konieshadow/fooocus-api – Run with an API on Replicate

konieshadow / fooocus-api

Third party Fooocus replicate model

Cold

Public
1.4M runs
L40S
GitHub
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

Prompt for image generation

Default: ""

negative_prompt

string

Shift + Return to add a new line

Negtive prompt for image generation

Default: ""

style_selections

string

Shift + Return to add a new line

Fooocus V2,Fooocus Enhance,Fooocus SharpFooocus V2,Fooocus Enhance,Fooocus Sharp

Fooocus styles applied for image generation, seperated by comma

Default: "Fooocus V2,Fooocus Enhance,Fooocus Sharp"

performance_selection

string

Performance selection

Default: "Speed"

aspect_ratios_selection

string

The generated image's size

Default: "1152*896"

image_number

integer

(minimum: 1, maximum: 8)

How many image to generate

Default: 1

image_seed

integer

Seed to generate image, -1 for random

Default: -1

sharpness

number

(minimum: 0, maximum: 30)

Default: 2

guidance_scale

number

(minimum: 1, maximum: 30)

Default: 4

refiner_switch

number

(minimum: 0.1, maximum: 1)

Default: 0.5

uov_input_image

file

Input image for upscale or variation, keep None for not upscale or variation

uov_method

string

An enumeration.

Default: "Disabled"

uov_upscale_value

number

Only when Upscale (Custom)

Default: 0

inpaint_additional_prompt

string

Shift + Return to add a new line

Prompt for image generation

Default: ""

inpaint_input_image

file

Input image for inpaint or outpaint, keep None for not inpaint or outpaint. Please noticed, `uov_input_image` has bigger priority is not None.

inpaint_input_mask

file

Input mask for inpaint

outpaint_selections

string

Shift + Return to add a new line

Outpaint expansion selections, literal 'Left', 'Right', 'Top', 'Bottom' seperated by comma

Default: ""

outpaint_distance_left

integer

Outpaint expansion distance from Left of the image

Default: 0

outpaint_distance_top

integer

Outpaint expansion distance from Top of the image

Default: 0

outpaint_distance_right

integer

Outpaint expansion distance from Right of the image

Default: 0

outpaint_distance_bottom

integer

Outpaint expansion distance from Bottom of the image

Default: 0

cn_img1

file

Input image for image prompt. If all cn_img[n] are None, image prompt will not applied.

cn_stop1

number

(minimum: 0, maximum: 1)

Stop at for image prompt, None for default value

cn_weight1

number

(minimum: 0, maximum: 2)

Weight for image prompt, None for default value

cn_type1

string

ControlNet type for image prompt

Default: "ImagePrompt"

cn_img2

file

Input image for image prompt. If all cn_img[n] are None, image prompt will not applied.

cn_stop2

number

(minimum: 0, maximum: 1)

Stop at for image prompt, None for default value

cn_weight2

number

(minimum: 0, maximum: 2)

Weight for image prompt, None for default value

cn_type2

string

ControlNet type for image prompt

Default: "ImagePrompt"

cn_img3

file

Input image for image prompt. If all cn_img[n] are None, image prompt will not applied.

cn_stop3

number

(minimum: 0, maximum: 1)

Stop at for image prompt, None for default value

cn_weight3

number

(minimum: 0, maximum: 2)

Weight for image prompt, None for default value

cn_type3

string

ControlNet type for image prompt

Default: "ImagePrompt"

cn_img4

file

Input image for image prompt. If all cn_img[n] are None, image prompt will not applied.

cn_stop4

number

(minimum: 0, maximum: 1)

Stop at for image prompt, None for default value

cn_weight4

number

(minimum: 0, maximum: 2)

Weight for image prompt, None for default value

cn_type4

string

ControlNet type for image prompt

Default: "ImagePrompt"

Run this model in Node.js with one line of code:

npx create-replicate --model=konieshadow/fooocus-api

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run konieshadow/fooocus-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "konieshadow/fooocus-api:fda927242b1db6affa1ece4f54c37f19b964666bf23b0d06ae2439067cd344a4",
  {
    input: {
      prompt: "forest elf",
      cn_type1: "ImagePrompt",
      cn_type2: "ImagePrompt",
      cn_type3: "ImagePrompt",
      cn_type4: "ImagePrompt",
      sharpness: 2,
      image_seed: 50403806253646856,
      uov_method: "Disabled",
      image_number: 1,
      guidance_scale: 4,
      refiner_switch: 0.5,
      negative_prompt: "",
      style_selections: "Fooocus V2,Fooocus Enhance,Fooocus Sharp",
      uov_upscale_value: 0,
      outpaint_selections: "",
      outpaint_distance_top: 0,
      performance_selection: "Speed",
      outpaint_distance_left: 0,
      aspect_ratios_selection: "1152*896",
      outpaint_distance_right: 0,
      outpaint_distance_bottom: 0,
      inpaint_additional_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run konieshadow/fooocus-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "konieshadow/fooocus-api:fda927242b1db6affa1ece4f54c37f19b964666bf23b0d06ae2439067cd344a4",
    input={
        "prompt": "forest elf",
        "cn_type1": "ImagePrompt",
        "cn_type2": "ImagePrompt",
        "cn_type3": "ImagePrompt",
        "cn_type4": "ImagePrompt",
        "sharpness": 2,
        "image_seed": 50403806253646856,
        "uov_method": "Disabled",
        "image_number": 1,
        "guidance_scale": 4,
        "refiner_switch": 0.5,
        "negative_prompt": "",
        "style_selections": "Fooocus V2,Fooocus Enhance,Fooocus Sharp",
        "uov_upscale_value": 0,
        "outpaint_selections": "",
        "outpaint_distance_top": 0,
        "performance_selection": "Speed",
        "outpaint_distance_left": 0,
        "aspect_ratios_selection": "1152*896",
        "outpaint_distance_right": 0,
        "outpaint_distance_bottom": 0,
        "inpaint_additional_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run konieshadow/fooocus-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "fda927242b1db6affa1ece4f54c37f19b964666bf23b0d06ae2439067cd344a4",
    "input": {
      "prompt": "forest elf",
      "cn_type1": "ImagePrompt",
      "cn_type2": "ImagePrompt",
      "cn_type3": "ImagePrompt",
      "cn_type4": "ImagePrompt",
      "sharpness": 2,
      "image_seed": 50403806253646856,
      "uov_method": "Disabled",
      "image_number": 1,
      "guidance_scale": 4,
      "refiner_switch": 0.5,
      "negative_prompt": "",
      "style_selections": "Fooocus V2,Fooocus Enhance,Fooocus Sharp",
      "uov_upscale_value": 0,
      "outpaint_selections": "",
      "outpaint_distance_top": 0,
      "performance_selection": "Speed",
      "outpaint_distance_left": 0,
      "aspect_ratios_selection": "1152*896",
      "outpaint_distance_right": 0,
      "outpaint_distance_bottom": 0,
      "inpaint_additional_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/konieshadow/fooocus-api@sha256:fda927242b1db6affa1ece4f54c37f19b964666bf23b0d06ae2439067cd344a4 \
  -i 'prompt="forest elf"' \
  -i 'cn_type1="ImagePrompt"' \
  -i 'cn_type2="ImagePrompt"' \
  -i 'cn_type3="ImagePrompt"' \
  -i 'cn_type4="ImagePrompt"' \
  -i 'sharpness=2' \
  -i 'image_seed=50403806253646856' \
  -i 'uov_method="Disabled"' \
  -i 'image_number=1' \
  -i 'guidance_scale=4' \
  -i 'refiner_switch=0.5' \
  -i 'negative_prompt=""' \
  -i 'style_selections="Fooocus V2,Fooocus Enhance,Fooocus Sharp"' \
  -i 'uov_upscale_value=0' \
  -i 'outpaint_selections=""' \
  -i 'outpaint_distance_top=0' \
  -i 'performance_selection="Speed"' \
  -i 'outpaint_distance_left=0' \
  -i 'aspect_ratios_selection="1152*896"' \
  -i 'outpaint_distance_right=0' \
  -i 'outpaint_distance_bottom=0' \
  -i 'inpaint_additional_prompt=""'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/konieshadow/fooocus-api@sha256:fda927242b1db6affa1ece4f54c37f19b964666bf23b0d06ae2439067cd344a4
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "forest elf",
      "cn_type1": "ImagePrompt",
      "cn_type2": "ImagePrompt",
      "cn_type3": "ImagePrompt",
      "cn_type4": "ImagePrompt",
      "sharpness": 2,
      "image_seed": 50403806253646856,
      "uov_method": "Disabled",
      "image_number": 1,
      "guidance_scale": 4,
      "refiner_switch": 0.5,
      "negative_prompt": "",
      "style_selections": "Fooocus V2,Fooocus Enhance,Fooocus Sharp",
      "uov_upscale_value": 0,
      "outpaint_selections": "",
      "outpaint_distance_top": 0,
      "performance_selection": "Speed",
      "outpaint_distance_left": 0,
      "aspect_ratios_selection": "1152*896",
      "outpaint_distance_right": 0,
      "outpaint_distance_bottom": 0,
      "inpaint_additional_prompt": ""
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2023-12-26T05:43:04.900623Z",
  "created_at": "2023-12-26T05:40:38.534817Z",
  "data_removed": false,
  "error": null,
  "id": "tfksyidb5dfvt26km6lhmaa57y",
  "input": {
    "prompt": "forest elf",
    "cn_type1": "ImagePrompt",
    "cn_type2": "ImagePrompt",
    "cn_type3": "ImagePrompt",
    "cn_type4": "ImagePrompt",
    "sharpness": 2,
    "image_seed": 50403806253646856,
    "uov_method": "Disabled",
    "image_number": 1,
    "guidance_scale": 4,
    "refiner_switch": 0.5,
    "negative_prompt": "",
    "style_selections": "Fooocus V2,Fooocus Enhance,Fooocus Sharp",
    "uov_upscale_value": 0,
    "outpaint_selections": "",
    "outpaint_distance_top": 0,
    "performance_selection": "Speed",
    "outpaint_distance_left": 0,
    "aspect_ratios_selection": "1152*896",
    "outpaint_distance_right": 0,
    "outpaint_distance_bottom": 0,
    "inpaint_additional_prompt": ""
  },
  "logs": "[Predictor Predict] Params: {'prompt': 'forest elf', 'negative_prompt': '', 'style_selections': ['Fooocus V2', 'Fooocus Enhance', 'Fooocus Sharp'], 'performance_selection': 'Speed', 'aspect_ratios_selection': '1152*896', 'image_number': 1, 'image_seed': 50403806253646856, 'sharpness': 2.0, 'guidance_scale': 4.0, 'base_model_name': 'juggernautXL_version6Rundiffusion.safetensors', 'refiner_model_name': 'None', 'refiner_switch': 0.5, 'loras': [['sd_xl_offset_example-lora_1.0.safetensors', 0.1]], 'uov_input_image': None, 'uov_method': 'Disabled', 'upscale_value': 0.0, 'outpaint_selections': [], 'outpaint_distance_left': 0, 'outpaint_distance_right': 0, 'outpaint_distance_top': 0, 'outpaint_distance_bottom': 0, 'inpaint_input_image': None, 'inpaint_additional_prompt': '', 'image_prompts': [], 'advanced_params': [False, 1.5, 0.8, 0.3, 7.0, 'dpmpp_2m_sde_gpu', 'karras', False, -1, -1, -1, -1, -1, -1, False, False, False, False, 0.25, 64, 128, 'joint', False, None, None, None, None, False, False, 'v2.6', 1.0, 0.618]}\n[Task Queue] Task queue is free, start task, seq=1\n[Parameters] Adaptive CFG = 7.0\n[Parameters] Sharpness = 2.0\n[Parameters] ADM Scale = 1.5 : 0.8 : 0.3\n[Parameters] CFG = 4.0\n[Parameters] Seed = 50403806253646856\n[Parameters] Sampler = dpmpp_2m_sde_gpu - karras\n[Parameters] Steps = 30 - 15\n[Fooocus] Initializing ...\n[Fooocus] Loading models ...\nRefiner unloaded.\nRequest to load LoRAs [['sd_xl_offset_example-lora_1.0.safetensors', 0.1]] for model [/src/repositories/Fooocus/models/checkpoints/juggernautXL_version6Rundiffusion.safetensors].\nLoaded LoRA [/src/repositories/Fooocus/models/loras/sd_xl_offset_example-lora_1.0.safetensors] for UNet [/src/repositories/Fooocus/models/checkpoints/juggernautXL_version6Rundiffusion.safetensors] with 788 keys at weight 0.1.\nRequested to load SDXLClipModel\nLoading 1 new model\nunload clone 1\n[Fooocus Model Management] Moving model(s) has taken 0.65 seconds\n[Fooocus] Processing prompts ...\n[Fooocus] Preparing Fooocus text #1 ...\n[Prompt Expansion] forest elf, sharp focus, intricate, cinematic light, clear, crisp detail, highly detailed, singular background, elegant, complex, magical, coherent, magic, ambient, amazing composition, professional, stunning, beautiful, symmetry, illuminated, gorgeous, pretty, marvelous, aesthetic, color, epic, shiny, wonderful, pure, perfect, focused, incredible, fancy,, artistic\n[Fooocus] Encoding positive #1 ...\n[Fooocus] Encoding negative #1 ...\n[Parameters] Denoising Strength = 1.0\n[Parameters] Initial Latent shape: Image Space (896, 1152)\nPreparation time: 3.22 seconds\n[Sampler] refiner_swap_method = joint\n[Sampler] sigma_min = 0.0291671771556139, sigma_max = 14.614643096923828\nRequested to load SDXL\nLoading 1 new model\n[Fooocus Model Management] Moving model(s) has taken 0.78 seconds\n  0%|          | 0/30 [00:00<?, ?it/s]\n  3%|▎         | 1/30 [00:00<00:15,  1.93it/s]\n  7%|▋         | 2/30 [00:00<00:09,  2.85it/s]\n 10%|█         | 3/30 [00:00<00:07,  3.38it/s]\n 13%|█▎        | 4/30 [00:01<00:07,  3.70it/s]\n 17%|█▋        | 5/30 [00:01<00:06,  3.90it/s]\n 20%|██        | 6/30 [00:01<00:05,  4.04it/s]\n 23%|██▎       | 7/30 [00:01<00:05,  4.11it/s]\n 27%|██▋       | 8/30 [00:02<00:05,  4.17it/s]\n 30%|███       | 9/30 [00:02<00:04,  4.21it/s]\n 33%|███▎      | 10/30 [00:02<00:04,  4.24it/s]\n 37%|███▋      | 11/30 [00:02<00:04,  4.26it/s]\n 40%|████      | 12/30 [00:03<00:04,  4.29it/s]\n 43%|████▎     | 13/30 [00:03<00:03,  4.30it/s]\n 47%|████▋     | 14/30 [00:03<00:03,  4.31it/s]\n 50%|█████     | 15/30 [00:03<00:03,  4.31it/s]\n 53%|█████▎    | 16/30 [00:03<00:03,  4.32it/s]\n 57%|█████▋    | 17/30 [00:04<00:03,  4.32it/s]\n 60%|██████    | 18/30 [00:04<00:02,  4.32it/s]\n 63%|██████▎   | 19/30 [00:04<00:02,  4.32it/s]\n 67%|██████▋   | 20/30 [00:04<00:02,  4.31it/s]\n 70%|███████   | 21/30 [00:05<00:02,  4.32it/s]\n 73%|███████▎  | 22/30 [00:05<00:01,  4.31it/s]\n 77%|███████▋  | 23/30 [00:05<00:01,  4.31it/s]\n 80%|████████  | 24/30 [00:05<00:01,  4.31it/s]\n 83%|████████▎ | 25/30 [00:06<00:01,  4.31it/s]\n 87%|████████▋ | 26/30 [00:06<00:00,  4.29it/s]\n 90%|█████████ | 27/30 [00:06<00:00,  4.29it/s]\n 93%|█████████▎| 28/30 [00:06<00:00,  4.30it/s]\n 97%|█████████▋| 29/30 [00:07<00:00,  4.32it/s]\n100%|██████████| 30/30 [00:07<00:00,  4.34it/s]\n100%|██████████| 30/30 [00:07<00:00,  4.15it/s]\nRequested to load AutoencoderKL\nLoading 1 new model\nImage generated with private log at: /src/repositories/Fooocus/outputs/2023-12-26/log.html\nGenerating and saving time: 8.69 seconds\n[Task Queue] Finish task, seq=1\n[Predictor Predict] Finished with 1 images",
  "metrics": {
    "predict_time": 13.431787,
    "total_time": 146.365806
  },
  "output": [
    "https://replicate.delivery/pbxt/fJldwXLDky3LWSBqlm12yNVTGY7XB4WfwP24Sbfw6uJQPDMkA/e977f103-26f5-409a-b154-23be0fb56060.png"
  ],
  "started_at": "2023-12-26T05:42:51.468836Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/tfksyidb5dfvt26km6lhmaa57y",
    "cancel": "https://api.replicate.com/v1/predictions/tfksyidb5dfvt26km6lhmaa57y/cancel"
  },
  "version": "a7e8fa2f96b01d02584de2b3029a8452b9bf0c8fa4127a6d1cfd406edfad54fb"
}

Generated in

13.4 seconds

Tweak itReport

[Predictor Predict] Params: {'prompt': 'forest elf', 'negative_prompt': '', 'style_selections': ['Fooocus V2', 'Fooocus Enhance', 'Fooocus Sharp'], 'performance_selection': 'Speed', 'aspect_ratios_selection': '1152*896', 'image_number': 1, 'image_seed': 50403806253646856, 'sharpness': 2.0, 'guidance_scale': 4.0, 'base_model_name': 'juggernautXL_version6Rundiffusion.safetensors', 'refiner_model_name': 'None', 'refiner_switch': 0.5, 'loras': [['sd_xl_offset_example-lora_1.0.safetensors', 0.1]], 'uov_input_image': None, 'uov_method': 'Disabled', 'upscale_value': 0.0, 'outpaint_selections': [], 'outpaint_distance_left': 0, 'outpaint_distance_right': 0, 'outpaint_distance_top': 0, 'outpaint_distance_bottom': 0, 'inpaint_input_image': None, 'inpaint_additional_prompt': '', 'image_prompts': [], 'advanced_params': [False, 1.5, 0.8, 0.3, 7.0, 'dpmpp_2m_sde_gpu', 'karras', False, -1, -1, -1, -1, -1, -1, False, False, False, False, 0.25, 64, 128, 'joint', False, None, None, None, None, False, False, 'v2.6', 1.0, 0.618]}
[Task Queue] Task queue is free, start task, seq=1
[Parameters] Adaptive CFG = 7.0
[Parameters] Sharpness = 2.0
[Parameters] ADM Scale = 1.5 : 0.8 : 0.3
[Parameters] CFG = 4.0
[Parameters] Seed = 50403806253646856
[Parameters] Sampler = dpmpp_2m_sde_gpu - karras
[Parameters] Steps = 30 - 15
[Fooocus] Initializing ...
[Fooocus] Loading models ...
Refiner unloaded.
Request to load LoRAs [['sd_xl_offset_example-lora_1.0.safetensors', 0.1]] for model [/src/repositories/Fooocus/models/checkpoints/juggernautXL_version6Rundiffusion.safetensors].
Loaded LoRA [/src/repositories/Fooocus/models/loras/sd_xl_offset_example-lora_1.0.safetensors] for UNet [/src/repositories/Fooocus/models/checkpoints/juggernautXL_version6Rundiffusion.safetensors] with 788 keys at weight 0.1.
Requested to load SDXLClipModel
Loading 1 new model
unload clone 1
[Fooocus Model Management] Moving model(s) has taken 0.65 seconds
[Fooocus] Processing prompts ...
[Fooocus] Preparing Fooocus text #1 ...
[Prompt Expansion] forest elf, sharp focus, intricate, cinematic light, clear, crisp detail, highly detailed, singular background, elegant, complex, magical, coherent, magic, ambient, amazing composition, professional, stunning, beautiful, symmetry, illuminated, gorgeous, pretty, marvelous, aesthetic, color, epic, shiny, wonderful, pure, perfect, focused, incredible, fancy,, artistic
[Fooocus] Encoding positive #1 ...
[Fooocus] Encoding negative #1 ...
[Parameters] Denoising Strength = 1.0
[Parameters] Initial Latent shape: Image Space (896, 1152)
Preparation time: 3.22 seconds
[Sampler] refiner_swap_method = joint
[Sampler] sigma_min = 0.0291671771556139, sigma_max = 14.614643096923828
Requested to load SDXL
Loading 1 new model
[Fooocus Model Management] Moving model(s) has taken 0.78 seconds
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Requested to load AutoencoderKL
Loading 1 new model
Image generated with private log at: /src/repositories/Fooocus/outputs/2023-12-26/log.html
Generating and saving time: 8.69 seconds
[Task Queue] Finish task, seq=1
[Predictor Predict] Finished with 1 images

This output was created using a different version of the model, konieshadow/fooocus-api:a7e8fa2f.

Examples

View more examples

Run time and cost

This model costs approximately $0.038 to run on Replicate, or 26 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 40 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Fooocus is an image generating software (based on Gradio).

Fooocus is a rethinking of Stable Diffusion and Midjourney’s designs:

Learned from Stable Diffusion, the software is offline, open source, and free.
Learned from Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images.

The Fooocus-API project export normal http apis from it.

And this model is base on Fooocus-API. It supports almost all generation methods in Fooocus gradio interface, generate same image with same parameters. Includes:

Text to Image
Image Upscale or Variation
Image Inpaint or Outpaint
Image Prompt

Loaded Fooocus version: 2.1.860