tstramer / mo-di-diffusion
- Public
- 46.7K runs
Prediction
tstramer/mo-di-diffusion:69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8eaID46ewezjqgjhsnh2tf6ywexic6iStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- seed
- 5
- width
- 512
- height
- 512
- prompt
- a magical princess with golden hair, modern disney style
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, modern disney style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "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 tstramer/mo-di-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/mo-di-diffusion:69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8ea", { input: { seed: 5, width: 512, height: 512, prompt: "a magical princess with golden hair, modern disney style", scheduler: "K-LMS", 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 replicateImport the client:import replicateRun tstramer/mo-di-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/mo-di-diffusion:69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8ea", input={ "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, modern disney style", "scheduler": "K-LMS", "num_outputs": 1, "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 tstramer/mo-di-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "tstramer/mo-di-diffusion:69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8ea", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, modern disney style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "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": "2022-11-07T21:40:56.619599Z", "created_at": "2022-11-07T21:34:06.636791Z", "data_removed": false, "error": null, "id": "46ewezjqgjhsnh2tf6ywexic6i", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, modern disney style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:05<04:52, 5.97s/it]\n 4%|▍ | 2/50 [00:06<02:05, 2.61s/it]\n 6%|▌ | 3/50 [00:06<01:12, 1.54s/it]\n 8%|▊ | 4/50 [00:06<00:47, 1.04s/it]\n 10%|█ | 5/50 [00:07<00:34, 1.32it/s]\n 12%|█▏ | 6/50 [00:07<00:25, 1.69it/s]\n 14%|█▍ | 7/50 [00:07<00:20, 2.06it/s]\n 16%|█▌ | 8/50 [00:07<00:17, 2.41it/s]\n 18%|█▊ | 9/50 [00:08<00:15, 2.71it/s]\n 20%|██ | 10/50 [00:08<00:13, 2.97it/s]\n 22%|██▏ | 11/50 [00:08<00:12, 3.18it/s]\n 24%|██▍ | 12/50 [00:08<00:11, 3.33it/s]\n 26%|██▌ | 13/50 [00:09<00:10, 3.45it/s]\n 28%|██▊ | 14/50 [00:09<00:10, 3.54it/s]\n 30%|███ | 15/50 [00:09<00:09, 3.61it/s]\n 32%|███▏ | 16/50 [00:09<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:10<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:10<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:10<00:08, 3.72it/s]\n 40%|████ | 20/50 [00:11<00:08, 3.74it/s]\n 42%|████▏ | 21/50 [00:11<00:07, 3.75it/s]\n 44%|████▍ | 22/50 [00:11<00:07, 3.75it/s]\n 46%|████▌ | 23/50 [00:11<00:07, 3.75it/s]\n 48%|████▊ | 24/50 [00:12<00:06, 3.75it/s]\n 50%|█████ | 25/50 [00:12<00:06, 3.74it/s]\n 52%|█████▏ | 26/50 [00:12<00:06, 3.73it/s]\n 54%|█████▍ | 27/50 [00:12<00:06, 3.73it/s]\n 56%|█████▌ | 28/50 [00:13<00:05, 3.75it/s]\n 58%|█████▊ | 29/50 [00:13<00:05, 3.76it/s]\n 60%|██████ | 30/50 [00:13<00:05, 3.76it/s]\n 62%|██████▏ | 31/50 [00:13<00:05, 3.75it/s]\n 64%|██████▍ | 32/50 [00:14<00:04, 3.75it/s]\n 66%|██████▌ | 33/50 [00:14<00:04, 3.75it/s]\n 68%|██████▊ | 34/50 [00:14<00:04, 3.74it/s]\n 70%|███████ | 35/50 [00:15<00:04, 3.74it/s]\n 72%|███████▏ | 36/50 [00:15<00:03, 3.74it/s]\n 74%|███████▍ | 37/50 [00:15<00:03, 3.75it/s]\n 76%|███████▌ | 38/50 [00:15<00:03, 3.73it/s]\n 78%|███████▊ | 39/50 [00:16<00:02, 3.71it/s]\n 80%|████████ | 40/50 [00:16<00:02, 3.72it/s]\n 82%|████████▏ | 41/50 [00:16<00:02, 3.73it/s]\n 84%|████████▍ | 42/50 [00:16<00:02, 3.72it/s]\n 86%|████████▌ | 43/50 [00:17<00:01, 3.71it/s]\n 88%|████████▊ | 44/50 [00:17<00:01, 3.71it/s]\n 90%|█████████ | 45/50 [00:17<00:01, 3.72it/s]\n 92%|█████████▏| 46/50 [00:17<00:01, 3.72it/s]\n 94%|█████████▍| 47/50 [00:18<00:00, 3.72it/s]\n 96%|█████████▌| 48/50 [00:18<00:00, 3.72it/s]\n 98%|█████████▊| 49/50 [00:18<00:00, 3.73it/s]\n100%|██████████| 50/50 [00:19<00:00, 3.73it/s]\n100%|██████████| 50/50 [00:19<00:00, 2.63it/s]", "metrics": { "predict_time": 35.732384, "total_time": 409.982808 }, "output": [ "https://replicate.delivery/pbxt/2WXeeovx3SikOkwQukhmwYoamInEMqQyeKmCu8cP740Rrl7fA/out-0.png" ], "started_at": "2022-11-07T21:40:20.887215Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/46ewezjqgjhsnh2tf6ywexic6i", "cancel": "https://api.replicate.com/v1/predictions/46ewezjqgjhsnh2tf6ywexic6i/cancel" }, "version": "69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8ea" }Generated inUsing seed: 5 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:05<04:52, 5.97s/it] 4%|▍ | 2/50 [00:06<02:05, 2.61s/it] 6%|▌ | 3/50 [00:06<01:12, 1.54s/it] 8%|▊ | 4/50 [00:06<00:47, 1.04s/it] 10%|█ | 5/50 [00:07<00:34, 1.32it/s] 12%|█▏ | 6/50 [00:07<00:25, 1.69it/s] 14%|█▍ | 7/50 [00:07<00:20, 2.06it/s] 16%|█▌ | 8/50 [00:07<00:17, 2.41it/s] 18%|█▊ | 9/50 [00:08<00:15, 2.71it/s] 20%|██ | 10/50 [00:08<00:13, 2.97it/s] 22%|██▏ | 11/50 [00:08<00:12, 3.18it/s] 24%|██▍ | 12/50 [00:08<00:11, 3.33it/s] 26%|██▌ | 13/50 [00:09<00:10, 3.45it/s] 28%|██▊ | 14/50 [00:09<00:10, 3.54it/s] 30%|███ | 15/50 [00:09<00:09, 3.61it/s] 32%|███▏ | 16/50 [00:09<00:09, 3.65it/s] 34%|███▍ | 17/50 [00:10<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:10<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:10<00:08, 3.72it/s] 40%|████ | 20/50 [00:11<00:08, 3.74it/s] 42%|████▏ | 21/50 [00:11<00:07, 3.75it/s] 44%|████▍ | 22/50 [00:11<00:07, 3.75it/s] 46%|████▌ | 23/50 [00:11<00:07, 3.75it/s] 48%|████▊ | 24/50 [00:12<00:06, 3.75it/s] 50%|█████ | 25/50 [00:12<00:06, 3.74it/s] 52%|█████▏ | 26/50 [00:12<00:06, 3.73it/s] 54%|█████▍ | 27/50 [00:12<00:06, 3.73it/s] 56%|█████▌ | 28/50 [00:13<00:05, 3.75it/s] 58%|█████▊ | 29/50 [00:13<00:05, 3.76it/s] 60%|██████ | 30/50 [00:13<00:05, 3.76it/s] 62%|██████▏ | 31/50 [00:13<00:05, 3.75it/s] 64%|██████▍ | 32/50 [00:14<00:04, 3.75it/s] 66%|██████▌ | 33/50 [00:14<00:04, 3.75it/s] 68%|██████▊ | 34/50 [00:14<00:04, 3.74it/s] 70%|███████ | 35/50 [00:15<00:04, 3.74it/s] 72%|███████▏ | 36/50 [00:15<00:03, 3.74it/s] 74%|███████▍ | 37/50 [00:15<00:03, 3.75it/s] 76%|███████▌ | 38/50 [00:15<00:03, 3.73it/s] 78%|███████▊ | 39/50 [00:16<00:02, 3.71it/s] 80%|████████ | 40/50 [00:16<00:02, 3.72it/s] 82%|████████▏ | 41/50 [00:16<00:02, 3.73it/s] 84%|████████▍ | 42/50 [00:16<00:02, 3.72it/s] 86%|████████▌ | 43/50 [00:17<00:01, 3.71it/s] 88%|████████▊ | 44/50 [00:17<00:01, 3.71it/s] 90%|█████████ | 45/50 [00:17<00:01, 3.72it/s] 92%|█████████▏| 46/50 [00:17<00:01, 3.72it/s] 94%|█████████▍| 47/50 [00:18<00:00, 3.72it/s] 96%|█████████▌| 48/50 [00:18<00:00, 3.72it/s] 98%|█████████▊| 49/50 [00:18<00:00, 3.73it/s] 100%|██████████| 50/50 [00:19<00:00, 3.73it/s] 100%|██████████| 50/50 [00:19<00:00, 2.63it/s]Prediction
tstramer/mo-di-diffusion:69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8eaID46szaan7bvhybcdfu4rm6lqh4eStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 5
- width
- 512
- height
- 512
- prompt
- simba from lion king, modern disney style
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "117"
{ "seed": 5, "width": 512, "height": 512, "prompt": "simba from lion king, modern disney style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "117" }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 tstramer/mo-di-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/mo-di-diffusion:69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8ea", { input: { seed: 5, width: 512, height: 512, prompt: "simba from lion king, modern disney style", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "117" } } ); // 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 tstramer/mo-di-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/mo-di-diffusion:69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8ea", input={ "seed": 5, "width": 512, "height": 512, "prompt": "simba from lion king, modern disney style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "117" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tstramer/mo-di-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "tstramer/mo-di-diffusion:69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8ea", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "simba from lion king, modern disney style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "117" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-08T02:01:41.068201Z", "created_at": "2022-11-08T01:58:54.061250Z", "data_removed": false, "error": null, "id": "46szaan7bvhybcdfu4rm6lqh4e", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "simba from lion king, modern disney style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "117" }, "logs": "Using seed: 5\n\n 0%| | 0/117 [00:00<?, ?it/s]\n 1%| | 1/117 [00:02<04:24, 2.28s/it]\n 3%|▎ | 3/117 [00:02<01:13, 1.55it/s]\n 4%|▍ | 5/117 [00:02<00:39, 2.84it/s]\n 6%|▌ | 7/117 [00:02<00:25, 4.26it/s]\n 8%|▊ | 9/117 [00:02<00:19, 5.64it/s]\n 9%|▉ | 11/117 [00:03<00:14, 7.08it/s]\n 11%|█ | 13/117 [00:03<00:12, 8.46it/s]\n 13%|█▎ | 15/117 [00:03<00:10, 9.66it/s]\n 15%|█▍ | 17/117 [00:03<00:09, 10.66it/s]\n 16%|█▌ | 19/117 [00:03<00:08, 11.13it/s]\n 18%|█▊ | 21/117 [00:03<00:08, 11.72it/s]\n 20%|█▉ | 23/117 [00:03<00:07, 12.15it/s]\n 21%|██▏ | 25/117 [00:04<00:07, 12.46it/s]\n 23%|██▎ | 27/117 [00:04<00:07, 12.72it/s]\n 25%|██▍ | 29/117 [00:04<00:06, 12.91it/s]\n 26%|██▋ | 31/117 [00:04<00:06, 13.02it/s]\n 28%|██▊ | 33/117 [00:04<00:06, 13.13it/s]\n 30%|██▉ | 35/117 [00:04<00:06, 13.18it/s]\n 32%|███▏ | 37/117 [00:04<00:06, 13.23it/s]\n 33%|███▎ | 39/117 [00:05<00:05, 13.32it/s]\n 35%|███▌ | 41/117 [00:05<00:05, 13.47it/s]\n 37%|███▋ | 43/117 [00:05<00:05, 13.57it/s]\n 38%|███▊ | 45/117 [00:05<00:05, 13.63it/s]\n 40%|████ | 47/117 [00:05<00:05, 13.57it/s]\n 42%|████▏ | 49/117 [00:05<00:04, 13.60it/s]\n 44%|████▎ | 51/117 [00:06<00:04, 13.61it/s]\n 45%|████▌ | 53/117 [00:06<00:04, 13.66it/s]\n 47%|████▋ | 55/117 [00:06<00:04, 13.64it/s]\n 49%|████▊ | 57/117 [00:06<00:04, 13.57it/s]\n 50%|█████ | 59/117 [00:06<00:04, 13.57it/s]\n 52%|█████▏ | 61/117 [00:06<00:04, 13.56it/s]\n 54%|█████▍ | 63/117 [00:06<00:03, 13.53it/s]\n 56%|█████▌ | 65/117 [00:07<00:03, 13.46it/s]\n 57%|█████▋ | 67/117 [00:07<00:03, 13.34it/s]\n 59%|█████▉ | 69/117 [00:07<00:03, 13.42it/s]\n 61%|██████ | 71/117 [00:07<00:03, 13.40it/s]\n 62%|██████▏ | 73/117 [00:07<00:03, 13.51it/s]\n 64%|██████▍ | 75/117 [00:07<00:03, 13.53it/s]\n 66%|██████▌ | 77/117 [00:07<00:02, 13.55it/s]\n 68%|██████▊ | 79/117 [00:08<00:02, 13.62it/s]\n 69%|██████▉ | 81/117 [00:08<00:02, 13.60it/s]\n 71%|███████ | 83/117 [00:08<00:02, 13.68it/s]\n 73%|███████▎ | 85/117 [00:08<00:02, 13.78it/s]\n 74%|███████▍ | 87/117 [00:08<00:02, 13.83it/s]\n 76%|███████▌ | 89/117 [00:08<00:02, 13.86it/s]\n 78%|███████▊ | 91/117 [00:08<00:01, 13.75it/s]\n 79%|███████▉ | 93/117 [00:09<00:01, 13.75it/s]\n 81%|████████ | 95/117 [00:09<00:01, 13.77it/s]\n 83%|████████▎ | 97/117 [00:09<00:01, 13.77it/s]\n 85%|████████▍ | 99/117 [00:09<00:01, 13.75it/s]\n 86%|████████▋ | 101/117 [00:09<00:01, 13.68it/s]\n 88%|████████▊ | 103/117 [00:09<00:01, 13.54it/s]\n 90%|████████▉ | 105/117 [00:09<00:00, 13.53it/s]\n 91%|█████████▏| 107/117 [00:10<00:00, 13.60it/s]\n 93%|█████████▎| 109/117 [00:10<00:00, 13.47it/s]\n 95%|█████████▍| 111/117 [00:10<00:00, 13.53it/s]\n 97%|█████████▋| 113/117 [00:10<00:00, 13.64it/s]\n 98%|█████████▊| 115/117 [00:10<00:00, 13.66it/s]\n100%|██████████| 117/117 [00:10<00:00, 13.66it/s]\n100%|██████████| 117/117 [00:10<00:00, 10.77it/s]", "metrics": { "predict_time": 14.369152, "total_time": 167.006951 }, "output": [ "https://replicate.delivery/pbxt/bIWbbbvqXLbfTycr18idOmrZ1wjnswgmzKnlBM7DoxwCV7ePA/out-0.png" ], "started_at": "2022-11-08T02:01:26.699049Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/46szaan7bvhybcdfu4rm6lqh4e", "cancel": "https://api.replicate.com/v1/predictions/46szaan7bvhybcdfu4rm6lqh4e/cancel" }, "version": "69687c28501955e2b88e15c8984049dcea52a44f385993271e885b1bf08ee8ea" }Generated inUsing seed: 5 0%| | 0/117 [00:00<?, ?it/s] 1%| | 1/117 [00:02<04:24, 2.28s/it] 3%|▎ | 3/117 [00:02<01:13, 1.55it/s] 4%|▍ | 5/117 [00:02<00:39, 2.84it/s] 6%|▌ | 7/117 [00:02<00:25, 4.26it/s] 8%|▊ | 9/117 [00:02<00:19, 5.64it/s] 9%|▉ | 11/117 [00:03<00:14, 7.08it/s] 11%|█ | 13/117 [00:03<00:12, 8.46it/s] 13%|█▎ | 15/117 [00:03<00:10, 9.66it/s] 15%|█▍ | 17/117 [00:03<00:09, 10.66it/s] 16%|█▌ | 19/117 [00:03<00:08, 11.13it/s] 18%|█▊ | 21/117 [00:03<00:08, 11.72it/s] 20%|█▉ | 23/117 [00:03<00:07, 12.15it/s] 21%|██▏ | 25/117 [00:04<00:07, 12.46it/s] 23%|██▎ | 27/117 [00:04<00:07, 12.72it/s] 25%|██▍ | 29/117 [00:04<00:06, 12.91it/s] 26%|██▋ | 31/117 [00:04<00:06, 13.02it/s] 28%|██▊ | 33/117 [00:04<00:06, 13.13it/s] 30%|██▉ | 35/117 [00:04<00:06, 13.18it/s] 32%|███▏ | 37/117 [00:04<00:06, 13.23it/s] 33%|███▎ | 39/117 [00:05<00:05, 13.32it/s] 35%|███▌ | 41/117 [00:05<00:05, 13.47it/s] 37%|███▋ | 43/117 [00:05<00:05, 13.57it/s] 38%|███▊ | 45/117 [00:05<00:05, 13.63it/s] 40%|████ | 47/117 [00:05<00:05, 13.57it/s] 42%|████▏ | 49/117 [00:05<00:04, 13.60it/s] 44%|████▎ | 51/117 [00:06<00:04, 13.61it/s] 45%|████▌ | 53/117 [00:06<00:04, 13.66it/s] 47%|████▋ | 55/117 [00:06<00:04, 13.64it/s] 49%|████▊ | 57/117 [00:06<00:04, 13.57it/s] 50%|█████ | 59/117 [00:06<00:04, 13.57it/s] 52%|█████▏ | 61/117 [00:06<00:04, 13.56it/s] 54%|█████▍ | 63/117 [00:06<00:03, 13.53it/s] 56%|█████▌ | 65/117 [00:07<00:03, 13.46it/s] 57%|█████▋ | 67/117 [00:07<00:03, 13.34it/s] 59%|█████▉ | 69/117 [00:07<00:03, 13.42it/s] 61%|██████ | 71/117 [00:07<00:03, 13.40it/s] 62%|██████▏ | 73/117 [00:07<00:03, 13.51it/s] 64%|██████▍ | 75/117 [00:07<00:03, 13.53it/s] 66%|██████▌ | 77/117 [00:07<00:02, 13.55it/s] 68%|██████▊ | 79/117 [00:08<00:02, 13.62it/s] 69%|██████▉ | 81/117 [00:08<00:02, 13.60it/s] 71%|███████ | 83/117 [00:08<00:02, 13.68it/s] 73%|███████▎ | 85/117 [00:08<00:02, 13.78it/s] 74%|███████▍ | 87/117 [00:08<00:02, 13.83it/s] 76%|███████▌ | 89/117 [00:08<00:02, 13.86it/s] 78%|███████▊ | 91/117 [00:08<00:01, 13.75it/s] 79%|███████▉ | 93/117 [00:09<00:01, 13.75it/s] 81%|████████ | 95/117 [00:09<00:01, 13.77it/s] 83%|████████▎ | 97/117 [00:09<00:01, 13.77it/s] 85%|████████▍ | 99/117 [00:09<00:01, 13.75it/s] 86%|████████▋ | 101/117 [00:09<00:01, 13.68it/s] 88%|████████▊ | 103/117 [00:09<00:01, 13.54it/s] 90%|████████▉ | 105/117 [00:09<00:00, 13.53it/s] 91%|█████████▏| 107/117 [00:10<00:00, 13.60it/s] 93%|█████████▎| 109/117 [00:10<00:00, 13.47it/s] 95%|█████████▍| 111/117 [00:10<00:00, 13.53it/s] 97%|█████████▋| 113/117 [00:10<00:00, 13.64it/s] 98%|█████████▊| 115/117 [00:10<00:00, 13.66it/s] 100%|██████████| 117/117 [00:10<00:00, 13.66it/s] 100%|██████████| 117/117 [00:10<00:00, 10.77it/s]
Want to make some of these yourself?
Run this model
