fermatresearch/sdxl-weighting-prompts – Run with an API on Replicate

fermatresearch / sdxl-weighting-prompts

SDXL with prompt weighting available using Compel's syntax. Check the Github link for the docs.

Cold

Public
3.3K runs
A100 (80GB)
GitHub

Iterate in playground

Run with an API

Playground API Examples Train README Versions

Input

prompt

string

Shift + Return to add a new line

a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4

Input prompt

Default: "An astronaut riding a rainbow unicorn"

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: ""

prompt_weighting

boolean

Use Compel for prompt weighting

Default: false

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: "K_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: 7.5

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: "expert_ensemble_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

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

npx create-replicate --model=fermatresearch/sdxl-weighting-prompts

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 fermatresearch/sdxl-weighting-prompts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fermatresearch/sdxl-weighting-prompts:66175a2993706e1721076d5c7f92f0c81ec6d065ec20717527f05dd8528a1fc7",
  {
    input: {
      seed: 12345,
      width: 1024,
      height: 1024,
      prompt: "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
      refine: "expert_ensemble_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "ugly, blurry, realistic",
      prompt_strength: 0.8,
      prompt_weighting: true,
      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 fermatresearch/sdxl-weighting-prompts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fermatresearch/sdxl-weighting-prompts:66175a2993706e1721076d5c7f92f0c81ec6d065ec20717527f05dd8528a1fc7",
    input={
        "seed": 12345,
        "width": 1024,
        "height": 1024,
        "prompt": "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
        "refine": "expert_ensemble_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "ugly, blurry, realistic",
        "prompt_strength": 0.8,
        "prompt_weighting": True,
        "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 fermatresearch/sdxl-weighting-prompts 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": "fermatresearch/sdxl-weighting-prompts:66175a2993706e1721076d5c7f92f0c81ec6d065ec20717527f05dd8528a1fc7",
    "input": {
      "seed": 12345,
      "width": 1024,
      "height": 1024,
      "prompt": "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
      "refine": "expert_ensemble_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "ugly, blurry, realistic",
      "prompt_strength": 0.8,
      "prompt_weighting": true,
      "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": "2023-08-17T14:18:05.208362Z",
  "created_at": "2023-08-17T14:17:56.886546Z",
  "data_removed": false,
  "error": null,
  "id": "us5s7izbrsgk35tbt7nj3hgqji",
  "input": {
    "seed": 12345,
    "width": 1024,
    "height": 1024,
    "prompt": "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
    "refine": "expert_ensemble_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "ugly, blurry, realistic",
    "prompt_strength": 0.8,
    "prompt_weighting": true,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 12345\nPrompt: a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4\nUsing Compel for prompt embeddings\ntxt2img mode\nPrompt embeddings calculated by Compel\n  0%|          | 0/40 [00:00<?, ?it/s]\n  2%|▎         | 1/40 [00:00<00:05,  6.87it/s]\n  5%|▌         | 2/40 [00:00<00:04,  7.66it/s]\n  8%|▊         | 3/40 [00:00<00:04,  8.07it/s]\n 10%|█         | 4/40 [00:00<00:04,  8.27it/s]\n 12%|█▎        | 5/40 [00:00<00:04,  8.38it/s]\n 15%|█▌        | 6/40 [00:00<00:04,  8.45it/s]\n 18%|█▊        | 7/40 [00:00<00:03,  8.50it/s]\n 20%|██        | 8/40 [00:00<00:03,  8.42it/s]\n 22%|██▎       | 9/40 [00:01<00:03,  8.47it/s]\n 25%|██▌       | 10/40 [00:01<00:03,  8.50it/s]\n 28%|██▊       | 11/40 [00:01<00:03,  8.52it/s]\n 30%|███       | 12/40 [00:01<00:03,  8.52it/s]\n 32%|███▎      | 13/40 [00:01<00:03,  8.53it/s]\n 35%|███▌      | 14/40 [00:01<00:03,  8.54it/s]\n 38%|███▊      | 15/40 [00:01<00:02,  8.42it/s]\n 40%|████      | 16/40 [00:01<00:02,  8.46it/s]\n 42%|████▎     | 17/40 [00:02<00:02,  8.25it/s]\n 45%|████▌     | 18/40 [00:02<00:02,  8.35it/s]\n 48%|████▊     | 19/40 [00:02<00:02,  8.41it/s]\n 50%|█████     | 20/40 [00:02<00:02,  8.45it/s]\n 52%|█████▎    | 21/40 [00:02<00:02,  8.48it/s]\n 55%|█████▌    | 22/40 [00:02<00:02,  8.51it/s]\n 57%|█████▊    | 23/40 [00:02<00:02,  8.50it/s]\n 60%|██████    | 24/40 [00:02<00:01,  8.52it/s]\n 62%|██████▎   | 25/40 [00:02<00:01,  8.50it/s]\n 65%|██████▌   | 26/40 [00:03<00:01,  8.51it/s]\n 68%|██████▊   | 27/40 [00:03<00:01,  8.53it/s]\n 70%|███████   | 28/40 [00:03<00:01,  8.54it/s]\n 72%|███████▎  | 29/40 [00:03<00:01,  8.55it/s]\n 75%|███████▌  | 30/40 [00:03<00:01,  8.55it/s]\n 78%|███████▊  | 31/40 [00:03<00:01,  8.55it/s]\n 80%|████████  | 32/40 [00:03<00:00,  8.53it/s]\n 82%|████████▎ | 33/40 [00:03<00:00,  8.53it/s]\n 85%|████████▌ | 34/40 [00:04<00:00,  8.50it/s]\n 88%|████████▊ | 35/40 [00:04<00:00,  8.34it/s]\n 90%|█████████ | 36/40 [00:04<00:00,  8.40it/s]\n 92%|█████████▎| 37/40 [00:04<00:00,  8.44it/s]\n 95%|█████████▌| 38/40 [00:04<00:00,  8.47it/s]\n 98%|█████████▊| 39/40 [00:04<00:00,  8.49it/s]\n100%|██████████| 40/40 [00:04<00:00,  8.44it/s]\n100%|██████████| 40/40 [00:04<00:00,  8.43it/s]\n  0%|          | 0/10 [00:00<?, ?it/s]\n 10%|█         | 1/10 [00:00<00:01,  7.72it/s]\n 20%|██        | 2/10 [00:00<00:01,  7.67it/s]\n 30%|███       | 3/10 [00:00<00:00,  7.67it/s]\n 40%|████      | 4/10 [00:00<00:00,  7.67it/s]\n 50%|█████     | 5/10 [00:00<00:00,  7.67it/s]\n 60%|██████    | 6/10 [00:00<00:00,  7.68it/s]\n 70%|███████   | 7/10 [00:00<00:00,  7.69it/s]\n 80%|████████  | 8/10 [00:01<00:00,  7.68it/s]\n 90%|█████████ | 9/10 [00:01<00:00,  7.67it/s]\n100%|██████████| 10/10 [00:01<00:00,  7.68it/s]\n100%|██████████| 10/10 [00:01<00:00,  7.68it/s]",
  "metrics": {
    "predict_time": 8.363248,
    "total_time": 8.321816
  },
  "output": [
    "https://replicate.delivery/pbxt/SvBffoE1nwjKpkDVCxAJIqutrTs2znWCyvY2bzfIrBQ5w71iA/out-0.png"
  ],
  "started_at": "2023-08-17T14:17:56.845114Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/us5s7izbrsgk35tbt7nj3hgqji",
    "cancel": "https://api.replicate.com/v1/predictions/us5s7izbrsgk35tbt7nj3hgqji/cancel"
  },
  "version": "66175a2993706e1721076d5c7f92f0c81ec6d065ec20717527f05dd8528a1fc7"
}

Generated in

8.4 seconds

Tweak itReport

Using seed: 12345
Prompt: a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4
Using Compel for prompt embeddings
txt2img mode
Prompt embeddings calculated by Compel
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Examples

View more examples

Run time and cost

This model costs approximately $0.0014 to run on Replicate, or 714 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 1 seconds.

Readme

This model doesn't have a readme.