datacte/proteus-v0.4-lightning – Run with an API on Replicate

datacte / proteus-v0.4-lightning

ProteusV0.4: The Style Update - enhances stylistic capabilities, similar to Midjourney's approach, rather than advancing prompt comprehension

Cold

Public
131.6K runs
L40S
GitHub
License

Iterate in the playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

3 fish in a fish tank wearing adorable outfits, best quality, hd3 fish in a fish tank wearing adorable outfits, best quality, hd

Input prompt

Default: "3 fish in a fish tank wearing adorable outfits, best quality, hd"

negative_prompt

string

Shift + Return to add a new line

nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy imagensfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image

Negative Input prompt

Default: "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image"

image

file

Input image for img2img or inpaint mode

mask

file

Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.

width

integer

Width of output image. Recommended 1024 or 1280

Default: 1024

height

integer

Height of output image. Recommended 1024 or 1280

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

scheduler

Default: "K_EULER_ANCESTRAL"

num_inference_steps

integer

(minimum: 1, maximum: 100)

Number of denoising steps

Default: 8

guidance_scale

number

(minimum: 0, maximum: 10)

Scale for classifier-free guidance

Default: 2

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image

Default: 0.8

seed

integer

Random seed. Leave blank to randomize the seed

apply_watermark

boolean

Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.

Default: true

disable_safety_checker

boolean

Disable safety checker for generated images. This feature is only available through the API. See https://replicate.com/docs/how-does-replicate-work#safety

Default: false

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

npx create-replicate --model=datacte/proteus-v0.4-lightning

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 datacte/proteus-v0.4-lightning using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "datacte/proteus-v0.4-lightning:21464a198e9baa3b583f93d8daaaa9e851b91ae1e32accb96ce5081a18a2d87c",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "3 fish in a fish tank wearing adorable outfits, best quality, hd",
      scheduler: "K_EULER_ANCESTRAL",
      num_outputs: 1,
      guidance_scale: 2,
      apply_watermark: true,
      negative_prompt: "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image",
      prompt_strength: 0.8,
      num_inference_steps: 8
    }
  }
);

// 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 datacte/proteus-v0.4-lightning using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "datacte/proteus-v0.4-lightning:21464a198e9baa3b583f93d8daaaa9e851b91ae1e32accb96ce5081a18a2d87c",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "3 fish in a fish tank wearing adorable outfits, best quality, hd",
        "scheduler": "K_EULER_ANCESTRAL",
        "num_outputs": 1,
        "guidance_scale": 2,
        "apply_watermark": True,
        "negative_prompt": "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image",
        "prompt_strength": 0.8,
        "num_inference_steps": 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 datacte/proteus-v0.4-lightning 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": "datacte/proteus-v0.4-lightning:21464a198e9baa3b583f93d8daaaa9e851b91ae1e32accb96ce5081a18a2d87c",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "3 fish in a fish tank wearing adorable outfits, best quality, hd",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_outputs": 1,
      "guidance_scale": 2,
      "apply_watermark": true,
      "negative_prompt": "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image",
      "prompt_strength": 0.8,
      "num_inference_steps": 8
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.7 seconds

Tweak itReport

Examples

View more examples

Run time and cost

This model costs approximately $0.036 to run on Replicate, or 27 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 37 seconds. The predict time for this model varies significantly based on the inputs.

Readme

ProteusV0.4: The Style Update Lightning Edition

This update enhances stylistic capabilities, similar to Midjourney’s approach, rather than advancing prompt comprehension. Methods used do not infringe on any copyrighted material.

Proteus

Proteus serves as a sophisticated enhancement over OpenDalleV1.1, leveraging its core functionalities to deliver superior outcomes. Key areas of advancement include heightened responsiveness to prompts and augmented creative capacities. To achieve this, it was fine-tuned using approximately 220,000 GPTV captioned images from copyright-free stock images (with some anime included), which were then normalized. Additionally, DPO (Direct Preference Optimization) was employed through a collection of 10,000 carefully selected high-quality, AI-generated image pairs.

In pursuit of optimal performance, numerous LORA (Low-Rank Adaptation) models are trained independently before being selectively incorporated into the principal model via dynamic application methods. These techniques involve targeting particular segments within the model while avoiding interference with other areas during the learning phase. Consequently, Proteus exhibits marked improvements in portraying intricate facial characteristics and lifelike skin textures, all while sustaining commendable proficiency across various aesthetic domains, notably surrealism, anime, and cartoon-style visualizations.

Settings for ProteusV0.4-Lightning

Use these settings for the best results with ProteusV0.4-Lightning :

CFG Scale: Use a CFG scale of 1 to 2

Steps: 4 to 10 steps for more detail, 8 steps for faster results.

Sampler: eular

Scheduler: normal

Resolution: 1280x1280 or 1024x1024

please also consider using these keep words to improve your prompts: best quality, HD, ~*~aesthetic~*~.

if you are having trouble coming up with prompts you can use this GPT I put together to help you refine the prompt. https://chat.openai.com/g/g-RziQNoydR-diffusion-master

Use it with 🧨 diffusers

import torch
from diffusers import (
    StableDiffusionXLPipeline, 
    EulerAncestralDiscreteScheduler,
    AutoencoderKL
)

# Load VAE component
vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix", 
    torch_dtype=torch.float16
)

# Configure the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "dataautogpt3/ProteusV0.4-Lightning", 
    vae=vae,
    torch_dtype=torch.float16
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')

# Define prompts and generate image
prompt = "black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed"
negative_prompt = "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image"

image = pipe(
    prompt, 
    negative_prompt=negative_prompt, 
    width=1024,
    height=1024,
    guidance_scale=2,
    num_inference_steps=8
).images[0]

please support the work I do through donating to me on: https://www.buymeacoffee.com/DataVoid or following me on https://twitter.com/DataPlusEngine