Versions
9ddc2c98
Latest
You're looking at a specific version of this model. Jump to the model overview.
marydotdev /sdxl-lego:9ddc2c98
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=marydotdev/sdxl-lego
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run marydotdev/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"marydotdev/sdxl-lego:9ddc2c9883e658f1317fd39b4d150ff79376cc8e63421a97ab5d0d5d757e1ab6",
{
input: {
width: 1024,
height: 1024,
prompt: "an eskimo igloo lego set",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.6,
num_outputs: 1,
guidance_scale: 15,
apply_watermark: true,
high_noise_frac: 0.8,
negative_prompt: "",
prompt_strength: 0.8,
num_inference_steps: 150
}
}
);
// 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 replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun marydotdev/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"marydotdev/sdxl-lego:9ddc2c9883e658f1317fd39b4d150ff79376cc8e63421a97ab5d0d5d757e1ab6",
input={
"width": 1024,
"height": 1024,
"prompt": "an eskimo igloo lego set",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 15,
"apply_watermark": True,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 150
}
)
print(output)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=<paste-your-token-here>Find your API token in your account settings.
Run marydotdev/sdxl-lego 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": "marydotdev/sdxl-lego:9ddc2c9883e658f1317fd39b4d150ff79376cc8e63421a97ab5d0d5d757e1ab6",
"input": {
"width": 1024,
"height": 1024,
"prompt": "an eskimo igloo lego set",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 15,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 150
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf you don’t 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/marydotdev/sdxl-lego@sha256:9ddc2c9883e658f1317fd39b4d150ff79376cc8e63421a97ab5d0d5d757e1ab6 \
-i 'width=1024' \
-i 'height=1024' \
-i 'prompt="an eskimo igloo lego set"' \
-i 'refine="no_refiner"' \
-i 'scheduler="K_EULER"' \
-i 'lora_scale=0.6' \
-i 'num_outputs=1' \
-i 'guidance_scale=15' \
-i 'apply_watermark=true' \
-i 'high_noise_frac=0.8' \
-i 'negative_prompt=""' \
-i 'prompt_strength=0.8' \
-i 'num_inference_steps=150'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/marydotdev/sdxl-lego@sha256:9ddc2c9883e658f1317fd39b4d150ff79376cc8e63421a97ab5d0d5d757e1ab6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "an eskimo igloo lego set", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 15, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 150 } }' \ 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 $0.030. Alternatively, try out our featured models for free.
Sign in with GitHub
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2023-11-12T05:17:14.407613Z",
"created_at": "2023-11-12T05:16:25.500469Z",
"data_removed": false,
"error": null,
"id": "fzskiadbi2lac77ytzqxdmdwty",
"input": {
"width": 1024,
"height": 1024,
"prompt": "an eskimo igloo lego set",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 15,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 150
},
"logs": "Using seed: 34815\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: an eskimo igloo lego set\ntxt2img mode\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:40, 3.66it/s]\n 1%|▏ | 2/150 [00:00<00:40, 3.65it/s]\n 2%|▏ | 3/150 [00:00<00:40, 3.65it/s]\n 3%|▎ | 4/150 [00:01<00:40, 3.65it/s]\n 3%|▎ | 5/150 [00:01<00:39, 3.65it/s]\n 4%|▍ | 6/150 [00:01<00:39, 3.64it/s]\n 5%|▍ | 7/150 [00:01<00:39, 3.64it/s]\n 5%|▌ | 8/150 [00:02<00:39, 3.64it/s]\n 6%|▌ | 9/150 [00:02<00:38, 3.64it/s]\n 7%|▋ | 10/150 [00:02<00:38, 3.64it/s]\n 7%|▋ | 11/150 [00:03<00:38, 3.64it/s]\n 8%|▊ | 12/150 [00:03<00:37, 3.63it/s]\n 9%|▊ | 13/150 [00:03<00:37, 3.63it/s]\n 9%|▉ | 14/150 [00:03<00:37, 3.63it/s]\n 10%|█ | 15/150 [00:04<00:37, 3.63it/s]\n 11%|█ | 16/150 [00:04<00:36, 3.63it/s]\n 11%|█▏ | 17/150 [00:04<00:36, 3.63it/s]\n 12%|█▏ | 18/150 [00:04<00:36, 3.63it/s]\n 13%|█▎ | 19/150 [00:05<00:36, 3.63it/s]\n 13%|█▎ | 20/150 [00:05<00:35, 3.63it/s]\n 14%|█▍ | 21/150 [00:05<00:35, 3.63it/s]\n 15%|█▍ | 22/150 [00:06<00:35, 3.63it/s]\n 15%|█▌ | 23/150 [00:06<00:34, 3.63it/s]\n 16%|█▌ | 24/150 [00:06<00:34, 3.63it/s]\n 17%|█▋ | 25/150 [00:06<00:34, 3.63it/s]\n 17%|█▋ | 26/150 [00:07<00:34, 3.63it/s]\n 18%|█▊ | 27/150 [00:07<00:33, 3.62it/s]\n 19%|█▊ | 28/150 [00:07<00:33, 3.62it/s]\n 19%|█▉ | 29/150 [00:07<00:33, 3.62it/s]\n 20%|██ | 30/150 [00:08<00:33, 3.62it/s]\n 21%|██ | 31/150 [00:08<00:32, 3.62it/s]\n 21%|██▏ | 32/150 [00:08<00:32, 3.62it/s]\n 22%|██▏ | 33/150 [00:09<00:32, 3.62it/s]\n 23%|██▎ | 34/150 [00:09<00:32, 3.62it/s]\n 23%|██▎ | 35/150 [00:09<00:31, 3.62it/s]\n 24%|██▍ | 36/150 [00:09<00:31, 3.62it/s]\n 25%|██▍ | 37/150 [00:10<00:31, 3.62it/s]\n 25%|██▌ | 38/150 [00:10<00:30, 3.62it/s]\n 26%|██▌ | 39/150 [00:10<00:30, 3.62it/s]\n 27%|██▋ | 40/150 [00:11<00:30, 3.62it/s]\n 27%|██▋ | 41/150 [00:11<00:30, 3.62it/s]\n 28%|██▊ | 42/150 [00:11<00:29, 3.62it/s]\n 29%|██▊ | 43/150 [00:11<00:29, 3.62it/s]\n 29%|██▉ | 44/150 [00:12<00:29, 3.62it/s]\n 30%|███ | 45/150 [00:12<00:29, 3.62it/s]\n 31%|███ | 46/150 [00:12<00:28, 3.61it/s]\n 31%|███▏ | 47/150 [00:12<00:28, 3.62it/s]\n 32%|███▏ | 48/150 [00:13<00:28, 3.62it/s]\n 33%|███▎ | 49/150 [00:13<00:27, 3.62it/s]\n 33%|███▎ | 50/150 [00:13<00:27, 3.62it/s]\n 34%|███▍ | 51/150 [00:14<00:27, 3.61it/s]\n 35%|███▍ | 52/150 [00:14<00:27, 3.62it/s]\n 35%|███▌ | 53/150 [00:14<00:26, 3.62it/s]\n 36%|███▌ | 54/150 [00:14<00:26, 3.62it/s]\n 37%|███▋ | 55/150 [00:15<00:26, 3.62it/s]\n 37%|███▋ | 56/150 [00:15<00:25, 3.62it/s]\n 38%|███▊ | 57/150 [00:15<00:25, 3.61it/s]\n 39%|███▊ | 58/150 [00:16<00:25, 3.58it/s]\n 39%|███▉ | 59/150 [00:16<00:25, 3.59it/s]\n 40%|████ | 60/150 [00:16<00:25, 3.59it/s]\n 41%|████ | 61/150 [00:16<00:24, 3.60it/s]\n 41%|████▏ | 62/150 [00:17<00:24, 3.60it/s]\n 42%|████▏ | 63/150 [00:17<00:24, 3.61it/s]\n 43%|████▎ | 64/150 [00:17<00:23, 3.61it/s]\n 43%|████▎ | 65/150 [00:17<00:23, 3.61it/s]\n 44%|████▍ | 66/150 [00:18<00:23, 3.61it/s]\n 45%|████▍ | 67/150 [00:18<00:22, 3.62it/s]\n 45%|████▌ | 68/150 [00:18<00:22, 3.61it/s]\n 46%|████▌ | 69/150 [00:19<00:22, 3.61it/s]\n 47%|████▋ | 70/150 [00:19<00:22, 3.61it/s]\n 47%|████▋ | 71/150 [00:19<00:21, 3.61it/s]\n 48%|████▊ | 72/150 [00:19<00:21, 3.62it/s]\n 49%|████▊ | 73/150 [00:20<00:21, 3.62it/s]\n 49%|████▉ | 74/150 [00:20<00:21, 3.62it/s]\n 50%|█████ | 75/150 [00:20<00:20, 3.62it/s]\n 51%|█████ | 76/150 [00:20<00:20, 3.61it/s]\n 51%|█████▏ | 77/150 [00:21<00:20, 3.61it/s]\n 52%|█████▏ | 78/150 [00:21<00:19, 3.61it/s]\n 53%|█████▎ | 79/150 [00:21<00:19, 3.62it/s]\n 53%|█████▎ | 80/150 [00:22<00:19, 3.61it/s]\n 54%|█████▍ | 81/150 [00:22<00:19, 3.61it/s]\n 55%|█████▍ | 82/150 [00:22<00:18, 3.61it/s]\n 55%|█████▌ | 83/150 [00:22<00:18, 3.61it/s]\n 56%|█████▌ | 84/150 [00:23<00:18, 3.61it/s]\n 57%|█████▋ | 85/150 [00:23<00:18, 3.61it/s]\n 57%|█████▋ | 86/150 [00:23<00:17, 3.61it/s]\n 58%|█████▊ | 87/150 [00:24<00:17, 3.61it/s]\n 59%|█████▊ | 88/150 [00:24<00:17, 3.61it/s]\n 59%|█████▉ | 89/150 [00:24<00:16, 3.61it/s]\n 60%|██████ | 90/150 [00:24<00:16, 3.60it/s]\n 61%|██████ | 91/150 [00:25<00:16, 3.61it/s]\n 61%|██████▏ | 92/150 [00:25<00:16, 3.60it/s]\n 62%|██████▏ | 93/150 [00:25<00:15, 3.60it/s]\n 63%|██████▎ | 94/150 [00:25<00:15, 3.60it/s]\n 63%|██████▎ | 95/150 [00:26<00:15, 3.61it/s]\n 64%|██████▍ | 96/150 [00:26<00:14, 3.60it/s]\n 65%|██████▍ | 97/150 [00:26<00:14, 3.61it/s]\n 65%|██████▌ | 98/150 [00:27<00:14, 3.61it/s]\n 66%|██████▌ | 99/150 [00:27<00:14, 3.61it/s]\n 67%|██████▋ | 100/150 [00:27<00:13, 3.61it/s]\n 67%|██████▋ | 101/150 [00:27<00:13, 3.61it/s]\n 68%|██████▊ | 102/150 [00:28<00:13, 3.61it/s]\n 69%|██████▊ | 103/150 [00:28<00:13, 3.61it/s]\n 69%|██████▉ | 104/150 [00:28<00:12, 3.61it/s]\n 70%|███████ | 105/150 [00:29<00:12, 3.61it/s]\n 71%|███████ | 106/150 [00:29<00:12, 3.61it/s]\n 71%|███████▏ | 107/150 [00:29<00:11, 3.61it/s]\n 72%|███████▏ | 108/150 [00:29<00:11, 3.61it/s]\n 73%|███████▎ | 109/150 [00:30<00:11, 3.61it/s]\n 73%|███████▎ | 110/150 [00:30<00:11, 3.61it/s]\n 74%|███████▍ | 111/150 [00:30<00:10, 3.61it/s]\n 75%|███████▍ | 112/150 [00:30<00:10, 3.61it/s]\n 75%|███████▌ | 113/150 [00:31<00:10, 3.61it/s]\n 76%|███████▌ | 114/150 [00:31<00:09, 3.61it/s]\n 77%|███████▋ | 115/150 [00:31<00:09, 3.61it/s]\n 77%|███████▋ | 116/150 [00:32<00:09, 3.61it/s]\n 78%|███████▊ | 117/150 [00:32<00:09, 3.61it/s]\n 79%|███████▊ | 118/150 [00:32<00:08, 3.61it/s]\n 79%|███████▉ | 119/150 [00:32<00:08, 3.61it/s]\n 80%|████████ | 120/150 [00:33<00:08, 3.61it/s]\n 81%|████████ | 121/150 [00:33<00:08, 3.61it/s]\n 81%|████████▏ | 122/150 [00:33<00:07, 3.61it/s]\n 82%|████████▏ | 123/150 [00:34<00:07, 3.60it/s]\n 83%|████████▎ | 124/150 [00:34<00:07, 3.61it/s]\n 83%|████████▎ | 125/150 [00:34<00:06, 3.61it/s]\n 84%|████████▍ | 126/150 [00:34<00:06, 3.61it/s]\n 85%|████████▍ | 127/150 [00:35<00:06, 3.61it/s]\n 85%|████████▌ | 128/150 [00:35<00:06, 3.61it/s]\n 86%|████████▌ | 129/150 [00:35<00:05, 3.61it/s]\n 87%|████████▋ | 130/150 [00:35<00:05, 3.61it/s]\n 87%|████████▋ | 131/150 [00:36<00:05, 3.61it/s]\n 88%|████████▊ | 132/150 [00:36<00:04, 3.61it/s]\n 89%|████████▊ | 133/150 [00:36<00:04, 3.61it/s]\n 89%|████████▉ | 134/150 [00:37<00:04, 3.61it/s]\n 90%|█████████ | 135/150 [00:37<00:04, 3.62it/s]\n 91%|█████████ | 136/150 [00:37<00:03, 3.61it/s]\n 91%|█████████▏| 137/150 [00:37<00:03, 3.61it/s]\n 92%|█████████▏| 138/150 [00:38<00:03, 3.61it/s]\n 93%|█████████▎| 139/150 [00:38<00:03, 3.61it/s]\n 93%|█████████▎| 140/150 [00:38<00:02, 3.61it/s]\n 94%|█████████▍| 141/150 [00:38<00:02, 3.61it/s]\n 95%|█████████▍| 142/150 [00:39<00:02, 3.61it/s]\n 95%|█████████▌| 143/150 [00:39<00:01, 3.61it/s]\n 96%|█████████▌| 144/150 [00:39<00:01, 3.61it/s]\n 97%|█████████▋| 145/150 [00:40<00:01, 3.61it/s]\n 97%|█████████▋| 146/150 [00:40<00:01, 3.61it/s]\n 98%|█████████▊| 147/150 [00:40<00:00, 3.61it/s]\n 99%|█████████▊| 148/150 [00:40<00:00, 3.61it/s]\n 99%|█████████▉| 149/150 [00:41<00:00, 3.61it/s]\n100%|██████████| 150/150 [00:41<00:00, 3.60it/s]\n100%|██████████| 150/150 [00:41<00:00, 3.62it/s]",
"metrics": {
"predict_time": 43.9795,
"total_time": 48.907144
},
"output": [
"https://replicate.delivery/pbxt/hMiqiRzQMeylbKZmd2fMYufavC7JF9Z2L1duNoWFEs4zOCvjA/out-0.png"
],
"started_at": "2023-11-12T05:16:30.428113Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/fzskiadbi2lac77ytzqxdmdwty",
"cancel": "https://api.replicate.com/v1/predictions/fzskiadbi2lac77ytzqxdmdwty/cancel"
},
"version": "9ddc2c9883e658f1317fd39b4d150ff79376cc8e63421a97ab5d0d5d757e1ab6"
}Generated in
Using seed: 34815
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: an eskimo igloo lego set
txt2img mode
0%| | 0/150 [00:00<?, ?it/s]
1%| | 1/150 [00:00<00:40, 3.66it/s]
1%|▏ | 2/150 [00:00<00:40, 3.65it/s]
2%|▏ | 3/150 [00:00<00:40, 3.65it/s]
3%|▎ | 4/150 [00:01<00:40, 3.65it/s]
3%|▎ | 5/150 [00:01<00:39, 3.65it/s]
4%|▍ | 6/150 [00:01<00:39, 3.64it/s]
5%|▍ | 7/150 [00:01<00:39, 3.64it/s]
5%|▌ | 8/150 [00:02<00:39, 3.64it/s]
6%|▌ | 9/150 [00:02<00:38, 3.64it/s]
7%|▋ | 10/150 [00:02<00:38, 3.64it/s]
7%|▋ | 11/150 [00:03<00:38, 3.64it/s]
8%|▊ | 12/150 [00:03<00:37, 3.63it/s]
9%|▊ | 13/150 [00:03<00:37, 3.63it/s]
9%|▉ | 14/150 [00:03<00:37, 3.63it/s]
10%|█ | 15/150 [00:04<00:37, 3.63it/s]
11%|█ | 16/150 [00:04<00:36, 3.63it/s]
11%|█▏ | 17/150 [00:04<00:36, 3.63it/s]
12%|█▏ | 18/150 [00:04<00:36, 3.63it/s]
13%|█▎ | 19/150 [00:05<00:36, 3.63it/s]
13%|█▎ | 20/150 [00:05<00:35, 3.63it/s]
14%|█▍ | 21/150 [00:05<00:35, 3.63it/s]
15%|█▍ | 22/150 [00:06<00:35, 3.63it/s]
15%|█▌ | 23/150 [00:06<00:34, 3.63it/s]
16%|█▌ | 24/150 [00:06<00:34, 3.63it/s]
17%|█▋ | 25/150 [00:06<00:34, 3.63it/s]
17%|█▋ | 26/150 [00:07<00:34, 3.63it/s]
18%|█▊ | 27/150 [00:07<00:33, 3.62it/s]
19%|█▊ | 28/150 [00:07<00:33, 3.62it/s]
19%|█▉ | 29/150 [00:07<00:33, 3.62it/s]
20%|██ | 30/150 [00:08<00:33, 3.62it/s]
21%|██ | 31/150 [00:08<00:32, 3.62it/s]
21%|██▏ | 32/150 [00:08<00:32, 3.62it/s]
22%|██▏ | 33/150 [00:09<00:32, 3.62it/s]
23%|██▎ | 34/150 [00:09<00:32, 3.62it/s]
23%|██▎ | 35/150 [00:09<00:31, 3.62it/s]
24%|██▍ | 36/150 [00:09<00:31, 3.62it/s]
25%|██▍ | 37/150 [00:10<00:31, 3.62it/s]
25%|██▌ | 38/150 [00:10<00:30, 3.62it/s]
26%|██▌ | 39/150 [00:10<00:30, 3.62it/s]
27%|██▋ | 40/150 [00:11<00:30, 3.62it/s]
27%|██▋ | 41/150 [00:11<00:30, 3.62it/s]
28%|██▊ | 42/150 [00:11<00:29, 3.62it/s]
29%|██▊ | 43/150 [00:11<00:29, 3.62it/s]
29%|██▉ | 44/150 [00:12<00:29, 3.62it/s]
30%|███ | 45/150 [00:12<00:29, 3.62it/s]
31%|███ | 46/150 [00:12<00:28, 3.61it/s]
31%|███▏ | 47/150 [00:12<00:28, 3.62it/s]
32%|███▏ | 48/150 [00:13<00:28, 3.62it/s]
33%|███▎ | 49/150 [00:13<00:27, 3.62it/s]
33%|███▎ | 50/150 [00:13<00:27, 3.62it/s]
34%|███▍ | 51/150 [00:14<00:27, 3.61it/s]
35%|███▍ | 52/150 [00:14<00:27, 3.62it/s]
35%|███▌ | 53/150 [00:14<00:26, 3.62it/s]
36%|███▌ | 54/150 [00:14<00:26, 3.62it/s]
37%|███▋ | 55/150 [00:15<00:26, 3.62it/s]
37%|███▋ | 56/150 [00:15<00:25, 3.62it/s]
38%|███▊ | 57/150 [00:15<00:25, 3.61it/s]
39%|███▊ | 58/150 [00:16<00:25, 3.58it/s]
39%|███▉ | 59/150 [00:16<00:25, 3.59it/s]
40%|████ | 60/150 [00:16<00:25, 3.59it/s]
41%|████ | 61/150 [00:16<00:24, 3.60it/s]
41%|████▏ | 62/150 [00:17<00:24, 3.60it/s]
42%|████▏ | 63/150 [00:17<00:24, 3.61it/s]
43%|████▎ | 64/150 [00:17<00:23, 3.61it/s]
43%|████▎ | 65/150 [00:17<00:23, 3.61it/s]
44%|████▍ | 66/150 [00:18<00:23, 3.61it/s]
45%|████▍ | 67/150 [00:18<00:22, 3.62it/s]
45%|████▌ | 68/150 [00:18<00:22, 3.61it/s]
46%|████▌ | 69/150 [00:19<00:22, 3.61it/s]
47%|████▋ | 70/150 [00:19<00:22, 3.61it/s]
47%|████▋ | 71/150 [00:19<00:21, 3.61it/s]
48%|████▊ | 72/150 [00:19<00:21, 3.62it/s]
49%|████▊ | 73/150 [00:20<00:21, 3.62it/s]
49%|████▉ | 74/150 [00:20<00:21, 3.62it/s]
50%|█████ | 75/150 [00:20<00:20, 3.62it/s]
51%|█████ | 76/150 [00:20<00:20, 3.61it/s]
51%|█████▏ | 77/150 [00:21<00:20, 3.61it/s]
52%|█████▏ | 78/150 [00:21<00:19, 3.61it/s]
53%|█████▎ | 79/150 [00:21<00:19, 3.62it/s]
53%|█████▎ | 80/150 [00:22<00:19, 3.61it/s]
54%|█████▍ | 81/150 [00:22<00:19, 3.61it/s]
55%|█████▍ | 82/150 [00:22<00:18, 3.61it/s]
55%|█████▌ | 83/150 [00:22<00:18, 3.61it/s]
56%|█████▌ | 84/150 [00:23<00:18, 3.61it/s]
57%|█████▋ | 85/150 [00:23<00:18, 3.61it/s]
57%|█████▋ | 86/150 [00:23<00:17, 3.61it/s]
58%|█████▊ | 87/150 [00:24<00:17, 3.61it/s]
59%|█████▊ | 88/150 [00:24<00:17, 3.61it/s]
59%|█████▉ | 89/150 [00:24<00:16, 3.61it/s]
60%|██████ | 90/150 [00:24<00:16, 3.60it/s]
61%|██████ | 91/150 [00:25<00:16, 3.61it/s]
61%|██████▏ | 92/150 [00:25<00:16, 3.60it/s]
62%|██████▏ | 93/150 [00:25<00:15, 3.60it/s]
63%|██████▎ | 94/150 [00:25<00:15, 3.60it/s]
63%|██████▎ | 95/150 [00:26<00:15, 3.61it/s]
64%|██████▍ | 96/150 [00:26<00:14, 3.60it/s]
65%|██████▍ | 97/150 [00:26<00:14, 3.61it/s]
65%|██████▌ | 98/150 [00:27<00:14, 3.61it/s]
66%|██████▌ | 99/150 [00:27<00:14, 3.61it/s]
67%|██████▋ | 100/150 [00:27<00:13, 3.61it/s]
67%|██████▋ | 101/150 [00:27<00:13, 3.61it/s]
68%|██████▊ | 102/150 [00:28<00:13, 3.61it/s]
69%|██████▊ | 103/150 [00:28<00:13, 3.61it/s]
69%|██████▉ | 104/150 [00:28<00:12, 3.61it/s]
70%|███████ | 105/150 [00:29<00:12, 3.61it/s]
71%|███████ | 106/150 [00:29<00:12, 3.61it/s]
71%|███████▏ | 107/150 [00:29<00:11, 3.61it/s]
72%|███████▏ | 108/150 [00:29<00:11, 3.61it/s]
73%|███████▎ | 109/150 [00:30<00:11, 3.61it/s]
73%|███████▎ | 110/150 [00:30<00:11, 3.61it/s]
74%|███████▍ | 111/150 [00:30<00:10, 3.61it/s]
75%|███████▍ | 112/150 [00:30<00:10, 3.61it/s]
75%|███████▌ | 113/150 [00:31<00:10, 3.61it/s]
76%|███████▌ | 114/150 [00:31<00:09, 3.61it/s]
77%|███████▋ | 115/150 [00:31<00:09, 3.61it/s]
77%|███████▋ | 116/150 [00:32<00:09, 3.61it/s]
78%|███████▊ | 117/150 [00:32<00:09, 3.61it/s]
79%|███████▊ | 118/150 [00:32<00:08, 3.61it/s]
79%|███████▉ | 119/150 [00:32<00:08, 3.61it/s]
80%|████████ | 120/150 [00:33<00:08, 3.61it/s]
81%|████████ | 121/150 [00:33<00:08, 3.61it/s]
81%|████████▏ | 122/150 [00:33<00:07, 3.61it/s]
82%|████████▏ | 123/150 [00:34<00:07, 3.60it/s]
83%|████████▎ | 124/150 [00:34<00:07, 3.61it/s]
83%|████████▎ | 125/150 [00:34<00:06, 3.61it/s]
84%|████████▍ | 126/150 [00:34<00:06, 3.61it/s]
85%|████████▍ | 127/150 [00:35<00:06, 3.61it/s]
85%|████████▌ | 128/150 [00:35<00:06, 3.61it/s]
86%|████████▌ | 129/150 [00:35<00:05, 3.61it/s]
87%|████████▋ | 130/150 [00:35<00:05, 3.61it/s]
87%|████████▋ | 131/150 [00:36<00:05, 3.61it/s]
88%|████████▊ | 132/150 [00:36<00:04, 3.61it/s]
89%|████████▊ | 133/150 [00:36<00:04, 3.61it/s]
89%|████████▉ | 134/150 [00:37<00:04, 3.61it/s]
90%|█████████ | 135/150 [00:37<00:04, 3.62it/s]
91%|█████████ | 136/150 [00:37<00:03, 3.61it/s]
91%|█████████▏| 137/150 [00:37<00:03, 3.61it/s]
92%|█████████▏| 138/150 [00:38<00:03, 3.61it/s]
93%|█████████▎| 139/150 [00:38<00:03, 3.61it/s]
93%|█████████▎| 140/150 [00:38<00:02, 3.61it/s]
94%|█████████▍| 141/150 [00:38<00:02, 3.61it/s]
95%|█████████▍| 142/150 [00:39<00:02, 3.61it/s]
95%|█████████▌| 143/150 [00:39<00:01, 3.61it/s]
96%|█████████▌| 144/150 [00:39<00:01, 3.61it/s]
97%|█████████▋| 145/150 [00:40<00:01, 3.61it/s]
97%|█████████▋| 146/150 [00:40<00:01, 3.61it/s]
98%|█████████▊| 147/150 [00:40<00:00, 3.61it/s]
99%|█████████▊| 148/150 [00:40<00:00, 3.61it/s]
99%|█████████▉| 149/150 [00:41<00:00, 3.61it/s]
100%|██████████| 150/150 [00:41<00:00, 3.60it/s]
100%|██████████| 150/150 [00:41<00:00, 3.62it/s]