Prediction
mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3dInput
{ "alpha": 1, "num_iter": 2, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "sketch_multi" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", { input: { alpha: 1, num_iter: 2, input_face: "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", pretrained: "sketch_multi" } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", input={ "alpha": 1, "num_iter": 2, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "sketch_multi" } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", "input": { "alpha": 1, "num_iter": 2, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "sketch_multi" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T17:28:10.189331Z", "created_at": "2022-02-04T17:27:09.324022Z", "data_removed": false, "error": null, "id": "yuv4oh7chnabnor2xg4mvdresu", "input": { "alpha": 1, "num_iter": 2, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "sketch_multi" }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt", "metrics": { "predict_time": 14.227985, "total_time": 60.865309 }, "output": [ { "file": "https://replicate.delivery/mgxm/ea4f0533-01f1-4331-b0f4-410174df5d8e/out.jpg" } ], "started_at": "2022-02-04T17:27:55.961346Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yuv4oh7chnabnor2xg4mvdresu", "cancel": "https://api.replicate.com/v1/predictions/yuv4oh7chnabnor2xg4mvdresu/cancel" }, "version": "45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt
Prediction
mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3dInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/af13d0b3-9b60-40f4-99f5-7ba95024ca39/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/bace600f-f213-4169-baa1-4d3b5a331c68/george.jpg", "preserve_color": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/af13d0b3-9b60-40f4-99f5-7ba95024ca39/iu.jpeg", style_img_0: "https://replicate.delivery/mgxm/bace600f-f213-4169-baa1-4d3b5a331c68/george.jpg", preserve_color: true } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/af13d0b3-9b60-40f4-99f5-7ba95024ca39/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/bace600f-f213-4169-baa1-4d3b5a331c68/george.jpg", "preserve_color": True } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/af13d0b3-9b60-40f4-99f5-7ba95024ca39/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/bace600f-f213-4169-baa1-4d3b5a331c68/george.jpg", "preserve_color": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T17:32:28.491507Z", "created_at": "2022-02-04T17:30:16.480625Z", "data_removed": false, "error": null, "id": "befeqvlrzzafbpv6peqnlhoixm", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/af13d0b3-9b60-40f4-99f5-7ba95024ca39/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/bace600f-f213-4169-baa1-4d3b5a331c68/george.jpg", "preserve_color": true }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\n\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<01:32, 2.15it/s]\n 1%| | 2/200 [00:00<01:32, 2.15it/s]\n 2%|▏ | 3/200 [00:01<01:32, 2.14it/s]\n 2%|▏ | 4/200 [00:01<01:31, 2.14it/s]\n 2%|▎ | 5/200 [00:02<01:31, 2.13it/s]\n 3%|▎ | 6/200 [00:02<01:31, 2.12it/s]\n 4%|▎ | 7/200 [00:03<01:31, 2.12it/s]\n 4%|▍ | 8/200 [00:03<01:30, 2.12it/s]\n 4%|▍ | 9/200 [00:04<01:29, 2.12it/s]\n 5%|▌ | 10/200 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"https://replicate.delivery/mgxm/3e1c53fc-16e7-473f-9a33-5af666264bbc/out.jpg" } ], "started_at": "2022-02-04T17:30:16.694226Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/befeqvlrzzafbpv6peqnlhoixm", "cancel": "https://api.replicate.com/v1/predictions/befeqvlrzzafbpv6peqnlhoixm/cancel" }, "version": "45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt 0%| | 0/200 [00:00<?, ?it/s] 0%| | 1/200 [00:00<01:32, 2.15it/s] 1%| | 2/200 [00:00<01:32, 2.15it/s] 2%|▏ | 3/200 [00:01<01:32, 2.14it/s] 2%|▏ | 4/200 [00:01<01:31, 2.14it/s] 2%|▎ | 5/200 [00:02<01:31, 2.13it/s] 3%|▎ | 6/200 [00:02<01:31, 2.12it/s] 4%|▎ | 7/200 [00:03<01:31, 2.12it/s] 4%|▍ | 8/200 [00:03<01:30, 2.12it/s] 4%|▍ | 9/200 [00:04<01:29, 2.12it/s] 5%|▌ | 10/200 [00:04<01:29, 2.12it/s] 6%|▌ | 11/200 [00:05<01:29, 2.11it/s] 6%|▌ | 12/200 [00:05<01:28, 2.12it/s] 6%|▋ | 13/200 [00:06<01:28, 2.12it/s] 7%|▋ | 14/200 [00:06<01:28, 2.11it/s] 8%|▊ | 15/200 [00:07<01:27, 2.11it/s] 8%|▊ | 16/200 [00:07<01:27, 2.11it/s] 8%|▊ | 17/200 [00:08<01:26, 2.11it/s] 9%|▉ | 18/200 [00:08<01:26, 2.11it/s] 10%|▉ | 19/200 [00:08<01:25, 2.11it/s] 10%|█ | 20/200 [00:09<01:25, 2.11it/s] 10%|█ | 21/200 [00:09<01:25, 2.10it/s] 11%|█ | 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Prediction
mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3dInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/56aa78ed-0e24-4af7-bb56-b648d025dc25/iu.jpeg", "pretrained": "art", "preserve_color": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/56aa78ed-0e24-4af7-bb56-b648d025dc25/iu.jpeg", pretrained: "art", preserve_color: true } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/56aa78ed-0e24-4af7-bb56-b648d025dc25/iu.jpeg", "pretrained": "art", "preserve_color": True } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/56aa78ed-0e24-4af7-bb56-b648d025dc25/iu.jpeg", "pretrained": "art", "preserve_color": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T17:38:49.109524Z", "created_at": "2022-02-04T17:38:36.862667Z", "data_removed": false, "error": null, "id": "u7hc7q45evh6tpmjo7nyxhigrq", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/56aa78ed-0e24-4af7-bb56-b648d025dc25/iu.jpeg", "pretrained": "art", "preserve_color": true }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt", "metrics": { "predict_time": 12.040058, "total_time": 12.246857 }, "output": [ { "file": "https://replicate.delivery/mgxm/1452ee0e-45c8-42d8-8bb0-384801f96099/out.jpg" } ], "started_at": "2022-02-04T17:38:37.069466Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/u7hc7q45evh6tpmjo7nyxhigrq", "cancel": "https://api.replicate.com/v1/predictions/u7hc7q45evh6tpmjo7nyxhigrq/cancel" }, "version": "45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt
Prediction
mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3dIDwtzzaasrwbcerbfnenpzpzjufiStatusSucceededSourceWebHardware–Total durationCreatedInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/a8b7a837-bae2-43de-9b66-cedbb758225b/iu.jpeg", "pretrained": "arcane_multi", "preserve_color": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/a8b7a837-bae2-43de-9b66-cedbb758225b/iu.jpeg", pretrained: "arcane_multi", preserve_color: true } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/a8b7a837-bae2-43de-9b66-cedbb758225b/iu.jpeg", "pretrained": "arcane_multi", "preserve_color": True } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/a8b7a837-bae2-43de-9b66-cedbb758225b/iu.jpeg", "pretrained": "arcane_multi", "preserve_color": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T17:45:11.110390Z", "created_at": "2022-02-04T17:44:59.269733Z", "data_removed": false, "error": null, "id": "wtzzaasrwbcerbfnenpzpzjufi", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/a8b7a837-bae2-43de-9b66-cedbb758225b/iu.jpeg", "pretrained": "arcane_multi", "preserve_color": true }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt", "metrics": { "predict_time": 11.633893, "total_time": 11.840657 }, "output": [ { "file": "https://replicate.delivery/mgxm/d000a101-beb3-4877-a6a1-ece5069f8925/out.jpg" } ], "started_at": "2022-02-04T17:44:59.476497Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wtzzaasrwbcerbfnenpzpzjufi", "cancel": "https://api.replicate.com/v1/predictions/wtzzaasrwbcerbfnenpzpzjufi/cancel" }, "version": "45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt
Prediction
mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3dInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/c1baf73d-87b5-453a-9ea6-66d6ca81105d/iu.jpeg", "pretrained": "jojo_yasuho", "preserve_color": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/c1baf73d-87b5-453a-9ea6-66d6ca81105d/iu.jpeg", pretrained: "jojo_yasuho", preserve_color: true } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/c1baf73d-87b5-453a-9ea6-66d6ca81105d/iu.jpeg", "pretrained": "jojo_yasuho", "preserve_color": True } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/c1baf73d-87b5-453a-9ea6-66d6ca81105d/iu.jpeg", "pretrained": "jojo_yasuho", "preserve_color": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T17:45:40.832663Z", "created_at": "2022-02-04T17:45:28.746875Z", "data_removed": false, "error": null, "id": "7nrc4wtmx5dk5mqegjxgawahkm", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/c1baf73d-87b5-453a-9ea6-66d6ca81105d/iu.jpeg", "pretrained": "jojo_yasuho", "preserve_color": true }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt", "metrics": { "predict_time": 11.874676, "total_time": 12.085788 }, "output": [ { "file": "https://replicate.delivery/mgxm/80f99fd3-cb67-463e-a8e9-82c80212bba0/out.jpg" } ], "started_at": "2022-02-04T17:45:28.957987Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7nrc4wtmx5dk5mqegjxgawahkm", "cancel": "https://api.replicate.com/v1/predictions/7nrc4wtmx5dk5mqegjxgawahkm/cancel" }, "version": "45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt
Prediction
mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3dInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/d1e93111-fa98-4e1a-af79-647963252756/iu.jpeg", "pretrained": "disney", "preserve_color": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/d1e93111-fa98-4e1a-af79-647963252756/iu.jpeg", pretrained: "disney", preserve_color: true } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/d1e93111-fa98-4e1a-af79-647963252756/iu.jpeg", "pretrained": "disney", "preserve_color": True } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/d1e93111-fa98-4e1a-af79-647963252756/iu.jpeg", "pretrained": "disney", "preserve_color": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T17:46:06.482212Z", "created_at": "2022-02-04T17:45:54.516334Z", "data_removed": false, "error": null, "id": "h6tv4qqw5ven5dframs3a4kepu", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/d1e93111-fa98-4e1a-af79-647963252756/iu.jpeg", "pretrained": "disney", "preserve_color": true }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt", "metrics": { "predict_time": 11.697183, "total_time": 11.965878 }, "output": [ { "file": "https://replicate.delivery/mgxm/d0fe5496-7784-4e26-8fe1-c3f884e136da/out.jpg" } ], "started_at": "2022-02-04T17:45:54.785029Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/h6tv4qqw5ven5dframs3a4kepu", "cancel": "https://api.replicate.com/v1/predictions/h6tv4qqw5ven5dframs3a4kepu/cancel" }, "version": "45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt
Prediction
mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3dInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/266c8a83-b349-46da-8837-7d11e1614abb/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/a15417b7-3715-4ec0-8d72-0c7bc19efcd2/vermeer.jpg", "preserve_color": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/266c8a83-b349-46da-8837-7d11e1614abb/iu.jpeg", style_img_0: "https://replicate.delivery/mgxm/a15417b7-3715-4ec0-8d72-0c7bc19efcd2/vermeer.jpg", preserve_color: true } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/266c8a83-b349-46da-8837-7d11e1614abb/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/a15417b7-3715-4ec0-8d72-0c7bc19efcd2/vermeer.jpg", "preserve_color": True } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/266c8a83-b349-46da-8837-7d11e1614abb/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/a15417b7-3715-4ec0-8d72-0c7bc19efcd2/vermeer.jpg", "preserve_color": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T17:54:14.780884Z", "created_at": "2022-02-04T17:52:17.954199Z", "data_removed": false, "error": null, "id": "wyqlgostpvemvbvpxqmzezmxvi", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/266c8a83-b349-46da-8837-7d11e1614abb/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/a15417b7-3715-4ec0-8d72-0c7bc19efcd2/vermeer.jpg", "preserve_color": true }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\n\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<01:30, 2.19it/s]\n 1%| | 2/200 [00:00<01:32, 2.15it/s]\n 2%|▏ | 3/200 [00:01<01:32, 2.13it/s]\n 2%|▏ | 4/200 [00:01<01:32, 2.13it/s]\n 2%|▎ | 5/200 [00:02<01:32, 2.11it/s]\n 3%|▎ | 6/200 [00:02<01:31, 2.11it/s]\n 4%|▎ | 7/200 [00:03<01:31, 2.11it/s]\n 4%|▍ | 8/200 [00:03<01:31, 2.11it/s]\n 4%|▍ | 9/200 [00:04<01:30, 2.10it/s]\n 5%|▌ | 10/200 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"https://replicate.delivery/mgxm/e1fefd88-3fba-490c-b608-f38341421427/out.jpg" } ], "started_at": "2022-02-04T17:52:18.178621Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wyqlgostpvemvbvpxqmzezmxvi", "cancel": "https://api.replicate.com/v1/predictions/wyqlgostpvemvbvpxqmzezmxvi/cancel" }, "version": "45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt 0%| | 0/200 [00:00<?, ?it/s] 0%| | 1/200 [00:00<01:30, 2.19it/s] 1%| | 2/200 [00:00<01:32, 2.15it/s] 2%|▏ | 3/200 [00:01<01:32, 2.13it/s] 2%|▏ | 4/200 [00:01<01:32, 2.13it/s] 2%|▎ | 5/200 [00:02<01:32, 2.11it/s] 3%|▎ | 6/200 [00:02<01:31, 2.11it/s] 4%|▎ | 7/200 [00:03<01:31, 2.11it/s] 4%|▍ | 8/200 [00:03<01:31, 2.11it/s] 4%|▍ | 9/200 [00:04<01:30, 2.10it/s] 5%|▌ | 10/200 [00:04<01:30, 2.10it/s] 6%|▌ | 11/200 [00:05<01:30, 2.10it/s] 6%|▌ | 12/200 [00:05<01:29, 2.10it/s] 6%|▋ | 13/200 [00:06<01:29, 2.10it/s] 7%|▋ | 14/200 [00:06<01:28, 2.09it/s] 8%|▊ | 15/200 [00:07<01:28, 2.10it/s] 8%|▊ | 16/200 [00:07<01:27, 2.09it/s] 8%|▊ | 17/200 [00:08<01:27, 2.09it/s] 9%|▉ | 18/200 [00:08<01:27, 2.09it/s] 10%|▉ | 19/200 [00:09<01:26, 2.09it/s] 10%|█ | 20/200 [00:09<01:26, 2.09it/s] 10%|█ | 21/200 [00:09<01:25, 2.09it/s] 11%|█ | 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Prediction
mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3dIDiatptmxybrbsjcaotxezrdeh2yStatusSucceededSourceWebHardware–Total durationCreatedInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/56c8bd63-6e42-4700-a15b-63b7c181001d/chris_hemsworth.jpeg", "style_img_0": "https://replicate.delivery/mgxm/da4cba64-f6e2-4dd5-9ad5-c3b48bd377fa/george.jpg", "preserve_color": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/56c8bd63-6e42-4700-a15b-63b7c181001d/chris_hemsworth.jpeg", style_img_0: "https://replicate.delivery/mgxm/da4cba64-f6e2-4dd5-9ad5-c3b48bd377fa/george.jpg", preserve_color: true } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/56c8bd63-6e42-4700-a15b-63b7c181001d/chris_hemsworth.jpeg", "style_img_0": "https://replicate.delivery/mgxm/da4cba64-f6e2-4dd5-9ad5-c3b48bd377fa/george.jpg", "preserve_color": True } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/56c8bd63-6e42-4700-a15b-63b7c181001d/chris_hemsworth.jpeg", "style_img_0": "https://replicate.delivery/mgxm/da4cba64-f6e2-4dd5-9ad5-c3b48bd377fa/george.jpg", "preserve_color": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T17:57:48.085473Z", "created_at": "2022-02-04T17:55:17.198794Z", "data_removed": false, "error": null, "id": "iatptmxybrbsjcaotxezrdeh2y", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/56c8bd63-6e42-4700-a15b-63b7c181001d/chris_hemsworth.jpeg", "style_img_0": "https://replicate.delivery/mgxm/da4cba64-f6e2-4dd5-9ad5-c3b48bd377fa/george.jpg", "preserve_color": true }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\n\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<01:33, 2.12it/s]\n 1%| | 2/200 [00:00<01:36, 2.05it/s]\n 2%|▏ | 3/200 [00:01<01:36, 2.05it/s]\n 2%|▏ | 4/200 [00:01<01:36, 2.04it/s]\n 2%|▎ | 5/200 [00:02<01:35, 2.04it/s]\n 3%|▎ | 6/200 [00:02<01:35, 2.03it/s]\n 4%|▎ | 7/200 [00:03<01:35, 2.03it/s]\n 4%|▍ | 8/200 [00:03<01:34, 2.03it/s]\n 4%|▍ | 9/200 [00:04<01:34, 2.02it/s]\n 5%|▌ | 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"https://replicate.delivery/mgxm/21466d80-bf03-4dab-b804-f203d7066917/out.jpg" } ], "started_at": "2022-02-04T17:55:17.384535Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iatptmxybrbsjcaotxezrdeh2y", "cancel": "https://api.replicate.com/v1/predictions/iatptmxybrbsjcaotxezrdeh2y/cancel" }, "version": "45f4421d0f4e0200e1dbe97bcda39468b0be533b3e51ea7babd9dc18e5182c3d" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt 0%| | 0/200 [00:00<?, ?it/s] 0%| | 1/200 [00:00<01:33, 2.12it/s] 1%| | 2/200 [00:00<01:36, 2.05it/s] 2%|▏ | 3/200 [00:01<01:36, 2.05it/s] 2%|▏ | 4/200 [00:01<01:36, 2.04it/s] 2%|▎ | 5/200 [00:02<01:35, 2.04it/s] 3%|▎ | 6/200 [00:02<01:35, 2.03it/s] 4%|▎ | 7/200 [00:03<01:35, 2.03it/s] 4%|▍ | 8/200 [00:03<01:34, 2.03it/s] 4%|▍ | 9/200 [00:04<01:34, 2.02it/s] 5%|▌ | 10/200 [00:04<01:33, 2.03it/s] 6%|▌ | 11/200 [00:05<01:33, 2.02it/s] 6%|▌ | 12/200 [00:05<01:33, 2.02it/s] 6%|▋ | 13/200 [00:06<01:32, 2.01it/s] 7%|▋ | 14/200 [00:06<01:32, 2.01it/s] 8%|▊ | 15/200 [00:07<01:31, 2.01it/s] 8%|▊ | 16/200 [00:07<01:31, 2.01it/s] 8%|▊ | 17/200 [00:08<01:31, 2.01it/s] 9%|▉ | 18/200 [00:08<01:30, 2.00it/s] 10%|▉ | 19/200 [00:09<01:30, 2.01it/s] 10%|█ | 20/200 [00:09<01:29, 2.00it/s] 10%|█ | 21/200 [00:10<01:29, 2.00it/s] 11%|█ | 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Prediction
mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0IDxaryoeyrrfebppfkwhsnakfgkqStatusSucceededSourceWebHardware–Total durationCreatedInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/2bdaa15b-699f-41c5-b2f8-79085924fbba/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/1fe3bb1b-356f-43a4-a622-bf84b726b9b8/brave-merida.jpg", "style_img_2": "https://replicate.delivery/mgxm/21e76689-4273-4fbe-8b65-2327466e1c5f/p_theincredibles_19751_52f1f47a.jpg", "preserve_color": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/2bdaa15b-699f-41c5-b2f8-79085924fbba/iu.jpeg", style_img_0: "https://replicate.delivery/mgxm/1fe3bb1b-356f-43a4-a622-bf84b726b9b8/brave-merida.jpg", style_img_2: "https://replicate.delivery/mgxm/21e76689-4273-4fbe-8b65-2327466e1c5f/p_theincredibles_19751_52f1f47a.jpg", preserve_color: true } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/2bdaa15b-699f-41c5-b2f8-79085924fbba/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/1fe3bb1b-356f-43a4-a622-bf84b726b9b8/brave-merida.jpg", "style_img_2": "https://replicate.delivery/mgxm/21e76689-4273-4fbe-8b65-2327466e1c5f/p_theincredibles_19751_52f1f47a.jpg", "preserve_color": True } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/2bdaa15b-699f-41c5-b2f8-79085924fbba/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/1fe3bb1b-356f-43a4-a622-bf84b726b9b8/brave-merida.jpg", "style_img_2": "https://replicate.delivery/mgxm/21e76689-4273-4fbe-8b65-2327466e1c5f/p_theincredibles_19751_52f1f47a.jpg", "preserve_color": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T20:50:35.594113Z", "created_at": "2022-02-04T20:46:46.689092Z", "data_removed": false, "error": null, "id": "xaryoeyrrfebppfkwhsnakfgkq", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/2bdaa15b-699f-41c5-b2f8-79085924fbba/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/1fe3bb1b-356f-43a4-a622-bf84b726b9b8/brave-merida.jpg", "style_img_2": "https://replicate.delivery/mgxm/21e76689-4273-4fbe-8b65-2327466e1c5f/p_theincredibles_19751_52f1f47a.jpg", "preserve_color": true }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\n\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<03:17, 1.01it/s]\n 1%| | 2/200 [00:01<03:16, 1.01it/s]\n 2%|▏ | 3/200 [00:02<03:16, 1.00it/s]\n 2%|▏ | 4/200 [00:03<03:15, 1.00it/s]\n 2%|▎ | 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1.04s/it]\n100%|██████████| 200/200 [03:26<00:00, 1.03s/it]", "metrics": { "predict_time": 228.69586, "total_time": 228.905021 }, "output": [ { "file": "https://replicate.delivery/mgxm/7932c4b8-c3ba-4dbc-9064-ac18c0af722c/out.jpg" } ], "started_at": "2022-02-04T20:46:46.898253Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xaryoeyrrfebppfkwhsnakfgkq", "cancel": "https://api.replicate.com/v1/predictions/xaryoeyrrfebppfkwhsnakfgkq/cancel" }, "version": "3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt 0%| | 0/200 [00:00<?, ?it/s] 0%| | 1/200 [00:00<03:17, 1.01it/s] 1%| | 2/200 [00:01<03:16, 1.01it/s] 2%|▏ | 3/200 [00:02<03:16, 1.00it/s] 2%|▏ | 4/200 [00:03<03:15, 1.00it/s] 2%|▎ | 5/200 [00:04<03:14, 1.00it/s] 3%|▎ | 6/200 [00:05<03:13, 1.00it/s] 4%|▎ | 7/200 [00:06<03:13, 1.00s/it] 4%|▍ | 8/200 [00:07<03:12, 1.00s/it] 4%|▍ | 9/200 [00:08<03:11, 1.00s/it] 5%|▌ | 10/200 [00:10<03:10, 1.00s/it] 6%|▌ | 11/200 [00:11<03:09, 1.00s/it] 6%|▌ | 12/200 [00:12<03:08, 1.00s/it] 6%|▋ | 13/200 [00:13<03:08, 1.01s/it] 7%|▋ | 14/200 [00:14<03:06, 1.00s/it] 8%|▊ | 15/200 [00:15<03:05, 1.01s/it] 8%|▊ | 16/200 [00:16<03:05, 1.01s/it] 8%|▊ | 17/200 [00:17<03:04, 1.01s/it] 9%|▉ | 18/200 [00:18<03:03, 1.01s/it] 10%|▉ | 19/200 [00:19<03:02, 1.01s/it] 10%|█ | 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Prediction
mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0IDczesfh5phbgbpmcshkqdh4wwliStatusSucceededSourceWebHardware–Total durationCreatedInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "sketch_multi", "style_img_0": "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", pretrained: "sketch_multi", style_img_0: "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", style_img_1: "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", style_img_2: "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", style_img_3: "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "sketch_multi", "style_img_0": "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "sketch_multi", "style_img_0": "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T20:54:54.359714Z", "created_at": "2022-02-04T20:54:41.934204Z", "data_removed": false, "error": null, "id": "czesfh5phbgbpmcshkqdh4wwli", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "sketch_multi", "style_img_0": "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt", "metrics": { "predict_time": 12.184346, "total_time": 12.42551 }, "output": [ { "file": "https://replicate.delivery/mgxm/2ec47e4e-c6ca-4b50-9633-2bb7a07e1212/out.jpg" } ], "started_at": "2022-02-04T20:54:42.175368Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/czesfh5phbgbpmcshkqdh4wwli", "cancel": "https://api.replicate.com/v1/predictions/czesfh5phbgbpmcshkqdh4wwli/cancel" }, "version": "3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt
Prediction
mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0Input
{ "alpha": 1, "num_iter": 50, "input_face": "https://replicate.delivery/mgxm/eff74db5-edbb-49b2-9dab-faffa85bd0c5/chris_hemsworth.jpeg", "style_img_0": "https://replicate.delivery/mgxm/71c9c02c-9002-4fda-9db5-0182c84d4039/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/25ee4ad0-77d5-4958-b836-cd826c5aa0b5/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/7af0a8c8-7972-47b0-86bd-aebda8f2a088/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/c09fbb42-5bf5-49b8-a33a-ce3f568da88e/sketch4.jpeg" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", { input: { alpha: 1, num_iter: 50, input_face: "https://replicate.delivery/mgxm/eff74db5-edbb-49b2-9dab-faffa85bd0c5/chris_hemsworth.jpeg", style_img_0: "https://replicate.delivery/mgxm/71c9c02c-9002-4fda-9db5-0182c84d4039/sketch.jpeg", style_img_1: "https://replicate.delivery/mgxm/25ee4ad0-77d5-4958-b836-cd826c5aa0b5/sketch2.jpeg", style_img_2: "https://replicate.delivery/mgxm/7af0a8c8-7972-47b0-86bd-aebda8f2a088/sketch3.jpeg", style_img_3: "https://replicate.delivery/mgxm/c09fbb42-5bf5-49b8-a33a-ce3f568da88e/sketch4.jpeg" } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", input={ "alpha": 1, "num_iter": 50, "input_face": "https://replicate.delivery/mgxm/eff74db5-edbb-49b2-9dab-faffa85bd0c5/chris_hemsworth.jpeg", "style_img_0": "https://replicate.delivery/mgxm/71c9c02c-9002-4fda-9db5-0182c84d4039/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/25ee4ad0-77d5-4958-b836-cd826c5aa0b5/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/7af0a8c8-7972-47b0-86bd-aebda8f2a088/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/c09fbb42-5bf5-49b8-a33a-ce3f568da88e/sketch4.jpeg" } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", "input": { "alpha": 1, "num_iter": 50, "input_face": "https://replicate.delivery/mgxm/eff74db5-edbb-49b2-9dab-faffa85bd0c5/chris_hemsworth.jpeg", "style_img_0": "https://replicate.delivery/mgxm/71c9c02c-9002-4fda-9db5-0182c84d4039/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/25ee4ad0-77d5-4958-b836-cd826c5aa0b5/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/7af0a8c8-7972-47b0-86bd-aebda8f2a088/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/c09fbb42-5bf5-49b8-a33a-ce3f568da88e/sketch4.jpeg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T21:16:42.583464Z", "created_at": "2022-02-04T21:12:39.200557Z", "data_removed": false, "error": null, "id": "wn6lwegmyjbffa4t7iih5tgwlq", "input": { "alpha": 1, "num_iter": 50, "input_face": "https://replicate.delivery/mgxm/eff74db5-edbb-49b2-9dab-faffa85bd0c5/chris_hemsworth.jpeg", "style_img_0": "https://replicate.delivery/mgxm/71c9c02c-9002-4fda-9db5-0182c84d4039/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/25ee4ad0-77d5-4958-b836-cd826c5aa0b5/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/7af0a8c8-7972-47b0-86bd-aebda8f2a088/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/c09fbb42-5bf5-49b8-a33a-ce3f568da88e/sketch4.jpeg" }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:03<03:01, 3.70s/it]\n 4%|▍ | 2/50 [00:07<02:58, 3.72s/it]\n 6%|▌ | 3/50 [00:11<02:55, 3.73s/it]\n 8%|▊ | 4/50 [00:14<02:51, 3.74s/it]\n 10%|█ | 5/50 [00:18<02:48, 3.75s/it]\n 12%|█▏ | 6/50 [00:22<02:45, 3.75s/it]\n 14%|█▍ | 7/50 [00:26<02:41, 3.76s/it]\n 16%|█▌ | 8/50 [00:30<02:38, 3.77s/it]\n 18%|█▊ | 9/50 [00:33<02:34, 3.78s/it]\n 20%|██ | 10/50 [00:37<02:31, 3.78s/it]\n 22%|██▏ | 11/50 [00:41<02:27, 3.79s/it]\n 24%|██▍ | 12/50 [00:45<02:24, 3.80s/it]\n 26%|██▌ | 13/50 [00:49<02:20, 3.80s/it]\n 28%|██▊ | 14/50 [00:52<02:17, 3.81s/it]\n 30%|███ | 15/50 [00:56<02:13, 3.82s/it]\n 32%|███▏ | 16/50 [01:00<02:09, 3.82s/it]\n 34%|███▍ | 17/50 [01:04<02:06, 3.83s/it]\n 36%|███▌ | 18/50 [01:08<02:02, 3.83s/it]\n 38%|███▊ | 19/50 [01:12<01:58, 3.84s/it]\n 40%|████ | 20/50 [01:15<01:55, 3.84s/it]\n 42%|████▏ | 21/50 [01:19<01:51, 3.84s/it]\n 44%|████▍ | 22/50 [01:23<01:47, 3.84s/it]\n 46%|████▌ | 23/50 [01:27<01:43, 3.85s/it]\n 48%|████▊ | 24/50 [01:31<01:40, 3.85s/it]\n 50%|█████ | 25/50 [01:35<01:36, 3.86s/it]\n 52%|█████▏ | 26/50 [01:39<01:32, 3.87s/it]\n 54%|█████▍ | 27/50 [01:42<01:29, 3.88s/it]\n 56%|█████▌ | 28/50 [01:46<01:25, 3.88s/it]\n 58%|█████▊ | 29/50 [01:50<01:21, 3.89s/it]\n 60%|██████ | 30/50 [01:54<01:17, 3.89s/it]\n 62%|██████▏ | 31/50 [01:58<01:14, 3.90s/it]\n 64%|██████▍ | 32/50 [02:02<01:10, 3.90s/it]\n 66%|██████▌ | 33/50 [02:06<01:06, 3.90s/it]\n 68%|██████▊ | 34/50 [02:10<01:02, 3.90s/it]\n 70%|███████ | 35/50 [02:14<00:58, 3.90s/it]\n 72%|███████▏ | 36/50 [02:18<00:54, 3.91s/it]\n 74%|███████▍ | 37/50 [02:22<00:50, 3.90s/it]\n 76%|███████▌ | 38/50 [02:25<00:46, 3.90s/it]\n 78%|███████▊ | 39/50 [02:29<00:42, 3.90s/it]\n 80%|████████ | 40/50 [02:33<00:38, 3.90s/it]\n 82%|████████▏ | 41/50 [02:37<00:35, 3.90s/it]\n 84%|████████▍ | 42/50 [02:41<00:31, 3.90s/it]\n 86%|████████▌ | 43/50 [02:45<00:27, 3.90s/it]\n 88%|████████▊ | 44/50 [02:49<00:23, 3.90s/it]\n 90%|█████████ | 45/50 [02:53<00:19, 3.90s/it]\n 92%|█████████▏| 46/50 [02:57<00:15, 3.90s/it]\n 94%|█████████▍| 47/50 [03:01<00:11, 3.91s/it]\n 96%|█████████▌| 48/50 [03:04<00:07, 3.91s/it]\n 98%|█████████▊| 49/50 [03:08<00:03, 3.91s/it]\n100%|██████████| 50/50 [03:12<00:00, 3.92s/it]\n100%|██████████| 50/50 [03:12<00:00, 3.86s/it]", "metrics": { "predict_time": 243.130902, "total_time": 243.382907 }, "output": [ { "file": "https://replicate.delivery/mgxm/ffadccf8-6363-4f96-9596-0ad2451f1da7/out.jpg" } ], "started_at": "2022-02-04T21:12:39.452562Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wn6lwegmyjbffa4t7iih5tgwlq", "cancel": "https://api.replicate.com/v1/predictions/wn6lwegmyjbffa4t7iih5tgwlq/cancel" }, "version": "3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:03<03:01, 3.70s/it] 4%|▍ | 2/50 [00:07<02:58, 3.72s/it] 6%|▌ | 3/50 [00:11<02:55, 3.73s/it] 8%|▊ | 4/50 [00:14<02:51, 3.74s/it] 10%|█ | 5/50 [00:18<02:48, 3.75s/it] 12%|█▏ | 6/50 [00:22<02:45, 3.75s/it] 14%|█▍ | 7/50 [00:26<02:41, 3.76s/it] 16%|█▌ | 8/50 [00:30<02:38, 3.77s/it] 18%|█▊ | 9/50 [00:33<02:34, 3.78s/it] 20%|██ | 10/50 [00:37<02:31, 3.78s/it] 22%|██▏ | 11/50 [00:41<02:27, 3.79s/it] 24%|██▍ | 12/50 [00:45<02:24, 3.80s/it] 26%|██▌ | 13/50 [00:49<02:20, 3.80s/it] 28%|██▊ | 14/50 [00:52<02:17, 3.81s/it] 30%|███ | 15/50 [00:56<02:13, 3.82s/it] 32%|███▏ | 16/50 [01:00<02:09, 3.82s/it] 34%|███▍ | 17/50 [01:04<02:06, 3.83s/it] 36%|███▌ | 18/50 [01:08<02:02, 3.83s/it] 38%|███▊ | 19/50 [01:12<01:58, 3.84s/it] 40%|████ | 20/50 [01:15<01:55, 3.84s/it] 42%|████▏ | 21/50 [01:19<01:51, 3.84s/it] 44%|████▍ | 22/50 [01:23<01:47, 3.84s/it] 46%|████▌ | 23/50 [01:27<01:43, 3.85s/it] 48%|████▊ | 24/50 [01:31<01:40, 3.85s/it] 50%|█████ | 25/50 [01:35<01:36, 3.86s/it] 52%|█████▏ | 26/50 [01:39<01:32, 3.87s/it] 54%|█████▍ | 27/50 [01:42<01:29, 3.88s/it] 56%|█████▌ | 28/50 [01:46<01:25, 3.88s/it] 58%|█████▊ | 29/50 [01:50<01:21, 3.89s/it] 60%|██████ | 30/50 [01:54<01:17, 3.89s/it] 62%|██████▏ | 31/50 [01:58<01:14, 3.90s/it] 64%|██████▍ | 32/50 [02:02<01:10, 3.90s/it] 66%|██████▌ | 33/50 [02:06<01:06, 3.90s/it] 68%|██████▊ | 34/50 [02:10<01:02, 3.90s/it] 70%|███████ | 35/50 [02:14<00:58, 3.90s/it] 72%|███████▏ | 36/50 [02:18<00:54, 3.91s/it] 74%|███████▍ | 37/50 [02:22<00:50, 3.90s/it] 76%|███████▌ | 38/50 [02:25<00:46, 3.90s/it] 78%|███████▊ | 39/50 [02:29<00:42, 3.90s/it] 80%|████████ | 40/50 [02:33<00:38, 3.90s/it] 82%|████████▏ | 41/50 [02:37<00:35, 3.90s/it] 84%|████████▍ | 42/50 [02:41<00:31, 3.90s/it] 86%|████████▌ | 43/50 [02:45<00:27, 3.90s/it] 88%|████████▊ | 44/50 [02:49<00:23, 3.90s/it] 90%|█████████ | 45/50 [02:53<00:19, 3.90s/it] 92%|█████████▏| 46/50 [02:57<00:15, 3.90s/it] 94%|█████████▍| 47/50 [03:01<00:11, 3.91s/it] 96%|█████████▌| 48/50 [03:04<00:07, 3.91s/it] 98%|█████████▊| 49/50 [03:08<00:03, 3.91s/it] 100%|██████████| 50/50 [03:12<00:00, 3.92s/it] 100%|██████████| 50/50 [03:12<00:00, 3.86s/it]
Prediction
mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0Input
{ "alpha": 1, "num_iter": 50, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", { input: { alpha: 1, num_iter: 50, input_face: "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", style_img_0: "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", style_img_1: "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", style_img_2: "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", style_img_3: "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", input={ "alpha": 1, "num_iter": 50, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", "input": { "alpha": 1, "num_iter": 50, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T21:20:51.822395Z", "created_at": "2022-02-04T21:17:00.500861Z", "data_removed": false, "error": null, "id": "t5jcvttvwja55j2thllqr2buz4", "input": { "alpha": 1, "num_iter": 50, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "style_img_0": "https://replicate.delivery/mgxm/e81e8d2c-da17-4bbb-ab17-155abea11704/sketch.jpeg", "style_img_1": "https://replicate.delivery/mgxm/22cac4bd-2024-4b15-be62-1fd3c785a7bb/sketch2.jpeg", "style_img_2": "https://replicate.delivery/mgxm/3de4b405-8042-4749-9bdd-21967fae2fbc/sketch3.jpeg", "style_img_3": "https://replicate.delivery/mgxm/3f5f141f-8dd9-4aaf-8e9c-e81c4555801e/sketch4.jpeg" }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\nLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:03<03:02, 3.73s/it]\n 4%|▍ | 2/50 [00:07<02:59, 3.74s/it]\n 6%|▌ | 3/50 [00:11<02:56, 3.75s/it]\n 8%|▊ | 4/50 [00:15<02:53, 3.76s/it]\n 10%|█ | 5/50 [00:18<02:49, 3.77s/it]\n 12%|█▏ | 6/50 [00:22<02:46, 3.78s/it]\n 14%|█▍ | 7/50 [00:26<02:42, 3.79s/it]\n 16%|█▌ | 8/50 [00:30<02:39, 3.79s/it]\n 18%|█▊ | 9/50 [00:34<02:35, 3.80s/it]\n 20%|██ | 10/50 [00:37<02:32, 3.81s/it]\n 22%|██▏ | 11/50 [00:41<02:28, 3.81s/it]\n 24%|██▍ | 12/50 [00:45<02:25, 3.82s/it]\n 26%|██▌ | 13/50 [00:49<02:21, 3.83s/it]\n 28%|██▊ | 14/50 [00:53<02:17, 3.83s/it]\n 30%|███ | 15/50 [00:57<02:14, 3.84s/it]\n 32%|███▏ | 16/50 [01:00<02:10, 3.84s/it]\n 34%|███▍ | 17/50 [01:04<02:06, 3.85s/it]\n 36%|███▌ | 18/50 [01:08<02:03, 3.85s/it]\n 38%|███▊ | 19/50 [01:12<01:59, 3.86s/it]\n 40%|████ | 20/50 [01:16<01:55, 3.86s/it]\n 42%|████▏ | 21/50 [01:20<01:52, 3.87s/it]\n 44%|████▍ | 22/50 [01:24<01:48, 3.87s/it]\n 46%|████▌ | 23/50 [01:28<01:44, 3.88s/it]\n 48%|████▊ | 24/50 [01:31<01:40, 3.88s/it]\n 50%|█████ | 25/50 [01:35<01:37, 3.88s/it]\n 52%|█████▏ | 26/50 [01:39<01:33, 3.88s/it]\n 54%|█████▍ | 27/50 [01:43<01:29, 3.89s/it]\n 56%|█████▌ | 28/50 [01:47<01:25, 3.89s/it]\n 58%|█████▊ | 29/50 [01:51<01:21, 3.89s/it]\n 60%|██████ | 30/50 [01:55<01:17, 3.89s/it]\n 62%|██████▏ | 31/50 [01:59<01:13, 3.89s/it]\n 64%|██████▍ | 32/50 [02:03<01:10, 3.89s/it]\n 66%|██████▌ | 33/50 [02:06<01:06, 3.89s/it]\n 68%|██████▊ | 34/50 [02:10<01:02, 3.90s/it]\n 70%|███████ | 35/50 [02:14<00:58, 3.90s/it]\n 72%|███████▏ | 36/50 [02:18<00:54, 3.90s/it]\n 74%|███████▍ | 37/50 [02:22<00:50, 3.90s/it]\n 76%|███████▌ | 38/50 [02:26<00:46, 3.91s/it]\n 78%|███████▊ | 39/50 [02:30<00:42, 3.91s/it]\n 80%|████████ | 40/50 [02:34<00:39, 3.91s/it]\n 82%|████████▏ | 41/50 [02:38<00:35, 3.91s/it]\n 84%|████████▍ | 42/50 [02:42<00:31, 3.91s/it]\n 86%|████████▌ | 43/50 [02:46<00:27, 3.92s/it]\n 88%|████████▊ | 44/50 [02:49<00:23, 3.92s/it]\n 90%|█████████ | 45/50 [02:53<00:19, 3.92s/it]\n 92%|█████████▏| 46/50 [02:57<00:15, 3.92s/it]\n 94%|█████████▍| 47/50 [03:01<00:11, 3.92s/it]\n 96%|█████████▌| 48/50 [03:05<00:07, 3.92s/it]\n 98%|█████████▊| 49/50 [03:09<00:03, 3.92s/it]\n100%|██████████| 50/50 [03:13<00:00, 3.92s/it]\n100%|██████████| 50/50 [03:13<00:00, 3.87s/it]", "metrics": { "predict_time": 230.993631, "total_time": 231.321534 }, "output": [ { "file": "https://replicate.delivery/mgxm/7741917d-06b3-4869-a31f-84ce66db39b5/out.jpg" } ], "started_at": "2022-02-04T21:17:00.828764Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/t5jcvttvwja55j2thllqr2buz4", "cancel": "https://api.replicate.com/v1/predictions/t5jcvttvwja55j2thllqr2buz4/cancel" }, "version": "3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:03<03:02, 3.73s/it] 4%|▍ | 2/50 [00:07<02:59, 3.74s/it] 6%|▌ | 3/50 [00:11<02:56, 3.75s/it] 8%|▊ | 4/50 [00:15<02:53, 3.76s/it] 10%|█ | 5/50 [00:18<02:49, 3.77s/it] 12%|█▏ | 6/50 [00:22<02:46, 3.78s/it] 14%|█▍ | 7/50 [00:26<02:42, 3.79s/it] 16%|█▌ | 8/50 [00:30<02:39, 3.79s/it] 18%|█▊ | 9/50 [00:34<02:35, 3.80s/it] 20%|██ | 10/50 [00:37<02:32, 3.81s/it] 22%|██▏ | 11/50 [00:41<02:28, 3.81s/it] 24%|██▍ | 12/50 [00:45<02:25, 3.82s/it] 26%|██▌ | 13/50 [00:49<02:21, 3.83s/it] 28%|██▊ | 14/50 [00:53<02:17, 3.83s/it] 30%|███ | 15/50 [00:57<02:14, 3.84s/it] 32%|███▏ | 16/50 [01:00<02:10, 3.84s/it] 34%|███▍ | 17/50 [01:04<02:06, 3.85s/it] 36%|███▌ | 18/50 [01:08<02:03, 3.85s/it] 38%|███▊ | 19/50 [01:12<01:59, 3.86s/it] 40%|████ | 20/50 [01:16<01:55, 3.86s/it] 42%|████▏ | 21/50 [01:20<01:52, 3.87s/it] 44%|████▍ | 22/50 [01:24<01:48, 3.87s/it] 46%|████▌ | 23/50 [01:28<01:44, 3.88s/it] 48%|████▊ | 24/50 [01:31<01:40, 3.88s/it] 50%|█████ | 25/50 [01:35<01:37, 3.88s/it] 52%|█████▏ | 26/50 [01:39<01:33, 3.88s/it] 54%|█████▍ | 27/50 [01:43<01:29, 3.89s/it] 56%|█████▌ | 28/50 [01:47<01:25, 3.89s/it] 58%|█████▊ | 29/50 [01:51<01:21, 3.89s/it] 60%|██████ | 30/50 [01:55<01:17, 3.89s/it] 62%|██████▏ | 31/50 [01:59<01:13, 3.89s/it] 64%|██████▍ | 32/50 [02:03<01:10, 3.89s/it] 66%|██████▌ | 33/50 [02:06<01:06, 3.89s/it] 68%|██████▊ | 34/50 [02:10<01:02, 3.90s/it] 70%|███████ | 35/50 [02:14<00:58, 3.90s/it] 72%|███████▏ | 36/50 [02:18<00:54, 3.90s/it] 74%|███████▍ | 37/50 [02:22<00:50, 3.90s/it] 76%|███████▌ | 38/50 [02:26<00:46, 3.91s/it] 78%|███████▊ | 39/50 [02:30<00:42, 3.91s/it] 80%|████████ | 40/50 [02:34<00:39, 3.91s/it] 82%|████████▏ | 41/50 [02:38<00:35, 3.91s/it] 84%|████████▍ | 42/50 [02:42<00:31, 3.91s/it] 86%|████████▌ | 43/50 [02:46<00:27, 3.92s/it] 88%|████████▊ | 44/50 [02:49<00:23, 3.92s/it] 90%|█████████ | 45/50 [02:53<00:19, 3.92s/it] 92%|█████████▏| 46/50 [02:57<00:15, 3.92s/it] 94%|█████████▍| 47/50 [03:01<00:11, 3.92s/it] 96%|█████████▌| 48/50 [03:05<00:07, 3.92s/it] 98%|█████████▊| 49/50 [03:09<00:03, 3.92s/it] 100%|██████████| 50/50 [03:13<00:00, 3.92s/it] 100%|██████████| 50/50 [03:13<00:00, 3.87s/it]
Prediction
mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0IDdjhaqqqjljfbdpbahj5ztyk4amStatusSucceededSourceWebHardware–Total durationCreatedInput
{ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "jojo", "preserve_color": false }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", { input: { alpha: 1, num_iter: 200, input_face: "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", pretrained: "jojo", preserve_color: false } } ); console.log(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 mchong6/jojogan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", input={ "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "jojo", "preserve_color": False } ) # The mchong6/jojogan model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/mchong6/jojogan/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run mchong6/jojogan 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": "mchong6/jojogan:3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "jojo", "preserve_color": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-04T21:24:11.333519Z", "created_at": "2022-02-04T21:23:59.615342Z", "data_removed": false, "error": null, "id": "djhaqqqjljfbdpbahj5ztyk4am", "input": { "alpha": 1, "num_iter": 200, "input_face": "https://replicate.delivery/mgxm/5ef0693c-8d77-4de1-8759-52c497a33925/iu.jpeg", "pretrained": "jojo", "preserve_color": false }, "logs": "Loading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt", "metrics": { "predict_time": 11.442153, "total_time": 11.718177 }, "output": [ { "file": "https://replicate.delivery/mgxm/d281c67a-2b18-4776-bda7-16c943ab4d27/out.jpg" } ], "started_at": "2022-02-04T21:23:59.891366Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/djhaqqqjljfbdpbahj5ztyk4am", "cancel": "https://api.replicate.com/v1/predictions/djhaqqqjljfbdpbahj5ztyk4am/cancel" }, "version": "3698093872606086201cc8fba38fe53b8fb9909136202c214b7503b305a8b5f0" }
Generated inLoading e4e over the pSp framework from checkpoint: models/e4e_ffhq_encode.pt
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