Prediction

vcollos/pedro:70fb2a10863495e25002c5c54f3721b8d636a6dfc2f6c82c4bc499332967d73d

Model

vcollos/pedro:70fb2a10

49zgsm0m5xrma0cknze9ns378m

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.5s

Created

5 months ago

Input

model: dev
prompt: professional portrait of Pedro in a studio with a dark Blue suit, 35mm
go_fast
lora_scale: 1
megapixels: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "professional portrait of Pedro in a studio with a dark Blue suit, 35mm",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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

const output = await replicate.run(
  "vcollos/pedro:70fb2a10863495e25002c5c54f3721b8d636a6dfc2f6c82c4bc499332967d73d",
  {
    input: {
      model: "dev",
      prompt: "professional portrait of Pedro in a studio with a dark Blue suit, 35mm",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

output = replicate.run(
    "vcollos/pedro:70fb2a10863495e25002c5c54f3721b8d636a6dfc2f6c82c4bc499332967d73d",
    input={
        "model": "dev",
        "prompt": "professional portrait of Pedro in a studio with a dark Blue suit, 35mm",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run vcollos/pedro 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": "70fb2a10863495e25002c5c54f3721b8d636a6dfc2f6c82c4bc499332967d73d",
    "input": {
      "model": "dev",
      "prompt": "professional portrait of Pedro in a studio with a dark Blue suit, 35mm",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-12-10T09:17:46.662922Z",
  "created_at": "2024-12-10T09:17:36.175000Z",
  "data_removed": false,
  "error": null,
  "id": "49zgsm0m5xrma0cknze9ns378m",
  "input": {
    "model": "dev",
    "prompt": "professional portrait of Pedro in a studio with a dark Blue suit, 35mm",
    "go_fast": false,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "2024-12-10 09:17:36.441 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-10 09:17:36.441 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  89%|████████▉ | 271/304 [00:00<00:00, 2693.66it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2616.16it/s]\n2024-12-10 09:17:36.558 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s\nfree=28270977695744\nDownloading weights\n2024-12-10T09:17:36Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpvs0yavwh/weights url=https://replicate.delivery/xezq/OdScz0wQ9hrfekLsv2besBKtIwYhq73qUYmRZWTrYxrFgtynA/trained_model.tar\n2024-12-10T09:17:40Z | INFO  | [ Complete ] dest=/tmp/tmpvs0yavwh/weights size=\"172 MB\" total_elapsed=3.851s url=https://replicate.delivery/xezq/OdScz0wQ9hrfekLsv2besBKtIwYhq73qUYmRZWTrYxrFgtynA/trained_model.tar\nDownloaded weights in 3.88s\n2024-12-10 09:17:40.436 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/a72323dc2b503686\n2024-12-10 09:17:40.509 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2024-12-10 09:17:40.509 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-10 09:17:40.509 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  89%|████████▉ | 271/304 [00:00<00:00, 2698.74it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2620.15it/s]\n2024-12-10 09:17:40.626 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s\nUsing seed: 11604\n0it [00:00, ?it/s]\n1it [00:00,  8.36it/s]\n2it [00:00,  5.87it/s]\n3it [00:00,  5.36it/s]\n4it [00:00,  5.15it/s]\n5it [00:00,  5.04it/s]\n6it [00:01,  4.97it/s]\n7it [00:01,  4.93it/s]\n8it [00:01,  4.91it/s]\n9it [00:01,  4.90it/s]\n10it [00:01,  4.88it/s]\n11it [00:02,  4.87it/s]\n12it [00:02,  4.86it/s]\n13it [00:02,  4.85it/s]\n14it [00:02,  4.85it/s]\n15it [00:03,  4.85it/s]\n16it [00:03,  4.85it/s]\n17it [00:03,  4.85it/s]\n18it [00:03,  4.85it/s]\n19it [00:03,  4.85it/s]\n20it [00:04,  4.84it/s]\n21it [00:04,  4.84it/s]\n22it [00:04,  4.85it/s]\n23it [00:04,  4.85it/s]\n24it [00:04,  4.85it/s]\n25it [00:05,  4.85it/s]\n26it [00:05,  4.85it/s]\n27it [00:05,  4.84it/s]\n28it [00:05,  4.85it/s]\n28it [00:05,  4.92it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 10.220674929,
    "total_time": 10.487922
  },
  "output": [
    "https://replicate.delivery/xezq/oPFbvVgg3eULf0ZlMtZSZlQuOhudYVWXvBCa6aN1YsO6kb5TA/out-0.webp"
  ],
  "started_at": "2024-12-10T09:17:36.442247Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-fpcta67m6td4ts4egv3bcug4ayj6rvd3cuhuignvh5mllrv2y2da",
    "get": "https://api.replicate.com/v1/predictions/49zgsm0m5xrma0cknze9ns378m",
    "cancel": "https://api.replicate.com/v1/predictions/49zgsm0m5xrma0cknze9ns378m/cancel"
  },
  "version": "70fb2a10863495e25002c5c54f3721b8d636a6dfc2f6c82c4bc499332967d73d"
}

Generated in

10.2 seconds

Tweak it ShareReport

2024-12-10 09:17:36.441 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-10 09:17:36.441 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  89%|████████▉ | 271/304 [00:00<00:00, 2693.66it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2616.16it/s]
2024-12-10 09:17:36.558 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s
free=28270977695744
Downloading weights
2024-12-10T09:17:36Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpvs0yavwh/weights url=https://replicate.delivery/xezq/OdScz0wQ9hrfekLsv2besBKtIwYhq73qUYmRZWTrYxrFgtynA/trained_model.tar
2024-12-10T09:17:40Z | INFO  | [ Complete ] dest=/tmp/tmpvs0yavwh/weights size="172 MB" total_elapsed=3.851s url=https://replicate.delivery/xezq/OdScz0wQ9hrfekLsv2besBKtIwYhq73qUYmRZWTrYxrFgtynA/trained_model.tar
Downloaded weights in 3.88s
2024-12-10 09:17:40.436 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/a72323dc2b503686
2024-12-10 09:17:40.509 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded
2024-12-10 09:17:40.509 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-10 09:17:40.509 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  89%|████████▉ | 271/304 [00:00<00:00, 2698.74it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2620.15it/s]
2024-12-10 09:17:40.626 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s
Using seed: 11604
0it [00:00, ?it/s]
1it [00:00,  8.36it/s]
2it [00:00,  5.87it/s]
3it [00:00,  5.36it/s]
4it [00:00,  5.15it/s]
5it [00:00,  5.04it/s]
6it [00:01,  4.97it/s]
7it [00:01,  4.93it/s]
8it [00:01,  4.91it/s]
9it [00:01,  4.90it/s]
10it [00:01,  4.88it/s]
11it [00:02,  4.87it/s]
12it [00:02,  4.86it/s]
13it [00:02,  4.85it/s]
14it [00:02,  4.85it/s]
15it [00:03,  4.85it/s]
16it [00:03,  4.85it/s]
17it [00:03,  4.85it/s]
18it [00:03,  4.85it/s]
19it [00:03,  4.85it/s]
20it [00:04,  4.84it/s]
21it [00:04,  4.84it/s]
22it [00:04,  4.85it/s]
23it [00:04,  4.85it/s]
24it [00:04,  4.85it/s]
25it [00:05,  4.85it/s]
26it [00:05,  4.85it/s]
27it [00:05,  4.84it/s]
28it [00:05,  4.85it/s]
28it [00:05,  4.92it/s]
Total safe images: 1 out of 1

Want to make some of these yourself?

Run this model