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workroomprds /jamesbooth1:d04969e3
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";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd",
{
input: {
width: 512,
height: 512,
prompt: "a compelling HDR portrait of a workroomprds person in 1950 film noir",
scheduler: "DDIM",
num_outputs: 1,
guidance_scale: 7.5,
prompt_strength: 0.8,
num_inference_steps: 50,
disable_safety_check: false
}
}
);
// 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd",
input={
"width": 512,
"height": 512,
"prompt": "a compelling HDR portrait of a workroomprds person in 1950 film noir",
"scheduler": "DDIM",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.8,
"num_inference_steps": 50,
"disable_safety_check": False
}
)
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 workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd",
"input": {
"width": 512,
"height": 512,
"prompt": "a compelling HDR portrait of a workroomprds person in 1950 film noir",
"scheduler": "DDIM",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.8,
"num_inference_steps": 50,
"disable_safety_check": false
}
}' \
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.
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Output
{
"completed_at": "2023-02-22T20:24:29.715346Z",
"created_at": "2023-02-22T20:24:19.225174Z",
"data_removed": false,
"error": null,
"id": "4kkvyaj2svf6riim2duotuyhbm",
"input": {
"width": 512,
"height": 512,
"prompt": "a compelling HDR portrait of a workroomprds person in 1950 film noir",
"scheduler": "DDIM",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.8,
"num_inference_steps": 50
},
"logs": "Using seed: 5284\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.94it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.61it/s]\n 6%|▌ | 3/50 [00:00<00:09, 4.97it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.15it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.25it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.29it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 5.30it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.37it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.42it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.45it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.46it/s]\n 24%|██▍ | 12/50 [00:02<00:06, 5.43it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.45it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.47it/s]\n 30%|███ | 15/50 [00:02<00:06, 5.49it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 5.50it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 5.48it/s]\n 36%|███▌ | 18/50 [00:03<00:05, 5.46it/s]\n 38%|███▊ | 19/50 [00:03<00:05, 5.46it/s]\n 40%|████ | 20/50 [00:03<00:05, 5.48it/s]\n 42%|████▏ | 21/50 [00:03<00:05, 5.49it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 5.49it/s]\n 46%|████▌ | 23/50 [00:04<00:04, 5.48it/s]\n 48%|████▊ | 24/50 [00:04<00:04, 5.47it/s]\n 50%|█████ | 25/50 [00:04<00:04, 5.47it/s]\n 52%|█████▏ | 26/50 [00:04<00:04, 5.48it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 5.49it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 5.47it/s]\n 58%|█████▊ | 29/50 [00:05<00:03, 5.47it/s]\n 60%|██████ | 30/50 [00:05<00:03, 5.47it/s]\n 62%|██████▏ | 31/50 [00:05<00:03, 5.47it/s]\n 64%|██████▍ | 32/50 [00:05<00:03, 5.49it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 5.48it/s]\n 68%|██████▊ | 34/50 [00:06<00:02, 5.49it/s]\n 70%|███████ | 35/50 [00:06<00:02, 5.48it/s]\n 72%|███████▏ | 36/50 [00:06<00:02, 5.48it/s]\n 74%|███████▍ | 37/50 [00:06<00:02, 5.48it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 5.48it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 5.47it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.44it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.41it/s]\n 84%|████████▍ | 42/50 [00:07<00:01, 5.42it/s]\n 86%|████████▌ | 43/50 [00:07<00:01, 5.41it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 5.43it/s]\n 90%|█████████ | 45/50 [00:08<00:00, 5.44it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.44it/s]\n 94%|█████████▍| 47/50 [00:08<00:00, 5.45it/s]\n 96%|█████████▌| 48/50 [00:08<00:00, 5.46it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 5.46it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.46it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.42it/s]",
"metrics": {
"predict_time": 10.421526,
"total_time": 10.490172
},
"output": [
"https://replicate.delivery/pbxt/UqKnsX3CLEIhARjVtggjAs895ZD6xpwqGVAuf0TKjuievChQA/out-0.png"
],
"started_at": "2023-02-22T20:24:19.293820Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/4kkvyaj2svf6riim2duotuyhbm",
"cancel": "https://api.replicate.com/v1/predictions/4kkvyaj2svf6riim2duotuyhbm/cancel"
},
"version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd"
}
Using seed: 5284
using txt2img
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