camenduru / attribute-control
Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions
Prediction
camenduru/attribute-control:2d3d06f5437eead1e38fa245b490c785b2d5b15698a7434d6ec87deba7dd3330IDc2rnh5tbxvicn7vduxslvjkyjuStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 0
- prompt
- a photo of a beautiful man
- num_images
- 6
- delay_relative
- 0.2
- guidance_scale
- 7.5
- pattern_target
- man
- prompt_negative
- pretrained_deltas
- person_age
{ "seed": 0, "prompt": "a photo of a beautiful man", "num_images": 6, "delay_relative": 0.2, "guidance_scale": 7.5, "pattern_target": "man", "prompt_negative": "", "pretrained_deltas": "person_age" }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 camenduru/attribute-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/attribute-control:2d3d06f5437eead1e38fa245b490c785b2d5b15698a7434d6ec87deba7dd3330", { input: { seed: 0, prompt: "a photo of a beautiful man", num_images: 6, delay_relative: 0.2, guidance_scale: 7.5, pattern_target: "man", prompt_negative: "", pretrained_deltas: "person_age" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun camenduru/attribute-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/attribute-control:2d3d06f5437eead1e38fa245b490c785b2d5b15698a7434d6ec87deba7dd3330", input={ "seed": 0, "prompt": "a photo of a beautiful man", "num_images": 6, "delay_relative": 0.2, "guidance_scale": 7.5, "pattern_target": "man", "prompt_negative": "", "pretrained_deltas": "person_age" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run camenduru/attribute-control 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": "camenduru/attribute-control:2d3d06f5437eead1e38fa245b490c785b2d5b15698a7434d6ec87deba7dd3330", "input": { "seed": 0, "prompt": "a photo of a beautiful man", "num_images": 6, "delay_relative": 0.2, "guidance_scale": 7.5, "pattern_target": "man", "prompt_negative": "", "pretrained_deltas": "person_age" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-31T02:37:52.932291Z", "created_at": "2024-03-31T02:34:34.633299Z", "data_removed": false, "error": null, "id": "c2rnh5tbxvicn7vduxslvjkyju", "input": { "seed": 0, "prompt": "a photo of a beautiful man", "num_images": 6, "delay_relative": 0.2, "guidance_scale": 7.5, "pattern_target": "man", "prompt_negative": "", "pretrained_deltas": "person_age" }, "logs": "0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:03, 2.37it/s]\n 20%|██ | 2/10 [00:00<00:02, 3.55it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.22it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.61it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.86it/s]\n 60%|██████ | 6/10 [00:01<00:00, 5.02it/s]\n 70%|███████ | 7/10 [00:01<00:00, 5.12it/s]\n 80%|████████ | 8/10 [00:01<00:00, 5.20it/s]\n 90%|█████████ | 9/10 [00:01<00:00, 5.26it/s]\n100%|██████████| 10/10 [00:02<00:00, 5.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.78it/s]\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:07, 5.38it/s]\n 5%|▌ | 2/40 [00:00<00:07, 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[00:01<00:06, 5.30it/s]\n 18%|█▊ | 7/40 [00:01<00:06, 5.30it/s]\n 20%|██ | 8/40 [00:01<00:06, 5.30it/s]\n 22%|██▎ | 9/40 [00:01<00:05, 5.30it/s]\n 25%|██▌ | 10/40 [00:01<00:05, 5.31it/s]\n 28%|██▊ | 11/40 [00:02<00:05, 5.30it/s]\n 30%|███ | 12/40 [00:02<00:05, 5.31it/s]\n 32%|███▎ | 13/40 [00:02<00:05, 5.30it/s]\n 35%|███▌ | 14/40 [00:02<00:04, 5.31it/s]\n 38%|███▊ | 15/40 [00:02<00:04, 5.30it/s]\n 40%|████ | 16/40 [00:03<00:04, 5.30it/s]\n 42%|████▎ | 17/40 [00:03<00:04, 5.31it/s]\n 45%|████▌ | 18/40 [00:03<00:04, 5.30it/s]\n 48%|████▊ | 19/40 [00:03<00:03, 5.30it/s]\n 50%|█████ | 20/40 [00:03<00:03, 5.30it/s]\n 52%|█████▎ | 21/40 [00:03<00:03, 5.31it/s]\n 55%|█████▌ | 22/40 [00:04<00:03, 5.30it/s]\n 57%|█████▊ | 23/40 [00:04<00:03, 5.30it/s]\n 60%|██████ | 24/40 [00:04<00:03, 5.29it/s]\n 62%|██████▎ | 25/40 [00:04<00:02, 5.30it/s]\n 65%|██████▌ | 26/40 [00:04<00:02, 5.30it/s]\n 68%|██████▊ | 27/40 [00:05<00:02, 5.30it/s]\n 70%|███████ | 28/40 [00:05<00:02, 5.30it/s]\n 72%|███████▎ | 29/40 [00:05<00:02, 5.30it/s]\n 75%|███████▌ | 30/40 [00:05<00:01, 5.30it/s]\n 78%|███████▊ | 31/40 [00:05<00:01, 5.29it/s]\n 80%|████████ | 32/40 [00:06<00:01, 5.29it/s]\n 82%|████████▎ | 33/40 [00:06<00:01, 5.29it/s]\n 85%|████████▌ | 34/40 [00:06<00:01, 5.29it/s]\n 88%|████████▊ | 35/40 [00:06<00:00, 5.29it/s]\n 90%|█████████ | 36/40 [00:06<00:00, 5.29it/s]\n 92%|█████████▎| 37/40 [00:06<00:00, 5.30it/s]\n 95%|█████████▌| 38/40 [00:07<00:00, 5.29it/s]\n 98%|█████████▊| 39/40 [00:07<00:00, 5.29it/s]\n100%|██████████| 40/40 [00:07<00:00, 5.29it/s]\n100%|██████████| 40/40 [00:07<00:00, 5.30it/s]", "metrics": { "predict_time": 69.882401, "total_time": 198.298992 }, "output": [ "https://replicate.delivery/pbxt/59oh8f5XZtVgGap1RYY6vuEG1rrPKZ1LMsU7V0rtgWze5nlSA/1.png", "https://replicate.delivery/pbxt/60VtB9eJxrWcUCuAm7MB14PFbnafwqcunZin6BdtXevenfsUC/2.png", "https://replicate.delivery/pbxt/MtNt8sE4fGy8KavYe8qrFnUK1yTlEnZ8Qx1lHe5RJ7jenfsUC/3.png", "https://replicate.delivery/pbxt/xcjBxEPqYvbZF5BFYhzCuZ8H78FfS41Y6tTZb33ZG1zf5nlSA/4.png", "https://replicate.delivery/pbxt/KJsU9YNSpHabJdQ3BOueDNLYJT4YEqj0CmO8T6aKfOWA6nlSA/5.png", "https://replicate.delivery/pbxt/Ijs75WxblE4TMNgeN2rC1NlTLWSbyHZlKxh8fuurXRBA6nlSA/6.png" ], "started_at": "2024-03-31T02:36:43.049890Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c2rnh5tbxvicn7vduxslvjkyju", "cancel": "https://api.replicate.com/v1/predictions/c2rnh5tbxvicn7vduxslvjkyju/cancel" }, "version": "2d3d06f5437eead1e38fa245b490c785b2d5b15698a7434d6ec87deba7dd3330" }Generated in0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:03, 2.37it/s] 20%|██ | 2/10 [00:00<00:02, 3.55it/s] 30%|███ | 3/10 [00:00<00:01, 4.22it/s] 40%|████ | 4/10 [00:00<00:01, 4.61it/s] 50%|█████ | 5/10 [00:01<00:01, 4.86it/s] 60%|██████ | 6/10 [00:01<00:00, 5.02it/s] 70%|███████ | 7/10 [00:01<00:00, 5.12it/s] 80%|████████ | 8/10 [00:01<00:00, 5.20it/s] 90%|█████████ | 9/10 [00:01<00:00, 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[00:07<00:00, 5.29it/s] 98%|█████████▊| 39/40 [00:07<00:00, 5.29it/s] 100%|██████████| 40/40 [00:07<00:00, 5.29it/s] 100%|██████████| 40/40 [00:07<00:00, 5.30it/s]Prediction
camenduru/attribute-control:2f75fb302a6ff587cad3d57fa113ea690c189ead618757cce9772b051d609062IDijymy3dbvqrfucok7jv5pb5qxmStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 0
- prompt
- a photo of a beautiful woman
- num_images
- 6
- delay_relative
- 0.2
- guidance_scale
- 7.5
- pattern_target
- woman
- prompt_negative
- pretrained_deltas
- person_age
{ "seed": 0, "prompt": "a photo of a beautiful woman", "num_images": 6, "delay_relative": 0.2, "guidance_scale": 7.5, "pattern_target": "woman", "prompt_negative": "", "pretrained_deltas": "person_age" }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 camenduru/attribute-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/attribute-control:2f75fb302a6ff587cad3d57fa113ea690c189ead618757cce9772b051d609062", { input: { seed: 0, prompt: "a photo of a beautiful woman", num_images: 6, delay_relative: 0.2, guidance_scale: 7.5, pattern_target: "woman", prompt_negative: "", pretrained_deltas: "person_age" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun camenduru/attribute-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/attribute-control:2f75fb302a6ff587cad3d57fa113ea690c189ead618757cce9772b051d609062", input={ "seed": 0, "prompt": "a photo of a beautiful woman", "num_images": 6, "delay_relative": 0.2, "guidance_scale": 7.5, "pattern_target": "woman", "prompt_negative": "", "pretrained_deltas": "person_age" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run camenduru/attribute-control 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": "camenduru/attribute-control:2f75fb302a6ff587cad3d57fa113ea690c189ead618757cce9772b051d609062", "input": { "seed": 0, "prompt": "a photo of a beautiful woman", "num_images": 6, "delay_relative": 0.2, "guidance_scale": 7.5, "pattern_target": "woman", "prompt_negative": "", "pretrained_deltas": "person_age" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-31T02:44:54.374518Z", "created_at": "2024-03-31T02:41:12.043393Z", "data_removed": false, "error": null, "id": "ijymy3dbvqrfucok7jv5pb5qxm", "input": { "seed": 0, "prompt": "a photo of a beautiful woman", "num_images": 6, "delay_relative": 0.2, "guidance_scale": 7.5, "pattern_target": "woman", "prompt_negative": "", "pretrained_deltas": "person_age" }, "logs": "0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:03, 2.44it/s]\n 20%|██ | 2/10 [00:00<00:02, 3.61it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.24it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.62it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.85it/s]\n 60%|██████ | 6/10 [00:01<00:00, 5.00it/s]\n 70%|███████ | 7/10 [00:01<00:00, 5.11it/s]\n 80%|████████ | 8/10 [00:01<00:00, 5.17it/s]\n 90%|█████████ | 9/10 [00:01<00:00, 5.22it/s]\n100%|██████████| 10/10 [00:02<00:00, 5.25it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.78it/s]\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:07, 5.34it/s]\n 5%|▌ | 2/40 [00:00<00:07, 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[00:01<00:06, 5.26it/s]\n 18%|█▊ | 7/40 [00:01<00:06, 5.25it/s]\n 20%|██ | 8/40 [00:01<00:06, 5.26it/s]\n 22%|██▎ | 9/40 [00:01<00:05, 5.26it/s]\n 25%|██▌ | 10/40 [00:01<00:05, 5.26it/s]\n 28%|██▊ | 11/40 [00:02<00:05, 5.26it/s]\n 30%|███ | 12/40 [00:02<00:05, 5.26it/s]\n 32%|███▎ | 13/40 [00:02<00:05, 5.27it/s]\n 35%|███▌ | 14/40 [00:02<00:04, 5.26it/s]\n 38%|███▊ | 15/40 [00:02<00:04, 5.26it/s]\n 40%|████ | 16/40 [00:03<00:04, 5.26it/s]\n 42%|████▎ | 17/40 [00:03<00:04, 5.26it/s]\n 45%|████▌ | 18/40 [00:03<00:04, 5.27it/s]\n 48%|████▊ | 19/40 [00:03<00:03, 5.27it/s]\n 50%|█████ | 20/40 [00:03<00:03, 5.26it/s]\n 52%|█████▎ | 21/40 [00:03<00:03, 5.26it/s]\n 55%|█████▌ | 22/40 [00:04<00:03, 5.26it/s]\n 57%|█████▊ | 23/40 [00:04<00:03, 5.26it/s]\n 60%|██████ | 24/40 [00:04<00:03, 5.26it/s]\n 62%|██████▎ | 25/40 [00:04<00:02, 5.26it/s]\n 65%|██████▌ | 26/40 [00:04<00:02, 5.26it/s]\n 68%|██████▊ | 27/40 [00:05<00:02, 5.25it/s]\n 70%|███████ | 28/40 [00:05<00:02, 5.25it/s]\n 72%|███████▎ | 29/40 [00:05<00:02, 5.26it/s]\n 75%|███████▌ | 30/40 [00:05<00:01, 5.26it/s]\n 78%|███████▊ | 31/40 [00:05<00:01, 5.25it/s]\n 80%|████████ | 32/40 [00:06<00:01, 5.25it/s]\n 82%|████████▎ | 33/40 [00:06<00:01, 5.25it/s]\n 85%|████████▌ | 34/40 [00:06<00:01, 5.26it/s]\n 88%|████████▊ | 35/40 [00:06<00:00, 5.26it/s]\n 90%|█████████ | 36/40 [00:06<00:00, 5.26it/s]\n 92%|█████████▎| 37/40 [00:07<00:00, 5.26it/s]\n 95%|█████████▌| 38/40 [00:07<00:00, 5.26it/s]\n 98%|█████████▊| 39/40 [00:07<00:00, 5.26it/s]\n100%|██████████| 40/40 [00:07<00:00, 5.26it/s]\n100%|██████████| 40/40 [00:07<00:00, 5.26it/s]", "metrics": { "predict_time": 65.408253, "total_time": 222.331125 }, "output": [ "https://replicate.delivery/pbxt/KjeeBXUrJYr1Ek5efUOMHrsVGuugCPj18SvfYawsaCvdEAtUC/1.png", "https://replicate.delivery/pbxt/Su5aaatfLzSVGCx7dfd7SQCk6VeWVvRD1SyyflBvfsSiEAtUC/2.png", "https://replicate.delivery/pbxt/4oVrcN3yX8YONh5TicNr4mcJvvZyySkeqKIQe3afOy5IBQLlA/3.png", "https://replicate.delivery/pbxt/WveFb7IluyVBX6Yfp1foWvirBxyQDCSaW9RZeKiJ0aSXCgWKB/4.png", "https://replicate.delivery/pbxt/ozsQMRia2dqTB9RjsrrINB0arS9O8P8DgVnYCAleEn9SA0SJA/5.png", "https://replicate.delivery/pbxt/fjiEUatqz1UtRKy8Q8MoxNpcCUkktKdcHVFTEG4tbhTTA0SJA/6.png" ], "started_at": "2024-03-31T02:43:48.966265Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ijymy3dbvqrfucok7jv5pb5qxm", "cancel": "https://api.replicate.com/v1/predictions/ijymy3dbvqrfucok7jv5pb5qxm/cancel" }, "version": "2f75fb302a6ff587cad3d57fa113ea690c189ead618757cce9772b051d609062" }Generated in0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:03, 2.44it/s] 20%|██ | 2/10 [00:00<00:02, 3.61it/s] 30%|███ | 3/10 [00:00<00:01, 4.24it/s] 40%|████ | 4/10 [00:00<00:01, 4.62it/s] 50%|█████ | 5/10 [00:01<00:01, 4.85it/s] 60%|██████ | 6/10 [00:01<00:00, 5.00it/s] 70%|███████ | 7/10 [00:01<00:00, 5.11it/s] 80%|████████ | 8/10 [00:01<00:00, 5.17it/s] 90%|█████████ | 9/10 [00:01<00:00, 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