neurowelt/keros-diffusion | Run with an API on Replicate

neurowelt / keros-diffusion

Controlling SD XL diffusion inference

Cold

Public
11 runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

psychedelic Obama wizard in Chernobyl, nuclear fallout, Hasserbladpsychedelic Obama wizard in Chernobyl, nuclear fallout, Hasserblad

Input prompt

Default: "An astronaut riding a rainbow unicorn"

richness

number

(minimum: 1, maximum: 3)

Low richness gives less detail and a misty look. High values make images stronger, rich, complex. Bugs expected for higher values

Default: 1.3

contrast

number

(minimum: 0.9, maximum: 1.1)

Quite delicate, can correct overexposure that guidance or richness create. Use 1.0 for no change.

Default: 1

texture

number

(minimum: 0.7, maximum: 1.3)

Low values give high local contrast and sharper changes between objects better for illustrations, glitchcore etc. High values give smooth textures, better for photos.

Default: 1

background

number

(minimum: 0.25, maximum: 0.3)

For historical reasons, 0.3 is a starting point equivalent to normal SDXL. 0.25 will (depending on prompt) cancel background objects if they shouldn't be there, allows for single color images or high contrast pure white and black.

Default: 0.25

focus

number

(minimum: 0, maximum: 0.5)

0.5 will have crispy sharp and contrasty images. 0.25 will be misty and delicate. Scaling is still non-linear, so 0.5 and 0.49 will have high difference compared 0.1 and 0.01 smaller. It's best to keep it as is.

Default: 0

variance

number

(minimum: -0.1, maximum: 0.1)

Hardest to control as it changes how other parameters operate. Low value of -0.1 is great for txt2img as it helps to get more interesting results and should be used with high **Richness** (`param1` of 1.0 up to ~2.5). 0.1 is great for img2img as it keeps structure of image but allows large changes in texture and style, to be used with **Richness** of 0.4 up to 1.0.

Default: 0.1

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: ""

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

Default: 1024

height

integer

Height of output image

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

scheduler

Default: "Keros Euler"

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 9

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

refine

string

Which refine style to use

Default: "no_refiner"

high_noise_frac

number

(minimum: 0, maximum: 1)

For expert_ensemble_refiner, the fraction of noise to use

Default: 0.8

refine_steps

integer

For base_image_refiner, the number of steps to refine, defaults to num_inference_steps

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

lora_scale

number

(minimum: 0, maximum: 1)

LoRA additive scale. Only applicable on trained models.

Default: 0.6

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](https://replicate.com/docs/how-does-replicate-work#safety)

Default: true

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

npx create-replicate --model=neurowelt/keros-diffusion

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 neurowelt/keros-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "neurowelt/keros-diffusion:6ce4619b235fdf79693471d80d2827e55ee3b7c8066ef76c96fb3b6143dc0a1e",
  {
    input: {
      focus: 0,
      width: 1024,
      height: 1024,
      prompt: "psychedelic Obama wizard in Chernobyl, nuclear fallout, Hasserblad",
      refine: "no_refiner",
      texture: 1,
      contrast: 1,
      richness: 1.3,
      variance: 0.1,
      scheduler: "Keros Euler",
      background: 0.25,
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 9,
      apply_watermark: true,
      high_noise_frac: 0.8,
      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 neurowelt/keros-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "neurowelt/keros-diffusion:6ce4619b235fdf79693471d80d2827e55ee3b7c8066ef76c96fb3b6143dc0a1e",
    input={
        "focus": 0,
        "width": 1024,
        "height": 1024,
        "prompt": "psychedelic Obama wizard in Chernobyl, nuclear fallout, Hasserblad",
        "refine": "no_refiner",
        "texture": 1,
        "contrast": 1,
        "richness": 1.3,
        "variance": 0.1,
        "scheduler": "Keros Euler",
        "background": 0.25,
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 9,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
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 neurowelt/keros-diffusion 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": "neurowelt/keros-diffusion:6ce4619b235fdf79693471d80d2827e55ee3b7c8066ef76c96fb3b6143dc0a1e",
    "input": {
      "focus": 0,
      "width": 1024,
      "height": 1024,
      "prompt": "psychedelic Obama wizard in Chernobyl, nuclear fallout, Hasserblad",
      "refine": "no_refiner",
      "texture": 1,
      "contrast": 1,
      "richness": 1.3,
      "variance": 0.1,
      "scheduler": "Keros Euler",
      "background": 0.25,
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 9,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "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

{
  "completed_at": "2024-09-16T11:20:40.272032Z",
  "created_at": "2024-09-16T11:17:07.918000Z",
  "data_removed": false,
  "error": null,
  "id": "q0wzx3qpssrgg0chza69hjqyjr",
  "input": {
    "focus": 0,
    "width": 1024,
    "height": 1024,
    "prompt": "psychedelic Obama wizard in Chernobyl, nuclear fallout, Hasserblad",
    "refine": "no_refiner",
    "texture": 1,
    "contrast": 1,
    "richness": 1.3,
    "variance": 0.1,
    "scheduler": "Keros Euler",
    "background": 0.25,
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 9,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 44650\nPrompt: psychedelic Obama wizard in Chernobyl, nuclear fallout, Hasserblad\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:26,  1.84it/s]\n  4%|▍         | 2/50 [00:00<00:16,  3.00it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.74it/s]\n  8%|▊         | 4/50 [00:01<00:10,  4.23it/s]\n 10%|█         | 5/50 [00:01<00:09,  4.56it/s]\n 12%|█▏        | 6/50 [00:01<00:09,  4.77it/s]\n 14%|█▍        | 7/50 [00:01<00:08,  4.91it/s]\n 16%|█▌        | 8/50 [00:01<00:08,  5.02it/s]\n 18%|█▊        | 9/50 [00:02<00:08,  5.09it/s]\n 20%|██        | 10/50 [00:02<00:07,  5.15it/s]\n 22%|██▏       | 11/50 [00:02<00:07,  5.18it/s]\n 24%|██▍       | 12/50 [00:02<00:07,  5.20it/s]\n 26%|██▌       | 13/50 [00:02<00:07,  5.22it/s]\n 28%|██▊       | 14/50 [00:03<00:06,  5.22it/s]\n 30%|███       | 15/50 [00:03<00:06,  5.23it/s]\n 32%|███▏      | 16/50 [00:03<00:06,  5.24it/s]\n 34%|███▍      | 17/50 [00:03<00:06,  5.24it/s]\n 36%|███▌      | 18/50 [00:03<00:06,  5.24it/s]\n 38%|███▊      | 19/50 [00:03<00:05,  5.24it/s]\n 40%|████      | 20/50 [00:04<00:05,  5.24it/s]\n 42%|████▏     | 21/50 [00:04<00:05,  5.24it/s]\n 44%|████▍     | 22/50 [00:04<00:05,  5.25it/s]\n 46%|████▌     | 23/50 [00:04<00:05,  5.25it/s]\n 48%|████▊     | 24/50 [00:04<00:04,  5.24it/s]\n 50%|█████     | 25/50 [00:05<00:04,  5.24it/s]\n 52%|█████▏    | 26/50 [00:05<00:04,  5.24it/s]\n 54%|█████▍    | 27/50 [00:05<00:04,  5.24it/s]\n 56%|█████▌    | 28/50 [00:05<00:04,  5.24it/s]\n 58%|█████▊    | 29/50 [00:05<00:04,  5.24it/s]\n 60%|██████    | 30/50 [00:06<00:03,  5.24it/s]\n 62%|██████▏   | 31/50 [00:06<00:03,  5.23it/s]\n 64%|██████▍   | 32/50 [00:06<00:03,  5.24it/s]\n 66%|██████▌   | 33/50 [00:06<00:03,  5.24it/s]\n 68%|██████▊   | 34/50 [00:06<00:03,  5.24it/s]\n 70%|███████   | 35/50 [00:07<00:02,  5.23it/s]\n 72%|███████▏  | 36/50 [00:07<00:02,  5.22it/s]\n 74%|███████▍  | 37/50 [00:07<00:02,  5.22it/s]\n 76%|███████▌  | 38/50 [00:07<00:02,  5.23it/s]\n 78%|███████▊  | 39/50 [00:07<00:02,  5.23it/s]\n 80%|████████  | 40/50 [00:07<00:01,  5.23it/s]\n 82%|████████▏ | 41/50 [00:08<00:01,  5.23it/s]\n 84%|████████▍ | 42/50 [00:08<00:01,  5.23it/s]\n 86%|████████▌ | 43/50 [00:08<00:01,  5.23it/s]\n 88%|████████▊ | 44/50 [00:08<00:01,  5.23it/s]\n 90%|█████████ | 45/50 [00:08<00:00,  5.23it/s]\n 92%|█████████▏| 46/50 [00:09<00:00,  5.23it/s]\n 94%|█████████▍| 47/50 [00:09<00:00,  5.23it/s]\n 96%|█████████▌| 48/50 [00:09<00:00,  5.23it/s]\n 98%|█████████▊| 49/50 [00:09<00:00,  5.23it/s]\n100%|██████████| 50/50 [00:09<00:00,  5.22it/s]\n100%|██████████| 50/50 [00:09<00:00,  5.06it/s]",
  "metrics": {
    "predict_time": 12.850988582,
    "total_time": 212.354032
  },
  "output": [
    "https://replicate.delivery/pbxt/rbQm1HDfoCUaIa0E11H1JVgpg5BeVPV9E12e3hpwo7lN046mA/out-0.png"
  ],
  "started_at": "2024-09-16T11:20:27.421044Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/q0wzx3qpssrgg0chza69hjqyjr",
    "cancel": "https://api.replicate.com/v1/predictions/q0wzx3qpssrgg0chza69hjqyjr/cancel"
  },
  "version": "6ce4619b235fdf79693471d80d2827e55ee3b7c8066ef76c96fb3b6143dc0a1e"
}

Generated in

12.9 seconds

Tweak it ShareReport View full prediction

Using seed: 44650
Prompt: psychedelic Obama wizard in Chernobyl, nuclear fallout, Hasserblad
txt2img mode
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Examples

View more examples

Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

Our aim at Keros AI was to give users more control over the effect of Stable Diffusion XL inference. We achieved that by manipulating the noise at scheduler step level.

To increase the control over the results of diffusion inference we introduce the following parameters:

Richness (1.0-3.0): Low richness gives not much detail or even objects and misty look. High values make images stronger, rich, complex, but bugs might also happen.
Contrast (0.9-1.1): Quite delicate, can correct overexposure that guidance or richness create. Use 1.0 for no change.
Texture (0.7-1.3): Low values give high local contrast and sharper changes between objects better for illustrations, glitchcore etc. High values give smooth textures, better for photos.
Background (0.25-0.3): For historical reasons, 0.3 is a starting point equivalent to normal SDXL. 0.25 will (depending on prompt) cancel background objects if they shouldn’t be there, allows for single color images or high contrast pure white and black.
Focus (0.0-0.5): 0.5 will have crispy sharp and contrasty images. 0.25 will be misty and delicate. Scaling is still non-linear, so 0.5 and 0.49 will have high difference compared 0.1 and 0.01 smaller. It’s best to keep it as is.
Variance (-0.1-0.1): Hardest to control as it changes how other parameters operate. Low value of -0.1 is great for txt2img as it helps to get more interesting results and should be used with high Richness (param1 of 1.0 up to ~2.5). 0.1 is great for img2img as it keeps structure of image but allows large changes in texture and style, to be used with Richness of 0.4 up to 1.0.