cjwbw / tcs-sdxl-lora
Trajectory Consistency Distillation
- Public
- 576 runs
-
L40S
- Paper
Prediction
cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95dIDa53bbjlbjvwkyjqu3a3ahyboz4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- eta
- 0.3
- prompt
- Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.
- negative_prompt
- num_inference_steps
- 4
{ "eta": 0.3, "prompt": "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", "negative_prompt": "", "num_inference_steps": 4 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", { input: { eta: 0.3, prompt: "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", negative_prompt: "", num_inference_steps: 4 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", input={ "eta": 0.3, "prompt": "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", "negative_prompt": "", "num_inference_steps": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/tcs-sdxl-lora 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": "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", "input": { "eta": 0.3, "prompt": "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", "negative_prompt": "", "num_inference_steps": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-04T00:34:41.641084Z", "created_at": "2024-03-04T00:33:10.032748Z", "data_removed": false, "error": null, "id": "a53bbjlbjvwkyjqu3a3ahyboz4", "input": { "eta": 0.3, "prompt": "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", "negative_prompt": "", "num_inference_steps": 4 }, "logs": "Using seed: 8124\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.22it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.15it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.17it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.78it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.00it/s]", "metrics": { "predict_time": 2.999298, "total_time": 91.608336 }, "output": "https://replicate.delivery/pbxt/W6FaMCAN7lIAFBqSVBZP9GVxPhX3qV50Guri6J3ShYLIJLnE/out.png", "started_at": "2024-03-04T00:34:38.641786Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a53bbjlbjvwkyjqu3a3ahyboz4", "cancel": "https://api.replicate.com/v1/predictions/a53bbjlbjvwkyjqu3a3ahyboz4/cancel" }, "version": "b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d" }Generated inUsing seed: 8124 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.22it/s] 50%|█████ | 2/4 [00:00<00:00, 6.15it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.17it/s] 100%|██████████| 4/4 [00:00<00:00, 7.78it/s] 100%|██████████| 4/4 [00:00<00:00, 7.00it/s]Prediction
cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95dIDeo4t32dbtfdc3ch4kctog64wemStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- eta
- 0.3
- prompt
- Beautiful man, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.
- negative_prompt
- num_inference_steps
- 8
{ "eta": 0.3, "prompt": "Beautiful man, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", "negative_prompt": "", "num_inference_steps": 8 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", { input: { eta: 0.3, prompt: "Beautiful man, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", negative_prompt: "", num_inference_steps: 8 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", input={ "eta": 0.3, "prompt": "Beautiful man, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", "negative_prompt": "", "num_inference_steps": 8 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/tcs-sdxl-lora 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": "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", "input": { "eta": 0.3, "prompt": "Beautiful man, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", "negative_prompt": "", "num_inference_steps": 8 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-04T00:44:15.887759Z", "created_at": "2024-03-04T00:42:34.331268Z", "data_removed": false, "error": null, "id": "eo4t32dbtfdc3ch4kctog64wem", "input": { "eta": 0.3, "prompt": "Beautiful man, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", "negative_prompt": "", "num_inference_steps": 8 }, "logs": "Using seed: 33644\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:00<00:01, 4.32it/s]\n 25%|██▌ | 2/8 [00:00<00:00, 6.23it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 7.22it/s]\n 50%|█████ | 4/8 [00:00<00:00, 7.81it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 8.17it/s]\n 75%|███████▌ | 6/8 [00:00<00:00, 8.40it/s]\n 88%|████████▊ | 7/8 [00:00<00:00, 8.57it/s]\n100%|██████████| 8/8 [00:01<00:00, 8.68it/s]\n100%|██████████| 8/8 [00:01<00:00, 7.88it/s]", "metrics": { "predict_time": 3.359364, "total_time": 101.556491 }, "output": "https://replicate.delivery/pbxt/hBEZiLkIkzLtAdngk7BajefyCTCIrdmLkoDiSJn7g8e8aZ5kA/out.png", "started_at": "2024-03-04T00:44:12.528395Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/eo4t32dbtfdc3ch4kctog64wem", "cancel": "https://api.replicate.com/v1/predictions/eo4t32dbtfdc3ch4kctog64wem/cancel" }, "version": "b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d" }Generated inUsing seed: 33644 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:00<00:01, 4.32it/s] 25%|██▌ | 2/8 [00:00<00:00, 6.23it/s] 38%|███▊ | 3/8 [00:00<00:00, 7.22it/s] 50%|█████ | 4/8 [00:00<00:00, 7.81it/s] 62%|██████▎ | 5/8 [00:00<00:00, 8.17it/s] 75%|███████▌ | 6/8 [00:00<00:00, 8.40it/s] 88%|████████▊ | 7/8 [00:00<00:00, 8.57it/s] 100%|██████████| 8/8 [00:01<00:00, 8.68it/s] 100%|██████████| 8/8 [00:01<00:00, 7.88it/s]Prediction
cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95dIDvnyeu3lbnammj4sqjoxjlo35yqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- eta
- 0.3
- prompt
- closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.
- negative_prompt
- num_inference_steps
- 16
{ "eta": 0.3, "prompt": "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.", "negative_prompt": "", "num_inference_steps": 16 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", { input: { eta: 0.3, prompt: "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.", negative_prompt: "", num_inference_steps: 16 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", input={ "eta": 0.3, "prompt": "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.", "negative_prompt": "", "num_inference_steps": 16 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/tcs-sdxl-lora 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": "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", "input": { "eta": 0.3, "prompt": "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.", "negative_prompt": "", "num_inference_steps": 16 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-04T00:46:08.955857Z", "created_at": "2024-03-04T00:46:04.880508Z", "data_removed": false, "error": null, "id": "vnyeu3lbnammj4sqjoxjlo35yq", "input": { "eta": 0.3, "prompt": "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.", "negative_prompt": "", "num_inference_steps": 16 }, "logs": "Using seed: 39033\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:01, 9.01it/s]\n 12%|█▎ | 2/16 [00:00<00:01, 8.93it/s]\n 19%|█▉ | 3/16 [00:00<00:01, 8.88it/s]\n 25%|██▌ | 4/16 [00:00<00:01, 8.88it/s]\n 31%|███▏ | 5/16 [00:00<00:01, 8.88it/s]\n 38%|███▊ | 6/16 [00:00<00:01, 8.86it/s]\n 44%|████▍ | 7/16 [00:00<00:01, 8.84it/s]\n 50%|█████ | 8/16 [00:00<00:00, 8.84it/s]\n 56%|█████▋ | 9/16 [00:01<00:00, 8.84it/s]\n 62%|██████▎ | 10/16 [00:01<00:00, 8.82it/s]\n 69%|██████▉ | 11/16 [00:01<00:00, 8.81it/s]\n 75%|███████▌ | 12/16 [00:01<00:00, 8.81it/s]\n 81%|████████▏ | 13/16 [00:01<00:00, 8.81it/s]\n 88%|████████▊ | 14/16 [00:01<00:00, 8.82it/s]\n 94%|█████████▍| 15/16 [00:01<00:00, 8.82it/s]\n100%|██████████| 16/16 [00:01<00:00, 8.83it/s]\n100%|██████████| 16/16 [00:01<00:00, 8.84it/s]", "metrics": { "predict_time": 4.032461, "total_time": 4.075349 }, "output": "https://replicate.delivery/pbxt/XxcqFfQLJCwXekEZH9Ef4hoSMxmXM1nR9d7cMxvqE8se8yyJB/out.png", "started_at": "2024-03-04T00:46:04.923396Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vnyeu3lbnammj4sqjoxjlo35yq", "cancel": "https://api.replicate.com/v1/predictions/vnyeu3lbnammj4sqjoxjlo35yq/cancel" }, "version": "b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d" }Generated inUsing seed: 39033 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:01, 9.01it/s] 12%|█▎ | 2/16 [00:00<00:01, 8.93it/s] 19%|█▉ | 3/16 [00:00<00:01, 8.88it/s] 25%|██▌ | 4/16 [00:00<00:01, 8.88it/s] 31%|███▏ | 5/16 [00:00<00:01, 8.88it/s] 38%|███▊ | 6/16 [00:00<00:01, 8.86it/s] 44%|████▍ | 7/16 [00:00<00:01, 8.84it/s] 50%|█████ | 8/16 [00:00<00:00, 8.84it/s] 56%|█████▋ | 9/16 [00:01<00:00, 8.84it/s] 62%|██████▎ | 10/16 [00:01<00:00, 8.82it/s] 69%|██████▉ | 11/16 [00:01<00:00, 8.81it/s] 75%|███████▌ | 12/16 [00:01<00:00, 8.81it/s] 81%|████████▏ | 13/16 [00:01<00:00, 8.81it/s] 88%|████████▊ | 14/16 [00:01<00:00, 8.82it/s] 94%|█████████▍| 15/16 [00:01<00:00, 8.82it/s] 100%|██████████| 16/16 [00:01<00:00, 8.83it/s] 100%|██████████| 16/16 [00:01<00:00, 8.84it/s]Prediction
cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95dID64wpt4dbhhrqorsrmzvph35jxmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- eta
- 0.3
- prompt
- Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.
- negative_prompt
- num_inference_steps
- 16
{ "eta": 0.3, "prompt": "Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.", "negative_prompt": "", "num_inference_steps": 16 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", { input: { eta: 0.3, prompt: "Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.", negative_prompt: "", num_inference_steps: 16 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", input={ "eta": 0.3, "prompt": "Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.", "negative_prompt": "", "num_inference_steps": 16 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/tcs-sdxl-lora 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": "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d", "input": { "eta": 0.3, "prompt": "Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.", "negative_prompt": "", "num_inference_steps": 16 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-04T00:46:57.347044Z", "created_at": "2024-03-04T00:46:47.287081Z", "data_removed": false, "error": null, "id": "64wpt4dbhhrqorsrmzvph35jxm", "input": { "eta": 0.3, "prompt": "Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.", "negative_prompt": "", "num_inference_steps": 16 }, "logs": "Using seed: 44028\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:01, 8.94it/s]\n 12%|█▎ | 2/16 [00:00<00:01, 8.85it/s]\n 19%|█▉ | 3/16 [00:00<00:01, 8.83it/s]\n 25%|██▌ | 4/16 [00:00<00:01, 8.82it/s]\n 31%|███▏ | 5/16 [00:00<00:01, 8.83it/s]\n 38%|███▊ | 6/16 [00:00<00:01, 8.85it/s]\n 44%|████▍ | 7/16 [00:00<00:01, 8.85it/s]\n 50%|█████ | 8/16 [00:00<00:00, 8.86it/s]\n 56%|█████▋ | 9/16 [00:01<00:00, 8.86it/s]\n 62%|██████▎ | 10/16 [00:01<00:00, 8.86it/s]\n 69%|██████▉ | 11/16 [00:01<00:00, 8.86it/s]\n 75%|███████▌ | 12/16 [00:01<00:00, 8.86it/s]\n 81%|████████▏ | 13/16 [00:01<00:00, 8.86it/s]\n 88%|████████▊ | 14/16 [00:01<00:00, 8.85it/s]\n 94%|█████████▍| 15/16 [00:01<00:00, 8.84it/s]\n100%|██████████| 16/16 [00:01<00:00, 8.86it/s]\n100%|██████████| 16/16 [00:01<00:00, 8.85it/s]", "metrics": { "predict_time": 4.030744, "total_time": 10.059963 }, "output": "https://replicate.delivery/pbxt/tFWf0eSkcGpVE0lNjM23tSXrHrV6TOfUFAjLYOa89oQBgZ5kA/out.png", "started_at": "2024-03-04T00:46:53.316300Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/64wpt4dbhhrqorsrmzvph35jxm", "cancel": "https://api.replicate.com/v1/predictions/64wpt4dbhhrqorsrmzvph35jxm/cancel" }, "version": "b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d" }Generated inUsing seed: 44028 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:01, 8.94it/s] 12%|█▎ | 2/16 [00:00<00:01, 8.85it/s] 19%|█▉ | 3/16 [00:00<00:01, 8.83it/s] 25%|██▌ | 4/16 [00:00<00:01, 8.82it/s] 31%|███▏ | 5/16 [00:00<00:01, 8.83it/s] 38%|███▊ | 6/16 [00:00<00:01, 8.85it/s] 44%|████▍ | 7/16 [00:00<00:01, 8.85it/s] 50%|█████ | 8/16 [00:00<00:00, 8.86it/s] 56%|█████▋ | 9/16 [00:01<00:00, 8.86it/s] 62%|██████▎ | 10/16 [00:01<00:00, 8.86it/s] 69%|██████▉ | 11/16 [00:01<00:00, 8.86it/s] 75%|███████▌ | 12/16 [00:01<00:00, 8.86it/s] 81%|████████▏ | 13/16 [00:01<00:00, 8.86it/s] 88%|████████▊ | 14/16 [00:01<00:00, 8.85it/s] 94%|█████████▍| 15/16 [00:01<00:00, 8.84it/s] 100%|██████████| 16/16 [00:01<00:00, 8.86it/s] 100%|██████████| 16/16 [00:01<00:00, 8.85it/s]
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