cottom / xsd_12kdsd
- Public
- 208 runs
-
L40S
Prediction
cottom/xsd_12kdsd:275e48e44744c87d8062fcbec7c7f99be7b10b120a639ea9d3bb80ca0ce8b1d9IDht73imlbuqeg4rsf4lmlgdhvq4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- prompt
- A photo of a room, 4k photo, highly detailed
- dry_run
- guidance_scale
- 7.5
- condition_scale
- 1
- negative_prompt
- anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured
- enable_depth_ctr
- image_resolution
- 1024
- detect_resolution
- 512
- enable_sketch_ctr
- num_inference_steps
- 30
- num_images_per_prompt
- 1
{ "image": "https://replicate.delivery/pbxt/Jeys9hsouLzzzHHSLnCCiNU5httOBXz7uv5kEKLp7i440naF/output_0%20%281%29.png", "prompt": "A photo of a room, 4k photo, highly detailed", "dry_run": false, "guidance_scale": 7.5, "condition_scale": 1, "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", "enable_depth_ctr": true, "image_resolution": 1024, "detect_resolution": 512, "enable_sketch_ctr": true, "num_inference_steps": 30, "num_images_per_prompt": 1 }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 cottom/xsd_12kdsd using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cottom/xsd_12kdsd:275e48e44744c87d8062fcbec7c7f99be7b10b120a639ea9d3bb80ca0ce8b1d9", { input: { image: "https://replicate.delivery/pbxt/Jeys9hsouLzzzHHSLnCCiNU5httOBXz7uv5kEKLp7i440naF/output_0%20%281%29.png", prompt: "A photo of a room, 4k photo, highly detailed", dry_run: false, guidance_scale: 7.5, condition_scale: 1, negative_prompt: "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", enable_depth_ctr: true, image_resolution: 1024, detect_resolution: 512, enable_sketch_ctr: true, num_inference_steps: 30, num_images_per_prompt: 1 } } ); // 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 cottom/xsd_12kdsd using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cottom/xsd_12kdsd:275e48e44744c87d8062fcbec7c7f99be7b10b120a639ea9d3bb80ca0ce8b1d9", input={ "image": "https://replicate.delivery/pbxt/Jeys9hsouLzzzHHSLnCCiNU5httOBXz7uv5kEKLp7i440naF/output_0%20%281%29.png", "prompt": "A photo of a room, 4k photo, highly detailed", "dry_run": False, "guidance_scale": 7.5, "condition_scale": 1, "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", "enable_depth_ctr": True, "image_resolution": 1024, "detect_resolution": 512, "enable_sketch_ctr": True, "num_inference_steps": 30, "num_images_per_prompt": 1 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cottom/xsd_12kdsd 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": "cottom/xsd_12kdsd:275e48e44744c87d8062fcbec7c7f99be7b10b120a639ea9d3bb80ca0ce8b1d9", "input": { "image": "https://replicate.delivery/pbxt/Jeys9hsouLzzzHHSLnCCiNU5httOBXz7uv5kEKLp7i440naF/output_0%20%281%29.png", "prompt": "A photo of a room, 4k photo, highly detailed", "dry_run": false, "guidance_scale": 7.5, "condition_scale": 1, "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", "enable_depth_ctr": true, "image_resolution": 1024, "detect_resolution": 512, "enable_sketch_ctr": true, "num_inference_steps": 30, "num_images_per_prompt": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/cottom/xsd_12kdsd@sha256:275e48e44744c87d8062fcbec7c7f99be7b10b120a639ea9d3bb80ca0ce8b1d9 \ -i 'image="https://replicate.delivery/pbxt/Jeys9hsouLzzzHHSLnCCiNU5httOBXz7uv5kEKLp7i440naF/output_0%20%281%29.png"' \ -i 'prompt="A photo of a room, 4k photo, highly detailed"' \ -i 'dry_run=false' \ -i 'guidance_scale=7.5' \ -i 'condition_scale=1' \ -i 'negative_prompt="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured"' \ -i 'enable_depth_ctr=true' \ -i 'image_resolution=1024' \ -i 'detect_resolution=512' \ -i 'enable_sketch_ctr=true' \ -i 'num_inference_steps=30' \ -i 'num_images_per_prompt=1'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/cottom/xsd_12kdsd@sha256:275e48e44744c87d8062fcbec7c7f99be7b10b120a639ea9d3bb80ca0ce8b1d9curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/Jeys9hsouLzzzHHSLnCCiNU5httOBXz7uv5kEKLp7i440naF/output_0%20%281%29.png", "prompt": "A photo of a room, 4k photo, highly detailed", "dry_run": false, "guidance_scale": 7.5, "condition_scale": 1, "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", "enable_depth_ctr": true, "image_resolution": 1024, "detect_resolution": 512, "enable_sketch_ctr": true, "num_inference_steps": 30, "num_images_per_prompt": 1 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-10-07T21:11:27.728357Z", "created_at": "2023-10-07T21:04:10.447264Z", "data_removed": false, "error": null, "id": "ht73imlbuqeg4rsf4lmlgdhvq4", "input": { "image": "https://replicate.delivery/pbxt/Jeys9hsouLzzzHHSLnCCiNU5httOBXz7uv5kEKLp7i440naF/output_0%20%281%29.png", "prompt": "A photo of a room, 4k photo, highly detailed", "dry_run": false, "guidance_scale": 7.5, "condition_scale": 1, "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", "enable_depth_ctr": true, "image_resolution": 1024, "detect_resolution": 512, "enable_sketch_ctr": true, "num_inference_steps": 30, "num_images_per_prompt": 1 }, "logs": "Run a single prediction on the model:\nOriginal width:1216, height:832\nAspect Ratio: 1.46\nresized width:1216, height:832\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:03, 8.43it/s]\n 7%|▋ | 2/30 [00:00<00:04, 6.59it/s]\n 10%|█ | 3/30 [00:00<00:04, 5.85it/s]\n 13%|█▎ | 4/30 [00:00<00:04, 5.55it/s]\n 17%|█▋ | 5/30 [00:00<00:04, 5.39it/s]\n 20%|██ | 6/30 [00:01<00:04, 5.29it/s]\n 23%|██▎ | 7/30 [00:01<00:04, 5.24it/s]\n 27%|██▋ | 8/30 [00:01<00:04, 5.21it/s]\n 30%|███ | 9/30 [00:01<00:04, 5.19it/s]\n 33%|███▎ | 10/30 [00:01<00:03, 5.17it/s]\n 37%|███▋ | 11/30 [00:02<00:03, 5.15it/s]\n 40%|████ | 12/30 [00:02<00:03, 5.14it/s]\n 43%|████▎ | 13/30 [00:02<00:03, 5.13it/s]\n 47%|████▋ | 14/30 [00:02<00:03, 5.13it/s]\n 50%|█████ | 15/30 [00:02<00:02, 5.12it/s]\n 53%|█████▎ | 16/30 [00:03<00:02, 5.12it/s]\n 57%|█████▋ | 17/30 [00:03<00:02, 5.11it/s]\n 60%|██████ | 18/30 [00:03<00:02, 5.11it/s]\n 63%|██████▎ | 19/30 [00:03<00:02, 5.11it/s]\n 67%|██████▋ | 20/30 [00:03<00:01, 5.11it/s]\n 70%|███████ | 21/30 [00:04<00:01, 5.11it/s]\n 73%|███████▎ | 22/30 [00:04<00:01, 5.11it/s]\n 77%|███████▋ | 23/30 [00:04<00:01, 5.11it/s]\n 80%|████████ | 24/30 [00:04<00:01, 5.11it/s]\n 83%|████████▎ | 25/30 [00:04<00:00, 5.11it/s]\n 87%|████████▋ | 26/30 [00:04<00:00, 5.11it/s]\n 90%|█████████ | 27/30 [00:05<00:00, 5.11it/s]\n 93%|█████████▎| 28/30 [00:05<00:00, 5.10it/s]\n 97%|█████████▋| 29/30 [00:05<00:00, 5.10it/s]\n100%|██████████| 30/30 [00:05<00:00, 5.10it/s]\n100%|██████████| 30/30 [00:05<00:00, 5.20it/s]", "metrics": { "predict_time": 15.493883, "total_time": 437.281093 }, "output": [ "https://replicate.delivery/pbxt/H6AwCR2OI3raOFfj7rZRjBe1YYGrCbW1QNASdcb3Vg2ebvXjA/output_0.png" ], "started_at": "2023-10-07T21:11:12.234474Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ht73imlbuqeg4rsf4lmlgdhvq4", "cancel": "https://api.replicate.com/v1/predictions/ht73imlbuqeg4rsf4lmlgdhvq4/cancel" }, "version": "275e48e44744c87d8062fcbec7c7f99be7b10b120a639ea9d3bb80ca0ce8b1d9" }Generated inRun a single prediction on the model: Original width:1216, height:832 Aspect Ratio: 1.46 resized width:1216, height:832 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:03, 8.43it/s] 7%|▋ | 2/30 [00:00<00:04, 6.59it/s] 10%|█ | 3/30 [00:00<00:04, 5.85it/s] 13%|█▎ | 4/30 [00:00<00:04, 5.55it/s] 17%|█▋ | 5/30 [00:00<00:04, 5.39it/s] 20%|██ | 6/30 [00:01<00:04, 5.29it/s] 23%|██▎ | 7/30 [00:01<00:04, 5.24it/s] 27%|██▋ | 8/30 [00:01<00:04, 5.21it/s] 30%|███ | 9/30 [00:01<00:04, 5.19it/s] 33%|███▎ | 10/30 [00:01<00:03, 5.17it/s] 37%|███▋ | 11/30 [00:02<00:03, 5.15it/s] 40%|████ | 12/30 [00:02<00:03, 5.14it/s] 43%|████▎ | 13/30 [00:02<00:03, 5.13it/s] 47%|████▋ | 14/30 [00:02<00:03, 5.13it/s] 50%|█████ | 15/30 [00:02<00:02, 5.12it/s] 53%|█████▎ | 16/30 [00:03<00:02, 5.12it/s] 57%|█████▋ | 17/30 [00:03<00:02, 5.11it/s] 60%|██████ | 18/30 [00:03<00:02, 5.11it/s] 63%|██████▎ | 19/30 [00:03<00:02, 5.11it/s] 67%|██████▋ | 20/30 [00:03<00:01, 5.11it/s] 70%|███████ | 21/30 [00:04<00:01, 5.11it/s] 73%|███████▎ | 22/30 [00:04<00:01, 5.11it/s] 77%|███████▋ | 23/30 [00:04<00:01, 5.11it/s] 80%|████████ | 24/30 [00:04<00:01, 5.11it/s] 83%|████████▎ | 25/30 [00:04<00:00, 5.11it/s] 87%|████████▋ | 26/30 [00:04<00:00, 5.11it/s] 90%|█████████ | 27/30 [00:05<00:00, 5.11it/s] 93%|█████████▎| 28/30 [00:05<00:00, 5.10it/s] 97%|█████████▋| 29/30 [00:05<00:00, 5.10it/s] 100%|██████████| 30/30 [00:05<00:00, 5.10it/s] 100%|██████████| 30/30 [00:05<00:00, 5.20it/s]
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