oranzino/mix-pro-v35:7a0d551f | Run with an API on Replicate

Version

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oranzino /mix-pro-v35:7a0d551f

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt. Use <1>, <2>, <3>, etc., to specify LoRA concepts

Default: "a photo of <1> riding a horse on mars"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

Default: ""

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

image

file

(Img2Img) Inital image to generate variations of. If this is not none, Img2Img will be invoked.

prompt_strength

number

(Img2Img) Prompt strength when providing the image. 1.0 corresponds to full destruction of information in init image

Default: 0.8

scheduler

string

Choose a scheduler.

Default: "DPMSolverMultistep"

lora_urls

string

Shift + Return to add a new line

List of urls for safetensors of lora models, seperated with | .

Default: ""

lora_scales

string

Shift + Return to add a new line

List of scales for safetensors of lora models, seperated with |

Default: "0.5"

seed

integer

Random seed. Leave blank to randomize the seed

adapter_condition_image

file

(T2I-adapter) Adapter Condition Image to gain extra control over generation. If this is not none, T2I adapter will be invoked. Width, Height of this image must match the above parameter, or dimension of the Img2Img image.

adapter_type

string

(T2I-adapter) Choose an adapter type for the additional condition.

Default: "sketch"

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

npx create-replicate --model=oranzino/mix-pro-v35

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";
import fs from "node:fs";

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

Run oranzino/mix-pro-v35 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "oranzino/mix-pro-v35:7a0d551f5933575946436b95ac37bd834f659d2ca487794bcedbbc02dc55c668",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "a photo of <1> riding a horse on mars",
      lora_urls: "",
      scheduler: "DPMSolverMultistep",
      lora_scales: "0.5",
      num_outputs: 1,
      adapter_type: "sketch",
      guidance_scale: 7.5,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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 oranzino/mix-pro-v35 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "oranzino/mix-pro-v35:7a0d551f5933575946436b95ac37bd834f659d2ca487794bcedbbc02dc55c668",
    input={
        "width": 512,
        "height": 512,
        "prompt": "a photo of <1> riding a horse on mars",
        "lora_urls": "",
        "scheduler": "DPMSolverMultistep",
        "lora_scales": "0.5",
        "num_outputs": 1,
        "adapter_type": "sketch",
        "guidance_scale": 7.5,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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 oranzino/mix-pro-v35 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": "oranzino/mix-pro-v35:7a0d551f5933575946436b95ac37bd834f659d2ca487794bcedbbc02dc55c668",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "a photo of <1> riding a horse on mars",
      "lora_urls": "",
      "scheduler": "DPMSolverMultistep",
      "lora_scales": "0.5",
      "num_outputs": 1,
      "adapter_type": "sketch",
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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