# Stable Diffusion with LoRAs API

> Text-to-image bi kwalunkwe mudell bażi Stable Diffusion flimkien ma’ LoRAs, embeddings, ControlNets u IP adapters

Jiġġenera immaġnijiet minn prompt testwali bl-użu ta’ mudell bażi mogħti b’URL jew HuggingFace ID f’`model_name`. Jistgħu jingħaqdu diversi `loras`, u huma appoġġjati embeddings, ControlNets, IP adapters u inputs IC Light. L-għażliet tas-sampler jinkludu ħafna schedulers u passi minn 1 sa 150. Jiġġenera minn 1 sa 8 immaġnijiet f’png jew jpeg.

- Model id: `lora`
- Made by: Open model
- Category: Text to Image
- Output: image
- Estimated price per run: $0.057 / image (cheapest run $0.057 (57 API credits))
- Run: `POST https://api.doitong.ru/v1/run/lora`
- Quote: `POST https://api.doitong.ru/v1/quote/lora`
- Docs page: https://doitong.com/mt/developers/models/lora

## Pricing

Price: **$0.057 / 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 $0.057 = 57 API credits (1 image × 57 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/lora` 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 |
|---|---|---|---|---|---|---|
| `model_name` |  | string | yes |  |  | Mudell bażiku għall-ġenerazzjoni tal-immaġni, identifikat permezz ta’ URL jew HuggingFace ID. |
| `unet_name` |  | string \| null | no |  |  | Għażla ta’ U-Net personalizzata għall-ġenerazzjoni tal-immaġnijiet; ipprovdi jew l-ID tagħha ta’ HuggingFace jew il-URL tagħha. |
| `variant` |  | string \| null | no |  |  | Għażla tal-varjant tal-mudell HuggingFace; 'fp16' huwa eżempju wieħed. |
| `prompt` |  | string | yes |  |  | Iddeskrivi l-immaġni li trid tiġi ġġenerata, billi tuża kemm jista' jkun dettall biex tikseb l-aħjar riżultati. |
| `negative_prompt` |  | string | no | `` |  | Prompt negattiv li jispeċifika dettalji mhux mixtieqa tal-immaġni. Jista' jeskludi kuluri, oġġetti, xenarji jew karatteristiċi u kwalitajiet fini, bħal mustaċċ, ċajpar jew riżoluzzjoni baxxa. |
| `prompt_weighting` |  | boolean | no | `false` |  | Toggle tal-piż tal-prompt: true ineħħi l-limitu ta' 77 token permezz tal-medja tal-embeddings u jippermetti s-sintassi tal-piżijiet. |
| `loras` |  | array of object {path, scale} | no | `[]` |  | LoRAs li l-effett magħqud tagħhom jipproduċi l-immaġni ġġenerata, mingħajr limitu fuq in-numru pprovdut. |
| `loras[].path` |  | string | yes |  |  | Post tal-piżijiet LoRA, speċifikat bħala URL jew mogħdija. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Multiplikatur applikat għall-piż LoRA qabel ma jingħaqad mal-mudell bażi. |
| `embeddings` |  | array of object {path, tokens} | no | `[]` |  | Embeddings applikati waqt il-ġenerazzjoni tal-immaġni biex jimmappjaw it-tokens tal-prompt ma' piżijiet tal-embedding. Bħalissa, embedding wieħed biss huwa appoġġjat. |
| `embeddings[].path` |  | string | yes |  |  | Piżijiet tal-embedding speċifikati permezz ta’ URL jew path tal-fajl. |
| `embeddings[].tokens` |  | array of string \| null | no |  |  | Tokens tal-prompt immappjati ma' piżijiet tal-embedding. Jekk tħallihom barra, jinħoloq token wieħed għal kull vettur tal-fajl tal-embedding, bl-isem <s0>, <s1>, ...; uża t-tokens immappjati fil-prompts tiegħek. |
| `controlnets` |  | array of object {path, config_url, variant, image_url, mask_url, conditioning_scale, start_percentage, end_percentage, ip_adapter_index} | no | `[]` |  | Control nets skedati biex jiġu applikati għall-immaġni f'timesteps speċifikati waqt il-ġenerazzjoni, mingħajr limitu fuq in-numru li tipprovdi. |
| `controlnets[].path` |  | string | yes |  |  | Il-post tal-piżijiet ControlNet, speċifikat bħala URL jew mogħdija. |
| `controlnets[].config_url` |  | string \| null | no |  |  | URL mhux obbligatorju li jindika l-fajl config.json ta’ ControlNet. |
| `controlnets[].variant` |  | string \| null | no |  |  | Varjant mhux obbligatorju għall-użu ma’ ċavetta ta’ repożitorju Hugging Face. |
| `controlnets[].image_url` |  | string (image URL) | yes |  |  | URL tal-immaġni pprovdut bħala l-input tal-ControlNet. |
| `controlnets[].mask_url` |  | string (image URL) \| null | no |  |  | Maskra tal-ControlNet, li meta tiġi pprovduta trid taqbel fid-daqs mal-immaġni ta' kontroll; il-wisa' u l-għoli għandhom ikunu diviżibbli bi 32. |
| `controlnets[].conditioning_scale` |  | number | no | `1` | 0 – 2 | Skalar applikat għall-piżijiet ControlNet qabel jingħaqdu mal-mudell bażi. |
| `controlnets[].start_percentage` |  | number | no | `0` | 0 – 1 | Perċentwal tat-timesteps totali li fih ControlNet jibda jinfluwenza l-ġenerazzjoni tal-immaġni. |
| `controlnets[].end_percentage` |  | number | no | `1` | 0 – 1 | Perċentwal tat-timesteps totali li fih ControlNet jieqaf jinfluwenza l-ġenerazzjoni tal-immaġni. |
| `controlnets[].ip_adapter_index` |  | integer \| null | no |  |  | Għażla tal-IP adapter skont l-indiċi għal InstantID ControlNet; ControlNets oħra m'għandhomx bżonn dan is-setting. |
| `controlnet_guess_mode` |  | boolean | no | `false` |  | Tirrestrinġi l-applikazzjoni tal-ControlNet għal tbassir kundizzjonali meta tkun true. |
| `ip_adapter` |  | array of object {ip_adapter_image_url, ip_adapter_mask_url, path, model_subfolder, weight_name, insight_face_model_path, scale, scale_json, unconditional_noising_factor, image_projection_shortcut} | no | `[]` |  | IP adapters użati biex jiġġeneraw immaġnijiet. Il-provvista ta' kwalunkwe entrata teħtieġ image_encoder_path. |
| `ip_adapter[].ip_adapter_image_url` |  | string (image URL) \| array of image URL | yes |  |  | URL tal-immaġni pprovdut bħala l-input tal-IP adapter. |
| `ip_adapter[].ip_adapter_mask_url` |  | string (image URL) \| null | no |  |  | Maskra tal-IP adapter, soġġetta għal restrizzjoni tad-daqs meta tiġi pprovduta: id-dimensjonijiet tagħha għandhom ikunu ugwali għal dawk tal-immaġni tal-adapter. |
| `ip_adapter[].path` |  | string | yes |  |  | Post tal-piżijiet tal-IP adapter, speċifikat bħala URL jew path. |
| `ip_adapter[].model_subfolder` |  | string \| null | no |  |  | Post tal-piżijiet tal-IP adapter, speċifikat bħala subfolder fid-direttorju tal-mudell. |
| `ip_adapter[].weight_name` |  | string \| null | no |  |  | Isem tal-fajl tal-piżijiet. |
| `ip_adapter[].insight_face_model_path` |  | string \| null | no |  |  | Post tal-piżijiet tal-mudell InsightFace, speċifikat bħala URL jew path. |
| `ip_adapter[].scale` |  | number | no | `1` | ≥ 0 | Fattur ta' skala għall-piżijiet tal-IP adapter, applikat qabel ma jingħaqdu mal-mudell bażi. |
| `ip_adapter[].scale_json` |  | object \| null | no |  |  | Fattur ta' skala għall-piżijiet tal-IP adapter, applikat qabel ma jingħaqdu mal-mudell bażi. |
| `ip_adapter[].unconditional_noising_factor` |  | number | no | `0` | 0 – 1 | Fattur li jikkontrolla n-noising mhux kondizzjonali għall-IP adapter. |
| `ip_adapter[].image_projection_shortcut` |  | boolean | no | `true` |  | Setting tal-shortcut tal-projezzjoni tal-immaġni, b'True bħala prestabbilit. Uża False għall-mudelli FaceID plus V1 u True għall-mudelli FaceID plus V2. |
| `image_encoder_path` |  | string \| null | no |  |  | Encoder tal-immaġni speċifikat b'URL HTTPS jew ID ta' Hugging Face, bil-kompatibbiltà determinata mill-piżijiet tal-IP-Adapter: SD1.5 juża image_encoder_subfolder='models/image_encoder', filwaqt li h94/IP-Adapter SDXL juża h94/IP-Adapter u image_encoder_subfolder='sdxl_models/image_encoder'. Encoder huwa meħtieġ kull meta ip_adapter ikun fih valur. |
| `image_encoder_subfolder` |  | string \| null | no |  |  | Subdirettorju fi ħdan image_encoder_path li fih il-piżijiet tal-encoder tal-immaġni. |
| `image_encoder_weight_name` |  | string \| null | no | `pytorch_model.bin` |  | Agħżel il-piżijiet tal-encoder tal-immaġni skont l-isem biex tużahom fil-ġenerazzjoni tal-immaġnijiet. |
| `ic_light_model_url` |  | string \| null | no |  |  | URL tal-mudell IC Light użat meta jiġu ġġenerati immaġnijiet. |
| `ic_light_model_background_image_url` |  | string (image URL) \| null | no |  |  | Sfond kompatibbli mal-mudell għall-ġenerazzjoni tal-immaġnijiet IC Light, ipprovdut bħala URL tal-immaġni. |
| `ic_light_image_url` |  | string (image URL) \| null | no |  |  | URL tal-immaġni tal-input għall-ġenerazzjoni bil-mudell IC Light. |
| `seed` |  | integer \| null | no |  |  | Seed għar-riproduzzjoni tal-immaġnijiet fi Stable Diffusion. Immaġni tista' tiġi riprodotta eżattament f'kull eżekuzzjoni billi l-verżjoni tal-mudell, il-prompt u s-seed jinżammu l-istess. |
| `image_size` |  | object {width, height} \| enum (string) \| null | no | `square_hd` | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | Dimensjonijiet tal-immaġni tal-output, magħżula minn presets jew speċifikati bħala wisa' u għoli personalizzati. Iż-żewġ dimensjonijiet personalizzati għandhom ikunu diviżibbli bi 8. |
| `num_inference_steps` |  | integer | no | `30` | 1 – 150 | Ikkontrolla kemm Stable Diffusion iwettaq passi ta' ġenerazzjoni; passi addizzjonali jistgħu jipproduċu immaġni finali b'aktar dettall. |
| `guidance_scale` |  | number | no | `7.5` | 0 – 20 | Qawwa tal-konformità mal-prompt għall-għażla tal-immaġni, espressa bħala CFG (gwida mingħajr klassifikatur). |
| `clip_skip` |  | integer | no | `0` | 0 – 2 | Taqbeż parti mill-ġenerazzjoni tal-immaġni biex tirrendi aktar malajr, u tipproduċi riżultati kemxejn differenti. |
| `scheduler` |  | enum (string) \| null | no |  | `DPM++ 2M`, `DPM++ 2M Karras`, `DPM++ 2M SDE`, `DPM++ 2M SDE Karras`, `Euler`, `Euler A`, `Euler (trailing timesteps)`, `LCM`, `LCM (trailing timesteps)`, `DDIM`, `TCD` | Scheduler jew sampler użat biex ineħħi l-istorbju mill-immaġni. |
| `timesteps` |  | object {method, array} | no | `{"array":[],"method":"default"}` |  | Override tal-iskeda tat-timestep tad-denoising, inkompatibbli ma' sigmas. Mingħajr override—il-prestabbilit—l-iskedulatur jikkalkula t-timesteps minn num_inference_steps; skeda personalizzata tagħmel num_inference_steps irrelevanti. Override jeħtieġ appoġġ tal-iskedulatur għall-argument timesteps biex jistabbilixxi t-timesteps. |
| `timesteps.method` |  | enum (string) | no | `default` | `default`, `array` | Agħżel kif jiġu determinati t-timesteps: 'default', is-setting prestabbilit, iħalli lill-iskedulatur juża `num_inference_steps`. Biex tipprovdi skeda ta' timestep int stess, agħżel 'array' u poġġi l-iskeda fil-qasam `array`. |
| `timesteps.array` |  | array of integer | no | `[]` |  | Skeda tat-timestep applikata meta l-metodu magħżul ikun 'custom'. |
| `sigmas` |  | object {method, array} | no | `{"array":[],"method":"default"}` |  | Override tal-iskeda tas-sigma tad-denoising, inkompatibbli ma' timesteps. Mingħajr override—il-prestabbilit—l-iskedulatur jikkalkula s-sigmas minn num_inference_steps; skeda personalizzata tagħmel num_inference_steps irrelevanti. Override jeħtieġ appoġġ tal-iskedulatur għall-argument sigmas biex jistabbilixxi s-sigmas. |
| `sigmas.method` |  | enum (string) | no | `default` | `default`, `array` | Agħżel kif jiġu determinati s-sigmas: 'default', is-setting prestabbilit, juża l-valuri tas-sigma tal-iskedulatur innifsu. Biex tipprovdi skeda tas-sigma int stess, agħżel 'custom' u poġġiha fil-qasam `array`. |
| `sigmas.array` |  | array of number | no | `[]` |  | Skeda tas-sigma applikata meta l-metodu magħżul ikun 'custom'. |
| `prediction_type` |  | enum (string) | no | `epsilon` | `v_prediction`, `epsilon` | Agħżel it-tip ta' tbassir għall-ġenerazzjoni tal-immaġnijiet; mingħajr għażla oħra, jintuża `epsilon`. |
| `rescale_betas_snr_zero` |  | boolean | no | `false` |  | Jikkontrolla jekk is-sampler jippermettix rescale_betas_snr_zero. |
| `image_format` |  | enum (string) | no | `png` | `jpeg`, `png` | Format tal-fajl għall-immaġni li tirriżulta. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 8 | Issettja kemm immaġnijiet tipproduċi talba waħda, u b'hekk tippermetti aktar ħin ta' ġenerazzjoni għal batch akbar. |
| `enable_safety_checker` |  | boolean | no | `false` |  | Attivazzjoni tal-kontroll tas-sikurezza; true jippermetti l-kontroll. |
| `tile_width` |  | integer | no | `4096` | 128 – 4,096 | Issettja d-daqs ta' kull tile waqt il-ġenerazzjoni tal-immaġni. |
| `tile_height` |  | integer | no | `4096` | 128 – 4,096 | Issettja d-daqs ta' kull tile waqt il-ġenerazzjoni tal-immaġni. |
| `tile_stride_width` |  | integer | no | `2048` | 64 – 2,048 | Issettja l-ispazjar bejn il-pożizzjonijiet konsekuttivi tat-tiles waqt il-ġenerazzjoni tal-immaġni. |
| `tile_stride_height` |  | integer | no | `2048` | 64 – 2,048 | Issettja l-ispazjar bejn il-pożizzjonijiet konsekuttivi tat-tiles waqt il-ġenerazzjoni tal-immaġni. |
| `eta` |  | number | no | `0` | 0 – 1 | Setting Eta użat meta jiġu ġġenerati immaġnijiet. |
| `debug_latents` |  | boolean | no | `false` |  | Isalva l-latents għad-debugging meta jkun true. |
| `debug_per_pass_latents` |  | boolean | no | `false` |  | Ippermetti d-debugging għal kull pass billi tissettja dan għal true; dan isalva l-latents ta' kull pass. |

File parameters (`ic_light_model_background_image_url`, `ic_light_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
{
  "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
  "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.ru/v1/run/lora?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
    "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.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/lora?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
    "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.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
    "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/lora", 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": "lora",
      "input": {
        "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
        "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": "lora",
  "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": 57,
    "usd": "0.057",
    "charged": 57,
    "refunded": 0
  },
  "input": {
    "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "output": {
    "images": [
      {
        "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,
    "has_nsfw_concepts": [
      false
    ],
    "debug_latents": {
      "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
    },
    "debug_per_pass_latents": {
      "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
    }
  },
  "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, content_type, file_name, file_size, width, height} | Informazzjoni tal-fajl għall-immaġnijiet iġġenerati. |
| `seed` | integer | Seed tal-ġenerazzjoni tal-immaġni, inkluż il-valur magħżul b’mod każwali jekk l-input ma kienx fih seed; inkella, jirrepeti l-valur ipprovdut. |
| `has_nsfw_concepts` | array of boolean | Jindika jekk kunċetti NSFW jidhrux fl-immaġnijiet ġġenerati. |
| `debug_latents` | object {url, content_type, file_name, file_size} \| null | Data latent maħżuna għad-debugging. |
| `debug_per_pass_latents` | object {url, content_type, file_name, file_size} \| null | Data latent għal kull pass maħżuna għad-debugging. |

## Related models

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- [FLUX.2 \[pro\]](https://doitong.com/developers/models/flux-2-pro.md) (`flux-2-pro`): $0.015 / megapixel of output image
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## Links

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