justmalhar/sxdl-sketchnotes – Run with an API on Replicate

justmalhar / sxdl-sketchnotes

SDXL Fine tuned on sketchnote style images. Prompt Prefix: a sketchnote photo of TOK

Cold

Public
15.6K runs
L40S
SDXL fine-tune

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

a sketchnote photo of TOK explaining types of sorting algorithmsa sketchnote photo of TOK explaining types of sorting algorithms

Input prompt

Default: "An astronaut riding a rainbow unicorn"

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: "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: "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: false

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

npx create-replicate --model=justmalhar/sxdl-sketchnotes

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 justmalhar/sxdl-sketchnotes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "a sketchnote photo of TOK explaining types of sorting algorithms",
      refine: "expert_ensemble_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 4,
      guidance_scale: 7.5,
      apply_watermark: false,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);
console.log(output);

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 justmalhar/sxdl-sketchnotes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "justmalhar/sxdl-sketchnotes:988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "a sketchnote photo of TOK explaining types of sorting algorithms",
        "refine": "expert_ensemble_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 4,
        "guidance_scale": 7.5,
        "apply_watermark": False,
        "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 justmalhar/sxdl-sketchnotes 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": "988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a sketchnote photo of TOK explaining types of sorting algorithms",
      "refine": "expert_ensemble_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 4,
      "guidance_scale": 7.5,
      "apply_watermark": false,
      "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": "2024-05-22T18:24:46.635355Z",
  "created_at": "2024-05-22T18:23:47.993000Z",
  "data_removed": false,
  "error": null,
  "id": "f02z3qwb35rgg0cfm65rgz7q30",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "a sketchnote photo of TOK explaining types of sorting algorithms",
    "refine": "expert_ensemble_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 4,
    "guidance_scale": 7.5,
    "apply_watermark": false,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 9272\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a sketchnote photo of <s0><s1> explaining types of sorting algorithms\ntxt2img mode\n  0%|          | 0/40 [00:00<?, ?it/s]\n  2%|▎         | 1/40 [00:00<00:35,  1.09it/s]\n  5%|▌         | 2/40 [00:01<00:34,  1.09it/s]\n  8%|▊         | 3/40 [00:02<00:34,  1.09it/s]\n 10%|█         | 4/40 [00:03<00:33,  1.09it/s]\n 12%|█▎        | 5/40 [00:04<00:32,  1.09it/s]\n 15%|█▌        | 6/40 [00:05<00:31,  1.08it/s]\n 18%|█▊        | 7/40 [00:06<00:30,  1.08it/s]\n 20%|██        | 8/40 [00:07<00:29,  1.08it/s]\n 22%|██▎       | 9/40 [00:08<00:28,  1.08it/s]\n 25%|██▌       | 10/40 [00:09<00:27,  1.08it/s]\n 28%|██▊       | 11/40 [00:10<00:26,  1.08it/s]\n 30%|███       | 12/40 [00:11<00:25,  1.08it/s]\n 32%|███▎      | 13/40 [00:11<00:24,  1.08it/s]\n 35%|███▌      | 14/40 [00:12<00:24,  1.08it/s]\n 38%|███▊      | 15/40 [00:13<00:23,  1.08it/s]\n 40%|████      | 16/40 [00:14<00:22,  1.08it/s]\n 42%|████▎     | 17/40 [00:15<00:21,  1.08it/s]\n 45%|████▌     | 18/40 [00:16<00:20,  1.08it/s]\n 48%|████▊     | 19/40 [00:17<00:19,  1.08it/s]\n 50%|█████     | 20/40 [00:18<00:18,  1.08it/s]\n 52%|█████▎    | 21/40 [00:19<00:17,  1.08it/s]\n 55%|█████▌    | 22/40 [00:20<00:16,  1.08it/s]\n 57%|█████▊    | 23/40 [00:21<00:15,  1.08it/s]\n 60%|██████    | 24/40 [00:22<00:14,  1.08it/s]\n 62%|██████▎   | 25/40 [00:23<00:13,  1.08it/s]\n 65%|██████▌   | 26/40 [00:24<00:12,  1.08it/s]\n 68%|██████▊   | 27/40 [00:24<00:12,  1.08it/s]\n 70%|███████   | 28/40 [00:25<00:11,  1.08it/s]\n 72%|███████▎  | 29/40 [00:26<00:10,  1.08it/s]\n 75%|███████▌  | 30/40 [00:27<00:09,  1.08it/s]\n 78%|███████▊  | 31/40 [00:28<00:08,  1.08it/s]\n 80%|████████  | 32/40 [00:29<00:07,  1.08it/s]\n 82%|████████▎ | 33/40 [00:30<00:06,  1.08it/s]\n 85%|████████▌ | 34/40 [00:31<00:05,  1.08it/s]\n 88%|████████▊ | 35/40 [00:32<00:04,  1.08it/s]\n 90%|█████████ | 36/40 [00:33<00:03,  1.08it/s]\n 92%|█████████▎| 37/40 [00:34<00:02,  1.08it/s]\n 95%|█████████▌| 38/40 [00:35<00:01,  1.08it/s]\n 98%|█████████▊| 39/40 [00:36<00:00,  1.08it/s]\n100%|██████████| 40/40 [00:36<00:00,  1.08it/s]\n100%|██████████| 40/40 [00:36<00:00,  1.08it/s]\n  0%|          | 0/10 [00:00<?, ?it/s]\n 10%|█         | 1/10 [00:00<00:08,  1.08it/s]\n 20%|██        | 2/10 [00:01<00:07,  1.08it/s]\n 30%|███       | 3/10 [00:02<00:06,  1.09it/s]\n 40%|████      | 4/10 [00:03<00:05,  1.08it/s]\n 50%|█████     | 5/10 [00:04<00:04,  1.08it/s]\n 60%|██████    | 6/10 [00:05<00:03,  1.08it/s]\n 70%|███████   | 7/10 [00:06<00:02,  1.08it/s]\n 80%|████████  | 8/10 [00:07<00:01,  1.08it/s]\n 90%|█████████ | 9/10 [00:08<00:00,  1.08it/s]\n100%|██████████| 10/10 [00:09<00:00,  1.08it/s]\n100%|██████████| 10/10 [00:09<00:00,  1.08it/s]",
  "metrics": {
    "predict_time": 53.310049,
    "total_time": 58.642355
  },
  "output": [
    "https://replicate.delivery/pbxt/J9or5jf3xZ31E6PsS4hA4lcRwzYs3faVmnJ6U3o8cOSspetlA/out-0.png",
    "https://replicate.delivery/pbxt/TSqUfoznfPp7TE39TLUeh3bYLEbTNWctNnve3oBHcE93m6bLB/out-1.png",
    "https://replicate.delivery/pbxt/I3dgpOKJR6Y7N1IeZi1xd2FWwHMSQAiuUsywJYeRg39tpetlA/out-2.png",
    "https://replicate.delivery/pbxt/gclBkGsjo4rOH9hfevFqMKFbuDSWZLHckJm95jtZuD3upetlA/out-3.png"
  ],
  "started_at": "2024-05-22T18:23:53.325306Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/f02z3qwb35rgg0cfm65rgz7q30",
    "cancel": "https://api.replicate.com/v1/predictions/f02z3qwb35rgg0cfm65rgz7q30/cancel"
  },
  "version": "988b3a09e1c2441fb4f22a04aeae435ddb0467357b7dec3337fef7ccc6f7df7c"
}

Generated in

53.3 seconds

Tweak it ShareReport

Using seed: 9272
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: a sketchnote photo of <s0><s1> explaining types of sorting algorithms
txt2img mode
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Examples

View more examples

Run time and cost

This model costs approximately $0.061 to run on Replicate, or 16 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 63 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Prompt Prefix: a sketchnote photo of TOK