Failed to load versions. Head to the versions page to see all versions for this model.
You're looking at a specific version of this model. Jump to the model overview.
Input
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
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.
pip install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
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.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
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": "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
}
}' \
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"
}
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