defaultA living room
typetext
ID
5qvbz5tgmsrgm0cgzen8gx0sag
Status
Succeeded
Source
Web
Hardware
A40 (Large)
Total duration
Created
by orbin-ahmed
Webhook
–
Input
- prompt
- A kids room
- upscale
- truedefaulttruetypeboolean
- num_inference_steps
- 20default20typenumericvalue typeinteger
- guidance_scale
- 7.5default7.5typenumericvalue typenumber
- seed
- 0typenumericvalue typeinteger
{
"guidance_scale": 7.5,
"num_inference_steps": 20,
"prompt": "A kids room",
"seed": 0,
"upscale": true
}Install Replicate’s Node.js client library:
npm install replicate
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_eQ7**********************************
This is your API token. Keep it to yourself.
Import and set up the client:
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"orbin-ahmed/360_pano:bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36",
{
input: {
guidance_scale: 7.5,
num_inference_steps: 20,
prompt: "A kids room",
seed: 0,
upscale: true
}
}
);
// To access the file URL:
console.log(output.url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicate
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_eQ7**********************************
This is your API token. Keep it to yourself.
Import the client:
import replicate
Run orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"orbin-ahmed/360_pano:bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36",
input={
"guidance_scale": 7.5,
"num_inference_steps": 20,
"prompt": "A kids room",
"seed": 0,
"upscale": True
}
)
# To access the file URL:
print(output.url())
#=> "http://example.com"
# To write the file to disk:
with open("my-image.png", "wb") as file:
file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_eQ7**********************************
This is your API token. Keep it to yourself.
Run orbin-ahmed/360_pano 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": "orbin-ahmed/360_pano:bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36",
"input": {
"guidance_scale": 7.5,
"num_inference_steps": 20,
"prompt": "A kids room",
"seed": 0,
"upscale": true
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{
"id": "5qvbz5tgmsrgm0cgzen8gx0sag",
"model": "orbin-ahmed/360_pano",
"version": "bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36",
"input": {
"guidance_scale": 7.5,
"num_inference_steps": 20,
"prompt": "A kids room",
"seed": 0,
"upscale": true
},
"logs": "Using seed: 15384\nTest with prompt: A kids room\nglobal seed: 15384\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:14, 1.33it/s]\n 15%|█▌ | 3/20 [00:00<00:04, 3.96it/s]\n 25%|██▌ | 5/20 [00:01<00:02, 6.16it/s]\n 35%|███▌ | 7/20 [00:01<00:01, 7.93it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 9.29it/s]\n 55%|█████▌ | 11/20 [00:01<00:00, 10.32it/s]\n 65%|██████▌ | 13/20 [00:01<00:00, 11.08it/s]\n 75%|███████▌ | 15/20 [00:01<00:00, 11.64it/s]\n 85%|████████▌ | 17/20 [00:01<00:00, 12.03it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 12.30it/s]\n100%|██████████| 20/20 [00:02<00:00, 9.02it/s]\ninputs: upscale=True, running upscaler.\nrunning upscaler step1. Initial super-resolution\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:02<00:11, 2.98s/it]\n 40%|████ | 2/5 [00:05<00:08, 2.72s/it]\n 60%|██████ | 3/5 [00:08<00:05, 2.64s/it]\n 80%|████████ | 4/5 [00:10<00:02, 2.59s/it]\n100%|██████████| 5/5 [00:13<00:00, 2.57s/it]\n100%|██████████| 5/5 [00:13<00:00, 2.62s/it]\nrunning upscaler step2. Super-resolution with Real-ESRGAN\nTile 1/45\nTile 2/45\nTile 3/45\nTile 4/45\nTile 5/45\nTile 6/45\nTile 7/45\nTile 8/45\nTile 9/45\nTile 10/45\nTile 11/45\nTile 12/45\nTile 13/45\nTile 14/45\nTile 15/45\nTile 16/45\nTile 17/45\nTile 18/45\nTile 19/45\nTile 20/45\nTile 21/45\nTile 22/45\nTile 23/45\nTile 24/45\nTile 25/45\nTile 26/45\nTile 27/45\nTile 28/45\nTile 29/45\nTile 30/45\nTile 31/45\nTile 32/45\nTile 33/45\nTile 34/45\nTile 35/45\nTile 36/45\nTile 37/45\nTile 38/45\nTile 39/45\nTile 40/45\nTile 41/45\nTile 42/45\nTile 43/45\nTile 44/45\nTile 45/45\ninputs: refinement=True, running refinement. This is a bit time-consuming.\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:12<00:48, 12.08s/it]\n 40%|████ | 2/5 [00:23<00:35, 11.84s/it]\n 60%|██████ | 3/5 [00:35<00:23, 11.81s/it]\n 80%|████████ | 4/5 [00:47<00:11, 11.78s/it]\n100%|██████████| 5/5 [00:59<00:00, 11.78s/it]\n100%|██████████| 5/5 [00:59<00:00, 11.81s/it]\nfinished",
"output": "https://replicate.delivery/pbxt/yf4PzoG74VySBiQ1ZxuC4YkDu53UepehXdjIsy9twBDwzQamA/result.png",
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2024-07-28T23:26:01.382Z",
"started_at": "2024-07-28T23:28:14.49815Z",
"completed_at": "2024-07-28T23:30:02.609626Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/5qvbz5tgmsrgm0cgzen8gx0sag/cancel",
"get": "https://api.replicate.com/v1/predictions/5qvbz5tgmsrgm0cgzen8gx0sag",
"web": "https://replicate.com/p/5qvbz5tgmsrgm0cgzen8gx0sag"
},
"metrics": {
"predict_time": 108.111475536,
"total_time": 241.227626
}
}Generated in
Using seed: 15384
Test with prompt: A kids room
global seed: 15384
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inputs: upscale=True, running upscaler.
running upscaler step1. Initial super-resolution
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running upscaler step2. Super-resolution with Real-ESRGAN
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inputs: refinement=True, running refinement. This is a bit time-consuming.
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40%|████ | 2/5 [00:23<00:35, 11.84s/it]
60%|██████ | 3/5 [00:35<00:23, 11.81s/it]
80%|████████ | 4/5 [00:47<00:11, 11.78s/it]
100%|██████████| 5/5 [00:59<00:00, 11.78s/it]
100%|██████████| 5/5 [00:59<00:00, 11.81s/it]
finished