rinnakk
/
japanese-stable-diffusion
Japanese-specific latent text-to-image diffusion model
Prediction
rinnakk/japanese-stable-diffusion:183d9142ID6cswdgufubhlvpl3umwuzl2qpqStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- サラリーマン 油絵
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "prompt": "サラリーマン 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 rinnakk/japanese-stable-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rinnakk/japanese-stable-diffusion:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be", { input: { prompt: "サラリーマン 油絵", num_outputs: 1, guidance_scale: 7.5, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun rinnakk/japanese-stable-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rinnakk/japanese-stable-diffusion:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be", input={ "prompt": "サラリーマン 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run rinnakk/japanese-stable-diffusion 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": "183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be", "input": { "prompt": "サラリーマン 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ 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/rinnakk/japanese-stable-diffusion@sha256:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be \ -i 'prompt="サラリーマン 油絵"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=50'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/rinnakk/japanese-stable-diffusion@sha256:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "prompt": "サラリーマン 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-09-17T22:25:55.638967Z", "created_at": "2022-09-17T22:25:40.947972Z", "data_removed": false, "error": null, "id": "6cswdgufubhlvpl3umwuzl2qpq", "input": { "prompt": "サラリーマン 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 13560\nGlobal seed set to 13560\n\n 0%| | 0/51 [00:00<?, ?it/s]\n 2%|▏ | 1/51 [00:00<00:08, 5.81it/s]\n 4%|▍ | 2/51 [00:00<00:11, 4.33it/s]\n 6%|▌ | 3/51 [00:00<00:11, 4.12it/s]\n 8%|▊ | 4/51 [00:00<00:11, 4.01it/s]\n 10%|▉ | 5/51 [00:01<00:11, 3.95it/s]\n 12%|█▏ | 6/51 [00:01<00:11, 3.89it/s]\n 14%|█▎ | 7/51 [00:01<00:11, 3.88it/s]\n 16%|█▌ | 8/51 [00:02<00:11, 3.87it/s]\n 18%|█▊ | 9/51 [00:02<00:10, 3.87it/s]\n 20%|█▉ | 10/51 [00:02<00:10, 3.84it/s]\n 22%|██▏ | 11/51 [00:02<00:10, 3.83it/s]\n 24%|██▎ | 12/51 [00:03<00:10, 3.84it/s]\n 25%|██▌ | 13/51 [00:03<00:09, 3.85it/s]\n 27%|██▋ | 14/51 [00:03<00:09, 3.83it/s]\n 29%|██▉ | 15/51 [00:03<00:09, 3.83it/s]\n 31%|███▏ | 16/51 [00:04<00:09, 3.83it/s]\n 33%|███▎ | 17/51 [00:04<00:08, 3.84it/s]\n 35%|███▌ | 18/51 [00:04<00:08, 3.83it/s]\n 37%|███▋ | 19/51 [00:04<00:08, 3.82it/s]\n 39%|███▉ | 20/51 [00:05<00:08, 3.81it/s]\n 41%|████ | 21/51 [00:05<00:07, 3.81it/s]\n 43%|████▎ | 22/51 [00:05<00:07, 3.81it/s]\n 45%|████▌ | 23/51 [00:05<00:07, 3.80it/s]\n 47%|████▋ | 24/51 [00:06<00:07, 3.80it/s]\n 49%|████▉ | 25/51 [00:06<00:06, 3.81it/s]\n 51%|█████ | 26/51 [00:06<00:06, 3.81it/s]\n 53%|█████▎ | 27/51 [00:06<00:06, 3.80it/s]\n 55%|█████▍ | 28/51 [00:07<00:06, 3.81it/s]\n 57%|█████▋ | 29/51 [00:07<00:05, 3.82it/s]\n 59%|█████▉ | 30/51 [00:07<00:05, 3.82it/s]\n 61%|██████ | 31/51 [00:08<00:05, 3.82it/s]\n 63%|██████▎ | 32/51 [00:08<00:04, 3.83it/s]\n 65%|██████▍ | 33/51 [00:08<00:04, 3.82it/s]\n 67%|██████▋ | 34/51 [00:08<00:04, 3.81it/s]\n 69%|██████▊ | 35/51 [00:09<00:04, 3.82it/s]\n 71%|███████ | 36/51 [00:09<00:03, 3.82it/s]\n 73%|███████▎ | 37/51 [00:09<00:03, 3.82it/s]\n 75%|███████▍ | 38/51 [00:09<00:03, 3.81it/s]\n 76%|███████▋ | 39/51 [00:10<00:03, 3.81it/s]\n 78%|███████▊ | 40/51 [00:10<00:02, 3.80it/s]\n 80%|████████ | 41/51 [00:10<00:02, 3.81it/s]\n 82%|████████▏ | 42/51 [00:10<00:02, 3.81it/s]\n 84%|████████▍ | 43/51 [00:11<00:02, 3.82it/s]\n 86%|████████▋ | 44/51 [00:11<00:01, 3.81it/s]\n 88%|████████▊ | 45/51 [00:11<00:01, 3.81it/s]\n 90%|█████████ | 46/51 [00:11<00:01, 3.80it/s]\n 92%|█████████▏| 47/51 [00:12<00:01, 3.79it/s]\n 94%|█████████▍| 48/51 [00:12<00:00, 3.79it/s]\n 96%|█████████▌| 49/51 [00:12<00:00, 3.79it/s]\n 98%|█████████▊| 50/51 [00:13<00:00, 3.79it/s]\n100%|██████████| 51/51 [00:13<00:00, 3.79it/s]\n100%|██████████| 51/51 [00:13<00:00, 3.84it/s]", "metrics": { "predict_time": 14.481305, "total_time": 14.690995 }, "output": [ "https://replicate.delivery/mgxm/fa193a69-05e6-471f-a8bf-96fe1ed65b1b/out-0.png" ], "started_at": "2022-09-17T22:25:41.157662Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6cswdgufubhlvpl3umwuzl2qpq", "cancel": "https://api.replicate.com/v1/predictions/6cswdgufubhlvpl3umwuzl2qpq/cancel" }, "version": "183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be" }Generated inUsing seed: 13560 Global seed set to 13560 0%| | 0/51 [00:00<?, ?it/s] 2%|▏ | 1/51 [00:00<00:08, 5.81it/s] 4%|▍ | 2/51 [00:00<00:11, 4.33it/s] 6%|▌ | 3/51 [00:00<00:11, 4.12it/s] 8%|▊ | 4/51 [00:00<00:11, 4.01it/s] 10%|▉ | 5/51 [00:01<00:11, 3.95it/s] 12%|█▏ | 6/51 [00:01<00:11, 3.89it/s] 14%|█▎ | 7/51 [00:01<00:11, 3.88it/s] 16%|█▌ | 8/51 [00:02<00:11, 3.87it/s] 18%|█▊ | 9/51 [00:02<00:10, 3.87it/s] 20%|█▉ | 10/51 [00:02<00:10, 3.84it/s] 22%|██▏ | 11/51 [00:02<00:10, 3.83it/s] 24%|██▎ | 12/51 [00:03<00:10, 3.84it/s] 25%|██▌ | 13/51 [00:03<00:09, 3.85it/s] 27%|██▋ | 14/51 [00:03<00:09, 3.83it/s] 29%|██▉ | 15/51 [00:03<00:09, 3.83it/s] 31%|███▏ | 16/51 [00:04<00:09, 3.83it/s] 33%|███▎ | 17/51 [00:04<00:08, 3.84it/s] 35%|███▌ | 18/51 [00:04<00:08, 3.83it/s] 37%|███▋ | 19/51 [00:04<00:08, 3.82it/s] 39%|███▉ | 20/51 [00:05<00:08, 3.81it/s] 41%|████ | 21/51 [00:05<00:07, 3.81it/s] 43%|████▎ | 22/51 [00:05<00:07, 3.81it/s] 45%|████▌ | 23/51 [00:05<00:07, 3.80it/s] 47%|████▋ | 24/51 [00:06<00:07, 3.80it/s] 49%|████▉ | 25/51 [00:06<00:06, 3.81it/s] 51%|█████ | 26/51 [00:06<00:06, 3.81it/s] 53%|█████▎ | 27/51 [00:06<00:06, 3.80it/s] 55%|█████▍ | 28/51 [00:07<00:06, 3.81it/s] 57%|█████▋ | 29/51 [00:07<00:05, 3.82it/s] 59%|█████▉ | 30/51 [00:07<00:05, 3.82it/s] 61%|██████ | 31/51 [00:08<00:05, 3.82it/s] 63%|██████▎ | 32/51 [00:08<00:04, 3.83it/s] 65%|██████▍ | 33/51 [00:08<00:04, 3.82it/s] 67%|██████▋ | 34/51 [00:08<00:04, 3.81it/s] 69%|██████▊ | 35/51 [00:09<00:04, 3.82it/s] 71%|███████ | 36/51 [00:09<00:03, 3.82it/s] 73%|███████▎ | 37/51 [00:09<00:03, 3.82it/s] 75%|███████▍ | 38/51 [00:09<00:03, 3.81it/s] 76%|███████▋ | 39/51 [00:10<00:03, 3.81it/s] 78%|███████▊ | 40/51 [00:10<00:02, 3.80it/s] 80%|████████ | 41/51 [00:10<00:02, 3.81it/s] 82%|████████▏ | 42/51 [00:10<00:02, 3.81it/s] 84%|████████▍ | 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rinnakk/japanese-stable-diffusion:183d9142IDrblrwajv4zaaza3fvxgf2yndu4StatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- 猫の肖像画 油絵
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "prompt": "猫の肖像画 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 rinnakk/japanese-stable-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rinnakk/japanese-stable-diffusion:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be", { input: { prompt: "猫の肖像画 油絵", num_outputs: 1, guidance_scale: 7.5, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun rinnakk/japanese-stable-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rinnakk/japanese-stable-diffusion:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be", input={ "prompt": "猫の肖像画 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run rinnakk/japanese-stable-diffusion 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": "183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be", "input": { "prompt": "猫の肖像画 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ 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/rinnakk/japanese-stable-diffusion@sha256:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be \ -i 'prompt="猫の肖像画 油絵"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=50'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/rinnakk/japanese-stable-diffusion@sha256:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "prompt": "猫の肖像画 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-09-17T22:27:10.800154Z", "created_at": "2022-09-17T22:26:55.960122Z", "data_removed": false, "error": null, "id": "rblrwajv4zaaza3fvxgf2yndu4", "input": { "prompt": "猫の肖像画 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 4659\nGlobal seed set to 4659\n\n 0%| | 0/51 [00:00<?, ?it/s]\n 2%|▏ | 1/51 [00:00<00:09, 5.35it/s]\n 4%|▍ | 2/51 [00:00<00:11, 4.31it/s]\n 6%|▌ | 3/51 [00:00<00:11, 4.07it/s]\n 8%|▊ | 4/51 [00:00<00:11, 3.97it/s]\n 10%|▉ | 5/51 [00:01<00:11, 3.89it/s]\n 12%|█▏ | 6/51 [00:01<00:11, 3.86it/s]\n 14%|█▎ | 7/51 [00:01<00:11, 3.84it/s]\n 16%|█▌ | 8/51 [00:02<00:11, 3.84it/s]\n 18%|█▊ | 9/51 [00:02<00:10, 3.82it/s]\n 20%|█▉ | 10/51 [00:02<00:10, 3.80it/s]\n 22%|██▏ | 11/51 [00:02<00:10, 3.81it/s]\n 24%|██▎ | 12/51 [00:03<00:10, 3.81it/s]\n 25%|██▌ | 13/51 [00:03<00:10, 3.80it/s]\n 27%|██▋ | 14/51 [00:03<00:09, 3.80it/s]\n 29%|██▉ | 15/51 [00:03<00:09, 3.80it/s]\n 31%|███▏ | 16/51 [00:04<00:09, 3.79it/s]\n 33%|███▎ | 17/51 [00:04<00:08, 3.79it/s]\n 35%|███▌ | 18/51 [00:04<00:08, 3.78it/s]\n 37%|███▋ | 19/51 [00:04<00:08, 3.78it/s]\n 39%|███▉ | 20/51 [00:05<00:08, 3.78it/s]\n 41%|████ | 21/51 [00:05<00:07, 3.78it/s]\n 43%|████▎ | 22/51 [00:05<00:07, 3.77it/s]\n 45%|████▌ | 23/51 [00:05<00:07, 3.77it/s]\n 47%|████▋ | 24/51 [00:06<00:07, 3.77it/s]\n 49%|████▉ | 25/51 [00:06<00:06, 3.76it/s]\n 51%|█████ | 26/51 [00:06<00:06, 3.76it/s]\n 53%|█████▎ | 27/51 [00:07<00:06, 3.77it/s]\n 55%|█████▍ | 28/51 [00:07<00:06, 3.77it/s]\n 57%|█████▋ | 29/51 [00:07<00:05, 3.78it/s]\n 59%|█████▉ | 30/51 [00:07<00:05, 3.78it/s]\n 61%|██████ | 31/51 [00:08<00:05, 3.78it/s]\n 63%|██████▎ | 32/51 [00:08<00:05, 3.77it/s]\n 65%|██████▍ | 33/51 [00:08<00:04, 3.77it/s]\n 67%|██████▋ | 34/51 [00:08<00:04, 3.78it/s]\n 69%|██████▊ | 35/51 [00:09<00:04, 3.76it/s]\n 71%|███████ | 36/51 [00:09<00:03, 3.75it/s]\n 73%|███████▎ | 37/51 [00:09<00:03, 3.76it/s]\n 75%|███████▍ | 38/51 [00:09<00:03, 3.77it/s]\n 76%|███████▋ | 39/51 [00:10<00:03, 3.77it/s]\n 78%|███████▊ | 40/51 [00:10<00:02, 3.77it/s]\n 80%|████████ | 41/51 [00:10<00:02, 3.77it/s]\n 82%|████████▏ | 42/51 [00:11<00:02, 3.77it/s]\n 84%|████████▍ | 43/51 [00:11<00:02, 3.77it/s]\n 86%|████████▋ | 44/51 [00:11<00:01, 3.76it/s]\n 88%|████████▊ | 45/51 [00:11<00:01, 3.75it/s]\n 90%|█████████ | 46/51 [00:12<00:01, 3.76it/s]\n 92%|█████████▏| 47/51 [00:12<00:01, 3.76it/s]\n 94%|█████████▍| 48/51 [00:12<00:00, 3.75it/s]\n 96%|█████████▌| 49/51 [00:12<00:00, 3.75it/s]\n 98%|█████████▊| 50/51 [00:13<00:00, 3.75it/s]\n100%|██████████| 51/51 [00:13<00:00, 3.75it/s]\n100%|██████████| 51/51 [00:13<00:00, 3.80it/s]", "metrics": { "predict_time": 14.633917, "total_time": 14.840032 }, "output": [ "https://replicate.delivery/mgxm/916bae42-a900-459d-9083-0cd09cf2b1a2/out-0.png" ], "started_at": "2022-09-17T22:26:56.166237Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rblrwajv4zaaza3fvxgf2yndu4", "cancel": "https://api.replicate.com/v1/predictions/rblrwajv4zaaza3fvxgf2yndu4/cancel" }, "version": "183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be" }Generated inUsing seed: 4659 Global seed set to 4659 0%| | 0/51 [00:00<?, ?it/s] 2%|▏ | 1/51 [00:00<00:09, 5.35it/s] 4%|▍ | 2/51 [00:00<00:11, 4.31it/s] 6%|▌ | 3/51 [00:00<00:11, 4.07it/s] 8%|▊ | 4/51 [00:00<00:11, 3.97it/s] 10%|▉ | 5/51 [00:01<00:11, 3.89it/s] 12%|█▏ | 6/51 [00:01<00:11, 3.86it/s] 14%|█▎ | 7/51 [00:01<00:11, 3.84it/s] 16%|█▌ | 8/51 [00:02<00:11, 3.84it/s] 18%|█▊ | 9/51 [00:02<00:10, 3.82it/s] 20%|█▉ | 10/51 [00:02<00:10, 3.80it/s] 22%|██▏ | 11/51 [00:02<00:10, 3.81it/s] 24%|██▎ | 12/51 [00:03<00:10, 3.81it/s] 25%|██▌ | 13/51 [00:03<00:10, 3.80it/s] 27%|██▋ | 14/51 [00:03<00:09, 3.80it/s] 29%|██▉ | 15/51 [00:03<00:09, 3.80it/s] 31%|███▏ | 16/51 [00:04<00:09, 3.79it/s] 33%|███▎ | 17/51 [00:04<00:08, 3.79it/s] 35%|███▌ | 18/51 [00:04<00:08, 3.78it/s] 37%|███▋ | 19/51 [00:04<00:08, 3.78it/s] 39%|███▉ | 20/51 [00:05<00:08, 3.78it/s] 41%|████ | 21/51 [00:05<00:07, 3.78it/s] 43%|████▎ | 22/51 [00:05<00:07, 3.77it/s] 45%|████▌ | 23/51 [00:05<00:07, 3.77it/s] 47%|████▋ | 24/51 [00:06<00:07, 3.77it/s] 49%|████▉ | 25/51 [00:06<00:06, 3.76it/s] 51%|█████ | 26/51 [00:06<00:06, 3.76it/s] 53%|█████▎ | 27/51 [00:07<00:06, 3.77it/s] 55%|█████▍ | 28/51 [00:07<00:06, 3.77it/s] 57%|█████▋ | 29/51 [00:07<00:05, 3.78it/s] 59%|█████▉ | 30/51 [00:07<00:05, 3.78it/s] 61%|██████ | 31/51 [00:08<00:05, 3.78it/s] 63%|██████▎ | 32/51 [00:08<00:05, 3.77it/s] 65%|██████▍ | 33/51 [00:08<00:04, 3.77it/s] 67%|██████▋ | 34/51 [00:08<00:04, 3.78it/s] 69%|██████▊ | 35/51 [00:09<00:04, 3.76it/s] 71%|███████ | 36/51 [00:09<00:03, 3.75it/s] 73%|███████▎ | 37/51 [00:09<00:03, 3.76it/s] 75%|███████▍ | 38/51 [00:09<00:03, 3.77it/s] 76%|███████▋ | 39/51 [00:10<00:03, 3.77it/s] 78%|███████▊ | 40/51 [00:10<00:02, 3.77it/s] 80%|████████ | 41/51 [00:10<00:02, 3.77it/s] 82%|████████▏ | 42/51 [00:11<00:02, 3.77it/s] 84%|████████▍ | 43/51 [00:11<00:02, 3.77it/s] 86%|████████▋ | 44/51 [00:11<00:01, 3.76it/s] 88%|████████▊ | 45/51 [00:11<00:01, 3.75it/s] 90%|█████████ | 46/51 [00:12<00:01, 3.76it/s] 92%|█████████▏| 47/51 [00:12<00:01, 3.76it/s] 94%|█████████▍| 48/51 [00:12<00:00, 3.75it/s] 96%|█████████▌| 49/51 [00:12<00:00, 3.75it/s] 98%|█████████▊| 50/51 [00:13<00:00, 3.75it/s] 100%|██████████| 51/51 [00:13<00:00, 3.75it/s] 100%|██████████| 51/51 [00:13<00:00, 3.80it/s]
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