Readme
Inferências do Dr Pedros para imagens de divulgação.
Dr. Pedro Jr
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";
import fs from "node:fs";
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.
{
"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
This model runs on Nvidia H100 GPU hardware. We don't yet have enough runs of this model to provide performance information.
Inferências do Dr Pedros para imagens de divulgação.
This model is warm. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
This model runs on H100 hardware which costs $0.001525 per second. View more.
Choose a file from your machine
Hint: you can also drag files onto the input
Choose a file from your machine
Hint: you can also drag files onto the input
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