typefile
ID
whgulqsfu5fojooawaoskdqj44
Status
Succeeded
Source
Web
Hardware
T4
Total duration
Created
by usamaehsan
Webhook
–
Input
- prompt
- underwater roomtypetext
- structure
- lineartdefaultlinearttypeenumvalue typestringoptionslineart
- num_samples
- 1default1typeenumvalue typestringoptions1, 4
- image_resolution
- 512default512typeenumvalue typestringoptions256, 512, 768
- ddim_steps
- 20default20typenumericvalue typeinteger
- strength
- 0.5default1typenumericvalue typenumber
- scale
- 5.76default9typenumeric-rangevalue typenumberminimum0.1maximum30
- preprocessor
- LineartdefaultLinearttypeenumvalue typestringoptionsLineart, Lineart_Coarse, None
- preprocessor_resolution
- 512default512typenumeric-rangevalue typeintegerminimum128maximum1024
- a_prompt
- Best quality, extremely detaileddefaultBest quality, extremely detailedtypetext
- n_prompt
- Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low qualitydefaultLongbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low qualitytypetext
{
"a_prompt": "Best quality, extremely detailed",
"ddim_steps": 20,
"image": "https://replicate.delivery/pbxt/IrAKyJh7d56Kgn0VDusM3BT5ZNOpWjKXwIHl8PB1CGD6gTnd/65b60fbebae5db9ea4b586d9b2d155ac.jpg",
"image_resolution": "512",
"n_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"num_samples": "1",
"preprocessor": "Lineart",
"preprocessor_resolution": 512,
"prompt": "underwater room",
"scale": 5.76,
"strength": 0.5,
"structure": "lineart"
}Install Replicate’s Node.js client library:
npm install replicate
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_WUC**********************************
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 usamaehsan/controlnet-1.1-x-realistic-vision-v2.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"usamaehsan/controlnet-1.1-x-realistic-vision-v2.0:7fbf4c86671738f97896c9cb4922705adfcdcf54a6edab193bb8c176c6b34a69",
{
input: {
a_prompt: "Best quality, extremely detailed",
ddim_steps: 20,
image: "https://replicate.delivery/pbxt/IrAKyJh7d56Kgn0VDusM3BT5ZNOpWjKXwIHl8PB1CGD6gTnd/65b60fbebae5db9ea4b586d9b2d155ac.jpg",
image_resolution: "512",
n_prompt: "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
num_samples: "1",
preprocessor: "Lineart",
preprocessor_resolution: 512,
prompt: "underwater room",
scale: 5.76,
strength: 0.5,
structure: "lineart"
}
}
);
// 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.
Install Replicate’s Python client library:
pip install replicate
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_WUC**********************************
This is your API token. Keep it to yourself.
Import the client:
import replicate
Run usamaehsan/controlnet-1.1-x-realistic-vision-v2.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"usamaehsan/controlnet-1.1-x-realistic-vision-v2.0:7fbf4c86671738f97896c9cb4922705adfcdcf54a6edab193bb8c176c6b34a69",
input={
"a_prompt": "Best quality, extremely detailed",
"ddim_steps": 20,
"image": "https://replicate.delivery/pbxt/IrAKyJh7d56Kgn0VDusM3BT5ZNOpWjKXwIHl8PB1CGD6gTnd/65b60fbebae5db9ea4b586d9b2d155ac.jpg",
"image_resolution": "512",
"n_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"num_samples": "1",
"preprocessor": "Lineart",
"preprocessor_resolution": 512,
"prompt": "underwater room",
"scale": 5.76,
"strength": 0.5,
"structure": "lineart"
}
)
# To access the file URL:
print(output[0].url())
#=> "http://example.com"
# To write the file to disk:
with open("my-image.png", "wb") as file:
file.write(output[0].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_WUC**********************************
This is your API token. Keep it to yourself.
Run usamaehsan/controlnet-1.1-x-realistic-vision-v2.0 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": "usamaehsan/controlnet-1.1-x-realistic-vision-v2.0:7fbf4c86671738f97896c9cb4922705adfcdcf54a6edab193bb8c176c6b34a69",
"input": {
"a_prompt": "Best quality, extremely detailed",
"ddim_steps": 20,
"image": "https://replicate.delivery/pbxt/IrAKyJh7d56Kgn0VDusM3BT5ZNOpWjKXwIHl8PB1CGD6gTnd/65b60fbebae5db9ea4b586d9b2d155ac.jpg",
"image_resolution": "512",
"n_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"num_samples": "1",
"preprocessor": "Lineart",
"preprocessor_resolution": 512,
"prompt": "underwater room",
"scale": 5.76,
"strength": 0.5,
"structure": "lineart"
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{
"id": "whgulqsfu5fojooawaoskdqj44",
"model": "usamaehsan/controlnet-1.1-x-realistic-vision-v2.0",
"version": "7fbf4c86671738f97896c9cb4922705adfcdcf54a6edab193bb8c176c6b34a69",
"input": {
"a_prompt": "Best quality, extremely detailed",
"ddim_steps": 20,
"image": "https://replicate.delivery/pbxt/IrAKyJh7d56Kgn0VDusM3BT5ZNOpWjKXwIHl8PB1CGD6gTnd/65b60fbebae5db9ea4b586d9b2d155ac.jpg",
"image_resolution": "512",
"n_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"num_samples": "1",
"preprocessor": "Lineart",
"preprocessor_resolution": 512,
"prompt": "underwater room",
"scale": 5.76,
"strength": 0.5,
"structure": "lineart"
},
"logs": "Global seed set to 731025\nData shape for DDIM sampling is (1, 4, 64, 80), eta 0.0\nRunning DDIM Sampling with 20 timesteps\nDDIM Sampler: 0%| | 0/20 [00:00<?, ?it/s]\nDDIM Sampler: 5%|▌ | 1/20 [00:01<00:20, 1.06s/it]\nDDIM Sampler: 10%|█ | 2/20 [00:02<00:18, 1.02s/it]\nDDIM Sampler: 15%|█▌ | 3/20 [00:03<00:17, 1.01s/it]\nDDIM Sampler: 20%|██ | 4/20 [00:04<00:16, 1.01s/it]\nDDIM Sampler: 25%|██▌ | 5/20 [00:05<00:15, 1.01s/it]\nDDIM Sampler: 30%|███ | 6/20 [00:06<00:14, 1.00s/it]\nDDIM Sampler: 35%|███▌ | 7/20 [00:07<00:13, 1.01s/it]\nDDIM Sampler: 40%|████ | 8/20 [00:08<00:12, 1.01s/it]\nDDIM Sampler: 45%|████▌ | 9/20 [00:09<00:11, 1.01s/it]\nDDIM Sampler: 50%|█████ | 10/20 [00:10<00:10, 1.01s/it]\nDDIM Sampler: 55%|█████▌ | 11/20 [00:11<00:09, 1.01s/it]\nDDIM Sampler: 60%|██████ | 12/20 [00:12<00:08, 1.01s/it]\nDDIM Sampler: 65%|██████▌ | 13/20 [00:13<00:07, 1.02s/it]\nDDIM Sampler: 70%|███████ | 14/20 [00:14<00:06, 1.02s/it]\nDDIM Sampler: 75%|███████▌ | 15/20 [00:15<00:05, 1.02s/it]\nDDIM Sampler: 80%|████████ | 16/20 [00:16<00:04, 1.01s/it]\nDDIM Sampler: 85%|████████▌ | 17/20 [00:17<00:03, 1.02s/it]\nDDIM Sampler: 90%|█████████ | 18/20 [00:18<00:02, 1.02s/it]\nDDIM Sampler: 95%|█████████▌| 19/20 [00:19<00:01, 1.02s/it]\nDDIM Sampler: 100%|██████████| 20/20 [00:20<00:00, 1.02s/it]\nDDIM Sampler: 100%|██████████| 20/20 [00:20<00:00, 1.02s/it]",
"output": [
"https://replicate.delivery/pbxt/LeOVrRM0rm24BC7LZocOOYsF70YL8Hc7vW1HCtYKAw0stzeQA/output_0.png",
"https://replicate.delivery/pbxt/LMr0UAn55UI6MRdgdwRqxehpV7f3XQsIe66EwDN6jHS12O7hA/output_1.png"
],
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2023-05-20T12:52:28.830209Z",
"started_at": "2023-05-20T13:01:46.062157Z",
"completed_at": "2023-05-20T13:02:18.969247Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/whgulqsfu5fojooawaoskdqj44/cancel",
"get": "https://api.replicate.com/v1/predictions/whgulqsfu5fojooawaoskdqj44"
},
"metrics": {
"predict_time": 32.90709,
"total_time": 590.139038
}
}Generated in
Global seed set to 731025
Data shape for DDIM sampling is (1, 4, 64, 80), eta 0.0
Running DDIM Sampling with 20 timesteps
DDIM Sampler: 0%| | 0/20 [00:00<?, ?it/s]
DDIM Sampler: 5%|▌ | 1/20 [00:01<00:20, 1.06s/it]
DDIM Sampler: 10%|█ | 2/20 [00:02<00:18, 1.02s/it]
DDIM Sampler: 15%|█▌ | 3/20 [00:03<00:17, 1.01s/it]
DDIM Sampler: 20%|██ | 4/20 [00:04<00:16, 1.01s/it]
DDIM Sampler: 25%|██▌ | 5/20 [00:05<00:15, 1.01s/it]
DDIM Sampler: 30%|███ | 6/20 [00:06<00:14, 1.00s/it]
DDIM Sampler: 35%|███▌ | 7/20 [00:07<00:13, 1.01s/it]
DDIM Sampler: 40%|████ | 8/20 [00:08<00:12, 1.01s/it]
DDIM Sampler: 45%|████▌ | 9/20 [00:09<00:11, 1.01s/it]
DDIM Sampler: 50%|█████ | 10/20 [00:10<00:10, 1.01s/it]
DDIM Sampler: 55%|█████▌ | 11/20 [00:11<00:09, 1.01s/it]
DDIM Sampler: 60%|██████ | 12/20 [00:12<00:08, 1.01s/it]
DDIM Sampler: 65%|██████▌ | 13/20 [00:13<00:07, 1.02s/it]
DDIM Sampler: 70%|███████ | 14/20 [00:14<00:06, 1.02s/it]
DDIM Sampler: 75%|███████▌ | 15/20 [00:15<00:05, 1.02s/it]
DDIM Sampler: 80%|████████ | 16/20 [00:16<00:04, 1.01s/it]
DDIM Sampler: 85%|████████▌ | 17/20 [00:17<00:03, 1.02s/it]
DDIM Sampler: 90%|█████████ | 18/20 [00:18<00:02, 1.02s/it]
DDIM Sampler: 95%|█████████▌| 19/20 [00:19<00:01, 1.02s/it]
DDIM Sampler: 100%|██████████| 20/20 [00:20<00:00, 1.02s/it]
DDIM Sampler: 100%|██████████| 20/20 [00:20<00:00, 1.02s/it]