# Seedream V4.5 Text to Image

> A next-generation text-to-image model by ByteDance, capable of high-fidelity generation, precise text rendering, and complex stylistic control for highly detailed visual compositions.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/bytedance/seedream-v4.5/text-to-image`
- **Model ID**: `bytedance/seedream-v4.5/text-to-image`
- **Category**: text-to-image
- **Kind**: inference
- **Tags**: stylized, transform

## Pricing

- **Price**: $0.04 per output

## Request Lifecycle

This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/bytedance/seedream-v4.5/text-to-image` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the model; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>` (exact key=value matches, AND-ed). The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/bytedance/seedream-v4.5/text-to-image/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/bytedance/seedream-v4.5/text-to-image/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/bytedance/seedream-v4.5/text-to-image/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`seed`** (`integer`, _optional_):
  Random seed to control the stochasticity of image generation.

- **`prompt`** (`string`, _required_):
  The text prompt used to generate the image

- **`image_size`** (`ImageSize | image_size_enum`, _optional_):
  The size of the generated image. Use a preset string (e.g. '4_3_1k') or a custom {width, height} object.
  - Default: `"4_3_1k"`
  - Options: `"1_1_512"`, `"1_1_1k"`, `"1_1_2k"`, `"1_1_4k"`, `"16_9_512"`, `"16_9_1k"`, `"16_9_2k"`, `"16_9_4k"`, `"9_16_512"`, `"9_16_1k"`, `"9_16_2k"`, `"9_16_4k"`, `"4_3_512"`, `"4_3_1k"`, `"4_3_2k"`, `"4_3_4k"`, `"3_4_512"`, `"3_4_1k"`, `"3_4_2k"`, `"3_4_4k"`, `"3_2_512"`, `"3_2_1k"`, `"3_2_2k"`, `"3_2_4k"`, `"2_3_512"`, `"2_3_1k"`, `"2_3_2k"`, `"2_3_4k"`, `"4_5_512"`, `"4_5_1k"`, `"4_5_2k"`, `"4_5_4k"`, `"5_4_512"`, `"5_4_1k"`, `"5_4_2k"`, `"5_4_4k"`, `"4_1_512"`, `"4_1_1k"`, `"4_1_2k"`, `"4_1_4k"`, `"1_4_512"`, `"1_4_1k"`, `"1_4_2k"`, `"1_4_4k"`, `"8_1_512"`, `"8_1_1k"`, `"8_1_2k"`, `"8_1_4k"`, `"1_8_512"`, `"1_8_1k"`, `"1_8_2k"`, `"1_8_4k"`, `"21_9_512"`, `"21_9_1k"`, `"21_9_2k"`, `"21_9_4k"`

- **`num_images`** (`integer`, _optional_):
  The number of images to generate. Each generated image is billed.
  - Default: `1`
  - Range: `1` to `4`

- **`enable_safety_checker`** (`boolean`, _optional_):
  If set to true, the safety checker will be enabled. This setting can only be configured via the API.
  - Default: `true`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "seed": 458102,
  "prompt": "A wide-angle landscape photograph of a bustling 1920s-style street corner on a rainy evening. The wet cobblestones reflect the warm, amber glow of classic streetlamps. A vintage, deep red tram is passing by, slightly blurred from motion. On the side of an elegant brick building, a large, ornate billboard clearly reads \"The Grand Continental\" in crisp, bold gold lettering. The scene captures a nostalgic, cinematic atmosphere with a shallow depth of field focusing on a smartly dressed gentleman in a trench coat holding a black umbrella in the foreground.",
  "image_size": "landscape_16_9",
  "max_images": 1,
  "num_images": 1,
  "enable_safety_checker": true
}
```

**Output**

```json
[
  "https://media.modelrunner.ai/3r4U1rMd0CNYHTGsVNuyR.png"
]
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/bytedance/seedream-v4.5/text-to-image \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "seed": 458102,
  "prompt": "A wide-angle landscape photograph of a bustling 1920s-style street corner on a rainy evening. The wet cobblestones reflect the warm, amber glow of classic streetlamps. A vintage, deep red tram is passing by, slightly blurred from motion. On the side of an elegant brick building, a large, ornate billboard clearly reads \"The Grand Continental\" in crisp, bold gold lettering. The scene captures a nostalgic, cinematic atmosphere with a shallow depth of field focusing on a smartly dressed gentleman in a trench coat holding a black umbrella in the foreground.",
  "image_size": "landscape_16_9",
  "max_images": 1,
  "num_images": 1,
  "enable_safety_checker": true
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

```javascript
import { modelrunner } from "@modelrunner/client";

const result = await modelrunner.subscribe("bytedance/seedream-v4.5/text-to-image", {
  input: {
  "seed": 458102,
  "prompt": "A wide-angle landscape photograph of a bustling 1920s-style street corner on a rainy evening. The wet cobblestones reflect the warm, amber glow of classic streetlamps. A vintage, deep red tram is passing by, slightly blurred from motion. On the side of an elegant brick building, a large, ornate billboard clearly reads \"The Grand Continental\" in crisp, bold gold lettering. The scene captures a nostalgic, cinematic atmosphere with a shallow depth of field focusing on a smartly dressed gentleman in a trench coat holding a black umbrella in the foreground.",
  "image_size": "landscape_16_9",
  "max_images": 1,
  "num_images": 1,
  "enable_safety_checker": true
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "bytedance/seedream-v4.5/text-to-image",
        arguments={
  "seed": 458102,
  "prompt": "A wide-angle landscape photograph of a bustling 1920s-style street corner on a rainy evening. The wet cobblestones reflect the warm, amber glow of classic streetlamps. A vintage, deep red tram is passing by, slightly blurred from motion. On the side of an elegant brick building, a large, ornate billboard clearly reads \"The Grand Continental\" in crisp, bold gold lettering. The scene captures a nostalgic, cinematic atmosphere with a shallow depth of field focusing on a smartly dressed gentleman in a trench coat holding a black umbrella in the foreground.",
  "image_size": "landscape_16_9",
  "max_images": 1,
  "num_images": 1,
  "enable_safety_checker": true
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/bytedance/seedream-v4.5/text-to-image)
- [OpenAPI Schema](https://modelrunner.ai/models/bytedance/seedream-v4.5/text-to-image/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/bytedance/seedream-v4.5/text-to-image/llms.txt)