{ e.preventDefault(); e.stopPropagation(); }} style={{ position: "relative", width: "100%", height, overflow: "hidden", cursor: "ew-resize", userSelect: "none", WebkitUserSelect: "none" }}> {}

{}

{}

{} {}

{}

{beforeLabel}

{afterLabel}

{} {garmentImage &&

e.stopPropagation()} onTouchStart={e => e.stopPropagation()} onClick={e => { e.preventDefault(); e.stopPropagation(); openLightbox(); }} title={`${garmentLabel} — click to enlarge`} style={{ position: "absolute", bottom: "12px", right: "12px", height: "140px", maxWidth: "40%", borderRadius: "8px", overflow: "hidden", cursor: "zoom-in", background: "rgba(255,255,255,0.95)", border: "2px solid rgba(255,255,255,0.9)", boxShadow: "0 2px 12px rgba(0,0,0,0.45)", zIndex: 5, display: "flex", alignItems: "center", justifyContent: "center" }}> {garmentLabel}

}

{} {garmentImage && }

; }; FLUX Outpainting extends an image naturally in any direction, filling new regions with contextually coherent content in a single call. Useful for aspect-ratio changes, banner generation, social media reformatting, or giving a composition more room to breathe. Try it interactively and for free in our [FLUX Outpainting demo](https://flux-tools.bfl.ai/outpainting). ## Example output Drag the slider to compare the input image padded onto the target canvas (left) with the outpainted result (right). No prompt was used — the model extended the scene on its own. ## Endpoint Submit an outpainting job: ```http theme={null} POST https://api.bfl.ai/v1/flux-tools/outpainting-v1 x-key: $BFL_API_KEY ``` Poll for the result: ```http theme={null} GET https://api.bfl.ai/v1/get_result?id= x-key: $BFL_API_KEY ``` ## Quick start The API uses an asynchronous workflow: POST your input image (base64) and the target canvas dimensions to the endpoint. The model extends the existing scene naturally — no prompt is needed. Use the returned `polling_url` to check status until the image is ready. ```python Python theme={null} #!/usr/bin/env python3 import base64 import os import time import requests API_KEY = os.environ["BFL_API_KEY"] BASE = "https://api.bfl.ai" HEADERS = {"accept": "application/json", "x-key": API_KEY, "Content-Type": "application/json"} IMAGE_PATH = "/path/to/input.png" WIDTH, HEIGHT = 1024, 1024 REFERENCE_OFFSET_X = 100 # None = center horizontally REFERENCE_OFFSET_Y = 50 # None = center vertically with open(IMAGE_PATH, "rb") as f: image_b64 = base64.b64encode(f.read()).decode() payload = { "input_image": image_b64, "width": WIDTH, "height": HEIGHT, "output_format": "png", } if REFERENCE_OFFSET_X is not None: payload["reference_offset_x"] = REFERENCE_OFFSET_X if REFERENCE_OFFSET_Y is not None: payload["reference_offset_y"] = REFERENCE_OFFSET_Y submit = requests.post(f"{BASE}/v1/flux-tools/outpainting-v1", headers=HEADERS, json=payload) submit.raise_for_status() meta = submit.json() task_id = meta["id"] poll_url = meta.get("polling_url", f"{BASE}/v1/get_result?id={task_id}") while True: r = requests.get(poll_url, headers={"accept": "application/json", "x-key": API_KEY}) r.raise_for_status() result = r.json() status = result.get("status") if status == "Ready": print("Result URL:", result["result"]["sample"]) break if status in {"Error", "Request Moderated", "Content Moderated", "Task not found"}: raise RuntimeError(f"Outpainting failed with status: {status} | payload: {result}") time.sleep(1) ``` ```bash cURL theme={null} IMAGE_B64="$(base64 < /path/to/input.png | tr -d '\n')" jq -n \ --arg img "$IMAGE_B64" \ '{ input_image: $img, width: 1024, height: 1024, reference_offset_x: 100, reference_offset_y: 50, output_format: "png" }' > /tmp/outpaint_request.json curl -sS -X POST "https://api.bfl.ai/v1/flux-tools/outpainting-v1" \ -H "accept: application/json" \ -H "Content-Type: application/json" \ -H "x-key: $BFL_API_KEY" \ --data-binary @/tmp/outpaint_request.json # Poll for the result curl -sS "https://api.bfl.ai/v1/get_result?id=YOUR_TASK_ID" \ -H "accept: application/json" \ -H "x-key: $BFL_API_KEY" ``` ## Request parameters | Parameter | Type | Required | Description | | -------------------- | ------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | | `input_image` | base64 string | Yes | Reference image to expand | | `width` | integer | Yes | Target canvas width in pixels (`>=64`). `width × height` must not exceed 4 MP | | `height` | integer | Yes | Target canvas height in pixels (`>=64`). `width × height` must not exceed 4 MP | | `reference_offset_x` | integer | No | Left offset (px) of the reference image's top-left corner on the canvas. Negative values allowed. `None` = center horizontally | | `reference_offset_y` | integer | No | Top offset (px) of the reference image's top-left corner on the canvas. Negative values allowed. `None` = center vertically | | `auto_crop` | boolean | No | If `true`, crop the reference image to the canvas bounds when it extends beyond the edges. Defaults to `false` (out-of-bounds placements return `422`) | | `mode` | string | No | Quality/speed trade-off: `high` (default) or `fast`. See [Choosing a mode](#choosing-a-mode) | | `output_format` | string | No | `png` (default) or `jpeg` | **Maximum canvas size: 4 MP.** The target canvas area (`width × height`) cannot exceed 4 megapixels (4,194,304 pixels). You're free to choose any aspect ratio within that budget — for example `2048 × 2048`, `2730 × 1536` (16:9), or `1024 × 4096` — as long as the two dimensions multiplied together stay at or below 4 MP. Requests above this limit return `422`. ### Image placement `reference_offset_x` and `reference_offset_y` set the top-left corner of the reference image on the output canvas. Drag the reference below to see how the offsets relate to the canvas: You have two options for placing the reference image on the output canvas: * **Centered (default)** — provide the image, set `width` and `height`, and leave `reference_offset_x` / `reference_offset_y` as `None`. The image is centered automatically. * **Custom position** — set `reference_offset_x` and `reference_offset_y` to control exactly where the top-left corner of the reference image lands on the canvas. Negative values are allowed; if any part of the reference falls outside the canvas, either set `auto_crop: true` or the request will return `422`. ## Choosing a mode The `mode` parameter trades quality for speed. Both modes accept the same request fields (`input_image`, `width`, `height`, `reference_offset_x` / `reference_offset_y`, `auto_crop`, `prompt`) and produce the same response format. | Mode | Speed | Best for | | ---------------- | -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `high` (default) | Slower | The highest-fidelity results — recommended whenever fine detail, prompt adherence, or consistency with complex content in the source image matters. | | `fast` | Significantly faster | Naturally extending most scenes — landscapes, backgrounds, textures, and products. May produce lower fidelity in the extended region than `high`. Typically lower cost as well. | If you omit `mode`, the request runs in `high` mode. ```python theme={null} payload = { "input_image": image_b64, "width": 1024, "height": 1024, "mode": "fast", # opt into the faster path "output_format": "png", } ``` **Fast mode requirements.** Fast mode encodes the reference and pads it in latent space, so a few extra constraints apply on top of the shared ones. Requests that violate them return `422`: * `input_image` must be a **base64-encoded image** — image URLs are not supported in `fast` mode. * The placed reference must be **at least 64 px per side** and have an **aspect ratio of at most 8:1**. * The canvas (`width × height`) plus internal alignment padding must stay **at or below 4 MP**. A canvas right at the 4 MP limit may be rejected because of the added padding — reduce it slightly if so. Any `reference_offset_x` / `reference_offset_y`, `width`, and `height` are otherwise accepted; you don't need to pre-align them. ## Response format ### Initial response ```json theme={null} { "id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx", "polling_url": "https://api.bfl.ai/v1/get_result?id=xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" } ``` Always poll the URL returned in the response. ### Polling response (success) ```json theme={null} { "id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx", "status": "Ready", "result": { "sample": "https://delivery.bfl.ai/..." } } ``` When `status` is `"Ready"`, use `result.sample`. Signed delivery URLs are only valid for **10 minutes**. Retrieve your result within this timeframe. ## Tips for best results * The model extends the existing scene naturally. The endpoint is tuned to continue the input image's content, lighting, and composition on its own. * The model was trained on green, blue, and magenta fill colors and performs best with those internally — no caller action needed; the server handles fill colors automatically. * Keep total output dimensions reasonable. The canvas area (`width × height`) is capped at 4 MP, and very large canvases or extreme aspect ratios may reduce quality even within that limit. ## Troubleshooting * **`403 Forbidden`** — your API key is missing or your project doesn't have access to this endpoint. * **`422` / validation errors** — check base64 encoding and that `width` / `height` are present, at least `64`, and that their product (`width × height`) does not exceed 4 MP. The endpoint rejects unknown fields: use `reference_offset_x` / `reference_offset_y` (not the older `bbox_x1` / `bbox_y1`). * **`422` in `fast` mode specifically** — fast mode adds extra geometry constraints (see [Choosing a mode](#choosing-a-mode)): pass a base64 image (not a URL), keep the placed reference ≥ 64 px per side and within an 8:1 aspect ratio, and leave headroom below 4 MP so internal alignment padding still fits. Switch to `mode: "high"` if your placement can't satisfy these. * **Visible seams** — give the model more canvas room around the reference image. For the full list of HTTP status codes and polling response types returned by the API, see the [Errors reference](/api_integration/errors).