Introduction
Fooocus capability related interfaces
Fooocus API task related interfaces
- job-queue
- query-job
- job-history
- stop
- ping
webhook
public requests body

Introduction

Fooocus API are provided more than a dozen REST interfaces now, I roughly divide it into two categories, the first is the ability to call Fooocus, such as generating images, refreshing models, and so on, and the second is related to Fooocus API itself, mainly related to task queries. I will try to illustrate their role and usage and provide examples in the following content.

Almost all interface parameters have default values, which means you only need to send the parameters you are interested in. The complete parameters and default values can be viewed in the table.

Fooocus capability related interfaces

text-to-image

Corresponding to the function of text to image in Fooocus

base info：

EndPoint: /v1/generation/text-to-image
Method: Post
DataType: json

requests params：

Name	Type	Description
prompt	string	prompt, default to empty string
negative_prompt	string	negative_prompt
style_selections	List[str]	list of style, must be supported style, you can get all supported style here
performance_selection	Enum	performance_selection, must be one of `Speed`, `Quality`, `Extreme Speed` default to `Speed`
aspect_ratios_selection	str	resolution, default to `1152*896`
image_number	int	the num of image to generate, default to 1 , max num is 32, note: Not a parallel interface
image_seed	int	seed, default to -1, meant random
sharpness	float	sharpness, default to 2.0 , 0-30
guidance_scale	float	guidance scale, default to 4.0 , 1-30
base_model_name	str	base model, default to `juggernautXL_version6Rundiffusion.safetensors`
refiner_model_name	str	refiner model, default to `None`
refiner_switch	float	refiner switch, default to 0.5
loras	List[Lora]	lora list, include conf, lora: Lora
advanced_params	AdvancedParams	Advanced params, AdvancedParams
require_base64	bool	require base64, default to False
save_meta	bool	save metadata to image, default True
meta_scheme	str	metadata scheme, default 'fooocus', only support 'fooocus' now
save_extension	str	extension for saved image, default 'png'
save_name	str	image name saved, default job_id + seq
read_wildcards_in_order	bool	read wildcards in order, default False
async_process	bool	is async, default to False
webhook_url	str	after async task completed, address for callback, default to None, refer to webhook

response params：

Most response have the same structure, but different parts will be specifically explained

This interface returns a universal response structure, refer to response

request example：

host = "http://127.0.0.1:8888"

def text2img(params: dict) -> dict:
    """
    text to image
    """
    result = requests.post(url=f"{host}/v1/generation/text-to-image",
                           data=json.dumps(params),
                           headers={"Content-Type": "application/json"})
    return result.json()

result =text2img({
    "prompt": "1girl sitting on the ground",
    "async_process": True})
print(result)

image-upscale-vary

Corresponding to the function of Upscale or Variation in Fooocus

the requests body for this interface based on text-to-image, so I will only list the difference with text-to-image

In addition, the interface provides two versions, and there is no functional difference between the two versions, mainly due to slight differences in request methods

base info：

EndPoint_V1: /v1/generation/image-upscale-vary
EndPoint_V2: /v2/generation/image-upscale-vary
Method: Post
DataType: form|json

V1

requests params

Name	Type	Description
input_image	string($binary)	binary image
uov_method	Enum	'Vary (Subtle)','Vary (Strong)','Upscale (1.5x)','Upscale (2x)','Upscale (Fast 2x)','Upscale (Custom)'
upscale_value	float	default to None , 1.0-5.0, magnification, only for uov_method is 'Upscale (Custom)'
style_selections	List[str]	list Fooocus style seg with comma
loras	str(List[Lora])	list for lora, with configure, lora: Lora, example: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]
advanced_params	str(AdvancedParams)	AdvancedParams, AdvancedParams: AdvancedParams, send with str, None is available

response params：

This interface returns a universal response structure, refer to response

requests example：

# headers should not contain {"Content-Type": "application/json"}

host = "http://127.0.0.1:8888"
image = open("./examples/imgs/bear.jpg", "rb").read()

def upscale_vary(image, params: dict) -> dict:
    """
    Upscale or Vary
    """
    response = requests.post(url=f"{host}/v1/generation/image-upscale-vary",
                        data=params,
                        files={"input_image": image})
    return response.json()

result =upscale_vary(image=image,
                     params={
                         "uov_method": "Upscale (2x)",
                         "async_process": True
                     })
print(json.dumps(result, indent=4, ensure_ascii=False))

V2

requests params

Name	Type	Description
uov_method	UpscaleOrVaryMethod	Enum type, value should one of 'Vary (Subtle)','Vary (Strong)','Upscale (1.5x)','Upscale (2x)','Upscale (Fast 2x)','Upscale (Custom)'
upscale_value	float	default to None , 1.0-5.0, magnification, only for uov_method is 'Upscale (Custom)'
input_image	str	input image, base64 str, or a URL

response params：

This interface returns a universal response structure, refer to response

requests params：

host = "http://127.0.0.1:8888"
image = open("./examples/imgs/bear.jpg", "rb").read()

def upscale_vary(image, params: dict) -> dict:
    """
    Upscale or Vary
    """
    params["input_image"] = base64.b64encode(image).decode('utf-8') 
    response = requests.post(url=f"{host}/v2/generation/image-upscale-vary",
                        data=json.dumps(params),
                        headers={"Content-Type": "application/json"},
                        timeout=300)
    return response.json()

result =upscale_vary(image=image,
                     params={
                         "uov_method": "Upscale (2x)",
                         "async_process": True
                     })
print(json.dumps(result, indent=4, ensure_ascii=False))

image-inpaint-outpaint

base info：

EndPoint_V1: /v1/generation/image-inpaint-outpaint
EndPoint_V2: /v2/generation/image-inpaint-outpaint
Method: Post
DataType: form|json

V1

requests params

Name	Type	Description
input_image	string($binary)	binary image
input_mask	string($binary)	binary image
inpaint_additional_prompt	string	additional_prompt
outpaint_selections	str	Image extension direction , 'Left', 'Right', 'Top', 'Bottom' seg with comma
outpaint_distance_left	int	Image extension distance, default to 0
outpaint_distance_right	int	Image extension distance, default to 0
outpaint_distance_top	int	Image extension distance, default to 0
outpaint_distance_bottom	int	Image extension distance, default to 0
style_selections	List[str]	list Fooocus style seg with comma
loras	str(List[Lora])	list for lora, with configure, lora: Lora, example: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]
advanced_params	str(AdvancedParams)	AdvancedParams, AdvancedParams: AdvancedParams, send with str, None is available

response params：

This interface returns a universal response structure, refer to response

requests example：

# example for inpaint outpaint v1
host = "http://127.0.0.1:8888"
image = open("./examples/imgs/bear.jpg", "rb").read()

def inpaint_outpaint(params: dict, input_image: bytes, input_mask: bytes = None) -> dict:
    """
    example for inpaint outpaint v1
    """
    response = requests.post(url=f"{host}/v1/generation/image-inpaint-outpaint",
                        data=params,
                        files={"input_image": input_image,
                               "input_mask": input_mask})
    return response.json()

# image extension example
result = inpaint_outpaint(params={
                            "outpaint_selections": "Left,Right",
                            "async_process": True},
                          input_image=image,
                          input_mask=None)
print(json.dumps(result, indent=4, ensure_ascii=False))

# image inpaint example
source = open("./examples/imgs/s.jpg", "rb").read()
mask = open("./examples/imgs/m.png", "rb").read()
result = inpaint_outpaint(params={
                            "prompt": "a cat",
                            "async_process": True},
                          input_image=source,
                          input_mask=mask)
print(json.dumps(result, indent=4, ensure_ascii=False))

V2

requests params

Name	Type	Description
input_image	str	input image, base64 str, or a URL
input_mask	str	input mask, base64 str, or a URL
inpaint_additional_prompt	str	additional prompt
outpaint_selections	List[OutpaintExpansion]	OutpaintExpansion is Enum, value should one of "Left", "Right", "Top", "Bottom"
outpaint_distance_left	int	Image extension distance, default to 0
outpaint_distance_right	int	Image extension distance, default to 0
outpaint_distance_top	int	Image extension distance, default to 0
outpaint_distance_bottom	int	Image extension distance, default to 0

response params：

This interface returns a universal response structure, refer to response response params

requests example：

# example for inpaint outpaint v2
host = "http://127.0.0.1:8888"
image = open("./examples/imgs/bear.jpg", "rb").read()

def inpaint_outpaint(params: dict) -> dict:
    """
    example for inpaint outpaint v2
    """
    response = requests.post(url=f"{host}/v2/generation/image-inpaint-outpaint",
                        data=json.dumps(params),
                        headers={"Content-Type": "application/json"})
    return response.json()

# image extension example
result = inpaint_outpaint(params={
                            "input_image": base64.b64encode(image).decode('utf-8'),
                            "input_mask": None,
                            "outpaint_selections": ["Left", "Right"],
                            "async_process": True})
print(json.dumps(result, indent=4, ensure_ascii=False))

# image inpaint example
source = open("./examples/imgs/s.jpg", "rb").read()
mask = open("./examples/imgs/m.png", "rb").read()
result = inpaint_outpaint(params={
                            "prompt": "a cat",
                            "input_image": base64.b64encode(source).decode('utf-8'),
                            "input_mask": base64.b64encode(mask).decode('utf-8'),
                            "async_process": True})
print(json.dumps(result, indent=4, ensure_ascii=False))

image-prompt

v0.3.27 has a break change. Interface based on change to inpaint-outpaint

after v0.3.27, this interface implements the functions of inpaint_outpaint and image-prompt.

Multi-function interface, which does not implement the functions of inpaint_outpaint and image-prompt at the same time in the same request

base info：

EndPoint_V1: /v1/generation/image-prompt
EndPoint_V2: /v2/generation/image-prompt
Method: Post
DataType: form|json

V1

requests params

Name	Type	Description
input_image	Bytes	binary image, use for inpaint
input_mask	Bytes	binary image mask, use for inpaint
inpaint_additional_prompt	str	inpaint additional prompt
outpaint_selections	str	Image extension direction , 'Left', 'Right', 'Top', 'Bottom' seg with comma
outpaint_distance_left	int	Image extension distance, default to 0
outpaint_distance_right	int	Image extension distance, default to 0
outpaint_distance_top	int	Image extension distance, default to 0
outpaint_distance_bottom	int	Image extension distance, default to 0
cn_img1	string($binary)	binary image
cn_stop1	float	default to 0.6
cn_weight1	float	default to 0.6
cn_type1	Enum	should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"
cn_img2	string($binary)	binary image
cn_stop2	float	default to 0.6
cn_weight2	float	default to 0.6
cn_type2	Enum	should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"
cn_img3	string($binary)	binary image
cn_stop3	float	default to 0.6
cn_weight3	float	default to 0.6
cn_type3	Enum	should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"
cn_img4	string($binary)	binary image
cn_stop4	float	default to 0.6
cn_weight4	float	default to 0.6
cn_type4	Enum	should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"
style_selections	List[str]	list Fooocus style seg with comma
loras	str(List[Lora])	list for lora, with configure, lora: Lora, example: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]
advanced_params	str(AdvancedParams)	AdvancedParams, AdvancedParams: AdvancedParams, send with str, None is available

response params：

This interface returns a universal response structure, refer to response response params

requests example：

# image_prompt v1 example
host = "http://127.0.0.1:8888"
image = open("./examples/imgs/bear.jpg", "rb").read()
source = open("./examples/imgs/s.jpg", "rb").read()
mask = open("./examples/imgs/m.png", "rb").read()

def image_prompt(params: dict,
                 input_image: bytes=None,
                 input_mask: bytes=None,
                 cn_img1: bytes=None,
                 cn_img2: bytes=None,
                 cn_img3: bytes=None,
                 cn_img4: bytes=None,) -> dict:
    """
    image prompt
    """
    response = requests.post(url=f"{host}/v1/generation/image-prompt",
                             data=params,
                             files={
                                 "input_image": input_image,
                                 "input_mask": input_mask,
                                 "cn_img1": cn_img1,
                                 "cn_img2": cn_img2,
                                 "cn_img3": cn_img3,
                                 "cn_img4": cn_img4,
                              })
    return response.json()

# image extend
params = {
    "outpaint_selections": ["Left", "Right"],
    "image_prompts": [] # required, can be empty list
}
result = image_prompt(params=params, input_image=image)
print(json.dumps(result, indent=4, ensure_ascii=False))

# inpaint

params = {
    "prompt": "1girl sitting on the chair",
    "image_prompts": [], # required, can be empty list
    "async_process": True
}
result = image_prompt(params=params, input_image=source, input_mask=mask)
print(json.dumps(result, indent=4, ensure_ascii=False))

# image prompt

params = {
    "prompt": "1girl sitting on the chair",
    "image_prompts": [
        {
            "cn_stop": 0.6,
            "cn_weight": 0.6,
            "cn_type": "ImagePrompt"
        },{
            "cn_stop": 0.6,
            "cn_weight": 0.6,
            "cn_type": "ImagePrompt"
        }]
    }
result = image_prompt(params=params, cn_img1=image, cn_img2=source)
print(json.dumps(result, indent=4, ensure_ascii=False))

V2

requests params

Name	Type	Description
input_image	str	base64 image, or a URL, use for inpaint
input_mask	str	base64 image mask, or a URL, use for inpaint
inpaint_additional_prompt	str	inpaint additional prompt
outpaint_selections	List[]	Image extension direction , 'Left', 'Right', 'Top', 'Bottom' seg with comma
outpaint_distance_left	int	Image extension distance, default to 0
outpaint_distance_right	int	Image extension distance, default to 0
outpaint_distance_top	int	Image extension distance, default to 0
outpaint_distance_bottom	int	Image extension distance, default to 0
image_prompts	List[ImagePrompt]	image list, include config, ImagePrompt struct：

ImagePrompt

Name	Type	Description
cn_img	str	input image, base64 str, or a URL
cn_stop	float	0-1, default to 0.5
cn_weight	float	weight, 0-2, default to 1.0
cn_type	ControlNetType	ControlNetType Enum, should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"

response params：

This interface returns a universal response structure, refer to response response params

requests example：

# image_prompt v2 example
host = "http://127.0.0.1:8888"
image = open("./examples/imgs/bear.jpg", "rb").read()
source = open("./examples/imgs/s.jpg", "rb").read()
mask = open("./examples/imgs/m.png", "rb").read()

def image_prompt(params: dict) -> dict:
    """
    image prompt
    """
    response = requests.post(url=f"{host}/v2/generation/image-prompt",
                             data=json.dumps(params),
                             headers={"Content-Type": "application/json"})
    return response.json()

# image extend
params = {
    "input_image": base64.b64encode(image).decode('utf-8'),
    "outpaint_selections": ["Left", "Right"],
    "image_prompts": [] # required, can be empty list
}
result = image_prompt(params)
print(json.dumps(result, indent=4, ensure_ascii=False))

# inpaint

params = {
    "prompt": "1girl sitting on the chair",
    "input_image": base64.b64encode(source).decode('utf-8'),
    "input_mask": base64.b64encode(mask).decode('utf-8'),
    "image_prompts": [], # required, can be empty list
    "async_process": True
}
result = image_prompt(params)
print(json.dumps(result, indent=4, ensure_ascii=False))

# image prompt

params = {
    "prompt": "1girl sitting on the chair",
    "image_prompts": [
        {
            "cn_img": base64.b64encode(source).decode('utf-8'),
            "cn_stop": 0.6,
            "cn_weight": 0.6,
            "cn_type": "ImagePrompt"
        },{
            "cn_img": base64.b64encode(image).decode('utf-8'),
            "cn_stop": 0.6,
            "cn_weight": 0.6,
            "cn_type": "ImagePrompt"
        }]
    }
result = image_prompt(params)
print(json.dumps(result, indent=4, ensure_ascii=False))

text to image with image prompt

this interface only provides v2 version

base info:

EndPoint: /v2/generation/text-to-image-with-ip
Method: Post
DataType: json

requests params

Name	Type	Description
image_prompts	List[ImagePrompt]	Image list

requests example:

# text to image with image prompt example
host = "http://127.0.0.1:8888"
image = open("./examples/imgs/bear.jpg", "rb").read()
source = open("./examples/imgs/s.jpg", "rb").read()
def image_prompt(params: dict) -> dict:
    """
    image prompt
    """
    response = requests.post(url=f"{host}/v2/generation/text-to-image-with-ip",
                             data=json.dumps(params),
                             headers={"Content-Type": "application/json"})
    return response.json()

params = {
    "prompt": "A bear",
    "image_prompts": [
        {
            "cn_img": base64.b64encode(source).decode('utf-8'),
            "cn_stop": 0.6,
            "cn_weight": 0.6,
            "cn_type": "ImagePrompt"
        },{
            "cn_img": base64.b64encode(image).decode('utf-8'),
            "cn_stop": 0.6,
            "cn_weight": 0.6,
            "cn_type": "ImagePrompt"
        }
    ]
}
result = image_prompt(params)
print(json.dumps(result, indent=4, ensure_ascii=False))

describe

base info：

EndPoint: /v1/tools/describe-image
Method: Post
DataType: form

requests params

Name	Type	Description
type	Enum	type, should be one of "Photo", "Anime"

requests example:

def describe_image(image: bytes,
                   params: dict = {"type": "Photo"}) -> dict:
    """
    describe-image
    """
    response = requests.post(url="http://127.0.0.1:8888/v1/tools/describe-image",
                        params=params,
                        files={
                            "image": image
                        },
                        timeout=30)
    return response.json()

response example:

{
  "describe": "a young woman posing with her hands behind her head"
}

all-models

base info：

EndPoint: /v1/engines/all-models
Method: Get

requests example:

def all_models() -> dict:
    """
    all-models
    """
    response = requests.get(url="http://127.0.0.1:8888/v1/engines/all-models",
                        timeout=30)
    return response.json()

response params:

{
  "model_filenames": [
    "juggernautXL_version6Rundiffusion.safetensors",
    "sd_xl_base_1.0_0.9vae.safetensors",
    "sd_xl_refiner_1.0_0.9vae.safetensors"
  ],
  "lora_filenames": [
    "sd_xl_offset_example-lora_1.0.safetensors"
  ]
}

refresh-models

base info：

EndPoint: /v1/engines/refresh-models
Method: Post

Removed, use all-models instead

requests example

def refresh() -> dict:
    """
    refresh-models
    """
    response = requests.post(url="http://127.0.0.1:8888/v1/engines/refresh-models",
                        timeout=30)
    return response.json()

response params

{
  "model_filenames": [
    "juggernautXL_version6Rundiffusion.safetensors",
    "sd_xl_base_1.0_0.9vae.safetensors",
    "sd_xl_refiner_1.0_0.9vae.safetensors"
  ],
  "lora_filenames": [
    "sd_xl_offset_example-lora_1.0.safetensors"
  ]
}

styles

base info：

EndPoint: /v1/engines/styles
Method: Get

requests example:

def styles() -> dict:
    """
    styles
    """
    response = requests.get(url="http://127.0.0.1:8888/v1/engines/styles",
                        timeout=30)
    return response.json()

response params:

[
  "Fooocus V2",
  "Fooocus Enhance",
  ...
  "Watercolor 2",
  "Whimsical And Playful"
]

Fooocus API task related interfaces

job-queue

base info：

EndPoint: /v1/engines/job-queue
Method: Get

requests example:

def job_queue() -> dict:
    """
    job-queue
    """
    response = requests.get(url="http://127.0.0.1:8888/v1/generation/job-queue",
                        timeout=30)
    return response.json()

response params:

{
  "running_size": 0,
  "finished_size": 1,
  "last_job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4"
}

query-job

base info：

EndPoint: /v1/generation/query-job
Method: Get

requests example:

def taskResult(task_id: str) -> dict:
    # get task status
    task_status = requests.get(url="http://127.0.0.1:8888/v1/generation/query-job",
                               params={"job_id": task_id,
                                       "require_step_preview": False},
                               timeout=30)

    return task_status.json()

response params:

{
  "job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4",
  "job_type": "Text to Image",
  "job_stage": "SUCCESS",
  "job_progress": 100,
  "job_status": "Finished",
  "job_step_preview": null,
  "job_result": [
    {
      "base64": null,
      "url": "http://127.0.0.1:8888/files/2023-11-27/b928e50e-3c09-4187-a3f9-1c12280bfd95.png",
      "seed": 8228839561385006000,
      "finish_reason": "SUCCESS"
    }
  ]
}

job-history

base info：

EndPoint: /v1/generation/job-history
Method: get

requests example:

def job-history() -> dict:
    """
    job-history
    """
    response = requests.get(url="http://127.0.0.1:8888/v1/generation/job-history",
                        timeout=30)
    return response.json()

response params:

{
  "queue": [],
  "history": [
    "job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4",
    "is_finished": True
  ]
}

stop

base info：

EndPoint: /v1/generation/stop
Method: post

requests example:

def stop() -> dict:
    """
    stop
    """
    response = requests.post(url="http://127.0.0.1:8888/v1/generation/stop",
                        timeout=30)
    return response.json()

response params:

{
  "msg": "success"
}

ping

base info：

EndPoint: /ping
Method: get

pong

webhook

You can specify an address through '--webhook_url' on the command line so that you can receive notifications after asynchronous tasks are completed

Here is a simple example to demonstrate how 'webhook' works

First，start a simple server using the following code:

from fastapi import FastAPI
import uvicorn

app = FastAPI()

@app.post("/status")
async def status(requests: dict):
    print(requests)

uvicorn.run(app, host="0.0.0.0", port=8000)

Then, start Fooocus API with --webhook-url http://host:8000/status

Submit a task in any way, and after completion, you will see the task completion information in the background of this simple server：

{'job_id': '717ec0b5-85df-4174-80d6-bddf93cd8248', 'job_result': [{'url': 'http://127.0.0.1:8888/files/2023-12-29/f1eca704-718e-4781-9d5f-82d41aa799d7.png', 'seed': '3283449865282320931'}]}

public requests params

AdvanceParams

Name	Type	Description
disable_preview	bool	disable preview, default to False
adm_scaler_positive	float	ADM Guidance Scaler, default to 1.5, range 0.1-3.0
adm_scaler_negative	float	negative ADM Guidance Scaler, default to 0.8, range 0.1-3.0
adm_scaler_end	float	ADM Guidance Scaler end value, default to 0.5, range 0.0-1.0
refiner_swap_method	str	refiner model swap method, default to `joint`
adaptive_cfg	float	CFG Mimicking from TSNR, default to 7.0, range 1.0-30.0
sampler_name	str	sampler, default to `default_sampler`
scheduler_name	str	scheduler, default to `default_scheduler`
overwrite_step	int	Forced Overwrite of Sampling Step, default to -1, range -1-200
overwrite_switch	int	Forced Overwrite of Refiner Switch Step, default to -1, range -1-200
overwrite_width	int	Forced Overwrite of Generating Width, default to -1, range -1-2048
overwrite_height	int	Forced Overwrite of Generating Height, default to -1, range -1-2048
overwrite_vary_strength	float	Forced Overwrite of Denoising Strength of "Vary", default to -1, range -1-1.0
overwrite_upscale_strength	float	Forced Overwrite of Denoising Strength of "Upscale", default to -1, range -1-1.0
mixing_image_prompt_and_vary_upscale	bool	Mixing Image Prompt and Vary/Upscale, default to False
mixing_image_prompt_and_inpaint	bool	Mixing Image Prompt and Inpaint, default to False
debugging_cn_preprocessor	bool	Debug Preprocessors, default to False
skipping_cn_preprocessor	bool	Skip Preprocessors, default to False
controlnet_softness	float	Softness of ControlNet, default to 0.25, range 0.0-1.0
canny_low_threshold	int	Canny Low Threshold, default to 64, range 1-255
canny_high_threshold	int	Canny High Threshold, default to 128, range 1-255
freeu_enabled	bool	FreeU enabled, default to False
freeu_b1	float	FreeU B1, default to 1.01
freeu_b2	float	FreeU B2, default to 1.02
freeu_s1	float	FreeU B3, default to 0.99
freeu_s2	float	FreeU B4, default to 0.95
debugging_inpaint_preprocessor	bool	Debug Inpaint Preprocessing, default to False
inpaint_disable_initial_latent	bool	Disable initial latent in inpaint, default to False
inpaint_engine	str	Inpaint Engine, default to `v2.6`
inpaint_strength	float	Inpaint Denoising Strength, default to 1.0, range 0.0-1.0
inpaint_respective_field	float	Inpaint Respective Field, default to 1.0, range 0.0-1.0
inpaint_mask_upload_checkbox	bool	upload mask, default False
invert_mask_checkbox	bool	revert mask, default False
inpaint_erode_or_dilate	int	Mask Erode or Dilate, default 0, -64-64

lora

Name	Type	Description
enabled	bool	enable lora
model_name	str	model name
weight	float	weight, default to 0.5

response

success response：

async_process: True

Name	Type	Description
job_id	int	job ID
job_type	str	job type
job_stage	str	job stage
job_progress	float	job progress
job_status	str	job status
job_step_preview	str	job preview
job_result	str	job result

async_process: False

Name	Type	Description
base64	str	base64 image, according to `require_base64` params determines whether it is null
url	str	result image url
seed	int	image seed
finish_reason	str	finish reason

fail response：

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api_doc_en.md

api_doc_en.md

Introduction

Fooocus capability related interfaces

text-to-image

image-upscale-vary

V1

V2

image-inpaint-outpaint

V1

V2

image-prompt

V1

V2

text to image with image prompt

describe

all-models

refresh-models

styles

Fooocus API task related interfaces

job-queue

query-job

job-history

stop

ping

webhook

public requests params

AdvanceParams

lora

response

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api_doc_en.md

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api_doc_en.md

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Introduction

Fooocus capability related interfaces

text-to-image

image-upscale-vary

V1

V2

image-inpaint-outpaint

V1

V2

image-prompt

V1

V2

text to image with image prompt

describe

all-models

refresh-models

styles

Fooocus API task related interfaces

job-queue

query-job

job-history

stop

ping

webhook

public requests params

AdvanceParams

lora

response