Stable Diffusion with LoRAs · Inpaint
統合
lora/inpaint
# 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/lora/inpaint?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.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/lora/inpaint?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"model_name": "stabilityai/stable-diffusion-xl-base-1.0",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/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/inpaint", 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"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/ja/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "lora/inpaint",
"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"
}
}
Doitong APIを通じてStable Diffusion with LoRAs · Inpaintモデル(lora/inpaint)を呼び出します。エージェント向けドキュメント: https://doitong.com/ja/developers/models/lora/inpaint.md
POST https://api.doitong.com/v1/run/lora/inpaint?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/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"
}
レスポンスのステータスが"queued"または"running"の場合、"succeeded"になるまでGET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20をポーリングしてください。ファイルはoutputs[].urlにあります。同じボディでPOST https://api.doitong.com/v1/quote/lora/inpaintを実行すると、実行前の正確な価格が返されます。
このリクエスト: ≈$0.184 184 APIクレジット このページで実行
新しいAPIキーです。 これは今だけ表示され、コードと下記のAPIキー欄にすでに入力されています。秘密にしてください。ブラウザで実行されるコードに決して含めないでください。
最初の結果がここに表示されます
以下でリクエストを作成し、APIキーで実行してください。出力はストレージにコピーされ、永久保存または無料で7日保存されます。
リクエストを作成
コントロールはモデルスキーマから生成されます。値を変更するとコードと価格が更新されます。
/run/lora/inpaint?wait=20
見積もり → $0.184
# 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/lora/inpaint?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.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/lora/inpaint?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"model_name": "stabilityai/stable-diffusion-xl-base-1.0",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/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/inpaint", 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"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "lora/inpaint",
"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
}
}
}
Stable Diffusion with LoRAs · Inpaintについて
`model_name` で指定した Stable Diffusion モデルを使い、`image_url` と白黒の `mask_url` で画像の一部を編集します。`noise_strength` は 0 から 1 の範囲です。マージ済み LoRA、埋め込み、ControlNet、IP adapter、複数のスケジューラーに対応し、ステップは 1 から 150 です。出力は png または jpeg です。
パラメータ
正式名称またはネイティブ名のいずれか一方を送信してください。両方は送信しないでください。 正式名称
model_name必須
unet_name任意
variant任意
prompt必須
negative_prompt任意
prompt_weighting任意
image_url任意
image
mask_url任意
mask
noise_strength任意
loras任意
loras[].path必須
loras[].scale任意
embeddings任意
embeddings[].path必須
embeddings[].tokens任意
controlnets任意
controlnets[].path必須
controlnets[].config_url任意
controlnets[].variant任意
controlnets[].image_url必須
controlnets[].mask_url任意
controlnets[].conditioning_scale任意
controlnets[].start_percentage任意
controlnets[].end_percentage任意
controlnets[].ip_adapter_index任意
controlnet_guess_mode任意
ip_adapter任意
ip_adapter[].ip_adapter_image_url必須
ip_adapter[].ip_adapter_mask_url任意
ip_adapter[].path必須
ip_adapter[].model_subfolder任意
ip_adapter[].weight_name任意
ip_adapter[].insight_face_model_path任意
ip_adapter[].scale任意
ip_adapter[].scale_json任意
ip_adapter[].unconditional_noising_factor任意
ip_adapter[].image_projection_shortcut任意
image_encoder_path任意
image_encoder_subfolder任意
image_encoder_weight_name任意
ic_light_model_url任意
ic_light_model_background_image_url任意
ic_light_image_url任意
seed任意
num_inference_steps任意
guidance_scale任意
clip_skip任意
scheduler任意
timesteps任意
timesteps.method任意
timesteps.array任意
sigmas任意
sigmas.method任意
sigmas.array任意
prediction_type任意
rescale_betas_snr_zero任意
image_format任意
num_images任意
num_outputs
enable_safety_checker任意
tile_width任意
tile_height任意
tile_stride_width任意
tile_stride_height任意
eta任意
debug_latents任意
debug_per_pass_latents任意
ファイルパラメータには、公開されたhttps:// URL、10 MBまでのdata: URI、またはアップロードしたファイルのfile_urlを指定します。 ファイル
出力
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "lora/inpaint",
"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": 184,
"usd": "0.184",
"charged": 184,
"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.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
出力フィールド
- images
- array of object {url, content_type, file_name, file_size, width, height}生成画像のファイル情報。
- seed
- integer画像生成シード。入力でシードを省略した場合はランダムに選ばれた値を含み、それ以外では指定値を繰り返します。
- has_nsfw_concepts
- array of boolean生成画像に NSFW 概念が含まれるかどうかを示します。
- debug_latents
- object {url, content_type, file_name, file_size} | nullデバッグ用に保存する潜在データ。
- debug_per_pass_latents
- object {url, content_type, file_name, file_size} | nullデバッグ用に保存するパスごとの潜在データ。
その他のStable Diffusion with LoRAsバリアント
その他のモデル: 画像編集
AIエージェント向け:このページのMarkdown版はこちらです: https://doitong.com/ja/developers/models/lora/inpaint.md. すべてのモデルとガイド: https://doitong.com/developers/llms.txt






