colinmcdonnell22 / spoot

{
  "model": "schnell",
  "prompt": "spoot character playing baseball",
  "go_fast": true,
  "lora_scale": 1.75,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "png",
  "guidance_scale": 5,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 4
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run colinmcdonnell22/spoot using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "colinmcdonnell22/spoot:29a70a544e154a4cfed0890cbf50a095b127987c78bfe1edff3800fb5e03e72d",
  {
    input: {
      model: "schnell",
      prompt: "spoot character playing baseball",
      go_fast: true,
      lora_scale: 1.75,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "png",
      guidance_scale: 5,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 4
    }
  }
);

// 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

Import the client:

import replicate

Run colinmcdonnell22/spoot using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "colinmcdonnell22/spoot:29a70a544e154a4cfed0890cbf50a095b127987c78bfe1edff3800fb5e03e72d",
    input={
        "model": "schnell",
        "prompt": "spoot character playing baseball",
        "go_fast": True,
        "lora_scale": 1.75,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "png",
        "guidance_scale": 5,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 4
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run colinmcdonnell22/spoot 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": "colinmcdonnell22/spoot:29a70a544e154a4cfed0890cbf50a095b127987c78bfe1edff3800fb5e03e72d",
    "input": {
      "model": "schnell",
      "prompt": "spoot character playing baseball",
      "go_fast": true,
      "lora_scale": 1.75,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "png",
      "guidance_scale": 5,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-12-14T18:52:00.784850Z",
  "created_at": "2024-12-14T18:51:59.365000Z",
  "data_removed": false,
  "error": null,
  "id": "ybe1c2k20nrm80ckrt1r53m0n0",
  "input": {
    "model": "schnell",
    "prompt": "spoot character playing baseball",
    "go_fast": true,
    "lora_scale": 1.75,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "png",
    "guidance_scale": 5,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 4
  },
  "logs": "2024-12-14 18:51:59.662 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-14 18:51:59.662 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 12228.88it/s]\n2024-12-14 18:51:59.688 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.026s\n2024-12-14 18:51:59.690 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/5010eb0c8a997edc\n2024-12-14 18:51:59.759 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2024-12-14 18:51:59.759 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-14 18:51:59.760 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  40%|████      | 122/304 [00:00<00:00, 1210.81it/s]\nApplying LoRA:  80%|████████  | 244/304 [00:00<00:00, 959.36it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 962.34it/s]\n2024-12-14 18:52:00.076 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.39s\nrunning quantized prediction\nUsing seed: 3997722200\n  0%|          | 0/4 [00:00<?, ?it/s]\n 50%|█████     | 2/4 [00:00<00:00, 18.79it/s]\n100%|██████████| 4/4 [00:00<00:00, 13.92it/s]\n100%|██████████| 4/4 [00:00<00:00, 14.48it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 1.121593454,
    "total_time": 1.41985
  },
  "output": [
    "https://replicate.delivery/xezq/C1faIU22PwQHS664yi9Cwo9noQf15tqZkPY6U3vWw2XQX46TA/out-0.png"
  ],
  "started_at": "2024-12-14T18:51:59.663257Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-k6ffbezzyf6tnv4zcbvt2alo4aih6hzedunml2dyjogkfoyl45ea",
    "get": "https://api.replicate.com/v1/predictions/ybe1c2k20nrm80ckrt1r53m0n0",
    "cancel": "https://api.replicate.com/v1/predictions/ybe1c2k20nrm80ckrt1r53m0n0/cancel"
  },
  "version": "29a70a544e154a4cfed0890cbf50a095b127987c78bfe1edff3800fb5e03e72d"
}

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