# Illusion Diffusion API

> ControlNet မှတစ်ဆင့် ထည့်သွင်းပုံပေါ်တွင် စာသားပရောမ့်ကို အခြေအနေသတ်မှတ်၍ illusion စတိုင်ပုံများကို ဖန်တီးသည်

လိုအပ်သော `image_url` နှင့် `prompt` ကို လက်ခံပြီး ပုံ၏ပုံသဏ္ဍာန်များကို ဖန်တီးထားသောပုံထဲသို့ ပေါင်းစပ်သည်။ optical-illusion အနုပညာအတွက် သင့်လျော်သည်။ မူရင်းပုံက ရလဒ်ကို မည်မျှအထိ လမ်းညွှန်မည်ကို သတ်မှတ်ရန် `controlnet_conditioning_scale`၊ `control_guidance_start` နှင့် `control_guidance_end` ကို ချိန်ညှိနိုင်သည်။ `negative_prompt`၊ 0 မှ 50 အထိ `guidance_scale`၊ inference step 0 မှ 80 နှင့် Euler သို့မဟုတ် DPM++ Karras SDE scheduler များကိုလည်း ထောက်ပံ့သည်။

- Model id: `illusion-diffusion`
- Made by: Open model
- Category: Text to Image
- Output: image
- Estimated price per run: $0.022 / run (cheapest run $0.022 (22 API credits))
- Run: `POST https://api.doitong.com/v1/run/illusion-diffusion`
- Quote: `POST https://api.doitong.com/v1/quote/illusion-diffusion`
- Docs page: https://doitong.com/my/developers/models/illusion-diffusion

## Pricing

Price: **$0.022 / run**. Estimated from the typical cost of one run. The quote is exactly what the run charges.

The full example input below costs $0.022 = 22 API credits (1 request × 22 API credits).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.com/v1/quote/illusion-diffusion` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

Send either the canonical name or the native field name (not both). Canonical names are the same across models.

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `image_url` | `image` | string (image URL) | yes |  |  | ထည့်သွင်းချက် ပုံ၏ URL။ |
| `prompt` |  | string | yes |  |  | ဖန်တီးလိုသောပုံကို အသေးစိတ်နိုင်သမျှ အသေးစိတ်ဖော်ပြပါ၊ ထိုသို့ပြုလုပ်ခြင်းဖြင့် အကောင်းဆုံးရလဒ် ရရှိမည်။ |
| `negative_prompt` |  | string | no | `` |  | ဖယ်ရှားလိုသော ပုံအသေးစိတ်များ၊ အရောင်များ၊ အရာဝတ္ထုများ၊ ရှုခင်းများ သို့မဟုတ် အသေးစိတ်အင်္ဂါရပ်များကို သတ်မှတ်သည့် အနုတ်လက္ခဏာ ပရောမ့်။ ဥပမာများမှာ နှုတ်ခမ်းမွှေး၊ ဝါးခြင်း သို့မဟုတ် ရုပ်ထွက်အရည်အသွေး နိမ့်ခြင်းတို့ ဖြစ်သည်။ |
| `guidance_scale` |  | number | no | `7.5` | ≤ 50, > 0 | ပုံရွေးချယ်မှုအတွက် ပရောမ့်လိုက်နာမှုအားကို CFG (Classifier Free Guidance) အဖြစ် ဖော်ပြထားသည်။ |
| `controlnet_conditioning_scale` |  | number | no | `1` |  | ControlNet conditioning တွင် အသုံးပြုသည့် scale။ |
| `control_guidance_start` |  | number | no | `0` | 0 – 1 |  |
| `control_guidance_end` |  | number | no | `1` | 0 – 1 |  |
| `seed` |  | integer \| null | no |  |  | ပုံဖန်တီးမှုအတွက် seed၊ ထည့်သွင်းချက်တွင် seed မပါပါက ကျပန်းရွေးချယ်ထားသောတန်ဖိုးကို ပါဝင်စေပြီး၊ ပါရှိပါက ပေးထားသောတန်ဖိုးကို ထပ်မံအသုံးပြုသည်။ |
| `scheduler` |  | enum (string) | no | `Euler` | `DPM++ Karras SDE`, `Euler` | ပုံမှ noise ဖယ်ရှားရန် အသုံးပြုသည့် scheduler သို့မဟုတ် sampler။ |
| `num_inference_steps` |  | integer | no | `40` | 0 – 80 | Stable Diffusion လုပ်ဆောင်မည့် ဖန်တီးမှုအဆင့် အရေအတွက်ကို ထိန်းချုပ်သည်။ အဆင့်များလေလေ နောက်ဆုံးပုံ ပိုမိုအသေးစိတ် ဖြစ်နိုင်သည်။ |
| `image_size` |  | object {width, height} \| enum (string) | no | `square_hd` | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | ကြိုတင်သတ်မှတ်ချက်များမှ ရွေးချယ်ထားသော သို့မဟုတ် စိတ်ကြိုက် width နှင့် height သတ်မှတ်ထားသော ထွက်ရလဒ်ပုံအရွယ်အစား။ စိတ်ကြိုက်အရွယ်အစားနှစ်ခုစလုံးသည် 8 ဖြင့်စားလို့ပြတ်ရမည်။ |
| `enable_safety_checker` |  | boolean | no | `true` |  | caller က ခွင့်ပြုချက်ရှိပါက false သတ်မှတ်ခြင်းဖြင့် စစ်ဆေးမှုကို လျှော့ချနိုင်သည်။ |

File parameters (`image`) take a public `https://` URL, a `data:` URI up to 10 MB, or the `file_url` of an upload from `POST /files`.

## Example input

Required parameters only:

```json
{
  "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/illusion-diffusion?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.com/v1";
const headers = {
  "Authorization": "Bearer " + process.env.DOITONG_API_KEY,
  "Content-Type": "application/json",
};

// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/illusion-diffusion?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);

// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
  res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
  request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
```

### Python

```python
import os
import requests

API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/illusion-diffusion", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
    raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])

# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
    request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
    raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
```

### MCP

The MCP server at `https://api.doitong.com/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "illusion-diffusion",
      "input": {
        "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "illusion-diffusion",
  "status": "succeeded",
  "created_at": "2026-10-20T12:00:00Z",
  "started_at": "2026-10-20T12:00:02Z",
  "completed_at": "2026-10-20T12:00:09Z",
  "queue_position": null,
  "cost": {
    "credits": 22,
    "usd": "0.022",
    "charged": 22,
    "refunded": 0
  },
  "input": {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "output": {
    "image": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768
    },
    "seed": 42
  },
  "outputs": [
    {
      "type": "image",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `image` | object {url, content_type, file_name, file_size, width, height} | ဖန်တီးထားသောပုံ၏ ဖိုင်အချက်အလက်။ |
| `seed` | integer | ပုံဖန်တီးမှုအတွက် seed၊ ထည့်သွင်းချက်တွင် seed မပါပါက ကျပန်းရွေးချယ်ထားသောတန်ဖိုးကို ပါဝင်စေပြီး၊ ပါရှိပါက ပေးထားသောတန်ဖိုးကို ထပ်မံအသုံးပြုသည်။ |

## Related models

- [FLUX.1 \[dev\]](https://doitong.com/developers/models/flux/dev.md) (`flux/dev`): $0.025 / megapixel
- [FLUX.1 \[schnell\]](https://doitong.com/developers/models/flux/schnell.md) (`flux/schnell`): $0.003 / megapixel
- [FLUX.2 \[dev\]](https://doitong.com/developers/models/flux-2.md) (`flux-2`): $0.012 / megapixel
- [FLUX.2 \[pro\]](https://doitong.com/developers/models/flux-2-pro.md) (`flux-2-pro`): $0.015 / megapixel of output image
- [GPT Image 2.5 Text to Image](https://doitong.com/developers/models/openai/gpt-image-2.5/flare/text-to-image.md) (`openai/gpt-image-2.5/flare/text-to-image`): from $0.008 / image
- [Ideogram V4.5](https://doitong.com/developers/models/ideogram/v4.5.md) (`ideogram/v4.5`): from $0.03 / image
- [Nano Banana 2](https://doitong.com/developers/models/nano-banana-2.md) (`nano-banana-2`): from $0.06 / image
- [Nano Banana Pro](https://doitong.com/developers/models/nano-banana-pro.md) (`nano-banana-pro`): from $0.15 / image

## Links

- HTML page: https://doitong.com/developers/models/illusion-diffusion
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=illusion-diffusion
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/illusion-diffusion