swk23/windu | Run with an API on Replicate

Warm

Public

19 runs

H100

Run with an API

Pricing

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

Prompt for generated image. If you include the `trigger_word` used in the training process you are more likely to activate the trained object, style, or concept in the resulting image.

image

file

Preview

Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.

mask

file

Preview

Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.

aspect_ratio

string

Aspect ratio for the generated image. If custom is selected, uses height and width below & will run in bf16 mode

Default: "1:1"

height

integer

(minimum: 256, maximum: 1440)

Height of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation

width

integer

(minimum: 256, maximum: 1440)

Width of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation

prompt_strength

number

(minimum: 0, maximum: 1)

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

Default: 0.8

model

string

Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps.

Default: "dev"

num_outputs

integer

(minimum: 1, maximum: 4)

Number of outputs to generate

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 50)

Number of denoising steps. More steps can give more detailed images, but take longer.

Default: 28

guidance_scale

number

(minimum: 0, maximum: 10)

Guidance scale for the diffusion process. Lower values can give more realistic images. Good values to try are 2, 2.5, 3 and 3.5

Default: 3

seed

integer

Random seed. Set for reproducible generation

output_format

string

Format of the output images

Default: "webp"

output_quality

integer

(minimum: 0, maximum: 100)

Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs

Default: 80

disable_safety_checker

boolean

Disable safety checker for generated images.

Default: false

go_fast

boolean

Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16

Default: false

megapixels

string

Approximate number of megapixels for generated image

Default: "1"

lora_scale

number

(minimum: -1, maximum: 3)

Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.

Default: 1

extra_lora

string

Shift + Return to add a new line

Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'

extra_lora_scale

number

(minimum: -1, maximum: 3)

Determines how strongly the extra LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.

Default: 1

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

npx create-replicate --model=swk23/windu

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 swk23/windu using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "swk23/windu:65f4bf32a31353e76b48659846fba61ecb49445c20a0d365f9a83fd151193847",
  {
    input: {
      mask: "https://replicate.delivery/pbxt/MW3Dyqdtn6JdKvOzr9qFJLdIzmivKKu25vFrWc1cXyJqL2Ru/test.png",
      image: "https://replicate.delivery/pbxt/MW3DzDSPvSGGCirpj7SByIOUEHcXJBBLSGvDe1PuUrpaPgbO/full.png",
      model: "dev",
      prompt: "windu sitting in a chair ",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "21:9",
      output_format: "jpg",
      guidance_scale: 3,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// 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 swk23/windu using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "swk23/windu:65f4bf32a31353e76b48659846fba61ecb49445c20a0d365f9a83fd151193847",
    input={
        "mask": "https://replicate.delivery/pbxt/MW3Dyqdtn6JdKvOzr9qFJLdIzmivKKu25vFrWc1cXyJqL2Ru/test.png",
        "image": "https://replicate.delivery/pbxt/MW3DzDSPvSGGCirpj7SByIOUEHcXJBBLSGvDe1PuUrpaPgbO/full.png",
        "model": "dev",
        "prompt": "windu sitting in a chair ",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "21:9",
        "output_format": "jpg",
        "guidance_scale": 3,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 swk23/windu 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": "swk23/windu:65f4bf32a31353e76b48659846fba61ecb49445c20a0d365f9a83fd151193847",
    "input": {
      "mask": "https://replicate.delivery/pbxt/MW3Dyqdtn6JdKvOzr9qFJLdIzmivKKu25vFrWc1cXyJqL2Ru/test.png",
      "image": "https://replicate.delivery/pbxt/MW3DzDSPvSGGCirpj7SByIOUEHcXJBBLSGvDe1PuUrpaPgbO/full.png",
      "model": "dev",
      "prompt": "windu sitting in a chair ",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "21:9",
      "output_format": "jpg",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  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/swk23/windu@sha256:65f4bf32a31353e76b48659846fba61ecb49445c20a0d365f9a83fd151193847 \
  -i 'mask="https://replicate.delivery/pbxt/MW3Dyqdtn6JdKvOzr9qFJLdIzmivKKu25vFrWc1cXyJqL2Ru/test.png"' \
  -i 'image="https://replicate.delivery/pbxt/MW3DzDSPvSGGCirpj7SByIOUEHcXJBBLSGvDe1PuUrpaPgbO/full.png"' \
  -i 'model="dev"' \
  -i 'prompt="windu sitting in a chair "' \
  -i 'go_fast=false' \
  -i 'lora_scale=1' \
  -i 'megapixels="1"' \
  -i 'num_outputs=1' \
  -i 'aspect_ratio="21:9"' \
  -i 'output_format="jpg"' \
  -i 'guidance_scale=3' \
  -i 'output_quality=80' \
  -i 'prompt_strength=0.8' \
  -i 'extra_lora_scale=1' \
  -i 'num_inference_steps=28'

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/swk23/windu@sha256:65f4bf32a31353e76b48659846fba61ecb49445c20a0d365f9a83fd151193847
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "mask": "https://replicate.delivery/pbxt/MW3Dyqdtn6JdKvOzr9qFJLdIzmivKKu25vFrWc1cXyJqL2Ru/test.png",
      "image": "https://replicate.delivery/pbxt/MW3DzDSPvSGGCirpj7SByIOUEHcXJBBLSGvDe1PuUrpaPgbO/full.png",
      "model": "dev",
      "prompt": "windu sitting in a chair ",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "21:9",
      "output_format": "jpg",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2025-02-17T17:29:18.098190Z",
  "created_at": "2025-02-17T17:29:05.542000Z",
  "data_removed": false,
  "error": null,
  "id": "0za0krys0srm80cn2ktabjavcr",
  "input": {
    "mask": "https://replicate.delivery/pbxt/MW3Dyqdtn6JdKvOzr9qFJLdIzmivKKu25vFrWc1cXyJqL2Ru/test.png",
    "image": "https://replicate.delivery/pbxt/MW3DzDSPvSGGCirpj7SByIOUEHcXJBBLSGvDe1PuUrpaPgbO/full.png",
    "model": "dev",
    "prompt": "windu sitting in a chair ",
    "go_fast": false,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "21:9",
    "output_format": "jpg",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "free=28122040393728\nDownloading weights\n2025-02-17T17:29:09Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpxb71_ofw/weights url=https://replicate.delivery/xezq/elmrBIPtlCyRYaRsePplNWBGJOrcq6QlPv2pAFje42I50xynA/trained_model.tar\n2025-02-17T17:29:11Z | INFO  | [ Complete ] dest=/tmp/tmpxb71_ofw/weights size=\"172 MB\" total_elapsed=1.324s url=https://replicate.delivery/xezq/elmrBIPtlCyRYaRsePplNWBGJOrcq6QlPv2pAFje42I50xynA/trained_model.tar\nDownloaded weights in 1.35s\nLoaded LoRAs in 1.90s\nUsing seed: 5644\nPrompt: windu sitting in a chair\nInput image size: 1536x640\n[!] Resizing input image from 1536x640 to 1440x608\n[!] inpaint mode\n  0%|          | 0/23 [00:00<?, ?it/s]\n  4%|▍         | 1/23 [00:00<00:19,  1.10it/s]\n  9%|▊         | 2/23 [00:01<00:10,  2.01it/s]\n 13%|█▎        | 3/23 [00:01<00:07,  2.71it/s]\n 17%|█▋        | 4/23 [00:01<00:05,  3.25it/s]\n 22%|██▏       | 5/23 [00:01<00:04,  3.66it/s]\n 26%|██▌       | 6/23 [00:01<00:04,  3.95it/s]\n 30%|███       | 7/23 [00:02<00:03,  4.17it/s]\n 35%|███▍      | 8/23 [00:02<00:03,  4.32it/s]\n 39%|███▉      | 9/23 [00:02<00:03,  4.43it/s]\n 43%|████▎     | 10/23 [00:02<00:02,  4.51it/s]\n 48%|████▊     | 11/23 [00:03<00:02,  4.56it/s]\n 52%|█████▏    | 12/23 [00:03<00:02,  4.60it/s]\n 57%|█████▋    | 13/23 [00:03<00:02,  4.62it/s]\n 61%|██████    | 14/23 [00:03<00:01,  4.64it/s]\n 65%|██████▌   | 15/23 [00:03<00:01,  4.66it/s]\n 70%|██████▉   | 16/23 [00:04<00:01,  4.67it/s]\n 74%|███████▍  | 17/23 [00:04<00:01,  4.67it/s]\n 78%|███████▊  | 18/23 [00:04<00:01,  4.68it/s]\n 83%|████████▎ | 19/23 [00:04<00:00,  4.68it/s]\n 87%|████████▋ | 20/23 [00:04<00:00,  4.68it/s]\n 91%|█████████▏| 21/23 [00:05<00:00,  4.66it/s]\n 96%|█████████▌| 22/23 [00:05<00:00,  4.67it/s]\n100%|██████████| 23/23 [00:05<00:00,  4.95it/s]\n100%|██████████| 23/23 [00:05<00:00,  4.13it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 8.681173174,
    "total_time": 12.55619
  },
  "output": [
    "https://replicate.delivery/xezq/NKc9yAJafxSf50I4hqASrl83NcSqxTRDmD1oeQI6TGRdfIBRB/out-0.jpg"
  ],
  "started_at": "2025-02-17T17:29:09.417017Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-nsrnjhks7kabyl7wgzyz5zqimyefejh6pkiajvcluqx24h3ccaqa",
    "get": "https://api.replicate.com/v1/predictions/0za0krys0srm80cn2ktabjavcr",
    "cancel": "https://api.replicate.com/v1/predictions/0za0krys0srm80cn2ktabjavcr/cancel"
  },
  "version": "65f4bf32a31353e76b48659846fba61ecb49445c20a0d365f9a83fd151193847"
}

Generated in

8.7 seconds

Tweak it Iterate in playground ShareReport View full prediction

free=28122040393728
Downloading weights
2025-02-17T17:29:09Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpxb71_ofw/weights url=https://replicate.delivery/xezq/elmrBIPtlCyRYaRsePplNWBGJOrcq6QlPv2pAFje42I50xynA/trained_model.tar
2025-02-17T17:29:11Z | INFO  | [ Complete ] dest=/tmp/tmpxb71_ofw/weights size="172 MB" total_elapsed=1.324s url=https://replicate.delivery/xezq/elmrBIPtlCyRYaRsePplNWBGJOrcq6QlPv2pAFje42I50xynA/trained_model.tar
Downloaded weights in 1.35s
Loaded LoRAs in 1.90s
Using seed: 5644
Prompt: windu sitting in a chair
Input image size: 1536x640
[!] Resizing input image from 1536x640 to 1440x608
[!] inpaint mode
  0%|          | 0/23 [00:00<?, ?it/s]
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 13%|█▎        | 3/23 [00:01<00:07,  2.71it/s]
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 57%|█████▋    | 13/23 [00:03<00:02,  4.62it/s]
 61%|██████    | 14/23 [00:03<00:01,  4.64it/s]
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 78%|███████▊  | 18/23 [00:04<00:01,  4.68it/s]
 83%|████████▎ | 19/23 [00:04<00:00,  4.68it/s]
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 96%|█████████▌| 22/23 [00:05<00:00,  4.67it/s]
100%|██████████| 23/23 [00:05<00:00,  4.95it/s]
100%|██████████| 23/23 [00:05<00:00,  4.13it/s]
Total safe images: 1 out of 1

Examples

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Run time and cost

This model runs on Nvidia H100 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

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