Input

prompt

string

Shift + Return to add a new line

Default: ""

width

integer

(minimum: 512, maximum: 2048)

The width of the image

Default: 1024

height

integer

(minimum: 512, maximum: 2048)

The height of the image

Default: 1024

output_format

string

Format of the output images

Default: "webp"

seed

integer

Set a seed for reproducibility. Random by default.

Including negative_prompt and 6 more...

negative_prompt

string

Shift + Return to add a new line

Things you do not want to see in your image

Default: ""

number_of_images

integer

(minimum: 1, maximum: 10)

The number of images to generate

Default: 1

cfg

number

(minimum: 0, maximum: 20)

The guidance scale tells the model how similar the output should be to the prompt.

Default: 3.5

sampler

string

An enumeration.

Default: "uni_pc"

scheduler

string

An enumeration.

Default: "normal"

shift

number

(minimum: 0, maximum: 10)

The timestep scheduling shift; shift values higher than 1.0 are better at managing noise in higher resolutions.

Default: 1.73

output_quality

integer

(minimum: 0, maximum: 100)

Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.

Default: 80

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

npx create-replicate --model=lucataco/aura-flow-v0.2

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 lucataco/aura-flow-v0.2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/aura-flow-v0.2:e784141e567364018b6c24297273e910b80c468c60ccb2a2a6642c034c7d43ee",
  {
    input: {
      cfg: 3.5,
      shift: 1,
      width: 1024,
      height: 1024,
      prompt: "A photo of a lavender cat",
      sampler: "uni_pc",
      scheduler: "normal",
      output_format: "webp",
      output_quality: 80,
      negative_prompt: "",
      number_of_images: 1
    }
  }
);

// 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 lucataco/aura-flow-v0.2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/aura-flow-v0.2:e784141e567364018b6c24297273e910b80c468c60ccb2a2a6642c034c7d43ee",
    input={
        "cfg": 3.5,
        "shift": 1,
        "width": 1024,
        "height": 1024,
        "prompt": "A photo of a lavender cat",
        "sampler": "uni_pc",
        "scheduler": "normal",
        "output_format": "webp",
        "output_quality": 80,
        "negative_prompt": "",
        "number_of_images": 1
    }
)

# 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 lucataco/aura-flow-v0.2 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": "lucataco/aura-flow-v0.2:e784141e567364018b6c24297273e910b80c468c60ccb2a2a6642c034c7d43ee",
    "input": {
      "cfg": 3.5,
      "shift": 1,
      "width": 1024,
      "height": 1024,
      "prompt": "A photo of a lavender cat",
      "sampler": "uni_pc",
      "scheduler": "normal",
      "output_format": "webp",
      "output_quality": 80,
      "negative_prompt": "",
      "number_of_images": 1
    }
  }' \
  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/lucataco/aura-flow-v0.2@sha256:e784141e567364018b6c24297273e910b80c468c60ccb2a2a6642c034c7d43ee \
  -i 'cfg=3.5' \
  -i 'shift=1' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="A photo of a lavender cat"' \
  -i 'sampler="uni_pc"' \
  -i 'scheduler="normal"' \
  -i 'output_format="webp"' \
  -i 'output_quality=80' \
  -i 'negative_prompt=""' \
  -i 'number_of_images=1'

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/lucataco/aura-flow-v0.2@sha256:e784141e567364018b6c24297273e910b80c468c60ccb2a2a6642c034c7d43ee
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "cfg": 3.5,
      "shift": 1,
      "width": 1024,
      "height": 1024,
      "prompt": "A photo of a lavender cat",
      "sampler": "uni_pc",
      "scheduler": "normal",
      "output_format": "webp",
      "output_quality": 80,
      "negative_prompt": "",
      "number_of_images": 1
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2024-07-27T18:20:07.143054Z",
  "created_at": "2024-07-27T18:19:54.146000Z",
  "data_removed": false,
  "error": null,
  "id": "kh41eknhm9rj00cgynnvh1excr",
  "input": {
    "cfg": 3.5,
    "shift": 1,
    "width": 1024,
    "height": 1024,
    "prompt": "A photo of a lavender cat",
    "sampler": "uni_pc",
    "scheduler": "normal",
    "output_format": "webp",
    "output_quality": 80,
    "negative_prompt": "",
    "number_of_images": 1
  },
  "logs": "Random seed set to: 1905931290\nRunning workflow\ngot prompt\nExecuting node 3, title: KSampler, class type: KSampler\n  0%|          | 0/25 [00:00<?, ?it/s]\n  8%|▊         | 2/25 [00:00<00:06,  3.52it/s]\n 12%|█▏        | 3/25 [00:01<00:08,  2.72it/s]\n 16%|█▌        | 4/25 [00:01<00:08,  2.44it/s]\n 20%|██        | 5/25 [00:02<00:08,  2.30it/s]\n 24%|██▍       | 6/25 [00:02<00:08,  2.22it/s]\n 28%|██▊       | 7/25 [00:02<00:08,  2.17it/s]\n 32%|███▏      | 8/25 [00:03<00:07,  2.13it/s]\n 36%|███▌      | 9/25 [00:03<00:07,  2.11it/s]\n 40%|████      | 10/25 [00:04<00:07,  2.10it/s]\n 44%|████▍     | 11/25 [00:04<00:06,  2.09it/s]\n 48%|████▊     | 12/25 [00:05<00:06,  2.08it/s]\n 52%|█████▏    | 13/25 [00:05<00:05,  2.08it/s]\n 56%|█████▌    | 14/25 [00:06<00:05,  2.08it/s]\n 60%|██████    | 15/25 [00:06<00:04,  2.07it/s]\n 64%|██████▍   | 16/25 [00:07<00:04,  2.07it/s]\n 68%|██████▊   | 17/25 [00:07<00:03,  2.07it/s]\n 72%|███████▏  | 18/25 [00:08<00:03,  2.07it/s]\n 76%|███████▌  | 19/25 [00:08<00:02,  2.07it/s]\n 80%|████████  | 20/25 [00:09<00:02,  2.07it/s]\n 84%|████████▍ | 21/25 [00:09<00:01,  2.07it/s]\n 88%|████████▊ | 22/25 [00:10<00:01,  2.06it/s]\n 92%|█████████▏| 23/25 [00:10<00:00,  2.06it/s]\n 96%|█████████▌| 24/25 [00:11<00:00,  2.06it/s]\n100%|██████████| 25/25 [00:12<00:00,  1.66it/s]\n100%|██████████| 25/25 [00:12<00:00,  2.07it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 12.45 seconds\noutputs:  {'9': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nComfyUI_00001_.png",
  "metrics": {
    "predict_time": 12.986397748,
    "total_time": 12.997054
  },
  "output": [
    "https://replicate.delivery/yhqm/nlA4mIDPNFocFNks4CG90QPffqXmq2bzgqsUJOiCXWyWxuMTA/ComfyUI_00001_.webp"
  ],
  "started_at": "2024-07-27T18:19:54.156656Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/kh41eknhm9rj00cgynnvh1excr",
    "cancel": "https://api.replicate.com/v1/predictions/kh41eknhm9rj00cgynnvh1excr/cancel"
  },
  "version": "e784141e567364018b6c24297273e910b80c468c60ccb2a2a6642c034c7d43ee"
}

Generated in

13.0 seconds

Tweak it Iterate in playground ShareReport View full prediction

Examples

View more examples

Run time and cost

This model costs approximately $0.077 to run on Replicate, or 12 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 56 seconds. The predict time for this model varies significantly based on the inputs.

Readme

AuraFlow-v0.2

The largest yet completely open sourced flow-based generation model that is capable of text-to-image generation. Created by @cloneofsimo and @fal.

Blog post:

https://blog.fal.ai/auraflow/

Weights:

https://huggingface.co/fal/AuraFlow