zsxkib/instant-id:f1ca369d – Run with an API on Replicate

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zsxkib /instant-id:f1ca369d

Playground API

Input

image

*file

Input face image

pose_image

file

(Optional) reference pose image

prompt

string

Shift + Return to add a new line

Input prompt

Default: "a person"

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: ""

sdxl_weights

string

Pick which base weights you want to use

Default: "stable-diffusion-xl-base-1.0"

face_detection_input_width

integer

(minimum: 640, maximum: 4096)

Width of the input image for face detection

Default: 640

face_detection_input_height

integer

(minimum: 640, maximum: 4096)

Height of the input image for face detection

Default: 640

scheduler

string

Scheduler

Default: "EulerDiscreteScheduler"

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 30

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 7.5

ip_adapter_scale

number

(minimum: 0, maximum: 1.5)

Scale for image adapter strength (for detail)

Default: 0.8

controlnet_conditioning_scale

number

(minimum: 0, maximum: 1.5)

Scale for IdentityNet strength (for fidelity)

Default: 0.8

enable_pose_controlnet

boolean

Enable Openpose ControlNet, overrides strength if set to false

Default: true

pose_strength

number

(minimum: 0, maximum: 1)

Openpose ControlNet strength, effective only if `enable_pose_controlnet` is true

Default: 0.4

enable_canny_controlnet

boolean

Enable Canny ControlNet, overrides strength if set to false

Default: false

canny_strength

number

(minimum: 0, maximum: 1)

Canny ControlNet strength, effective only if `enable_canny_controlnet` is true

Default: 0.3

enable_depth_controlnet

boolean

Enable Depth ControlNet, overrides strength if set to false

Default: false

depth_strength

number

(minimum: 0, maximum: 1)

Depth ControlNet strength, effective only if `enable_depth_controlnet` is true

Default: 0.5

enable_lcm

boolean

Enable Fast Inference with LCM (Latent Consistency Models) - speeds up inference steps, trade-off is the quality of the generated image. Performs better with close-up portrait face images

Default: false

lcm_num_inference_steps

integer

(minimum: 1, maximum: 10)

Only used when `enable_lcm` is set to True, Number of denoising steps when using LCM

Default: 5

lcm_guidance_scale

number

(minimum: 1, maximum: 20)

Only used when `enable_lcm` is set to True, Scale for classifier-free guidance when using LCM

Default: 1.5

enhance_nonface_region

boolean

Enhance non-face region

Default: true

output_format

string

Format of the output images

Default: "webp"

output_quality

integer

(minimum: 0, maximum: 100)

Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.

Default: 80

seed

integer

Random seed. Leave blank to randomize the seed

num_outputs

integer

(minimum: 1, maximum: 8)

Number of images to output

Default: 1

disable_safety_checker

boolean

Disable safety checker for generated images

Default: false

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

npx create-replicate --model=zsxkib/instant-id

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 zsxkib/instant-id using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zsxkib/instant-id:f1ca369da43885a347690a98f6b710afbf5f167cb9bf13bd5af512ba4a9f7b63",
  {
    input: {
      prompt: "a person",
      scheduler: "EulerDiscreteScheduler",
      enable_lcm: false,
      num_outputs: 1,
      sdxl_weights: "stable-diffusion-xl-base-1.0",
      output_format: "webp",
      pose_strength: 0.4,
      canny_strength: 0.3,
      depth_strength: 0.5,
      guidance_scale: 7.5,
      output_quality: 80,
      negative_prompt: "",
      ip_adapter_scale: 0.8,
      lcm_guidance_scale: 1.5,
      num_inference_steps: 30,
      enable_pose_controlnet: true,
      enhance_nonface_region: true,
      enable_canny_controlnet: false,
      enable_depth_controlnet: false,
      lcm_num_inference_steps: 5,
      face_detection_input_width: 640,
      face_detection_input_height: 640,
      controlnet_conditioning_scale: 0.8
    }
  }
);
console.log(output);

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 zsxkib/instant-id using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zsxkib/instant-id:f1ca369da43885a347690a98f6b710afbf5f167cb9bf13bd5af512ba4a9f7b63",
    input={
        "prompt": "a person",
        "scheduler": "EulerDiscreteScheduler",
        "enable_lcm": False,
        "num_outputs": 1,
        "sdxl_weights": "stable-diffusion-xl-base-1.0",
        "output_format": "webp",
        "pose_strength": 0.4,
        "canny_strength": 0.3,
        "depth_strength": 0.5,
        "guidance_scale": 7.5,
        "output_quality": 80,
        "negative_prompt": "",
        "ip_adapter_scale": 0.8,
        "lcm_guidance_scale": 1.5,
        "num_inference_steps": 30,
        "enable_pose_controlnet": True,
        "enhance_nonface_region": True,
        "enable_canny_controlnet": False,
        "enable_depth_controlnet": False,
        "lcm_num_inference_steps": 5,
        "face_detection_input_width": 640,
        "face_detection_input_height": 640,
        "controlnet_conditioning_scale": 0.8
    }
)
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 zsxkib/instant-id 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": "f1ca369da43885a347690a98f6b710afbf5f167cb9bf13bd5af512ba4a9f7b63",
    "input": {
      "prompt": "a person",
      "scheduler": "EulerDiscreteScheduler",
      "enable_lcm": false,
      "num_outputs": 1,
      "sdxl_weights": "stable-diffusion-xl-base-1.0",
      "output_format": "webp",
      "pose_strength": 0.4,
      "canny_strength": 0.3,
      "depth_strength": 0.5,
      "guidance_scale": 7.5,
      "output_quality": 80,
      "negative_prompt": "",
      "ip_adapter_scale": 0.8,
      "lcm_guidance_scale": 1.5,
      "num_inference_steps": 30,
      "enable_pose_controlnet": true,
      "enhance_nonface_region": true,
      "enable_canny_controlnet": false,
      "enable_depth_controlnet": false,
      "lcm_num_inference_steps": 5,
      "face_detection_input_width": 640,
      "face_detection_input_height": 640,
      "controlnet_conditioning_scale": 0.8
    }
  }' \
  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/zsxkib/instant-id@sha256:f1ca369da43885a347690a98f6b710afbf5f167cb9bf13bd5af512ba4a9f7b63 \
  -i 'prompt="a person"' \
  -i 'scheduler="EulerDiscreteScheduler"' \
  -i 'enable_lcm=false' \
  -i 'num_outputs=1' \
  -i 'sdxl_weights="stable-diffusion-xl-base-1.0"' \
  -i 'output_format="webp"' \
  -i 'pose_strength=0.4' \
  -i 'canny_strength=0.3' \
  -i 'depth_strength=0.5' \
  -i 'guidance_scale=7.5' \
  -i 'output_quality=80' \
  -i 'negative_prompt=""' \
  -i 'ip_adapter_scale=0.8' \
  -i 'lcm_guidance_scale=1.5' \
  -i 'num_inference_steps=30' \
  -i 'enable_pose_controlnet=true' \
  -i 'enhance_nonface_region=true' \
  -i 'enable_canny_controlnet=false' \
  -i 'enable_depth_controlnet=false' \
  -i 'lcm_num_inference_steps=5' \
  -i 'face_detection_input_width=640' \
  -i 'face_detection_input_height=640' \
  -i 'controlnet_conditioning_scale=0.8'

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/zsxkib/instant-id@sha256:f1ca369da43885a347690a98f6b710afbf5f167cb9bf13bd5af512ba4a9f7b63
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "a person",
      "scheduler": "EulerDiscreteScheduler",
      "enable_lcm": false,
      "num_outputs": 1,
      "sdxl_weights": "stable-diffusion-xl-base-1.0",
      "output_format": "webp",
      "pose_strength": 0.4,
      "canny_strength": 0.3,
      "depth_strength": 0.5,
      "guidance_scale": 7.5,
      "output_quality": 80,
      "negative_prompt": "",
      "ip_adapter_scale": 0.8,
      "lcm_guidance_scale": 1.5,
      "num_inference_steps": 30,
      "enable_pose_controlnet": true,
      "enhance_nonface_region": true,
      "enable_canny_controlnet": false,
      "enable_depth_controlnet": false,
      "lcm_num_inference_steps": 5,
      "face_detection_input_width": 640,
      "face_detection_input_height": 640,
      "controlnet_conditioning_scale": 0.8
    }
  }' \
  http://localhost:5000/predictions

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

Output

No output yet! Press "Submit" to start a prediction.