chenxwh / ominicontrol-spatial
Minimal and Universal Control for Diffusion Transformer - demo for Spatially aligned control
Prediction
chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917IDqqcsy4zmdsrme0cm3wbvt2x1kcStatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- image
- model
- coloring
- prompt
- A breathtaking scene of nature featuring a serene blue lake surrounded by vibrant maple trees in fiery shades of red, orange, and gold, creating a stunning autumnal contrast
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/MF6YY6T8ZyUrLQfFulpCaHfF9ey8IKuf2Hz0MwP4HPQCdalG/color.jpeg", "model": "coloring", "prompt": "A breathtaking scene of nature featuring a serene blue lake surrounded by vibrant maple trees in fiery shades of red, orange, and gold, creating a stunning autumnal contrast", "guidance_scale": 7.5, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run chenxwh/ominicontrol-spatial using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", { input: { image: "https://replicate.delivery/pbxt/MF6YY6T8ZyUrLQfFulpCaHfF9ey8IKuf2Hz0MwP4HPQCdalG/color.jpeg", model: "coloring", prompt: "A breathtaking scene of nature featuring a serene blue lake surrounded by vibrant maple trees in fiery shades of red, orange, and gold, creating a stunning autumnal contrast", guidance_scale: 7.5, num_inference_steps: 50 } } ); // 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 chenxwh/ominicontrol-spatial using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", input={ "image": "https://replicate.delivery/pbxt/MF6YY6T8ZyUrLQfFulpCaHfF9ey8IKuf2Hz0MwP4HPQCdalG/color.jpeg", "model": "coloring", "prompt": "A breathtaking scene of nature featuring a serene blue lake surrounded by vibrant maple trees in fiery shades of red, orange, and gold, creating a stunning autumnal contrast", "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run chenxwh/ominicontrol-spatial 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": "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", "input": { "image": "https://replicate.delivery/pbxt/MF6YY6T8ZyUrLQfFulpCaHfF9ey8IKuf2Hz0MwP4HPQCdalG/color.jpeg", "model": "coloring", "prompt": "A breathtaking scene of nature featuring a serene blue lake surrounded by vibrant maple trees in fiery shades of red, orange, and gold, creating a stunning autumnal contrast", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-31T23:41:07.605072Z", "created_at": "2024-12-31T23:40:51.182000Z", "data_removed": false, "error": null, "id": "qqcsy4zmdsrme0cm3wbvt2x1kc", "input": { "image": "https://replicate.delivery/pbxt/MF6YY6T8ZyUrLQfFulpCaHfF9ey8IKuf2Hz0MwP4HPQCdalG/color.jpeg", "model": "coloring", "prompt": "A breathtaking scene of nature featuring a serene blue lake surrounded by vibrant maple trees in fiery shades of red, orange, and gold, creating a stunning autumnal contrast", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 53351\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:15, 3.16it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.35it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.24it/s]\n 8%|▊ | 4/50 [00:01<00:14, 3.19it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.16it/s]\n 12%|█▏ | 6/50 [00:01<00:13, 3.14it/s]\n 14%|█▍ | 7/50 [00:02<00:13, 3.14it/s]\n 16%|█▌ | 8/50 [00:02<00:13, 3.13it/s]\n 18%|█▊ | 9/50 [00:02<00:13, 3.13it/s]\n 20%|██ | 10/50 [00:03<00:12, 3.12it/s]\n 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3.10it/s]\n 68%|██████▊ | 34/50 [00:10<00:05, 3.10it/s]\n 70%|███████ | 35/50 [00:11<00:04, 3.11it/s]\n 72%|███████▏ | 36/50 [00:11<00:04, 3.11it/s]\n 74%|███████▍ | 37/50 [00:11<00:04, 3.10it/s]\n 76%|███████▌ | 38/50 [00:12<00:03, 3.11it/s]\n 78%|███████▊ | 39/50 [00:12<00:03, 3.11it/s]\n 80%|████████ | 40/50 [00:12<00:03, 3.10it/s]\n 82%|████████▏ | 41/50 [00:13<00:02, 3.10it/s]\n 84%|████████▍ | 42/50 [00:13<00:02, 3.10it/s]\n 86%|████████▌ | 43/50 [00:13<00:02, 3.10it/s]\n 88%|████████▊ | 44/50 [00:14<00:01, 3.10it/s]\n 90%|█████████ | 45/50 [00:14<00:01, 3.10it/s]\n 92%|█████████▏| 46/50 [00:14<00:01, 3.10it/s]\n 94%|█████████▍| 47/50 [00:15<00:00, 3.10it/s]\n 96%|█████████▌| 48/50 [00:15<00:00, 3.10it/s]\n 98%|█████████▊| 49/50 [00:15<00:00, 3.10it/s]\n100%|██████████| 50/50 [00:16<00:00, 3.10it/s]\n100%|██████████| 50/50 [00:16<00:00, 3.12it/s]", "metrics": { "predict_time": 16.415232333, "total_time": 16.423072 }, "output": "https://replicate.delivery/xezq/3PW4Te1fW5ihKkmtcHfjMDjdCVuE10PubVJ1CPoFrJqnYGBoA/out.png", "started_at": "2024-12-31T23:40:51.189840Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-oyrmfhze2kzlqkz47qejwygn2yksczjhzog7ltfxpjnsomkf2ira", "get": "https://api.replicate.com/v1/predictions/qqcsy4zmdsrme0cm3wbvt2x1kc", "cancel": "https://api.replicate.com/v1/predictions/qqcsy4zmdsrme0cm3wbvt2x1kc/cancel" }, "version": "1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917" }Generated inUsing seed: 53351 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:15, 3.16it/s] 4%|▍ | 2/50 [00:00<00:14, 3.35it/s] 6%|▌ | 3/50 [00:00<00:14, 3.24it/s] 8%|▊ | 4/50 [00:01<00:14, 3.19it/s] 10%|█ | 5/50 [00:01<00:14, 3.16it/s] 12%|█▏ | 6/50 [00:01<00:13, 3.14it/s] 14%|█▍ | 7/50 [00:02<00:13, 3.14it/s] 16%|█▌ | 8/50 [00:02<00:13, 3.13it/s] 18%|█▊ | 9/50 [00:02<00:13, 3.13it/s] 20%|██ | 10/50 [00:03<00:12, 3.12it/s] 22%|██▏ | 11/50 [00:03<00:12, 3.12it/s] 24%|██▍ | 12/50 [00:03<00:12, 3.12it/s] 26%|██▌ | 13/50 [00:04<00:11, 3.12it/s] 28%|██▊ | 14/50 [00:04<00:11, 3.12it/s] 30%|███ | 15/50 [00:04<00:11, 3.11it/s] 32%|███▏ | 16/50 [00:05<00:10, 3.11it/s] 34%|███▍ | 17/50 [00:05<00:10, 3.11it/s] 36%|███▌ | 18/50 [00:05<00:10, 3.11it/s] 38%|███▊ | 19/50 [00:06<00:09, 3.11it/s] 40%|████ | 20/50 [00:06<00:09, 3.11it/s] 42%|████▏ | 21/50 [00:06<00:09, 3.11it/s] 44%|████▍ | 22/50 [00:07<00:08, 3.11it/s] 46%|████▌ | 23/50 [00:07<00:08, 3.11it/s] 48%|████▊ | 24/50 [00:07<00:08, 3.11it/s] 50%|█████ | 25/50 [00:07<00:08, 3.11it/s] 52%|█████▏ | 26/50 [00:08<00:07, 3.11it/s] 54%|█████▍ | 27/50 [00:08<00:07, 3.11it/s] 56%|█████▌ | 28/50 [00:08<00:07, 3.11it/s] 58%|█████▊ | 29/50 [00:09<00:06, 3.11it/s] 60%|██████ | 30/50 [00:09<00:06, 3.11it/s] 62%|██████▏ | 31/50 [00:09<00:06, 3.11it/s] 64%|██████▍ | 32/50 [00:10<00:05, 3.11it/s] 66%|██████▌ | 33/50 [00:10<00:05, 3.10it/s] 68%|██████▊ | 34/50 [00:10<00:05, 3.10it/s] 70%|███████ | 35/50 [00:11<00:04, 3.11it/s] 72%|███████▏ | 36/50 [00:11<00:04, 3.11it/s] 74%|███████▍ | 37/50 [00:11<00:04, 3.10it/s] 76%|███████▌ | 38/50 [00:12<00:03, 3.11it/s] 78%|███████▊ | 39/50 [00:12<00:03, 3.11it/s] 80%|████████ | 40/50 [00:12<00:03, 3.10it/s] 82%|████████▏ | 41/50 [00:13<00:02, 3.10it/s] 84%|████████▍ | 42/50 [00:13<00:02, 3.10it/s] 86%|████████▌ | 43/50 [00:13<00:02, 3.10it/s] 88%|████████▊ | 44/50 [00:14<00:01, 3.10it/s] 90%|█████████ | 45/50 [00:14<00:01, 3.10it/s] 92%|█████████▏| 46/50 [00:14<00:01, 3.10it/s] 94%|█████████▍| 47/50 [00:15<00:00, 3.10it/s] 96%|█████████▌| 48/50 [00:15<00:00, 3.10it/s] 98%|█████████▊| 49/50 [00:15<00:00, 3.10it/s] 100%|██████████| 50/50 [00:16<00:00, 3.10it/s] 100%|██████████| 50/50 [00:16<00:00, 3.12it/s]Prediction
chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917ID8akda4e8v5rma0cm3wfbn9fd3cStatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- image
- model
- fill
- prompt
- The Mona Lisa is wearing a white VR headset with "OMINI" written on it.
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/MF6fbaFCPPXLDUYYRQZhP0S93K3HQzzZDduHwxXQNAWiOgdb/masked.png", "model": "fill", "prompt": "The Mona Lisa is wearing a white VR headset with \"OMINI\" written on it.", "guidance_scale": 7.5, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run chenxwh/ominicontrol-spatial using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", { input: { image: "https://replicate.delivery/pbxt/MF6fbaFCPPXLDUYYRQZhP0S93K3HQzzZDduHwxXQNAWiOgdb/masked.png", model: "fill", prompt: "The Mona Lisa is wearing a white VR headset with \"OMINI\" written on it.", guidance_scale: 7.5, num_inference_steps: 50 } } ); // 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 chenxwh/ominicontrol-spatial using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", input={ "image": "https://replicate.delivery/pbxt/MF6fbaFCPPXLDUYYRQZhP0S93K3HQzzZDduHwxXQNAWiOgdb/masked.png", "model": "fill", "prompt": "The Mona Lisa is wearing a white VR headset with \"OMINI\" written on it.", "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run chenxwh/ominicontrol-spatial 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": "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", "input": { "image": "https://replicate.delivery/pbxt/MF6fbaFCPPXLDUYYRQZhP0S93K3HQzzZDduHwxXQNAWiOgdb/masked.png", "model": "fill", "prompt": "The Mona Lisa is wearing a white VR headset with \\"OMINI\\" written on it.", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-31T23:48:35.195807Z", "created_at": "2024-12-31T23:48:18.777000Z", "data_removed": false, "error": null, "id": "8akda4e8v5rma0cm3wfbn9fd3c", "input": { "image": "https://replicate.delivery/pbxt/MF6fbaFCPPXLDUYYRQZhP0S93K3HQzzZDduHwxXQNAWiOgdb/masked.png", "model": "fill", "prompt": "The Mona Lisa is wearing a white VR headset with \"OMINI\" written on it.", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 32895\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:15, 3.19it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.39it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.27it/s]\n 8%|▊ | 4/50 [00:01<00:14, 3.22it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.19it/s]\n 12%|█▏ | 6/50 [00:01<00:13, 3.18it/s]\n 14%|█▍ | 7/50 [00:02<00:13, 3.16it/s]\n 16%|█▌ | 8/50 [00:02<00:13, 3.16it/s]\n 18%|█▊ | 9/50 [00:02<00:13, 3.15it/s]\n 20%|██ | 10/50 [00:03<00:12, 3.14it/s]\n 22%|██▏ | 11/50 [00:03<00:12, 3.14it/s]\n 24%|██▍ | 12/50 [00:03<00:12, 3.14it/s]\n 26%|██▌ | 13/50 [00:04<00:11, 3.13it/s]\n 28%|██▊ | 14/50 [00:04<00:11, 3.13it/s]\n 30%|███ | 15/50 [00:04<00:11, 3.12it/s]\n 32%|███▏ | 16/50 [00:05<00:10, 3.12it/s]\n 34%|███▍ | 17/50 [00:05<00:10, 3.13it/s]\n 36%|███▌ | 18/50 [00:05<00:10, 3.12it/s]\n 38%|███▊ | 19/50 [00:06<00:09, 3.11it/s]\n 40%|████ | 20/50 [00:06<00:09, 3.11it/s]\n 42%|████▏ | 21/50 [00:06<00:09, 3.11it/s]\n 44%|████▍ | 22/50 [00:06<00:09, 3.10it/s]\n 46%|████▌ | 23/50 [00:07<00:08, 3.10it/s]\n 48%|████▊ | 24/50 [00:07<00:08, 3.11it/s]\n 50%|█████ | 25/50 [00:07<00:08, 3.10it/s]\n 52%|█████▏ | 26/50 [00:08<00:07, 3.11it/s]\n 54%|█████▍ | 27/50 [00:08<00:07, 3.10it/s]\n 56%|█████▌ | 28/50 [00:08<00:07, 3.10it/s]\n 58%|█████▊ | 29/50 [00:09<00:06, 3.10it/s]\n 60%|██████ | 30/50 [00:09<00:06, 3.10it/s]\n 62%|██████▏ | 31/50 [00:09<00:06, 3.10it/s]\n 64%|██████▍ | 32/50 [00:10<00:05, 3.10it/s]\n 66%|██████▌ | 33/50 [00:10<00:05, 3.10it/s]\n 68%|██████▊ | 34/50 [00:10<00:05, 3.11it/s]\n 70%|███████ | 35/50 [00:11<00:04, 3.11it/s]\n 72%|███████▏ | 36/50 [00:11<00:04, 3.11it/s]\n 74%|███████▍ | 37/50 [00:11<00:04, 3.11it/s]\n 76%|███████▌ | 38/50 [00:12<00:03, 3.10it/s]\n 78%|███████▊ | 39/50 [00:12<00:03, 3.10it/s]\n 80%|████████ | 40/50 [00:12<00:03, 3.10it/s]\n 82%|████████▏ | 41/50 [00:13<00:02, 3.10it/s]\n 84%|████████▍ | 42/50 [00:13<00:02, 3.09it/s]\n 86%|████████▌ | 43/50 [00:13<00:02, 3.10it/s]\n 88%|████████▊ | 44/50 [00:14<00:01, 3.10it/s]\n 90%|█████████ | 45/50 [00:14<00:01, 3.10it/s]\n 92%|█████████▏| 46/50 [00:14<00:01, 3.10it/s]\n 94%|█████████▍| 47/50 [00:15<00:00, 3.10it/s]\n 96%|█████████▌| 48/50 [00:15<00:00, 3.09it/s]\n 98%|█████████▊| 49/50 [00:15<00:00, 3.09it/s]\n100%|██████████| 50/50 [00:16<00:00, 3.09it/s]\n100%|██████████| 50/50 [00:16<00:00, 3.12it/s]", "metrics": { "predict_time": 16.411346081, "total_time": 16.418807 }, "output": "https://replicate.delivery/xezq/N8TXdUfGht2WNqNAufhyuZMpep0PhRqxHgCk04AnISGmmGBoA/out.png", "started_at": "2024-12-31T23:48:18.784461Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-mzad2jq7z3am3jbqlndv3l4clchmcqmy724qqtoelndmobl7u2cq", "get": "https://api.replicate.com/v1/predictions/8akda4e8v5rma0cm3wfbn9fd3c", "cancel": "https://api.replicate.com/v1/predictions/8akda4e8v5rma0cm3wfbn9fd3c/cancel" }, "version": "1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917" }Generated inUsing seed: 32895 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:15, 3.19it/s] 4%|▍ | 2/50 [00:00<00:14, 3.39it/s] 6%|▌ | 3/50 [00:00<00:14, 3.27it/s] 8%|▊ | 4/50 [00:01<00:14, 3.22it/s] 10%|█ | 5/50 [00:01<00:14, 3.19it/s] 12%|█▏ | 6/50 [00:01<00:13, 3.18it/s] 14%|█▍ | 7/50 [00:02<00:13, 3.16it/s] 16%|█▌ | 8/50 [00:02<00:13, 3.16it/s] 18%|█▊ | 9/50 [00:02<00:13, 3.15it/s] 20%|██ | 10/50 [00:03<00:12, 3.14it/s] 22%|██▏ | 11/50 [00:03<00:12, 3.14it/s] 24%|██▍ | 12/50 [00:03<00:12, 3.14it/s] 26%|██▌ | 13/50 [00:04<00:11, 3.13it/s] 28%|██▊ | 14/50 [00:04<00:11, 3.13it/s] 30%|███ | 15/50 [00:04<00:11, 3.12it/s] 32%|███▏ | 16/50 [00:05<00:10, 3.12it/s] 34%|███▍ | 17/50 [00:05<00:10, 3.13it/s] 36%|███▌ | 18/50 [00:05<00:10, 3.12it/s] 38%|███▊ | 19/50 [00:06<00:09, 3.11it/s] 40%|████ | 20/50 [00:06<00:09, 3.11it/s] 42%|████▏ | 21/50 [00:06<00:09, 3.11it/s] 44%|████▍ | 22/50 [00:06<00:09, 3.10it/s] 46%|████▌ | 23/50 [00:07<00:08, 3.10it/s] 48%|████▊ | 24/50 [00:07<00:08, 3.11it/s] 50%|█████ | 25/50 [00:07<00:08, 3.10it/s] 52%|█████▏ | 26/50 [00:08<00:07, 3.11it/s] 54%|█████▍ | 27/50 [00:08<00:07, 3.10it/s] 56%|█████▌ | 28/50 [00:08<00:07, 3.10it/s] 58%|█████▊ | 29/50 [00:09<00:06, 3.10it/s] 60%|██████ | 30/50 [00:09<00:06, 3.10it/s] 62%|██████▏ | 31/50 [00:09<00:06, 3.10it/s] 64%|██████▍ | 32/50 [00:10<00:05, 3.10it/s] 66%|██████▌ | 33/50 [00:10<00:05, 3.10it/s] 68%|██████▊ | 34/50 [00:10<00:05, 3.11it/s] 70%|███████ | 35/50 [00:11<00:04, 3.11it/s] 72%|███████▏ | 36/50 [00:11<00:04, 3.11it/s] 74%|███████▍ | 37/50 [00:11<00:04, 3.11it/s] 76%|███████▌ | 38/50 [00:12<00:03, 3.10it/s] 78%|███████▊ | 39/50 [00:12<00:03, 3.10it/s] 80%|████████ | 40/50 [00:12<00:03, 3.10it/s] 82%|████████▏ | 41/50 [00:13<00:02, 3.10it/s] 84%|████████▍ | 42/50 [00:13<00:02, 3.09it/s] 86%|████████▌ | 43/50 [00:13<00:02, 3.10it/s] 88%|████████▊ | 44/50 [00:14<00:01, 3.10it/s] 90%|█████████ | 45/50 [00:14<00:01, 3.10it/s] 92%|█████████▏| 46/50 [00:14<00:01, 3.10it/s] 94%|█████████▍| 47/50 [00:15<00:00, 3.10it/s] 96%|█████████▌| 48/50 [00:15<00:00, 3.09it/s] 98%|█████████▊| 49/50 [00:15<00:00, 3.09it/s] 100%|██████████| 50/50 [00:16<00:00, 3.09it/s] 100%|██████████| 50/50 [00:16<00:00, 3.12it/s]Prediction
chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917IDd6vh4jpswnrmc0cm3wftbb1sb4StatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- image
- model
- depth
- prompt
- basketball in the net
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/MF6ghOb46cxRkqjNKKiNTcTsIIhQlUyy4LsaoKqokO3bZ5Fr/basket.jpg", "model": "depth", "prompt": "basketball in the net", "guidance_scale": 7.5, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run chenxwh/ominicontrol-spatial using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", { input: { image: "https://replicate.delivery/pbxt/MF6ghOb46cxRkqjNKKiNTcTsIIhQlUyy4LsaoKqokO3bZ5Fr/basket.jpg", model: "depth", prompt: "basketball in the net", guidance_scale: 7.5, num_inference_steps: 50 } } ); // 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 chenxwh/ominicontrol-spatial using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", input={ "image": "https://replicate.delivery/pbxt/MF6ghOb46cxRkqjNKKiNTcTsIIhQlUyy4LsaoKqokO3bZ5Fr/basket.jpg", "model": "depth", "prompt": "basketball in the net", "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run chenxwh/ominicontrol-spatial 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": "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", "input": { "image": "https://replicate.delivery/pbxt/MF6ghOb46cxRkqjNKKiNTcTsIIhQlUyy4LsaoKqokO3bZ5Fr/basket.jpg", "model": "depth", "prompt": "basketball in the net", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-31T23:49:55.285536Z", "created_at": "2024-12-31T23:49:28.677000Z", "data_removed": false, "error": null, "id": "d6vh4jpswnrmc0cm3wftbb1sb4", "input": { "image": "https://replicate.delivery/pbxt/MF6ghOb46cxRkqjNKKiNTcTsIIhQlUyy4LsaoKqokO3bZ5Fr/basket.jpg", "model": "depth", "prompt": "basketball in the net", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 45981\nDevice set to use cuda\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:15, 3.15it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.34it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.22it/s]\n 8%|▊ | 4/50 [00:01<00:14, 3.18it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.15it/s]\n 12%|█▏ | 6/50 [00:01<00:14, 3.13it/s]\n 14%|█▍ | 7/50 [00:02<00:13, 3.12it/s]\n 16%|█▌ | 8/50 [00:02<00:13, 3.12it/s]\n 18%|█▊ | 9/50 [00:02<00:13, 3.11it/s]\n 20%|██ | 10/50 [00:03<00:12, 3.11it/s]\n 22%|██▏ | 11/50 [00:03<00:12, 3.10it/s]\n 24%|██▍ | 12/50 [00:03<00:12, 3.09it/s]\n 26%|██▌ | 13/50 [00:04<00:11, 3.09it/s]\n 28%|██▊ | 14/50 [00:04<00:11, 3.09it/s]\n 30%|███ | 15/50 [00:04<00:11, 3.09it/s]\n 32%|███▏ | 16/50 [00:05<00:11, 3.08it/s]\n 34%|███▍ | 17/50 [00:05<00:10, 3.08it/s]\n 36%|███▌ | 18/50 [00:05<00:10, 3.08it/s]\n 38%|███▊ | 19/50 [00:06<00:10, 3.08it/s]\n 40%|████ | 20/50 [00:06<00:09, 3.09it/s]\n 42%|████▏ | 21/50 [00:06<00:09, 3.08it/s]\n 44%|████▍ | 22/50 [00:07<00:09, 3.09it/s]\n 46%|████▌ | 23/50 [00:07<00:08, 3.09it/s]\n 48%|████▊ | 24/50 [00:07<00:08, 3.08it/s]\n 50%|█████ | 25/50 [00:08<00:08, 3.08it/s]\n 52%|█████▏ | 26/50 [00:08<00:07, 3.08it/s]\n 54%|█████▍ | 27/50 [00:08<00:07, 3.07it/s]\n 56%|█████▌ | 28/50 [00:09<00:07, 3.07it/s]\n 58%|█████▊ | 29/50 [00:09<00:06, 3.07it/s]\n 60%|██████ | 30/50 [00:09<00:06, 3.08it/s]\n 62%|██████▏ | 31/50 [00:10<00:06, 3.07it/s]\n 64%|██████▍ | 32/50 [00:10<00:05, 3.07it/s]\n 66%|██████▌ | 33/50 [00:10<00:05, 3.07it/s]\n 68%|██████▊ | 34/50 [00:10<00:05, 3.08it/s]\n 70%|███████ | 35/50 [00:11<00:04, 3.08it/s]\n 72%|███████▏ | 36/50 [00:11<00:04, 3.07it/s]\n 74%|███████▍ | 37/50 [00:11<00:04, 3.07it/s]\n 76%|███████▌ | 38/50 [00:12<00:03, 3.07it/s]\n 78%|███████▊ | 39/50 [00:12<00:03, 3.07it/s]\n 80%|████████ | 40/50 [00:12<00:03, 3.07it/s]\n 82%|████████▏ | 41/50 [00:13<00:02, 3.07it/s]\n 84%|████████▍ | 42/50 [00:13<00:02, 3.07it/s]\n 86%|████████▌ | 43/50 [00:13<00:02, 3.07it/s]\n 88%|████████▊ | 44/50 [00:14<00:01, 3.07it/s]\n 90%|█████████ | 45/50 [00:14<00:01, 3.07it/s]\n 92%|█████████▏| 46/50 [00:14<00:01, 3.07it/s]\n 94%|█████████▍| 47/50 [00:15<00:00, 3.08it/s]\n 96%|█████████▌| 48/50 [00:15<00:00, 3.08it/s]\n 98%|█████████▊| 49/50 [00:15<00:00, 3.09it/s]\n100%|██████████| 50/50 [00:16<00:00, 3.08it/s]\n100%|██████████| 50/50 [00:16<00:00, 3.09it/s]", "metrics": { "predict_time": 26.600814582, "total_time": 26.608536 }, "output": "https://replicate.delivery/xezq/12wu8VnM5XZTMBooDp4noJ2BsD511MRbvIAgSmE6efMjUjAUA/out.png", "started_at": "2024-12-31T23:49:28.684722Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-ozmzcqqkw2iz6uoua2cyaoh6sgzvw25sx4kbngll7c6gnv4iwwla", "get": "https://api.replicate.com/v1/predictions/d6vh4jpswnrmc0cm3wftbb1sb4", "cancel": "https://api.replicate.com/v1/predictions/d6vh4jpswnrmc0cm3wftbb1sb4/cancel" }, "version": "1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917" }Generated inUsing seed: 45981 Device set to use cuda 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:15, 3.15it/s] 4%|▍ | 2/50 [00:00<00:14, 3.34it/s] 6%|▌ | 3/50 [00:00<00:14, 3.22it/s] 8%|▊ | 4/50 [00:01<00:14, 3.18it/s] 10%|█ | 5/50 [00:01<00:14, 3.15it/s] 12%|█▏ | 6/50 [00:01<00:14, 3.13it/s] 14%|█▍ | 7/50 [00:02<00:13, 3.12it/s] 16%|█▌ | 8/50 [00:02<00:13, 3.12it/s] 18%|█▊ | 9/50 [00:02<00:13, 3.11it/s] 20%|██ | 10/50 [00:03<00:12, 3.11it/s] 22%|██▏ | 11/50 [00:03<00:12, 3.10it/s] 24%|██▍ | 12/50 [00:03<00:12, 3.09it/s] 26%|██▌ | 13/50 [00:04<00:11, 3.09it/s] 28%|██▊ | 14/50 [00:04<00:11, 3.09it/s] 30%|███ | 15/50 [00:04<00:11, 3.09it/s] 32%|███▏ | 16/50 [00:05<00:11, 3.08it/s] 34%|███▍ | 17/50 [00:05<00:10, 3.08it/s] 36%|███▌ | 18/50 [00:05<00:10, 3.08it/s] 38%|███▊ | 19/50 [00:06<00:10, 3.08it/s] 40%|████ | 20/50 [00:06<00:09, 3.09it/s] 42%|████▏ | 21/50 [00:06<00:09, 3.08it/s] 44%|████▍ | 22/50 [00:07<00:09, 3.09it/s] 46%|████▌ | 23/50 [00:07<00:08, 3.09it/s] 48%|████▊ | 24/50 [00:07<00:08, 3.08it/s] 50%|█████ | 25/50 [00:08<00:08, 3.08it/s] 52%|█████▏ | 26/50 [00:08<00:07, 3.08it/s] 54%|█████▍ | 27/50 [00:08<00:07, 3.07it/s] 56%|█████▌ | 28/50 [00:09<00:07, 3.07it/s] 58%|█████▊ | 29/50 [00:09<00:06, 3.07it/s] 60%|██████ | 30/50 [00:09<00:06, 3.08it/s] 62%|██████▏ | 31/50 [00:10<00:06, 3.07it/s] 64%|██████▍ | 32/50 [00:10<00:05, 3.07it/s] 66%|██████▌ | 33/50 [00:10<00:05, 3.07it/s] 68%|██████▊ | 34/50 [00:10<00:05, 3.08it/s] 70%|███████ | 35/50 [00:11<00:04, 3.08it/s] 72%|███████▏ | 36/50 [00:11<00:04, 3.07it/s] 74%|███████▍ | 37/50 [00:11<00:04, 3.07it/s] 76%|███████▌ | 38/50 [00:12<00:03, 3.07it/s] 78%|███████▊ | 39/50 [00:12<00:03, 3.07it/s] 80%|████████ | 40/50 [00:12<00:03, 3.07it/s] 82%|████████▏ | 41/50 [00:13<00:02, 3.07it/s] 84%|████████▍ | 42/50 [00:13<00:02, 3.07it/s] 86%|████████▌ | 43/50 [00:13<00:02, 3.07it/s] 88%|████████▊ | 44/50 [00:14<00:01, 3.07it/s] 90%|█████████ | 45/50 [00:14<00:01, 3.07it/s] 92%|█████████▏| 46/50 [00:14<00:01, 3.07it/s] 94%|█████████▍| 47/50 [00:15<00:00, 3.08it/s] 96%|█████████▌| 48/50 [00:15<00:00, 3.08it/s] 98%|█████████▊| 49/50 [00:15<00:00, 3.09it/s] 100%|██████████| 50/50 [00:16<00:00, 3.08it/s] 100%|██████████| 50/50 [00:16<00:00, 3.09it/s]Prediction
chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917IDwz19xybrthrmc0cm3wkbhyyzh8StatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- image
- model
- canny
- prompt
- a colorful bird singing on a tree branch
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/MF6nZVDiGtG6FlVSSiH1o9LGauIc8q40BtfrlN1KE1J3oADt/bird.png", "model": "canny", "prompt": "a colorful bird singing on a tree branch", "guidance_scale": 7.5, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run chenxwh/ominicontrol-spatial using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", { input: { image: "https://replicate.delivery/pbxt/MF6nZVDiGtG6FlVSSiH1o9LGauIc8q40BtfrlN1KE1J3oADt/bird.png", model: "canny", prompt: "a colorful bird singing on a tree branch", guidance_scale: 7.5, num_inference_steps: 50 } } ); // 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 chenxwh/ominicontrol-spatial using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", input={ "image": "https://replicate.delivery/pbxt/MF6nZVDiGtG6FlVSSiH1o9LGauIc8q40BtfrlN1KE1J3oADt/bird.png", "model": "canny", "prompt": "a colorful bird singing on a tree branch", "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run chenxwh/ominicontrol-spatial 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": "chenxwh/ominicontrol-spatial:1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917", "input": { "image": "https://replicate.delivery/pbxt/MF6nZVDiGtG6FlVSSiH1o9LGauIc8q40BtfrlN1KE1J3oADt/bird.png", "model": "canny", "prompt": "a colorful bird singing on a tree branch", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-31T23:58:09.359993Z", "created_at": "2024-12-31T23:56:42.580000Z", "data_removed": false, "error": null, "id": "wz19xybrthrmc0cm3wkbhyyzh8", "input": { "image": "https://replicate.delivery/pbxt/MF6nZVDiGtG6FlVSSiH1o9LGauIc8q40BtfrlN1KE1J3oADt/bird.png", "model": "canny", "prompt": "a colorful bird singing on a tree branch", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 2265\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 2.92it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.28it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.24it/s]\n 8%|▊ | 4/50 [00:01<00:14, 3.22it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.21it/s]\n 12%|█▏ | 6/50 [00:01<00:13, 3.20it/s]\n 14%|█▍ | 7/50 [00:02<00:13, 3.20it/s]\n 16%|█▌ | 8/50 [00:02<00:13, 3.19it/s]\n 18%|█▊ | 9/50 [00:02<00:12, 3.19it/s]\n 20%|██ | 10/50 [00:03<00:12, 3.18it/s]\n 22%|██▏ | 11/50 [00:03<00:12, 3.18it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.18it/s]\n 26%|██▌ | 13/50 [00:04<00:11, 3.19it/s]\n 28%|██▊ | 14/50 [00:04<00:11, 3.18it/s]\n 30%|███ | 15/50 [00:04<00:10, 3.18it/s]\n 32%|███▏ | 16/50 [00:05<00:10, 3.18it/s]\n 34%|███▍ | 17/50 [00:05<00:10, 3.18it/s]\n 36%|███▌ | 18/50 [00:05<00:10, 3.18it/s]\n 38%|███▊ | 19/50 [00:05<00:09, 3.18it/s]\n 40%|████ | 20/50 [00:06<00:09, 3.18it/s]\n 42%|████▏ | 21/50 [00:06<00:09, 3.17it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.17it/s]\n 46%|████▌ | 23/50 [00:07<00:08, 3.17it/s]\n 48%|████▊ | 24/50 [00:07<00:08, 3.17it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.17it/s]\n 52%|█████▏ | 26/50 [00:08<00:07, 3.17it/s]\n 54%|█████▍ | 27/50 [00:08<00:07, 3.17it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.17it/s]\n 58%|█████▊ | 29/50 [00:09<00:06, 3.17it/s]\n 60%|██████ | 30/50 [00:09<00:06, 3.17it/s]\n 62%|██████▏ | 31/50 [00:09<00:06, 3.17it/s]\n 64%|██████▍ | 32/50 [00:10<00:05, 3.17it/s]\n 66%|██████▌ | 33/50 [00:10<00:05, 3.17it/s]\n 68%|██████▊ | 34/50 [00:10<00:05, 3.17it/s]\n 70%|███████ | 35/50 [00:11<00:04, 3.17it/s]\n 72%|███████▏ | 36/50 [00:11<00:04, 3.17it/s]\n 74%|███████▍ | 37/50 [00:11<00:04, 3.17it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.17it/s]\n 78%|███████▊ | 39/50 [00:12<00:03, 3.17it/s]\n 80%|████████ | 40/50 [00:12<00:03, 3.17it/s]\n 82%|████████▏ | 41/50 [00:12<00:02, 3.17it/s]\n 84%|████████▍ | 42/50 [00:13<00:02, 3.17it/s]\n 86%|████████▌ | 43/50 [00:13<00:02, 3.17it/s]\n 88%|████████▊ | 44/50 [00:13<00:01, 3.17it/s]\n 90%|█████████ | 45/50 [00:14<00:01, 3.16it/s]\n 92%|█████████▏| 46/50 [00:14<00:01, 3.16it/s]\n 94%|█████████▍| 47/50 [00:14<00:00, 3.16it/s]\n 96%|█████████▌| 48/50 [00:15<00:00, 3.16it/s]\n 98%|█████████▊| 49/50 [00:15<00:00, 3.16it/s]\n100%|██████████| 50/50 [00:15<00:00, 3.16it/s]\n100%|██████████| 50/50 [00:15<00:00, 3.17it/s]", "metrics": { "predict_time": 16.533189879, "total_time": 86.779993 }, "output": "https://replicate.delivery/xezq/LRGbkeqE1p04EqC9PWDk6ln7Nh8LNhDp90ZpjUrKBfwRcjAUA/out.png", "started_at": "2024-12-31T23:57:52.826803Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-672dux6qnqx2pj3rsqnj36tewvmanjhshdjb7wiwmfa5cxrzad6q", "get": "https://api.replicate.com/v1/predictions/wz19xybrthrmc0cm3wkbhyyzh8", "cancel": "https://api.replicate.com/v1/predictions/wz19xybrthrmc0cm3wkbhyyzh8/cancel" }, "version": "1b9423cc867733ca0fcf0bc9e677a731fd8654206fc6f3fbddb7279cd91ce917" }Generated inUsing seed: 2265 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 2.92it/s] 4%|▍ | 2/50 [00:00<00:14, 3.28it/s] 6%|▌ | 3/50 [00:00<00:14, 3.24it/s] 8%|▊ | 4/50 [00:01<00:14, 3.22it/s] 10%|█ | 5/50 [00:01<00:14, 3.21it/s] 12%|█▏ | 6/50 [00:01<00:13, 3.20it/s] 14%|█▍ | 7/50 [00:02<00:13, 3.20it/s] 16%|█▌ | 8/50 [00:02<00:13, 3.19it/s] 18%|█▊ | 9/50 [00:02<00:12, 3.19it/s] 20%|██ | 10/50 [00:03<00:12, 3.18it/s] 22%|██▏ | 11/50 [00:03<00:12, 3.18it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.18it/s] 26%|██▌ | 13/50 [00:04<00:11, 3.19it/s] 28%|██▊ | 14/50 [00:04<00:11, 3.18it/s] 30%|███ | 15/50 [00:04<00:10, 3.18it/s] 32%|███▏ | 16/50 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