publu / rubberducky
Trained to create rubber ducks. That is all.
- Public
- 542 runs
-
T4
Prediction
publu/rubberducky:8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0IDasdxrqg37fbmvdnoolrjz77ka4StatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a photo of a rubber duck ducky, cartoon, baking hat
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a photo of a rubber duck ducky, cartoon, baking hat", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 publu/rubberducky using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "publu/rubberducky:8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0", { input: { width: 512, height: 512, prompt: "a photo of a rubber duck ducky, cartoon, baking hat", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run publu/rubberducky using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "publu/rubberducky:8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0", input={ "width": 512, "height": 512, "prompt": "a photo of a rubber duck ducky, cartoon, baking hat", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "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 publu/rubberducky 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": "publu/rubberducky:8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0", "input": { "width": 512, "height": 512, "prompt": "a photo of a rubber duck ducky, cartoon, baking hat", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-03-29T14:12:44.297135Z", "created_at": "2023-03-29T14:10:02.545422Z", "data_removed": false, "error": null, "id": "asdxrqg37fbmvdnoolrjz77ka4", "input": { "width": 512, "height": 512, "prompt": "a photo of a rubber duck ducky, cartoon, baking hat", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 26842\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<02:16, 2.79s/it]\n 4%|▍ | 2/50 [00:02<01:00, 1.25s/it]\n 6%|▌ | 3/50 [00:03<00:35, 1.31it/s]\n 8%|▊ | 4/50 [00:03<00:24, 1.89it/s]\n 10%|█ | 5/50 [00:03<00:18, 2.49it/s]\n 12%|█▏ | 6/50 [00:03<00:14, 3.08it/s]\n 14%|█▍ | 7/50 [00:03<00:11, 3.59it/s]\n 16%|█▌ | 8/50 [00:04<00:10, 4.07it/s]\n 18%|█▊ | 9/50 [00:04<00:09, 4.46it/s]\n 20%|██ | 10/50 [00:04<00:08, 4.76it/s]\n 22%|██▏ | 11/50 [00:04<00:07, 5.02it/s]\n 24%|██▍ | 12/50 [00:04<00:07, 5.21it/s]\n 26%|██▌ | 13/50 [00:04<00:06, 5.29it/s]\n 28%|██▊ | 14/50 [00:05<00:06, 5.37it/s]\n 30%|███ | 15/50 [00:05<00:06, 5.45it/s]\n 32%|███▏ | 16/50 [00:05<00:06, 5.53it/s]\n 34%|███▍ | 17/50 [00:05<00:05, 5.58it/s]\n 36%|███▌ | 18/50 [00:05<00:05, 5.58it/s]\n 38%|███▊ | 19/50 [00:05<00:05, 5.56it/s]\n 40%|████ | 20/50 [00:06<00:05, 5.59it/s]\n 42%|████▏ | 21/50 [00:06<00:05, 5.64it/s]\n 44%|████▍ | 22/50 [00:06<00:04, 5.64it/s]\n 46%|████▌ | 23/50 [00:06<00:04, 5.66it/s]\n 48%|████▊ | 24/50 [00:06<00:04, 5.66it/s]\n 50%|█████ | 25/50 [00:07<00:04, 5.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:04, 5.62it/s]\n 54%|█████▍ | 27/50 [00:07<00:04, 5.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:03, 5.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:03, 5.66it/s]\n 60%|██████ | 30/50 [00:07<00:03, 5.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:03, 5.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:03, 5.66it/s]\n 66%|██████▌ | 33/50 [00:08<00:03, 5.63it/s]\n 68%|██████▊ | 34/50 [00:08<00:02, 5.62it/s]\n 70%|███████ | 35/50 [00:08<00:02, 5.62it/s]\n 72%|███████▏ | 36/50 [00:08<00:02, 5.64it/s]\n 74%|███████▍ | 37/50 [00:09<00:02, 5.64it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 5.62it/s]\n 78%|███████▊ | 39/50 [00:09<00:01, 5.61it/s]\n 80%|████████ | 40/50 [00:09<00:01, 5.64it/s]\n 82%|████████▏ | 41/50 [00:09<00:01, 5.64it/s]\n 84%|████████▍ | 42/50 [00:10<00:01, 5.64it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 5.63it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 5.64it/s]\n 90%|█████████ | 45/50 [00:10<00:00, 5.65it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 5.63it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 5.61it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 5.60it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 5.63it/s]\n100%|██████████| 50/50 [00:11<00:00, 5.64it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.36it/s]", "metrics": { "predict_time": 14.106561, "total_time": 161.751713 }, "output": [ "https://replicate.delivery/pbxt/l6yMGrZw777bLdU9tCSL6994uDICvaGhHNMBP1Jys2DX5HLE/out-0.png" ], "started_at": "2023-03-29T14:12:30.190574Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/asdxrqg37fbmvdnoolrjz77ka4", "cancel": "https://api.replicate.com/v1/predictions/asdxrqg37fbmvdnoolrjz77ka4/cancel" }, "version": "8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0" }
Generated inUsing seed: 26842 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<02:16, 2.79s/it] 4%|▍ | 2/50 [00:02<01:00, 1.25s/it] 6%|▌ | 3/50 [00:03<00:35, 1.31it/s] 8%|▊ | 4/50 [00:03<00:24, 1.89it/s] 10%|█ | 5/50 [00:03<00:18, 2.49it/s] 12%|█▏ | 6/50 [00:03<00:14, 3.08it/s] 14%|█▍ | 7/50 [00:03<00:11, 3.59it/s] 16%|█▌ | 8/50 [00:04<00:10, 4.07it/s] 18%|█▊ | 9/50 [00:04<00:09, 4.46it/s] 20%|██ | 10/50 [00:04<00:08, 4.76it/s] 22%|██▏ | 11/50 [00:04<00:07, 5.02it/s] 24%|██▍ | 12/50 [00:04<00:07, 5.21it/s] 26%|██▌ | 13/50 [00:04<00:06, 5.29it/s] 28%|██▊ | 14/50 [00:05<00:06, 5.37it/s] 30%|███ | 15/50 [00:05<00:06, 5.45it/s] 32%|███▏ | 16/50 [00:05<00:06, 5.53it/s] 34%|███▍ | 17/50 [00:05<00:05, 5.58it/s] 36%|███▌ | 18/50 [00:05<00:05, 5.58it/s] 38%|███▊ | 19/50 [00:05<00:05, 5.56it/s] 40%|████ | 20/50 [00:06<00:05, 5.59it/s] 42%|████▏ | 21/50 [00:06<00:05, 5.64it/s] 44%|████▍ | 22/50 [00:06<00:04, 5.64it/s] 46%|████▌ | 23/50 [00:06<00:04, 5.66it/s] 48%|████▊ | 24/50 [00:06<00:04, 5.66it/s] 50%|█████ | 25/50 [00:07<00:04, 5.65it/s] 52%|█████▏ | 26/50 [00:07<00:04, 5.62it/s] 54%|█████▍ | 27/50 [00:07<00:04, 5.63it/s] 56%|█████▌ | 28/50 [00:07<00:03, 5.66it/s] 58%|█████▊ | 29/50 [00:07<00:03, 5.66it/s] 60%|██████ | 30/50 [00:07<00:03, 5.64it/s] 62%|██████▏ | 31/50 [00:08<00:03, 5.65it/s] 64%|██████▍ | 32/50 [00:08<00:03, 5.66it/s] 66%|██████▌ | 33/50 [00:08<00:03, 5.63it/s] 68%|██████▊ | 34/50 [00:08<00:02, 5.62it/s] 70%|███████ | 35/50 [00:08<00:02, 5.62it/s] 72%|███████▏ | 36/50 [00:08<00:02, 5.64it/s] 74%|███████▍ | 37/50 [00:09<00:02, 5.64it/s] 76%|███████▌ | 38/50 [00:09<00:02, 5.62it/s] 78%|███████▊ | 39/50 [00:09<00:01, 5.61it/s] 80%|████████ | 40/50 [00:09<00:01, 5.64it/s] 82%|████████▏ | 41/50 [00:09<00:01, 5.64it/s] 84%|████████▍ | 42/50 [00:10<00:01, 5.64it/s] 86%|████████▌ | 43/50 [00:10<00:01, 5.63it/s] 88%|████████▊ | 44/50 [00:10<00:01, 5.64it/s] 90%|█████████ | 45/50 [00:10<00:00, 5.65it/s] 92%|█████████▏| 46/50 [00:10<00:00, 5.63it/s] 94%|█████████▍| 47/50 [00:10<00:00, 5.61it/s] 96%|█████████▌| 48/50 [00:11<00:00, 5.60it/s] 98%|█████████▊| 49/50 [00:11<00:00, 5.63it/s] 100%|██████████| 50/50 [00:11<00:00, 5.64it/s] 100%|██████████| 50/50 [00:11<00:00, 4.36it/s]
Prediction
publu/rubberducky:8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0ID4hxymfwewngr7csn4cswg3la3uStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a photo of a chef rubber duck ducky, cartoon
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- negative_prompt
- many ducks, bad duck anatomy
- prompt_strength
- 0.8
- num_inference_steps
- 50
- disable_safety_check
{ "width": 512, "height": 512, "prompt": "a photo of a chef rubber duck ducky, cartoon", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "many ducks, bad duck anatomy", "prompt_strength": 0.8, "num_inference_steps": 50, "disable_safety_check": true }
Install Replicate’s Node.js client library:npm install replicate
Import 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 publu/rubberducky using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "publu/rubberducky:8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0", { input: { width: 512, height: 512, prompt: "a photo of a chef rubber duck ducky, cartoon", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, negative_prompt: "many ducks, bad duck anatomy", prompt_strength: 0.8, num_inference_steps: 50, disable_safety_check: true } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run publu/rubberducky using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "publu/rubberducky:8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0", input={ "width": 512, "height": 512, "prompt": "a photo of a chef rubber duck ducky, cartoon", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "many ducks, bad duck anatomy", "prompt_strength": 0.8, "num_inference_steps": 50, "disable_safety_check": True } ) # 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 publu/rubberducky 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": "publu/rubberducky:8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0", "input": { "width": 512, "height": 512, "prompt": "a photo of a chef rubber duck ducky, cartoon", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "many ducks, bad duck anatomy", "prompt_strength": 0.8, "num_inference_steps": 50, "disable_safety_check": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-03-29T16:06:15.834543Z", "created_at": "2023-03-29T16:06:05.790633Z", "data_removed": false, "error": null, "id": "4hxymfwewngr7csn4cswg3la3u", "input": { "width": 512, "height": 512, "prompt": "a photo of a chef rubber duck ducky, cartoon", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "many ducks, bad duck anatomy", "prompt_strength": 0.8, "num_inference_steps": 50, "disable_safety_check": true }, "logs": "Using seed: 23513\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 4.01it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.73it/s]\n 6%|▌ | 3/50 [00:00<00:09, 5.03it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.19it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.28it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.28it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 5.28it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.32it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.36it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.38it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.41it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 5.38it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.36it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.35it/s]\n 30%|███ | 15/50 [00:02<00:06, 5.38it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 5.40it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 5.38it/s]\n 36%|███▌ | 18/50 [00:03<00:05, 5.36it/s]\n 38%|███▊ | 19/50 [00:03<00:05, 5.34it/s]\n 40%|████ | 20/50 [00:03<00:05, 5.36it/s]\n 42%|████▏ | 21/50 [00:03<00:05, 5.39it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 5.38it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 5.35it/s]\n 48%|████▊ | 24/50 [00:04<00:04, 5.32it/s]\n 50%|█████ | 25/50 [00:04<00:04, 5.36it/s]\n 52%|█████▏ | 26/50 [00:04<00:04, 5.38it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 5.36it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 5.34it/s]\n 58%|█████▊ | 29/50 [00:05<00:03, 5.33it/s]\n 60%|██████ | 30/50 [00:05<00:03, 5.33it/s]\n 62%|██████▏ | 31/50 [00:05<00:03, 5.32it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 5.34it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 5.35it/s]\n 68%|██████▊ | 34/50 [00:06<00:02, 5.36it/s]\n 70%|███████ | 35/50 [00:06<00:02, 5.33it/s]\n 72%|███████▏ | 36/50 [00:06<00:02, 5.32it/s]\n 74%|███████▍ | 37/50 [00:06<00:02, 5.36it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 5.35it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 5.35it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.35it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.31it/s]\n 84%|████████▍ | 42/50 [00:07<00:01, 5.34it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 5.36it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 5.37it/s]\n 90%|█████████ | 45/50 [00:08<00:00, 5.34it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.33it/s]\n 94%|█████████▍| 47/50 [00:08<00:00, 5.32it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 5.31it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 5.32it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.34it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.32it/s]", "metrics": { "predict_time": 9.920352, "total_time": 10.04391 }, "output": [ "https://replicate.delivery/pbxt/8kqkNJHxWraPOl29OdZqfLkuQ4a9SrSi4cCe6Uda0HD3PhsQA/out-0.png" ], "started_at": "2023-03-29T16:06:05.914191Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4hxymfwewngr7csn4cswg3la3u", "cancel": "https://api.replicate.com/v1/predictions/4hxymfwewngr7csn4cswg3la3u/cancel" }, "version": "8ab090227b436ce29a48230c167c81dc7b3022b35d0930121b210dbca5a614a0" }
Generated inUsing seed: 23513 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:12, 4.01it/s] 4%|▍ | 2/50 [00:00<00:10, 4.73it/s] 6%|▌ | 3/50 [00:00<00:09, 5.03it/s] 8%|▊ | 4/50 [00:00<00:08, 5.19it/s] 10%|█ | 5/50 [00:00<00:08, 5.28it/s] 12%|█▏ | 6/50 [00:01<00:08, 5.28it/s] 14%|█▍ | 7/50 [00:01<00:08, 5.28it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.32it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.36it/s] 20%|██ | 10/50 [00:01<00:07, 5.38it/s] 22%|██▏ | 11/50 [00:02<00:07, 5.41it/s] 24%|██▍ | 12/50 [00:02<00:07, 5.38it/s] 26%|██▌ | 13/50 [00:02<00:06, 5.36it/s] 28%|██▊ | 14/50 [00:02<00:06, 5.35it/s] 30%|███ | 15/50 [00:02<00:06, 5.38it/s] 32%|███▏ | 16/50 [00:03<00:06, 5.40it/s] 34%|███▍ | 17/50 [00:03<00:06, 5.38it/s] 36%|███▌ | 18/50 [00:03<00:05, 5.36it/s] 38%|███▊ | 19/50 [00:03<00:05, 5.34it/s] 40%|████ | 20/50 [00:03<00:05, 5.36it/s] 42%|████▏ | 21/50 [00:03<00:05, 5.39it/s] 44%|████▍ | 22/50 [00:04<00:05, 5.38it/s] 46%|████▌ | 23/50 [00:04<00:05, 5.35it/s] 48%|████▊ | 24/50 [00:04<00:04, 5.32it/s] 50%|█████ | 25/50 [00:04<00:04, 5.36it/s] 52%|█████▏ | 26/50 [00:04<00:04, 5.38it/s] 54%|█████▍ | 27/50 [00:05<00:04, 5.36it/s] 56%|█████▌ | 28/50 [00:05<00:04, 5.34it/s] 58%|█████▊ | 29/50 [00:05<00:03, 5.33it/s] 60%|██████ | 30/50 [00:05<00:03, 5.33it/s] 62%|██████▏ | 31/50 [00:05<00:03, 5.32it/s] 64%|██████▍ | 32/50 [00:06<00:03, 5.34it/s] 66%|██████▌ | 33/50 [00:06<00:03, 5.35it/s] 68%|██████▊ | 34/50 [00:06<00:02, 5.36it/s] 70%|███████ | 35/50 [00:06<00:02, 5.33it/s] 72%|███████▏ | 36/50 [00:06<00:02, 5.32it/s] 74%|███████▍ | 37/50 [00:06<00:02, 5.36it/s] 76%|███████▌ | 38/50 [00:07<00:02, 5.35it/s] 78%|███████▊ | 39/50 [00:07<00:02, 5.35it/s] 80%|████████ | 40/50 [00:07<00:01, 5.35it/s] 82%|████████▏ | 41/50 [00:07<00:01, 5.31it/s] 84%|████████▍ | 42/50 [00:07<00:01, 5.34it/s] 86%|████████▌ | 43/50 [00:08<00:01, 5.36it/s] 88%|████████▊ | 44/50 [00:08<00:01, 5.37it/s] 90%|█████████ | 45/50 [00:08<00:00, 5.34it/s] 92%|█████████▏| 46/50 [00:08<00:00, 5.33it/s] 94%|█████████▍| 47/50 [00:08<00:00, 5.32it/s] 96%|█████████▌| 48/50 [00:09<00:00, 5.31it/s] 98%|█████████▊| 49/50 [00:09<00:00, 5.32it/s] 100%|██████████| 50/50 [00:09<00:00, 5.34it/s] 100%|██████████| 50/50 [00:09<00:00, 5.32it/s]
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