mikelyndon / controlnet-annotators
ControlNet annotators - the initial image that is fed into a stable diffusion pipeline with ControlNet
- Public
- 357 runs
-
T4
Prediction
mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1aIDofcbnpeigzhqdd5v77ft3bbpbuStatusSucceededSourceWebHardwareT4Total durationCreatedInput
{ "type": "openpose", "image": "https://replicate.delivery/pbxt/IpqgnP7Yp2FjjGR58XVf7MQlpb0yM0F4HBikRGq5rhxowgcS/pose2.png" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", { input: { type: "openpose", image: "https://replicate.delivery/pbxt/IpqgnP7Yp2FjjGR58XVf7MQlpb0yM0F4HBikRGq5rhxowgcS/pose2.png" } } ); // 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 replicate
Import the client:import replicate
Run mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", input={ "type": "openpose", "image": "https://replicate.delivery/pbxt/IpqgnP7Yp2FjjGR58XVf7MQlpb0yM0F4HBikRGq5rhxowgcS/pose2.png" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run mikelyndon/controlnet-annotators 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": "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", "input": { "type": "openpose", "image": "https://replicate.delivery/pbxt/IpqgnP7Yp2FjjGR58XVf7MQlpb0yM0F4HBikRGq5rhxowgcS/pose2.png" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-16T19:51:09.094474Z", "created_at": "2023-05-16T19:50:00.443992Z", "data_removed": false, "error": null, "id": "ofcbnpeigzhqdd5v77ft3bbpbu", "input": { "type": "openpose", "image": "https://replicate.delivery/pbxt/IpqgnP7Yp2FjjGR58XVf7MQlpb0yM0F4HBikRGq5rhxowgcS/pose2.png" }, "logs": null, "metrics": { "predict_time": 7.894639, "total_time": 68.650482 }, "output": "https://replicate.delivery/pbxt/mw2CFeVnymWxPazSL3pIzSrjvwVavve2wGl7WfBGJaKZFy4hA/out.png", "started_at": "2023-05-16T19:51:01.199835Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ofcbnpeigzhqdd5v77ft3bbpbu", "cancel": "https://api.replicate.com/v1/predictions/ofcbnpeigzhqdd5v77ft3bbpbu/cancel" }, "version": "2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a" }
Prediction
mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1aIDoe6nu3fkqncrjjmlaqqrkezdfeStatusSucceededSourceWebHardwareT4Total durationCreatedInput
{ "type": "canny", "image": "https://replicate.delivery/pbxt/IpqiRqczd9bltMSLwKpQsDMoXl6KWecPX2ljPm1INsck6aPX/pose2.png" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", { input: { type: "canny", image: "https://replicate.delivery/pbxt/IpqiRqczd9bltMSLwKpQsDMoXl6KWecPX2ljPm1INsck6aPX/pose2.png" } } ); // 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 replicate
Import the client:import replicate
Run mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", input={ "type": "canny", "image": "https://replicate.delivery/pbxt/IpqiRqczd9bltMSLwKpQsDMoXl6KWecPX2ljPm1INsck6aPX/pose2.png" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run mikelyndon/controlnet-annotators 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": "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", "input": { "type": "canny", "image": "https://replicate.delivery/pbxt/IpqiRqczd9bltMSLwKpQsDMoXl6KWecPX2ljPm1INsck6aPX/pose2.png" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-16T19:51:45.272498Z", "created_at": "2023-05-16T19:51:44.569961Z", "data_removed": false, "error": null, "id": "oe6nu3fkqncrjjmlaqqrkezdfe", "input": { "type": "canny", "image": "https://replicate.delivery/pbxt/IpqiRqczd9bltMSLwKpQsDMoXl6KWecPX2ljPm1INsck6aPX/pose2.png" }, "logs": null, "metrics": { "predict_time": 0.645795, "total_time": 0.702537 }, "output": "https://replicate.delivery/pbxt/zeHVBpAdW9yoN6E0BGYfjUGLGVfq6eMBqrfywgzSTbmCaIjHC/out.png", "started_at": "2023-05-16T19:51:44.626703Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/oe6nu3fkqncrjjmlaqqrkezdfe", "cancel": "https://api.replicate.com/v1/predictions/oe6nu3fkqncrjjmlaqqrkezdfe/cancel" }, "version": "2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a" }
Prediction
mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1aIDex3323rr6rf77gt3xjywklnf2aStatusSucceededSourceWebHardwareT4Total durationCreatedInput
{ "type": "normal", "image": "https://replicate.delivery/pbxt/IpqikkiWX0hGLD0LNjtJtB0e1splVB92xxb4Oj5nB0pMs68P/pose2.png" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", { input: { type: "normal", image: "https://replicate.delivery/pbxt/IpqikkiWX0hGLD0LNjtJtB0e1splVB92xxb4Oj5nB0pMs68P/pose2.png" } } ); // 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 replicate
Import the client:import replicate
Run mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", input={ "type": "normal", "image": "https://replicate.delivery/pbxt/IpqikkiWX0hGLD0LNjtJtB0e1splVB92xxb4Oj5nB0pMs68P/pose2.png" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run mikelyndon/controlnet-annotators 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": "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", "input": { "type": "normal", "image": "https://replicate.delivery/pbxt/IpqikkiWX0hGLD0LNjtJtB0e1splVB92xxb4Oj5nB0pMs68P/pose2.png" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-16T19:52:06.626096Z", "created_at": "2023-05-16T19:52:03.545436Z", "data_removed": false, "error": null, "id": "ex3323rr6rf77gt3xjywklnf2a", "input": { "type": "normal", "image": "https://replicate.delivery/pbxt/IpqikkiWX0hGLD0LNjtJtB0e1splVB92xxb4Oj5nB0pMs68P/pose2.png" }, "logs": "Loading base model ()...Done.\nRemoving last two layers (global_pool & classifier).", "metrics": { "predict_time": 3.030552, "total_time": 3.08066 }, "output": "https://replicate.delivery/pbxt/vejudPFZEdQhQS3YZ9yCj7RHEN76sumCBjMSQ6fgG3BmDZ8QA/out.png", "started_at": "2023-05-16T19:52:03.595544Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ex3323rr6rf77gt3xjywklnf2a", "cancel": "https://api.replicate.com/v1/predictions/ex3323rr6rf77gt3xjywklnf2a/cancel" }, "version": "2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a" }
Prediction
mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1aIDkewoneplwzepbdlykwgiexprreStatusSucceededSourceWebHardwareT4Total durationCreatedInput
{ "type": "seg", "image": "https://replicate.delivery/pbxt/IpqjPSIAcx08SF6vqBHlfIEsMynrqW10Qo7g45JElppvmhh4/room2.jpg" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", { input: { type: "seg", image: "https://replicate.delivery/pbxt/IpqjPSIAcx08SF6vqBHlfIEsMynrqW10Qo7g45JElppvmhh4/room2.jpg" } } ); // 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 replicate
Import the client:import replicate
Run mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", input={ "type": "seg", "image": "https://replicate.delivery/pbxt/IpqjPSIAcx08SF6vqBHlfIEsMynrqW10Qo7g45JElppvmhh4/room2.jpg" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run mikelyndon/controlnet-annotators 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": "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", "input": { "type": "seg", "image": "https://replicate.delivery/pbxt/IpqjPSIAcx08SF6vqBHlfIEsMynrqW10Qo7g45JElppvmhh4/room2.jpg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-16T19:52:50.137840Z", "created_at": "2023-05-16T19:52:45.117734Z", "data_removed": false, "error": null, "id": "kewoneplwzepbdlykwgiexprre", "input": { "type": "seg", "image": "https://replicate.delivery/pbxt/IpqjPSIAcx08SF6vqBHlfIEsMynrqW10Qo7g45JElppvmhh4/room2.jpg" }, "logs": null, "metrics": { "predict_time": 4.979748, "total_time": 5.020106 }, "output": "https://replicate.delivery/pbxt/6Bjfq0NlJP0dFaM4LCbhz5s4Xfqu99Bu4GTpJqfeQ35GRkxDB/out.png", "started_at": "2023-05-16T19:52:45.158092Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kewoneplwzepbdlykwgiexprre", "cancel": "https://api.replicate.com/v1/predictions/kewoneplwzepbdlykwgiexprre/cancel" }, "version": "2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a" }
Prediction
mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1aIDzrl6ohdh55ftrlpnouz5rkdk4uStatusSucceededSourceWebHardwareT4Total durationCreatedInput
{ "type": "mlsd", "image": "https://replicate.delivery/pbxt/Ipqjgvf3ia0WwSwRtINxAEKnr34p9Mjis58fvjmS44xExOUI/room2.jpg" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", { input: { type: "mlsd", image: "https://replicate.delivery/pbxt/Ipqjgvf3ia0WwSwRtINxAEKnr34p9Mjis58fvjmS44xExOUI/room2.jpg" } } ); // 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 replicate
Import the client:import replicate
Run mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", input={ "type": "mlsd", "image": "https://replicate.delivery/pbxt/Ipqjgvf3ia0WwSwRtINxAEKnr34p9Mjis58fvjmS44xExOUI/room2.jpg" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run mikelyndon/controlnet-annotators 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": "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", "input": { "type": "mlsd", "image": "https://replicate.delivery/pbxt/Ipqjgvf3ia0WwSwRtINxAEKnr34p9Mjis58fvjmS44xExOUI/room2.jpg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-16T19:53:03.981251Z", "created_at": "2023-05-16T19:53:02.734323Z", "data_removed": false, "error": null, "id": "zrl6ohdh55ftrlpnouz5rkdk4u", "input": { "type": "mlsd", "image": "https://replicate.delivery/pbxt/Ipqjgvf3ia0WwSwRtINxAEKnr34p9Mjis58fvjmS44xExOUI/room2.jpg" }, "logs": null, "metrics": { "predict_time": 1.205176, "total_time": 1.246928 }, "output": "https://replicate.delivery/pbxt/8cAfjQfOOKjJkUQSkgdxI6IMl5hUf2xrMoKEKFy8Wd5eRkxDB/out.png", "started_at": "2023-05-16T19:53:02.776075Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zrl6ohdh55ftrlpnouz5rkdk4u", "cancel": "https://api.replicate.com/v1/predictions/zrl6ohdh55ftrlpnouz5rkdk4u/cancel" }, "version": "2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a" }
Prediction
mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1aID662qzwcoxna5ferxwy46fsz4uiStatusSucceededSourceWebHardwareT4Total durationCreatedInput
{ "type": "depth", "image": "https://replicate.delivery/pbxt/Ipqk4RIIH1NzJfBKaSTKkNoQDuF4mOCCoo50GVq2bBraxPsE/pose2.png" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", { input: { type: "depth", image: "https://replicate.delivery/pbxt/Ipqk4RIIH1NzJfBKaSTKkNoQDuF4mOCCoo50GVq2bBraxPsE/pose2.png" } } ); // 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 replicate
Import the client:import replicate
Run mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", input={ "type": "depth", "image": "https://replicate.delivery/pbxt/Ipqk4RIIH1NzJfBKaSTKkNoQDuF4mOCCoo50GVq2bBraxPsE/pose2.png" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run mikelyndon/controlnet-annotators 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": "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", "input": { "type": "depth", "image": "https://replicate.delivery/pbxt/Ipqk4RIIH1NzJfBKaSTKkNoQDuF4mOCCoo50GVq2bBraxPsE/pose2.png" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-16T19:53:28.124548Z", "created_at": "2023-05-16T19:53:27.253048Z", "data_removed": false, "error": null, "id": "662qzwcoxna5ferxwy46fsz4ui", "input": { "type": "depth", "image": "https://replicate.delivery/pbxt/Ipqk4RIIH1NzJfBKaSTKkNoQDuF4mOCCoo50GVq2bBraxPsE/pose2.png" }, "logs": null, "metrics": { "predict_time": 0.83025, "total_time": 0.8715 }, "output": "https://replicate.delivery/pbxt/SeI9WbNk7ozVcagLyBIvFCnNbW5Pw514Vw0uVT2cWd8biMeQA/out.png", "started_at": "2023-05-16T19:53:27.294298Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/662qzwcoxna5ferxwy46fsz4ui", "cancel": "https://api.replicate.com/v1/predictions/662qzwcoxna5ferxwy46fsz4ui/cancel" }, "version": "2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a" }
Prediction
mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1aID5ei2vngprzbxhpih6m25yvddjaStatusSucceededSourceWebHardwareT4Total durationCreatedInput
{ "type": "hed", "image": "https://replicate.delivery/pbxt/IrZZkVsVAkNa3qO1ydwA6Yz9Gs5uIPYvcgnnDwxe7qqZRPFy/room2.jpg" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", { input: { type: "hed", image: "https://replicate.delivery/pbxt/IrZZkVsVAkNa3qO1ydwA6Yz9Gs5uIPYvcgnnDwxe7qqZRPFy/room2.jpg" } } ); // 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 replicate
Import the client:import replicate
Run mikelyndon/controlnet-annotators using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", input={ "type": "hed", "image": "https://replicate.delivery/pbxt/IrZZkVsVAkNa3qO1ydwA6Yz9Gs5uIPYvcgnnDwxe7qqZRPFy/room2.jpg" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())
To learn more, take a look at the guide on getting started with Python.
Run mikelyndon/controlnet-annotators 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": "mikelyndon/controlnet-annotators:2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a", "input": { "type": "hed", "image": "https://replicate.delivery/pbxt/IrZZkVsVAkNa3qO1ydwA6Yz9Gs5uIPYvcgnnDwxe7qqZRPFy/room2.jpg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-21T16:28:04.920846Z", "created_at": "2023-05-21T16:23:51.958697Z", "data_removed": false, "error": null, "id": "5ei2vngprzbxhpih6m25yvddja", "input": { "type": "hed", "image": "https://replicate.delivery/pbxt/IrZZkVsVAkNa3qO1ydwA6Yz9Gs5uIPYvcgnnDwxe7qqZRPFy/room2.jpg" }, "logs": null, "metrics": { "predict_time": 4.570044, "total_time": 252.962149 }, "output": "https://replicate.delivery/pbxt/5MgH2nEkk6KfYKhEHkdG8V9D5fOvtm5jYdu83FHtrj3Uif7hA/out.png", "started_at": "2023-05-21T16:28:00.350802Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5ei2vngprzbxhpih6m25yvddja", "cancel": "https://api.replicate.com/v1/predictions/5ei2vngprzbxhpih6m25yvddja/cancel" }, "version": "2a07e5f142c94ea238549300935f767a911b587167aa4786f885745ebbb2ad1a" }
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