You're looking at a specific version of this model. Jump to the model overview.
carlpuvosx /cerveza-presidente:48375433
Input
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variableexport 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 carlpuvosx/cerveza-presidente using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"carlpuvosx/cerveza-presidente:48375433dbf9da71de56813ad7fa5a82cb5a1b9132b4d63ac9e75e572d049237",
{
input: {
model: "dev",
prompt: "TOKcerveza-Presidente A photo of a green bottle of Presidente Light beer with a white label and red stripes. The bottle is covered in frost and appears to be cold. The beer is in a clear glass bottle, and the label is in Spanish. The bottle is positioned against a white background. The image is in the style of a product advertisement.",
go_fast: false,
lora_scale: 1,
megapixels: "1",
num_outputs: 4,
aspect_ratio: "2:3",
output_format: "png",
guidance_scale: 3.5,
output_quality: 90,
prompt_strength: 0.8,
extra_lora_scale: 1,
num_inference_steps: 28
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run carlpuvosx/cerveza-presidente using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"carlpuvosx/cerveza-presidente:48375433dbf9da71de56813ad7fa5a82cb5a1b9132b4d63ac9e75e572d049237",
input={
"model": "dev",
"prompt": "TOKcerveza-Presidente A photo of a green bottle of Presidente Light beer with a white label and red stripes. The bottle is covered in frost and appears to be cold. The beer is in a clear glass bottle, and the label is in Spanish. The bottle is positioned against a white background. The image is in the style of a product advertisement.",
"go_fast": False,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 4,
"aspect_ratio": "2:3",
"output_format": "png",
"guidance_scale": 3.5,
"output_quality": 90,
"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 variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run carlpuvosx/cerveza-presidente 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": "48375433dbf9da71de56813ad7fa5a82cb5a1b9132b4d63ac9e75e572d049237",
"input": {
"model": "dev",
"prompt": "TOKcerveza-Presidente A photo of a green bottle of Presidente Light beer with a white label and red stripes. The bottle is covered in frost and appears to be cold. The beer is in a clear glass bottle, and the label is in Spanish. The bottle is positioned against a white background. The image is in the style of a product advertisement.",
"go_fast": false,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 4,
"aspect_ratio": "2:3",
"output_format": "png",
"guidance_scale": 3.5,
"output_quality": 90,
"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.
Add a payment method to run this model.
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2024-09-07T06:45:30.946605Z",
"created_at": "2024-09-07T06:44:51.177000Z",
"data_removed": false,
"error": null,
"id": "jvbx53bkd5rm00chscx8v7tvtw",
"input": {
"model": "dev",
"prompt": "TOKcerveza-Presidente A photo of a green bottle of Presidente Light beer with a white label and red stripes. The bottle is covered in frost and appears to be cold. The beer is in a clear glass bottle, and the label is in Spanish. The bottle is positioned against a white background. The image is in the style of a product advertisement.",
"lora_scale": 1,
"num_outputs": 4,
"aspect_ratio": "2:3",
"output_format": "png",
"guidance_scale": 3.5,
"output_quality": 90,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 28
},
"logs": "Using seed: 9697\nPrompt: TOKcerveza-Presidente A photo of a green bottle of Presidente Light beer with a white label and red stripes. The bottle is covered in frost and appears to be cold. The beer is in a clear glass bottle, and the label is in Spanish. The bottle is positioned against a white background. The image is in the style of a product advertisement.\n[!] txt2img mode\nUsing dev model\nfree=8344466460672\nDownloading weights\n2024-09-07T06:44:51Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp_x77qwpw/weights url=https://replicate.delivery/yhqm/UoEDg80Zeeg2uUXMVxoJZMT9C5x0AtfjZNIk2gmndav32u0mA/trained_model.tar\n2024-09-07T06:44:53Z | INFO | [ Complete ] dest=/tmp/tmp_x77qwpw/weights size=\"172 MB\" total_elapsed=1.859s url=https://replicate.delivery/yhqm/UoEDg80Zeeg2uUXMVxoJZMT9C5x0AtfjZNIk2gmndav32u0mA/trained_model.tar\nDownloaded weights in 1.89s\nLoaded LoRAs in 9.15s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:01<00:27, 1.02s/it]\n 7%|▋ | 2/28 [00:01<00:23, 1.10it/s]\n 11%|█ | 3/28 [00:02<00:24, 1.04it/s]\n 14%|█▍ | 4/28 [00:03<00:23, 1.01it/s]\n 18%|█▊ | 5/28 [00:04<00:23, 1.00s/it]\n 21%|██▏ | 6/28 [00:05<00:22, 1.01s/it]\n 25%|██▌ | 7/28 [00:06<00:21, 1.01s/it]\n 29%|██▊ | 8/28 [00:08<00:20, 1.02s/it]\n 32%|███▏ | 9/28 [00:09<00:19, 1.02s/it]\n 36%|███▌ | 10/28 [00:10<00:18, 1.02s/it]\n 39%|███▉ | 11/28 [00:11<00:17, 1.02s/it]\n 43%|████▎ | 12/28 [00:12<00:16, 1.02s/it]\n 46%|████▋ | 13/28 [00:13<00:15, 1.03s/it]\n 50%|█████ | 14/28 [00:14<00:14, 1.03s/it]\n 54%|█████▎ | 15/28 [00:15<00:13, 1.03s/it]\n 57%|█████▋ | 16/28 [00:16<00:12, 1.03s/it]\n 61%|██████ | 17/28 [00:17<00:11, 1.03s/it]\n 64%|██████▍ | 18/28 [00:18<00:10, 1.03s/it]\n 68%|██████▊ | 19/28 [00:19<00:09, 1.03s/it]\n 71%|███████▏ | 20/28 [00:20<00:08, 1.03s/it]\n 75%|███████▌ | 21/28 [00:21<00:07, 1.03s/it]\n 79%|███████▊ | 22/28 [00:22<00:06, 1.03s/it]\n 82%|████████▏ | 23/28 [00:23<00:05, 1.03s/it]\n 86%|████████▌ | 24/28 [00:24<00:04, 1.03s/it]\n 89%|████████▉ | 25/28 [00:25<00:03, 1.03s/it]\n 93%|█████████▎| 26/28 [00:26<00:02, 1.03s/it]\n 96%|█████████▋| 27/28 [00:27<00:01, 1.03s/it]\n100%|██████████| 28/28 [00:28<00:00, 1.03s/it]\n100%|██████████| 28/28 [00:28<00:00, 1.02s/it]",
"metrics": {
"predict_time": 39.762537465,
"total_time": 39.769605
},
"output": [
"https://replicate.delivery/yhqm/a8bfhNDTYJzexEelUTGIkTc78214qFyuN7nk4oRfl2uqIqpNB/out-0.png",
"https://replicate.delivery/yhqm/9T42l0JVC8JEBda5aOWjLs2jeKu5uRiGiW6sVfg6ciTKiaaTA/out-1.png",
"https://replicate.delivery/yhqm/UBEz6nYFCzoiFJXf88qWqHEnXdJoTOpAeNCbuXJ09HSKiaaTA/out-2.png",
"https://replicate.delivery/yhqm/Vudho7plxwYzItxJ4qbfQQgzhSrJP1hmvhceBuPBaz4KiaaTA/out-3.png"
],
"started_at": "2024-09-07T06:44:51.184067Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/jvbx53bkd5rm00chscx8v7tvtw",
"cancel": "https://api.replicate.com/v1/predictions/jvbx53bkd5rm00chscx8v7tvtw/cancel"
},
"version": "48375433dbf9da71de56813ad7fa5a82cb5a1b9132b4d63ac9e75e572d049237"
}
Using seed: 9697
Prompt: TOKcerveza-Presidente A photo of a green bottle of Presidente Light beer with a white label and red stripes. The bottle is covered in frost and appears to be cold. The beer is in a clear glass bottle, and the label is in Spanish. The bottle is positioned against a white background. The image is in the style of a product advertisement.
[!] txt2img mode
Using dev model
free=8344466460672
Downloading weights
2024-09-07T06:44:51Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp_x77qwpw/weights url=https://replicate.delivery/yhqm/UoEDg80Zeeg2uUXMVxoJZMT9C5x0AtfjZNIk2gmndav32u0mA/trained_model.tar
2024-09-07T06:44:53Z | INFO | [ Complete ] dest=/tmp/tmp_x77qwpw/weights size="172 MB" total_elapsed=1.859s url=https://replicate.delivery/yhqm/UoEDg80Zeeg2uUXMVxoJZMT9C5x0AtfjZNIk2gmndav32u0mA/trained_model.tar
Downloaded weights in 1.89s
Loaded LoRAs in 9.15s
0%| | 0/28 [00:00<?, ?it/s]
4%|▎ | 1/28 [00:01<00:27, 1.02s/it]
7%|▋ | 2/28 [00:01<00:23, 1.10it/s]
11%|█ | 3/28 [00:02<00:24, 1.04it/s]
14%|█▍ | 4/28 [00:03<00:23, 1.01it/s]
18%|█▊ | 5/28 [00:04<00:23, 1.00s/it]
21%|██▏ | 6/28 [00:05<00:22, 1.01s/it]
25%|██▌ | 7/28 [00:06<00:21, 1.01s/it]
29%|██▊ | 8/28 [00:08<00:20, 1.02s/it]
32%|███▏ | 9/28 [00:09<00:19, 1.02s/it]
36%|███▌ | 10/28 [00:10<00:18, 1.02s/it]
39%|███▉ | 11/28 [00:11<00:17, 1.02s/it]
43%|████▎ | 12/28 [00:12<00:16, 1.02s/it]
46%|████▋ | 13/28 [00:13<00:15, 1.03s/it]
50%|█████ | 14/28 [00:14<00:14, 1.03s/it]
54%|█████▎ | 15/28 [00:15<00:13, 1.03s/it]
57%|█████▋ | 16/28 [00:16<00:12, 1.03s/it]
61%|██████ | 17/28 [00:17<00:11, 1.03s/it]
64%|██████▍ | 18/28 [00:18<00:10, 1.03s/it]
68%|██████▊ | 19/28 [00:19<00:09, 1.03s/it]
71%|███████▏ | 20/28 [00:20<00:08, 1.03s/it]
75%|███████▌ | 21/28 [00:21<00:07, 1.03s/it]
79%|███████▊ | 22/28 [00:22<00:06, 1.03s/it]
82%|████████▏ | 23/28 [00:23<00:05, 1.03s/it]
86%|████████▌ | 24/28 [00:24<00:04, 1.03s/it]
89%|████████▉ | 25/28 [00:25<00:03, 1.03s/it]
93%|█████████▎| 26/28 [00:26<00:02, 1.03s/it]
96%|█████████▋| 27/28 [00:27<00:01, 1.03s/it]
100%|██████████| 28/28 [00:28<00:00, 1.03s/it]
100%|██████████| 28/28 [00:28<00:00, 1.02s/it]