catacolabs / sdxl-ad-inpaint
Product advertising image generator using SDXL (Updated 1 year, 9 months ago)
Prediction
catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359IDmiigvsdbbueqfj3g36vellzjbyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 47712
- image
- prompt
- a shoe in the desert sand
- img_size
- 1024, 1024
- apply_img
- scheduler
- K_EULER
- product_fill
- 80
- guidance_scale
- 7.5
- condition_scale
- 0.9
- negative_prompt
- low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement
- num_refine_steps
- 5
- num_inference_steps
- 40
{ "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7t2wLqOLoZpXhPMdww1YLG3rBMld2SGtXa2IVFXwCph7Yo/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "1024, 1024", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { seed: 47712, image: "https://replicate.delivery/pbxt/JX7t2wLqOLoZpXhPMdww1YLG3rBMld2SGtXa2IVFXwCph7Yo/shoe.jpg", prompt: "a shoe in the desert sand", img_size: "1024, 1024", apply_img: true, scheduler: "K_EULER", product_fill: "80", guidance_scale: 7.5, condition_scale: 0.9, negative_prompt: "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", num_refine_steps: 5, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7t2wLqOLoZpXhPMdww1YLG3rBMld2SGtXa2IVFXwCph7Yo/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "1024, 1024", "apply_img": True, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7t2wLqOLoZpXhPMdww1YLG3rBMld2SGtXa2IVFXwCph7Yo/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "1024, 1024", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-15T18:36:13.343921Z", "created_at": "2023-09-15T18:35:56.337347Z", "data_removed": false, "error": null, "id": "miigvsdbbueqfj3g36vellzjby", "input": { "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7t2wLqOLoZpXhPMdww1YLG3rBMld2SGtXa2IVFXwCph7Yo/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "1024, 1024", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 }, "logs": "Using seed: 47712\nProduct img W:684, H:514\nScale factor: 0.8\nFinal img W: 1024, H:1024\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.39it/s]\n 5%|▌ | 2/40 [00:00<00:11, 3.38it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.37it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.37it/s]\n 12%|█▎ | 5/40 [00:01<00:10, 3.38it/s]\n 15%|█▌ | 6/40 [00:01<00:10, 3.38it/s]\n 18%|█▊ | 7/40 [00:02<00:09, 3.38it/s]\n 20%|██ | 8/40 [00:02<00:09, 3.38it/s]\n 22%|██▎ | 9/40 [00:02<00:09, 3.38it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.38it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.38it/s]\n 30%|███ | 12/40 [00:03<00:08, 3.38it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.38it/s]\n 35%|███▌ | 14/40 [00:04<00:07, 3.38it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.38it/s]\n 40%|████ | 16/40 [00:04<00:07, 3.38it/s]\n 42%|████▎ | 17/40 [00:05<00:06, 3.38it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.38it/s]\n 48%|████▊ | 19/40 [00:05<00:06, 3.38it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.37it/s]\n 52%|█████▎ | 21/40 [00:06<00:05, 3.37it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.37it/s]\n 57%|█████▊ | 23/40 [00:06<00:05, 3.37it/s]\n 60%|██████ | 24/40 [00:07<00:04, 3.37it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.37it/s]\n 65%|██████▌ | 26/40 [00:07<00:04, 3.37it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.37it/s]\n 70%|███████ | 28/40 [00:08<00:03, 3.37it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.37it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.37it/s]\n 78%|███████▊ | 31/40 [00:09<00:02, 3.37it/s]\n 80%|████████ | 32/40 [00:09<00:02, 3.36it/s]\n 82%|████████▎ | 33/40 [00:09<00:02, 3.36it/s]\n 85%|████████▌ | 34/40 [00:10<00:01, 3.36it/s]\n 88%|████████▊ | 35/40 [00:10<00:01, 3.36it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.36it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.36it/s]\n 95%|█████████▌| 38/40 [00:11<00:00, 3.36it/s]\n 98%|█████████▊| 39/40 [00:11<00:00, 3.36it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.36it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.37it/s]\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:00<00:00, 4.34it/s]\n100%|██████████| 1/1 [00:00<00:00, 4.34it/s]", "metrics": { "predict_time": 16.987033, "total_time": 17.006574 }, "output": "https://pbxt.replicate.delivery/91os4aXMkbLWFNyMGy5RudtIiMPvYL14ce8yufRrgqFcYlkRA/8-out.png", "started_at": "2023-09-15T18:35:56.356888Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/miigvsdbbueqfj3g36vellzjby", "cancel": "https://api.replicate.com/v1/predictions/miigvsdbbueqfj3g36vellzjby/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 47712 Product img W:684, H:514 Scale factor: 0.8 Final img W: 1024, H:1024 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.39it/s] 5%|▌ | 2/40 [00:00<00:11, 3.38it/s] 8%|▊ | 3/40 [00:00<00:10, 3.37it/s] 10%|█ | 4/40 [00:01<00:10, 3.37it/s] 12%|█▎ | 5/40 [00:01<00:10, 3.38it/s] 15%|█▌ | 6/40 [00:01<00:10, 3.38it/s] 18%|█▊ | 7/40 [00:02<00:09, 3.38it/s] 20%|██ | 8/40 [00:02<00:09, 3.38it/s] 22%|██▎ | 9/40 [00:02<00:09, 3.38it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.38it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.38it/s] 30%|███ | 12/40 [00:03<00:08, 3.38it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.38it/s] 35%|███▌ | 14/40 [00:04<00:07, 3.38it/s] 38%|███▊ | 15/40 [00:04<00:07, 3.38it/s] 40%|████ | 16/40 [00:04<00:07, 3.38it/s] 42%|████▎ | 17/40 [00:05<00:06, 3.38it/s] 45%|████▌ | 18/40 [00:05<00:06, 3.38it/s] 48%|████▊ | 19/40 [00:05<00:06, 3.38it/s] 50%|█████ | 20/40 [00:05<00:05, 3.37it/s] 52%|█████▎ | 21/40 [00:06<00:05, 3.37it/s] 55%|█████▌ | 22/40 [00:06<00:05, 3.37it/s] 57%|█████▊ | 23/40 [00:06<00:05, 3.37it/s] 60%|██████ | 24/40 [00:07<00:04, 3.37it/s] 62%|██████▎ | 25/40 [00:07<00:04, 3.37it/s] 65%|██████▌ | 26/40 [00:07<00:04, 3.37it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.37it/s] 70%|███████ | 28/40 [00:08<00:03, 3.37it/s] 72%|███████▎ | 29/40 [00:08<00:03, 3.37it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.37it/s] 78%|███████▊ | 31/40 [00:09<00:02, 3.37it/s] 80%|████████ | 32/40 [00:09<00:02, 3.36it/s] 82%|████████▎ | 33/40 [00:09<00:02, 3.36it/s] 85%|████████▌ | 34/40 [00:10<00:01, 3.36it/s] 88%|████████▊ | 35/40 [00:10<00:01, 3.36it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.36it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.36it/s] 95%|█████████▌| 38/40 [00:11<00:00, 3.36it/s] 98%|█████████▊| 39/40 [00:11<00:00, 3.36it/s] 100%|██████████| 40/40 [00:11<00:00, 3.36it/s] 100%|██████████| 40/40 [00:11<00:00, 3.37it/s] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 4.34it/s] 100%|██████████| 1/1 [00:00<00:00, 4.34it/s]Prediction
catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359IDiwcc2etb2gtpgocpc5yzdhpd5aStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 47712
- image
- prompt
- a shoe in the desert sand
- img_size
- 1536, 640
- apply_img
- scheduler
- K_EULER
- product_fill
- 80
- guidance_scale
- 7.5
- condition_scale
- 0.9
- negative_prompt
- low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement
- num_refine_steps
- 5
- num_inference_steps
- 40
{ "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7wepAPwQVbRlsO9wyKf3rF684VKL5p1wwxYORxuqlCgbgG/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "1536, 640", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { seed: 47712, image: "https://replicate.delivery/pbxt/JX7wepAPwQVbRlsO9wyKf3rF684VKL5p1wwxYORxuqlCgbgG/shoe.jpg", prompt: "a shoe in the desert sand", img_size: "1536, 640", apply_img: true, scheduler: "K_EULER", product_fill: "80", guidance_scale: 7.5, condition_scale: 0.9, negative_prompt: "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", num_refine_steps: 5, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7wepAPwQVbRlsO9wyKf3rF684VKL5p1wwxYORxuqlCgbgG/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "1536, 640", "apply_img": True, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7wepAPwQVbRlsO9wyKf3rF684VKL5p1wwxYORxuqlCgbgG/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "1536, 640", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-15T18:37:08.666280Z", "created_at": "2023-09-15T18:36:52.646992Z", "data_removed": false, "error": null, "id": "iwcc2etb2gtpgocpc5yzdhpd5a", "input": { "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7wepAPwQVbRlsO9wyKf3rF684VKL5p1wwxYORxuqlCgbgG/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "1536, 640", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 }, "logs": "Using seed: 47712\nProduct img W:684, H:514\nScale factor: 0.8\nFinal img W: 1536, H:640\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.33it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.48it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.53it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.54it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.56it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.56it/s]\n 18%|█▊ | 7/40 [00:01<00:09, 3.56it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.56it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.56it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.57it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.57it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.56it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.57it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.56it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.57it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.57it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.57it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.57it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.57it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.56it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.56it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.56it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.56it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.56it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.56it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.56it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.56it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.56it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.56it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.56it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.56it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.56it/s]\n 82%|████████▎ | 33/40 [00:09<00:01, 3.56it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.56it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.56it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.56it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.56it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.56it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.56it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.56it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.56it/s]\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:00<00:00, 4.46it/s]\n100%|██████████| 1/1 [00:00<00:00, 4.45it/s]", "metrics": { "predict_time": 16.043137, "total_time": 16.019288 }, "output": "https://pbxt.replicate.delivery/Sre5rPucjDTxCadoJ3Jd7aMVY8hV1Ftgsy7YXfuzCYJTZlkRA/8-out.png", "started_at": "2023-09-15T18:36:52.623143Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iwcc2etb2gtpgocpc5yzdhpd5a", "cancel": "https://api.replicate.com/v1/predictions/iwcc2etb2gtpgocpc5yzdhpd5a/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 47712 Product img W:684, H:514 Scale factor: 0.8 Final img W: 1536, H:640 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.33it/s] 5%|▌ | 2/40 [00:00<00:10, 3.48it/s] 8%|▊ | 3/40 [00:00<00:10, 3.53it/s] 10%|█ | 4/40 [00:01<00:10, 3.54it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.56it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.56it/s] 18%|█▊ | 7/40 [00:01<00:09, 3.56it/s] 20%|██ | 8/40 [00:02<00:08, 3.56it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.56it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.57it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.57it/s] 30%|███ | 12/40 [00:03<00:07, 3.56it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.57it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.56it/s] 38%|███▊ | 15/40 [00:04<00:07, 3.57it/s] 40%|████ | 16/40 [00:04<00:06, 3.57it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.57it/s] 45%|████▌ | 18/40 [00:05<00:06, 3.57it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.57it/s] 50%|█████ | 20/40 [00:05<00:05, 3.56it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.56it/s] 55%|█████▌ | 22/40 [00:06<00:05, 3.56it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.56it/s] 60%|██████ | 24/40 [00:06<00:04, 3.56it/s] 62%|██████▎ | 25/40 [00:07<00:04, 3.56it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.56it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.56it/s] 70%|███████ | 28/40 [00:07<00:03, 3.56it/s] 72%|███████▎ | 29/40 [00:08<00:03, 3.56it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.56it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.56it/s] 80%|████████ | 32/40 [00:08<00:02, 3.56it/s] 82%|████████▎ | 33/40 [00:09<00:01, 3.56it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.56it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.56it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.56it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.56it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.56it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.56it/s] 100%|██████████| 40/40 [00:11<00:00, 3.56it/s] 100%|██████████| 40/40 [00:11<00:00, 3.56it/s] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 4.46it/s] 100%|██████████| 1/1 [00:00<00:00, 4.45it/s]Prediction
catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359IDfc4gaqdbvpfeax2taqiwyfnjhyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 47712
- image
- prompt
- a shoe in the desert sand
- img_size
- 704, 1408
- apply_img
- scheduler
- K_EULER
- product_fill
- 80
- guidance_scale
- 7.5
- condition_scale
- 0.9
- negative_prompt
- low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement
- num_refine_steps
- 5
- num_inference_steps
- 40
{ "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7x7rWQRclzXKHQFq8A79nvicOGapri21uFr5PlO3Uxnn6i/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "704, 1408", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { seed: 47712, image: "https://replicate.delivery/pbxt/JX7x7rWQRclzXKHQFq8A79nvicOGapri21uFr5PlO3Uxnn6i/shoe.jpg", prompt: "a shoe in the desert sand", img_size: "704, 1408", apply_img: true, scheduler: "K_EULER", product_fill: "80", guidance_scale: 7.5, condition_scale: 0.9, negative_prompt: "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", num_refine_steps: 5, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7x7rWQRclzXKHQFq8A79nvicOGapri21uFr5PlO3Uxnn6i/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "704, 1408", "apply_img": True, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7x7rWQRclzXKHQFq8A79nvicOGapri21uFr5PlO3Uxnn6i/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "704, 1408", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-15T18:38:12.770983Z", "created_at": "2023-09-15T18:37:56.533331Z", "data_removed": false, "error": null, "id": "fc4gaqdbvpfeax2taqiwyfnjhy", "input": { "seed": 47712, "image": "https://replicate.delivery/pbxt/JX7x7rWQRclzXKHQFq8A79nvicOGapri21uFr5PlO3Uxnn6i/shoe.jpg", "prompt": "a shoe in the desert sand", "img_size": "704, 1408", "apply_img": true, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.9, "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement", "num_refine_steps": 5, "num_inference_steps": 40 }, "logs": "Using seed: 47712\nProduct img W:684, H:514\nScale factor: 0.8\nFinal img W: 704, H:1408\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.27it/s]\n 5%|▌ | 2/40 [00:00<00:11, 3.41it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.46it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.48it/s]\n 12%|█▎ | 5/40 [00:01<00:10, 3.49it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.49it/s]\n 18%|█▊ | 7/40 [00:02<00:09, 3.49it/s]\n 20%|██ | 8/40 [00:02<00:09, 3.50it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.50it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.50it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.50it/s]\n 30%|███ | 12/40 [00:03<00:08, 3.50it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.50it/s]\n 35%|███▌ | 14/40 [00:04<00:07, 3.49it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.50it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.50it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.49it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.49it/s]\n 48%|████▊ | 19/40 [00:05<00:06, 3.49it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.49it/s]\n 52%|█████▎ | 21/40 [00:06<00:05, 3.49it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.49it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.49it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.49it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.49it/s]\n 65%|██████▌ | 26/40 [00:07<00:04, 3.49it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.49it/s]\n 70%|███████ | 28/40 [00:08<00:03, 3.49it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.49it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.49it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.49it/s]\n 80%|████████ | 32/40 [00:09<00:02, 3.49it/s]\n 82%|████████▎ | 33/40 [00:09<00:02, 3.49it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.49it/s]\n 88%|████████▊ | 35/40 [00:10<00:01, 3.49it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.49it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.49it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.49it/s]\n 98%|█████████▊| 39/40 [00:11<00:00, 3.49it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.49it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.49it/s]\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:00<00:00, 4.44it/s]\n100%|██████████| 1/1 [00:00<00:00, 4.44it/s]", "metrics": { "predict_time": 16.28614, "total_time": 16.237652 }, "output": "https://pbxt.replicate.delivery/GGJgjgtT7gYZKhpFaye5ii2wYBTiza9J6u3yGO8ikFgJtSyIA/8-out.png", "started_at": "2023-09-15T18:37:56.484843Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fc4gaqdbvpfeax2taqiwyfnjhy", "cancel": "https://api.replicate.com/v1/predictions/fc4gaqdbvpfeax2taqiwyfnjhy/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 47712 Product img W:684, H:514 Scale factor: 0.8 Final img W: 704, H:1408 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.27it/s] 5%|▌ | 2/40 [00:00<00:11, 3.41it/s] 8%|▊ | 3/40 [00:00<00:10, 3.46it/s] 10%|█ | 4/40 [00:01<00:10, 3.48it/s] 12%|█▎ | 5/40 [00:01<00:10, 3.49it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.49it/s] 18%|█▊ | 7/40 [00:02<00:09, 3.49it/s] 20%|██ | 8/40 [00:02<00:09, 3.50it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.50it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.50it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.50it/s] 30%|███ | 12/40 [00:03<00:08, 3.50it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.50it/s] 35%|███▌ | 14/40 [00:04<00:07, 3.49it/s] 38%|███▊ | 15/40 [00:04<00:07, 3.50it/s] 40%|████ | 16/40 [00:04<00:06, 3.50it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.49it/s] 45%|████▌ | 18/40 [00:05<00:06, 3.49it/s] 48%|████▊ | 19/40 [00:05<00:06, 3.49it/s] 50%|█████ | 20/40 [00:05<00:05, 3.49it/s] 52%|█████▎ | 21/40 [00:06<00:05, 3.49it/s] 55%|█████▌ | 22/40 [00:06<00:05, 3.49it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.49it/s] 60%|██████ | 24/40 [00:06<00:04, 3.49it/s] 62%|██████▎ | 25/40 [00:07<00:04, 3.49it/s] 65%|██████▌ | 26/40 [00:07<00:04, 3.49it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.49it/s] 70%|███████ | 28/40 [00:08<00:03, 3.49it/s] 72%|███████▎ | 29/40 [00:08<00:03, 3.49it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.49it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.49it/s] 80%|████████ | 32/40 [00:09<00:02, 3.49it/s] 82%|████████▎ | 33/40 [00:09<00:02, 3.49it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.49it/s] 88%|████████▊ | 35/40 [00:10<00:01, 3.49it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.49it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.49it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.49it/s] 98%|█████████▊| 39/40 [00:11<00:00, 3.49it/s] 100%|██████████| 40/40 [00:11<00:00, 3.49it/s] 100%|██████████| 40/40 [00:11<00:00, 3.49it/s] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 4.44it/s] 100%|██████████| 1/1 [00:00<00:00, 4.44it/s]Prediction
catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359IDvhroqttby6kocim755245syvxiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 24603
- image
- prompt
- a modern sofa in a contemporary living room, stylish decor
- img_size
- 1024, 1024
- apply_img
- scheduler
- K_EULER
- product_fill
- 80
- guidance_scale
- 7.5
- condition_scale
- 0.8
- negative_prompt
- num_refine_steps
- 20
- num_inference_steps
- 40
{ "seed": 24603, "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { seed: 24603, image: "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", prompt: "a modern sofa in a contemporary living room, stylish decor", img_size: "1024, 1024", apply_img: false, scheduler: "K_EULER", product_fill: "80", guidance_scale: 7.5, condition_scale: 0.8, negative_prompt: "", num_refine_steps: 20, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "seed": 24603, "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1024, 1024", "apply_img": False, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "seed": 24603, "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-15T18:43:09.006396Z", "created_at": "2023-09-15T18:42:51.245376Z", "data_removed": false, "error": null, "id": "vhroqttby6kocim755245syvxi", "input": { "seed": 24603, "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }, "logs": "Using seed: 24603\nProduct img W:666, H:375\nScale factor: 0.8\nFinal img W: 1024, H:1024\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.38it/s]\n 5%|▌ | 2/40 [00:00<00:11, 3.38it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.38it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.37it/s]\n 12%|█▎ | 5/40 [00:01<00:10, 3.37it/s]\n 15%|█▌ | 6/40 [00:01<00:10, 3.37it/s]\n 18%|█▊ | 7/40 [00:02<00:09, 3.37it/s]\n 20%|██ | 8/40 [00:02<00:09, 3.37it/s]\n 22%|██▎ | 9/40 [00:02<00:09, 3.37it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.36it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.36it/s]\n 30%|███ | 12/40 [00:03<00:08, 3.36it/s]\n 32%|███▎ | 13/40 [00:03<00:08, 3.36it/s]\n 35%|███▌ | 14/40 [00:04<00:07, 3.36it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.32it/s]\n 40%|████ | 16/40 [00:04<00:07, 3.32it/s]\n 42%|████▎ | 17/40 [00:05<00:06, 3.33it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.33it/s]\n 48%|████▊ | 19/40 [00:05<00:06, 3.34it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.34it/s]\n 52%|█████▎ | 21/40 [00:06<00:05, 3.34it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.35it/s]\n 57%|█████▊ | 23/40 [00:06<00:05, 3.34it/s]\n 60%|██████ | 24/40 [00:07<00:04, 3.34it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.34it/s]\n 65%|██████▌ | 26/40 [00:07<00:04, 3.34it/s]\n 68%|██████▊ | 27/40 [00:08<00:03, 3.34it/s]\n 70%|███████ | 28/40 [00:08<00:03, 3.34it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.34it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.34it/s]\n 78%|███████▊ | 31/40 [00:09<00:02, 3.35it/s]\n 80%|████████ | 32/40 [00:09<00:02, 3.34it/s]\n 82%|████████▎ | 33/40 [00:09<00:02, 3.34it/s]\n 85%|████████▌ | 34/40 [00:10<00:01, 3.34it/s]\n 88%|████████▊ | 35/40 [00:10<00:01, 3.34it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.34it/s]\n 92%|█████████▎| 37/40 [00:11<00:00, 3.34it/s]\n 95%|█████████▌| 38/40 [00:11<00:00, 3.34it/s]\n 98%|█████████▊| 39/40 [00:11<00:00, 3.34it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.34it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.35it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.31it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.30it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.29it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.29it/s]", "metrics": { "predict_time": 17.757526, "total_time": 17.76102 }, "output": "https://pbxt.replicate.delivery/ORbuWtoy0y6NI9f4DrJ2fxs92LgviBaOlzOVdYTr3pT8eKJjA/7-out.png", "started_at": "2023-09-15T18:42:51.248870Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vhroqttby6kocim755245syvxi", "cancel": "https://api.replicate.com/v1/predictions/vhroqttby6kocim755245syvxi/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 24603 Product img W:666, H:375 Scale factor: 0.8 Final img W: 1024, H:1024 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.38it/s] 5%|▌ | 2/40 [00:00<00:11, 3.38it/s] 8%|▊ | 3/40 [00:00<00:10, 3.38it/s] 10%|█ | 4/40 [00:01<00:10, 3.37it/s] 12%|█▎ | 5/40 [00:01<00:10, 3.37it/s] 15%|█▌ | 6/40 [00:01<00:10, 3.37it/s] 18%|█▊ | 7/40 [00:02<00:09, 3.37it/s] 20%|██ | 8/40 [00:02<00:09, 3.37it/s] 22%|██▎ | 9/40 [00:02<00:09, 3.37it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.36it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.36it/s] 30%|███ | 12/40 [00:03<00:08, 3.36it/s] 32%|███▎ | 13/40 [00:03<00:08, 3.36it/s] 35%|███▌ | 14/40 [00:04<00:07, 3.36it/s] 38%|███▊ | 15/40 [00:04<00:07, 3.32it/s] 40%|████ | 16/40 [00:04<00:07, 3.32it/s] 42%|████▎ | 17/40 [00:05<00:06, 3.33it/s] 45%|████▌ | 18/40 [00:05<00:06, 3.33it/s] 48%|████▊ | 19/40 [00:05<00:06, 3.34it/s] 50%|█████ | 20/40 [00:05<00:05, 3.34it/s] 52%|█████▎ | 21/40 [00:06<00:05, 3.34it/s] 55%|█████▌ | 22/40 [00:06<00:05, 3.35it/s] 57%|█████▊ | 23/40 [00:06<00:05, 3.34it/s] 60%|██████ | 24/40 [00:07<00:04, 3.34it/s] 62%|██████▎ | 25/40 [00:07<00:04, 3.34it/s] 65%|██████▌ | 26/40 [00:07<00:04, 3.34it/s] 68%|██████▊ | 27/40 [00:08<00:03, 3.34it/s] 70%|███████ | 28/40 [00:08<00:03, 3.34it/s] 72%|███████▎ | 29/40 [00:08<00:03, 3.34it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.34it/s] 78%|███████▊ | 31/40 [00:09<00:02, 3.35it/s] 80%|████████ | 32/40 [00:09<00:02, 3.34it/s] 82%|████████▎ | 33/40 [00:09<00:02, 3.34it/s] 85%|████████▌ | 34/40 [00:10<00:01, 3.34it/s] 88%|████████▊ | 35/40 [00:10<00:01, 3.34it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.34it/s] 92%|█████████▎| 37/40 [00:11<00:00, 3.34it/s] 95%|█████████▌| 38/40 [00:11<00:00, 3.34it/s] 98%|█████████▊| 39/40 [00:11<00:00, 3.34it/s] 100%|██████████| 40/40 [00:11<00:00, 3.34it/s] 100%|██████████| 40/40 [00:11<00:00, 3.35it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.31it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s] 50%|█████ | 3/6 [00:00<00:00, 4.30it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.29it/s] 100%|██████████| 6/6 [00:01<00:00, 4.28it/s] 100%|██████████| 6/6 [00:01<00:00, 4.29it/s]Prediction
catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359IDcn2v6ulb2kpwwfqvqmfts425ceStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- a modern sofa in a contemporary living room, stylish decor
- img_size
- 1024, 1024
- apply_img
- scheduler
- K_EULER
- product_fill
- 80
- guidance_scale
- 7.5
- condition_scale
- 0.8
- negative_prompt
- num_refine_steps
- 20
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { image: "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", prompt: "a modern sofa in a contemporary living room, stylish decor", img_size: "1024, 1024", apply_img: false, scheduler: "K_EULER", product_fill: "80", guidance_scale: 7.5, condition_scale: 0.8, negative_prompt: "", num_refine_steps: 20, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1024, 1024", "apply_img": False, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-15T18:44:04.924234Z", "created_at": "2023-09-15T18:43:47.432399Z", "data_removed": false, "error": null, "id": "cn2v6ulb2kpwwfqvqmfts425ce", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }, "logs": "Using seed: 41446\nProduct img W:666, H:375\nScale factor: 0.8\nFinal img W: 1024, H:1024\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.37it/s]\n 5%|▌ | 2/40 [00:00<00:11, 3.36it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.37it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.36it/s]\n 12%|█▎ | 5/40 [00:01<00:10, 3.36it/s]\n 15%|█▌ | 6/40 [00:01<00:10, 3.36it/s]\n 18%|█▊ | 7/40 [00:02<00:09, 3.36it/s]\n 20%|██ | 8/40 [00:02<00:09, 3.36it/s]\n 22%|██▎ | 9/40 [00:02<00:09, 3.36it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.36it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.36it/s]\n 30%|███ | 12/40 [00:03<00:08, 3.36it/s]\n 32%|███▎ | 13/40 [00:03<00:08, 3.36it/s]\n 35%|███▌ | 14/40 [00:04<00:07, 3.35it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.35it/s]\n 40%|████ | 16/40 [00:04<00:07, 3.36it/s]\n 42%|████▎ | 17/40 [00:05<00:06, 3.36it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.35it/s]\n 48%|████▊ | 19/40 [00:05<00:06, 3.35it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.35it/s]\n 52%|█████▎ | 21/40 [00:06<00:05, 3.35it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.35it/s]\n 57%|█████▊ | 23/40 [00:06<00:05, 3.35it/s]\n 60%|██████ | 24/40 [00:07<00:04, 3.34it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.34it/s]\n 65%|██████▌ | 26/40 [00:07<00:04, 3.34it/s]\n 68%|██████▊ | 27/40 [00:08<00:03, 3.34it/s]\n 70%|███████ | 28/40 [00:08<00:03, 3.34it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.34it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.34it/s]\n 78%|███████▊ | 31/40 [00:09<00:02, 3.34it/s]\n 80%|████████ | 32/40 [00:09<00:02, 3.34it/s]\n 82%|████████▎ | 33/40 [00:09<00:02, 3.34it/s]\n 85%|████████▌ | 34/40 [00:10<00:01, 3.34it/s]\n 88%|████████▊ | 35/40 [00:10<00:01, 3.34it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.34it/s]\n 92%|█████████▎| 37/40 [00:11<00:00, 3.34it/s]\n 95%|█████████▌| 38/40 [00:11<00:00, 3.34it/s]\n 98%|█████████▊| 39/40 [00:11<00:00, 3.34it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.34it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.35it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.32it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.29it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.28it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]", "metrics": { "predict_time": 17.485488, "total_time": 17.491835 }, "output": "https://pbxt.replicate.delivery/WxvK33oWqd7HLRg0hlnmOBEaNdAf4P3780MvFfnf9JspfVSGB/7-out.png", "started_at": "2023-09-15T18:43:47.438746Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cn2v6ulb2kpwwfqvqmfts425ce", "cancel": "https://api.replicate.com/v1/predictions/cn2v6ulb2kpwwfqvqmfts425ce/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 41446 Product img W:666, H:375 Scale factor: 0.8 Final img W: 1024, H:1024 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.37it/s] 5%|▌ | 2/40 [00:00<00:11, 3.36it/s] 8%|▊ | 3/40 [00:00<00:10, 3.37it/s] 10%|█ | 4/40 [00:01<00:10, 3.36it/s] 12%|█▎ | 5/40 [00:01<00:10, 3.36it/s] 15%|█▌ | 6/40 [00:01<00:10, 3.36it/s] 18%|█▊ | 7/40 [00:02<00:09, 3.36it/s] 20%|██ | 8/40 [00:02<00:09, 3.36it/s] 22%|██▎ | 9/40 [00:02<00:09, 3.36it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.36it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.36it/s] 30%|███ | 12/40 [00:03<00:08, 3.36it/s] 32%|███▎ | 13/40 [00:03<00:08, 3.36it/s] 35%|███▌ | 14/40 [00:04<00:07, 3.35it/s] 38%|███▊ | 15/40 [00:04<00:07, 3.35it/s] 40%|████ | 16/40 [00:04<00:07, 3.36it/s] 42%|████▎ | 17/40 [00:05<00:06, 3.36it/s] 45%|████▌ | 18/40 [00:05<00:06, 3.35it/s] 48%|████▊ | 19/40 [00:05<00:06, 3.35it/s] 50%|█████ | 20/40 [00:05<00:05, 3.35it/s] 52%|█████▎ | 21/40 [00:06<00:05, 3.35it/s] 55%|█████▌ | 22/40 [00:06<00:05, 3.35it/s] 57%|█████▊ | 23/40 [00:06<00:05, 3.35it/s] 60%|██████ | 24/40 [00:07<00:04, 3.34it/s] 62%|██████▎ | 25/40 [00:07<00:04, 3.34it/s] 65%|██████▌ | 26/40 [00:07<00:04, 3.34it/s] 68%|██████▊ | 27/40 [00:08<00:03, 3.34it/s] 70%|███████ | 28/40 [00:08<00:03, 3.34it/s] 72%|███████▎ | 29/40 [00:08<00:03, 3.34it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.34it/s] 78%|███████▊ | 31/40 [00:09<00:02, 3.34it/s] 80%|████████ | 32/40 [00:09<00:02, 3.34it/s] 82%|████████▎ | 33/40 [00:09<00:02, 3.34it/s] 85%|████████▌ | 34/40 [00:10<00:01, 3.34it/s] 88%|████████▊ | 35/40 [00:10<00:01, 3.34it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.34it/s] 92%|█████████▎| 37/40 [00:11<00:00, 3.34it/s] 95%|█████████▌| 38/40 [00:11<00:00, 3.34it/s] 98%|█████████▊| 39/40 [00:11<00:00, 3.34it/s] 100%|██████████| 40/40 [00:11<00:00, 3.34it/s] 100%|██████████| 40/40 [00:11<00:00, 3.35it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.32it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s] 50%|█████ | 3/6 [00:00<00:00, 4.29it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.28it/s] 100%|██████████| 6/6 [00:01<00:00, 4.28it/s] 100%|██████████| 6/6 [00:01<00:00, 4.28it/s]Prediction
catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359ID5rpne6lb5eviftodd6scbtbkhiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- a modern sofa in a contemporary living room, stylish decor
- img_size
- 1408, 704
- apply_img
- scheduler
- K_EULER
- product_fill
- 80
- guidance_scale
- 7.5
- condition_scale
- 0.8
- negative_prompt
- num_refine_steps
- 20
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1408, 704", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { image: "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", prompt: "a modern sofa in a contemporary living room, stylish decor", img_size: "1408, 704", apply_img: false, scheduler: "K_EULER", product_fill: "80", guidance_scale: 7.5, condition_scale: 0.8, negative_prompt: "", num_refine_steps: 20, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1408, 704", "apply_img": False, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1408, 704", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-15T18:46:20.599092Z", "created_at": "2023-09-15T18:46:03.471012Z", "data_removed": false, "error": null, "id": "5rpne6lb5eviftodd6scbtbkhi", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JX7yjB7jAgeCC1tUmWUUZlWg3IDWW9vLqIjwqWOlj9p6zyyn/sofa.png", "prompt": "a modern sofa in a contemporary living room, stylish decor", "img_size": "1408, 704", "apply_img": false, "scheduler": "K_EULER", "product_fill": "80", "guidance_scale": 7.5, "condition_scale": 0.8, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }, "logs": "Using seed: 48169\nProduct img W:666, H:375\nScale factor: 0.8\nFinal img W: 1408, H:704\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.51it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.49it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.48it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.48it/s]\n 12%|█▎ | 5/40 [00:01<00:10, 3.47it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.47it/s]\n 18%|█▊ | 7/40 [00:02<00:09, 3.47it/s]\n 20%|██ | 8/40 [00:02<00:09, 3.47it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.46it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.46it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.46it/s]\n 30%|███ | 12/40 [00:03<00:08, 3.46it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.46it/s]\n 35%|███▌ | 14/40 [00:04<00:07, 3.46it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.46it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.46it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.46it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.46it/s]\n 48%|████▊ | 19/40 [00:05<00:06, 3.45it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.45it/s]\n 52%|█████▎ | 21/40 [00:06<00:05, 3.45it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.45it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.45it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.45it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.45it/s]\n 65%|██████▌ | 26/40 [00:07<00:04, 3.45it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.45it/s]\n 70%|███████ | 28/40 [00:08<00:03, 3.45it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.45it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.45it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.45it/s]\n 80%|████████ | 32/40 [00:09<00:02, 3.45it/s]\n 82%|████████▎ | 33/40 [00:09<00:02, 3.45it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.44it/s]\n 88%|████████▊ | 35/40 [00:10<00:01, 3.45it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.45it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.44it/s]\n 95%|█████████▌| 38/40 [00:11<00:00, 3.44it/s]\n 98%|█████████▊| 39/40 [00:11<00:00, 3.44it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.44it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.45it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.48it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.47it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.46it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.44it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.44it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.44it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.45it/s]", "metrics": { "predict_time": 17.130095, "total_time": 17.12808 }, "output": "https://pbxt.replicate.delivery/qJVdeBdUjpwZLyX0KB6TBB9F0mavFvh4K6LK6xIiQE69wSyIA/7-out.png", "started_at": "2023-09-15T18:46:03.468997Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5rpne6lb5eviftodd6scbtbkhi", "cancel": "https://api.replicate.com/v1/predictions/5rpne6lb5eviftodd6scbtbkhi/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 48169 Product img W:666, H:375 Scale factor: 0.8 Final img W: 1408, H:704 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.51it/s] 5%|▌ | 2/40 [00:00<00:10, 3.49it/s] 8%|▊ | 3/40 [00:00<00:10, 3.48it/s] 10%|█ | 4/40 [00:01<00:10, 3.48it/s] 12%|█▎ | 5/40 [00:01<00:10, 3.47it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.47it/s] 18%|█▊ | 7/40 [00:02<00:09, 3.47it/s] 20%|██ | 8/40 [00:02<00:09, 3.47it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.46it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.46it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.46it/s] 30%|███ | 12/40 [00:03<00:08, 3.46it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.46it/s] 35%|███▌ | 14/40 [00:04<00:07, 3.46it/s] 38%|███▊ | 15/40 [00:04<00:07, 3.46it/s] 40%|████ | 16/40 [00:04<00:06, 3.46it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.46it/s] 45%|████▌ | 18/40 [00:05<00:06, 3.46it/s] 48%|████▊ | 19/40 [00:05<00:06, 3.45it/s] 50%|█████ | 20/40 [00:05<00:05, 3.45it/s] 52%|█████▎ | 21/40 [00:06<00:05, 3.45it/s] 55%|█████▌ | 22/40 [00:06<00:05, 3.45it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.45it/s] 60%|██████ | 24/40 [00:06<00:04, 3.45it/s] 62%|██████▎ | 25/40 [00:07<00:04, 3.45it/s] 65%|██████▌ | 26/40 [00:07<00:04, 3.45it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.45it/s] 70%|███████ | 28/40 [00:08<00:03, 3.45it/s] 72%|███████▎ | 29/40 [00:08<00:03, 3.45it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.45it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.45it/s] 80%|████████ | 32/40 [00:09<00:02, 3.45it/s] 82%|████████▎ | 33/40 [00:09<00:02, 3.45it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.44it/s] 88%|████████▊ | 35/40 [00:10<00:01, 3.45it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.45it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.44it/s] 95%|█████████▌| 38/40 [00:11<00:00, 3.44it/s] 98%|█████████▊| 39/40 [00:11<00:00, 3.44it/s] 100%|██████████| 40/40 [00:11<00:00, 3.44it/s] 100%|██████████| 40/40 [00:11<00:00, 3.45it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.48it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.47it/s] 50%|█████ | 3/6 [00:00<00:00, 4.46it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.44it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.44it/s] 100%|██████████| 6/6 [00:01<00:00, 4.44it/s] 100%|██████████| 6/6 [00:01<00:00, 4.45it/s]Prediction
catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359IDpe624olbamtdau44k5xftmdeyaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 43137
- image
- prompt
- a bottle in the mountains
- img_size
- 1024, 1024
- apply_img
- scheduler
- K_EULER
- product_fill
- Original
- guidance_scale
- 7.5
- condition_scale
- 0.7
- negative_prompt
- num_refine_steps
- 20
- num_inference_steps
- 40
{ "seed": 43137, "image": "https://replicate.delivery/pbxt/JXEDRJyInkqDarZM6vftt6qx5D5CLF7MycSfGsvDTpinah9l/orange-bottle.jpg", "prompt": "a bottle in the mountains", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { seed: 43137, image: "https://replicate.delivery/pbxt/JXEDRJyInkqDarZM6vftt6qx5D5CLF7MycSfGsvDTpinah9l/orange-bottle.jpg", prompt: "a bottle in the mountains", img_size: "1024, 1024", apply_img: false, scheduler: "K_EULER", product_fill: "Original", guidance_scale: 7.5, condition_scale: 0.7, negative_prompt: "", num_refine_steps: 20, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "seed": 43137, "image": "https://replicate.delivery/pbxt/JXEDRJyInkqDarZM6vftt6qx5D5CLF7MycSfGsvDTpinah9l/orange-bottle.jpg", "prompt": "a bottle in the mountains", "img_size": "1024, 1024", "apply_img": False, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "seed": 43137, "image": "https://replicate.delivery/pbxt/JXEDRJyInkqDarZM6vftt6qx5D5CLF7MycSfGsvDTpinah9l/orange-bottle.jpg", "prompt": "a bottle in the mountains", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-16T00:53:46.201526Z", "created_at": "2023-09-16T00:53:26.739110Z", "data_removed": false, "error": null, "id": "pe624olbamtdau44k5xftmdeya", "input": { "seed": 43137, "image": "https://replicate.delivery/pbxt/JXEDRJyInkqDarZM6vftt6qx5D5CLF7MycSfGsvDTpinah9l/orange-bottle.jpg", "prompt": "a bottle in the mountains", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }, "logs": "Using seed: 43137\nProduct img W:409, H:612\nScale factor: 1.0\nFinal img W: 1024, H:1024\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.40it/s]\n 5%|▌ | 2/40 [00:00<00:11, 3.38it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.38it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.37it/s]\n 12%|█▎ | 5/40 [00:01<00:10, 3.37it/s]\n 15%|█▌ | 6/40 [00:01<00:10, 3.37it/s]\n 18%|█▊ | 7/40 [00:02<00:09, 3.37it/s]\n 20%|██ | 8/40 [00:02<00:09, 3.36it/s]\n 22%|██▎ | 9/40 [00:02<00:09, 3.36it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.36it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.36it/s]\n 30%|███ | 12/40 [00:03<00:08, 3.36it/s]\n 32%|███▎ | 13/40 [00:03<00:08, 3.36it/s]\n 35%|███▌ | 14/40 [00:04<00:07, 3.36it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.36it/s]\n 40%|████ | 16/40 [00:04<00:07, 3.37it/s]\n 42%|████▎ | 17/40 [00:05<00:06, 3.37it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.38it/s]\n 48%|████▊ | 19/40 [00:05<00:06, 3.38it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.38it/s]\n 52%|█████▎ | 21/40 [00:06<00:05, 3.38it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.38it/s]\n 57%|█████▊ | 23/40 [00:06<00:05, 3.38it/s]\n 60%|██████ | 24/40 [00:07<00:04, 3.38it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.38it/s]\n 65%|██████▌ | 26/40 [00:07<00:04, 3.38it/s]\n 68%|██████▊ | 27/40 [00:08<00:03, 3.38it/s]\n 70%|███████ | 28/40 [00:08<00:03, 3.38it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.38it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.38it/s]\n 78%|███████▊ | 31/40 [00:09<00:02, 3.38it/s]\n 80%|████████ | 32/40 [00:09<00:02, 3.38it/s]\n 82%|████████▎ | 33/40 [00:09<00:02, 3.38it/s]\n 85%|████████▌ | 34/40 [00:10<00:01, 3.38it/s]\n 88%|████████▊ | 35/40 [00:10<00:01, 3.38it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.38it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.38it/s]\n 95%|█████████▌| 38/40 [00:11<00:00, 3.38it/s]\n 98%|█████████▊| 39/40 [00:11<00:00, 3.38it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.38it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.37it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.33it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.33it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.33it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.31it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.31it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.32it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.32it/s]", "metrics": { "predict_time": 19.487974, "total_time": 19.462416 }, "output": "https://pbxt.replicate.delivery/SkspieXVUegD90PCxwd3pFrSP01mnYZdvLMn304sr75Z6qkRA/7-out.png", "started_at": "2023-09-16T00:53:26.713552Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pe624olbamtdau44k5xftmdeya", "cancel": "https://api.replicate.com/v1/predictions/pe624olbamtdau44k5xftmdeya/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 43137 Product img W:409, H:612 Scale factor: 1.0 Final img W: 1024, H:1024 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.40it/s] 5%|▌ | 2/40 [00:00<00:11, 3.38it/s] 8%|▊ | 3/40 [00:00<00:10, 3.38it/s] 10%|█ | 4/40 [00:01<00:10, 3.37it/s] 12%|█▎ | 5/40 [00:01<00:10, 3.37it/s] 15%|█▌ | 6/40 [00:01<00:10, 3.37it/s] 18%|█▊ | 7/40 [00:02<00:09, 3.37it/s] 20%|██ | 8/40 [00:02<00:09, 3.36it/s] 22%|██▎ | 9/40 [00:02<00:09, 3.36it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.36it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.36it/s] 30%|███ | 12/40 [00:03<00:08, 3.36it/s] 32%|███▎ | 13/40 [00:03<00:08, 3.36it/s] 35%|███▌ | 14/40 [00:04<00:07, 3.36it/s] 38%|███▊ | 15/40 [00:04<00:07, 3.36it/s] 40%|████ | 16/40 [00:04<00:07, 3.37it/s] 42%|████▎ | 17/40 [00:05<00:06, 3.37it/s] 45%|████▌ | 18/40 [00:05<00:06, 3.38it/s] 48%|████▊ | 19/40 [00:05<00:06, 3.38it/s] 50%|█████ | 20/40 [00:05<00:05, 3.38it/s] 52%|█████▎ | 21/40 [00:06<00:05, 3.38it/s] 55%|█████▌ | 22/40 [00:06<00:05, 3.38it/s] 57%|█████▊ | 23/40 [00:06<00:05, 3.38it/s] 60%|██████ | 24/40 [00:07<00:04, 3.38it/s] 62%|██████▎ | 25/40 [00:07<00:04, 3.38it/s] 65%|██████▌ | 26/40 [00:07<00:04, 3.38it/s] 68%|██████▊ | 27/40 [00:08<00:03, 3.38it/s] 70%|███████ | 28/40 [00:08<00:03, 3.38it/s] 72%|███████▎ | 29/40 [00:08<00:03, 3.38it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.38it/s] 78%|███████▊ | 31/40 [00:09<00:02, 3.38it/s] 80%|████████ | 32/40 [00:09<00:02, 3.38it/s] 82%|████████▎ | 33/40 [00:09<00:02, 3.38it/s] 85%|████████▌ | 34/40 [00:10<00:01, 3.38it/s] 88%|████████▊ | 35/40 [00:10<00:01, 3.38it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.38it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.38it/s] 95%|█████████▌| 38/40 [00:11<00:00, 3.38it/s] 98%|█████████▊| 39/40 [00:11<00:00, 3.38it/s] 100%|██████████| 40/40 [00:11<00:00, 3.38it/s] 100%|██████████| 40/40 [00:11<00:00, 3.37it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.33it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.33it/s] 50%|█████ | 3/6 [00:00<00:00, 4.33it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.31it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.31it/s] 100%|██████████| 6/6 [00:01<00:00, 4.32it/s] 100%|██████████| 6/6 [00:01<00:00, 4.32it/s]Prediction
catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359IDfgfiawlbpgndblvma6rq2yqw4uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- a bottle in the snow
- img_size
- 1024, 1024
- apply_img
- scheduler
- K_EULER
- product_fill
- Original
- guidance_scale
- 7.5
- condition_scale
- 0.7
- negative_prompt
- num_refine_steps
- 10
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JXEH6mLymvT2H0UOiIBEezYI3TKcCKhtFCUAiAGV6uJvUxiG/orange-bottle.jpg", "prompt": "a bottle in the snow", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 10, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { image: "https://replicate.delivery/pbxt/JXEH6mLymvT2H0UOiIBEezYI3TKcCKhtFCUAiAGV6uJvUxiG/orange-bottle.jpg", prompt: "a bottle in the snow", img_size: "1024, 1024", apply_img: false, scheduler: "K_EULER", product_fill: "Original", guidance_scale: 7.5, condition_scale: 0.7, negative_prompt: "", num_refine_steps: 10, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "image": "https://replicate.delivery/pbxt/JXEH6mLymvT2H0UOiIBEezYI3TKcCKhtFCUAiAGV6uJvUxiG/orange-bottle.jpg", "prompt": "a bottle in the snow", "img_size": "1024, 1024", "apply_img": False, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 10, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "image": "https://replicate.delivery/pbxt/JXEH6mLymvT2H0UOiIBEezYI3TKcCKhtFCUAiAGV6uJvUxiG/orange-bottle.jpg", "prompt": "a bottle in the snow", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 10, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-16T00:57:33.009193Z", "created_at": "2023-09-16T00:57:16.268381Z", "data_removed": false, "error": null, "id": "fgfiawlbpgndblvma6rq2yqw4u", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JXEH6mLymvT2H0UOiIBEezYI3TKcCKhtFCUAiAGV6uJvUxiG/orange-bottle.jpg", "prompt": "a bottle in the snow", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 10, "num_inference_steps": 40 }, "logs": "Using seed: 32097\nProduct img W:409, H:612\nScale factor: 1.0\nFinal img W: 1024, H:1024\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.39it/s]\n 5%|▌ | 2/40 [00:00<00:11, 3.39it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.40it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.40it/s]\n 12%|█▎ | 5/40 [00:01<00:10, 3.39it/s]\n 15%|█▌ | 6/40 [00:01<00:10, 3.39it/s]\n 18%|█▊ | 7/40 [00:02<00:09, 3.39it/s]\n 20%|██ | 8/40 [00:02<00:09, 3.39it/s]\n 22%|██▎ | 9/40 [00:02<00:09, 3.39it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.39it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.39it/s]\n 30%|███ | 12/40 [00:03<00:08, 3.39it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.39it/s]\n 35%|███▌ | 14/40 [00:04<00:07, 3.39it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.38it/s]\n 40%|████ | 16/40 [00:04<00:07, 3.38it/s]\n 42%|████▎ | 17/40 [00:05<00:06, 3.38it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.38it/s]\n 48%|████▊ | 19/40 [00:05<00:06, 3.38it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.38it/s]\n 52%|█████▎ | 21/40 [00:06<00:05, 3.38it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.38it/s]\n 57%|█████▊ | 23/40 [00:06<00:05, 3.38it/s]\n 60%|██████ | 24/40 [00:07<00:04, 3.38it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.38it/s]\n 65%|██████▌ | 26/40 [00:07<00:04, 3.37it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.38it/s]\n 70%|███████ | 28/40 [00:08<00:03, 3.38it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.38it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.38it/s]\n 78%|███████▊ | 31/40 [00:09<00:02, 3.37it/s]\n 80%|████████ | 32/40 [00:09<00:02, 3.37it/s]\n 82%|████████▎ | 33/40 [00:09<00:02, 3.37it/s]\n 85%|████████▌ | 34/40 [00:10<00:01, 3.37it/s]\n 88%|████████▊ | 35/40 [00:10<00:01, 3.37it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.37it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.37it/s]\n 95%|█████████▌| 38/40 [00:11<00:00, 3.37it/s]\n 98%|█████████▊| 39/40 [00:11<00:00, 3.37it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.37it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.38it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.34it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.32it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.32it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.32it/s]", "metrics": { "predict_time": 16.75474, "total_time": 16.740812 }, "output": "https://pbxt.replicate.delivery/uEqLWiVkwqowDp4PMlisem5XhdcCApKeUdERNGenrJN47VJjA/7-out.png", "started_at": "2023-09-16T00:57:16.254453Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fgfiawlbpgndblvma6rq2yqw4u", "cancel": "https://api.replicate.com/v1/predictions/fgfiawlbpgndblvma6rq2yqw4u/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 32097 Product img W:409, H:612 Scale factor: 1.0 Final img W: 1024, H:1024 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.39it/s] 5%|▌ | 2/40 [00:00<00:11, 3.39it/s] 8%|▊ | 3/40 [00:00<00:10, 3.40it/s] 10%|█ | 4/40 [00:01<00:10, 3.40it/s] 12%|█▎ | 5/40 [00:01<00:10, 3.39it/s] 15%|█▌ | 6/40 [00:01<00:10, 3.39it/s] 18%|█▊ | 7/40 [00:02<00:09, 3.39it/s] 20%|██ | 8/40 [00:02<00:09, 3.39it/s] 22%|██▎ | 9/40 [00:02<00:09, 3.39it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.39it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.39it/s] 30%|███ | 12/40 [00:03<00:08, 3.39it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.39it/s] 35%|███▌ | 14/40 [00:04<00:07, 3.39it/s] 38%|███▊ | 15/40 [00:04<00:07, 3.38it/s] 40%|████ | 16/40 [00:04<00:07, 3.38it/s] 42%|████▎ | 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catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359IDrg5kftdboeqcx3qoupqnl4j2aaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 43274
- image
- prompt
- a bottle in the forest
- img_size
- 1024, 1024
- apply_img
- scheduler
- K_EULER
- product_fill
- Original
- guidance_scale
- 7.5
- condition_scale
- 0.7
- negative_prompt
- num_refine_steps
- 20
- num_inference_steps
- 40
{ "seed": 43274, "image": "https://replicate.delivery/pbxt/JXEIL2FvR21qwTLpDicA2Lvi7rmwrLpU1ehsUZulQNCF98ya/orange-bottle.jpg", "prompt": "a bottle in the forest", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", { input: { seed: 43274, image: "https://replicate.delivery/pbxt/JXEIL2FvR21qwTLpDicA2Lvi7rmwrLpU1ehsUZulQNCF98ya/orange-bottle.jpg", prompt: "a bottle in the forest", img_size: "1024, 1024", apply_img: false, scheduler: "K_EULER", product_fill: "Original", guidance_scale: 7.5, condition_scale: 0.7, negative_prompt: "", num_refine_steps: 20, num_inference_steps: 40 } } ); // 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 catacolabs/sdxl-ad-inpaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", input={ "seed": 43274, "image": "https://replicate.delivery/pbxt/JXEIL2FvR21qwTLpDicA2Lvi7rmwrLpU1ehsUZulQNCF98ya/orange-bottle.jpg", "prompt": "a bottle in the forest", "img_size": "1024, 1024", "apply_img": False, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run catacolabs/sdxl-ad-inpaint 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": "catacolabs/sdxl-ad-inpaint:9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359", "input": { "seed": 43274, "image": "https://replicate.delivery/pbxt/JXEIL2FvR21qwTLpDicA2Lvi7rmwrLpU1ehsUZulQNCF98ya/orange-bottle.jpg", "prompt": "a bottle in the forest", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-16T00:58:51.308585Z", "created_at": "2023-09-16T00:58:33.652908Z", "data_removed": false, "error": null, "id": "rg5kftdboeqcx3qoupqnl4j2aa", "input": { "seed": 43274, "image": "https://replicate.delivery/pbxt/JXEIL2FvR21qwTLpDicA2Lvi7rmwrLpU1ehsUZulQNCF98ya/orange-bottle.jpg", "prompt": "a bottle in the forest", "img_size": "1024, 1024", "apply_img": false, "scheduler": "K_EULER", "product_fill": "Original", "guidance_scale": 7.5, "condition_scale": 0.7, "negative_prompt": "", "num_refine_steps": 20, "num_inference_steps": 40 }, "logs": "Using seed: 43274\nProduct img W:409, H:612\nScale factor: 1.0\nFinal img W: 1024, H:1024\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:11, 3.39it/s]\n 5%|▌ | 2/40 [00:00<00:11, 3.39it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.39it/s]\n 10%|█ | 4/40 [00:01<00:10, 3.39it/s]\n 12%|█▎ | 5/40 [00:01<00:10, 3.39it/s]\n 15%|█▌ | 6/40 [00:01<00:10, 3.39it/s]\n 18%|█▊ | 7/40 [00:02<00:09, 3.40it/s]\n 20%|██ | 8/40 [00:02<00:09, 3.39it/s]\n 22%|██▎ | 9/40 [00:02<00:09, 3.39it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.39it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.39it/s]\n 30%|███ | 12/40 [00:03<00:08, 3.39it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.39it/s]\n 35%|███▌ | 14/40 [00:04<00:07, 3.39it/s]\n 38%|███▊ | 15/40 [00:04<00:07, 3.39it/s]\n 40%|████ | 16/40 [00:04<00:07, 3.39it/s]\n 42%|████▎ | 17/40 [00:05<00:06, 3.39it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.38it/s]\n 48%|████▊ | 19/40 [00:05<00:06, 3.38it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.38it/s]\n 52%|█████▎ | 21/40 [00:06<00:05, 3.38it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.38it/s]\n 57%|█████▊ | 23/40 [00:06<00:05, 3.38it/s]\n 60%|██████ | 24/40 [00:07<00:04, 3.38it/s]\n 62%|██████▎ | 25/40 [00:07<00:04, 3.38it/s]\n 65%|██████▌ | 26/40 [00:07<00:04, 3.37it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.37it/s]\n 70%|███████ | 28/40 [00:08<00:03, 3.37it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.37it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.37it/s]\n 78%|███████▊ | 31/40 [00:09<00:02, 3.37it/s]\n 80%|████████ | 32/40 [00:09<00:02, 3.37it/s]\n 82%|████████▎ | 33/40 [00:09<00:02, 3.37it/s]\n 85%|████████▌ | 34/40 [00:10<00:01, 3.37it/s]\n 88%|████████▊ | 35/40 [00:10<00:01, 3.36it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.37it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.37it/s]\n 95%|█████████▌| 38/40 [00:11<00:00, 3.37it/s]\n 98%|█████████▊| 39/40 [00:11<00:00, 3.37it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.37it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.38it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.35it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.33it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.33it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.31it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.31it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.31it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.32it/s]", "metrics": { "predict_time": 17.651395, "total_time": 17.655677 }, "output": "https://pbxt.replicate.delivery/LF3Dhhgdeo0oSCVCvdJvP3fCj2la5WpIVNDbyF8uFMLKfVJjA/7-out.png", "started_at": "2023-09-16T00:58:33.657190Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rg5kftdboeqcx3qoupqnl4j2aa", "cancel": "https://api.replicate.com/v1/predictions/rg5kftdboeqcx3qoupqnl4j2aa/cancel" }, "version": "9c0cb4c579c54432431d96c70924afcca18983de872e8a221777fb1416253359" }Generated inUsing seed: 43274 Product img W:409, H:612 Scale factor: 1.0 Final img W: 1024, H:1024 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.39it/s] 5%|▌ | 2/40 [00:00<00:11, 3.39it/s] 8%|▊ | 3/40 [00:00<00:10, 3.39it/s] 10%|█ | 4/40 [00:01<00:10, 3.39it/s] 12%|█▎ | 5/40 [00:01<00:10, 3.39it/s] 15%|█▌ | 6/40 [00:01<00:10, 3.39it/s] 18%|█▊ | 7/40 [00:02<00:09, 3.40it/s] 20%|██ | 8/40 [00:02<00:09, 3.39it/s] 22%|██▎ | 9/40 [00:02<00:09, 3.39it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.39it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.39it/s] 30%|███ | 12/40 [00:03<00:08, 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3.36it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.37it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.37it/s] 95%|█████████▌| 38/40 [00:11<00:00, 3.37it/s] 98%|█████████▊| 39/40 [00:11<00:00, 3.37it/s] 100%|██████████| 40/40 [00:11<00:00, 3.37it/s] 100%|██████████| 40/40 [00:11<00:00, 3.38it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.35it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.33it/s] 50%|█████ | 3/6 [00:00<00:00, 4.33it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.31it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.31it/s] 100%|██████████| 6/6 [00:01<00:00, 4.31it/s] 100%|██████████| 6/6 [00:01<00:00, 4.32it/s]
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