fofr
/
sdxl-matrix-code
An SDXL fine-tune based on Matrix Code art
- Public
- 280 runs
-
L40S
Prediction
fofr/sdxl-matrix-code:b30d790aIDc7lcns3brimcm6vkgudxvhf3wiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1024
- height
- 1024
- prompt
- A landscape photo in the style of TOK, detailed, 8k
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.9
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.95
- negative_prompt
- skintones, garish, low quality, jpeg artifacts, black and white
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK, detailed, 8k", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.9, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "skintones, garish, low quality, jpeg artifacts, black and white", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Set theREPLICATE_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 fofr/sdxl-matrix-code using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-matrix-code:b30d790aac7564f348fe93c3c7e152ca87e03373b913164392258eec1666895a", { input: { width: 1024, height: 1024, prompt: "A landscape photo in the style of TOK, detailed, 8k", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.9, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.95, negative_prompt: "skintones, garish, low quality, jpeg artifacts, black and white", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the client:import replicate
Run fofr/sdxl-matrix-code using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-matrix-code:b30d790aac7564f348fe93c3c7e152ca87e03373b913164392258eec1666895a", input={ "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK, detailed, 8k", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.9, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.95, "negative_prompt": "skintones, garish, low quality, jpeg artifacts, black and white", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-matrix-code 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": "b30d790aac7564f348fe93c3c7e152ca87e03373b913164392258eec1666895a", "input": { "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK, detailed, 8k", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.9, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "skintones, garish, low quality, jpeg artifacts, black and white", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-11T19:40:21.325250Z", "created_at": "2023-08-11T19:39:25.385624Z", "data_removed": false, "error": null, "id": "c7lcns3brimcm6vkgudxvhf3wi", "input": { "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK, detailed, 8k", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.9, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "skintones, garish, low quality, jpeg artifacts, black and white", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 35589\nPrompt: A landscape photo in the style of <s0><s1>, detailed, 8k\ntxt2img mode\n 0%| | 0/47 [00:00<?, ?it/s]\n 2%|▏ | 1/47 [00:01<00:46, 1.01s/it]\n 4%|▍ | 2/47 [00:02<00:45, 1.01s/it]\n 6%|▋ | 3/47 [00:03<00:44, 1.02s/it]\n 9%|▊ | 4/47 [00:04<00:43, 1.01s/it]\n 11%|█ | 5/47 [00:05<00:42, 1.01s/it]\n 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it]\n 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it]\n 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it]\n 19%|█▉ | 9/47 [00:09<00:38, 1.01s/it]\n 21%|██▏ | 10/47 [00:10<00:37, 1.01s/it]\n 23%|██▎ | 11/47 [00:11<00:36, 1.01s/it]\n 26%|██▌ | 12/47 [00:12<00:35, 1.01s/it]\n 28%|██▊ | 13/47 [00:13<00:34, 1.01s/it]\n 30%|██▉ | 14/47 [00:14<00:33, 1.01s/it]\n 32%|███▏ | 15/47 [00:15<00:32, 1.01s/it]\n 34%|███▍ | 16/47 [00:16<00:31, 1.01s/it]\n 36%|███▌ | 17/47 [00:17<00:30, 1.01s/it]\n 38%|███▊ | 18/47 [00:18<00:29, 1.01s/it]\n 40%|████ | 19/47 [00:19<00:28, 1.02s/it]\n 43%|████▎ | 20/47 [00:20<00:27, 1.01s/it]\n 45%|████▍ | 21/47 [00:21<00:26, 1.02s/it]\n 47%|████▋ | 22/47 [00:22<00:25, 1.02s/it]\n 49%|████▉ | 23/47 [00:23<00:24, 1.02s/it]\n 51%|█████ | 24/47 [00:24<00:23, 1.02s/it]\n 53%|█████▎ | 25/47 [00:25<00:22, 1.02s/it]\n 55%|█████▌ | 26/47 [00:26<00:21, 1.02s/it]\n 57%|█████▋ | 27/47 [00:27<00:20, 1.02s/it]\n 60%|█████▉ | 28/47 [00:28<00:19, 1.02s/it]\n 62%|██████▏ | 29/47 [00:29<00:18, 1.02s/it]\n 64%|██████▍ | 30/47 [00:30<00:17, 1.02s/it]\n 66%|██████▌ | 31/47 [00:31<00:16, 1.02s/it]\n 68%|██████▊ | 32/47 [00:32<00:15, 1.02s/it]\n 70%|███████ | 33/47 [00:33<00:14, 1.02s/it]\n 72%|███████▏ | 34/47 [00:34<00:13, 1.02s/it]\n 74%|███████▍ | 35/47 [00:35<00:12, 1.02s/it]\n 77%|███████▋ | 36/47 [00:36<00:11, 1.02s/it]\n 79%|███████▊ | 37/47 [00:37<00:10, 1.02s/it]\n 81%|████████ | 38/47 [00:38<00:09, 1.02s/it]\n 83%|████████▎ | 39/47 [00:39<00:08, 1.02s/it]\n 85%|████████▌ | 40/47 [00:40<00:07, 1.02s/it]\n 87%|████████▋ | 41/47 [00:41<00:06, 1.02s/it]\n 89%|████████▉ | 42/47 [00:42<00:05, 1.02s/it]\n 91%|█████████▏| 43/47 [00:43<00:04, 1.02s/it]\n 94%|█████████▎| 44/47 [00:44<00:03, 1.02s/it]\n 96%|█████████▌| 45/47 [00:45<00:02, 1.02s/it]\n 98%|█████████▊| 46/47 [00:46<00:01, 1.02s/it]\n100%|██████████| 47/47 [00:47<00:00, 1.02s/it]\n100%|██████████| 47/47 [00:47<00:00, 1.02s/it]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.21it/s]\n 67%|██████▋ | 2/3 [00:01<00:00, 1.21it/s]\n100%|██████████| 3/3 [00:02<00:00, 1.21it/s]\n100%|██████████| 3/3 [00:02<00:00, 1.21it/s]", "metrics": { "predict_time": 55.924204, "total_time": 55.939626 }, "output": [ "https://replicate.delivery/pbxt/lxgUOA1XqhZPMFLU4925kxWECPNWLoZZbedoPhBo32KRBisIA/out-0.png", "https://replicate.delivery/pbxt/oftOTjfkcfAXjIo2iEdfAjDeRrwK0RRweZojlLerHZorRBisIA/out-1.png", "https://replicate.delivery/pbxt/nNqbN00XnM4xAZy8tvBgDnbBw0g5cGLQtyhh0TXE7sLpARWE/out-2.png", "https://replicate.delivery/pbxt/XFbev1erjQlIfI0XhXcfyE8AoXM0zAfYKlXqA9E9ZkwrUgILC/out-3.png" ], "started_at": "2023-08-11T19:39:25.401046Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c7lcns3brimcm6vkgudxvhf3wi", "cancel": "https://api.replicate.com/v1/predictions/c7lcns3brimcm6vkgudxvhf3wi/cancel" }, "version": "b30d790aac7564f348fe93c3c7e152ca87e03373b913164392258eec1666895a" }
Generated inUsing seed: 35589 Prompt: A landscape photo in the style of <s0><s1>, detailed, 8k txt2img mode 0%| | 0/47 [00:00<?, ?it/s] 2%|▏ | 1/47 [00:01<00:46, 1.01s/it] 4%|▍ | 2/47 [00:02<00:45, 1.01s/it] 6%|▋ | 3/47 [00:03<00:44, 1.02s/it] 9%|▊ | 4/47 [00:04<00:43, 1.01s/it] 11%|█ | 5/47 [00:05<00:42, 1.01s/it] 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it] 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it] 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it] 19%|█▉ | 9/47 [00:09<00:38, 1.01s/it] 21%|██▏ | 10/47 [00:10<00:37, 1.01s/it] 23%|██▎ | 11/47 [00:11<00:36, 1.01s/it] 26%|██▌ | 12/47 [00:12<00:35, 1.01s/it] 28%|██▊ | 13/47 [00:13<00:34, 1.01s/it] 30%|██▉ | 14/47 [00:14<00:33, 1.01s/it] 32%|███▏ | 15/47 [00:15<00:32, 1.01s/it] 34%|███▍ | 16/47 [00:16<00:31, 1.01s/it] 36%|███▌ | 17/47 [00:17<00:30, 1.01s/it] 38%|███▊ | 18/47 [00:18<00:29, 1.01s/it] 40%|████ | 19/47 [00:19<00:28, 1.02s/it] 43%|████▎ | 20/47 [00:20<00:27, 1.01s/it] 45%|████▍ | 21/47 [00:21<00:26, 1.02s/it] 47%|████▋ | 22/47 [00:22<00:25, 1.02s/it] 49%|████▉ | 23/47 [00:23<00:24, 1.02s/it] 51%|█████ | 24/47 [00:24<00:23, 1.02s/it] 53%|█████▎ | 25/47 [00:25<00:22, 1.02s/it] 55%|█████▌ | 26/47 [00:26<00:21, 1.02s/it] 57%|█████▋ | 27/47 [00:27<00:20, 1.02s/it] 60%|█████▉ | 28/47 [00:28<00:19, 1.02s/it] 62%|██████▏ | 29/47 [00:29<00:18, 1.02s/it] 64%|██████▍ | 30/47 [00:30<00:17, 1.02s/it] 66%|██████▌ | 31/47 [00:31<00:16, 1.02s/it] 68%|██████▊ | 32/47 [00:32<00:15, 1.02s/it] 70%|███████ | 33/47 [00:33<00:14, 1.02s/it] 72%|███████▏ | 34/47 [00:34<00:13, 1.02s/it] 74%|███████▍ | 35/47 [00:35<00:12, 1.02s/it] 77%|███████▋ | 36/47 [00:36<00:11, 1.02s/it] 79%|███████▊ | 37/47 [00:37<00:10, 1.02s/it] 81%|████████ | 38/47 [00:38<00:09, 1.02s/it] 83%|████████▎ | 39/47 [00:39<00:08, 1.02s/it] 85%|████████▌ | 40/47 [00:40<00:07, 1.02s/it] 87%|████████▋ | 41/47 [00:41<00:06, 1.02s/it] 89%|████████▉ | 42/47 [00:42<00:05, 1.02s/it] 91%|█████████▏| 43/47 [00:43<00:04, 1.02s/it] 94%|█████████▎| 44/47 [00:44<00:03, 1.02s/it] 96%|█████████▌| 45/47 [00:45<00:02, 1.02s/it] 98%|█████████▊| 46/47 [00:46<00:01, 1.02s/it] 100%|██████████| 47/47 [00:47<00:00, 1.02s/it] 100%|██████████| 47/47 [00:47<00:00, 1.02s/it] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.21it/s] 67%|██████▋ | 2/3 [00:01<00:00, 1.21it/s] 100%|██████████| 3/3 [00:02<00:00, 1.21it/s] 100%|██████████| 3/3 [00:02<00:00, 1.21it/s]
Prediction
fofr/sdxl-matrix-code:b30d790aIDrg3qmrtbbou2yh7yntl2dnt3gyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Set theREPLICATE_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 fofr/sdxl-matrix-code using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-matrix-code:b30d790aac7564f348fe93c3c7e152ca87e03373b913164392258eec1666895a", { input: { width: 1024, height: 1024, prompt: "A photo in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the client:import replicate
Run fofr/sdxl-matrix-code using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-matrix-code:b30d790aac7564f348fe93c3c7e152ca87e03373b913164392258eec1666895a", input={ "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-matrix-code 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": "b30d790aac7564f348fe93c3c7e152ca87e03373b913164392258eec1666895a", "input": { "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-11T19:30:15.943131Z", "created_at": "2023-08-11T19:29:57.473770Z", "data_removed": false, "error": null, "id": "rg3qmrtbbou2yh7yntl2dnt3gy", "input": { "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 35257\nPrompt: A photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:37, 1.31it/s]\n 4%|▍ | 2/50 [00:01<00:22, 2.11it/s]\n 6%|▌ | 3/50 [00:01<00:17, 2.61it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.95it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.17it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.32it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.43it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.51it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.56it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.60it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:05<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:08<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.54it/s]", "metrics": { "predict_time": 16.715543, "total_time": 18.469361 }, "output": [ "https://replicate.delivery/pbxt/uMSu4eB2fnu3p0dByZcjEITdJeiaqBP12bx252VVNrcMyHyiA/out-0.png" ], "started_at": "2023-08-11T19:29:59.227588Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rg3qmrtbbou2yh7yntl2dnt3gy", "cancel": "https://api.replicate.com/v1/predictions/rg3qmrtbbou2yh7yntl2dnt3gy/cancel" }, "version": "b30d790aac7564f348fe93c3c7e152ca87e03373b913164392258eec1666895a" }
Generated inUsing seed: 35257 Prompt: A photo in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:37, 1.31it/s] 4%|▍ | 2/50 [00:01<00:22, 2.11it/s] 6%|▌ | 3/50 [00:01<00:17, 2.61it/s] 8%|▊ | 4/50 [00:01<00:15, 2.95it/s] 10%|█ | 5/50 [00:01<00:14, 3.17it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.32it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.43it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.51it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.56it/s] 20%|██ | 10/50 [00:03<00:11, 3.60it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:05<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.67it/s] 50%|█████ | 25/50 [00:07<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:08<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:10<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.66it/s] 80%|████████ | 40/50 [00:11<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:14<00:00, 3.65it/s] 100%|██████████| 50/50 [00:14<00:00, 3.54it/s]
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