zhouzhengjun/lora_openjourney_v4 | Run with an API on Replicate

zhouzhengjun / lora_openjourney_v4

Lora & openjourney V4

Cold

Public
18.8K runs
A100 (80GB)

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

(((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,(((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,

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.

mask

file

Black and white image to use as mask for inpainting over the image provided. Black pixels are inpainted and white pixels are preserved

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: "K_EULER_ANCESTRAL"

lora_urls

string

Shift + Return to add a new line

https://replicate.delivery/pbxt/Mf7QBwNXrehQ3k6GwMPpi8bqy0cer9x1NqogXVWylWC9l6YhA/tmp28kwa2ceclexz5tc90001zun1iy5b8x3wzip.safetensorshttps://replicate.delivery/pbxt/Mf7QBwNXrehQ3k6GwMPpi8bqy0cer9x1NqogXVWylWC9l6YhA/tmp28kwa2ceclexz5tc90001zun1iy5b8x3wzip.safetensors

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

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

npx create-replicate --model=zhouzhengjun/lora_openjourney_v4

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 zhouzhengjun/lora_openjourney_v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zhouzhengjun/lora_openjourney_v4:f8e5074f993f6852679bdac9f604590827f11698fdbfc3f68a1f0c3395b46db6",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "(((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,",
      lora_urls: "https://replicate.delivery/pbxt/Mf7QBwNXrehQ3k6GwMPpi8bqy0cer9x1NqogXVWylWC9l6YhA/tmp28kwa2ceclexz5tc90001zun1iy5b8x3wzip.safetensors",
      scheduler: "K_EULER_ANCESTRAL",
      lora_scales: "0.6",
      num_outputs: 1,
      guidance_scale: 3.25,
      negative_prompt: "easynegative, bad-picture-chill-75v",
      prompt_strength: 0.9,
      num_inference_steps: 31
    }
  }
);

// 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 zhouzhengjun/lora_openjourney_v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zhouzhengjun/lora_openjourney_v4:f8e5074f993f6852679bdac9f604590827f11698fdbfc3f68a1f0c3395b46db6",
    input={
        "width": 512,
        "height": 512,
        "prompt": "(((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,",
        "lora_urls": "https://replicate.delivery/pbxt/Mf7QBwNXrehQ3k6GwMPpi8bqy0cer9x1NqogXVWylWC9l6YhA/tmp28kwa2ceclexz5tc90001zun1iy5b8x3wzip.safetensors",
        "scheduler": "K_EULER_ANCESTRAL",
        "lora_scales": "0.6",
        "num_outputs": 1,
        "guidance_scale": 3.25,
        "negative_prompt": "easynegative, bad-picture-chill-75v",
        "prompt_strength": 0.9,
        "num_inference_steps": 31
    }
)

# 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 zhouzhengjun/lora_openjourney_v4 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": "zhouzhengjun/lora_openjourney_v4:f8e5074f993f6852679bdac9f604590827f11698fdbfc3f68a1f0c3395b46db6",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "(((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,",
      "lora_urls": "https://replicate.delivery/pbxt/Mf7QBwNXrehQ3k6GwMPpi8bqy0cer9x1NqogXVWylWC9l6YhA/tmp28kwa2ceclexz5tc90001zun1iy5b8x3wzip.safetensors",
      "scheduler": "K_EULER_ANCESTRAL",
      "lora_scales": "0.6",
      "num_outputs": 1,
      "guidance_scale": 3.25,
      "negative_prompt": "easynegative, bad-picture-chill-75v",
      "prompt_strength": 0.9,
      "num_inference_steps": 31
    }
  }' \
  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/zhouzhengjun/lora_openjourney_v4@sha256:f8e5074f993f6852679bdac9f604590827f11698fdbfc3f68a1f0c3395b46db6 \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="(((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,"' \
  -i 'lora_urls="https://replicate.delivery/pbxt/Mf7QBwNXrehQ3k6GwMPpi8bqy0cer9x1NqogXVWylWC9l6YhA/tmp28kwa2ceclexz5tc90001zun1iy5b8x3wzip.safetensors"' \
  -i 'scheduler="K_EULER_ANCESTRAL"' \
  -i 'lora_scales="0.6"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=3.25' \
  -i 'negative_prompt="easynegative, bad-picture-chill-75v"' \
  -i 'prompt_strength=0.9' \
  -i 'num_inference_steps=31'

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/zhouzhengjun/lora_openjourney_v4@sha256:f8e5074f993f6852679bdac9f604590827f11698fdbfc3f68a1f0c3395b46db6
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "(((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,",
      "lora_urls": "https://replicate.delivery/pbxt/Mf7QBwNXrehQ3k6GwMPpi8bqy0cer9x1NqogXVWylWC9l6YhA/tmp28kwa2ceclexz5tc90001zun1iy5b8x3wzip.safetensors",
      "scheduler": "K_EULER_ANCESTRAL",
      "lora_scales": "0.6",
      "num_outputs": 1,
      "guidance_scale": 3.25,
      "negative_prompt": "easynegative, bad-picture-chill-75v",
      "prompt_strength": 0.9,
      "num_inference_steps": 31
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-04-20T10:51:38.537121Z",
  "created_at": "2023-04-20T10:51:34.616238Z",
  "data_removed": false,
  "error": null,
  "id": "xbwykzzdrzaczdkydodvcqmkka",
  "input": {
    "width": "512",
    "height": "512",
    "prompt": "(((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,",
    "lora_urls": "https://replicate.delivery/pbxt/Mf7QBwNXrehQ3k6GwMPpi8bqy0cer9x1NqogXVWylWC9l6YhA/tmp28kwa2ceclexz5tc90001zun1iy5b8x3wzip.safetensors",
    "scheduler": "K_EULER_ANCESTRAL",
    "lora_scales": "0.6",
    "num_outputs": 1,
    "guidance_scale": 3.25,
    "negative_prompt": "easynegative, bad-picture-chill-75v",
    "prompt_strength": 0.9,
    "num_inference_steps": 31
  },
  "logs": "Using seed: 8651\nGenerating image of 512 x 512 with prompt: (((masterpiece))),(((bestquality))),((ultra-detailed)),(illustration),((anextremelydelicateandbeautiful)),dynamicangle,floating,(beautifuldetailedeyes),(detailedlight) (1girl), solo , floating_hair,glowingeyes,green hair,greeneyes <1>, twintails, halterdress,\nThe requested LoRAs are loaded.\nThe config attributes {'clip_sample_range': 1.0} were passed to EulerDiscreteScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\nThe config attributes {'clip_sample_range': 1.0} were passed to EulerAncestralDiscreteScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['twintails, halterdress,']\n  0%|          | 0/31 [00:00<?, ?it/s]\n  6%|▋         | 2/31 [00:00<00:02, 11.13it/s]\n 13%|█▎        | 4/31 [00:00<00:02, 11.14it/s]\n 19%|█▉        | 6/31 [00:00<00:02, 11.23it/s]\n 26%|██▌       | 8/31 [00:00<00:02, 11.25it/s]\n 32%|███▏      | 10/31 [00:00<00:01, 11.30it/s]\n 39%|███▊      | 12/31 [00:01<00:01, 11.33it/s]\n 45%|████▌     | 14/31 [00:01<00:01, 11.02it/s]\n 52%|█████▏    | 16/31 [00:01<00:01, 11.10it/s]\n 58%|█████▊    | 18/31 [00:01<00:01, 11.18it/s]\n 65%|██████▍   | 20/31 [00:01<00:00, 11.23it/s]\n 71%|███████   | 22/31 [00:01<00:00, 11.22it/s]\n 77%|███████▋  | 24/31 [00:02<00:00, 11.21it/s]\n 84%|████████▍ | 26/31 [00:02<00:00, 11.11it/s]\n 90%|█████████ | 28/31 [00:02<00:00, 11.04it/s]\n 97%|█████████▋| 30/31 [00:02<00:00, 11.11it/s]\n100%|██████████| 31/31 [00:02<00:00, 11.16it/s]",
  "metrics": {
    "predict_time": 3.802227,
    "total_time": 3.920883
  },
  "output": [
    "https://replicate.delivery/pbxt/xSvYgfGIXEzHOK2fUfIkksiIhanhqOm5xDt3TJQ1VkCyZZnhA/out-0.png"
  ],
  "started_at": "2023-04-20T10:51:34.734894Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/xbwykzzdrzaczdkydodvcqmkka",
    "cancel": "https://api.replicate.com/v1/predictions/xbwykzzdrzaczdkydodvcqmkka/cancel"
  },
  "version": "f8e5074f993f6852679bdac9f604590827f11698fdbfc3f68a1f0c3395b46db6"
}

Generated in

3.8 seconds

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Examples

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Run time and cost

This model costs approximately $0.011 to run on Replicate, or 90 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 A100 (80GB) GPU hardware. Predictions typically complete within 9 seconds.

Readme

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Model description

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Intended use

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Ethical considerations

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Caveats and recommendations

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