datacte/mobius
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mobius
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datacte/mobius
Mobius: Redefining State-of-the-Art in Debiased Diffusion Models
datacte/proteus-v0.1
ProteusV0.1 uses OpenDalleV1.1 as a base and further refines prompt adherence and stylistic capabilities to a measurable degree
datacte/proteus-v0.2
Proteus v0.2 shows subtle yet significant improvements over Version 0.1. It demonstrates enhanced prompt understanding that surpasses MJ6, while also approaching its stylistic capabilities.
datacte/proteus-v0.3
ProteusV0.3: The Anime Update
datacte/proteus-v0.4-lightning
ProteusV0.4: The Style Update - enhances stylistic capabilities, similar to Midjourney's approach, rather than advancing prompt comprehension
datacte/proteus-v0.4
ProteusV0.4: The Style Update
datacte/proteus-v0.5
ProteusV0.5 is the latest full release built as a sophisticated enhancement over OpenDalleV1.1
datacte/prometheusv1
PrometheusV1 is presumed to be the first full rank finetune of Playground v2.5
datacte/flux-aesthetic-anime
Flux lora, trained on the unique style and aesthetic of ghibli retro anime
datacte/flux-synthetic-anime
Flux lora, use "1980s anime screengrab", "VHS quality", or "syntheticanime" to trigger image generation
Prediction
datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624fIDs1kkmbgf19rj00cjd1ar3c1dg0StatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedby @datacteInput
- width
- 1024
- height
- 1024
- prompt
- The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 3
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 3, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run datacte/mobius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", { input: { width: 1024, height: 1024, prompt: "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 3, 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 replicateImport the client:import replicateRun datacte/mobius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", input={ "width": 1024, "height": 1024, "prompt": "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 3, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run datacte/mobius 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": "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", "input": { "width": 1024, "height": 1024, "prompt": "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 3, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-07T18:54:23.926538Z", "created_at": "2024-10-07T18:54:17.866000Z", "data_removed": false, "error": null, "id": "s1kkmbgf19rj00cjd1ar3c1dg0", "input": { "width": 1024, "height": 1024, "prompt": "The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 3, "num_inference_steps": 50 }, "logs": "Using seed: 21520\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 8.95it/s]\n 6%|▌ | 3/50 [00:00<00:04, 10.84it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.83it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.61it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.44it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.33it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.24it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.18it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.14it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 9.11it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 9.09it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.07it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.06it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.06it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.05it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.05it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 9.05it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.04it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 9.04it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 9.04it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.04it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.04it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.04it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.04it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.04it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 9.04it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.04it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.04it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 9.04it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.04it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.04it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.04it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.04it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.04it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 9.04it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 9.04it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.04it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.04it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.04it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.04it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.04it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.04it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.04it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 9.04it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 9.04it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 9.04it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.04it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.04it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.12it/s]", "metrics": { "predict_time": 6.052366482, "total_time": 6.060538 }, "output": [ "https://replicate.delivery/yhqm/rSV3LUK0laYpHhoMCvIoaFGHlOHkixw8ShLW2WpQ8fwvAPyJA/out-0.png" ], "started_at": "2024-10-07T18:54:17.874172Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/s1kkmbgf19rj00cjd1ar3c1dg0", "cancel": "https://api.replicate.com/v1/predictions/s1kkmbgf19rj00cjd1ar3c1dg0/cancel" }, "version": "197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f" }Generated inUsing seed: 21520 0%| | 0/50 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datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624fID8v4q89m6yhrj20cjd1b9y3sa4cStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 7
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run datacte/mobius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", { input: { width: 1024, height: 1024, prompt: "Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 7, 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 replicateImport the client:import replicateRun datacte/mobius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", input={ "width": 1024, "height": 1024, "prompt": "Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run datacte/mobius 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": "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", "input": { "width": 1024, "height": 1024, "prompt": "Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d\'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-07T18:56:00.183735Z", "created_at": "2024-10-07T18:55:54.100000Z", "data_removed": false, "error": null, "id": "8v4q89m6yhrj20cjd1b9y3sa4c", "input": { "width": 1024, "height": 1024, "prompt": "Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 50 }, "logs": "Using seed: 58492\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 8.95it/s]\n 6%|▌ | 3/50 [00:00<00:04, 10.82it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.81it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.59it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.43it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.32it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.24it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.18it/s]\n 22%|██▏ | 11/50 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"https://replicate.delivery/yhqm/wryLK772h15bJJ0FmDYTJHOcAQHRwIyVz3BnE3eeftUBG8InA/out-0.png" ], "started_at": "2024-10-07T18:55:54.109219Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/8v4q89m6yhrj20cjd1b9y3sa4c", "cancel": "https://api.replicate.com/v1/predictions/8v4q89m6yhrj20cjd1b9y3sa4c/cancel" }, "version": "197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f" }Generated inUsing seed: 58492 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:05, 8.95it/s] 6%|▌ | 3/50 [00:00<00:04, 10.82it/s] 10%|█ | 5/50 [00:00<00:04, 9.81it/s] 12%|█▏ | 6/50 [00:00<00:04, 9.59it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.43it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.32it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.24it/s] 20%|██ | 10/50 [00:01<00:04, 9.18it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.14it/s] 24%|██▍ | 12/50 [00:01<00:04, 9.11it/s] 26%|██▌ | 13/50 [00:01<00:04, 9.09it/s] 28%|██▊ | 14/50 [00:01<00:03, 9.07it/s] 30%|███ | 15/50 [00:01<00:03, 9.06it/s] 32%|███▏ | 16/50 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datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624fID78hp15tfndrj20cjd1cssnyj7cStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- anime boy, protagonist, best quality
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 7
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "anime boy, protagonist, best quality", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run datacte/mobius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", { input: { width: 1024, height: 1024, prompt: "anime boy, protagonist, best quality", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 7, 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 replicateImport the client:import replicateRun datacte/mobius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", input={ "width": 1024, "height": 1024, "prompt": "anime boy, protagonist, best quality", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run datacte/mobius 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": "datacte/mobius:197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f", "input": { "width": 1024, "height": 1024, "prompt": "anime boy, protagonist, best quality", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-07T18:59:02.656586Z", "created_at": "2024-10-07T18:58:56.555000Z", "data_removed": false, "error": null, "id": "78hp15tfndrj20cjd1cssnyj7c", "input": { "width": 1024, "height": 1024, "prompt": "anime boy, protagonist, best quality", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 50 }, "logs": "Using seed: 54551\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 8.94it/s]\n 6%|▌ | 3/50 [00:00<00:04, 10.81it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.82it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.60it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.44it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.32it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.23it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.18it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.14it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 9.11it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 9.09it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.07it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.06it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.06it/s]\n 34%|███▍ 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[00:04<00:01, 9.02it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.02it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.02it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.03it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.03it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.03it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.03it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 9.03it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 9.04it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 9.04it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.02it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.02it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.12it/s]", "metrics": { "predict_time": 6.090890986, "total_time": 6.101586 }, "output": [ "https://replicate.delivery/yhqm/gLJoBFC2WfSNCqetVuD8vfUc72ExfcNM7WLVEsaSYTFbX4ROB/out-0.png" ], "started_at": "2024-10-07T18:58:56.565695Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/78hp15tfndrj20cjd1cssnyj7c", "cancel": "https://api.replicate.com/v1/predictions/78hp15tfndrj20cjd1cssnyj7c/cancel" }, "version": "197f2145583f80c7c3ec520d2a1080aa7986601e1612e417ccd6e4f50fe0624f" }Generated inUsing seed: 54551 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:05, 8.94it/s] 6%|▌ | 3/50 [00:00<00:04, 10.81it/s] 10%|█ | 5/50 [00:00<00:04, 9.82it/s] 12%|█▏ | 6/50 [00:00<00:04, 9.60it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.44it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.32it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.23it/s] 20%|██ | 10/50 [00:01<00:04, 9.18it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.14it/s] 24%|██▍ | 12/50 [00:01<00:04, 9.11it/s] 26%|██▌ | 13/50 [00:01<00:04, 9.09it/s] 28%|██▊ | 14/50 [00:01<00:03, 9.07it/s] 30%|███ | 15/50 [00:01<00:03, 9.06it/s] 32%|███▏ | 16/50 [00:01<00:03, 9.06it/s] 34%|███▍ | 17/50 [00:01<00:03, 9.05it/s] 36%|███▌ | 18/50 [00:01<00:03, 9.05it/s] 38%|███▊ | 19/50 [00:02<00:03, 9.04it/s] 40%|████ | 20/50 [00:02<00:03, 9.04it/s] 42%|████▏ | 21/50 [00:02<00:03, 9.04it/s] 44%|████▍ | 22/50 [00:02<00:03, 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