0xtuba/archillect-lora – Run with an API on Replicate

0xtuba / archillect-lora

Generates images in the style of Archillect

Warm

Public
5.8K runs
H100

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

small empty room with many computer screens in the style of ARCHLLCT, grayscalesmall empty room with many computer screens in the style of ARCHLLCT, grayscale

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.

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

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

Including image and 7 more...

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

npx create-replicate --model=0xtuba/archillect-lora

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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42",
  {
    input: {
      model: "dev",
      prompt: "small empty room with many computer screens in the style of ARCHLLCT, grayscale",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42",
    input={
        "model": "dev",
        "prompt": "small empty room with many computer screens in the style of ARCHLLCT, grayscale",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
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 0xtuba/archillect-lora 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": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42",
    "input": {
      "model": "dev",
      "prompt": "small empty room with many computer screens in the style of ARCHLLCT, grayscale",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "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.

Output

{
  "completed_at": "2024-09-05T10:36:20.467623Z",
  "created_at": "2024-09-05T10:36:02.760000Z",
  "data_removed": false,
  "error": null,
  "id": "p9sprtk591rm40chr70srw8fwr",
  "input": {
    "model": "dev",
    "prompt": "small empty room with many computer screens in the style of ARCHLLCT, grayscale",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 49085\nPrompt: small empty room with many computer screens in the style of ARCHLLCT, grayscale\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.08s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.46it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.91it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.69it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.60it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.55it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.52it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.51it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.49it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.49it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.48it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.48it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.47it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.47it/s]\n 50%|█████     | 14/28 [00:03<00:04,  3.47it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.47it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.47it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.47it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.46it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.47it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.46it/s]\n 75%|███████▌  | 21/28 [00:06<00:02,  3.46it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.46it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.46it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.46it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.46it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.47it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.45it/s]\n100%|██████████| 28/28 [00:08<00:00,  3.46it/s]\n100%|██████████| 28/28 [00:08<00:00,  3.49it/s]",
  "metrics": {
    "predict_time": 17.69888967,
    "total_time": 17.707623
  },
  "output": [
    "https://replicate.delivery/yhqm/O0eNA4366iUPHqW5DLdnLjZdgoXwtEzZLe7huLppYafJdnzmA/out-0.webp"
  ],
  "started_at": "2024-09-05T10:36:02.768734Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/p9sprtk591rm40chr70srw8fwr",
    "cancel": "https://api.replicate.com/v1/predictions/p9sprtk591rm40chr70srw8fwr/cancel"
  },
  "version": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42"
}

Generated in

17.7 seconds

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Examples

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Run time and cost

This model costs approximately $0.022 to run on Replicate, or 45 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 H100 GPU hardware. Predictions typically complete within 15 seconds.

Readme

Use the keywords “in the style of ARCHLLCT”. Works well with “grayscale”