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

> FLUX.1 [dev] per editar maletgs cun ina carta da midadas che definescha la fermezza per regiun

Transformescha ina maletg d’origin regiun per regiun, cun ina carta da midadas che controlla quant che mintga zona vegn modifitgada. Pretenda `prompt`, `image_url` e `change_map_image_url`. Accepta er LoRAs, ControlNets, IP-Adapters ed ina maletg da referenza. `num_inference_steps` va da 1 fin 50 e `num_images` fin a 10, cun output png u 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/rm/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 |  |  | Prompt che gida la generaziun dal maletg. |
| `image_size` |  | object {width, height} \| enum (string) | no |  | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | Dimensiuns dal maletg generà. |
| `num_inference_steps` |  | integer | no | `28` | 1 – 50 | Dumber da pass d'inferenzia da manar a fin. |
| `seed` |  | integer \| null | no |  |  | Semenza per maletgs reproducibels: in prompt ed ina semenza identics produceschan il medem maletg sch'è la versiun dal model invariada. |
| `loras` |  | array of object {path, scale} | no | `[]` |  | LoRAs da las qualas l'effect cumina produce il maletg generà, senza limit dal dumber furnì. |
| `loras[].path` |  | string | yes |  |  | Lieu dals pais da LoRA, specificà sco URL u percurs. |
| `loras[].scale` |  | object \| number | no | `1` |  | Scalaziun applitgada a las ponderaziuns LoRA avant ch'ellas vegnan fusiunadas cun il model da basa. Furnir in dicziunari da la furma {"layer_name":layer_scale} per definir scalaziuns individualas; ils nivels betg inditgads utiliseschan 1.0. |
| `control_loras` |  | array of object {path, scale, control_image_url, preprocess} | no | `[]` |  | LoRAs dal maletg da control fusiunadas en ina configuraziun communabla per generar il maletg final; il dumber è illimità. |
| `control_loras[].path` |  | string | yes |  |  | Lieu dals pais da LoRA, specificà sco URL u percurs. |
| `control_loras[].scale` |  | object \| number | no | `1` |  | Scalaziun applitgada a las ponderaziuns LoRA avant ch'ellas vegnan fusiunadas cun il model da basa. Furnir in dicziunari da la furma {"layer_name":layer_scale} per definir scalaziuns individualas; ils nivels betg inditgads utiliseschan 1.0. |
| `control_loras[].control_image_url` |  | string (image URL) | yes |  |  | URL dal maletg che furnescha il maletg da control. |
| `control_loras[].preprocess` |  | enum (string) | no | `None` | `canny`, `depth`, `None` | Metoda da precessament applitgada al maletg furnì. |
| `controlnets` |  | array of object {path, config_url, variant, control_image_url, mask_image_url, mask_threshold, conditioning_scale, start_percentage, end_percentage} | no | `[]` |  | Configuraziun ControlNet per la generaziun da maletgs, actualmain limitada a in sulet ControlNet. |
| `controlnets[].path` |  | string | yes |  |  | Lieu da la ponderaziun ControlNet, inditgà sco URL u via. |
| `controlnets[].config_url` |  | string \| null | no |  |  | URL facultativ che mussa il datoteca config.json da ControlNet. |
| `controlnets[].variant` |  | string \| null | no |  |  | Varianta facultativa per l'utilisaziun cun ina clav da repositori Hugging Face. |
| `controlnets[].control_image_url` |  | string (image URL) | yes |  |  | URL dal maletg che furnescha il maletg da control. |
| `controlnets[].mask_image_url` |  | string (image URL) | no |  |  | URL da la mascrina colliada cun il maletg da control. |
| `controlnets[].mask_threshold` |  | number | no | `0.5` | 0.01 – 0.99 | Valur da la limita da la mascrina. |
| `controlnets[].conditioning_scale` |  | number | no | `1` | 0 – 2 | Scalaziun applitgada a las ponderaziuns ControlNet avant ch'ellas vegnan fusiunadas cun il model da basa. |
| `controlnets[].start_percentage` |  | number | no | `0` | 0 – 1 | Per tschient dal total da pass temporals nua che ControlNet cumenza a influenzar la generaziun dal maletg. |
| `controlnets[].end_percentage` |  | number | no | `1` | 0 – 1 | Per tschient dal total da pass temporals nua che ControlNet chalenda d'influenzar la generaziun dal maletg. |
| `controlnet_unions` |  | array of object {path, config_url, variant, controls} | no | `[]` |  | Configuraziun da l'unificaziun ControlNet per la generaziun da maletgs, suttamessa a la restricziun actuala d'in sulet ControlNet. |
| `controlnet_unions[].path` |  | string | yes |  |  | Lieu da la ponderaziun ControlNet, inditgà sco URL u via. |
| `controlnet_unions[].config_url` |  | string \| null | no |  |  | URL facultativ che mussa il datoteca config.json da ControlNet. |
| `controlnet_unions[].variant` |  | string \| null | no |  |  | Varianta facultativa per l'utilisaziun cun ina clav da repositori Hugging Face. |
| `controlnet_unions[].controls` |  | array of object {control_image_url, mask_image_url, control_mode, conditioning_scale, mask_threshold, start_percentage, end_percentage} | yes |  |  | Maletgs da control e modas associadas furnids a ControlNet. |
| `controlnet_unions[].controls[].control_image_url` |  | string (image URL) | yes |  |  | URL dal maletg che furnescha il maletg da control. |
| `controlnet_unions[].controls[].mask_image_url` |  | string (image URL) | no |  |  | URL da la mascrina colliada cun il maletg da control. |
| `controlnet_unions[].controls[].control_mode` |  | enum (string) | yes |  | `canny`, `tile`, `depth`, `blur`, `pose`, `gray`, `low-quality` | Moda da control utilisada da Flux Controlnet Union. Opziuns: canny dirigia la generaziun cun urs; tile cun tiles; depth cun ina charta da profunditad en scala da grisch; blur renda il maletg stgir; pose dirigia la generaziun cun la posa dal maletg; gray renda il maletg en scala da grisch; low-quality producescha ina versiun da bassa qualitad dal maletg. |
| `controlnet_unions[].controls[].conditioning_scale` |  | number | no | `1` | 0 – 2 | Scalaziun applitgada a las ponderaziuns ControlNet avant ch'ellas vegnan fusiunadas cun il model da basa. |
| `controlnet_unions[].controls[].mask_threshold` |  | number | no | `0.5` | 0.01 – 0.99 | Valur da la limita da la mascrina. |
| `controlnet_unions[].controls[].start_percentage` |  | number | no | `0` | 0 – 1 | Per tschient dal total da pass temporals nua che ControlNet cumenza a influenzar la generaziun dal maletg. |
| `controlnet_unions[].controls[].end_percentage` |  | number | no | `1` | 0 – 1 | Per tschient dal total da pass temporals nua che ControlNet chalenda d'influenzar la generaziun dal maletg. |
| `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 | `[]` |  | IP-Adapter applitgà durant la generaziun dal maletg. |
| `ip_adapters[].path` |  | string | yes |  |  | Lieu da IP-Adapter sin Hugging Face. |
| `ip_adapters[].subfolder` |  | string \| null | no |  |  | Sotscartella che cuntegna las ponderaziuns ip_adapter. |
| `ip_adapters[].weight_name` |  | string \| null | no |  |  | Num da datoteca Safetensors per las ponderaziuns da IP-Adapter. |
| `ip_adapters[].image_encoder_path` |  | string | yes |  |  | Lieu da l'encodader da maletgs IP-Adapter, sco 'openai/clip-vit-large-patch14'. |
| `ip_adapters[].image_encoder_subfolder` |  | string \| null | no |  |  | Sotscartella che cuntegna las ponderaziuns per l'encodader da maletgs. |
| `ip_adapters[].image_encoder_weight_name` |  | string \| null | no |  |  | Num da l'encodader da maletgs. |
| `ip_adapters[].image_url` |  | string (image URL) | yes |  |  | URL dal maletg da cundiziunament furnì a IP-Adapter. |
| `ip_adapters[].mask_image_url` |  | string (image URL) \| null | no |  |  | URL da la mascrina colliada cun il maletg da control. |
| `ip_adapters[].mask_threshold` |  | number | no | `0.5` | 0.01 – 0.99 | Valur da la limita da la mascrina. |
| `ip_adapters[].scale` |  | number | yes |  |  | Factor da scalaziun da IP-Adapter. |
| `easycontrols` |  | array of object {control_method_url, scale, image_url, image_control_type} | no | `[]` |  | Inputs EasyControl applitgads durant la generaziun dal maletg. |
| `easycontrols[].control_method_url` |  | string | yes |  |  | URL da la ponderaziun safetensor da la metoda da control, u in identificader sustegnì: `canny`, `depth`, `hedsketch`, `inpainting`, `pose`, `seg`, `subject`, `ghibli`. |
| `easycontrols[].scale` |  | number | no | `1` | 0 – 2 | Factor da scalaziun da la metoda da control. |
| `easycontrols[].image_url` |  | string (image URL) | yes |  |  | URL dal maletg che furnescha in input da control. |
| `easycontrols[].image_control_type` |  | enum (string) | yes |  | `subject`, `spatial` | Moda da control dal maletg; accepta `spatial` u `subject`. |
| `fill_image` |  | object {fill_image_url} \| null | no |  |  | Maletg input che dirigia la generaziun e po furnir cuntegn per regiuns mascradas. |
| `fill_image.fill_image_url` |  | string (image URL) \| array of image URL | no | `[]` |  | URLs da maletgs per emplenir cun redux prompting. |
| `guidance_scale` |  | number | no | `3.5` | 0 – 20 | Furza d'aderenza al prompt per la selecziun dal maletg, exprimida sco CFG (Classifier Free Guidance). |
| `real_cfg_scale` |  | number | no | `3.5` | 0 – 5 | Furza d'aderenza al prompt per la selecziun dal maletg, exprimida sco CFG (Classifier Free Guidance). |
| `use_real_cfg` |  | boolean | no | `false` |  | Interrupziun CFG classica, activada automaticamain cun XLabs IP-Adapter v1. Il cumportament da guidance correspunda a quel da models sco SD1.5 e SDXL. |
| `use_cfg_zero` |  | boolean | no | `false` |  | Activescha il sampling d'inizialisaziun CFG-zero, descrit a https://arxiv.org/abs/2503.18886. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 10 | Fixar quant maletgs generar; questa tscherna è fixada a 1 cura che l'output vegn tramess en streaming. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Controlla da segirezza che restituescha maletgs nairs enstagl da maletgs marcads sco privlus. La valur true activa la controlla; false la deactivescha mo per contos autorisads, entant che dumondas senza autorisaziun vegnan controlladas tuttina. |
| `reference_image_url` |  | string (image URL) | no |  |  | URL dal maletg utilisà per la guidance Reference-Only. |
| `reference_strength` |  | number | no | `0.65` | -3 – 3 | Influenza dal maletg da referenza durant la generaziun reference_only; senza maletg da referenza furnì n'ha questa regulaziun nagin effect. |
| `reference_start` |  | number | no | `0` | 0 – 1 | Punct da partenza da la guidance da referenza, exprimì sco pertschient da tut ils pass temporals. |
| `reference_end` |  | number | no | `1` | 0 – 1 | Punct final da la guidance da referenza, exprimì sco pertschient da tut ils pass temporals. |
| `base_shift` |  | number | no | `0.5` | 0.01 – 5 | Midada da basa applitgada als pass temporals planisads. |
| `max_shift` |  | number | no | `1.15` | 0.01 – 5 | Midada maximala applitgada als pass temporals planisads. |
| `output_format` |  | enum (string) | no | `png` | `jpeg`, `png` | Format da datoteca dal maletg final. |
| `use_beta_schedule` |  | boolean | no | `false` |  | Controlla sche beta sigmas vegnan utilisads. |
| `sigma_schedule` |  | string \| null | no |  |  | Plan da sigma utilisà durant la denoisaziun. |
| `scheduler` |  | enum (string) | no | `euler` | `euler`, `dpmpp_2m` | Planisader utilisà durant la denoisaziun. |
| `negative_prompt` |  | string | no | `` |  | Prompt negativ elavurà per standard cun NAG per evitar las caracteristicas nungiavischadas inditgadas en maletgs generads. |
| `nag_scale` |  | number | no | `3` | ≤ 10, > 1 | Evitaziun dal prompt negativ controllada da la scala NAG: augmentar ella renda il maletg generà main sumegliant a la descripziun negativa. |
| `nag_tau` |  | number | no | `2.5` | > 0 | Configuraziun NAG tau per la normalisaziun dal stadi zuppà; è recumandà da la laschar invariada. Augmentar ella flaivlenta la normalisaziun e po chaschunar divergenzas nunspetgadas da l'original. |
| `nag_alpha` |  | number | no | `0.25` | ≤ 1, > 0 | NAG alpha definescha quant che il prompt positiv redirigia la guidance normalisada che unescha ils prompts positiv e negativ. Questa ponderaziun finala dat al prompt positiv dapli influenza tar regulaziuns bassas e reducescha la redirecziun tar regulaziuns autas. |
| `nag_end` |  | number | no | `0.25` | ≤ 1, > 0 | Limita NAG exprimida sco proporziun dals pass da generaziun. Ils processaders d'attenziun originals da FLUX surpiglian suenter che NAG ha funcziunà durant questa proporziun iniziala. |
| `image_url` | `image` | string (image URL) | yes |  |  | URL dal maletg che furnescha il maletg inizial. |
| `change_map_image_url` |  | string (image URL) | yes |  |  | URL che mussa la charta da midadas. |
| `strength` |  | number | no | `0.85` | 0.01 – 1 | Furma da la diffusiun differenziala, da la preservaziun da l'original a 0.0 fin a la renovaziun cumpletta a 1.0. |

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} | Infurmaziuns da la datoteca per ils maletgs generads. |
| `timings` | object |  |
| `seed` | integer | Seed da la generaziun dal maletg, inclusa la valur tschernida casualmain sche l'input n'ha betg furnì in seed; autramain repeta la valur furnida. |
| `has_nsfw_concepts` | array of boolean | Inditgescha sche concepts NSFW cumparan en ils maletgs generads. |
| `prompt` | string | Prompt utilisà durant la generaziun dal maletg. |

## Related models

- [FLUX.1 \[dev\] with Controlnets and Loras](https://doitong.com/developers/models/flux-general.md) (`flux-general`): $0.30 / image
- [FLUX.1 \[dev\] with Controlnets and Loras · Image to Image](https://doitong.com/developers/models/flux-general/image-to-image.md) (`flux-general/image-to-image`): $0.15 / image
- [FLUX.1 \[dev\] with Controlnets and Loras · Inpainting](https://doitong.com/developers/models/flux-general/inpainting.md) (`flux-general/inpainting`): $0.192 / image
- [FLUX.1 \[dev\] with Controlnets and Loras · Rf Inversion](https://doitong.com/developers/models/flux-general/rf-inversion.md) (`flux-general/rf-inversion`): $0.025 / image
- [FLUX.1 Kontext \[pro\]](https://doitong.com/developers/models/flux-pro/kontext.md) (`flux-pro/kontext`): $0.04 / image
- [GPT Image 2.5 Edit](https://doitong.com/developers/models/openai/gpt-image-2.5/flare/edit.md) (`openai/gpt-image-2.5/flare/edit`): from $0.12 / image
- [Nano Banana 2 Edit](https://doitong.com/developers/models/nano-banana-2/edit.md) (`nano-banana-2/edit`): from $0.06 / image
- [Nano Banana Pro Edit](https://doitong.com/developers/models/nano-banana-pro/edit.md) (`nano-banana-pro/edit`): from $0.15 / image

## 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