cudanexus/stickergp | Run with an API on Replicate

cudanexus / stickergp

Cold

Public
387 runs
T4

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=cudanexus/stickergp

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

const output = await replicate.run(
  "cudanexus/stickergp:6ffeac3440d7f79fe412021e0a2d28b03a43062c090cc0c625d3a73f0aa1f827",
  {
    input: {
      steps: 20,
      width: 1024,
      height: 1024,
      prompt: "a cute dog ",
      upscale: false,
      upscale_steps: 10,
      negative_prompt: ""
    }
  }
);

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

output = replicate.run(
    "cudanexus/stickergp:6ffeac3440d7f79fe412021e0a2d28b03a43062c090cc0c625d3a73f0aa1f827",
    input={
        "steps": 20,
        "width": 1024,
        "height": 1024,
        "prompt": "a cute dog ",
        "upscale": False,
        "upscale_steps": 10,
        "negative_prompt": ""
    }
)
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 cudanexus/stickergp 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": "cudanexus/stickergp:6ffeac3440d7f79fe412021e0a2d28b03a43062c090cc0c625d3a73f0aa1f827",
    "input": {
      "steps": 20,
      "width": 1024,
      "height": 1024,
      "prompt": "a cute dog ",
      "upscale": false,
      "upscale_steps": 10,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-03-16T22:30:00.039291Z",
  "created_at": "2024-03-16T22:29:32.214331Z",
  "data_removed": false,
  "error": null,
  "id": "rz3bx3zbvokmbcihijp3hvp6ei",
  "input": {
    "steps": 20,
    "width": 1024,
    "height": 1024,
    "prompt": "a cute dog ",
    "upscale": false,
    "upscale_steps": 10,
    "negative_prompt": ""
  },
  "logs": "Random seed set to: 3470438042\nChecking inputs\n====================================\nChecking weights\n✅ dreamshaper_8.safetensors\n✅ artificialguybr/StickersRedmond.safetensors\n✅ albedobaseXL_v13.safetensors\n✅ RMBG-1.4/model.pth\n✅ 4x-AnimeSharp.pth\n====================================\nRunning workflow\ngot prompt\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): StickersRedmond(1,1)\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:16,  1.13it/s]\n 10%|█         | 2/20 [00:01<00:16,  1.12it/s]\n 15%|█▌        | 3/20 [00:02<00:15,  1.11it/s]\n 20%|██        | 4/20 [00:03<00:14,  1.10it/s]\n 25%|██▌       | 5/20 [00:04<00:13,  1.11it/s]\n 30%|███       | 6/20 [00:05<00:12,  1.10it/s]\n 35%|███▌      | 7/20 [00:06<00:11,  1.10it/s]\n 40%|████      | 8/20 [00:07<00:10,  1.10it/s]\n 45%|████▌     | 9/20 [00:08<00:10,  1.09it/s]\n 50%|█████     | 10/20 [00:09<00:09,  1.09it/s]\n 55%|█████▌    | 11/20 [00:10<00:08,  1.09it/s]\n 60%|██████    | 12/20 [00:10<00:07,  1.09it/s]\n 65%|██████▌   | 13/20 [00:11<00:06,  1.08it/s]\n 70%|███████   | 14/20 [00:12<00:05,  1.07it/s]\n 75%|███████▌  | 15/20 [00:13<00:04,  1.07it/s]\n 80%|████████  | 16/20 [00:14<00:03,  1.07it/s]\n 85%|████████▌ | 17/20 [00:15<00:02,  1.07it/s]\n 90%|█████████ | 18/20 [00:16<00:01,  1.07it/s]\n 95%|█████████▌| 19/20 [00:17<00:00,  1.07it/s]\n100%|██████████| 20/20 [00:18<00:00,  1.08it/s]\n100%|██████████| 20/20 [00:18<00:00,  1.09it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nExecuting node 9, title: 🧹BRIA RMBG, class type: BRIA_RMBG_Zho\nExecuting node 10, title: Save Image, class type: SaveImage\nPrompt executed in 26.98 seconds\noutputs:  {'4': {'images': [{'filename': 'ComfyUI_temp_tzosl_00001_.png', 'subfolder': '', 'type': 'temp'}]}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}, '10': {'images': [{'filename': 'ComfyUI_00002_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00002_.png\nComfyUI_00001_.png",
  "metrics": {
    "predict_time": 27.809204,
    "total_time": 27.82496
  },
  "output": [
    "https://replicate.delivery/pbxt/WT0S1FTNDd58FRCoH7Gvt8Nje8V7uvqpz7qqUZ65x1xze8gSA/ComfyUI_00002_.png",
    "https://replicate.delivery/pbxt/meCHP58LH0XzSSsZeeqCIMcDqc1RzqVyAenEDS59C8zfsnHUC/ComfyUI_00001_.png"
  ],
  "started_at": "2024-03-16T22:29:32.230087Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/rz3bx3zbvokmbcihijp3hvp6ei",
    "cancel": "https://api.replicate.com/v1/predictions/rz3bx3zbvokmbcihijp3hvp6ei/cancel"
  },
  "version": "6ffeac3440d7f79fe412021e0a2d28b03a43062c090cc0c625d3a73f0aa1f827"
}

Generated in

27.8 seconds

Tweak it Report View full prediction

Random seed set to: 3470438042
Checking inputs
====================================
Checking weights
✅ dreamshaper_8.safetensors
✅ artificialguybr/StickersRedmond.safetensors
✅ albedobaseXL_v13.safetensors
✅ RMBG-1.4/model.pth
✅ 4x-AnimeSharp.pth
====================================
Running workflow
got prompt
Executing node 2, title: Efficient Loader, class type: Efficient Loader
Requested to load SDXLClipModel
Loading 1 new model
----------------------------------------
Efficient Loader Models Cache:
Ckpt:
[1] albedobaseXL_v13
Lora:
[1] base_ckpt: albedobaseXL_v13
lora(mod,clip): StickersRedmond(1,1)
Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient)
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Executing node 5, title: Save Image, class type: SaveImage
Executing node 9, title: 🧹BRIA RMBG, class type: BRIA_RMBG_Zho
Executing node 10, title: Save Image, class type: SaveImage
Prompt executed in 26.98 seconds
outputs:  {'4': {'images': [{'filename': 'ComfyUI_temp_tzosl_00001_.png', 'subfolder': '', 'type': 'temp'}]}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}, '10': {'images': [{'filename': 'ComfyUI_00002_.png', 'subfolder': '', 'type': 'output'}]}}
====================================
Contents of /tmp/outputs:
ComfyUI_00002_.png
ComfyUI_00001_.png

Examples

View more examples

Run time and cost

This model costs approximately $0.061 to run on Replicate, or 16 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 T4 GPU hardware. Predictions typically complete within 5 minutes. The predict time for this model varies significantly based on the inputs.

Readme

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