FLUX.1 [dev] with Controlnets and Loras · Differential Diffusion
Integrēt
flux-general/differential-diffusion
# 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/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": "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.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/flux-general/differential-diffusion?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"change_map_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/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": "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"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/lv/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": "flux-general/differential-diffusion",
"input": {
"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"
}
}
Izsauciet FLUX.1 [dev] with Controlnets and Loras · Differential Diffusion modeli (flux-general/differential-diffusion), izmantojot Doitong API. Dokumentācija aģentiem: https://doitong.com/lv/developers/models/flux-general/differential-diffusion.md
POST https://api.doitong.com/v1/run/flux-general/differential-diffusion?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/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"
}
Ja atbildes statuss ir "queued" vai "running", periodiski izsauciet GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20, līdz tas kļūst par "succeeded"; faili atrodas outputs[].url. POST https://api.doitong.com/v1/quote/flux-general/differential-diffusion ar to pašu pamattekstu atgriež precīzu cenu pirms izpildes.
Šis pieprasījums: ≈0,138 $ 138 API kredīti Izpildīt šajā lapā
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Izveidots vietnē Doitong
Doitong kopienas publiskie darbi, kas izveidoti ar FLUX.1 [dev] with Controlnets and Loras. Izmantojiet uzvedni atkārtoti ar vienu klikšķi.
Close-up portrait seorang wanita berkulit pucat berdiri setengah tubuh di dalam air berwarna lembut kebiruan, dikelilingi bunga teratai berkelopak penuh yang bermekaran indah di permukaan air. Rambutnya pirang pucat tersanggul longgar dengan beberapa helaian tipis jatuh alami membingkai sisi...
Close-up portrait seorang wanita berkulit pucat berdiri setengah tubuh di dalam air berwarna lembut kebiruan, dikelilingi bunga teratai berkelopak penuh yang bermekaran indah di permukaan air. Rambutnya pirang pucat tersanggul longgar dengan beberapa helaian tipis jatuh alami membingkai sisi...
Prompt Details Seorang wanita berkulit pucat berdiri setengah tubuh di dalam air berwarna lembut kebiruan, dikelilingi bunga teratai berkelopak penuh yang bermekaran indah di permukaan air. Rambutnya pirang pucat tersanggul longgar dengan beberapa helaian tipis jatuh alami membingkai sisi...
Seorang wanita berkulit pucat berdiri setengah tubuh di dalam air berwarna lembut kebiruan, dikelilingi bunga teratai berkelopak penuh yang bermekaran indah di permukaan air. Rambutnya pirang pucat tersanggul longgar dengan beberapa helaian tipis jatuh alami membingkai sisi wajahnya. Ia memeluk...
"Potret sinematik ultra-realistis close-up seorang anak laki-laki kecil (balita) yang sangat menggemaskan dengan mata cokelat besar yang ekspresif, rambut hitam berantakan yang tebal, mengenakan kaus putih kerah henley. Dia berpose dengan kedua tangan terkatup di depan dada, menunjukkan senyum...
"Potret sinematik ultra-realistis close-up seorang anak laki-laki kecil (balita) yang sangat menggemaskan dengan mata cokelat besar yang ekspresif, rambut hitam berantakan yang tebal, mengenakan kaus putih kerah henley. Dia berpose dengan kedua tangan terkatup di depan dada, menunjukkan senyum...
"Potret sinematik ultra-realistis close-up seorang anak laki-laki kecil (balita) yang sangat menggemaskan dengan mata cokelat besar yang ekspresif, rambut hitam berantakan yang tebal, mengenakan kaus putih kerah henley. Dia berpose dengan kedua tangan terkatup di depan dada, menunjukkan senyum...
Foto sinematik hiper-realistis resolusi tinggi menampilkan seorang pria muda Indonesia dengan tubuh ramping dan proporsional, rambut hitam pendek agak acak, mengenakan jaket kulit hitam, celana riding fit, dan sepatu motor. Ia sedang melakukan aksi freestyle ekstrem dengan motor sport 500cc...
Foto sinematik hiper-realistis resolusi tinggi menampilkan seorang pria muda Indonesia dengan tubuh ramping dan proporsional, rambut hitam pendek agak acak, mengenakan jaket kulit hitam dan celana riding fit. Ia sedang bersiap melakukan freestyle menggunakan motor sport 500cc berwarna merah,...
Izveidot pieprasījumu
Vadīklas tiek ģenerētas no modeļa shēmas. Kods un cena tiek atjaunināti, kad tos maināt.
/run/flux-general/differential-diffusion?wait=20
cena → 0,138 $
# 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/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": "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.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/flux-general/differential-diffusion?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"change_map_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/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": "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"])
{
"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": "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
}
}
}
Palaist šo pieprasījumu
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Par FLUX.1 [dev] with Controlnets and Loras · Differential Diffusion
Pārveido avota attēlu pa apgabaliem, izmantojot izmaiņu karti, kas kontrolē katras zonas pārveidošanas apjomu. Nepieciešami `prompt`, `image_url` un `change_map_image_url`. Atbalsta arī LoRA, ControlNet, IP-Adapter un atsauces attēlu. `num_inference_steps` ir no 1 līdz 50, `num_images` — līdz 10, ar png vai jpeg izvadi.
Parametri
Nosūti vai nu kanonisko nosaukumu, vai vietējo nosaukumu, bet ne abus. Kan啤niskie nosaukumi
promptobligāts
image_sizeneobligāts
num_inference_stepsneobligāts
seedneobligāts
lorasneobligāts
loras[].pathobligāts
loras[].scaleneobligāts
control_lorasneobligāts
control_loras[].pathobligāts
control_loras[].scaleneobligāts
control_loras[].control_image_urlobligāts
control_loras[].preprocessneobligāts
controlnetsneobligāts
controlnets[].pathobligāts
controlnets[].config_urlneobligāts
controlnets[].variantneobligāts
controlnets[].control_image_urlobligāts
controlnets[].mask_image_urlneobligāts
controlnets[].mask_thresholdneobligāts
controlnets[].conditioning_scaleneobligāts
controlnets[].start_percentageneobligāts
controlnets[].end_percentageneobligāts
controlnet_unionsneobligāts
controlnet_unions[].pathobligāts
controlnet_unions[].config_urlneobligāts
controlnet_unions[].variantneobligāts
controlnet_unions[].controlsobligāts
controlnet_unions[].controls[].control_image_urlobligāts
controlnet_unions[].controls[].mask_image_urlneobligāts
controlnet_unions[].controls[].control_modeobligāts
controlnet_unions[].controls[].conditioning_scaleneobligāts
controlnet_unions[].controls[].mask_thresholdneobligāts
controlnet_unions[].controls[].start_percentageneobligāts
controlnet_unions[].controls[].end_percentageneobligāts
ip_adaptersneobligāts
ip_adapters[].pathobligāts
ip_adapters[].subfolderneobligāts
ip_adapters[].weight_nameneobligāts
ip_adapters[].image_encoder_pathobligāts
ip_adapters[].image_encoder_subfolderneobligāts
ip_adapters[].image_encoder_weight_nameneobligāts
ip_adapters[].image_urlobligāts
ip_adapters[].mask_image_urlneobligāts
ip_adapters[].mask_thresholdneobligāts
ip_adapters[].scaleobligāts
easycontrolsneobligāts
easycontrols[].control_method_urlobligāts
easycontrols[].scaleneobligāts
easycontrols[].image_urlobligāts
easycontrols[].image_control_typeobligāts
fill_imageneobligāts
fill_image.fill_image_urlneobligāts
guidance_scaleneobligāts
real_cfg_scaleneobligāts
use_real_cfgneobligāts
use_cfg_zeroneobligāts
num_imagesneobligāts
num_outputs
enable_safety_checkerneobligāts
reference_image_urlneobligāts
reference_strengthneobligāts
reference_startneobligāts
reference_endneobligāts
base_shiftneobligāts
max_shiftneobligāts
output_formatneobligāts
use_beta_scheduleneobligāts
sigma_scheduleneobligāts
schedulerneobligāts
negative_promptneobligāts
nag_scaleneobligāts
nag_tauneobligāts
nag_alphaneobligāts
nag_endneobligāts
image_urlobligāts
image
change_map_image_urlobligāts
strengthneobligāts
Failu parametriem nepieciešams publisks https:// URL, data: URI līdz 10 MB vai augšupielādes file_url. Faili
Izvade
{
"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.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Izvades lauki
- images
- array of object {url, width, height, content_type}Ģenerēto attēlu failu informācija.
- timings
- object
- seed
- integerAttēla ģenerēšanas sēkla, tostarp nejauši izvēlētā vērtība, ja ievadē sēkla nebija norādīta; pretējā gadījumā atkārto norādīto vērtību.
- has_nsfw_concepts
- array of booleanNorāda, vai ģenerētajos attēlos parādās NSFW koncepcijas.
- prompt
- stringAttēla ģenerēšanas laikā izmantotā uzvedne.
Citi FLUX.1 [dev] with Controlnets and Loras varianti
Vairāk modeļu: Attēlu rediģēšana
AI aģentiem: šī lapa Markdown formātā ir pieejama https://doitong.com/lv/developers/models/flux-general/differential-diffusion.md. Visi modeļi un ceļveži: https://doitong.com/developers/llms.txt






