Fooocus Serverless API

Fooocus enables high-quality image generation effortlessly, combining the best of Stable Diffusion and Midjourney.

~20.24s

POST /v2/fooocus · submit + poll

 1# pip install "segmind>=1.1.0"
 2# export SEGMIND_API_KEY="YOUR_API_KEY"
 3import segmind
 4
 5# Async (v2): submit to the queue and block until COMPLETED.
 6# run() returns the final result dict (600s deadline, 1.0s poll by default).
 7result = segmind.run(
 8    "fooocus",
 9    prompt="cinematic 3D caricature of a woman, smiling, looking happy",
10    negative_prompt="lowquality, badquality, sketches",
11    steps=30,
12    samples=1,
13    styles="V2,Enhance,Sharp",
14    aspect_ratio="1024*1024",
15    seed=1849415,
16    guidance_scale=4,
17    scheduler="DPM++ 2M SDE Karras",
18    base_model="protovisionxl",
19    faceswap_img="https://segmind-sd-models.s3.amazonaws.com/display_images/fooocus-face-image.jpg",
20    faceswap_cn_stop=0.9,
21    faceswap_cn_weight=0.8,
22    imageprompt_img="https://segmind-sd-models.s3.amazonaws.com/display_images/fooocus-image-prompt.png",
23    imageprompt_cn_stop=0.5,
24    imageprompt_cn_weight=0.6,
25    pyracanny_cn_stop=0.5,
26    pyracanny_cn_weight=1,
27    cpds_cn_stop=0.5,
28    cpds_cn_weight=1,
29    base64=False,
30)
31print(result["status"])                      # COMPLETED
32print(result.get("output"))                  # model output (e.g. media URL)
33print(result["metrics"]["inference_time"])   # server compute seconds
34
35# --- Or submit + poll manually (track request_id, control the cadence) ---
36from segmind import SegmindClient, InferenceFailed, InferenceTimeout
37
38client = SegmindClient()                      # reads SEGMIND_API_KEY
39payload = {
40    "prompt": "cinematic 3D caricature of a woman, smiling, looking happy",
41    "negative_prompt": "lowquality, badquality, sketches",
42    "steps": 30,
43    "samples": 1,
44    "styles": "V2,Enhance,Sharp",
45    "aspect_ratio": "1024*1024",
46    "seed": 1849415,
47    "guidance_scale": 4,
48    "scheduler": "DPM++ 2M SDE Karras",
49    "base_model": "protovisionxl",
50    "faceswap_img": "https://segmind-sd-models.s3.amazonaws.com/display_images/fooocus-face-image.jpg",
51    "faceswap_cn_stop": 0.9,
52    "faceswap_cn_weight": 0.8,
53    "imageprompt_img": "https://segmind-sd-models.s3.amazonaws.com/display_images/fooocus-image-prompt.png",
54    "imageprompt_cn_stop": 0.5,
55    "imageprompt_cn_weight": 0.6,
56    "pyracanny_cn_stop": 0.5,
57    "pyracanny_cn_weight": 1,
58    "cpds_cn_stop": 0.5,
59    "cpds_cn_weight": 1,
60    "base64": False,
61}
62job = client.submit_async("fooocus", **payload)
63print(job.request_id)                         # available immediately
64try:
65    result = job.wait(timeout=600, interval=1.0)
66except InferenceTimeout as e:
67    print("still running:", e.request_id)
68except InferenceFailed as e:
69    print("failed:", e.detail)

 1# pip install "segmind>=1.1.0"
 2# export SEGMIND_API_KEY="YOUR_API_KEY"
 3import segmind
 4
 5# Async (v2): submit to the queue and block until COMPLETED.
 6# run() returns the final result dict (600s deadline, 1.0s poll by default).
 7result = segmind.run(
 8    "fooocus",
 9    prompt="cinematic 3D caricature of a woman, smiling, looking happy",
10    negative_prompt="lowquality, badquality, sketches",
11    steps=30,
12    samples=1,
13    styles="V2,Enhance,Sharp",
14    aspect_ratio="1024*1024",
15    seed=1849415,
16    guidance_scale=4,
17    scheduler="DPM++ 2M SDE Karras",
18    base_model="protovisionxl",
19    faceswap_img="https://segmind-sd-models.s3.amazonaws.com/display_images/fooocus-face-image.jpg",
20    faceswap_cn_stop=0.9,
21    faceswap_cn_weight=0.8,
22    imageprompt_img="https://segmind-sd-models.s3.amazonaws.com/display_images/fooocus-image-prompt.png",
23    imageprompt_cn_stop=0.5,
24    imageprompt_cn_weight=0.6,
25    pyracanny_cn_stop=0.5,
26    pyracanny_cn_weight=1,
27    cpds_cn_stop=0.5,
28    cpds_cn_weight=1,
29    base64=False,
30)
31print(result["status"])                      # COMPLETED
32print(result.get("output"))                  # model output (e.g. media URL)
33print(result["metrics"]["inference_time"])   # server compute seconds
34
35# --- Or submit + poll manually (track request_id, control the cadence) ---
36from segmind import SegmindClient, InferenceFailed, InferenceTimeout
37
38client = SegmindClient()                      # reads SEGMIND_API_KEY
39payload = {
40    "prompt": "cinematic 3D caricature of a woman, smiling, looking happy",
41    "negative_prompt": "lowquality, badquality, sketches",
42    "steps": 30,
43    "samples": 1,
44    "styles": "V2,Enhance,Sharp",
45    "aspect_ratio": "1024*1024",
46    "seed": 1849415,
47    "guidance_scale": 4,
48    "scheduler": "DPM++ 2M SDE Karras",
49    "base_model": "protovisionxl",
50    "faceswap_img": "https://segmind-sd-models.s3.amazonaws.com/display_images/fooocus-face-image.jpg",
51    "faceswap_cn_stop": 0.9,
52    "faceswap_cn_weight": 0.8,
53    "imageprompt_img": "https://segmind-sd-models.s3.amazonaws.com/display_images/fooocus-image-prompt.png",
54    "imageprompt_cn_stop": 0.5,
55    "imageprompt_cn_weight": 0.6,
56    "pyracanny_cn_stop": 0.5,
57    "pyracanny_cn_weight": 1,
58    "cpds_cn_stop": 0.5,
59    "cpds_cn_weight": 1,
60    "base64": False,
61}
62job = client.submit_async("fooocus", **payload)
63print(job.request_id)                         # available immediately
64try:
65    result = job.wait(timeout=600, interval=1.0)
66except InferenceTimeout as e:
67    print("still running:", e.request_id)
68except InferenceFailed as e:
69    print("failed:", e.detail)

API Endpoint

POSThttps://api.segmind.com/v1/fooocus

Parameters

promptrequired

string

Prompt to render

aspect_ratiooptional

string

output image aspect ratio

Default: "1024*1024"

Allowed values (28 total):

"704*1408""704*1344""768*1344""768*1280""832*1216""832*1152""896*1152""896*1088""960*1088""768*1024"+18 more

base_modeloptional

string

Model for inference

Default: "juggernaut_v8"

Allowed values :

"juggernaut_v8""Unstable_diffusers_v11""protovisionxl""realism_engine_v3""realVisXL""fluentlyXL"

base64optional

boolean

Base64 encoding of the output image.

Default: false

cpds_cn_stopoptional

number

image propmt

Default: 0.5Range: 0 - 1.5

cpds_cn_weightoptional

number

image propmt

Default: 1Range: 0 - 1.5

cpds_imgoptional

string (uri)

cpds_img

faceswap_cn_stopoptional

number

image propmt

Default: 0.9Range: 0 - 1.5

faceswap_cn_weightoptional

number

image propmt

Default: 0.8Range: 0 - 1.5

faceswap_imgoptional

string (uri)

faceswap_img

guidance_scaleoptional

number

Scale for classifier-free guidance

Default: 4Range: 1 - 25

imageprompt_cn_stopoptional

number

image prompt

Default: 0.5Range: 0 - 1.5

imageprompt_cn_weightoptional

number

image propmt

Default: 0.6Range: 0 - 1.5

imageprompt_imgoptional

string (uri)

faceswap_img

negative_promptoptional

string

Prompts to exclude, eg. bad anatomy, bad hands, missing fingers

pyracanny_cn_stopoptional

number

image propmt

Default: 0.5Range: 0 - 1.5

pyracanny_cn_weightoptional

number

image propmt

Default: 1Range: 0 - 1.5

pyracanny_imgoptional

string (uri)

pyracanny_img

samplesoptional

integer

Number images to generate.

Default: 1Range: 1 - 4

scheduleroptional

string

Type of scheduler.

Default: "DPM++ SDE"

Allowed values (29 total):

"DPM++ SDE Karras""DPM++ 2M Karras""DPM++ 2M SDE Karras""DPM++ 2M SDE Heun Karras""DPM++ 3M SDE Karras""LMS Karras""DPM2 Karras""DPM2 a Karras""DPM++ 2S a Karras""DPM++ 2M SDE Exponential"+19 more

seedoptional

integer

Seed for image generation.

Default: -1Range: -1 - 999999999999999

stepsoptional

integer

Number of denoising steps.

Default: 30Range: 20 - 100

stylesoptional

string

Style selection

Default: "V2,Enhance,Sharp"

Response Type

Returns: Image

Asynchronous requests (v2)

Use Async for video, long-running (>~60s), or high-concurrency workloads; Sync is simplest for fast image & LLM calls. Async submits a request and you poll it to completion.

1
POST /v2/fooocus
Submit — returns request_id, status_url, response_url
2
GET /v2/requests/{id}/status
Poll — until COMPLETED or FAILED
3
GET /v2/requests/{id}
Result — final response body

Status states

QUEUED— Accepted, waiting for a worker

PROCESSING— Running on a worker

COMPLETED— Done — result body is ready

FAILED— Errored (incl. content/RAI blocks)

A FAILED request is served as HTTP 422 — the body still carries the error detail.
An unknown or expired request_id returns HTTP 404.
Results are retained for 1 hour, then expire.
Content / RAI blocks surface as FAILED, not a separate state.
Track completion by polling the status endpoint.

Common Error Codes

The API returns standard HTTP status codes. Detailed error messages are provided in the response body.

400

Bad Request

Invalid parameters or request format

401

Unauthorized

Missing or invalid API key

403

Forbidden

Insufficient permissions

404

Not Found

Model or endpoint not found

406

Insufficient Credits

Not enough credits to process request

429

Rate Limited

Too many requests

500

Server Error

Internal server error

502

Bad Gateway

Service temporarily unavailable

504

Timeout

Request timed out