tuannha / instant-character
Tencent Instant Character
Prediction
tuannha/instant-character:cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6fID2vxx06evpsrm80cp8vxsffjj5cStatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- lora
- ghibli_style
- seed
- -1
- width
- 768
- height
- 1344
- prompt
- a character in the library
- guidance_scale
- 3.5
- num_inference_steps
- 28
{ "lora": "ghibli_style", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 }
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 tuannha/instant-character using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tuannha/instant-character:cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6f", { input: { lora: "ghibli_style", seed: -1, width: 768, height: 1344, prompt: "a character in the library", subject_image: "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", guidance_scale: 3.5, num_inference_steps: 28 } } ); // 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 tuannha/instant-character using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tuannha/instant-character:cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6f", input={ "lora": "ghibli_style", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 } ) # 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 tuannha/instant-character 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": "tuannha/instant-character:cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6f", "input": { "lora": "ghibli_style", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-04-18T03:40:52.931853Z", "created_at": "2025-04-18T03:40:33.078000Z", "data_removed": false, "error": null, "id": "2vxx06evpsrm80cp8vxsffjj5c", "input": { "lora": "ghibli_style", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 }, "logs": "loading lora in transformer ...: 0%| | 0/988 [00:00<?, ?it/s]\nloading lora in transformer ...: 53%|█████▎ | 528/988 [00:00<00:00, 5258.64it/s]\nloading lora in transformer ...: 100%|██████████| 988/988 [00:00<00:00, 3879.36it/s]\nloading lora in text_encoder ...: 0it [00:00, ?it/s]\nloading lora in text_encoder ...: 0it [00:00, ?it/s]\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:17, 1.58it/s]\n 7%|▋ | 2/28 [00:01<00:13, 1.87it/s]\n 11%|█ | 3/28 [00:01<00:14, 1.73it/s]\n 14%|█▍ | 4/28 [00:02<00:14, 1.67it/s]\n 18%|█▊ | 5/28 [00:02<00:14, 1.63it/s]\n 21%|██▏ | 6/28 [00:03<00:13, 1.61it/s]\n 25%|██▌ | 7/28 [00:04<00:13, 1.60it/s]\n 29%|██▊ | 8/28 [00:04<00:12, 1.59it/s]\n 32%|███▏ | 9/28 [00:05<00:11, 1.59it/s]\n 36%|███▌ | 10/28 [00:06<00:11, 1.58it/s]\n 39%|███▉ | 11/28 [00:06<00:10, 1.58it/s]\n 43%|████▎ | 12/28 [00:07<00:10, 1.58it/s]\n 46%|████▋ | 13/28 [00:08<00:09, 1.58it/s]\n 50%|█████ | 14/28 [00:08<00:08, 1.58it/s]\n 54%|█████▎ | 15/28 [00:09<00:08, 1.57it/s]\n 57%|█████▋ | 16/28 [00:09<00:07, 1.57it/s]\n 61%|██████ | 17/28 [00:10<00:06, 1.57it/s]\n 64%|██████▍ | 18/28 [00:11<00:06, 1.57it/s]\n 68%|██████▊ | 19/28 [00:11<00:05, 1.57it/s]\n 71%|███████▏ | 20/28 [00:12<00:05, 1.57it/s]\n 75%|███████▌ | 21/28 [00:13<00:04, 1.57it/s]\n 79%|███████▊ | 22/28 [00:13<00:03, 1.57it/s]\n 82%|████████▏ | 23/28 [00:14<00:03, 1.57it/s]\n 86%|████████▌ | 24/28 [00:15<00:02, 1.56it/s]\n 89%|████████▉ | 25/28 [00:15<00:01, 1.56it/s]\n 93%|█████████▎| 26/28 [00:16<00:01, 1.56it/s]\n 96%|█████████▋| 27/28 [00:17<00:00, 1.56it/s]\n100%|██████████| 28/28 [00:17<00:00, 1.56it/s]\n100%|██████████| 28/28 [00:17<00:00, 1.59it/s]\nloading lora in transformer ...: 0%| | 0/988 [00:00<?, ?it/s]\nloading lora in transformer ...: 65%|██████▍ | 640/988 [00:00<00:00, 6389.53it/s]\nloading lora in transformer ...: 100%|██████████| 988/988 [00:00<00:00, 4527.79it/s]\nloading lora in text_encoder ...: 0it [00:00, ?it/s]\nloading lora in text_encoder ...: 0it [00:00, ?it/s]", "metrics": { "predict_time": 19.846660952, "total_time": 19.853853 }, "output": "https://replicate.delivery/xezq/wtf5hwaR2EwgVaEsMDU41pBCcjntw5wVOG99ujj9NPWi37RKA/73dbbbd9-314a-4cd8-8022-cb77a6ae877b.png", "started_at": "2025-04-18T03:40:33.085192Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-np7dnpvqloa3gsnjhwf3yxlha7kmsdujlxxhg4li7gixiobwpkva", "get": "https://api.replicate.com/v1/predictions/2vxx06evpsrm80cp8vxsffjj5c", "cancel": "https://api.replicate.com/v1/predictions/2vxx06evpsrm80cp8vxsffjj5c/cancel" }, "version": "cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6f" }
Generated inloading lora in transformer ...: 0%| | 0/988 [00:00<?, ?it/s] loading lora in transformer ...: 53%|█████▎ | 528/988 [00:00<00:00, 5258.64it/s] loading lora in transformer ...: 100%|██████████| 988/988 [00:00<00:00, 3879.36it/s] loading lora in text_encoder ...: 0it [00:00, ?it/s] loading lora in text_encoder ...: 0it [00:00, ?it/s] 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:17, 1.58it/s] 7%|▋ | 2/28 [00:01<00:13, 1.87it/s] 11%|█ | 3/28 [00:01<00:14, 1.73it/s] 14%|█▍ | 4/28 [00:02<00:14, 1.67it/s] 18%|█▊ | 5/28 [00:02<00:14, 1.63it/s] 21%|██▏ | 6/28 [00:03<00:13, 1.61it/s] 25%|██▌ | 7/28 [00:04<00:13, 1.60it/s] 29%|██▊ | 8/28 [00:04<00:12, 1.59it/s] 32%|███▏ | 9/28 [00:05<00:11, 1.59it/s] 36%|███▌ | 10/28 [00:06<00:11, 1.58it/s] 39%|███▉ | 11/28 [00:06<00:10, 1.58it/s] 43%|████▎ | 12/28 [00:07<00:10, 1.58it/s] 46%|████▋ | 13/28 [00:08<00:09, 1.58it/s] 50%|█████ | 14/28 [00:08<00:08, 1.58it/s] 54%|█████▎ | 15/28 [00:09<00:08, 1.57it/s] 57%|█████▋ | 16/28 [00:09<00:07, 1.57it/s] 61%|██████ | 17/28 [00:10<00:06, 1.57it/s] 64%|██████▍ | 18/28 [00:11<00:06, 1.57it/s] 68%|██████▊ | 19/28 [00:11<00:05, 1.57it/s] 71%|███████▏ | 20/28 [00:12<00:05, 1.57it/s] 75%|███████▌ | 21/28 [00:13<00:04, 1.57it/s] 79%|███████▊ | 22/28 [00:13<00:03, 1.57it/s] 82%|████████▏ | 23/28 [00:14<00:03, 1.57it/s] 86%|████████▌ | 24/28 [00:15<00:02, 1.56it/s] 89%|████████▉ | 25/28 [00:15<00:01, 1.56it/s] 93%|█████████▎| 26/28 [00:16<00:01, 1.56it/s] 96%|█████████▋| 27/28 [00:17<00:00, 1.56it/s] 100%|██████████| 28/28 [00:17<00:00, 1.56it/s] 100%|██████████| 28/28 [00:17<00:00, 1.59it/s] loading lora in transformer ...: 0%| | 0/988 [00:00<?, ?it/s] loading lora in transformer ...: 65%|██████▍ | 640/988 [00:00<00:00, 6389.53it/s] loading lora in transformer ...: 100%|██████████| 988/988 [00:00<00:00, 4527.79it/s] loading lora in text_encoder ...: 0it [00:00, ?it/s] loading lora in text_encoder ...: 0it [00:00, ?it/s]
Prediction
tuannha/instant-character:cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6fID3bbzmadkb5rma0cp8vyadcwrw4StatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- lora
- none
- seed
- -1
- width
- 768
- height
- 1344
- prompt
- a character in the library
- guidance_scale
- 3.5
- num_inference_steps
- 28
{ "lora": "none", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr90sERw0hkj2xwe6gSUzppLzJnyX3KpN7ok9yFPmld3jFBl/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 }
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 tuannha/instant-character using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tuannha/instant-character:cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6f", { input: { lora: "none", seed: -1, width: 768, height: 1344, prompt: "a character in the library", subject_image: "https://replicate.delivery/pbxt/Mr90sERw0hkj2xwe6gSUzppLzJnyX3KpN7ok9yFPmld3jFBl/face.png", guidance_scale: 3.5, num_inference_steps: 28 } } ); // 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 tuannha/instant-character using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tuannha/instant-character:cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6f", input={ "lora": "none", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr90sERw0hkj2xwe6gSUzppLzJnyX3KpN7ok9yFPmld3jFBl/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 } ) # 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 tuannha/instant-character 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": "tuannha/instant-character:cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6f", "input": { "lora": "none", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr90sERw0hkj2xwe6gSUzppLzJnyX3KpN7ok9yFPmld3jFBl/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-04-18T03:41:47.461116Z", "created_at": "2025-04-18T03:41:28.281000Z", "data_removed": false, "error": null, "id": "3bbzmadkb5rma0cp8vyadcwrw4", "input": { "lora": "none", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr90sERw0hkj2xwe6gSUzppLzJnyX3KpN7ok9yFPmld3jFBl/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 }, "logs": "0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:17, 1.57it/s]\n 7%|▋ | 2/28 [00:01<00:13, 1.86it/s]\n 11%|█ | 3/28 [00:01<00:14, 1.72it/s]\n 14%|█▍ | 4/28 [00:02<00:14, 1.66it/s]\n 18%|█▊ | 5/28 [00:03<00:14, 1.63it/s]\n 21%|██▏ | 6/28 [00:03<00:13, 1.60it/s]\n 25%|██▌ | 7/28 [00:04<00:13, 1.59it/s]\n 29%|██▊ | 8/28 [00:04<00:12, 1.58it/s]\n 32%|███▏ | 9/28 [00:05<00:12, 1.58it/s]\n 36%|███▌ | 10/28 [00:06<00:11, 1.57it/s]\n 39%|███▉ | 11/28 [00:06<00:10, 1.57it/s]\n 43%|████▎ | 12/28 [00:07<00:10, 1.57it/s]\n 46%|████▋ | 13/28 [00:08<00:09, 1.57it/s]\n 50%|█████ | 14/28 [00:08<00:08, 1.57it/s]\n 54%|█████▎ | 15/28 [00:09<00:08, 1.56it/s]\n 57%|█████▋ | 16/28 [00:10<00:07, 1.56it/s]\n 61%|██████ | 17/28 [00:10<00:07, 1.56it/s]\n 64%|██████▍ | 18/28 [00:11<00:06, 1.56it/s]\n 68%|██████▊ | 19/28 [00:11<00:05, 1.56it/s]\n 71%|███████▏ | 20/28 [00:12<00:05, 1.56it/s]\n 75%|███████▌ | 21/28 [00:13<00:04, 1.56it/s]\n 79%|███████▊ | 22/28 [00:13<00:03, 1.56it/s]\n 82%|████████▏ | 23/28 [00:14<00:03, 1.56it/s]\n 86%|████████▌ | 24/28 [00:15<00:02, 1.56it/s]\n 89%|████████▉ | 25/28 [00:15<00:01, 1.56it/s]\n 93%|█████████▎| 26/28 [00:16<00:01, 1.56it/s]\n 96%|█████████▋| 27/28 [00:17<00:00, 1.55it/s]\n100%|██████████| 28/28 [00:17<00:00, 1.55it/s]\n100%|██████████| 28/28 [00:17<00:00, 1.58it/s]", "metrics": { "predict_time": 19.173149953, "total_time": 19.180116 }, "output": "https://replicate.delivery/xezq/TZrXeXN5mLQLc6wUTuARHNTOd8Z2mmGtznWOkAigJZq937RKA/5fc6336a-dfee-4f12-9059-9f69155f63ef.png", "started_at": "2025-04-18T03:41:28.287966Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-n5hd2yh7dm7qigfiyvirbjpzjlv33yc53uhgm3w4nuh4pp2abtnq", "get": "https://api.replicate.com/v1/predictions/3bbzmadkb5rma0cp8vyadcwrw4", "cancel": "https://api.replicate.com/v1/predictions/3bbzmadkb5rma0cp8vyadcwrw4/cancel" }, "version": "cf2799a4d552fd49b3ef7d58cbcbb147d6f098ec42a307e2e857955e35c7bd6f" }
Generated in0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:17, 1.57it/s] 7%|▋ | 2/28 [00:01<00:13, 1.86it/s] 11%|█ | 3/28 [00:01<00:14, 1.72it/s] 14%|█▍ | 4/28 [00:02<00:14, 1.66it/s] 18%|█▊ | 5/28 [00:03<00:14, 1.63it/s] 21%|██▏ | 6/28 [00:03<00:13, 1.60it/s] 25%|██▌ | 7/28 [00:04<00:13, 1.59it/s] 29%|██▊ | 8/28 [00:04<00:12, 1.58it/s] 32%|███▏ | 9/28 [00:05<00:12, 1.58it/s] 36%|███▌ | 10/28 [00:06<00:11, 1.57it/s] 39%|███▉ | 11/28 [00:06<00:10, 1.57it/s] 43%|████▎ | 12/28 [00:07<00:10, 1.57it/s] 46%|████▋ | 13/28 [00:08<00:09, 1.57it/s] 50%|█████ | 14/28 [00:08<00:08, 1.57it/s] 54%|█████▎ | 15/28 [00:09<00:08, 1.56it/s] 57%|█████▋ | 16/28 [00:10<00:07, 1.56it/s] 61%|██████ | 17/28 [00:10<00:07, 1.56it/s] 64%|██████▍ | 18/28 [00:11<00:06, 1.56it/s] 68%|██████▊ | 19/28 [00:11<00:05, 1.56it/s] 71%|███████▏ | 20/28 [00:12<00:05, 1.56it/s] 75%|███████▌ | 21/28 [00:13<00:04, 1.56it/s] 79%|███████▊ | 22/28 [00:13<00:03, 1.56it/s] 82%|████████▏ | 23/28 [00:14<00:03, 1.56it/s] 86%|████████▌ | 24/28 [00:15<00:02, 1.56it/s] 89%|████████▉ | 25/28 [00:15<00:01, 1.56it/s] 93%|█████████▎| 26/28 [00:16<00:01, 1.56it/s] 96%|█████████▋| 27/28 [00:17<00:00, 1.55it/s] 100%|██████████| 28/28 [00:17<00:00, 1.55it/s] 100%|██████████| 28/28 [00:17<00:00, 1.58it/s]
Prediction
tuannha/instant-character:df5eed34fa9c812acf62d3ca79874daf9b5e78c2bee11f4ada182a55dd5c1712IDfkbjv9dzg1rmc0cp8w6byyyqm8StatusSucceededSourceWebHardwareL40STotal durationCreatedInput
- lora
- makoto_shinkai
- seed
- -1
- width
- 768
- height
- 1344
- prompt
- a character in the library
- guidance_scale
- 3.5
- num_inference_steps
- 28
{ "lora": "makoto_shinkai", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 }
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 tuannha/instant-character using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tuannha/instant-character:df5eed34fa9c812acf62d3ca79874daf9b5e78c2bee11f4ada182a55dd5c1712", { input: { lora: "makoto_shinkai", seed: -1, width: 768, height: 1344, prompt: "a character in the library", subject_image: "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", guidance_scale: 3.5, num_inference_steps: 28 } } ); // 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 tuannha/instant-character using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tuannha/instant-character:df5eed34fa9c812acf62d3ca79874daf9b5e78c2bee11f4ada182a55dd5c1712", input={ "lora": "makoto_shinkai", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 } ) # 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 tuannha/instant-character 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": "tuannha/instant-character:df5eed34fa9c812acf62d3ca79874daf9b5e78c2bee11f4ada182a55dd5c1712", "input": { "lora": "makoto_shinkai", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-04-18T04:02:14.514209Z", "created_at": "2025-04-18T03:58:59.968000Z", "data_removed": false, "error": null, "id": "fkbjv9dzg1rmc0cp8w6byyyqm8", "input": { "lora": "makoto_shinkai", "seed": -1, "width": 768, "height": 1344, "prompt": "a character in the library", "subject_image": "https://replicate.delivery/pbxt/Mr9015ajTj5WrUEuW6YyWbxxRifCNCo0SbiA0L0F4vqjQmXS/face.png", "guidance_scale": 3.5, "num_inference_steps": 28 }, "logs": "loading lora in transformer ...: 0%| | 0/988 [00:00<?, ?it/s]\nloading lora in transformer ...: 43%|████▎ | 426/988 [00:00<00:00, 4211.85it/s]\nloading lora in transformer ...: 86%|████████▌ | 848/988 [00:00<00:00, 3301.67it/s]\nloading lora in transformer ...: 100%|██████████| 988/988 [00:00<00:00, 3335.27it/s]\nloading lora in text_encoder ...: 0%| | 0/216 [00:00<?, ?it/s]\nloading lora in text_encoder ...: 100%|██████████| 216/216 [00:00<00:00, 22635.09it/s]\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:17, 1.58it/s]\n 7%|▋ | 2/28 [00:01<00:13, 1.88it/s]\n 11%|█ | 3/28 [00:01<00:14, 1.74it/s]\n 14%|█▍ | 4/28 [00:02<00:14, 1.68it/s]\n 18%|█▊ | 5/28 [00:02<00:13, 1.65it/s]\n 21%|██▏ | 6/28 [00:03<00:13, 1.63it/s]\n 25%|██▌ | 7/28 [00:04<00:12, 1.62it/s]\n 29%|██▊ | 8/28 [00:04<00:12, 1.61it/s]\n 32%|███▏ | 9/28 [00:05<00:11, 1.61it/s]\n 36%|███▌ | 10/28 [00:06<00:11, 1.60it/s]\n 39%|███▉ | 11/28 [00:06<00:10, 1.60it/s]\n 43%|████▎ | 12/28 [00:07<00:10, 1.60it/s]\n 46%|████▋ | 13/28 [00:07<00:09, 1.59it/s]\n 50%|█████ | 14/28 [00:08<00:08, 1.59it/s]\n 54%|█████▎ | 15/28 [00:09<00:08, 1.59it/s]\n 57%|█████▋ | 16/28 [00:09<00:07, 1.59it/s]\n 61%|██████ | 17/28 [00:10<00:06, 1.59it/s]\n 64%|██████▍ | 18/28 [00:11<00:06, 1.59it/s]\n 68%|██████▊ | 19/28 [00:11<00:05, 1.59it/s]\n 71%|███████▏ | 20/28 [00:12<00:05, 1.59it/s]\n 75%|███████▌ | 21/28 [00:13<00:04, 1.59it/s]\n 79%|███████▊ | 22/28 [00:13<00:03, 1.59it/s]\n 82%|████████▏ | 23/28 [00:14<00:03, 1.59it/s]\n 86%|████████▌ | 24/28 [00:14<00:02, 1.59it/s]\n 89%|████████▉ | 25/28 [00:15<00:01, 1.59it/s]\n 93%|█████████▎| 26/28 [00:16<00:01, 1.58it/s]\n 96%|█████████▋| 27/28 [00:16<00:00, 1.58it/s]\n100%|██████████| 28/28 [00:17<00:00, 1.58it/s]\n100%|██████████| 28/28 [00:17<00:00, 1.60it/s]\nloading lora in transformer ...: 0%| | 0/988 [00:00<?, ?it/s]\nloading lora in transformer ...: 64%|██████▍ | 634/988 [00:00<00:00, 6305.32it/s]\nloading lora in transformer ...: 100%|██████████| 988/988 [00:00<00:00, 4428.84it/s]\nloading lora in text_encoder ...: 0%| | 0/216 [00:00<?, ?it/s]\nloading lora in text_encoder ...: 100%|██████████| 216/216 [00:00<00:00, 36176.56it/s]", "metrics": { "predict_time": 20.073047417, "total_time": 194.546209 }, "output": "https://replicate.delivery/xezq/jhkCLpB0bD6tFhmm5lzGKzAdEBc3rFGMKttTj2NtUipxAeRKA/f24770a9-d562-4b99-aa8d-babb842d27ba.png", "started_at": "2025-04-18T04:01:54.441162Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-vqy75zksl7haxckncqj343pthktlfjqepeg5xea2lfao4yyw66ga", "get": "https://api.replicate.com/v1/predictions/fkbjv9dzg1rmc0cp8w6byyyqm8", "cancel": "https://api.replicate.com/v1/predictions/fkbjv9dzg1rmc0cp8w6byyyqm8/cancel" }, "version": "df5eed34fa9c812acf62d3ca79874daf9b5e78c2bee11f4ada182a55dd5c1712" }
Generated inloading lora in transformer ...: 0%| | 0/988 [00:00<?, ?it/s] loading lora in transformer ...: 43%|████▎ | 426/988 [00:00<00:00, 4211.85it/s] loading lora in transformer ...: 86%|████████▌ | 848/988 [00:00<00:00, 3301.67it/s] loading lora in transformer ...: 100%|██████████| 988/988 [00:00<00:00, 3335.27it/s] loading lora in text_encoder ...: 0%| | 0/216 [00:00<?, ?it/s] loading lora in text_encoder ...: 100%|██████████| 216/216 [00:00<00:00, 22635.09it/s] 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:17, 1.58it/s] 7%|▋ | 2/28 [00:01<00:13, 1.88it/s] 11%|█ | 3/28 [00:01<00:14, 1.74it/s] 14%|█▍ | 4/28 [00:02<00:14, 1.68it/s] 18%|█▊ | 5/28 [00:02<00:13, 1.65it/s] 21%|██▏ | 6/28 [00:03<00:13, 1.63it/s] 25%|██▌ | 7/28 [00:04<00:12, 1.62it/s] 29%|██▊ | 8/28 [00:04<00:12, 1.61it/s] 32%|███▏ | 9/28 [00:05<00:11, 1.61it/s] 36%|███▌ | 10/28 [00:06<00:11, 1.60it/s] 39%|███▉ | 11/28 [00:06<00:10, 1.60it/s] 43%|████▎ | 12/28 [00:07<00:10, 1.60it/s] 46%|████▋ | 13/28 [00:07<00:09, 1.59it/s] 50%|█████ | 14/28 [00:08<00:08, 1.59it/s] 54%|█████▎ | 15/28 [00:09<00:08, 1.59it/s] 57%|█████▋ | 16/28 [00:09<00:07, 1.59it/s] 61%|██████ | 17/28 [00:10<00:06, 1.59it/s] 64%|██████▍ | 18/28 [00:11<00:06, 1.59it/s] 68%|██████▊ | 19/28 [00:11<00:05, 1.59it/s] 71%|███████▏ | 20/28 [00:12<00:05, 1.59it/s] 75%|███████▌ | 21/28 [00:13<00:04, 1.59it/s] 79%|███████▊ | 22/28 [00:13<00:03, 1.59it/s] 82%|████████▏ | 23/28 [00:14<00:03, 1.59it/s] 86%|████████▌ | 24/28 [00:14<00:02, 1.59it/s] 89%|████████▉ | 25/28 [00:15<00:01, 1.59it/s] 93%|█████████▎| 26/28 [00:16<00:01, 1.58it/s] 96%|█████████▋| 27/28 [00:16<00:00, 1.58it/s] 100%|██████████| 28/28 [00:17<00:00, 1.58it/s] 100%|██████████| 28/28 [00:17<00:00, 1.60it/s] loading lora in transformer ...: 0%| | 0/988 [00:00<?, ?it/s] loading lora in transformer ...: 64%|██████▍ | 634/988 [00:00<00:00, 6305.32it/s] loading lora in transformer ...: 100%|██████████| 988/988 [00:00<00:00, 4428.84it/s] loading lora in text_encoder ...: 0%| | 0/216 [00:00<?, ?it/s] loading lora in text_encoder ...: 100%|██████████| 216/216 [00:00<00:00, 36176.56it/s]
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