grabielairu / sdxl-junji-ito
An SDXL fine-tune based on Junji Ito's manga drawings (Updated 1 year, 9 months ago)
- Public
- 301 runs
-
L40S
- SDXL fine-tune
Prediction
grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfcIDxct44vdbntidt4dxcr77jqkubiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @grabielairuInput
- width
- 1200
- height
- 1000
- prompt
- TOK woman reading a book
- 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": 1200, "height": 1000, "prompt": "TOK woman reading a book", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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 grabielairu/sdxl-junji-ito using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", { input: { width: 1200, height: 1000, prompt: "TOK woman reading a book", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, 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 replicateImport the client:import replicateRun grabielairu/sdxl-junji-ito using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", input={ "width": 1200, "height": 1000, "prompt": "TOK woman reading a book", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "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.
Run grabielairu/sdxl-junji-ito 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": "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", "input": { "width": 1200, "height": 1000, "prompt": "TOK woman reading a book", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-09-11T21:28:01.037604Z", "created_at": "2023-09-11T21:27:42.022221Z", "data_removed": false, "error": null, "id": "xct44vdbntidt4dxcr77jqkubi", "input": { "width": 1200, "height": 1000, "prompt": "TOK woman reading a book", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 37018\nPrompt: <s0><s1> woman reading a book\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 3.03it/s]\n 4%|▍ | 2/50 [00:00<00:15, 3.01it/s]\n 6%|▌ | 3/50 [00:00<00:15, 3.00it/s]\n 8%|▊ | 4/50 [00:01<00:15, 3.00it/s]\n 10%|█ | 5/50 [00:01<00:15, 3.00it/s]\n 12%|█▏ | 6/50 [00:02<00:14, 2.99it/s]\n 14%|█▍ | 7/50 [00:02<00:14, 2.99it/s]\n 16%|█▌ | 8/50 [00:02<00:14, 2.99it/s]\n 18%|█▊ | 9/50 [00:03<00:13, 2.99it/s]\n 20%|██ | 10/50 [00:03<00:13, 2.99it/s]\n 22%|██▏ | 11/50 [00:03<00:13, 2.99it/s]\n 24%|██▍ | 12/50 [00:04<00:12, 2.99it/s]\n 26%|██▌ | 13/50 [00:04<00:12, 2.99it/s]\n 28%|██▊ | 14/50 [00:04<00:12, 2.99it/s]\n 30%|███ | 15/50 [00:05<00:11, 2.99it/s]\n 32%|███▏ | 16/50 [00:05<00:11, 2.99it/s]\n 34%|███▍ | 17/50 [00:05<00:11, 2.99it/s]\n 36%|███▌ | 18/50 [00:06<00:10, 2.99it/s]\n 38%|███▊ | 19/50 [00:06<00:10, 2.99it/s]\n 40%|████ | 20/50 [00:06<00:10, 2.99it/s]\n 42%|████▏ | 21/50 [00:07<00:09, 2.99it/s]\n 44%|████▍ | 22/50 [00:07<00:09, 2.99it/s]\n 46%|████▌ | 23/50 [00:07<00:09, 2.99it/s]\n 48%|████▊ | 24/50 [00:08<00:08, 2.99it/s]\n 50%|█████ | 25/50 [00:08<00:08, 2.99it/s]\n 52%|█████▏ | 26/50 [00:08<00:08, 2.99it/s]\n 54%|█████▍ | 27/50 [00:09<00:07, 2.99it/s]\n 56%|█████▌ | 28/50 [00:09<00:07, 2.98it/s]\n 58%|█████▊ | 29/50 [00:09<00:07, 2.98it/s]\n 60%|██████ | 30/50 [00:10<00:06, 2.98it/s]\n 62%|██████▏ | 31/50 [00:10<00:06, 2.98it/s]\n 64%|██████▍ | 32/50 [00:10<00:06, 2.98it/s]\n 66%|██████▌ | 33/50 [00:11<00:05, 2.98it/s]\n 68%|██████▊ | 34/50 [00:11<00:05, 2.98it/s]\n 70%|███████ | 35/50 [00:11<00:05, 2.98it/s]\n 72%|███████▏ | 36/50 [00:12<00:04, 2.98it/s]\n 74%|███████▍ | 37/50 [00:12<00:04, 2.98it/s]\n 76%|███████▌ | 38/50 [00:12<00:04, 2.98it/s]\n 78%|███████▊ | 39/50 [00:13<00:03, 2.98it/s]\n 80%|████████ | 40/50 [00:13<00:03, 2.98it/s]\n 82%|████████▏ | 41/50 [00:13<00:03, 2.98it/s]\n 84%|████████▍ | 42/50 [00:14<00:02, 2.98it/s]\n 86%|████████▌ | 43/50 [00:14<00:02, 2.98it/s]\n 88%|████████▊ | 44/50 [00:14<00:02, 2.98it/s]\n 90%|█████████ | 45/50 [00:15<00:01, 2.98it/s]\n 92%|█████████▏| 46/50 [00:15<00:01, 2.98it/s]\n 94%|█████████▍| 47/50 [00:15<00:01, 2.98it/s]\n 96%|█████████▌| 48/50 [00:16<00:00, 2.98it/s]\n 98%|█████████▊| 49/50 [00:16<00:00, 2.98it/s]\n100%|██████████| 50/50 [00:16<00:00, 2.98it/s]\n100%|██████████| 50/50 [00:16<00:00, 2.99it/s]", "metrics": { "predict_time": 19.012986, "total_time": 19.015383 }, "output": [ "https://pbxt.replicate.delivery/RynuVe97C7SehE6ixKjbOW5YMYpzsC2f42enSfXv68K4LcaMC/out-0.png" ], "started_at": "2023-09-11T21:27:42.024618Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xct44vdbntidt4dxcr77jqkubi", "cancel": "https://api.replicate.com/v1/predictions/xct44vdbntidt4dxcr77jqkubi/cancel" }, "version": "c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc" }Generated inUsing seed: 37018 Prompt: <s0><s1> woman reading a book txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 3.03it/s] 4%|▍ | 2/50 [00:00<00:15, 3.01it/s] 6%|▌ | 3/50 [00:00<00:15, 3.00it/s] 8%|▊ | 4/50 [00:01<00:15, 3.00it/s] 10%|█ | 5/50 [00:01<00:15, 3.00it/s] 12%|█▏ | 6/50 [00:02<00:14, 2.99it/s] 14%|█▍ | 7/50 [00:02<00:14, 2.99it/s] 16%|█▌ | 8/50 [00:02<00:14, 2.99it/s] 18%|█▊ | 9/50 [00:03<00:13, 2.99it/s] 20%|██ | 10/50 [00:03<00:13, 2.99it/s] 22%|██▏ | 11/50 [00:03<00:13, 2.99it/s] 24%|██▍ | 12/50 [00:04<00:12, 2.99it/s] 26%|██▌ | 13/50 [00:04<00:12, 2.99it/s] 28%|██▊ | 14/50 [00:04<00:12, 2.99it/s] 30%|███ | 15/50 [00:05<00:11, 2.99it/s] 32%|███▏ | 16/50 [00:05<00:11, 2.99it/s] 34%|███▍ | 17/50 [00:05<00:11, 2.99it/s] 36%|███▌ | 18/50 [00:06<00:10, 2.99it/s] 38%|███▊ | 19/50 [00:06<00:10, 2.99it/s] 40%|████ | 20/50 [00:06<00:10, 2.99it/s] 42%|████▏ | 21/50 [00:07<00:09, 2.99it/s] 44%|████▍ | 22/50 [00:07<00:09, 2.99it/s] 46%|████▌ | 23/50 [00:07<00:09, 2.99it/s] 48%|████▊ | 24/50 [00:08<00:08, 2.99it/s] 50%|█████ | 25/50 [00:08<00:08, 2.99it/s] 52%|█████▏ | 26/50 [00:08<00:08, 2.99it/s] 54%|█████▍ | 27/50 [00:09<00:07, 2.99it/s] 56%|█████▌ | 28/50 [00:09<00:07, 2.98it/s] 58%|█████▊ | 29/50 [00:09<00:07, 2.98it/s] 60%|██████ | 30/50 [00:10<00:06, 2.98it/s] 62%|██████▏ | 31/50 [00:10<00:06, 2.98it/s] 64%|██████▍ | 32/50 [00:10<00:06, 2.98it/s] 66%|██████▌ | 33/50 [00:11<00:05, 2.98it/s] 68%|██████▊ | 34/50 [00:11<00:05, 2.98it/s] 70%|███████ | 35/50 [00:11<00:05, 2.98it/s] 72%|███████▏ | 36/50 [00:12<00:04, 2.98it/s] 74%|███████▍ | 37/50 [00:12<00:04, 2.98it/s] 76%|███████▌ | 38/50 [00:12<00:04, 2.98it/s] 78%|███████▊ | 39/50 [00:13<00:03, 2.98it/s] 80%|████████ | 40/50 [00:13<00:03, 2.98it/s] 82%|████████▏ | 41/50 [00:13<00:03, 2.98it/s] 84%|████████▍ | 42/50 [00:14<00:02, 2.98it/s] 86%|████████▌ | 43/50 [00:14<00:02, 2.98it/s] 88%|████████▊ | 44/50 [00:14<00:02, 2.98it/s] 90%|█████████ | 45/50 [00:15<00:01, 2.98it/s] 92%|█████████▏| 46/50 [00:15<00:01, 2.98it/s] 94%|█████████▍| 47/50 [00:15<00:01, 2.98it/s] 96%|█████████▌| 48/50 [00:16<00:00, 2.98it/s] 98%|█████████▊| 49/50 [00:16<00:00, 2.98it/s] 100%|██████████| 50/50 [00:16<00:00, 2.98it/s] 100%|██████████| 50/50 [00:16<00:00, 2.99it/s]Prediction
grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfcID7uggiytblgt2gazjq5en7njln4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1200
- height
- 1000
- prompt
- TOK drawing of children playing in a park
- 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": 1200, "height": 1000, "prompt": "TOK drawing of children playing in a park", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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 grabielairu/sdxl-junji-ito using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", { input: { width: 1200, height: 1000, prompt: "TOK drawing of children playing in a park", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, 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 replicateImport the client:import replicateRun grabielairu/sdxl-junji-ito using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", input={ "width": 1200, "height": 1000, "prompt": "TOK drawing of children playing in a park", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "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.
Run grabielairu/sdxl-junji-ito 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": "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", "input": { "width": 1200, "height": 1000, "prompt": "TOK drawing of children playing in a park", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-09-11T21:31:12.487422Z", "created_at": "2023-09-11T21:30:53.914653Z", "data_removed": false, "error": null, "id": "7uggiytblgt2gazjq5en7njln4", "input": { "width": 1200, "height": 1000, "prompt": "TOK drawing of children playing in a park", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 64129\nPrompt: <s0><s1> drawing of children playing in a park\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 3.03it/s]\n 4%|▍ | 2/50 [00:00<00:15, 3.02it/s]\n 6%|▌ | 3/50 [00:00<00:15, 3.01it/s]\n 8%|▊ | 4/50 [00:01<00:15, 3.01it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.01it/s]\n 12%|█▏ | 6/50 [00:01<00:14, 3.00it/s]\n 14%|█▍ | 7/50 [00:02<00:14, 3.00it/s]\n 16%|█▌ | 8/50 [00:02<00:14, 3.00it/s]\n 18%|█▊ | 9/50 [00:02<00:13, 3.00it/s]\n 20%|██ | 10/50 [00:03<00:13, 3.00it/s]\n 22%|██▏ | 11/50 [00:03<00:13, 3.00it/s]\n 24%|██▍ | 12/50 [00:03<00:12, 3.00it/s]\n 26%|██▌ | 13/50 [00:04<00:12, 2.99it/s]\n 28%|██▊ | 14/50 [00:04<00:12, 2.99it/s]\n 30%|███ | 15/50 [00:05<00:11, 2.99it/s]\n 32%|███▏ | 16/50 [00:05<00:11, 2.99it/s]\n 34%|███▍ | 17/50 [00:05<00:11, 2.99it/s]\n 36%|███▌ | 18/50 [00:06<00:10, 2.99it/s]\n 38%|███▊ | 19/50 [00:06<00:10, 2.99it/s]\n 40%|████ | 20/50 [00:06<00:10, 2.99it/s]\n 42%|████▏ | 21/50 [00:07<00:09, 2.99it/s]\n 44%|████▍ | 22/50 [00:07<00:09, 2.99it/s]\n 46%|████▌ | 23/50 [00:07<00:09, 2.99it/s]\n 48%|████▊ | 24/50 [00:08<00:08, 2.99it/s]\n 50%|█████ | 25/50 [00:08<00:08, 2.99it/s]\n 52%|█████▏ | 26/50 [00:08<00:08, 2.99it/s]\n 54%|█████▍ | 27/50 [00:09<00:07, 2.99it/s]\n 56%|█████▌ | 28/50 [00:09<00:07, 2.99it/s]\n 58%|█████▊ | 29/50 [00:09<00:07, 2.99it/s]\n 60%|██████ | 30/50 [00:10<00:06, 2.99it/s]\n 62%|██████▏ | 31/50 [00:10<00:06, 2.99it/s]\n 64%|██████▍ | 32/50 [00:10<00:06, 2.99it/s]\n 66%|██████▌ | 33/50 [00:11<00:05, 2.99it/s]\n 68%|██████▊ | 34/50 [00:11<00:05, 2.99it/s]\n 70%|███████ | 35/50 [00:11<00:05, 2.99it/s]\n 72%|███████▏ | 36/50 [00:12<00:04, 2.98it/s]\n 74%|███████▍ | 37/50 [00:12<00:04, 2.98it/s]\n 76%|███████▌ | 38/50 [00:12<00:04, 2.99it/s]\n 78%|███████▊ | 39/50 [00:13<00:03, 2.98it/s]\n 80%|████████ | 40/50 [00:13<00:03, 2.98it/s]\n 82%|████████▏ | 41/50 [00:13<00:03, 2.99it/s]\n 84%|████████▍ | 42/50 [00:14<00:02, 2.99it/s]\n 86%|████████▌ | 43/50 [00:14<00:02, 2.98it/s]\n 88%|████████▊ | 44/50 [00:14<00:02, 2.98it/s]\n 90%|█████████ | 45/50 [00:15<00:01, 2.98it/s]\n 92%|█████████▏| 46/50 [00:15<00:01, 2.98it/s]\n 94%|█████████▍| 47/50 [00:15<00:01, 2.98it/s]\n 96%|█████████▌| 48/50 [00:16<00:00, 2.99it/s]\n 98%|█████████▊| 49/50 [00:16<00:00, 2.98it/s]\n100%|██████████| 50/50 [00:16<00:00, 2.98it/s]\n100%|██████████| 50/50 [00:16<00:00, 2.99it/s]", "metrics": { "predict_time": 18.57008, "total_time": 18.572769 }, "output": [ "https://pbxt.replicate.delivery/69Vn8l3uXB5iBVRjLwgkNdrkXSyMlU5Bl9fX1QcdC41PypxIA/out-0.png" ], "started_at": "2023-09-11T21:30:53.917342Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7uggiytblgt2gazjq5en7njln4", "cancel": "https://api.replicate.com/v1/predictions/7uggiytblgt2gazjq5en7njln4/cancel" }, "version": "c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc" }Generated inUsing seed: 64129 Prompt: <s0><s1> drawing of children playing in a park txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 3.03it/s] 4%|▍ | 2/50 [00:00<00:15, 3.02it/s] 6%|▌ | 3/50 [00:00<00:15, 3.01it/s] 8%|▊ | 4/50 [00:01<00:15, 3.01it/s] 10%|█ | 5/50 [00:01<00:14, 3.01it/s] 12%|█▏ | 6/50 [00:01<00:14, 3.00it/s] 14%|█▍ | 7/50 [00:02<00:14, 3.00it/s] 16%|█▌ | 8/50 [00:02<00:14, 3.00it/s] 18%|█▊ | 9/50 [00:02<00:13, 3.00it/s] 20%|██ | 10/50 [00:03<00:13, 3.00it/s] 22%|██▏ | 11/50 [00:03<00:13, 3.00it/s] 24%|██▍ | 12/50 [00:03<00:12, 3.00it/s] 26%|██▌ | 13/50 [00:04<00:12, 2.99it/s] 28%|██▊ | 14/50 [00:04<00:12, 2.99it/s] 30%|███ | 15/50 [00:05<00:11, 2.99it/s] 32%|███▏ | 16/50 [00:05<00:11, 2.99it/s] 34%|███▍ | 17/50 [00:05<00:11, 2.99it/s] 36%|███▌ | 18/50 [00:06<00:10, 2.99it/s] 38%|███▊ | 19/50 [00:06<00:10, 2.99it/s] 40%|████ | 20/50 [00:06<00:10, 2.99it/s] 42%|████▏ | 21/50 [00:07<00:09, 2.99it/s] 44%|████▍ | 22/50 [00:07<00:09, 2.99it/s] 46%|████▌ | 23/50 [00:07<00:09, 2.99it/s] 48%|████▊ | 24/50 [00:08<00:08, 2.99it/s] 50%|█████ | 25/50 [00:08<00:08, 2.99it/s] 52%|█████▏ | 26/50 [00:08<00:08, 2.99it/s] 54%|█████▍ | 27/50 [00:09<00:07, 2.99it/s] 56%|█████▌ | 28/50 [00:09<00:07, 2.99it/s] 58%|█████▊ | 29/50 [00:09<00:07, 2.99it/s] 60%|██████ | 30/50 [00:10<00:06, 2.99it/s] 62%|██████▏ | 31/50 [00:10<00:06, 2.99it/s] 64%|██████▍ | 32/50 [00:10<00:06, 2.99it/s] 66%|██████▌ | 33/50 [00:11<00:05, 2.99it/s] 68%|██████▊ | 34/50 [00:11<00:05, 2.99it/s] 70%|███████ | 35/50 [00:11<00:05, 2.99it/s] 72%|███████▏ | 36/50 [00:12<00:04, 2.98it/s] 74%|███████▍ | 37/50 [00:12<00:04, 2.98it/s] 76%|███████▌ | 38/50 [00:12<00:04, 2.99it/s] 78%|███████▊ | 39/50 [00:13<00:03, 2.98it/s] 80%|████████ | 40/50 [00:13<00:03, 2.98it/s] 82%|████████▏ | 41/50 [00:13<00:03, 2.99it/s] 84%|████████▍ | 42/50 [00:14<00:02, 2.99it/s] 86%|████████▌ | 43/50 [00:14<00:02, 2.98it/s] 88%|████████▊ | 44/50 [00:14<00:02, 2.98it/s] 90%|█████████ | 45/50 [00:15<00:01, 2.98it/s] 92%|█████████▏| 46/50 [00:15<00:01, 2.98it/s] 94%|█████████▍| 47/50 [00:15<00:01, 2.98it/s] 96%|█████████▌| 48/50 [00:16<00:00, 2.99it/s] 98%|█████████▊| 49/50 [00:16<00:00, 2.98it/s] 100%|██████████| 50/50 [00:16<00:00, 2.98it/s] 100%|██████████| 50/50 [00:16<00:00, 2.99it/s]Prediction
grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfcIDhs7cccdbarjxsgzpobev3f257mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK drawing of puppy with eight eyes and spiral tail
- refine
- no_refiner
- scheduler
- DDIM
- 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": "TOK drawing of puppy with eight eyes and spiral tail", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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 grabielairu/sdxl-junji-ito using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", { input: { width: 1024, height: 1024, prompt: "TOK drawing of puppy with eight eyes and spiral tail", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, 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 replicateImport the client:import replicateRun grabielairu/sdxl-junji-ito using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", input={ "width": 1024, "height": 1024, "prompt": "TOK drawing of puppy with eight eyes and spiral tail", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "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.
Run grabielairu/sdxl-junji-ito 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": "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", "input": { "width": 1024, "height": 1024, "prompt": "TOK drawing of puppy with eight eyes and spiral tail", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-09-11T21:33:05.566899Z", "created_at": "2023-09-11T21:32:49.874201Z", "data_removed": false, "error": null, "id": "hs7cccdbarjxsgzpobev3f257m", "input": { "width": 1024, "height": 1024, "prompt": "TOK drawing of puppy with eight eyes and spiral tail", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 25392\nPrompt: <s0><s1> drawing of puppy with eight eyes and spiral tail\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<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 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<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 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.691063, "total_time": 15.692698 }, "output": [ "https://pbxt.replicate.delivery/izRUePFFKtRPdSFJqsLsRHtCLmvRWb6YjM4arhjobsTIzpxIA/out-0.png" ], "started_at": "2023-09-11T21:32:49.875836Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hs7cccdbarjxsgzpobev3f257m", "cancel": "https://api.replicate.com/v1/predictions/hs7cccdbarjxsgzpobev3f257m/cancel" }, "version": "c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc" }Generated inUsing seed: 25392 Prompt: <s0><s1> drawing of puppy with eight eyes and spiral tail txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<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 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<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 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfcIDrli7bkdbgmrfzztmbhbavgjq3aStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a sandwich in TOK style
- refine
- no_refiner
- scheduler
- DDIM
- 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 sandwich in TOK style", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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 grabielairu/sdxl-junji-ito using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", { input: { width: 1024, height: 1024, prompt: "a sandwich in TOK style", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, 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 replicateImport the client:import replicateRun grabielairu/sdxl-junji-ito using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", input={ "width": 1024, "height": 1024, "prompt": "a sandwich in TOK style", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "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.
Run grabielairu/sdxl-junji-ito 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": "grabielairu/sdxl-junji-ito:c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc", "input": { "width": 1024, "height": 1024, "prompt": "a sandwich in TOK style", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-09-11T21:34:09.466687Z", "created_at": "2023-09-11T21:33:54.054812Z", "data_removed": false, "error": null, "id": "rli7bkdbgmrfzztmbhbavgjq3a", "input": { "width": 1024, "height": 1024, "prompt": "a sandwich in TOK style", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 55679\nPrompt: a sandwich in <s0><s1> style\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<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 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<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 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.382459, "total_time": 15.411875 }, "output": [ "https://pbxt.replicate.delivery/zCp8G317P8bFHp6pSJa3LprppkrAVlT3SRupyEm6T8I050YE/out-0.png" ], "started_at": "2023-09-11T21:33:54.084228Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rli7bkdbgmrfzztmbhbavgjq3a", "cancel": "https://api.replicate.com/v1/predictions/rli7bkdbgmrfzztmbhbavgjq3a/cancel" }, "version": "c56d9d53a582747dc6c229000d5e630f014800b990b8e50f2211cc745be2fbfc" }Generated inUsing seed: 55679 Prompt: a sandwich in <s0><s1> style txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<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 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<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 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]
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