matt-bornstein / nspm1
Fine-tuned model based on Nick St. Pierre's latest Midjourney model!
- Public
- 2.8K runs
- Paper
Prediction
matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01fIDsgggsywhczeq5msre4dv35dl5yStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a 19th century photograph of nspm
- scheduler
- DDIM
- num_outputs
- "4"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a 19th century photograph of nspm", "scheduler": "DDIM", "num_outputs": "4", "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 matt-bornstein/nspm1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", { input: { width: 512, height: 512, prompt: "a 19th century photograph of nspm", scheduler: "DDIM", num_outputs: "4", 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 matt-bornstein/nspm1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", input={ "width": 512, "height": 512, "prompt": "a 19th century photograph of nspm", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "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 matt-bornstein/nspm1 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": "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", "input": { "width": 512, "height": 512, "prompt": "a 19th century photograph of nspm", "scheduler": "DDIM", "num_outputs": "4", "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": "2023-02-20T01:18:20.764416Z", "created_at": "2023-02-20T01:18:12.591066Z", "data_removed": false, "error": null, "id": "sgggsywhczeq5msre4dv35dl5y", "input": { "width": 512, "height": 512, "prompt": "a 19th century photograph of nspm", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 20304\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:07, 6.50it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.89it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.62it/s]\n 8%|▊ | 4/50 [00:00<00:05, 9.02it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.26it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.41it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.52it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.59it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.64it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.66it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.67it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 9.69it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 9.70it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.72it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.73it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.73it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.74it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.74it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 9.73it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.73it/s]\n 42%|████▏ | 21/50 [00:02<00:02, 9.72it/s]\n 44%|████▍ | 22/50 [00:02<00:02, 9.72it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.72it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.72it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.72it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.73it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.73it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 9.71it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.70it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.70it/s]\n 62%|██████▏ | 31/50 [00:03<00:01, 9.70it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.71it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.71it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.70it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.71it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.72it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 9.73it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 9.72it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.71it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.71it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.71it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.72it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.72it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.71it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.71it/s]\n 92%|█████████▏| 46/50 [00:04<00:00, 9.70it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 9.70it/s]\n 96%|█████████▌| 48/50 [00:04<00:00, 9.69it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.69it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.71it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.61it/s]", "metrics": { "predict_time": 8.109449, "total_time": 8.17335 }, "output": [ "https://replicate.delivery/pbxt/5GYtyKQt2QKQMBseBM50dT7XwzSWwepd1lGKccpqj0GaxHgQA/out-0.png", "https://replicate.delivery/pbxt/WeDPvbCPnNXmFCILPQduucbFwcxLDqDE8Zxdl93cV3qt4DQIA/out-1.png", "https://replicate.delivery/pbxt/XAfkqvvJf4qNvUHlvoIbnOzdXefyxAJfAHZbflwR7cl8W8BIE/out-2.png", "https://replicate.delivery/pbxt/AJhsdfmylgXtMS7Uf6MhvmZmr5uphsKuaCFVzcmJeeYxFfAEC/out-3.png" ], "started_at": "2023-02-20T01:18:12.654967Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sgggsywhczeq5msre4dv35dl5y", "cancel": "https://api.replicate.com/v1/predictions/sgggsywhczeq5msre4dv35dl5y/cancel" }, "version": "6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f" }
Generated inUsing seed: 20304 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:07, 6.50it/s] 4%|▍ | 2/50 [00:00<00:06, 7.89it/s] 6%|▌ | 3/50 [00:00<00:05, 8.62it/s] 8%|▊ | 4/50 [00:00<00:05, 9.02it/s] 10%|█ | 5/50 [00:00<00:04, 9.26it/s] 12%|█▏ | 6/50 [00:00<00:04, 9.41it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.52it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.59it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.64it/s] 20%|██ | 10/50 [00:01<00:04, 9.66it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.67it/s] 24%|██▍ | 12/50 [00:01<00:03, 9.69it/s] 26%|██▌ | 13/50 [00:01<00:03, 9.70it/s] 28%|██▊ | 14/50 [00:01<00:03, 9.72it/s] 30%|███ | 15/50 [00:01<00:03, 9.73it/s] 32%|███▏ | 16/50 [00:01<00:03, 9.73it/s] 34%|███▍ | 17/50 [00:01<00:03, 9.74it/s] 36%|███▌ | 18/50 [00:01<00:03, 9.74it/s] 38%|███▊ | 19/50 [00:02<00:03, 9.73it/s] 40%|████ | 20/50 [00:02<00:03, 9.73it/s] 42%|████▏ | 21/50 [00:02<00:02, 9.72it/s] 44%|████▍ | 22/50 [00:02<00:02, 9.72it/s] 46%|████▌ | 23/50 [00:02<00:02, 9.72it/s] 48%|████▊ | 24/50 [00:02<00:02, 9.72it/s] 50%|█████ | 25/50 [00:02<00:02, 9.72it/s] 52%|█████▏ | 26/50 [00:02<00:02, 9.73it/s] 54%|█████▍ | 27/50 [00:02<00:02, 9.73it/s] 56%|█████▌ | 28/50 [00:02<00:02, 9.71it/s] 58%|█████▊ | 29/50 [00:03<00:02, 9.70it/s] 60%|██████ | 30/50 [00:03<00:02, 9.70it/s] 62%|██████▏ | 31/50 [00:03<00:01, 9.70it/s] 64%|██████▍ | 32/50 [00:03<00:01, 9.71it/s] 66%|██████▌ | 33/50 [00:03<00:01, 9.71it/s] 68%|██████▊ | 34/50 [00:03<00:01, 9.70it/s] 70%|███████ | 35/50 [00:03<00:01, 9.71it/s] 72%|███████▏ | 36/50 [00:03<00:01, 9.72it/s] 74%|███████▍ | 37/50 [00:03<00:01, 9.73it/s] 76%|███████▌ | 38/50 [00:03<00:01, 9.72it/s] 78%|███████▊ | 39/50 [00:04<00:01, 9.71it/s] 80%|████████ | 40/50 [00:04<00:01, 9.71it/s] 82%|████████▏ | 41/50 [00:04<00:00, 9.71it/s] 84%|████████▍ | 42/50 [00:04<00:00, 9.72it/s] 86%|████████▌ | 43/50 [00:04<00:00, 9.72it/s] 88%|████████▊ | 44/50 [00:04<00:00, 9.71it/s] 90%|█████████ | 45/50 [00:04<00:00, 9.71it/s] 92%|█████████▏| 46/50 [00:04<00:00, 9.70it/s] 94%|█████████▍| 47/50 [00:04<00:00, 9.70it/s] 96%|█████████▌| 48/50 [00:04<00:00, 9.69it/s] 98%|█████████▊| 49/50 [00:05<00:00, 9.69it/s] 100%|██████████| 50/50 [00:05<00:00, 9.71it/s] 100%|██████████| 50/50 [00:05<00:00, 9.61it/s]
Prediction
matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01fIDlkkmdyvw5vbbrdvbcl4yvipfnyStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- baby nspm dancing in a japanese temple
- scheduler
- DDIM
- num_outputs
- "4"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "baby nspm dancing in a japanese temple", "scheduler": "DDIM", "num_outputs": "4", "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 matt-bornstein/nspm1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", { input: { width: 512, height: 512, prompt: "baby nspm dancing in a japanese temple", scheduler: "DDIM", num_outputs: "4", 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 matt-bornstein/nspm1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", input={ "width": 512, "height": 512, "prompt": "baby nspm dancing in a japanese temple", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "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 matt-bornstein/nspm1 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": "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", "input": { "width": 512, "height": 512, "prompt": "baby nspm dancing in a japanese temple", "scheduler": "DDIM", "num_outputs": "4", "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": "2023-02-20T01:19:51.622658Z", "created_at": "2023-02-20T01:19:43.376060Z", "data_removed": false, "error": null, "id": "lkkmdyvw5vbbrdvbcl4yvipfny", "input": { "width": 512, "height": 512, "prompt": "baby nspm dancing in a japanese temple", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 7870\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.92it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.94it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.18it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.73it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.06it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.27it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.41it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.51it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.57it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.62it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.65it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 9.66it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 9.67it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.68it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.69it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.70it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.70it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.70it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 9.69it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.68it/s]\n 42%|████▏ | 21/50 [00:02<00:02, 9.69it/s]\n 44%|████▍ | 22/50 [00:02<00:02, 9.69it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.69it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.67it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.66it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.68it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.70it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 9.71it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.72it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.72it/s]\n 62%|██████▏ | 31/50 [00:03<00:01, 9.71it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.71it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.72it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.73it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.71it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.71it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 9.73it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 9.73it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.72it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.72it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.72it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.72it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.72it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.72it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.72it/s]\n 92%|█████████▏| 46/50 [00:04<00:00, 9.72it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 9.73it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 9.73it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.73it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.71it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.60it/s]", "metrics": { "predict_time": 8.183786, "total_time": 8.246598 }, "output": [ "https://replicate.delivery/pbxt/vJhvALOr3joeOSa9qS2daIRFxq3Ije69qaU4WYbFuCO1yHgQA/out-0.png", "https://replicate.delivery/pbxt/X3GeKfvhbEpyy0VMdKKeLoaMNEkbonj23Kb3txzy6g7tlPAhA/out-1.png", "https://replicate.delivery/pbxt/4bSNsb37AzKuN9MC2xvZqfG8u9lUDuIRWeNoRxGZdYj2yHgQA/out-2.png", "https://replicate.delivery/pbxt/ft5GkbnvuRzEf0VSQm5s85kFwIeHNI9BBMZ0A1yeuwe5WeBIE/out-3.png" ], "started_at": "2023-02-20T01:19:43.438872Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lkkmdyvw5vbbrdvbcl4yvipfny", "cancel": "https://api.replicate.com/v1/predictions/lkkmdyvw5vbbrdvbcl4yvipfny/cancel" }, "version": "6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f" }
Generated inUsing seed: 7870 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.92it/s] 4%|▍ | 2/50 [00:00<00:06, 7.94it/s] 6%|▌ | 3/50 [00:00<00:05, 8.18it/s] 8%|▊ | 4/50 [00:00<00:05, 8.73it/s] 10%|█ | 5/50 [00:00<00:04, 9.06it/s] 12%|█▏ | 6/50 [00:00<00:04, 9.27it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.41it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.51it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.57it/s] 20%|██ | 10/50 [00:01<00:04, 9.62it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.65it/s] 24%|██▍ | 12/50 [00:01<00:03, 9.66it/s] 26%|██▌ | 13/50 [00:01<00:03, 9.67it/s] 28%|██▊ | 14/50 [00:01<00:03, 9.68it/s] 30%|███ | 15/50 [00:01<00:03, 9.69it/s] 32%|███▏ | 16/50 [00:01<00:03, 9.70it/s] 34%|███▍ | 17/50 [00:01<00:03, 9.70it/s] 36%|███▌ | 18/50 [00:01<00:03, 9.70it/s] 38%|███▊ | 19/50 [00:02<00:03, 9.69it/s] 40%|████ | 20/50 [00:02<00:03, 9.68it/s] 42%|████▏ | 21/50 [00:02<00:02, 9.69it/s] 44%|████▍ | 22/50 [00:02<00:02, 9.69it/s] 46%|████▌ | 23/50 [00:02<00:02, 9.69it/s] 48%|████▊ | 24/50 [00:02<00:02, 9.67it/s] 50%|█████ | 25/50 [00:02<00:02, 9.66it/s] 52%|█████▏ | 26/50 [00:02<00:02, 9.68it/s] 54%|█████▍ | 27/50 [00:02<00:02, 9.70it/s] 56%|█████▌ | 28/50 [00:02<00:02, 9.71it/s] 58%|█████▊ | 29/50 [00:03<00:02, 9.72it/s] 60%|██████ | 30/50 [00:03<00:02, 9.72it/s] 62%|██████▏ | 31/50 [00:03<00:01, 9.71it/s] 64%|██████▍ | 32/50 [00:03<00:01, 9.71it/s] 66%|██████▌ | 33/50 [00:03<00:01, 9.72it/s] 68%|██████▊ | 34/50 [00:03<00:01, 9.73it/s] 70%|███████ | 35/50 [00:03<00:01, 9.71it/s] 72%|███████▏ | 36/50 [00:03<00:01, 9.71it/s] 74%|███████▍ | 37/50 [00:03<00:01, 9.73it/s] 76%|███████▌ | 38/50 [00:03<00:01, 9.73it/s] 78%|███████▊ | 39/50 [00:04<00:01, 9.72it/s] 80%|████████ | 40/50 [00:04<00:01, 9.72it/s] 82%|████████▏ | 41/50 [00:04<00:00, 9.72it/s] 84%|████████▍ | 42/50 [00:04<00:00, 9.72it/s] 86%|████████▌ | 43/50 [00:04<00:00, 9.72it/s] 88%|████████▊ | 44/50 [00:04<00:00, 9.72it/s] 90%|█████████ | 45/50 [00:04<00:00, 9.72it/s] 92%|█████████▏| 46/50 [00:04<00:00, 9.72it/s] 94%|█████████▍| 47/50 [00:04<00:00, 9.73it/s] 96%|█████████▌| 48/50 [00:05<00:00, 9.73it/s] 98%|█████████▊| 49/50 [00:05<00:00, 9.73it/s] 100%|██████████| 50/50 [00:05<00:00, 9.71it/s] 100%|██████████| 50/50 [00:05<00:00, 9.60it/s]
Prediction
matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01fIDzsb6tznd2zg5xajlsez7greff4StatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- nspm sitting on the roof of a building, looking out over new york city at night, ultra realistic
- scheduler
- DDIM
- num_outputs
- "4"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "nspm sitting on the roof of a building, looking out over new york city at night, ultra realistic", "scheduler": "DDIM", "num_outputs": "4", "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 matt-bornstein/nspm1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", { input: { width: 512, height: 512, prompt: "nspm sitting on the roof of a building, looking out over new york city at night, ultra realistic", scheduler: "DDIM", num_outputs: "4", 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 matt-bornstein/nspm1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", input={ "width": 512, "height": 512, "prompt": "nspm sitting on the roof of a building, looking out over new york city at night, ultra realistic", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "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 matt-bornstein/nspm1 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": "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", "input": { "width": 512, "height": 512, "prompt": "nspm sitting on the roof of a building, looking out over new york city at night, ultra realistic", "scheduler": "DDIM", "num_outputs": "4", "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": "2023-02-20T01:21:24.803991Z", "created_at": "2023-02-20T01:21:16.072866Z", "data_removed": false, "error": null, "id": "zsb6tznd2zg5xajlsez7greff4", "input": { "width": 512, "height": 512, "prompt": "nspm sitting on the roof of a building, looking out over new york city at night, ultra realistic", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 44586\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 8.08it/s]\n 4%|▍ | 2/50 [00:00<00:05, 8.31it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.45it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.91it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.18it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.34it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.46it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.53it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.58it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.62it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.65it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 9.67it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 9.68it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.68it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.69it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.70it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.70it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.71it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 9.72it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.72it/s]\n 42%|████▏ | 21/50 [00:02<00:02, 9.73it/s]\n 44%|████▍ | 22/50 [00:02<00:02, 9.73it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.72it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.72it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.70it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.71it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.71it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 9.70it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.70it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.70it/s]\n 62%|██████▏ | 31/50 [00:03<00:01, 9.70it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.70it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.69it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.69it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.67it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.67it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 9.68it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 9.69it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.69it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.68it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.70it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.72it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.72it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.71it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.71it/s]\n 92%|█████████▏| 46/50 [00:04<00:00, 9.71it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 9.72it/s]\n 96%|█████████▌| 48/50 [00:04<00:00, 9.71it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.70it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.71it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.61it/s]", "metrics": { "predict_time": 8.673108, "total_time": 8.731125 }, "output": [ "https://replicate.delivery/pbxt/nXZytQlezr2JVSJn7SgQeKnPNh0W7vWJcWel9RJCzwykoPAhA/out-0.png", "https://replicate.delivery/pbxt/xJGvfOSZAr1UDKGLCwklKBaLaeLTrkBgyYr1At6fX9bloPAhA/out-1.png", "https://replicate.delivery/pbxt/e3lOvJYxSaXnWiwvdNeH34YfN442C0k0GbtfeOXcmqRYieBIE/out-2.png", "https://replicate.delivery/pbxt/Dv5mDzOCAezBYCejm8bUCffRlgpIZwSD99phuwQb5h6QRfAEC/out-3.png" ], "started_at": "2023-02-20T01:21:16.130883Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zsb6tznd2zg5xajlsez7greff4", "cancel": "https://api.replicate.com/v1/predictions/zsb6tznd2zg5xajlsez7greff4/cancel" }, "version": "6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f" }
Generated inUsing seed: 44586 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 8.08it/s] 4%|▍ | 2/50 [00:00<00:05, 8.31it/s] 6%|▌ | 3/50 [00:00<00:05, 8.45it/s] 8%|▊ | 4/50 [00:00<00:05, 8.91it/s] 10%|█ | 5/50 [00:00<00:04, 9.18it/s] 12%|█▏ | 6/50 [00:00<00:04, 9.34it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.46it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.53it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.58it/s] 20%|██ | 10/50 [00:01<00:04, 9.62it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.65it/s] 24%|██▍ | 12/50 [00:01<00:03, 9.67it/s] 26%|██▌ | 13/50 [00:01<00:03, 9.68it/s] 28%|██▊ | 14/50 [00:01<00:03, 9.68it/s] 30%|███ | 15/50 [00:01<00:03, 9.69it/s] 32%|███▏ | 16/50 [00:01<00:03, 9.70it/s] 34%|███▍ | 17/50 [00:01<00:03, 9.70it/s] 36%|███▌ | 18/50 [00:01<00:03, 9.71it/s] 38%|███▊ | 19/50 [00:02<00:03, 9.72it/s] 40%|████ | 20/50 [00:02<00:03, 9.72it/s] 42%|████▏ | 21/50 [00:02<00:02, 9.73it/s] 44%|████▍ | 22/50 [00:02<00:02, 9.73it/s] 46%|████▌ | 23/50 [00:02<00:02, 9.72it/s] 48%|████▊ | 24/50 [00:02<00:02, 9.72it/s] 50%|█████ | 25/50 [00:02<00:02, 9.70it/s] 52%|█████▏ | 26/50 [00:02<00:02, 9.71it/s] 54%|█████▍ | 27/50 [00:02<00:02, 9.71it/s] 56%|█████▌ | 28/50 [00:02<00:02, 9.70it/s] 58%|█████▊ | 29/50 [00:03<00:02, 9.70it/s] 60%|██████ | 30/50 [00:03<00:02, 9.70it/s] 62%|██████▏ | 31/50 [00:03<00:01, 9.70it/s] 64%|██████▍ | 32/50 [00:03<00:01, 9.70it/s] 66%|██████▌ | 33/50 [00:03<00:01, 9.69it/s] 68%|██████▊ | 34/50 [00:03<00:01, 9.69it/s] 70%|███████ | 35/50 [00:03<00:01, 9.67it/s] 72%|███████▏ | 36/50 [00:03<00:01, 9.67it/s] 74%|███████▍ | 37/50 [00:03<00:01, 9.68it/s] 76%|███████▌ | 38/50 [00:03<00:01, 9.69it/s] 78%|███████▊ | 39/50 [00:04<00:01, 9.69it/s] 80%|████████ | 40/50 [00:04<00:01, 9.68it/s] 82%|████████▏ | 41/50 [00:04<00:00, 9.70it/s] 84%|████████▍ | 42/50 [00:04<00:00, 9.72it/s] 86%|████████▌ | 43/50 [00:04<00:00, 9.72it/s] 88%|████████▊ | 44/50 [00:04<00:00, 9.71it/s] 90%|█████████ | 45/50 [00:04<00:00, 9.71it/s] 92%|█████████▏| 46/50 [00:04<00:00, 9.71it/s] 94%|█████████▍| 47/50 [00:04<00:00, 9.72it/s] 96%|█████████▌| 48/50 [00:04<00:00, 9.71it/s] 98%|█████████▊| 49/50 [00:05<00:00, 9.70it/s] 100%|██████████| 50/50 [00:05<00:00, 9.71it/s] 100%|██████████| 50/50 [00:05<00:00, 9.61it/s]
Prediction
matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01fIDrs4tkymqyrat7ckxovqr5lje7qStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- Professional portrait of nspm as a young jedi woman with a lightsabre, star wars, by artgerm and moebius, beautiful, hyperrealism, highly detailed, 8k, intricate, closeup, dynamic dramatic dark moody lighting, shadows, artstation, concept art, octane render, 8k
- scheduler
- DDIM
- num_outputs
- "4"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "Professional portrait of nspm as a young jedi woman with a lightsabre, star wars, by artgerm and moebius, beautiful, hyperrealism, highly detailed, 8k, intricate, closeup, dynamic dramatic dark moody lighting, shadows, artstation, concept art, octane render, 8k", "scheduler": "DDIM", "num_outputs": "4", "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 matt-bornstein/nspm1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", { input: { width: 512, height: 512, prompt: "Professional portrait of nspm as a young jedi woman with a lightsabre, star wars, by artgerm and moebius, beautiful, hyperrealism, highly detailed, 8k, intricate, closeup, dynamic dramatic dark moody lighting, shadows, artstation, concept art, octane render, 8k", scheduler: "DDIM", num_outputs: "4", 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 matt-bornstein/nspm1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", input={ "width": 512, "height": 512, "prompt": "Professional portrait of nspm as a young jedi woman with a lightsabre, star wars, by artgerm and moebius, beautiful, hyperrealism, highly detailed, 8k, intricate, closeup, dynamic dramatic dark moody lighting, shadows, artstation, concept art, octane render, 8k", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "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 matt-bornstein/nspm1 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": "matt-bornstein/nspm1:6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f", "input": { "width": 512, "height": 512, "prompt": "Professional portrait of nspm as a young jedi woman with a lightsabre, star wars, by artgerm and moebius, beautiful, hyperrealism, highly detailed, 8k, intricate, closeup, dynamic dramatic dark moody lighting, shadows, artstation, concept art, octane render, 8k", "scheduler": "DDIM", "num_outputs": "4", "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": "2023-02-20T01:25:52.297587Z", "created_at": "2023-02-20T01:25:44.051314Z", "data_removed": false, "error": null, "id": "rs4tkymqyrat7ckxovqr5lje7q", "input": { "width": 512, "height": 512, "prompt": "Professional portrait of nspm as a young jedi woman with a lightsabre, star wars, by artgerm and moebius, beautiful, hyperrealism, highly detailed, 8k, intricate, closeup, dynamic dramatic dark moody lighting, shadows, artstation, concept art, octane render, 8k", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 33227\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.90it/s]\n 4%|▍ | 2/50 [00:00<00:05, 8.42it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.95it/s]\n 8%|▊ | 4/50 [00:00<00:04, 9.24it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.39it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.48it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.55it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.60it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.64it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.66it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.67it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 9.68it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 9.69it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.70it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.69it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.69it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.70it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.71it/s]\n 38%|███▊ | 19/50 [00:01<00:03, 9.70it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.69it/s]\n 42%|████▏ | 21/50 [00:02<00:02, 9.70it/s]\n 44%|████▍ | 22/50 [00:02<00:02, 9.71it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.71it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.68it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.67it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.69it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.69it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 9.68it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.69it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.71it/s]\n 62%|██████▏ | 31/50 [00:03<00:01, 9.71it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.71it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.70it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.70it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.71it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.71it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 9.70it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 9.69it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.70it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.70it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.69it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.70it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.71it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.69it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.68it/s]\n 92%|█████████▏| 46/50 [00:04<00:00, 9.69it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 9.69it/s]\n 96%|█████████▌| 48/50 [00:04<00:00, 9.68it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.68it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.69it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.63it/s]", "metrics": { "predict_time": 8.182938, "total_time": 8.246273 }, "output": [ "https://replicate.delivery/pbxt/gxlzrYRsGRIPOxBzGD99fGpkPxbKq3AcA10bQP7g4KMP8DQIA/out-0.png", "https://replicate.delivery/pbxt/M4HB1afCMbyAJ6QAbNhOdu4CdS23LO0fFVUULwrYbHzewPAhA/out-1.png", "https://replicate.delivery/pbxt/NE7CjZoIJ7rrAx2weSTKIYJ7OWKfueuiFyXpTf4fjzVeHeDQIA/out-2.png", "https://replicate.delivery/pbxt/Lf8Wei3fuXHx9pQ0eYPppfLesMeI95nfne1QNdWLfpNQ8hfAEC/out-3.png" ], "started_at": "2023-02-20T01:25:44.114649Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rs4tkymqyrat7ckxovqr5lje7q", "cancel": "https://api.replicate.com/v1/predictions/rs4tkymqyrat7ckxovqr5lje7q/cancel" }, "version": "6ca8d8299a9bfe947c807e2eab145b8edd5d432644ba16c4073122b63940b01f" }
Generated inUsing seed: 33227 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.90it/s] 4%|▍ | 2/50 [00:00<00:05, 8.42it/s] 6%|▌ | 3/50 [00:00<00:05, 8.95it/s] 8%|▊ | 4/50 [00:00<00:04, 9.24it/s] 10%|█ | 5/50 [00:00<00:04, 9.39it/s] 12%|█▏ | 6/50 [00:00<00:04, 9.48it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.55it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.60it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.64it/s] 20%|██ | 10/50 [00:01<00:04, 9.66it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.67it/s] 24%|██▍ | 12/50 [00:01<00:03, 9.68it/s] 26%|██▌ | 13/50 [00:01<00:03, 9.69it/s] 28%|██▊ | 14/50 [00:01<00:03, 9.70it/s] 30%|███ | 15/50 [00:01<00:03, 9.69it/s] 32%|███▏ | 16/50 [00:01<00:03, 9.69it/s] 34%|███▍ | 17/50 [00:01<00:03, 9.70it/s] 36%|███▌ | 18/50 [00:01<00:03, 9.71it/s] 38%|███▊ | 19/50 [00:01<00:03, 9.70it/s] 40%|████ | 20/50 [00:02<00:03, 9.69it/s] 42%|████▏ | 21/50 [00:02<00:02, 9.70it/s] 44%|████▍ | 22/50 [00:02<00:02, 9.71it/s] 46%|████▌ | 23/50 [00:02<00:02, 9.71it/s] 48%|████▊ | 24/50 [00:02<00:02, 9.68it/s] 50%|█████ | 25/50 [00:02<00:02, 9.67it/s] 52%|█████▏ | 26/50 [00:02<00:02, 9.69it/s] 54%|█████▍ | 27/50 [00:02<00:02, 9.69it/s] 56%|█████▌ | 28/50 [00:02<00:02, 9.68it/s] 58%|█████▊ | 29/50 [00:03<00:02, 9.69it/s] 60%|██████ | 30/50 [00:03<00:02, 9.71it/s] 62%|██████▏ | 31/50 [00:03<00:01, 9.71it/s] 64%|██████▍ | 32/50 [00:03<00:01, 9.71it/s] 66%|██████▌ | 33/50 [00:03<00:01, 9.70it/s] 68%|██████▊ | 34/50 [00:03<00:01, 9.70it/s] 70%|███████ | 35/50 [00:03<00:01, 9.71it/s] 72%|███████▏ | 36/50 [00:03<00:01, 9.71it/s] 74%|███████▍ | 37/50 [00:03<00:01, 9.70it/s] 76%|███████▌ | 38/50 [00:03<00:01, 9.69it/s] 78%|███████▊ | 39/50 [00:04<00:01, 9.70it/s] 80%|████████ | 40/50 [00:04<00:01, 9.70it/s] 82%|████████▏ | 41/50 [00:04<00:00, 9.69it/s] 84%|████████▍ | 42/50 [00:04<00:00, 9.70it/s] 86%|████████▌ | 43/50 [00:04<00:00, 9.71it/s] 88%|████████▊ | 44/50 [00:04<00:00, 9.69it/s] 90%|█████████ | 45/50 [00:04<00:00, 9.68it/s] 92%|█████████▏| 46/50 [00:04<00:00, 9.69it/s] 94%|█████████▍| 47/50 [00:04<00:00, 9.69it/s] 96%|█████████▌| 48/50 [00:04<00:00, 9.68it/s] 98%|█████████▊| 49/50 [00:05<00:00, 9.68it/s] 100%|██████████| 50/50 [00:05<00:00, 9.69it/s] 100%|██████████| 50/50 [00:05<00:00, 9.63it/s]
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