompt, *, duration)	Generate a sound effect
generate_speech(text, *, voice_id, model, language, speed, pitch)	Text to speech
transcribe(audio_url, *, language)	Speech to text
generate_3d(mode, model, *, image, prompt, quality, format, options)	Start a 3D mesh job
get_3d_status(job_id) / wait_for_3d(job_id, ...)	Poll a 3D job
tryon(person_image_url, *, garment_image_url, garment_id, category, garments, ...)	Start a virtual try-on job
get_tryon_status(job_id) / wait_for_tryon(job_id, ...)	Poll a try-on job

Language

Method	Description
chat(messages, *, model, temperature, max_tokens)	Chat completion. stream=True raises ValueError
chat_claude(messages, *, model, temperature, max_tokens, system)	Premium Claude-class chat
chat_bedrock(messages, *, model, temperature, max_tokens, system)	Chat via Bedrock
analyze_image(image_url, *, features)	Vision analysis
enhance_prompt(prompt, *, style)	Rewrite a prompt for image models
translate(text, target_language, *, source_language)	Translate text
gabriel_classify(prompt, *, language, context, enhance_prompt)	Route a request to a feature + model
gabriel_stream(prompt, *, language, context)	Same, streamed as SSE frames
gabriel_suggest(partial, *, tab, page)	Autocomplete suggestions
gabriel_recommend(*, page, credits_remaining, has_brand, recent_actions)	Idle recommendations

Stability tools

stability_tools(), stability_run(tool_id, image_base64, ...), and the named wrappers stability_upscale, stability_remove_background, stability_erase, stability_inpaint, stability_outpaint, stability_search_replace, stability_recolor, stability_style_transfer.

Billing, tiers and webhooks — all amounts in USD

Method	Description
get_balance()	Tier, credit counters, wallet balance, overage
get_credits()	Credit breakdown and per-operation costs
get_pricing() / get_plans()	Public price catalogue, subscription plans
estimate_cost(operations)	Price a batch before running it
get_topup_packages() / create_topup(package)	Wallet top-up catalogue and checkout
get_wallet() / topup_wallet(amount_usd, *, pay_currency)	Wallet state and an arbitrary-amount top-up
get_transactions(*, page, page_size, type_filter) / get_invoices()	History
set_overage_limit(hard_limit_usd, *, project_id)	Hard monthly overage cap
get_tier_catalog() / get_current_tier() / compare_tiers() / subscribe_tier(slug)	API tier plans
apply_enterprise(company_name, contact_email, expected_usage, use_case, *, notes)	Enterprise enquiry
list_webhooks() / create_webhook(name, url, events, *, headers) / update_webhook(id, **kw) / delete_webhook(id) / test_webhook(id) / get_webhook_logs(id)	Webhook management

topup_wallet(pay_currency="pln") keeps the wallet in USD while letting a Polish customer pay at Stripe in PLN (BLIK, card, bank transfer).

Storage (/v1/storage/s3/*) and per-endpoint analytics (GET /v1/usage) have no SDK wrappers yet — call them over HTTP.

Type Safety

The SDK ships a py.typed marker and full annotations on every parameter. Responses are returned as dict[str, Any] — the API's JSON, unwrapped:

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

result = client.generate_image(prompt="test")
url: str = result["images"][0]
credits: float = result["credits_used"]
charged: float = result["billing"]["usd_charged"]

fotohub.models also ships Pydantic v2 models (ImageGenerationResponse, ImageResult, …), but no client method returns them — they are not exported from the package root and are kept only for callers that want to validate a payload themselves.

Requirements

Dependency	Version
Python	>= 3.9
httpx	>= 0.24
pydantic	>= 2.0

Contributing

We welcome contributions! To get started:

# Clone the repository
git clone https://github.com/fotohubapp/sdk-python.git
cd sdk-python

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check .

# Run type checking
mypy fotohub/

Please ensure all tests pass and type checks are clean before submitting a pull request.

Links

• Documentation
• API Reference
• FOTOhub Platform
• Changelog
• Issue Tracker

License

MIT License. See LICENSE for details.