Readme
Transform your image or QR code like never before. this model from Hugging Face https://huggingface.co/diffusers/controlnet-canny-sdxl-1.0
Transform your image or QR code like never before
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";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run qr2ai/img2paint_controlnet using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"qr2ai/img2paint_controlnet:592691cf624bb863fe5a01673badff425607ba56dbc499a74bbfdacd3ec0da55",
{
input: {
seed: 130264517,
image: "https://replicate.delivery/pbxt/Jn9H4XwOEn7reK9HAMsUa1dNkdf7C6oQKFpIb4Q7wD3ldHVv/Al-HamduLillah.jpg",
prompt: "Large Original Yellow Sunflower Landscape Oil Painting abstract arts",
condition_scale: 0.65,
negative_prompt: "low quality, bad quality, sketches, nsfw",
num_inference_steps: 200
}
}
);
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 variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run qr2ai/img2paint_controlnet using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"qr2ai/img2paint_controlnet:592691cf624bb863fe5a01673badff425607ba56dbc499a74bbfdacd3ec0da55",
input={
"seed": 130264517,
"image": "https://replicate.delivery/pbxt/Jn9H4XwOEn7reK9HAMsUa1dNkdf7C6oQKFpIb4Q7wD3ldHVv/Al-HamduLillah.jpg",
"prompt": "Large Original Yellow Sunflower Landscape Oil Painting abstract arts",
"condition_scale": 0.65,
"negative_prompt": "low quality, bad quality, sketches, nsfw",
"num_inference_steps": 200
}
)
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 qr2ai/img2paint_controlnet using Replicateβs 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": "592691cf624bb863fe5a01673badff425607ba56dbc499a74bbfdacd3ec0da55",
"input": {
"seed": 130264517,
"image": "https://replicate.delivery/pbxt/Jn9H4XwOEn7reK9HAMsUa1dNkdf7C6oQKFpIb4Q7wD3ldHVv/Al-HamduLillah.jpg",
"prompt": "Large Original Yellow Sunflower Landscape Oil Painting abstract arts",
"condition_scale": 0.65,
"negative_prompt": "low quality, bad quality, sketches, nsfw",
"num_inference_steps": 200
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicateβs HTTP API reference docs.
brew install cog
If you donβt have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/qr2ai/img2paint_controlnet@sha256:592691cf624bb863fe5a01673badff425607ba56dbc499a74bbfdacd3ec0da55 \
-i 'seed=130264517' \
-i 'image="https://replicate.delivery/pbxt/Jn9H4XwOEn7reK9HAMsUa1dNkdf7C6oQKFpIb4Q7wD3ldHVv/Al-HamduLillah.jpg"' \
-i 'prompt="Large Original Yellow Sunflower Landscape Oil Painting abstract arts"' \
-i 'condition_scale=0.65' \
-i 'negative_prompt="low quality, bad quality, sketches, nsfw"' \
-i 'num_inference_steps=200'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/qr2ai/img2paint_controlnet@sha256:592691cf624bb863fe5a01673badff425607ba56dbc499a74bbfdacd3ec0da55
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 130264517, "image": "https://replicate.delivery/pbxt/Jn9H4XwOEn7reK9HAMsUa1dNkdf7C6oQKFpIb4Q7wD3ldHVv/Al-HamduLillah.jpg", "prompt": "Large Original Yellow Sunflower Landscape Oil Painting abstract arts", "condition_scale": 0.65, "negative_prompt": "low quality, bad quality, sketches, nsfw", "num_inference_steps": 200 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Each run costs approximately $0.13. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-10-30T22:43:44.980094Z",
"created_at": "2023-10-30T22:41:45.118141Z",
"data_removed": false,
"error": null,
"id": "zmt5i7lbn3lle4dk4lfcvm5huy",
"input": {
"seed": 130264517,
"image": "https://replicate.delivery/pbxt/Jn9H4XwOEn7reK9HAMsUa1dNkdf7C6oQKFpIb4Q7wD3ldHVv/Al-HamduLillah.jpg",
"prompt": "Large Original Yellow Sunflower Landscape Oil Painting abstract arts",
"qr_data": null,
"condition_scale": 0.65,
"negative_prompt": "low quality, bad quality, sketches, nsfw",
"num_inference_steps": 200
},
"logs": "Using seed: 130264517\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<02:34, 1.28it/s]\n 1%| | 2/200 [00:01<01:37, 2.03it/s]\n 2%|β | 3/200 [00:01<01:19, 2.49it/s]\n 2%|β | 4/200 [00:01<01:10, 2.77it/s]\n 2%|β | 5/200 [00:01<01:05, 2.96it/s]\n 3%|β | 6/200 [00:02<01:02, 3.09it/s]\n 4%|β | 7/200 [00:02<01:00, 3.18it/s]\n 4%|β | 8/200 [00:02<00:59, 3.24it/s]\n 4%|β | 9/200 [00:03<00:58, 3.28it/s]\n 5%|β | 10/200 [00:03<00:57, 3.31it/s]\n 6%|β | 11/200 [00:03<00:56, 3.33it/s]\n 6%|β | 12/200 [00:04<00:56, 3.34it/s]\n 6%|β | 13/200 [00:04<00:55, 3.35it/s]\n 7%|β | 14/200 [00:04<00:55, 3.35it/s]\n 8%|β | 15/200 [00:04<00:55, 3.36it/s]\n 8%|β | 16/200 [00:05<00:54, 3.36it/s]\n 8%|β | 17/200 [00:05<00:54, 3.36it/s]\n 9%|β | 18/200 [00:05<00:54, 3.36it/s]\n 10%|β | 19/200 [00:06<00:53, 3.36it/s]\n 10%|β | 20/200 [00:06<00:53, 3.36it/s]\n 10%|β | 21/200 [00:06<00:53, 3.36it/s]\n 11%|β | 22/200 [00:07<00:53, 3.36it/s]\n 12%|ββ | 23/200 [00:07<00:52, 3.36it/s]\n 12%|ββ | 24/200 [00:07<00:52, 3.36it/s]\n 12%|ββ | 25/200 [00:07<00:52, 3.36it/s]\n 13%|ββ | 26/200 [00:08<00:51, 3.36it/s]\n 14%|ββ | 27/200 [00:08<00:51, 3.36it/s]\n 14%|ββ | 28/200 [00:08<00:51, 3.36it/s]\n 14%|ββ | 29/200 [00:09<00:50, 3.36it/s]\n 15%|ββ | 30/200 [00:09<00:50, 3.36it/s]\n 16%|ββ | 31/200 [00:09<00:50, 3.35it/s]\n 16%|ββ | 32/200 [00:09<00:50, 3.35it/s]\n 16%|ββ | 33/200 [00:10<00:49, 3.35it/s]\n 17%|ββ | 34/200 [00:10<00:49, 3.35it/s]\n 18%|ββ | 35/200 [00:10<00:49, 3.35it/s]\n 18%|ββ | 36/200 [00:11<00:49, 3.35it/s]\n 18%|ββ | 37/200 [00:11<00:48, 3.35it/s]\n 19%|ββ | 38/200 [00:11<00:48, 3.35it/s]\n 20%|ββ | 39/200 [00:12<00:48, 3.35it/s]\n 20%|ββ | 40/200 [00:12<00:47, 3.34it/s]\n 20%|ββ | 41/200 [00:12<00:47, 3.35it/s]\n 21%|ββ | 42/200 [00:12<00:47, 3.35it/s]\n 22%|βββ | 43/200 [00:13<00:46, 3.35it/s]\n 22%|βββ | 44/200 [00:13<00:46, 3.35it/s]\n 22%|βββ | 45/200 [00:13<00:46, 3.35it/s]\n 23%|βββ | 46/200 [00:14<00:45, 3.35it/s]\n 24%|βββ | 47/200 [00:14<00:45, 3.35it/s]\n 24%|βββ | 48/200 [00:14<00:45, 3.36it/s]\n 24%|βββ | 49/200 [00:15<00:44, 3.36it/s]\n 25%|βββ | 50/200 [00:15<00:44, 3.36it/s]\n 26%|βββ | 51/200 [00:15<00:44, 3.37it/s]\n 26%|βββ | 52/200 [00:15<00:43, 3.37it/s]\n 26%|βββ | 53/200 [00:16<00:43, 3.37it/s]\n 27%|βββ | 54/200 [00:16<00:43, 3.37it/s]\n 28%|βββ | 55/200 [00:16<00:43, 3.37it/s]\n 28%|βββ | 56/200 [00:17<00:42, 3.37it/s]\n 28%|βββ | 57/200 [00:17<00:42, 3.37it/s]\n 29%|βββ | 58/200 [00:17<00:42, 3.37it/s]\n 30%|βββ | 59/200 [00:18<00:41, 3.37it/s]\n 30%|βββ | 60/200 [00:18<00:41, 3.37it/s]\n 30%|βββ | 61/200 [00:18<00:41, 3.37it/s]\n 31%|βββ | 62/200 [00:18<00:40, 3.37it/s]\n 32%|ββββ | 63/200 [00:19<00:40, 3.37it/s]\n 32%|ββββ | 64/200 [00:19<00:40, 3.37it/s]\n 32%|ββββ | 65/200 [00:19<00:40, 3.37it/s]\n 33%|ββββ | 66/200 [00:20<00:39, 3.37it/s]\n 34%|ββββ | 67/200 [00:20<00:39, 3.36it/s]\n 34%|ββββ | 68/200 [00:20<00:39, 3.36it/s]\n 34%|ββββ | 69/200 [00:20<00:38, 3.37it/s]\n 35%|ββββ | 70/200 [00:21<00:38, 3.37it/s]\n 36%|ββββ | 71/200 [00:21<00:38, 3.37it/s]\n 36%|ββββ | 72/200 [00:21<00:38, 3.37it/s]\n 36%|ββββ | 73/200 [00:22<00:37, 3.37it/s]\n 37%|ββββ | 74/200 [00:22<00:37, 3.36it/s]\n 38%|ββββ | 75/200 [00:22<00:37, 3.36it/s]\n 38%|ββββ | 76/200 [00:23<00:36, 3.36it/s]\n 38%|ββββ | 77/200 [00:23<00:36, 3.36it/s]\n 39%|ββββ | 78/200 [00:23<00:36, 3.36it/s]\n 40%|ββββ | 79/200 [00:23<00:35, 3.36it/s]\n 40%|ββββ | 80/200 [00:24<00:35, 3.36it/s]\n 40%|ββββ | 81/200 [00:24<00:35, 3.36it/s]\n 41%|ββββ | 82/200 [00:24<00:35, 3.36it/s]\n 42%|βββββ | 83/200 [00:25<00:34, 3.36it/s]\n 42%|βββββ | 84/200 [00:25<00:34, 3.36it/s]\n 42%|βββββ | 85/200 [00:25<00:34, 3.36it/s]\n 43%|βββββ | 86/200 [00:26<00:34, 3.32it/s]\n 44%|βββββ | 87/200 [00:26<00:33, 3.33it/s]\n 44%|βββββ | 88/200 [00:26<00:33, 3.34it/s]\n 44%|βββββ | 89/200 [00:26<00:33, 3.34it/s]\n 45%|βββββ | 90/200 [00:27<00:32, 3.35it/s]\n 46%|βββββ | 91/200 [00:27<00:32, 3.35it/s]\n 46%|βββββ | 92/200 [00:27<00:32, 3.35it/s]\n 46%|βββββ | 93/200 [00:28<00:31, 3.35it/s]\n 47%|βββββ | 94/200 [00:28<00:31, 3.36it/s]\n 48%|βββββ | 95/200 [00:28<00:31, 3.36it/s]\n 48%|βββββ | 96/200 [00:29<00:30, 3.36it/s]\n 48%|βββββ | 97/200 [00:29<00:30, 3.35it/s]\n 49%|βββββ | 98/200 [00:29<00:30, 3.35it/s]\n 50%|βββββ | 99/200 [00:29<00:30, 3.35it/s]\n 50%|βββββ | 100/200 [00:30<00:29, 3.35it/s]\n 50%|βββββ | 101/200 [00:30<00:29, 3.35it/s]\n 51%|βββββ | 102/200 [00:30<00:29, 3.35it/s]\n 52%|ββββββ | 103/200 [00:31<00:28, 3.35it/s]\n 52%|ββββββ | 104/200 [00:31<00:28, 3.35it/s]\n 52%|ββββββ | 105/200 [00:31<00:28, 3.34it/s]\n 53%|ββββββ | 106/200 [00:32<00:28, 3.35it/s]\n 54%|ββββββ | 107/200 [00:32<00:27, 3.35it/s]\n 54%|ββββββ | 108/200 [00:32<00:27, 3.35it/s]\n 55%|ββββββ | 109/200 [00:32<00:27, 3.35it/s]\n 55%|ββββββ | 110/200 [00:33<00:26, 3.35it/s]\n 56%|ββββββ | 111/200 [00:33<00:26, 3.35it/s]\n 56%|ββββββ | 112/200 [00:33<00:26, 3.34it/s]\n 56%|ββββββ | 113/200 [00:34<00:26, 3.35it/s]\n 57%|ββββββ | 114/200 [00:34<00:25, 3.34it/s]\n 57%|ββββββ | 115/200 [00:34<00:25, 3.34it/s]\n 58%|ββββββ | 116/200 [00:35<00:25, 3.34it/s]\n 58%|ββββββ | 117/200 [00:35<00:24, 3.34it/s]\n 59%|ββββββ | 118/200 [00:35<00:24, 3.34it/s]\n 60%|ββββββ | 119/200 [00:35<00:24, 3.34it/s]\n 60%|ββββββ | 120/200 [00:36<00:23, 3.34it/s]\n 60%|ββββββ | 121/200 [00:36<00:23, 3.34it/s]\n 61%|ββββββ | 122/200 [00:36<00:23, 3.34it/s]\n 62%|βββββββ | 123/200 [00:37<00:23, 3.34it/s]\n 62%|βββββββ | 124/200 [00:37<00:22, 3.34it/s]\n 62%|βββββββ | 125/200 [00:37<00:22, 3.34it/s]\n 63%|βββββββ | 126/200 [00:38<00:22, 3.34it/s]\n 64%|βββββββ | 127/200 [00:38<00:21, 3.34it/s]\n 64%|βββββββ | 128/200 [00:38<00:21, 3.33it/s]\n 64%|βββββββ | 129/200 [00:38<00:21, 3.34it/s]\n 65%|βββββββ | 130/200 [00:39<00:20, 3.34it/s]\n 66%|βββββββ | 131/200 [00:39<00:20, 3.34it/s]\n 66%|βββββββ | 132/200 [00:39<00:20, 3.33it/s]\n 66%|βββββββ | 133/200 [00:40<00:20, 3.34it/s]\n 67%|βββββββ | 134/200 [00:40<00:19, 3.34it/s]\n 68%|βββββββ | 135/200 [00:40<00:19, 3.34it/s]\n 68%|βββββββ | 136/200 [00:41<00:19, 3.34it/s]\n 68%|βββββββ | 137/200 [00:41<00:18, 3.34it/s]\n 69%|βββββββ | 138/200 [00:41<00:18, 3.34it/s]\n 70%|βββββββ | 139/200 [00:41<00:18, 3.34it/s]\n 70%|βββββββ | 140/200 [00:42<00:17, 3.33it/s]\n 70%|βββββββ | 141/200 [00:42<00:17, 3.34it/s]\n 71%|βββββββ | 142/200 [00:42<00:17, 3.34it/s]\n 72%|ββββββββ | 143/200 [00:43<00:17, 3.34it/s]\n 72%|ββββββββ | 144/200 [00:43<00:16, 3.33it/s]\n 72%|ββββββββ | 145/200 [00:43<00:16, 3.33it/s]\n 73%|ββββββββ | 146/200 [00:44<00:16, 3.33it/s]\n 74%|ββββββββ | 147/200 [00:44<00:15, 3.33it/s]\n 74%|ββββββββ | 148/200 [00:44<00:15, 3.33it/s]\n 74%|ββββββββ | 149/200 [00:44<00:15, 3.34it/s]\n 75%|ββββββββ | 150/200 [00:45<00:14, 3.34it/s]\n 76%|ββββββββ | 151/200 [00:45<00:14, 3.34it/s]\n 76%|ββββββββ | 152/200 [00:45<00:14, 3.34it/s]\n 76%|ββββββββ | 153/200 [00:46<00:14, 3.34it/s]\n 77%|ββββββββ | 154/200 [00:46<00:13, 3.34it/s]\n 78%|ββββββββ | 155/200 [00:46<00:13, 3.34it/s]\n 78%|ββββββββ | 156/200 [00:47<00:13, 3.34it/s]\n 78%|ββββββββ | 157/200 [00:47<00:12, 3.34it/s]\n 79%|ββββββββ | 158/200 [00:47<00:12, 3.34it/s]\n 80%|ββββββββ | 159/200 [00:47<00:12, 3.33it/s]\n 80%|ββββββββ | 160/200 [00:48<00:11, 3.33it/s]\n 80%|ββββββββ | 161/200 [00:48<00:11, 3.33it/s]\n 81%|ββββββββ | 162/200 [00:48<00:11, 3.33it/s]\n 82%|βββββββββ | 163/200 [00:49<00:11, 3.33it/s]\n 82%|βββββββββ | 164/200 [00:49<00:10, 3.33it/s]\n 82%|βββββββββ | 165/200 [00:49<00:10, 3.33it/s]\n 83%|βββββββββ | 166/200 [00:50<00:10, 3.33it/s]\n 84%|βββββββββ | 167/200 [00:50<00:09, 3.33it/s]\n 84%|βββββββββ | 168/200 [00:50<00:09, 3.33it/s]\n 84%|βββββββββ | 169/200 [00:50<00:09, 3.33it/s]\n 85%|βββββββββ | 170/200 [00:51<00:09, 3.33it/s]\n 86%|βββββββββ | 171/200 [00:51<00:08, 3.33it/s]\n 86%|βββββββββ | 172/200 [00:51<00:08, 3.33it/s]\n 86%|βββββββββ | 173/200 [00:52<00:08, 3.33it/s]\n 87%|βββββββββ | 174/200 [00:52<00:07, 3.33it/s]\n 88%|βββββββββ | 175/200 [00:52<00:07, 3.33it/s]\n 88%|βββββββββ | 176/200 [00:53<00:07, 3.33it/s]\n 88%|βββββββββ | 177/200 [00:53<00:06, 3.33it/s]\n 89%|βββββββββ | 178/200 [00:53<00:06, 3.33it/s]\n 90%|βββββββββ | 179/200 [00:53<00:06, 3.33it/s]\n 90%|βββββββββ | 180/200 [00:54<00:06, 3.33it/s]\n 90%|βββββββββ | 181/200 [00:54<00:05, 3.33it/s]\n 91%|βββββββββ | 182/200 [00:54<00:05, 3.33it/s]\n 92%|ββββββββββ| 183/200 [00:55<00:05, 3.33it/s]\n 92%|ββββββββββ| 184/200 [00:55<00:04, 3.33it/s]\n 92%|ββββββββββ| 185/200 [00:55<00:04, 3.33it/s]\n 93%|ββββββββββ| 186/200 [00:56<00:04, 3.33it/s]\n 94%|ββββββββββ| 187/200 [00:56<00:03, 3.33it/s]\n 94%|ββββββββββ| 188/200 [00:56<00:03, 3.33it/s]\n 94%|ββββββββββ| 189/200 [00:56<00:03, 3.33it/s]\n 95%|ββββββββββ| 190/200 [00:57<00:03, 3.33it/s]\n 96%|ββββββββββ| 191/200 [00:57<00:02, 3.33it/s]\n 96%|ββββββββββ| 192/200 [00:57<00:02, 3.33it/s]\n 96%|ββββββββββ| 193/200 [00:58<00:02, 3.33it/s]\n 97%|ββββββββββ| 194/200 [00:58<00:01, 3.33it/s]\n 98%|ββββββββββ| 195/200 [00:58<00:01, 3.33it/s]\n 98%|ββββββββββ| 196/200 [00:59<00:01, 3.33it/s]\n 98%|ββββββββββ| 197/200 [00:59<00:00, 3.33it/s]\n 99%|ββββββββββ| 198/200 [00:59<00:00, 3.33it/s]\n100%|ββββββββββ| 199/200 [00:59<00:00, 3.33it/s]\n100%|ββββββββββ| 200/200 [01:00<00:00, 3.33it/s]\n100%|ββββββββββ| 200/200 [01:00<00:00, 3.32it/s]",
"metrics": {
"predict_time": 63.166646,
"total_time": 119.861953
},
"output": "https://pbxt.replicate.delivery/N7Nbj35GZk4nNtq1cM2syhTakCgB20BG5LeNnew6eXrBd8mjA/output.png",
"started_at": "2023-10-30T22:42:41.813448Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/zmt5i7lbn3lle4dk4lfcvm5huy",
"cancel": "https://api.replicate.com/v1/predictions/zmt5i7lbn3lle4dk4lfcvm5huy/cancel"
},
"version": "592691cf624bb863fe5a01673badff425607ba56dbc499a74bbfdacd3ec0da55"
}
Using seed: 130264517
0%| | 0/200 [00:00<?, ?it/s]
0%| | 1/200 [00:00<02:34, 1.28it/s]
1%| | 2/200 [00:01<01:37, 2.03it/s]
2%|β | 3/200 [00:01<01:19, 2.49it/s]
2%|β | 4/200 [00:01<01:10, 2.77it/s]
2%|β | 5/200 [00:01<01:05, 2.96it/s]
3%|β | 6/200 [00:02<01:02, 3.09it/s]
4%|β | 7/200 [00:02<01:00, 3.18it/s]
4%|β | 8/200 [00:02<00:59, 3.24it/s]
4%|β | 9/200 [00:03<00:58, 3.28it/s]
5%|β | 10/200 [00:03<00:57, 3.31it/s]
6%|β | 11/200 [00:03<00:56, 3.33it/s]
6%|β | 12/200 [00:04<00:56, 3.34it/s]
6%|β | 13/200 [00:04<00:55, 3.35it/s]
7%|β | 14/200 [00:04<00:55, 3.35it/s]
8%|β | 15/200 [00:04<00:55, 3.36it/s]
8%|β | 16/200 [00:05<00:54, 3.36it/s]
8%|β | 17/200 [00:05<00:54, 3.36it/s]
9%|β | 18/200 [00:05<00:54, 3.36it/s]
10%|β | 19/200 [00:06<00:53, 3.36it/s]
10%|β | 20/200 [00:06<00:53, 3.36it/s]
10%|β | 21/200 [00:06<00:53, 3.36it/s]
11%|β | 22/200 [00:07<00:53, 3.36it/s]
12%|ββ | 23/200 [00:07<00:52, 3.36it/s]
12%|ββ | 24/200 [00:07<00:52, 3.36it/s]
12%|ββ | 25/200 [00:07<00:52, 3.36it/s]
13%|ββ | 26/200 [00:08<00:51, 3.36it/s]
14%|ββ | 27/200 [00:08<00:51, 3.36it/s]
14%|ββ | 28/200 [00:08<00:51, 3.36it/s]
14%|ββ | 29/200 [00:09<00:50, 3.36it/s]
15%|ββ | 30/200 [00:09<00:50, 3.36it/s]
16%|ββ | 31/200 [00:09<00:50, 3.35it/s]
16%|ββ | 32/200 [00:09<00:50, 3.35it/s]
16%|ββ | 33/200 [00:10<00:49, 3.35it/s]
17%|ββ | 34/200 [00:10<00:49, 3.35it/s]
18%|ββ | 35/200 [00:10<00:49, 3.35it/s]
18%|ββ | 36/200 [00:11<00:49, 3.35it/s]
18%|ββ | 37/200 [00:11<00:48, 3.35it/s]
19%|ββ | 38/200 [00:11<00:48, 3.35it/s]
20%|ββ | 39/200 [00:12<00:48, 3.35it/s]
20%|ββ | 40/200 [00:12<00:47, 3.34it/s]
20%|ββ | 41/200 [00:12<00:47, 3.35it/s]
21%|ββ | 42/200 [00:12<00:47, 3.35it/s]
22%|βββ | 43/200 [00:13<00:46, 3.35it/s]
22%|βββ | 44/200 [00:13<00:46, 3.35it/s]
22%|βββ | 45/200 [00:13<00:46, 3.35it/s]
23%|βββ | 46/200 [00:14<00:45, 3.35it/s]
24%|βββ | 47/200 [00:14<00:45, 3.35it/s]
24%|βββ | 48/200 [00:14<00:45, 3.36it/s]
24%|βββ | 49/200 [00:15<00:44, 3.36it/s]
25%|βββ | 50/200 [00:15<00:44, 3.36it/s]
26%|βββ | 51/200 [00:15<00:44, 3.37it/s]
26%|βββ | 52/200 [00:15<00:43, 3.37it/s]
26%|βββ | 53/200 [00:16<00:43, 3.37it/s]
27%|βββ | 54/200 [00:16<00:43, 3.37it/s]
28%|βββ | 55/200 [00:16<00:43, 3.37it/s]
28%|βββ | 56/200 [00:17<00:42, 3.37it/s]
28%|βββ | 57/200 [00:17<00:42, 3.37it/s]
29%|βββ | 58/200 [00:17<00:42, 3.37it/s]
30%|βββ | 59/200 [00:18<00:41, 3.37it/s]
30%|βββ | 60/200 [00:18<00:41, 3.37it/s]
30%|βββ | 61/200 [00:18<00:41, 3.37it/s]
31%|βββ | 62/200 [00:18<00:40, 3.37it/s]
32%|ββββ | 63/200 [00:19<00:40, 3.37it/s]
32%|ββββ | 64/200 [00:19<00:40, 3.37it/s]
32%|ββββ | 65/200 [00:19<00:40, 3.37it/s]
33%|ββββ | 66/200 [00:20<00:39, 3.37it/s]
34%|ββββ | 67/200 [00:20<00:39, 3.36it/s]
34%|ββββ | 68/200 [00:20<00:39, 3.36it/s]
34%|ββββ | 69/200 [00:20<00:38, 3.37it/s]
35%|ββββ | 70/200 [00:21<00:38, 3.37it/s]
36%|ββββ | 71/200 [00:21<00:38, 3.37it/s]
36%|ββββ | 72/200 [00:21<00:38, 3.37it/s]
36%|ββββ | 73/200 [00:22<00:37, 3.37it/s]
37%|ββββ | 74/200 [00:22<00:37, 3.36it/s]
38%|ββββ | 75/200 [00:22<00:37, 3.36it/s]
38%|ββββ | 76/200 [00:23<00:36, 3.36it/s]
38%|ββββ | 77/200 [00:23<00:36, 3.36it/s]
39%|ββββ | 78/200 [00:23<00:36, 3.36it/s]
40%|ββββ | 79/200 [00:23<00:35, 3.36it/s]
40%|ββββ | 80/200 [00:24<00:35, 3.36it/s]
40%|ββββ | 81/200 [00:24<00:35, 3.36it/s]
41%|ββββ | 82/200 [00:24<00:35, 3.36it/s]
42%|βββββ | 83/200 [00:25<00:34, 3.36it/s]
42%|βββββ | 84/200 [00:25<00:34, 3.36it/s]
42%|βββββ | 85/200 [00:25<00:34, 3.36it/s]
43%|βββββ | 86/200 [00:26<00:34, 3.32it/s]
44%|βββββ | 87/200 [00:26<00:33, 3.33it/s]
44%|βββββ | 88/200 [00:26<00:33, 3.34it/s]
44%|βββββ | 89/200 [00:26<00:33, 3.34it/s]
45%|βββββ | 90/200 [00:27<00:32, 3.35it/s]
46%|βββββ | 91/200 [00:27<00:32, 3.35it/s]
46%|βββββ | 92/200 [00:27<00:32, 3.35it/s]
46%|βββββ | 93/200 [00:28<00:31, 3.35it/s]
47%|βββββ | 94/200 [00:28<00:31, 3.36it/s]
48%|βββββ | 95/200 [00:28<00:31, 3.36it/s]
48%|βββββ | 96/200 [00:29<00:30, 3.36it/s]
48%|βββββ | 97/200 [00:29<00:30, 3.35it/s]
49%|βββββ | 98/200 [00:29<00:30, 3.35it/s]
50%|βββββ | 99/200 [00:29<00:30, 3.35it/s]
50%|βββββ | 100/200 [00:30<00:29, 3.35it/s]
50%|βββββ | 101/200 [00:30<00:29, 3.35it/s]
51%|βββββ | 102/200 [00:30<00:29, 3.35it/s]
52%|ββββββ | 103/200 [00:31<00:28, 3.35it/s]
52%|ββββββ | 104/200 [00:31<00:28, 3.35it/s]
52%|ββββββ | 105/200 [00:31<00:28, 3.34it/s]
53%|ββββββ | 106/200 [00:32<00:28, 3.35it/s]
54%|ββββββ | 107/200 [00:32<00:27, 3.35it/s]
54%|ββββββ | 108/200 [00:32<00:27, 3.35it/s]
55%|ββββββ | 109/200 [00:32<00:27, 3.35it/s]
55%|ββββββ | 110/200 [00:33<00:26, 3.35it/s]
56%|ββββββ | 111/200 [00:33<00:26, 3.35it/s]
56%|ββββββ | 112/200 [00:33<00:26, 3.34it/s]
56%|ββββββ | 113/200 [00:34<00:26, 3.35it/s]
57%|ββββββ | 114/200 [00:34<00:25, 3.34it/s]
57%|ββββββ | 115/200 [00:34<00:25, 3.34it/s]
58%|ββββββ | 116/200 [00:35<00:25, 3.34it/s]
58%|ββββββ | 117/200 [00:35<00:24, 3.34it/s]
59%|ββββββ | 118/200 [00:35<00:24, 3.34it/s]
60%|ββββββ | 119/200 [00:35<00:24, 3.34it/s]
60%|ββββββ | 120/200 [00:36<00:23, 3.34it/s]
60%|ββββββ | 121/200 [00:36<00:23, 3.34it/s]
61%|ββββββ | 122/200 [00:36<00:23, 3.34it/s]
62%|βββββββ | 123/200 [00:37<00:23, 3.34it/s]
62%|βββββββ | 124/200 [00:37<00:22, 3.34it/s]
62%|βββββββ | 125/200 [00:37<00:22, 3.34it/s]
63%|βββββββ | 126/200 [00:38<00:22, 3.34it/s]
64%|βββββββ | 127/200 [00:38<00:21, 3.34it/s]
64%|βββββββ | 128/200 [00:38<00:21, 3.33it/s]
64%|βββββββ | 129/200 [00:38<00:21, 3.34it/s]
65%|βββββββ | 130/200 [00:39<00:20, 3.34it/s]
66%|βββββββ | 131/200 [00:39<00:20, 3.34it/s]
66%|βββββββ | 132/200 [00:39<00:20, 3.33it/s]
66%|βββββββ | 133/200 [00:40<00:20, 3.34it/s]
67%|βββββββ | 134/200 [00:40<00:19, 3.34it/s]
68%|βββββββ | 135/200 [00:40<00:19, 3.34it/s]
68%|βββββββ | 136/200 [00:41<00:19, 3.34it/s]
68%|βββββββ | 137/200 [00:41<00:18, 3.34it/s]
69%|βββββββ | 138/200 [00:41<00:18, 3.34it/s]
70%|βββββββ | 139/200 [00:41<00:18, 3.34it/s]
70%|βββββββ | 140/200 [00:42<00:17, 3.33it/s]
70%|βββββββ | 141/200 [00:42<00:17, 3.34it/s]
71%|βββββββ | 142/200 [00:42<00:17, 3.34it/s]
72%|ββββββββ | 143/200 [00:43<00:17, 3.34it/s]
72%|ββββββββ | 144/200 [00:43<00:16, 3.33it/s]
72%|ββββββββ | 145/200 [00:43<00:16, 3.33it/s]
73%|ββββββββ | 146/200 [00:44<00:16, 3.33it/s]
74%|ββββββββ | 147/200 [00:44<00:15, 3.33it/s]
74%|ββββββββ | 148/200 [00:44<00:15, 3.33it/s]
74%|ββββββββ | 149/200 [00:44<00:15, 3.34it/s]
75%|ββββββββ | 150/200 [00:45<00:14, 3.34it/s]
76%|ββββββββ | 151/200 [00:45<00:14, 3.34it/s]
76%|ββββββββ | 152/200 [00:45<00:14, 3.34it/s]
76%|ββββββββ | 153/200 [00:46<00:14, 3.34it/s]
77%|ββββββββ | 154/200 [00:46<00:13, 3.34it/s]
78%|ββββββββ | 155/200 [00:46<00:13, 3.34it/s]
78%|ββββββββ | 156/200 [00:47<00:13, 3.34it/s]
78%|ββββββββ | 157/200 [00:47<00:12, 3.34it/s]
79%|ββββββββ | 158/200 [00:47<00:12, 3.34it/s]
80%|ββββββββ | 159/200 [00:47<00:12, 3.33it/s]
80%|ββββββββ | 160/200 [00:48<00:11, 3.33it/s]
80%|ββββββββ | 161/200 [00:48<00:11, 3.33it/s]
81%|ββββββββ | 162/200 [00:48<00:11, 3.33it/s]
82%|βββββββββ | 163/200 [00:49<00:11, 3.33it/s]
82%|βββββββββ | 164/200 [00:49<00:10, 3.33it/s]
82%|βββββββββ | 165/200 [00:49<00:10, 3.33it/s]
83%|βββββββββ | 166/200 [00:50<00:10, 3.33it/s]
84%|βββββββββ | 167/200 [00:50<00:09, 3.33it/s]
84%|βββββββββ | 168/200 [00:50<00:09, 3.33it/s]
84%|βββββββββ | 169/200 [00:50<00:09, 3.33it/s]
85%|βββββββββ | 170/200 [00:51<00:09, 3.33it/s]
86%|βββββββββ | 171/200 [00:51<00:08, 3.33it/s]
86%|βββββββββ | 172/200 [00:51<00:08, 3.33it/s]
86%|βββββββββ | 173/200 [00:52<00:08, 3.33it/s]
87%|βββββββββ | 174/200 [00:52<00:07, 3.33it/s]
88%|βββββββββ | 175/200 [00:52<00:07, 3.33it/s]
88%|βββββββββ | 176/200 [00:53<00:07, 3.33it/s]
88%|βββββββββ | 177/200 [00:53<00:06, 3.33it/s]
89%|βββββββββ | 178/200 [00:53<00:06, 3.33it/s]
90%|βββββββββ | 179/200 [00:53<00:06, 3.33it/s]
90%|βββββββββ | 180/200 [00:54<00:06, 3.33it/s]
90%|βββββββββ | 181/200 [00:54<00:05, 3.33it/s]
91%|βββββββββ | 182/200 [00:54<00:05, 3.33it/s]
92%|ββββββββββ| 183/200 [00:55<00:05, 3.33it/s]
92%|ββββββββββ| 184/200 [00:55<00:04, 3.33it/s]
92%|ββββββββββ| 185/200 [00:55<00:04, 3.33it/s]
93%|ββββββββββ| 186/200 [00:56<00:04, 3.33it/s]
94%|ββββββββββ| 187/200 [00:56<00:03, 3.33it/s]
94%|ββββββββββ| 188/200 [00:56<00:03, 3.33it/s]
94%|ββββββββββ| 189/200 [00:56<00:03, 3.33it/s]
95%|ββββββββββ| 190/200 [00:57<00:03, 3.33it/s]
96%|ββββββββββ| 191/200 [00:57<00:02, 3.33it/s]
96%|ββββββββββ| 192/200 [00:57<00:02, 3.33it/s]
96%|ββββββββββ| 193/200 [00:58<00:02, 3.33it/s]
97%|ββββββββββ| 194/200 [00:58<00:01, 3.33it/s]
98%|ββββββββββ| 195/200 [00:58<00:01, 3.33it/s]
98%|ββββββββββ| 196/200 [00:59<00:01, 3.33it/s]
98%|ββββββββββ| 197/200 [00:59<00:00, 3.33it/s]
99%|ββββββββββ| 198/200 [00:59<00:00, 3.33it/s]
100%|ββββββββββ| 199/200 [00:59<00:00, 3.33it/s]
100%|ββββββββββ| 200/200 [01:00<00:00, 3.33it/s]
100%|ββββββββββ| 200/200 [01:00<00:00, 3.32it/s]
This model costs approximately $0.13 to run on Replicate, or 7 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 136 seconds. The predict time for this model varies significantly based on the inputs.
Transform your image or QR code like never before. this model from Hugging Face https://huggingface.co/diffusers/controlnet-canny-sdxl-1.0
This model is cold. 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.
Choose a file from your machine
Hint: you can also drag files onto the input
Using seed: 130264517
0%| | 0/200 [00:00<?, ?it/s]
0%| | 1/200 [00:00<02:34, 1.28it/s]
1%| | 2/200 [00:01<01:37, 2.03it/s]
2%|β | 3/200 [00:01<01:19, 2.49it/s]
2%|β | 4/200 [00:01<01:10, 2.77it/s]
2%|β | 5/200 [00:01<01:05, 2.96it/s]
3%|β | 6/200 [00:02<01:02, 3.09it/s]
4%|β | 7/200 [00:02<01:00, 3.18it/s]
4%|β | 8/200 [00:02<00:59, 3.24it/s]
4%|β | 9/200 [00:03<00:58, 3.28it/s]
5%|β | 10/200 [00:03<00:57, 3.31it/s]
6%|β | 11/200 [00:03<00:56, 3.33it/s]
6%|β | 12/200 [00:04<00:56, 3.34it/s]
6%|β | 13/200 [00:04<00:55, 3.35it/s]
7%|β | 14/200 [00:04<00:55, 3.35it/s]
8%|β | 15/200 [00:04<00:55, 3.36it/s]
8%|β | 16/200 [00:05<00:54, 3.36it/s]
8%|β | 17/200 [00:05<00:54, 3.36it/s]
9%|β | 18/200 [00:05<00:54, 3.36it/s]
10%|β | 19/200 [00:06<00:53, 3.36it/s]
10%|β | 20/200 [00:06<00:53, 3.36it/s]
10%|β | 21/200 [00:06<00:53, 3.36it/s]
11%|β | 22/200 [00:07<00:53, 3.36it/s]
12%|ββ | 23/200 [00:07<00:52, 3.36it/s]
12%|ββ | 24/200 [00:07<00:52, 3.36it/s]
12%|ββ | 25/200 [00:07<00:52, 3.36it/s]
13%|ββ | 26/200 [00:08<00:51, 3.36it/s]
14%|ββ | 27/200 [00:08<00:51, 3.36it/s]
14%|ββ | 28/200 [00:08<00:51, 3.36it/s]
14%|ββ | 29/200 [00:09<00:50, 3.36it/s]
15%|ββ | 30/200 [00:09<00:50, 3.36it/s]
16%|ββ | 31/200 [00:09<00:50, 3.35it/s]
16%|ββ | 32/200 [00:09<00:50, 3.35it/s]
16%|ββ | 33/200 [00:10<00:49, 3.35it/s]
17%|ββ | 34/200 [00:10<00:49, 3.35it/s]
18%|ββ | 35/200 [00:10<00:49, 3.35it/s]
18%|ββ | 36/200 [00:11<00:49, 3.35it/s]
18%|ββ | 37/200 [00:11<00:48, 3.35it/s]
19%|ββ | 38/200 [00:11<00:48, 3.35it/s]
20%|ββ | 39/200 [00:12<00:48, 3.35it/s]
20%|ββ | 40/200 [00:12<00:47, 3.34it/s]
20%|ββ | 41/200 [00:12<00:47, 3.35it/s]
21%|ββ | 42/200 [00:12<00:47, 3.35it/s]
22%|βββ | 43/200 [00:13<00:46, 3.35it/s]
22%|βββ | 44/200 [00:13<00:46, 3.35it/s]
22%|βββ | 45/200 [00:13<00:46, 3.35it/s]
23%|βββ | 46/200 [00:14<00:45, 3.35it/s]
24%|βββ | 47/200 [00:14<00:45, 3.35it/s]
24%|βββ | 48/200 [00:14<00:45, 3.36it/s]
24%|βββ | 49/200 [00:15<00:44, 3.36it/s]
25%|βββ | 50/200 [00:15<00:44, 3.36it/s]
26%|βββ | 51/200 [00:15<00:44, 3.37it/s]
26%|βββ | 52/200 [00:15<00:43, 3.37it/s]
26%|βββ | 53/200 [00:16<00:43, 3.37it/s]
27%|βββ | 54/200 [00:16<00:43, 3.37it/s]
28%|βββ | 55/200 [00:16<00:43, 3.37it/s]
28%|βββ | 56/200 [00:17<00:42, 3.37it/s]
28%|βββ | 57/200 [00:17<00:42, 3.37it/s]
29%|βββ | 58/200 [00:17<00:42, 3.37it/s]
30%|βββ | 59/200 [00:18<00:41, 3.37it/s]
30%|βββ | 60/200 [00:18<00:41, 3.37it/s]
30%|βββ | 61/200 [00:18<00:41, 3.37it/s]
31%|βββ | 62/200 [00:18<00:40, 3.37it/s]
32%|ββββ | 63/200 [00:19<00:40, 3.37it/s]
32%|ββββ | 64/200 [00:19<00:40, 3.37it/s]
32%|ββββ | 65/200 [00:19<00:40, 3.37it/s]
33%|ββββ | 66/200 [00:20<00:39, 3.37it/s]
34%|ββββ | 67/200 [00:20<00:39, 3.36it/s]
34%|ββββ | 68/200 [00:20<00:39, 3.36it/s]
34%|ββββ | 69/200 [00:20<00:38, 3.37it/s]
35%|ββββ | 70/200 [00:21<00:38, 3.37it/s]
36%|ββββ | 71/200 [00:21<00:38, 3.37it/s]
36%|ββββ | 72/200 [00:21<00:38, 3.37it/s]
36%|ββββ | 73/200 [00:22<00:37, 3.37it/s]
37%|ββββ | 74/200 [00:22<00:37, 3.36it/s]
38%|ββββ | 75/200 [00:22<00:37, 3.36it/s]
38%|ββββ | 76/200 [00:23<00:36, 3.36it/s]
38%|ββββ | 77/200 [00:23<00:36, 3.36it/s]
39%|ββββ | 78/200 [00:23<00:36, 3.36it/s]
40%|ββββ | 79/200 [00:23<00:35, 3.36it/s]
40%|ββββ | 80/200 [00:24<00:35, 3.36it/s]
40%|ββββ | 81/200 [00:24<00:35, 3.36it/s]
41%|ββββ | 82/200 [00:24<00:35, 3.36it/s]
42%|βββββ | 83/200 [00:25<00:34, 3.36it/s]
42%|βββββ | 84/200 [00:25<00:34, 3.36it/s]
42%|βββββ | 85/200 [00:25<00:34, 3.36it/s]
43%|βββββ | 86/200 [00:26<00:34, 3.32it/s]
44%|βββββ | 87/200 [00:26<00:33, 3.33it/s]
44%|βββββ | 88/200 [00:26<00:33, 3.34it/s]
44%|βββββ | 89/200 [00:26<00:33, 3.34it/s]
45%|βββββ | 90/200 [00:27<00:32, 3.35it/s]
46%|βββββ | 91/200 [00:27<00:32, 3.35it/s]
46%|βββββ | 92/200 [00:27<00:32, 3.35it/s]
46%|βββββ | 93/200 [00:28<00:31, 3.35it/s]
47%|βββββ | 94/200 [00:28<00:31, 3.36it/s]
48%|βββββ | 95/200 [00:28<00:31, 3.36it/s]
48%|βββββ | 96/200 [00:29<00:30, 3.36it/s]
48%|βββββ | 97/200 [00:29<00:30, 3.35it/s]
49%|βββββ | 98/200 [00:29<00:30, 3.35it/s]
50%|βββββ | 99/200 [00:29<00:30, 3.35it/s]
50%|βββββ | 100/200 [00:30<00:29, 3.35it/s]
50%|βββββ | 101/200 [00:30<00:29, 3.35it/s]
51%|βββββ | 102/200 [00:30<00:29, 3.35it/s]
52%|ββββββ | 103/200 [00:31<00:28, 3.35it/s]
52%|ββββββ | 104/200 [00:31<00:28, 3.35it/s]
52%|ββββββ | 105/200 [00:31<00:28, 3.34it/s]
53%|ββββββ | 106/200 [00:32<00:28, 3.35it/s]
54%|ββββββ | 107/200 [00:32<00:27, 3.35it/s]
54%|ββββββ | 108/200 [00:32<00:27, 3.35it/s]
55%|ββββββ | 109/200 [00:32<00:27, 3.35it/s]
55%|ββββββ | 110/200 [00:33<00:26, 3.35it/s]
56%|ββββββ | 111/200 [00:33<00:26, 3.35it/s]
56%|ββββββ | 112/200 [00:33<00:26, 3.34it/s]
56%|ββββββ | 113/200 [00:34<00:26, 3.35it/s]
57%|ββββββ | 114/200 [00:34<00:25, 3.34it/s]
57%|ββββββ | 115/200 [00:34<00:25, 3.34it/s]
58%|ββββββ | 116/200 [00:35<00:25, 3.34it/s]
58%|ββββββ | 117/200 [00:35<00:24, 3.34it/s]
59%|ββββββ | 118/200 [00:35<00:24, 3.34it/s]
60%|ββββββ | 119/200 [00:35<00:24, 3.34it/s]
60%|ββββββ | 120/200 [00:36<00:23, 3.34it/s]
60%|ββββββ | 121/200 [00:36<00:23, 3.34it/s]
61%|ββββββ | 122/200 [00:36<00:23, 3.34it/s]
62%|βββββββ | 123/200 [00:37<00:23, 3.34it/s]
62%|βββββββ | 124/200 [00:37<00:22, 3.34it/s]
62%|βββββββ | 125/200 [00:37<00:22, 3.34it/s]
63%|βββββββ | 126/200 [00:38<00:22, 3.34it/s]
64%|βββββββ | 127/200 [00:38<00:21, 3.34it/s]
64%|βββββββ | 128/200 [00:38<00:21, 3.33it/s]
64%|βββββββ | 129/200 [00:38<00:21, 3.34it/s]
65%|βββββββ | 130/200 [00:39<00:20, 3.34it/s]
66%|βββββββ | 131/200 [00:39<00:20, 3.34it/s]
66%|βββββββ | 132/200 [00:39<00:20, 3.33it/s]
66%|βββββββ | 133/200 [00:40<00:20, 3.34it/s]
67%|βββββββ | 134/200 [00:40<00:19, 3.34it/s]
68%|βββββββ | 135/200 [00:40<00:19, 3.34it/s]
68%|βββββββ | 136/200 [00:41<00:19, 3.34it/s]
68%|βββββββ | 137/200 [00:41<00:18, 3.34it/s]
69%|βββββββ | 138/200 [00:41<00:18, 3.34it/s]
70%|βββββββ | 139/200 [00:41<00:18, 3.34it/s]
70%|βββββββ | 140/200 [00:42<00:17, 3.33it/s]
70%|βββββββ | 141/200 [00:42<00:17, 3.34it/s]
71%|βββββββ | 142/200 [00:42<00:17, 3.34it/s]
72%|ββββββββ | 143/200 [00:43<00:17, 3.34it/s]
72%|ββββββββ | 144/200 [00:43<00:16, 3.33it/s]
72%|ββββββββ | 145/200 [00:43<00:16, 3.33it/s]
73%|ββββββββ | 146/200 [00:44<00:16, 3.33it/s]
74%|ββββββββ | 147/200 [00:44<00:15, 3.33it/s]
74%|ββββββββ | 148/200 [00:44<00:15, 3.33it/s]
74%|ββββββββ | 149/200 [00:44<00:15, 3.34it/s]
75%|ββββββββ | 150/200 [00:45<00:14, 3.34it/s]
76%|ββββββββ | 151/200 [00:45<00:14, 3.34it/s]
76%|ββββββββ | 152/200 [00:45<00:14, 3.34it/s]
76%|ββββββββ | 153/200 [00:46<00:14, 3.34it/s]
77%|ββββββββ | 154/200 [00:46<00:13, 3.34it/s]
78%|ββββββββ | 155/200 [00:46<00:13, 3.34it/s]
78%|ββββββββ | 156/200 [00:47<00:13, 3.34it/s]
78%|ββββββββ | 157/200 [00:47<00:12, 3.34it/s]
79%|ββββββββ | 158/200 [00:47<00:12, 3.34it/s]
80%|ββββββββ | 159/200 [00:47<00:12, 3.33it/s]
80%|ββββββββ | 160/200 [00:48<00:11, 3.33it/s]
80%|ββββββββ | 161/200 [00:48<00:11, 3.33it/s]
81%|ββββββββ | 162/200 [00:48<00:11, 3.33it/s]
82%|βββββββββ | 163/200 [00:49<00:11, 3.33it/s]
82%|βββββββββ | 164/200 [00:49<00:10, 3.33it/s]
82%|βββββββββ | 165/200 [00:49<00:10, 3.33it/s]
83%|βββββββββ | 166/200 [00:50<00:10, 3.33it/s]
84%|βββββββββ | 167/200 [00:50<00:09, 3.33it/s]
84%|βββββββββ | 168/200 [00:50<00:09, 3.33it/s]
84%|βββββββββ | 169/200 [00:50<00:09, 3.33it/s]
85%|βββββββββ | 170/200 [00:51<00:09, 3.33it/s]
86%|βββββββββ | 171/200 [00:51<00:08, 3.33it/s]
86%|βββββββββ | 172/200 [00:51<00:08, 3.33it/s]
86%|βββββββββ | 173/200 [00:52<00:08, 3.33it/s]
87%|βββββββββ | 174/200 [00:52<00:07, 3.33it/s]
88%|βββββββββ | 175/200 [00:52<00:07, 3.33it/s]
88%|βββββββββ | 176/200 [00:53<00:07, 3.33it/s]
88%|βββββββββ | 177/200 [00:53<00:06, 3.33it/s]
89%|βββββββββ | 178/200 [00:53<00:06, 3.33it/s]
90%|βββββββββ | 179/200 [00:53<00:06, 3.33it/s]
90%|βββββββββ | 180/200 [00:54<00:06, 3.33it/s]
90%|βββββββββ | 181/200 [00:54<00:05, 3.33it/s]
91%|βββββββββ | 182/200 [00:54<00:05, 3.33it/s]
92%|ββββββββββ| 183/200 [00:55<00:05, 3.33it/s]
92%|ββββββββββ| 184/200 [00:55<00:04, 3.33it/s]
92%|ββββββββββ| 185/200 [00:55<00:04, 3.33it/s]
93%|ββββββββββ| 186/200 [00:56<00:04, 3.33it/s]
94%|ββββββββββ| 187/200 [00:56<00:03, 3.33it/s]
94%|ββββββββββ| 188/200 [00:56<00:03, 3.33it/s]
94%|ββββββββββ| 189/200 [00:56<00:03, 3.33it/s]
95%|ββββββββββ| 190/200 [00:57<00:03, 3.33it/s]
96%|ββββββββββ| 191/200 [00:57<00:02, 3.33it/s]
96%|ββββββββββ| 192/200 [00:57<00:02, 3.33it/s]
96%|ββββββββββ| 193/200 [00:58<00:02, 3.33it/s]
97%|ββββββββββ| 194/200 [00:58<00:01, 3.33it/s]
98%|ββββββββββ| 195/200 [00:58<00:01, 3.33it/s]
98%|ββββββββββ| 196/200 [00:59<00:01, 3.33it/s]
98%|ββββββββββ| 197/200 [00:59<00:00, 3.33it/s]
99%|ββββββββββ| 198/200 [00:59<00:00, 3.33it/s]
100%|ββββββββββ| 199/200 [00:59<00:00, 3.33it/s]
100%|ββββββββββ| 200/200 [01:00<00:00, 3.33it/s]
100%|ββββββββββ| 200/200 [01:00<00:00, 3.32it/s]