orbin-ahmed/360_pano
Prediction
orbin-ahmed/360_pano:bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36ID5qvbz5tgmsrgm0cgzen8gx0sagStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @orbin-ahmedInput
- seed
- 0
- prompt
- A kids room
- upscale
- guidance_scale
- 7.5
- num_inference_steps
- 20
{ "seed": 0, "prompt": "A kids room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 }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 orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "orbin-ahmed/360_pano:bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36", { input: { seed: 0, prompt: "A kids room", upscale: true, guidance_scale: 7.5, num_inference_steps: 20 } } ); // 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 orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "orbin-ahmed/360_pano:bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36", input={ "seed": 0, "prompt": "A kids room", "upscale": True, "guidance_scale": 7.5, "num_inference_steps": 20 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run orbin-ahmed/360_pano 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": "orbin-ahmed/360_pano:bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36", "input": { "seed": 0, "prompt": "A kids room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-07-28T23:30:02.609626Z", "created_at": "2024-07-28T23:26:01.382000Z", "data_removed": false, "error": null, "id": "5qvbz5tgmsrgm0cgzen8gx0sag", "input": { "seed": 0, "prompt": "A kids room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 }, "logs": "Using seed: 15384\nTest with prompt: A kids room\nglobal seed: 15384\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:14, 1.33it/s]\n 15%|█▌ | 3/20 [00:00<00:04, 3.96it/s]\n 25%|██▌ | 5/20 [00:01<00:02, 6.16it/s]\n 35%|███▌ | 7/20 [00:01<00:01, 7.93it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 9.29it/s]\n 55%|█████▌ | 11/20 [00:01<00:00, 10.32it/s]\n 65%|██████▌ | 13/20 [00:01<00:00, 11.08it/s]\n 75%|███████▌ | 15/20 [00:01<00:00, 11.64it/s]\n 85%|████████▌ | 17/20 [00:01<00:00, 12.03it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 12.30it/s]\n100%|██████████| 20/20 [00:02<00:00, 9.02it/s]\ninputs: upscale=True, running upscaler.\nrunning upscaler step1. Initial super-resolution\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:02<00:11, 2.98s/it]\n 40%|████ | 2/5 [00:05<00:08, 2.72s/it]\n 60%|██████ | 3/5 [00:08<00:05, 2.64s/it]\n 80%|████████ | 4/5 [00:10<00:02, 2.59s/it]\n100%|██████████| 5/5 [00:13<00:00, 2.57s/it]\n100%|██████████| 5/5 [00:13<00:00, 2.62s/it]\nrunning upscaler step2. Super-resolution with Real-ESRGAN\nTile 1/45\nTile 2/45\nTile 3/45\nTile 4/45\nTile 5/45\nTile 6/45\nTile 7/45\nTile 8/45\nTile 9/45\nTile 10/45\nTile 11/45\nTile 12/45\nTile 13/45\nTile 14/45\nTile 15/45\nTile 16/45\nTile 17/45\nTile 18/45\nTile 19/45\nTile 20/45\nTile 21/45\nTile 22/45\nTile 23/45\nTile 24/45\nTile 25/45\nTile 26/45\nTile 27/45\nTile 28/45\nTile 29/45\nTile 30/45\nTile 31/45\nTile 32/45\nTile 33/45\nTile 34/45\nTile 35/45\nTile 36/45\nTile 37/45\nTile 38/45\nTile 39/45\nTile 40/45\nTile 41/45\nTile 42/45\nTile 43/45\nTile 44/45\nTile 45/45\ninputs: refinement=True, running refinement. This is a bit time-consuming.\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:12<00:48, 12.08s/it]\n 40%|████ | 2/5 [00:23<00:35, 11.84s/it]\n 60%|██████ | 3/5 [00:35<00:23, 11.81s/it]\n 80%|████████ | 4/5 [00:47<00:11, 11.78s/it]\n100%|██████████| 5/5 [00:59<00:00, 11.78s/it]\n100%|██████████| 5/5 [00:59<00:00, 11.81s/it]\nfinished", "metrics": { "predict_time": 108.111475536, "total_time": 241.227626 }, "output": "https://replicate.delivery/pbxt/yf4PzoG74VySBiQ1ZxuC4YkDu53UepehXdjIsy9twBDwzQamA/result.png", "started_at": "2024-07-28T23:28:14.498150Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5qvbz5tgmsrgm0cgzen8gx0sag", "cancel": "https://api.replicate.com/v1/predictions/5qvbz5tgmsrgm0cgzen8gx0sag/cancel" }, "version": "bfd01bb5d6c5f9fb4a036049fb8ab770beb6a0d5872ef7ef1c04f4ca26781a36" }Generated inUsing seed: 15384 Test with prompt: A kids room global seed: 15384 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:14, 1.33it/s] 15%|█▌ | 3/20 [00:00<00:04, 3.96it/s] 25%|██▌ | 5/20 [00:01<00:02, 6.16it/s] 35%|███▌ | 7/20 [00:01<00:01, 7.93it/s] 45%|████▌ | 9/20 [00:01<00:01, 9.29it/s] 55%|█████▌ | 11/20 [00:01<00:00, 10.32it/s] 65%|██████▌ | 13/20 [00:01<00:00, 11.08it/s] 75%|███████▌ | 15/20 [00:01<00:00, 11.64it/s] 85%|████████▌ | 17/20 [00:01<00:00, 12.03it/s] 95%|█████████▌| 19/20 [00:02<00:00, 12.30it/s] 100%|██████████| 20/20 [00:02<00:00, 9.02it/s] inputs: upscale=True, running upscaler. running upscaler step1. Initial super-resolution 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:02<00:11, 2.98s/it] 40%|████ | 2/5 [00:05<00:08, 2.72s/it] 60%|██████ | 3/5 [00:08<00:05, 2.64s/it] 80%|████████ | 4/5 [00:10<00:02, 2.59s/it] 100%|██████████| 5/5 [00:13<00:00, 2.57s/it] 100%|██████████| 5/5 [00:13<00:00, 2.62s/it] running upscaler step2. Super-resolution with Real-ESRGAN Tile 1/45 Tile 2/45 Tile 3/45 Tile 4/45 Tile 5/45 Tile 6/45 Tile 7/45 Tile 8/45 Tile 9/45 Tile 10/45 Tile 11/45 Tile 12/45 Tile 13/45 Tile 14/45 Tile 15/45 Tile 16/45 Tile 17/45 Tile 18/45 Tile 19/45 Tile 20/45 Tile 21/45 Tile 22/45 Tile 23/45 Tile 24/45 Tile 25/45 Tile 26/45 Tile 27/45 Tile 28/45 Tile 29/45 Tile 30/45 Tile 31/45 Tile 32/45 Tile 33/45 Tile 34/45 Tile 35/45 Tile 36/45 Tile 37/45 Tile 38/45 Tile 39/45 Tile 40/45 Tile 41/45 Tile 42/45 Tile 43/45 Tile 44/45 Tile 45/45 inputs: refinement=True, running refinement. This is a bit time-consuming. 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:12<00:48, 12.08s/it] 40%|████ | 2/5 [00:23<00:35, 11.84s/it] 60%|██████ | 3/5 [00:35<00:23, 11.81s/it] 80%|████████ | 4/5 [00:47<00:11, 11.78s/it] 100%|██████████| 5/5 [00:59<00:00, 11.78s/it] 100%|██████████| 5/5 [00:59<00:00, 11.81s/it] finishedPrediction
orbin-ahmed/360_pano:3aceec69383b79b43ad425aaff441a4d921f66f228f143de657fb94e56a4a0e7ID3xhwzsthhxrgp0cgzcdrepm1zcStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- prompt
- A living room
- upscale
- guidance_scale
- 7.5
- num_inference_steps
- 20
{ "prompt": "A living room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 }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 orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "orbin-ahmed/360_pano:3aceec69383b79b43ad425aaff441a4d921f66f228f143de657fb94e56a4a0e7", { input: { prompt: "A living room", upscale: true, guidance_scale: 7.5, num_inference_steps: 20 } } ); // 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 orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "orbin-ahmed/360_pano:3aceec69383b79b43ad425aaff441a4d921f66f228f143de657fb94e56a4a0e7", input={ "prompt": "A living room", "upscale": True, "guidance_scale": 7.5, "num_inference_steps": 20 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run orbin-ahmed/360_pano 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": "orbin-ahmed/360_pano:3aceec69383b79b43ad425aaff441a4d921f66f228f143de657fb94e56a4a0e7", "input": { "prompt": "A living room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-07-28T20:54:23.266125Z", "created_at": "2024-07-28T20:49:49.967000Z", "data_removed": false, "error": null, "id": "3xhwzsthhxrgp0cgzcdrepm1zc", "input": { "prompt": "A living room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 }, "logs": "Using seed: 62475\nTest with prompt: A living room\nglobal seed: 62475\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:17, 1.06it/s]\n 15%|█▌ | 3/20 [00:01<00:05, 3.30it/s]\n 25%|██▌ | 5/20 [00:01<00:02, 5.36it/s]\n 35%|███▌ | 7/20 [00:01<00:01, 7.13it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 8.59it/s]\n 55%|█████▌ | 11/20 [00:01<00:00, 9.74it/s]\n 65%|██████▌ | 13/20 [00:01<00:00, 10.62it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 11.26it/s]\n 85%|████████▌ | 17/20 [00:02<00:00, 11.74it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 12.09it/s]\n100%|██████████| 20/20 [00:02<00:00, 8.29it/s]\ninputs: upscale=True, running upscaler.\nrunning upscaler step1. Initial super-resolution\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:03<00:12, 3.07s/it]\n 40%|████ | 2/5 [00:05<00:08, 2.78s/it]\n 60%|██████ | 3/5 [00:08<00:05, 2.68s/it]\n 80%|████████ | 4/5 [00:10<00:02, 2.63s/it]\n100%|██████████| 5/5 [00:13<00:00, 2.61s/it]\n100%|██████████| 5/5 [00:13<00:00, 2.67s/it]\nrunning upscaler step2. Super-resolution with Real-ESRGAN\nTile 1/45\nTile 2/45\nTile 3/45\nTile 4/45\nTile 5/45\nTile 6/45\nTile 7/45\nTile 8/45\nTile 9/45\nTile 10/45\nTile 11/45\nTile 12/45\nTile 13/45\nTile 14/45\nTile 15/45\nTile 16/45\nTile 17/45\nTile 18/45\nTile 19/45\nTile 20/45\nTile 21/45\nTile 22/45\nTile 23/45\nTile 24/45\nTile 25/45\nTile 26/45\nTile 27/45\nTile 28/45\nTile 29/45\nTile 30/45\nTile 31/45\nTile 32/45\nTile 33/45\nTile 34/45\nTile 35/45\nTile 36/45\nTile 37/45\nTile 38/45\nTile 39/45\nTile 40/45\nTile 41/45\nTile 42/45\nTile 43/45\nTile 44/45\nTile 45/45\ninputs: refinement=True, running refinement. This is a bit time-consuming.\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:12<00:48, 12.12s/it]\n 40%|████ | 2/5 [00:23<00:35, 11.95s/it]\n 60%|██████ | 3/5 [00:35<00:23, 11.89s/it]\n 80%|████████ | 4/5 [00:47<00:11, 11.87s/it]\n100%|██████████| 5/5 [00:59<00:00, 11.86s/it]\n100%|██████████| 5/5 [00:59<00:00, 11.89s/it]\nfinished", "metrics": { "predict_time": 133.885373954, "total_time": 273.299125 }, "output": "https://replicate.delivery/pbxt/PWJBJ1qVosZzJxu5JJefTOUIGhKesP1KxJ7mnh6D5SM8PMamA/result.png", "started_at": "2024-07-28T20:52:09.380751Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3xhwzsthhxrgp0cgzcdrepm1zc", "cancel": "https://api.replicate.com/v1/predictions/3xhwzsthhxrgp0cgzcdrepm1zc/cancel" }, "version": "3aceec69383b79b43ad425aaff441a4d921f66f228f143de657fb94e56a4a0e7" }Generated inUsing seed: 62475 Test with prompt: A living room global seed: 62475 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:17, 1.06it/s] 15%|█▌ | 3/20 [00:01<00:05, 3.30it/s] 25%|██▌ | 5/20 [00:01<00:02, 5.36it/s] 35%|███▌ | 7/20 [00:01<00:01, 7.13it/s] 45%|████▌ | 9/20 [00:01<00:01, 8.59it/s] 55%|█████▌ | 11/20 [00:01<00:00, 9.74it/s] 65%|██████▌ | 13/20 [00:01<00:00, 10.62it/s] 75%|███████▌ | 15/20 [00:02<00:00, 11.26it/s] 85%|████████▌ | 17/20 [00:02<00:00, 11.74it/s] 95%|█████████▌| 19/20 [00:02<00:00, 12.09it/s] 100%|██████████| 20/20 [00:02<00:00, 8.29it/s] inputs: upscale=True, running upscaler. running upscaler step1. Initial super-resolution 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:03<00:12, 3.07s/it] 40%|████ | 2/5 [00:05<00:08, 2.78s/it] 60%|██████ | 3/5 [00:08<00:05, 2.68s/it] 80%|████████ | 4/5 [00:10<00:02, 2.63s/it] 100%|██████████| 5/5 [00:13<00:00, 2.61s/it] 100%|██████████| 5/5 [00:13<00:00, 2.67s/it] running upscaler step2. Super-resolution with Real-ESRGAN Tile 1/45 Tile 2/45 Tile 3/45 Tile 4/45 Tile 5/45 Tile 6/45 Tile 7/45 Tile 8/45 Tile 9/45 Tile 10/45 Tile 11/45 Tile 12/45 Tile 13/45 Tile 14/45 Tile 15/45 Tile 16/45 Tile 17/45 Tile 18/45 Tile 19/45 Tile 20/45 Tile 21/45 Tile 22/45 Tile 23/45 Tile 24/45 Tile 25/45 Tile 26/45 Tile 27/45 Tile 28/45 Tile 29/45 Tile 30/45 Tile 31/45 Tile 32/45 Tile 33/45 Tile 34/45 Tile 35/45 Tile 36/45 Tile 37/45 Tile 38/45 Tile 39/45 Tile 40/45 Tile 41/45 Tile 42/45 Tile 43/45 Tile 44/45 Tile 45/45 inputs: refinement=True, running refinement. This is a bit time-consuming. 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:12<00:48, 12.12s/it] 40%|████ | 2/5 [00:23<00:35, 11.95s/it] 60%|██████ | 3/5 [00:35<00:23, 11.89s/it] 80%|████████ | 4/5 [00:47<00:11, 11.87s/it] 100%|██████████| 5/5 [00:59<00:00, 11.86s/it] 100%|██████████| 5/5 [00:59<00:00, 11.89s/it] finishedPrediction
orbin-ahmed/360_pano:dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68ID31f1hv4pxnrgg0cgzfpa0arrr4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- prompt
- A office room
- upscale
- guidance_scale
- 7.5
- num_inference_steps
- 20
{ "image": "https://replicate.delivery/pbxt/LLkBi1pJTvVmbuPMFYZnwcWqlbV7RpY2Q18QkV9AAtImYZYJ/home_office_1.jpg", "prompt": "A office room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 }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 orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "orbin-ahmed/360_pano:dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68", { input: { image: "https://replicate.delivery/pbxt/LLkBi1pJTvVmbuPMFYZnwcWqlbV7RpY2Q18QkV9AAtImYZYJ/home_office_1.jpg", prompt: "A office room", upscale: true, guidance_scale: 7.5, num_inference_steps: 20 } } ); // 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 orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "orbin-ahmed/360_pano:dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68", input={ "image": "https://replicate.delivery/pbxt/LLkBi1pJTvVmbuPMFYZnwcWqlbV7RpY2Q18QkV9AAtImYZYJ/home_office_1.jpg", "prompt": "A office room", "upscale": True, "guidance_scale": 7.5, "num_inference_steps": 20 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run orbin-ahmed/360_pano 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": "orbin-ahmed/360_pano:dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68", "input": { "image": "https://replicate.delivery/pbxt/LLkBi1pJTvVmbuPMFYZnwcWqlbV7RpY2Q18QkV9AAtImYZYJ/home_office_1.jpg", "prompt": "A office room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-07-29T00:41:43.556976Z", "created_at": "2024-07-29T00:38:24.749000Z", "data_removed": false, "error": null, "id": "31f1hv4pxnrgg0cgzfpa0arrr4", "input": { "image": "https://replicate.delivery/pbxt/LLkBi1pJTvVmbuPMFYZnwcWqlbV7RpY2Q18QkV9AAtImYZYJ/home_office_1.jpg", "prompt": "A office room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 }, "logs": "Using seed: 63695\nMask Loaded\nImage Pipeline is running!\nTest with prompt: A office room\nglobal seed: 63695\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:09, 2.02it/s]\n 10%|█ | 2/20 [00:00<00:04, 3.68it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 4.99it/s]\n 20%|██ | 4/20 [00:00<00:02, 6.00it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 6.75it/s]\n 30%|███ | 6/20 [00:01<00:01, 7.30it/s]\n 35%|███▌ | 7/20 [00:01<00:01, 7.69it/s]\n 40%|████ | 8/20 [00:01<00:01, 7.98it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 8.18it/s]\n 50%|█████ | 10/20 [00:01<00:01, 8.32it/s]\n 55%|█████▌ | 11/20 [00:01<00:01, 8.42it/s]\n 60%|██████ | 12/20 [00:01<00:00, 8.49it/s]\n 65%|██████▌ | 13/20 [00:01<00:00, 8.53it/s]\n 70%|███████ | 14/20 [00:01<00:00, 8.57it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 8.59it/s]\n 80%|████████ | 16/20 [00:02<00:00, 8.60it/s]\n 85%|████████▌ | 17/20 [00:02<00:00, 8.61it/s]\n 90%|█████████ | 18/20 [00:02<00:00, 8.61it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 8.62it/s]\n100%|██████████| 20/20 [00:02<00:00, 8.62it/s]\n100%|██████████| 20/20 [00:02<00:00, 7.43it/s]\ninputs: upscale=True, running upscaler.\nrunning upscaler step1. Initial super-resolution\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:03<00:12, 3.00s/it]\n 40%|████ | 2/5 [00:05<00:08, 2.77s/it]\n 60%|██████ | 3/5 [00:08<00:05, 2.68s/it]\n 80%|████████ | 4/5 [00:10<00:02, 2.65s/it]\n100%|██████████| 5/5 [00:13<00:00, 2.63s/it]\n100%|██████████| 5/5 [00:13<00:00, 2.67s/it]\nrunning upscaler step2. Super-resolution with Real-ESRGAN\nTile 1/45\nTile 2/45\nTile 3/45\nTile 4/45\nTile 5/45\nTile 6/45\nTile 7/45\nTile 8/45\nTile 9/45\nTile 10/45\nTile 11/45\nTile 12/45\nTile 13/45\nTile 14/45\nTile 15/45\nTile 16/45\nTile 17/45\nTile 18/45\nTile 19/45\nTile 20/45\nTile 21/45\nTile 22/45\nTile 23/45\nTile 24/45\nTile 25/45\nTile 26/45\nTile 27/45\nTile 28/45\nTile 29/45\nTile 30/45\nTile 31/45\nTile 32/45\nTile 33/45\nTile 34/45\nTile 35/45\nTile 36/45\nTile 37/45\nTile 38/45\nTile 39/45\nTile 40/45\nTile 41/45\nTile 42/45\nTile 43/45\nTile 44/45\nTile 45/45\ninputs: refinement=True, running refinement. This is a bit time-consuming.\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:12<00:49, 12.26s/it]\n 40%|████ | 2/5 [00:24<00:36, 12.02s/it]\n 60%|██████ | 3/5 [00:36<00:23, 11.96s/it]\n 80%|████████ | 4/5 [00:48<00:11, 11.98s/it]\n100%|██████████| 5/5 [00:59<00:00, 11.95s/it]\n100%|██████████| 5/5 [00:59<00:00, 11.98s/it]\nfinished", "metrics": { "predict_time": 115.879393804, "total_time": 198.807976 }, "output": "https://replicate.delivery/pbxt/cEP8OS1kuaKBE5z5JUiAxEBhpq1byJQCNLOZtQ9XsfeGdJNTA/result.png", "started_at": "2024-07-29T00:39:47.677582Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/31f1hv4pxnrgg0cgzfpa0arrr4", "cancel": "https://api.replicate.com/v1/predictions/31f1hv4pxnrgg0cgzfpa0arrr4/cancel" }, "version": "dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68" }Generated inUsing seed: 63695 Mask Loaded Image Pipeline is running! Test with prompt: A office room global seed: 63695 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:09, 2.02it/s] 10%|█ | 2/20 [00:00<00:04, 3.68it/s] 15%|█▌ | 3/20 [00:00<00:03, 4.99it/s] 20%|██ | 4/20 [00:00<00:02, 6.00it/s] 25%|██▌ | 5/20 [00:00<00:02, 6.75it/s] 30%|███ | 6/20 [00:01<00:01, 7.30it/s] 35%|███▌ | 7/20 [00:01<00:01, 7.69it/s] 40%|████ | 8/20 [00:01<00:01, 7.98it/s] 45%|████▌ | 9/20 [00:01<00:01, 8.18it/s] 50%|█████ | 10/20 [00:01<00:01, 8.32it/s] 55%|█████▌ | 11/20 [00:01<00:01, 8.42it/s] 60%|██████ | 12/20 [00:01<00:00, 8.49it/s] 65%|██████▌ | 13/20 [00:01<00:00, 8.53it/s] 70%|███████ | 14/20 [00:01<00:00, 8.57it/s] 75%|███████▌ | 15/20 [00:02<00:00, 8.59it/s] 80%|████████ | 16/20 [00:02<00:00, 8.60it/s] 85%|████████▌ | 17/20 [00:02<00:00, 8.61it/s] 90%|█████████ | 18/20 [00:02<00:00, 8.61it/s] 95%|█████████▌| 19/20 [00:02<00:00, 8.62it/s] 100%|██████████| 20/20 [00:02<00:00, 8.62it/s] 100%|██████████| 20/20 [00:02<00:00, 7.43it/s] inputs: upscale=True, running upscaler. running upscaler step1. Initial super-resolution 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:03<00:12, 3.00s/it] 40%|████ | 2/5 [00:05<00:08, 2.77s/it] 60%|██████ | 3/5 [00:08<00:05, 2.68s/it] 80%|████████ | 4/5 [00:10<00:02, 2.65s/it] 100%|██████████| 5/5 [00:13<00:00, 2.63s/it] 100%|██████████| 5/5 [00:13<00:00, 2.67s/it] running upscaler step2. Super-resolution with Real-ESRGAN Tile 1/45 Tile 2/45 Tile 3/45 Tile 4/45 Tile 5/45 Tile 6/45 Tile 7/45 Tile 8/45 Tile 9/45 Tile 10/45 Tile 11/45 Tile 12/45 Tile 13/45 Tile 14/45 Tile 15/45 Tile 16/45 Tile 17/45 Tile 18/45 Tile 19/45 Tile 20/45 Tile 21/45 Tile 22/45 Tile 23/45 Tile 24/45 Tile 25/45 Tile 26/45 Tile 27/45 Tile 28/45 Tile 29/45 Tile 30/45 Tile 31/45 Tile 32/45 Tile 33/45 Tile 34/45 Tile 35/45 Tile 36/45 Tile 37/45 Tile 38/45 Tile 39/45 Tile 40/45 Tile 41/45 Tile 42/45 Tile 43/45 Tile 44/45 Tile 45/45 inputs: refinement=True, running refinement. This is a bit time-consuming. 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:12<00:49, 12.26s/it] 40%|████ | 2/5 [00:24<00:36, 12.02s/it] 60%|██████ | 3/5 [00:36<00:23, 11.96s/it] 80%|████████ | 4/5 [00:48<00:11, 11.98s/it] 100%|██████████| 5/5 [00:59<00:00, 11.95s/it] 100%|██████████| 5/5 [00:59<00:00, 11.98s/it] finishedPrediction
orbin-ahmed/360_pano:dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68IDecjjgze2x9rj00cgzfvtnmq734StatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- image
- prompt
- A bed room
- upscale
- guidance_scale
- 7.5
- num_inference_steps
- 20
{ "image": "https://replicate.delivery/pbxt/LLkU6zGedVqcWQpqYXzRD3cNanBVsUVPiP7Us6JreOGODP82/test.jpg", "prompt": "A bed room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 }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 orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "orbin-ahmed/360_pano:dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68", { input: { image: "https://replicate.delivery/pbxt/LLkU6zGedVqcWQpqYXzRD3cNanBVsUVPiP7Us6JreOGODP82/test.jpg", prompt: "A bed room", upscale: true, guidance_scale: 7.5, num_inference_steps: 20 } } ); // 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 orbin-ahmed/360_pano using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "orbin-ahmed/360_pano:dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68", input={ "image": "https://replicate.delivery/pbxt/LLkU6zGedVqcWQpqYXzRD3cNanBVsUVPiP7Us6JreOGODP82/test.jpg", "prompt": "A bed room", "upscale": True, "guidance_scale": 7.5, "num_inference_steps": 20 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run orbin-ahmed/360_pano 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": "orbin-ahmed/360_pano:dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68", "input": { "image": "https://replicate.delivery/pbxt/LLkU6zGedVqcWQpqYXzRD3cNanBVsUVPiP7Us6JreOGODP82/test.jpg", "prompt": "A bed room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-07-29T00:54:14.583415Z", "created_at": "2024-07-29T00:50:36.906000Z", "data_removed": false, "error": null, "id": "ecjjgze2x9rj00cgzfvtnmq734", "input": { "image": "https://replicate.delivery/pbxt/LLkU6zGedVqcWQpqYXzRD3cNanBVsUVPiP7Us6JreOGODP82/test.jpg", "prompt": "A bed room", "upscale": true, "guidance_scale": 7.5, "num_inference_steps": 20 }, "logs": "Using seed: 40374\nMask Loaded\nImage Pipeline is running!\nTest with prompt: A bed room\nglobal seed: 40374\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:09, 1.90it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 5.33it/s]\n 25%|██▌ | 5/20 [00:00<00:01, 7.87it/s]\n 35%|███▌ | 7/20 [00:00<00:01, 9.77it/s]\n 45%|████▌ | 9/20 [00:01<00:00, 11.16it/s]\n 55%|█████▌ | 11/20 [00:01<00:00, 12.17it/s]\n 65%|██████▌ | 13/20 [00:01<00:00, 12.89it/s]\n 75%|███████▌ | 15/20 [00:01<00:00, 13.38it/s]\n 85%|████████▌ | 17/20 [00:01<00:00, 13.74it/s]\n 95%|█████████▌| 19/20 [00:01<00:00, 13.99it/s]\n100%|██████████| 20/20 [00:01<00:00, 10.91it/s]\ninputs: upscale=True, running upscaler.\nrunning upscaler step1. Initial super-resolution\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:01<00:07, 1.85s/it]\n 40%|████ | 2/5 [00:03<00:04, 1.63s/it]\n 60%|██████ | 3/5 [00:04<00:03, 1.56s/it]\n 80%|████████ | 4/5 [00:06<00:01, 1.53s/it]\n100%|██████████| 5/5 [00:07<00:00, 1.51s/it]\n100%|██████████| 5/5 [00:07<00:00, 1.56s/it]\nrunning upscaler step2. Super-resolution with Real-ESRGAN\nTile 1/45\nTile 2/45\nTile 3/45\nTile 4/45\nTile 5/45\nTile 6/45\nTile 7/45\nTile 8/45\nTile 9/45\nTile 10/45\nTile 11/45\nTile 12/45\nTile 13/45\nTile 14/45\nTile 15/45\nTile 16/45\nTile 17/45\nTile 18/45\nTile 19/45\nTile 20/45\nTile 21/45\nTile 22/45\nTile 23/45\nTile 24/45\nTile 25/45\nTile 26/45\nTile 27/45\nTile 28/45\nTile 29/45\nTile 30/45\nTile 31/45\nTile 32/45\nTile 33/45\nTile 34/45\nTile 35/45\nTile 36/45\nTile 37/45\nTile 38/45\nTile 39/45\nTile 40/45\nTile 41/45\nTile 42/45\nTile 43/45\nTile 44/45\nTile 45/45\ninputs: refinement=True, running refinement. This is a bit time-consuming.\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:07<00:28, 7.04s/it]\n 40%|████ | 2/5 [00:13<00:20, 6.89s/it]\n 60%|██████ | 3/5 [00:20<00:13, 6.84s/it]\n 80%|████████ | 4/5 [00:27<00:06, 6.82s/it]\n100%|██████████| 5/5 [00:34<00:00, 6.81s/it]\n100%|██████████| 5/5 [00:34<00:00, 6.84s/it]\nfinished", "metrics": { "predict_time": 73.360964185, "total_time": 217.677415 }, "output": "https://replicate.delivery/yhqm/BDfvqFY7dtRWDSDWSJyPPdXrce1beZbOtdTX3dfa8QJYjm0MB/result.png", "started_at": "2024-07-29T00:53:01.222451Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ecjjgze2x9rj00cgzfvtnmq734", "cancel": "https://api.replicate.com/v1/predictions/ecjjgze2x9rj00cgzfvtnmq734/cancel" }, "version": "dc653997df860cbabc5cc46b75b9fc77eef955ca7d1af9c00fc5ba3ada6ffe68" }Generated inUsing seed: 40374 Mask Loaded Image Pipeline is running! Test with prompt: A bed room global seed: 40374 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:09, 1.90it/s] 15%|█▌ | 3/20 [00:00<00:03, 5.33it/s] 25%|██▌ | 5/20 [00:00<00:01, 7.87it/s] 35%|███▌ | 7/20 [00:00<00:01, 9.77it/s] 45%|████▌ | 9/20 [00:01<00:00, 11.16it/s] 55%|█████▌ | 11/20 [00:01<00:00, 12.17it/s] 65%|██████▌ | 13/20 [00:01<00:00, 12.89it/s] 75%|███████▌ | 15/20 [00:01<00:00, 13.38it/s] 85%|████████▌ | 17/20 [00:01<00:00, 13.74it/s] 95%|█████████▌| 19/20 [00:01<00:00, 13.99it/s] 100%|██████████| 20/20 [00:01<00:00, 10.91it/s] inputs: upscale=True, running upscaler. running upscaler step1. Initial super-resolution 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:01<00:07, 1.85s/it] 40%|████ | 2/5 [00:03<00:04, 1.63s/it] 60%|██████ | 3/5 [00:04<00:03, 1.56s/it] 80%|████████ | 4/5 [00:06<00:01, 1.53s/it] 100%|██████████| 5/5 [00:07<00:00, 1.51s/it] 100%|██████████| 5/5 [00:07<00:00, 1.56s/it] running upscaler step2. Super-resolution with Real-ESRGAN Tile 1/45 Tile 2/45 Tile 3/45 Tile 4/45 Tile 5/45 Tile 6/45 Tile 7/45 Tile 8/45 Tile 9/45 Tile 10/45 Tile 11/45 Tile 12/45 Tile 13/45 Tile 14/45 Tile 15/45 Tile 16/45 Tile 17/45 Tile 18/45 Tile 19/45 Tile 20/45 Tile 21/45 Tile 22/45 Tile 23/45 Tile 24/45 Tile 25/45 Tile 26/45 Tile 27/45 Tile 28/45 Tile 29/45 Tile 30/45 Tile 31/45 Tile 32/45 Tile 33/45 Tile 34/45 Tile 35/45 Tile 36/45 Tile 37/45 Tile 38/45 Tile 39/45 Tile 40/45 Tile 41/45 Tile 42/45 Tile 43/45 Tile 44/45 Tile 45/45 inputs: refinement=True, running refinement. This is a bit time-consuming. 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:07<00:28, 7.04s/it] 40%|████ | 2/5 [00:13<00:20, 6.89s/it] 60%|██████ | 3/5 [00:20<00:13, 6.84s/it] 80%|████████ | 4/5 [00:27<00:06, 6.82s/it] 100%|██████████| 5/5 [00:34<00:00, 6.81s/it] 100%|██████████| 5/5 [00:34<00:00, 6.84s/it] finished
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