参数对照表

In next table, AdvancedParams is replaced with adp, and the rule of name change is to unify with Fooocus.

Fooocus-API	FooocusAPI	备注
prompt	prompt
negative_prompt	negative_prompt
style_selections	style_selections
performance_selection	performance_selection
aspect_ratios_selection	aspect_ratios_selection
image_number	image_number
image_seed	image_seed
sharpness	sharpness
guidance_scale	guidance_scale
base_model_name	base_model_name
refiner_model_name	refiner_model_name
refiner_switch	refiner_switch
loras	loras	same, a list of lora obj
	input_image_checkbox	this is always true
	current_tab	need not care this
uov_method	uov_method
input_image	uov_input_image	use variable name in Fooocus
outpaint_selections	outpaint_selections
input_image	inpaint_input_image	use variable name in Fooocus
inpaint_additional_prompt	inpaint_additional_prompt
input_mask	inpaint_mask_image_upload	use variable name in Fooocus
adp.disable_preview	disable_preview
adp.disable_intermediate_results	disable_intermediate_results
adp.disable_seed_increment	disable_seed_increment
adp.black_out_nsfw	black_out_nsfw
adp.adm_scaler_positive	adm_scaler_positive
adp.adm_scaler_negative	adm_scaler_negative
adp.adm_scaler_end	adm_scaler_end
adp.adaptive_cfg	adaptive_cfg
adp.clip_skip	clip_skip
adp.sampler_name	sampler_name
adp.scheduler_name	scheduler_name
adp.vae_name	vae_name
adp.overwrite_step	overwrite_step
adp.overwrite_switch	overwrite_switch
adp.overwrite_width	overwrite_width
adp.overwrite_height	overwrite_height
adp.overwrite_vary_strength	overwrite_vary_strength
adp.overwrite_upscale_strength	overwrite_upscale_strength
adp.mixing_image_prompt_and_vary_upscale	mixing_image_prompt_and_vary_upscale
adp.mixing_image_prompt_and_inpaint	mixing_image_prompt_and_inpaint
adp.debugging_cn_preprocessor	debugging_cn_preprocessor
adp.skipping_cn_preprocessor	skipping_cn_preprocessor
adp.canny_low_threshold	canny_low_threshold
adp.canny_high_threshold	canny_high_threshold
adp.refiner_swap_method	refiner_swap_method
adp.controlnet_softness	controlnet_softness
adp.freeu_enabled	freeu_enabled
adp.freeu_b1	freeu_b1
adp.freeu_b2	freeu_b2
adp.freeu_s1	freeu_s1
adp.freeu_s2	freeu_s2
adp.debugging_inpaint_preprocessor	debugging_inpaint_preprocessor
adp.inpaint_disable_initial_latent	inpaint_disable_initial_latent
adp.inpaint_engine	inpaint_engine
adp.inpaint_strength	inpaint_strength
adp.inpaint_respective_field	inpaint_respective_field
adp.inpaint_mask_upload_checkbox	inpaint_mask_upload_checkbox
adp.invert_mask_checkbox	invert_mask_checkbox
adp.inpaint_erode_or_dilate	inpaint_erode_or_dilate
image_prompts	controlnet_image	just change name
	generate_image_grid	new, default is better
outpaint_distance_left	outpaint_distance	merge these to one
outpaint_distance_right		use a list to pass these four
outpaint_distance_top		exp: [100, 50, 0, 0]
outpaint_distance_bottom		Directions are: left, up, right, down
upscale_value	upscale_multiple	name change only
	preset	new, use this this specified preset
	stream_output	new, similar to LLM streaming output
save_meta	save_metadata_to_images	name change only
meta_scheme	metadata_scheme	name change only
save_extension	output_format	name change only
save_name		remove
read_wildcards_in_order	read_wildcards_in_order
require_base64	require_base64	will be remove
async_process	async_process
webhook_url	webhook_url

simple is:

All AdvancedParams move to upper level
Modify some params name
- input_image -> inpaint_input_image
- inpaint_mask -> inpaint_mask_image_upload
- input_image -> uov_input_image
- image_prompts -> controlnet_image
- upscale_value -> upscale_value
- save_meta -> upscale_multiple
- meta_scheme -> save_metadata_to_images
- save_extension -> output_format
Remove some params
- save_name
Add some params
- input_image_checkbox
- current_tab
- generate_image_grid
- preset
- stream_output
Merge some params
- outpaint_distance_left,right,top,bottom 四个参数合并为 outpaint_distance

Example for four types of return

async task

specify async_process as True

import requests
import json

endpoint = "http://127.0.0.1:7866/v1/engine/generate/"

params = {
    "prompt": "",
    "negative_prompt": "",
    "performance_selection": "Lightning",
    "async_process": True,
    "webhook_url": ""
}

res = requests.post(
    url=endpoint,
    data=json.dumps(params),
    timeout=60
)

print(res.json())

output will be like this:

{'id': -1, 'task_id': '85c10c81e9e2482d90a64c3704137d3a', 'req_params': {}, 'in_queue_mills': -1, 'start_mills': -1, 'finish_mills': -1, 'task_status': 'pending', 'progress': -1, 'preview': '', 'webhook_url': '', 'result': []}

use task_id request http://127.0.0.1:7866/tasks/{task_id} to get task info, if this task is currently running, return should be include preview

example for return

# pending
{
    "id": -1,
    "in_queue_mills": 1720085748199,
    "finish_mills": null,
    "progress": null,
    "result": null,
    "req_params": {
        # full request params
        ...
    },
    "task_id": "85c10c81e9e2482d90a64c3704137d3a",
    "start_mills": null,
    "task_status": null,
    "webhook_url": ""
}

# running
{
    "id": -1,
    "task_id": "85c10c81e9e2482d90a64c3704137d3a",
    "req_params": {
        ...
    },
    "in_queue_mills": 1720086131653,
    "start_mills": 1720086131865,
    "finish_mills": -1,
    "task_status": "running",
    "progress": 18,
    "preview": "a long text",
    "webhook_url": "",
    "result": []
}

# finished
{
    "id": 71,
    "in_queue_mills": 1720085748199,
    "finish_mills": 1720085770046,
    "progress": 100,
    "result": [
        "http://127.0.0.1:7866/outputs/2024-07-04/2024-07-04_17-36-09_5201.png"
    ],
    "req_params": {
        ...
    },
    "task_id": "85c10c81e9e2482d90a64c3704137d3a",
    "start_mills": 1720085748425,
    "task_status": "finished",
    "webhook_url": ""
}

streaming output

this is like LLM streaming output, you will recieve from server until finish, refer to the above example:

import requests
import json

endpoint = "http://127.0.0.1:7866/v1/engine/generate/"

params = {
    "prompt": "",
    "negative_prompt": "",
    "performance_selection": "Lightning",
    "stream_output": True,
    "webhook_url": ""
}

res = requests.post(
    url=endpoint,
    data=json.dumps(params),
    stream=True,
    timeout=60
)

for line in res.iter_lines():
    if line:
        print(line.decode('utf-8'))

you will get response like this:

data: {"progress": 2, "preview": null, "message": "Loading models ...", "images": []}
data:
data: {"progress": 13, "preview": null, "message": "Preparing task 1/1 ...", "images": []}
data:
data: {"progress": 13, "preview": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAA...", 'message': 'Sampling step 1/4, image 1/1 ...', 'images': []}
data:
data: {"progress": 34, "preview": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAA...", 'message': 'Sampling step 2/4, image 1/1 ...', 'images': []}
data:
data: {"progress": 56, "preview": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAA...", 'message': 'Sampling step 3/4, image 1/1 ...', 'images': []}
data:
data: {"progress": 78, "preview": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAA...", 'message': 'Sampling step 4/4, image 1/1 ...', 'images': []}
data:
data: {"progress": 100, "preview": null, "message": "Saving image 1/1 to system ...", "images": []}
data:
data: {"progress": 100, "preview": null, "message": "Finished", "images": ["http://10.0.0.245:7866/outputs/2024-07-05/2024-07-05_09-31-10_1752.png"]}
data:

just modify our code:

import requests
import json

endpoint = "http://127.0.0.1:7866/v1/engine/generate/"

params = {
    "prompt": "",
    "negative_prompt": "",
    "performance_selection": "Lightning",
    "stream_output": True,
    "webhook_url": ""
}

res = requests.post(
    url=endpoint,
    data=json.dumps(params),
    stream=True,
    timeout=60
)

for line in res.iter_lines(chunk_size=8192):
    line = line.decode('utf-8').split('\n')[0]

    try:
        json_data = json.loads(line[6:])
        if json_data["preview"] is not None:
            json_data["preview"] = "data:image/png;base64,iVBORw0KGgoAAAANSU..."
    except json.decoder.JSONDecodeError:
        continue
    print(json_data)

you will get this:

{'progress': 13, 'preview': None, 'message': 'Preparing task 1/1 ...', 'images': []}
{'progress': 13, 'preview': 'data:image/png;base64,iVBORw0KGgoAAAANSU...', 'message': 'Sampling step 1/4, image 1/1 ...', 'images': []}
{'progress': 34, 'preview': 'data:image/png;base64,iVBORw0KGgoAAAANSU...', 'message': 'Sampling step 2/4, image 1/1 ...', 'images': []}
{'progress': 56, 'preview': 'data:image/png;base64,iVBORw0KGgoAAAANSU...', 'message': 'Sampling step 3/4, image 1/1 ...', 'images': []}
{'progress': 78, 'preview': 'data:image/png;base64,iVBORw0KGgoAAAANSU...', 'message': 'Sampling step 4/4, image 1/1 ...', 'images': []}
{'progress': 100, 'preview': None, 'message': 'Saving image 1/1 to system ...', 'images': []}
{'progress': 100, 'preview': None, 'message': 'Finished', 'images': ['http://10.0.0.245:7866/outputs/2024-07-05/2024-07-05_10-02-22_2536.png']}

it is better for frontend i think (but i am not good at this). with AI, i generate a example.html, click Generate button, you will get a page with preview and progress.

binary output

this is simple, just set Accept: image/xxx in headers, image_number force to 1, higher priority than stream_output and async_process, you will get a binary image output, format is what you set in headers. (jpg, jpeg, png, webp are supported)

import requests
import json
from PIL import Image
from io import BytesIO
import matplotlib.pyplot as plt

endpoint = "http://127.0.0.1:7866/v1/engine/generate/"

headers = {
    "Accept": "image/png"
}

params = {
    "prompt": "",
    "negative_prompt": "",
    "performance_selection": "Lightning",
    "webhook_url": ""
}

res = requests.post(
    url=endpoint,
    data=json.dumps(params),
    headers=headers,
    timeout=60
)

image_stream = BytesIO(res.content)
image = Image.open(image_stream)

plt.imshow(image)
plt.show()

syncronous output

use default params, endpoint will be a sync interface, the return format is the same as query by id.

import requests
import json

endpoint = "http://127.0.0.1:7866/v1/engine/generate/"

params = {
    "prompt": "",
    "negative_prompt": "",
    "performance_selection": "Lightning",
    "webhook_url": ""
}

res = requests.post(
    url=endpoint,
    data=json.dumps(params),
    timeout=60
)

print(res.json())

task query

Unlike Fooocus-API, the history saving will be automatic without a retention switch. The database is used with SQLite3 and stored in outputs/db.sqlite3. Taking lessons from the previous version, the table structure has been greatly simplified, and request parameters are stored as JSON in the req_params field. To reduce read and write operations, database operations are only performed when tasks enter and complete the queue. It is only used for generating records, and task status tracking is completed in memory.

In addition, this version will retain input images, uploaded images will calculate hash values and be saved in the inputs directory, and the image parameters in the database's req_params will be replaced with url information for saving, which means more complete historical record preservation, whether it is text-to-image or image-to-image or other types of images.

/tasks

This is a compound interface, but its return format is fixed. The interface will always return JSON data in the following format, regardless of how the parameters are specified.

{
    "history": [],
    "current": [],  # Although it is a list, there will be no more than one element in it.
    "pending": []
}

All elements have a format that matches the scheme in the database, except for current which has an additional preview, as shown in the following figure:

more usage, see below:

The return format of this interface is always fixed, regardless of how the parameters are adjusted.

curl http://localhost:7866/tasks?query=current
# only return current task, other value for query include 'all', 'pending', 'history'

curl http://localhost:7866/tasks?query=history&page=3&page_size=5
# history and pending supports pagination and page size.

curl http://localhost:7866/tasks?query=history&start_at=2024-07-03T12:22:30
# You can specify a time range for the query, which will return all records within that time period. The time format is ISO8601, and if you do not specify end_at, it will be set to the current time.

curl http://localhost:7866/tasks?query=history&start_at=2024-07-03T12:22:30&action=delete
# Delete tasks within a specified time range, including database records and generated files. This is the only supported deletion method at present (input files will not be deleted).

curl http://localhost:7866/tasks/38ba92b188a64233a7336218cd902865
# This will return the information of the task, but it is just a dictionary. It is equivalent to taking the task with the specified task_id from the list above. If it happens to be the current task, it will also include preview. (Although it may look similar, this is actually another interface.)

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migration.md

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参数对照表

Example for four types of return

async task

streaming output

binary output

syncronous output

task query

/tasks

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migration.md

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参数对照表

Example for four types of return

async task

streaming output

binary output

syncronous output

task query

/tasks