# FLUX.1 [dev] with Controlnets and Loras · Differential Diffusion API

> 由變化圖驅動的 FLUX.1 [dev] 影像編輯，可設定各區域的修改強度

逐區域轉換來源影像，使用變化圖控制各區域的修改程度。需要 `prompt`、`image_url` 和 `change_map_image_url`。也接受 LoRA、ControlNet、IP-Adapter 與參考影像。`num_inference_steps` 為 1 到 50，`num_images` 最多為 10，輸出格式為 png 或 jpeg。

- Model id: `flux-general/differential-diffusion`
- Made by: Black Forest Labs
- Category: Image Editing
- Output: image
- Estimated price per run: $0.138 / image (cheapest run $0.138 (138 API credits))
- Run: `POST https://api.doitong.ru/v1/run/flux-general/differential-diffusion`
- Quote: `POST https://api.doitong.ru/v1/quote/flux-general/differential-diffusion`
- Docs page: https://doitong.com/zh-TW/developers/models/flux-general/differential-diffusion

## Pricing

Price: **$0.138 / image**. Estimated from the typical cost of one run, per output (`num_images`). The quote is exactly what the run charges.

The full example input below costs $0.138 = 138 API credits (1 image × 138 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.ru/v1/quote/flux-general/differential-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 |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  |  | 引導圖像生成的提示詞。 |
| `image_size` |  | object {width, height} \| enum (string) | no |  | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | 生成圖像的尺寸。 |
| `num_inference_steps` |  | integer | no | `28` | 1 – 50 | 要執行的推論步數。 |
| `seed` |  | integer \| null | no |  |  | 可重現圖像的種子：模型版本不變時，相同的提示詞與種子會產生相同圖像。 |
| `loras` |  | array of object {path, scale} | no | `[]` |  | 其組合效果會產生生成圖像的 LoRA，數量不限。 |
| `loras[].path` |  | string | yes |  |  | LoRA 權重的位置，以 URL 或路徑指定。 |
| `loras[].scale` |  | object \| number | no | `1` |  | LoRA 權重合併至基礎模型前套用的縮放。提供形式為 {"layer_name":layer_scale} 的字典可設定個別層級縮放；未指定的層級使用 1.0。 |
| `control_loras` |  | array of object {path, scale, control_image_url, preprocess} | no | `[]` |  | 合併為組合設定、用於生成最終圖像的控制圖像 LoRA；提供的數量不限。 |
| `control_loras[].path` |  | string | yes |  |  | LoRA 權重的位置，以 URL 或路徑指定。 |
| `control_loras[].scale` |  | object \| number | no | `1` |  | LoRA 權重合併至基礎模型前套用的縮放。提供形式為 {"layer_name":layer_scale} 的字典可設定個別層級縮放；未指定的層級使用 1.0。 |
| `control_loras[].control_image_url` |  | string (image URL) | yes |  |  | 提供控制圖像的圖像 URL。 |
| `control_loras[].preprocess` |  | enum (string) | no | `None` | `canny`, `depth`, `None` | 套用於提供圖像的預處理方法。 |
| `controlnets` |  | array of object {path, config_url, variant, control_image_url, mask_image_url, mask_threshold, conditioning_scale, start_percentage, end_percentage} | no | `[]` |  | 用於圖像生成的 ControlNet 設定，目前限用單一 ControlNet。 |
| `controlnets[].path` |  | string | yes |  |  | ControlNet 權重位置，可指定為 URL 或路徑。 |
| `controlnets[].config_url` |  | string \| null | no |  |  | 可選的 URL，指向 ControlNet 的 config.json 檔案。 |
| `controlnets[].variant` |  | string \| null | no |  |  | 搭配 Hugging Face 儲存庫金鑰使用的可選變體。 |
| `controlnets[].control_image_url` |  | string (image URL) | yes |  |  | 提供控制圖像的圖像 URL。 |
| `controlnets[].mask_image_url` |  | string (image URL) | no |  |  | 與控制圖像相關聯的遮罩 URL。 |
| `controlnets[].mask_threshold` |  | number | no | `0.5` | 0.01 – 0.99 | 遮罩閾值。 |
| `controlnets[].conditioning_scale` |  | number | no | `1` | 0 – 2 | ControlNet 權重合併至基礎模型前套用的縮放。 |
| `controlnets[].start_percentage` |  | number | no | `0` | 0 – 1 | ControlNet 開始影響圖像生成時所占總時間步的百分比。 |
| `controlnets[].end_percentage` |  | number | no | `1` | 0 – 1 | ControlNet 停止影響圖像生成時所占總時間步的百分比。 |
| `controlnet_unions` |  | array of object {path, config_url, variant, controls} | no | `[]` |  | 用於圖像生成的 ControlNet union 設定，受目前單一 ControlNet 限制。 |
| `controlnet_unions[].path` |  | string | yes |  |  | ControlNet 權重位置，可指定為 URL 或路徑。 |
| `controlnet_unions[].config_url` |  | string \| null | no |  |  | 可選的 URL，指向 ControlNet 的 config.json 檔案。 |
| `controlnet_unions[].variant` |  | string \| null | no |  |  | 搭配 Hugging Face 儲存庫金鑰使用的可選變體。 |
| `controlnet_unions[].controls` |  | array of object {control_image_url, mask_image_url, control_mode, conditioning_scale, mask_threshold, start_percentage, end_percentage} | yes |  |  | 提供給 ControlNet 的控制圖像及相關模式。 |
| `controlnet_unions[].controls[].control_image_url` |  | string (image URL) | yes |  |  | 提供控制圖像的圖像 URL。 |
| `controlnet_unions[].controls[].mask_image_url` |  | string (image URL) | no |  |  | 與控制圖像相關聯的遮罩 URL。 |
| `controlnet_unions[].controls[].control_mode` |  | enum (string) | yes |  | `canny`, `tile`, `depth`, `blur`, `pose`, `gray`, `low-quality` | Flux Controlnet Union 使用的控制模式。選項：canny 以邊緣引導生成；tile 以圖格引導；depth 以灰階深度圖引導；blur 模糊圖像；pose 以圖像姿勢引導生成；gray 將圖像轉為灰階；low-quality 產生低畫質版本的圖像。 |
| `controlnet_unions[].controls[].conditioning_scale` |  | number | no | `1` | 0 – 2 | ControlNet 權重合併至基礎模型前套用的縮放。 |
| `controlnet_unions[].controls[].mask_threshold` |  | number | no | `0.5` | 0.01 – 0.99 | 遮罩閾值。 |
| `controlnet_unions[].controls[].start_percentage` |  | number | no | `0` | 0 – 1 | ControlNet 開始影響圖像生成時所占總時間步的百分比。 |
| `controlnet_unions[].controls[].end_percentage` |  | number | no | `1` | 0 – 1 | ControlNet 停止影響圖像生成時所占總時間步的百分比。 |
| `ip_adapters` |  | array of object {path, subfolder, weight_name, image_encoder_path, image_encoder_subfolder, image_encoder_weight_name, image_url, mask_image_url, mask_threshold, scale} | no | `[]` |  | 圖像生成期間套用的 IP-Adapter。 |
| `ip_adapters[].path` |  | string | yes |  |  | Hugging Face 上的 IP-Adapter 位置。 |
| `ip_adapters[].subfolder` |  | string \| null | no |  |  | 包含 ip_adapter 權重的子資料夾。 |
| `ip_adapters[].weight_name` |  | string \| null | no |  |  | IP-Adapter 權重的 Safetensors 檔名。 |
| `ip_adapters[].image_encoder_path` |  | string | yes |  |  | IP-Adapter 圖像編碼器位置，例如 'openai/clip-vit-large-patch14'。 |
| `ip_adapters[].image_encoder_subfolder` |  | string \| null | no |  |  | 包含圖像編碼器權重的子資料夾。 |
| `ip_adapters[].image_encoder_weight_name` |  | string \| null | no |  |  | 圖像編碼器名稱。 |
| `ip_adapters[].image_url` |  | string (image URL) | yes |  |  | 提供給 IP-Adapter 的條件圖像 URL。 |
| `ip_adapters[].mask_image_url` |  | string (image URL) \| null | no |  |  | 與控制圖像相關聯的遮罩 URL。 |
| `ip_adapters[].mask_threshold` |  | number | no | `0.5` | 0.01 – 0.99 | 遮罩閾值。 |
| `ip_adapters[].scale` |  | number | yes |  |  | IP-Adapter 縮放係數。 |
| `easycontrols` |  | array of object {control_method_url, scale, image_url, image_control_type} | no | `[]` |  | 圖像生成期間套用的 EasyControl 輸入。 |
| `easycontrols[].control_method_url` |  | string | yes |  |  | 控制方法的 safetensor 權重 URL，或支援的識別碼：`canny`、`depth`、`hedsketch`、`inpainting`、`pose`、`seg`、`subject`、`ghibli`。 |
| `easycontrols[].scale` |  | number | no | `1` | 0 – 2 | 控制方法縮放係數。 |
| `easycontrols[].image_url` |  | string (image URL) | yes |  |  | 提供控制輸入的圖像 URL。 |
| `easycontrols[].image_control_type` |  | enum (string) | yes |  | `subject`, `spatial` | 圖像控制模式；接受 `spatial` 或 `subject`。 |
| `fill_image` |  | object {fill_image_url} \| null | no |  |  | 引導生成並可為遮罩區域提供內容的輸入圖像。 |
| `fill_image.fill_image_url` |  | string (image URL) \| array of image URL | no | `[]` |  | 透過 redux prompting 填入內容的圖像 URL。 |
| `guidance_scale` |  | number | no | `3.5` | 0 – 20 | 圖像選取的提示詞遵循強度，以 CFG (Classifier Free Guidance) 表示。 |
| `real_cfg_scale` |  | number | no | `3.5` | 0 – 5 | 圖像選取的提示詞遵循強度，以 CFG (Classifier Free Guidance) 表示。 |
| `use_real_cfg` |  | boolean | no | `false` |  | 傳統 CFG 開關，搭配 XLabs IP-Adapter v1 時會自動啟用。引導行為與 SD1.5 和 SDXL 等模型相同。 |
| `use_cfg_zero` |  | boolean | no | `false` |  | 啟用 CFG-zero 初始化取樣，說明見 https://arxiv.org/abs/2503.18886。 |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 10 | 設定要生成的圖像數量；串流輸出時，此選項固定為 1。 |
| `enable_safety_checker` |  | boolean | no | `true` |  | 安全檢查，會以黑色圖像取代任何被標記為不安全的圖像。true 會啟用檢查；false 僅對獲得授權的帳戶停用檢查，未獲授權的請求無論如何都會接受檢查。 |
| `reference_image_url` |  | string (image URL) | no |  |  | 用於 Reference-Only 引導的圖像 URL。 |
| `reference_strength` |  | number | no | `0.65` | -3 – 3 | reference_only 生成期間參考圖像的影響；未提供參考圖像時，此強度設定不會生效。 |
| `reference_start` |  | number | no | `0` | 0 – 1 | 參考引導起點，以所有時間步的百分比表示。 |
| `reference_end` |  | number | no | `1` | 0 – 1 | 參考引導終點，以所有時間步的百分比表示。 |
| `base_shift` |  | number | no | `0.5` | 0.01 – 5 | 套用於排程時間步的基準位移。 |
| `max_shift` |  | number | no | `1.15` | 0.01 – 5 | 套用於排程時間步的最大位移。 |
| `output_format` |  | enum (string) | no | `png` | `jpeg`, `png` | 生成圖像的檔案格式。 |
| `use_beta_schedule` |  | boolean | no | `false` |  | 控制是否使用 beta sigmas。 |
| `sigma_schedule` |  | string \| null | no |  |  | 去噪期間使用的 Sigma 排程。 |
| `scheduler` |  | enum (string) | no | `euler` | `euler`, `dpmpp_2m` | 去噪期間使用的排程器。 |
| `negative_prompt` |  | string | no | `` |  | 預設以 NAG 處理的負面提示詞，用於抑制生成圖像中的指定不想要特徵。 |
| `nag_scale` |  | number | no | `3` | ≤ 10, > 1 | 由 NAG scale 控制的負面提示詞規避；提高此值會使生成圖像較不像負面描述。 |
| `nag_tau` |  | number | no | `2.5` | > 0 | 用於隱藏狀態正規化的 NAG tau 設定；建議維持不變。提高此值會削弱正規化，並可能導致圖像意外偏離原圖。 |
| `nag_alpha` |  | number | no | `0.25` | ≤ 1, > 0 | NAG alpha 設定正面提示詞重新導向正規化引導的程度，該引導結合正面與負面提示詞。此最終加權在較低設定下讓正面提示詞影響更大，在較高設定下減少重新導向。 |
| `nag_end` |  | number | no | `0.25` | ≤ 1, > 0 | NAG 截止點，以生成步驟的比例表示。NAG 執行該初始比例後，FLUX 的原始注意力處理器會接手。 |
| `image_url` | `image` | string (image URL) | yes |  |  | 提供初始圖像的圖像 URL。 |
| `change_map_image_url` |  | string (image URL) | yes |  |  | 指向變更圖的 URL。 |
| `strength` |  | number | no | `0.85` | 0.01 – 1 | 差異擴散強度，範圍從 0.0 時保留原圖到 1.0 時完全重製。 |

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

A fuller example:

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

## 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.ru/v1/run/flux-general/differential-diffusion?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "change_map_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }'

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

### JavaScript

```javascript
const API = "https://api.doitong.ru/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/flux-general/differential-diffusion?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "change_map_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }),
});
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.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

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

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/flux-general/differential-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.ru/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": "flux-general/differential-diffusion",
      "input": {
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
        "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
        "change_map_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
      },
      "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": "flux-general/differential-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": 138,
    "usd": "0.138",
    "charged": 138,
    "refunded": 0
  },
  "input": {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "change_map_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "output": {
    "images": [
      {
        "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
        "width": 1024,
        "height": 768,
        "content_type": "image/jpeg"
      }
    ],
    "timings": {},
    "seed": 42,
    "has_nsfw_concepts": [
      false
    ],
    "prompt": "string"
  },
  "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.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `images` | array of object {url, width, height, content_type} | 生成圖像的檔案資訊。 |
| `timings` | object |  |
| `seed` | integer | 圖像生成種子；若輸入未提供種子，則包含隨機選取的值，否則會重複提供的值。 |
| `has_nsfw_concepts` | array of boolean | 指出生成的圖像中是否出現 NSFW 概念。 |
| `prompt` | string | 圖像生成期間使用的提示詞。 |

## Related models

- [FLUX.1 \[dev\] with Controlnets and Loras](https://doitong.com/developers/models/flux-general.md) (`flux-general`): $0.30 / image
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## Links

- HTML page: https://doitong.com/developers/models/flux-general/differential-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=flux-general/differential-diffusion
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/flux-general/differential-diffusion