cloneofsimo/avatar:f72c64e7 – Run with an API on Replicate

Version

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cloneofsimo /avatar:f72c64e7

Input

prompt

string

Shift + Return to add a new line

a photo of <1> riding a horse on mars, avatarart stylea photo of <1> riding a horse on mars, avatarart style

Input prompt. Use <1>, <2>, <3>, etc., to specify LoRA concepts

Default: "a photo of <1> riding a horse on mars"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

Default: ""

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

scheduler

string

Choose a scheduler.

Default: "DPMSolverMultistep"

lora_urls

string

Shift + Return to add a new line

List of urls for safetensors of lora models, seperated with | . If provided, it will override all above options.

Default: ""

lora_scales

string

Shift + Return to add a new line

List of scales for safetensors of lora models, seperated with |

Default: "0.3"

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=cloneofsimo/avatar

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

const output = await replicate.run(
  "cloneofsimo/avatar:f72c64e7bdc9dfa0204a9700aca5c038d2f43c032ee97292b15ee7365651fe78",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "a photo of <1> riding a horse on mars, avatarart style",
      lora_urls: "",
      scheduler: "DPMSolverMultistep",
      lora_scales: "0.3",
      num_outputs: 1,
      guidance_scale: 7.5,
      negative_prompt: "",
      num_inference_steps: 50
    }
  }
);
console.log(output);

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

output = replicate.run(
    "cloneofsimo/avatar:f72c64e7bdc9dfa0204a9700aca5c038d2f43c032ee97292b15ee7365651fe78",
    input={
        "width": 512,
        "height": 512,
        "prompt": "a photo of <1> riding a horse on mars, avatarart style",
        "lora_urls": "",
        "scheduler": "DPMSolverMultistep",
        "lora_scales": "0.3",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "negative_prompt": "",
        "num_inference_steps": 50
    }
)
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 cloneofsimo/avatar 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": "f72c64e7bdc9dfa0204a9700aca5c038d2f43c032ee97292b15ee7365651fe78",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "a photo of <1> riding a horse on mars, avatarart style",
      "lora_urls": "",
      "scheduler": "DPMSolverMultistep",
      "lora_scales": "0.3",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "num_inference_steps": 50
    }
  }' \
  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/cloneofsimo/avatar@sha256:f72c64e7bdc9dfa0204a9700aca5c038d2f43c032ee97292b15ee7365651fe78 \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="a photo of <1> riding a horse on mars, avatarart style"' \
  -i 'lora_urls=""' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'lora_scales="0.3"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=50'

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/cloneofsimo/avatar@sha256:f72c64e7bdc9dfa0204a9700aca5c038d2f43c032ee97292b15ee7365651fe78
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "a photo of <1> riding a horse on mars, avatarart style",
      "lora_urls": "",
      "scheduler": "DPMSolverMultistep",
      "lora_scales": "0.3",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-02-07T09:12:48.567509Z",
  "created_at": "2023-02-07T09:12:24.348184Z",
  "data_removed": false,
  "error": null,
  "id": "6py6tchzlbb5bco6vundazwmde",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "a photo of <1> riding a horse on mars, avatarart style",
    "scheduler": "DPMSolverMultistep",
    "lora_scales": "0.3",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 41696\nNo LoRA models provided, using default model...\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:24,  1.97it/s]\n  4%|▍         | 2/50 [00:00<00:22,  2.15it/s]\n  6%|▌         | 3/50 [00:01<00:21,  2.20it/s]\n  8%|▊         | 4/50 [00:01<00:20,  2.23it/s]\n 10%|█         | 5/50 [00:02<00:20,  2.24it/s]\n 12%|█▏        | 6/50 [00:02<00:19,  2.25it/s]\n 14%|█▍        | 7/50 [00:03<00:19,  2.25it/s]\n 16%|█▌        | 8/50 [00:03<00:18,  2.25it/s]\n 18%|█▊        | 9/50 [00:04<00:18,  2.26it/s]\n 20%|██        | 10/50 [00:04<00:17,  2.26it/s]\n 22%|██▏       | 11/50 [00:04<00:17,  2.26it/s]\n 24%|██▍       | 12/50 [00:05<00:16,  2.26it/s]\n 26%|██▌       | 13/50 [00:05<00:16,  2.26it/s]\n 28%|██▊       | 14/50 [00:06<00:15,  2.26it/s]\n 30%|███       | 15/50 [00:06<00:15,  2.26it/s]\n 32%|███▏      | 16/50 [00:07<00:15,  2.26it/s]\n 34%|███▍      | 17/50 [00:07<00:14,  2.26it/s]\n 36%|███▌      | 18/50 [00:08<00:14,  2.26it/s]\n 38%|███▊      | 19/50 [00:08<00:13,  2.25it/s]\n 40%|████      | 20/50 [00:08<00:13,  2.26it/s]\n 42%|████▏     | 21/50 [00:09<00:12,  2.25it/s]\n 44%|████▍     | 22/50 [00:09<00:12,  2.25it/s]\n 46%|████▌     | 23/50 [00:10<00:12,  2.25it/s]\n 48%|████▊     | 24/50 [00:10<00:11,  2.24it/s]\n 50%|█████     | 25/50 [00:11<00:11,  2.24it/s]\n 52%|█████▏    | 26/50 [00:11<00:10,  2.23it/s]\n 54%|█████▍    | 27/50 [00:12<00:10,  2.24it/s]\n 56%|█████▌    | 28/50 [00:12<00:09,  2.24it/s]\n 58%|█████▊    | 29/50 [00:12<00:09,  2.24it/s]\n 60%|██████    | 30/50 [00:13<00:08,  2.24it/s]\n 62%|██████▏   | 31/50 [00:13<00:08,  2.24it/s]\n 64%|██████▍   | 32/50 [00:14<00:08,  2.24it/s]\n 66%|██████▌   | 33/50 [00:14<00:07,  2.24it/s]\n 68%|██████▊   | 34/50 [00:15<00:07,  2.24it/s]\n 70%|███████   | 35/50 [00:15<00:06,  2.25it/s]\n 72%|███████▏  | 36/50 [00:16<00:06,  2.24it/s]\n 74%|███████▍  | 37/50 [00:16<00:05,  2.24it/s]\n 76%|███████▌  | 38/50 [00:16<00:05,  2.24it/s]\n 78%|███████▊  | 39/50 [00:17<00:04,  2.24it/s]\n 80%|████████  | 40/50 [00:17<00:04,  2.24it/s]\n 82%|████████▏ | 41/50 [00:18<00:04,  2.24it/s]\n 84%|████████▍ | 42/50 [00:18<00:03,  2.23it/s]\n 86%|████████▌ | 43/50 [00:19<00:03,  2.23it/s]\n 88%|████████▊ | 44/50 [00:19<00:02,  2.22it/s]\n 90%|█████████ | 45/50 [00:20<00:02,  2.22it/s]\n 92%|█████████▏| 46/50 [00:20<00:01,  2.23it/s]\n 94%|█████████▍| 47/50 [00:20<00:01,  2.23it/s]\n 96%|█████████▌| 48/50 [00:21<00:00,  2.23it/s]\n 98%|█████████▊| 49/50 [00:21<00:00,  2.23it/s]\n100%|██████████| 50/50 [00:22<00:00,  2.23it/s]\n100%|██████████| 50/50 [00:22<00:00,  2.24it/s]",
  "metrics": {
    "predict_time": 23.280949,
    "total_time": 24.219325
  },
  "output": [
    "https://replicate.delivery/pbxt/lLGfls5zK0Te5ELLvMb2yqRmTe2b2gvVAkFjpy2S6RBhA53gA/out-0.png"
  ],
  "started_at": "2023-02-07T09:12:25.286560Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/6py6tchzlbb5bco6vundazwmde",
    "cancel": "https://api.replicate.com/v1/predictions/6py6tchzlbb5bco6vundazwmde/cancel"
  },
  "version": "f72c64e7bdc9dfa0204a9700aca5c038d2f43c032ee97292b15ee7365651fe78"
}

Generated in

23.3 seconds

Tweak itReport

Using seed: 41696
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