lucataco / sdxl-controlnet-depth
SDXL ControlNet - Depth
Prediction
lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9IDcgekvctbear65ui5vgwiahmn34StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 25087
- image
- prompt
- spiderman lecture, photorealistic
- condition_scale
- 0.5
- num_inference_steps
- 30
{ "seed": 25087, "image": "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png", "prompt": "spiderman lecture, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", { input: { seed: 25087, image: "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png", prompt: "spiderman lecture, photorealistic", condition_scale: 0.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", input={ "seed": 25087, "image": "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png", "prompt": "spiderman lecture, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/sdxl-controlnet-depth 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": "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", "input": { "seed": 25087, "image": "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png", "prompt": "spiderman lecture, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T22:47:53.749512Z", "created_at": "2023-09-12T22:45:45.734709Z", "data_removed": false, "error": null, "id": "cgekvctbear65ui5vgwiahmn34", "input": { "seed": 25087, "image": "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png", "prompt": "spiderman lecture, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 }, "logs": "Using seed: 25087\nOriginal width:458, height:458\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:09, 3.05it/s]\n 7%|▋ | 2/30 [00:00<00:08, 3.26it/s]\n 10%|█ | 3/30 [00:00<00:08, 3.33it/s]\n 13%|█▎ | 4/30 [00:01<00:07, 3.34it/s]\n 17%|█▋ | 5/30 [00:01<00:07, 3.36it/s]\n 20%|██ | 6/30 [00:01<00:07, 3.38it/s]\n 23%|██▎ | 7/30 [00:02<00:06, 3.38it/s]\n 27%|██▋ | 8/30 [00:02<00:06, 3.38it/s]\n 30%|███ | 9/30 [00:02<00:06, 3.39it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.39it/s]\n 37%|███▋ | 11/30 [00:03<00:05, 3.38it/s]\n 40%|████ | 12/30 [00:03<00:05, 3.35it/s]\n 43%|████▎ | 13/30 [00:03<00:05, 3.37it/s]\n 47%|████▋ | 14/30 [00:04<00:04, 3.37it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.37it/s]\n 53%|█████▎ | 16/30 [00:04<00:04, 3.38it/s]\n 57%|█████▋ | 17/30 [00:05<00:03, 3.38it/s]\n 60%|██████ | 18/30 [00:05<00:03, 3.38it/s]\n 63%|██████▎ | 19/30 [00:05<00:03, 3.38it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.38it/s]\n 70%|███████ | 21/30 [00:06<00:02, 3.38it/s]\n 73%|███████▎ | 22/30 [00:06<00:02, 3.38it/s]\n 77%|███████▋ | 23/30 [00:06<00:02, 3.38it/s]\n 80%|████████ | 24/30 [00:07<00:01, 3.38it/s]\n 83%|████████▎ | 25/30 [00:07<00:01, 3.38it/s]\n 87%|████████▋ | 26/30 [00:07<00:01, 3.38it/s]\n 90%|█████████ | 27/30 [00:08<00:00, 3.38it/s]\n 93%|█████████▎| 28/30 [00:08<00:00, 3.38it/s]\n 97%|█████████▋| 29/30 [00:08<00:00, 3.38it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.38it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.37it/s]", "metrics": { "predict_time": 12.481869, "total_time": 128.014803 }, "output": "https://pbxt.replicate.delivery/lvhWUVZ8cqaaDRyNwcoP5SxZnZYO8lG3JXqRmNFAM5Fmc6YE/output.png", "started_at": "2023-09-12T22:47:41.267643Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cgekvctbear65ui5vgwiahmn34", "cancel": "https://api.replicate.com/v1/predictions/cgekvctbear65ui5vgwiahmn34/cancel" }, "version": "5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9" }Generated inUsing seed: 25087 Original width:458, height:458 Aspect Ratio: 1.00 new_width:1024, new_height:1024 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:09, 3.05it/s] 7%|▋ | 2/30 [00:00<00:08, 3.26it/s] 10%|█ | 3/30 [00:00<00:08, 3.33it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.34it/s] 17%|█▋ | 5/30 [00:01<00:07, 3.36it/s] 20%|██ | 6/30 [00:01<00:07, 3.38it/s] 23%|██▎ | 7/30 [00:02<00:06, 3.38it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.38it/s] 30%|███ | 9/30 [00:02<00:06, 3.39it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.39it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.38it/s] 40%|████ | 12/30 [00:03<00:05, 3.35it/s] 43%|████▎ | 13/30 [00:03<00:05, 3.37it/s] 47%|████▋ | 14/30 [00:04<00:04, 3.37it/s] 50%|█████ | 15/30 [00:04<00:04, 3.37it/s] 53%|█████▎ | 16/30 [00:04<00:04, 3.38it/s] 57%|█████▋ | 17/30 [00:05<00:03, 3.38it/s] 60%|██████ | 18/30 [00:05<00:03, 3.38it/s] 63%|██████▎ | 19/30 [00:05<00:03, 3.38it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.38it/s] 70%|███████ | 21/30 [00:06<00:02, 3.38it/s] 73%|███████▎ | 22/30 [00:06<00:02, 3.38it/s] 77%|███████▋ | 23/30 [00:06<00:02, 3.38it/s] 80%|████████ | 24/30 [00:07<00:01, 3.38it/s] 83%|████████▎ | 25/30 [00:07<00:01, 3.38it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.38it/s] 90%|█████████ | 27/30 [00:08<00:00, 3.38it/s] 93%|█████████▎| 28/30 [00:08<00:00, 3.38it/s] 97%|█████████▋| 29/30 [00:08<00:00, 3.38it/s] 100%|██████████| 30/30 [00:08<00:00, 3.38it/s] 100%|██████████| 30/30 [00:08<00:00, 3.37it/s]Prediction
lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9IDa7fcbstb575ts3rrnr3wyxqxuaStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 1339
- image
- prompt
- stormtrooper lecture, photorealistic
- condition_scale
- 0.5
- num_inference_steps
- 30
{ "seed": 1339, "image": "https://replicate.delivery/pbxt/JLl4ye7Giv1pSVZBXgeUpPFMHPilb2YLi7zqvEqzk78PC6rx/demo.png", "prompt": "stormtrooper lecture, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", { input: { seed: 1339, image: "https://replicate.delivery/pbxt/JLl4ye7Giv1pSVZBXgeUpPFMHPilb2YLi7zqvEqzk78PC6rx/demo.png", prompt: "stormtrooper lecture, photorealistic", condition_scale: 0.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", input={ "seed": 1339, "image": "https://replicate.delivery/pbxt/JLl4ye7Giv1pSVZBXgeUpPFMHPilb2YLi7zqvEqzk78PC6rx/demo.png", "prompt": "stormtrooper lecture, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/sdxl-controlnet-depth 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": "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", "input": { "seed": 1339, "image": "https://replicate.delivery/pbxt/JLl4ye7Giv1pSVZBXgeUpPFMHPilb2YLi7zqvEqzk78PC6rx/demo.png", "prompt": "stormtrooper lecture, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T22:48:27.044423Z", "created_at": "2023-09-12T22:48:15.696375Z", "data_removed": false, "error": null, "id": "a7fcbstb575ts3rrnr3wyxqxua", "input": { "seed": 1339, "image": "https://replicate.delivery/pbxt/JLl4ye7Giv1pSVZBXgeUpPFMHPilb2YLi7zqvEqzk78PC6rx/demo.png", "prompt": "stormtrooper lecture, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 }, "logs": "Using seed: 1339\nOriginal width:458, height:458\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:08, 3.41it/s]\n 7%|▋ | 2/30 [00:00<00:08, 3.40it/s]\n 10%|█ | 3/30 [00:00<00:07, 3.40it/s]\n 13%|█▎ | 4/30 [00:01<00:07, 3.39it/s]\n 17%|█▋ | 5/30 [00:01<00:07, 3.38it/s]\n 20%|██ | 6/30 [00:01<00:07, 3.38it/s]\n 23%|██▎ | 7/30 [00:02<00:06, 3.38it/s]\n 27%|██▋ | 8/30 [00:02<00:06, 3.38it/s]\n 30%|███ | 9/30 [00:02<00:06, 3.38it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.38it/s]\n 37%|███▋ | 11/30 [00:03<00:05, 3.38it/s]\n 40%|████ | 12/30 [00:03<00:05, 3.37it/s]\n 43%|████▎ | 13/30 [00:03<00:05, 3.37it/s]\n 47%|████▋ | 14/30 [00:04<00:04, 3.37it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.37it/s]\n 53%|█████▎ | 16/30 [00:04<00:04, 3.37it/s]\n 57%|█████▋ | 17/30 [00:05<00:03, 3.37it/s]\n 60%|██████ | 18/30 [00:05<00:03, 3.37it/s]\n 63%|██████▎ | 19/30 [00:05<00:03, 3.38it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.38it/s]\n 70%|███████ | 21/30 [00:06<00:02, 3.39it/s]\n 73%|███████▎ | 22/30 [00:06<00:02, 3.39it/s]\n 77%|███████▋ | 23/30 [00:06<00:02, 3.39it/s]\n 80%|████████ | 24/30 [00:07<00:01, 3.39it/s]\n 83%|████████▎ | 25/30 [00:07<00:01, 3.39it/s]\n 87%|████████▋ | 26/30 [00:07<00:01, 3.39it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.39it/s]\n 93%|█████████▎| 28/30 [00:08<00:00, 3.39it/s]\n 97%|█████████▋| 29/30 [00:08<00:00, 3.39it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.39it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.38it/s]", "metrics": { "predict_time": 11.373073, "total_time": 11.348048 }, "output": "https://pbxt.replicate.delivery/JaKlVF9Hffpwx0tOv2YPBM7ORys4n0q39BTo16fnbvc1lTHjA/output.png", "started_at": "2023-09-12T22:48:15.671350Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a7fcbstb575ts3rrnr3wyxqxua", "cancel": "https://api.replicate.com/v1/predictions/a7fcbstb575ts3rrnr3wyxqxua/cancel" }, "version": "5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9" }Generated inUsing seed: 1339 Original width:458, height:458 Aspect Ratio: 1.00 new_width:1024, new_height:1024 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:08, 3.41it/s] 7%|▋ | 2/30 [00:00<00:08, 3.40it/s] 10%|█ | 3/30 [00:00<00:07, 3.40it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.39it/s] 17%|█▋ | 5/30 [00:01<00:07, 3.38it/s] 20%|██ | 6/30 [00:01<00:07, 3.38it/s] 23%|██▎ | 7/30 [00:02<00:06, 3.38it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.38it/s] 30%|███ | 9/30 [00:02<00:06, 3.38it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.38it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.38it/s] 40%|████ | 12/30 [00:03<00:05, 3.37it/s] 43%|████▎ | 13/30 [00:03<00:05, 3.37it/s] 47%|████▋ | 14/30 [00:04<00:04, 3.37it/s] 50%|█████ | 15/30 [00:04<00:04, 3.37it/s] 53%|█████▎ | 16/30 [00:04<00:04, 3.37it/s] 57%|█████▋ | 17/30 [00:05<00:03, 3.37it/s] 60%|██████ | 18/30 [00:05<00:03, 3.37it/s] 63%|██████▎ | 19/30 [00:05<00:03, 3.38it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.38it/s] 70%|███████ | 21/30 [00:06<00:02, 3.39it/s] 73%|███████▎ | 22/30 [00:06<00:02, 3.39it/s] 77%|███████▋ | 23/30 [00:06<00:02, 3.39it/s] 80%|████████ | 24/30 [00:07<00:01, 3.39it/s] 83%|████████▎ | 25/30 [00:07<00:01, 3.39it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.39it/s] 90%|█████████ | 27/30 [00:07<00:00, 3.39it/s] 93%|█████████▎| 28/30 [00:08<00:00, 3.39it/s] 97%|█████████▋| 29/30 [00:08<00:00, 3.39it/s] 100%|██████████| 30/30 [00:08<00:00, 3.39it/s] 100%|██████████| 30/30 [00:08<00:00, 3.38it/s]Prediction
lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9IDueu5wr3bygt72drpp2h5hybbleStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 27095
- image
- prompt
- Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic
- condition_scale
- 0.5
- num_inference_steps
- 30
{ "seed": 27095, "image": "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", "prompt": "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", { input: { seed: 27095, image: "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", prompt: "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", condition_scale: 0.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", input={ "seed": 27095, "image": "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", "prompt": "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/sdxl-controlnet-depth 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": "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", "input": { "seed": 27095, "image": "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", "prompt": "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T22:49:09.226966Z", "created_at": "2023-09-12T22:48:58.138950Z", "data_removed": false, "error": null, "id": "ueu5wr3bygt72drpp2h5hybble", "input": { "seed": 27095, "image": "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", "prompt": "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 }, "logs": "Using seed: 27095\nOriginal width:800, height:534\nAspect Ratio: 1.50\nnew_width:1216, new_height:832\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:09, 3.21it/s]\n 7%|▋ | 2/30 [00:00<00:08, 3.37it/s]\n 10%|█ | 3/30 [00:00<00:07, 3.41it/s]\n 13%|█▎ | 4/30 [00:01<00:07, 3.41it/s]\n 17%|█▋ | 5/30 [00:01<00:07, 3.43it/s]\n 20%|██ | 6/30 [00:01<00:06, 3.44it/s]\n 23%|██▎ | 7/30 [00:02<00:06, 3.44it/s]\n 27%|██▋ | 8/30 [00:02<00:06, 3.44it/s]\n 30%|███ | 9/30 [00:02<00:06, 3.45it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.45it/s]\n 37%|███▋ | 11/30 [00:03<00:05, 3.44it/s]\n 40%|████ | 12/30 [00:03<00:05, 3.45it/s]\n 43%|████▎ | 13/30 [00:03<00:04, 3.46it/s]\n 47%|████▋ | 14/30 [00:04<00:04, 3.46it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.46it/s]\n 53%|█████▎ | 16/30 [00:04<00:04, 3.47it/s]\n 57%|█████▋ | 17/30 [00:04<00:03, 3.47it/s]\n 60%|██████ | 18/30 [00:05<00:03, 3.47it/s]\n 63%|██████▎ | 19/30 [00:05<00:03, 3.47it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.47it/s]\n 70%|███████ | 21/30 [00:06<00:02, 3.47it/s]\n 73%|███████▎ | 22/30 [00:06<00:02, 3.47it/s]\n 77%|███████▋ | 23/30 [00:06<00:02, 3.47it/s]\n 80%|████████ | 24/30 [00:06<00:01, 3.47it/s]\n 83%|████████▎ | 25/30 [00:07<00:01, 3.47it/s]\n 87%|████████▋ | 26/30 [00:07<00:01, 3.47it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.47it/s]\n 93%|█████████▎| 28/30 [00:08<00:00, 3.47it/s]\n 97%|█████████▋| 29/30 [00:08<00:00, 3.47it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.47it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.46it/s]", "metrics": { "predict_time": 11.079993, "total_time": 11.088016 }, "output": "https://pbxt.replicate.delivery/Ni0D1vfMioRGFaDgWHBOyyua873FCKch52fVRrA2LBzkzpjRA/output.png", "started_at": "2023-09-12T22:48:58.146973Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ueu5wr3bygt72drpp2h5hybble", "cancel": "https://api.replicate.com/v1/predictions/ueu5wr3bygt72drpp2h5hybble/cancel" }, "version": "5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9" }Generated inUsing seed: 27095 Original width:800, height:534 Aspect Ratio: 1.50 new_width:1216, new_height:832 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:09, 3.21it/s] 7%|▋ | 2/30 [00:00<00:08, 3.37it/s] 10%|█ | 3/30 [00:00<00:07, 3.41it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.41it/s] 17%|█▋ | 5/30 [00:01<00:07, 3.43it/s] 20%|██ | 6/30 [00:01<00:06, 3.44it/s] 23%|██▎ | 7/30 [00:02<00:06, 3.44it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.44it/s] 30%|███ | 9/30 [00:02<00:06, 3.45it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.45it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.44it/s] 40%|████ | 12/30 [00:03<00:05, 3.45it/s] 43%|████▎ | 13/30 [00:03<00:04, 3.46it/s] 47%|████▋ | 14/30 [00:04<00:04, 3.46it/s] 50%|█████ | 15/30 [00:04<00:04, 3.46it/s] 53%|█████▎ | 16/30 [00:04<00:04, 3.47it/s] 57%|█████▋ | 17/30 [00:04<00:03, 3.47it/s] 60%|██████ | 18/30 [00:05<00:03, 3.47it/s] 63%|██████▎ | 19/30 [00:05<00:03, 3.47it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.47it/s] 70%|███████ | 21/30 [00:06<00:02, 3.47it/s] 73%|███████▎ | 22/30 [00:06<00:02, 3.47it/s] 77%|███████▋ | 23/30 [00:06<00:02, 3.47it/s] 80%|████████ | 24/30 [00:06<00:01, 3.47it/s] 83%|████████▎ | 25/30 [00:07<00:01, 3.47it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.47it/s] 90%|█████████ | 27/30 [00:07<00:00, 3.47it/s] 93%|█████████▎| 28/30 [00:08<00:00, 3.47it/s] 97%|█████████▋| 29/30 [00:08<00:00, 3.47it/s] 100%|██████████| 30/30 [00:08<00:00, 3.47it/s] 100%|██████████| 30/30 [00:08<00:00, 3.46it/s]Prediction
lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9IDqsllmwtbcs5i5xgw4zngjgdgaeStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 39035
- image
- prompt
- Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic
- condition_scale
- 0.5
- num_inference_steps
- 30
{ "seed": 39035, "image": "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", "prompt": "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", { input: { seed: 39035, image: "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", prompt: "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", condition_scale: 0.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", input={ "seed": 39035, "image": "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", "prompt": "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/sdxl-controlnet-depth 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": "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", "input": { "seed": 39035, "image": "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", "prompt": "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T22:49:36.847960Z", "created_at": "2023-09-12T22:49:25.737212Z", "data_removed": false, "error": null, "id": "qsllmwtbcs5i5xgw4zngjgdgae", "input": { "seed": 39035, "image": "https://replicate.delivery/pbxt/JOGUi29yzacQHdsW1gqFcSmxELopCmEUYfFKQHMXt1KhBOvt/empty_salon.jpg", "prompt": "Sofa and table, cinematic, contour, lighting, highly detailed, summer, golden hour, photorealistic", "condition_scale": 0.5, "num_inference_steps": 30 }, "logs": "Using seed: 39035\nOriginal width:800, height:534\nAspect Ratio: 1.50\nnew_width:1216, new_height:832\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:08, 3.49it/s]\n 7%|▋ | 2/30 [00:00<00:08, 3.47it/s]\n 10%|█ | 3/30 [00:00<00:07, 3.47it/s]\n 13%|█▎ | 4/30 [00:01<00:07, 3.45it/s]\n 17%|█▋ | 5/30 [00:01<00:07, 3.46it/s]\n 20%|██ | 6/30 [00:01<00:06, 3.45it/s]\n 23%|██▎ | 7/30 [00:02<00:06, 3.45it/s]\n 27%|██▋ | 8/30 [00:02<00:06, 3.45it/s]\n 30%|███ | 9/30 [00:02<00:06, 3.45it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.46it/s]\n 37%|███▋ | 11/30 [00:03<00:05, 3.47it/s]\n 40%|████ | 12/30 [00:03<00:05, 3.47it/s]\n 43%|████▎ | 13/30 [00:03<00:04, 3.47it/s]\n 47%|████▋ | 14/30 [00:04<00:04, 3.47it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.47it/s]\n 53%|█████▎ | 16/30 [00:04<00:04, 3.48it/s]\n 57%|█████▋ | 17/30 [00:04<00:03, 3.48it/s]\n 60%|██████ | 18/30 [00:05<00:03, 3.47it/s]\n 63%|██████▎ | 19/30 [00:05<00:03, 3.48it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.47it/s]\n 70%|███████ | 21/30 [00:06<00:02, 3.47it/s]\n 73%|███████▎ | 22/30 [00:06<00:02, 3.47it/s]\n 77%|███████▋ | 23/30 [00:06<00:02, 3.48it/s]\n 80%|████████ | 24/30 [00:06<00:01, 3.47it/s]\n 83%|████████▎ | 25/30 [00:07<00:01, 3.47it/s]\n 87%|████████▋ | 26/30 [00:07<00:01, 3.47it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.47it/s]\n 93%|█████████▎| 28/30 [00:08<00:00, 3.47it/s]\n 97%|█████████▋| 29/30 [00:08<00:00, 3.47it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.47it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.47it/s]", "metrics": { "predict_time": 11.078298, "total_time": 11.110748 }, "output": "https://pbxt.replicate.delivery/w9Bj5nTpHdqWOVRjVisQmB0dOUM0XEip06prMfQ12VmfzpjRA/output.png", "started_at": "2023-09-12T22:49:25.769662Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qsllmwtbcs5i5xgw4zngjgdgae", "cancel": "https://api.replicate.com/v1/predictions/qsllmwtbcs5i5xgw4zngjgdgae/cancel" }, "version": "5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9" }Generated inUsing seed: 39035 Original width:800, height:534 Aspect Ratio: 1.50 new_width:1216, new_height:832 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:08, 3.49it/s] 7%|▋ | 2/30 [00:00<00:08, 3.47it/s] 10%|█ | 3/30 [00:00<00:07, 3.47it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.45it/s] 17%|█▋ | 5/30 [00:01<00:07, 3.46it/s] 20%|██ | 6/30 [00:01<00:06, 3.45it/s] 23%|██▎ | 7/30 [00:02<00:06, 3.45it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.45it/s] 30%|███ | 9/30 [00:02<00:06, 3.45it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.46it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.47it/s] 40%|████ | 12/30 [00:03<00:05, 3.47it/s] 43%|████▎ | 13/30 [00:03<00:04, 3.47it/s] 47%|████▋ | 14/30 [00:04<00:04, 3.47it/s] 50%|█████ | 15/30 [00:04<00:04, 3.47it/s] 53%|█████▎ | 16/30 [00:04<00:04, 3.48it/s] 57%|█████▋ | 17/30 [00:04<00:03, 3.48it/s] 60%|██████ | 18/30 [00:05<00:03, 3.47it/s] 63%|██████▎ | 19/30 [00:05<00:03, 3.48it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.47it/s] 70%|███████ | 21/30 [00:06<00:02, 3.47it/s] 73%|███████▎ | 22/30 [00:06<00:02, 3.47it/s] 77%|███████▋ | 23/30 [00:06<00:02, 3.48it/s] 80%|████████ | 24/30 [00:06<00:01, 3.47it/s] 83%|████████▎ | 25/30 [00:07<00:01, 3.47it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.47it/s] 90%|█████████ | 27/30 [00:07<00:00, 3.47it/s] 93%|█████████▎| 28/30 [00:08<00:00, 3.47it/s] 97%|█████████▋| 29/30 [00:08<00:00, 3.47it/s] 100%|██████████| 30/30 [00:08<00:00, 3.47it/s] 100%|██████████| 30/30 [00:08<00:00, 3.47it/s]Prediction
lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9IDaoc6hsdbcyp74xusuflnpiuueeStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 6685
- image
- prompt
- dog, meme
- condition_scale
- 0.5
- num_inference_steps
- 30
{ "seed": 6685, "image": "https://replicate.delivery/pbxt/JW7V0ZaYRpt5HfGUsTxzTWbDrwfFmj8IpkgjJxGInHPgFujl/Screenshot%202023-08-22%20at%2011.54.23%20PM.png", "prompt": "dog, meme", "condition_scale": 0.5, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", { input: { seed: 6685, image: "https://replicate.delivery/pbxt/JW7V0ZaYRpt5HfGUsTxzTWbDrwfFmj8IpkgjJxGInHPgFujl/Screenshot%202023-08-22%20at%2011.54.23%20PM.png", prompt: "dog, meme", condition_scale: 0.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", input={ "seed": 6685, "image": "https://replicate.delivery/pbxt/JW7V0ZaYRpt5HfGUsTxzTWbDrwfFmj8IpkgjJxGInHPgFujl/Screenshot%202023-08-22%20at%2011.54.23%20PM.png", "prompt": "dog, meme", "condition_scale": 0.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/sdxl-controlnet-depth 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": "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9", "input": { "seed": 6685, "image": "https://replicate.delivery/pbxt/JW7V0ZaYRpt5HfGUsTxzTWbDrwfFmj8IpkgjJxGInHPgFujl/Screenshot%202023-08-22%20at%2011.54.23%20PM.png", "prompt": "dog, meme", "condition_scale": 0.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T22:50:06.217589Z", "created_at": "2023-09-12T22:49:55.513608Z", "data_removed": false, "error": null, "id": "aoc6hsdbcyp74xusuflnpiuuee", "input": { "seed": 6685, "image": "https://replicate.delivery/pbxt/JW7V0ZaYRpt5HfGUsTxzTWbDrwfFmj8IpkgjJxGInHPgFujl/Screenshot%202023-08-22%20at%2011.54.23%20PM.png", "prompt": "dog, meme", "condition_scale": 0.5, "num_inference_steps": 30 }, "logs": "Using seed: 6685\nOriginal width:324, height:400\nAspect Ratio: 0.81\nnew_width:896, new_height:1088\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:08, 3.31it/s]\n 7%|▋ | 2/30 [00:00<00:08, 3.47it/s]\n 10%|█ | 3/30 [00:00<00:07, 3.52it/s]\n 13%|█▎ | 4/30 [00:01<00:07, 3.54it/s]\n 17%|█▋ | 5/30 [00:01<00:07, 3.55it/s]\n 20%|██ | 6/30 [00:01<00:06, 3.56it/s]\n 23%|██▎ | 7/30 [00:01<00:06, 3.56it/s]\n 27%|██▋ | 8/30 [00:02<00:06, 3.57it/s]\n 30%|███ | 9/30 [00:02<00:05, 3.57it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.57it/s]\n 37%|███▋ | 11/30 [00:03<00:05, 3.58it/s]\n 40%|████ | 12/30 [00:03<00:05, 3.58it/s]\n 43%|████▎ | 13/30 [00:03<00:04, 3.58it/s]\n 47%|████▋ | 14/30 [00:03<00:04, 3.59it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.59it/s]\n 53%|█████▎ | 16/30 [00:04<00:03, 3.59it/s]\n 57%|█████▋ | 17/30 [00:04<00:03, 3.59it/s]\n 60%|██████ | 18/30 [00:05<00:03, 3.59it/s]\n 63%|██████▎ | 19/30 [00:05<00:03, 3.59it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.59it/s]\n 70%|███████ | 21/30 [00:05<00:02, 3.59it/s]\n 73%|███████▎ | 22/30 [00:06<00:02, 3.59it/s]\n 77%|███████▋ | 23/30 [00:06<00:01, 3.59it/s]\n 80%|████████ | 24/30 [00:06<00:01, 3.59it/s]\n 83%|████████▎ | 25/30 [00:06<00:01, 3.59it/s]\n 87%|████████▋ | 26/30 [00:07<00:01, 3.59it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.58it/s]\n 93%|█████████▎| 28/30 [00:07<00:00, 3.58it/s]\n 97%|█████████▋| 29/30 [00:08<00:00, 3.58it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.58it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.57it/s]", "metrics": { "predict_time": 10.70738, "total_time": 10.703981 }, "output": "https://pbxt.replicate.delivery/MB0L58gBMhr8HJXSEzYj7a0vAkDZuFl2zfbvPh1RCwrO60xIA/output.png", "started_at": "2023-09-12T22:49:55.510209Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/aoc6hsdbcyp74xusuflnpiuuee", "cancel": "https://api.replicate.com/v1/predictions/aoc6hsdbcyp74xusuflnpiuuee/cancel" }, "version": "5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9" }Generated inUsing seed: 6685 Original width:324, height:400 Aspect Ratio: 0.81 new_width:896, new_height:1088 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:08, 3.31it/s] 7%|▋ | 2/30 [00:00<00:08, 3.47it/s] 10%|█ | 3/30 [00:00<00:07, 3.52it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.54it/s] 17%|█▋ | 5/30 [00:01<00:07, 3.55it/s] 20%|██ | 6/30 [00:01<00:06, 3.56it/s] 23%|██▎ | 7/30 [00:01<00:06, 3.56it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.57it/s] 30%|███ | 9/30 [00:02<00:05, 3.57it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.57it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.58it/s] 40%|████ | 12/30 [00:03<00:05, 3.58it/s] 43%|████▎ | 13/30 [00:03<00:04, 3.58it/s] 47%|████▋ | 14/30 [00:03<00:04, 3.59it/s] 50%|█████ | 15/30 [00:04<00:04, 3.59it/s] 53%|█████▎ | 16/30 [00:04<00:03, 3.59it/s] 57%|█████▋ | 17/30 [00:04<00:03, 3.59it/s] 60%|██████ | 18/30 [00:05<00:03, 3.59it/s] 63%|██████▎ | 19/30 [00:05<00:03, 3.59it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.59it/s] 70%|███████ | 21/30 [00:05<00:02, 3.59it/s] 73%|███████▎ | 22/30 [00:06<00:02, 3.59it/s] 77%|███████▋ | 23/30 [00:06<00:01, 3.59it/s] 80%|████████ | 24/30 [00:06<00:01, 3.59it/s] 83%|████████▎ | 25/30 [00:06<00:01, 3.59it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.59it/s] 90%|█████████ | 27/30 [00:07<00:00, 3.58it/s] 93%|█████████▎| 28/30 [00:07<00:00, 3.58it/s] 97%|█████████▋| 29/30 [00:08<00:00, 3.58it/s] 100%|██████████| 30/30 [00:08<00:00, 3.58it/s] 100%|██████████| 30/30 [00:08<00:00, 3.57it/s]
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