jakedahn/flux-latentpop:c5e4432e – Run with an API on Replicate

Version

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jakedahn /flux-latentpop:c5e4432e

Input

prompt

*string

Shift + Return to add a new line

screenprint tshirt design, a happy cat holding a sign that says "I LOVE REPLICATE", LNTP illustration stylescreenprint tshirt design, a happy cat holding a sign that says "I LOVE REPLICATE", LNTP illustration style

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.

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"

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

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

Including image and 10 more...

image

file

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

mask

file

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

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"

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

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=jakedahn/flux-latentpop

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 jakedahn/flux-latentpop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 20
    }
  }
);

// 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 jakedahn/flux-latentpop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 20
    }
)
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 jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "screenprint tshirt design, a happy cat holding a sign that says \\"I LOVE REPLICATE\\", LNTP illustration style",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T22:04:30.262889Z",
  "created_at": "2024-09-25T22:04:04.682000Z",
  "data_removed": false,
  "error": null,
  "id": "0g92prkys9rm20cj5cv8qenjr0",
  "input": {
    "model": "dev",
    "prompt": "screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 20,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 4485\nPrompt: screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 8.77s\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:05,  3.51it/s]\n 10%|█         | 2/20 [00:00<00:04,  3.97it/s]\n 15%|█▌        | 3/20 [00:00<00:04,  3.74it/s]\n 20%|██        | 4/20 [00:01<00:04,  3.64it/s]\n 25%|██▌       | 5/20 [00:01<00:04,  3.59it/s]\n 30%|███       | 6/20 [00:01<00:03,  3.56it/s]\n 35%|███▌      | 7/20 [00:01<00:03,  3.55it/s]\n 40%|████      | 8/20 [00:02<00:03,  3.53it/s]\n 45%|████▌     | 9/20 [00:02<00:03,  3.53it/s]\n 50%|█████     | 10/20 [00:02<00:02,  3.52it/s]\n 55%|█████▌    | 11/20 [00:03<00:02,  3.52it/s]\n 60%|██████    | 12/20 [00:03<00:02,  3.51it/s]\n 65%|██████▌   | 13/20 [00:03<00:01,  3.51it/s]\n 70%|███████   | 14/20 [00:03<00:01,  3.51it/s]\n 75%|███████▌  | 15/20 [00:04<00:01,  3.51it/s]\n 80%|████████  | 16/20 [00:04<00:01,  3.51it/s]\n 85%|████████▌ | 17/20 [00:04<00:00,  3.51it/s]\n 90%|█████████ | 18/20 [00:05<00:00,  3.50it/s]\n 95%|█████████▌| 19/20 [00:05<00:00,  3.50it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.50it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.54it/s]",
  "metrics": {
    "predict_time": 14.983668764,
    "total_time": 25.580889
  },
  "output": [
    "https://replicate.delivery/yhqm/6oe5Mae5xNrptUvEdwdWLxKkiEIWdPpgfJd44JkXSmUdXHBnA/out-0.webp"
  ],
  "started_at": "2024-09-25T22:04:15.279220Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/0g92prkys9rm20cj5cv8qenjr0",
    "cancel": "https://api.replicate.com/v1/predictions/0g92prkys9rm20cj5cv8qenjr0/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

15.0 seconds

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