lucataco / realvisxl2-lcm

RealvisXL-v2.0 with LCM LoRA - requires fewer steps (4 to 8 instead of the original 40 to 50)

Cold

Public
292.7K runs
L40S
GitHub
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

dark shot, front shot, photo of a 25 y.o latino man, perfect eyes, natural skin, skin moles, looks at viewer, cinematic shotdark shot, front shot, photo of a 25 y.o latino man, perfect eyes, natural skin, skin moles, looks at viewer, cinematic shot

Input prompt

Default: "dark shot, front shot, closeup photo of a 25 y.o latino man, perfect eyes, natural skin, skin moles, looks at viewer, cinematic shot"

negative_prompt

string

Shift + Return to add a new line

(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth

Negative Input prompt

Default: "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth"

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: "LCM"

num_inference_steps

integer

(minimum: 1, maximum: 20)

Number of denoising steps

Default: 6

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 2

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

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

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

Default: false

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

npx create-replicate --model=lucataco/realvisxl2-lcm

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 lucataco/realvisxl2-lcm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/realvisxl2-lcm:479633443fc6588e1e8ae764b79cdb3702d0c196e0cb2de6db39ce577383be77",
  {
    input: {
      seed: 34694,
      width: 1024,
      height: 1024,
      prompt: "dark shot, front shot, photo of a 25 y.o latino man, perfect eyes, natural skin, skin moles, looks at viewer, cinematic shot",
      scheduler: "LCM",
      num_outputs: 1,
      guidance_scale: 2,
      apply_watermark: true,
      negative_prompt: "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
      prompt_strength: 0.8,
      num_inference_steps: 6
    }
  }
);

// 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/realvisxl2-lcm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/realvisxl2-lcm:479633443fc6588e1e8ae764b79cdb3702d0c196e0cb2de6db39ce577383be77",
    input={
        "seed": 34694,
        "width": 1024,
        "height": 1024,
        "prompt": "dark shot, front shot, photo of a 25 y.o latino man, perfect eyes, natural skin, skin moles, looks at viewer, cinematic shot",
        "scheduler": "LCM",
        "num_outputs": 1,
        "guidance_scale": 2,
        "apply_watermark": True,
        "negative_prompt": "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
        "prompt_strength": 0.8,
        "num_inference_steps": 6
    }
)
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 lucataco/realvisxl2-lcm 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/realvisxl2-lcm:479633443fc6588e1e8ae764b79cdb3702d0c196e0cb2de6db39ce577383be77",
    "input": {
      "seed": 34694,
      "width": 1024,
      "height": 1024,
      "prompt": "dark shot, front shot, photo of a 25 y.o latino man, perfect eyes, natural skin, skin moles, looks at viewer, cinematic shot",
      "scheduler": "LCM",
      "num_outputs": 1,
      "guidance_scale": 2,
      "apply_watermark": true,
      "negative_prompt": "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
      "prompt_strength": 0.8,
      "num_inference_steps": 6
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-15T23:06:59.108210Z",
  "created_at": "2023-11-15T23:06:56.321159Z",
  "data_removed": false,
  "error": null,
  "id": "xbpexxtb6uqgh77hbusyq34tca",
  "input": {
    "seed": 34694,
    "width": 1024,
    "height": 1024,
    "prompt": "dark shot, front shot, photo of a 25 y.o latino man, perfect eyes, natural skin, skin moles, looks at viewer, cinematic shot",
    "scheduler": "LCM",
    "num_outputs": 1,
    "guidance_scale": 2,
    "apply_watermark": true,
    "negative_prompt": "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
    "prompt_strength": 0.8,
    "num_inference_steps": 6
  },
  "logs": "Using seed: 34694\nPrompt: dark shot, front shot, photo of a 25 y.o latino man, perfect eyes, natural skin, skin moles, looks at viewer, cinematic shot\ntxt2img mode\nThe config attributes {'skip_prk_steps': True} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n  0%|          | 0/6 [00:00<?, ?it/s]\n 17%|█▋        | 1/6 [00:00<00:00,  8.94it/s]\n 33%|███▎      | 2/6 [00:00<00:00,  8.86it/s]\n 50%|█████     | 3/6 [00:00<00:00,  8.87it/s]\n 67%|██████▋   | 4/6 [00:00<00:00,  8.86it/s]\n 83%|████████▎ | 5/6 [00:00<00:00,  8.84it/s]\n100%|██████████| 6/6 [00:00<00:00,  8.82it/s]\n100%|██████████| 6/6 [00:00<00:00,  8.84it/s]",
  "metrics": {
    "predict_time": 2.750163,
    "total_time": 2.787051
  },
  "output": [
    "https://replicate.delivery/pbxt/ILTzFdAenk1JLKVb7jZbEDUOUJGz7hSFFSArhCnxin7ICY8IA/out-0.png"
  ],
  "started_at": "2023-11-15T23:06:56.358047Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/xbpexxtb6uqgh77hbusyq34tca",
    "cancel": "https://api.replicate.com/v1/predictions/xbpexxtb6uqgh77hbusyq34tca/cancel"
  },
  "version": "479633443fc6588e1e8ae764b79cdb3702d0c196e0cb2de6db39ce577383be77"
}

Generated in

2.8 seconds

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Examples

View more examples

Run time and cost

This model costs approximately $0.0013 to run on Replicate, or 769 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 2 seconds. The predict time for this model varies significantly based on the inputs.

Readme

About

Cog Implementation of the model: SG161222/RealVisXL_V2.0 with LCM LoRA for faster inference speeds