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saehoonkim / mindall-e
text-to-image generation
Prediction
saehoonkim/mindall-e:a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256IDnhkvjlm46zfndj4u6bsrvghw3uStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "0"
- prompt
- A painting of a monkey with sunglasses in the frame
- num_samples
- "4"
{ "seed": "0", "prompt": "A painting of a monkey with sunglasses in the frame", "num_samples": "4" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "saehoonkim/mindall-e:a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256", { input: { seed: "0", prompt: "A painting of a monkey with sunglasses in the frame", num_samples: "4" } } ); 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 replicateImport the client:import replicateRun saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "saehoonkim/mindall-e:a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256", input={ "seed": "0", "prompt": "A painting of a monkey with sunglasses in the frame", "num_samples": "4" } ) # The saehoonkim/mindall-e 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/saehoonkim/mindall-e/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run saehoonkim/mindall-e 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": "saehoonkim/mindall-e:a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256", "input": { "seed": "0", "prompt": "A painting of a monkey with sunglasses in the frame", "num_samples": "4" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-20T23:55:14.376634Z", "created_at": "2021-12-20T23:54:00.882689Z", "data_removed": false, "error": null, "id": "nhkvjlm46zfndj4u6bsrvghw3u", "input": { "seed": "0", "prompt": "A painting of a monkey with sunglasses in the frame", "num_samples": "4" }, "logs": "\n 0%| | 0/256 [00:00<?, ?it/s]\n 0%| | 1/256 [00:00<01:27, 2.92it/s]\n 1%| | 3/256 [00:00<01:05, 3.89it/s]\n 2%|▏ | 5/256 [00:00<00:49, 5.09it/s]\n 3%|▎ | 7/256 [00:00<00:38, 6.49it/s]\n 4%|▎ | 9/256 [00:00<00:30, 8.06it/s]\n 4%|▍ | 11/256 [00:00<00:25, 9.68it/s]\n 5%|▌ | 13/256 [00:01<00:21, 11.26it/s]\n 6%|▌ | 15/256 [00:01<00:18, 12.71it/s]\n 7%|▋ | 17/256 [00:01<00:17, 13.96it/s]\n 7%|▋ | 19/256 [00:01<00:15, 14.98it/s]\n 8%|▊ | 21/256 [00:01<00:14, 15.75it/s]\n 9%|▉ | 23/256 [00:01<00:14, 16.32it/s]\n 10%|▉ | 25/256 [00:01<00:13, 16.75it/s]\n 11%|█ | 27/256 [00:01<00:13, 17.06it/s]\n 11%|█▏ | 29/256 [00:01<00:13, 17.28it/s]\n 12%|█▏ | 31/256 [00:02<00:19, 11.62it/s]\n 13%|█▎ | 33/256 [00:02<00:17, 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10.52it/s]\n 96%|█████████▌| 245/256 [00:19<00:00, 11.33it/s]\n 96%|█████████▋| 247/256 [00:19<00:00, 9.52it/s]\n 97%|█████████▋| 249/256 [00:19<00:00, 10.49it/s]\n 98%|█████████▊| 251/256 [00:19<00:00, 11.28it/s]\n 99%|█████████▉| 253/256 [00:19<00:00, 9.52it/s]\n100%|█████████▉| 255/256 [00:20<00:00, 10.47it/s]\n100%|██████████| 256/256 [00:20<00:00, 12.73it/s]", "metrics": { "predict_time": 22.519527, "total_time": 73.493945 }, "output": [ { "file": "https://replicate.delivery/mgxm/0b561e91-1b5a-4100-94ba-b31f50d2155d/out.png" } ], "started_at": "2021-12-20T23:54:51.857107Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nhkvjlm46zfndj4u6bsrvghw3u", "cancel": "https://api.replicate.com/v1/predictions/nhkvjlm46zfndj4u6bsrvghw3u/cancel" }, "version": "a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256" }Generated in0%| | 0/256 [00:00<?, ?it/s] 0%| | 1/256 [00:00<01:27, 2.92it/s] 1%| | 3/256 [00:00<01:05, 3.89it/s] 2%|▏ | 5/256 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saehoonkim/mindall-e:be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643aIDrmlz3ef5uzeizewx4udlhmxax4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- A horse eating a cupcake
- num_samples
- "4"
{ "seed": 0, "prompt": "A horse eating a cupcake", "num_samples": "4" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "saehoonkim/mindall-e:be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643a", { input: { seed: 0, prompt: "A horse eating a cupcake", num_samples: "4" } } ); 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 replicateImport the client:import replicateRun saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "saehoonkim/mindall-e:be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643a", input={ "seed": 0, "prompt": "A horse eating a cupcake", "num_samples": "4" } ) # The saehoonkim/mindall-e 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/saehoonkim/mindall-e/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run saehoonkim/mindall-e 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": "saehoonkim/mindall-e:be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643a", "input": { "seed": 0, "prompt": "A horse eating a cupcake", "num_samples": "4" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-21T00:23:11.742778Z", "created_at": "2021-12-21T00:22:25.577439Z", "data_removed": false, "error": null, "id": "rmlz3ef5uzeizewx4udlhmxax4", "input": { "seed": 0, "prompt": "A horse eating a cupcake", "num_samples": "4" }, "logs": "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n\n 0%| | 0/256 [00:00<?, ?it/s]\n 0%| | 1/256 [00:00<01:31, 2.79it/s]\n 1%| | 3/256 [00:00<01:09, 3.67it/s]\n 2%|▏ | 5/256 [00:00<00:53, 4.71it/s]\n 3%|▎ | 7/256 [00:00<00:42, 5.87it/s]\n 4%|▎ | 9/256 [00:00<00:35, 6.99it/s]\n 4%|▍ | 11/256 [00:01<00:29, 8.17it/s]\n 5%|▌ | 13/256 [00:01<00:26, 9.26it/s]\n 6%|▌ | 15/256 [00:01<00:23, 10.21it/s]\n 7%|▋ | 17/256 [00:01<00:21, 10.97it/s]\n 7%|▋ | 19/256 [00:01<00:20, 11.57it/s]\n 8%|▊ | 21/256 [00:01<00:19, 11.95it/s]\n 9%|▉ | 23/256 [00:02<00:19, 12.19it/s]\n 10%|▉ | 25/256 [00:02<00:18, 12.42it/s]\n 11%|█ | 27/256 [00:02<00:18, 12.56it/s]\n 11%|█▏ | 29/256 [00:02<00:17, 12.65it/s]\n 12%|█▏ | 31/256 [00:02<00:17, 12.66it/s]\n 13%|█▎ | 33/256 [00:02<00:17, 12.64it/s]\n 14%|█▎ | 35/256 [00:02<00:17, 12.54it/s]\n 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7.44it/s]\n 96%|█████████▌| 246/256 [00:25<00:01, 7.43it/s]\n 96%|█████████▋| 247/256 [00:25<00:01, 7.42it/s]\n 97%|█████████▋| 248/256 [00:25<00:01, 7.42it/s]\n 97%|█████████▋| 249/256 [00:26<00:00, 7.40it/s]\n 98%|█████████▊| 250/256 [00:26<00:00, 7.38it/s]\n 98%|█████████▊| 251/256 [00:26<00:00, 7.36it/s]\n 98%|█████████▊| 252/256 [00:26<00:00, 7.34it/s]\n 99%|█████████▉| 253/256 [00:26<00:00, 7.35it/s]\n 99%|█████████▉| 254/256 [00:26<00:00, 7.33it/s]\n100%|█████████▉| 255/256 [00:26<00:00, 7.32it/s]\n100%|██████████| 256/256 [00:27<00:00, 7.30it/s]\n100%|██████████| 256/256 [00:27<00:00, 9.47it/s]", "metrics": { "predict_time": 30.866151, "total_time": 46.165339 }, "output": [ { "file": "https://replicate.delivery/mgxm/49e14eb2-d112-4cb4-919d-63b2283a456c/out.png" } ], "started_at": "2021-12-21T00:22:40.876627Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rmlz3ef5uzeizewx4udlhmxax4", "cancel": "https://api.replicate.com/v1/predictions/rmlz3ef5uzeizewx4udlhmxax4/cancel" }, "version": "be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643a" }Generated inhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... 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saehoonkim/mindall-e:6c4d732882f90d0fe29ab2f8124f854828e547ecd590f45602621d8f4dcba8b9IDzyf5456hx5h7lhgokhaiomzvmaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "0"
- prompt
- A painting of a monkey with sunglasses in the frame
- num_samples
- "1"
{ "seed": "0", "prompt": "A painting of a monkey with sunglasses in the frame", "num_samples": "1" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "saehoonkim/mindall-e:6c4d732882f90d0fe29ab2f8124f854828e547ecd590f45602621d8f4dcba8b9", { input: { seed: "0", prompt: "A painting of a monkey with sunglasses in the frame", num_samples: "1" } } ); 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 replicateImport the client:import replicateRun saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "saehoonkim/mindall-e:6c4d732882f90d0fe29ab2f8124f854828e547ecd590f45602621d8f4dcba8b9", input={ "seed": "0", "prompt": "A painting of a monkey with sunglasses in the frame", "num_samples": "1" } ) # The saehoonkim/mindall-e 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/saehoonkim/mindall-e/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run saehoonkim/mindall-e 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": "saehoonkim/mindall-e:6c4d732882f90d0fe29ab2f8124f854828e547ecd590f45602621d8f4dcba8b9", "input": { "seed": "0", "prompt": "A painting of a monkey with sunglasses in the frame", "num_samples": "1" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-20T23:05:30.456234Z", "created_at": "2021-12-20T23:03:25.433307Z", "data_removed": false, "error": null, "id": "zyf5456hx5h7lhgokhaiomzvma", "input": { "seed": "0", "prompt": "A painting of a monkey with sunglasses in the frame", "num_samples": "1" }, "logs": "\n 0%| | 0/256 [00:00<?, ?it/s]\n 0%| | 1/256 [00:00<01:19, 3.20it/s]\n 1%| | 3/256 [00:00<01:00, 4.20it/s]\n 2%|▏ | 5/256 [00:00<00:45, 5.46it/s]\n 3%|▎ | 7/256 [00:00<00:35, 6.92it/s]\n 4%|▎ | 9/256 [00:00<00:29, 8.51it/s]\n 4%|▍ | 11/256 [00:00<00:24, 10.14it/s]\n 5%|▌ | 13/256 [00:00<00:20, 11.71it/s]\n 6%|▌ | 15/256 [00:01<00:18, 13.12it/s]\n 7%|▋ | 17/256 [00:01<00:16, 14.33it/s]\n 7%|▋ | 19/256 [00:01<00:15, 15.29it/s]\n 8%|▊ | 21/256 [00:01<00:14, 16.01it/s]\n 9%|▉ | 23/256 [00:01<00:14, 16.54it/s]\n 10%|▉ | 25/256 [00:01<00:13, 16.93it/s]\n 11%|█ | 27/256 [00:01<00:13, 17.20it/s]\n 11%|█▏ | 29/256 [00:01<00:13, 17.41it/s]\n 12%|█▏ | 31/256 [00:02<00:19, 11.68it/s]\n 13%|█▎ | 33/256 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[00:18<00:01, 10.57it/s]\n 96%|█████████▌| 245/256 [00:18<00:00, 11.39it/s]\n 96%|█████████▋| 247/256 [00:19<00:00, 9.59it/s]\n 97%|█████████▋| 249/256 [00:19<00:00, 10.56it/s]\n 98%|█████████▊| 251/256 [00:19<00:00, 11.36it/s]\n 99%|█████████▉| 253/256 [00:19<00:00, 9.44it/s]\n100%|█████████▉| 255/256 [00:19<00:00, 10.42it/s]\n100%|██████████| 256/256 [00:19<00:00, 12.80it/s]", "metrics": { "predict_time": 21.475619, "total_time": 125.022927 }, "output": [ { "file": "https://replicate.delivery/mgxm/1ffe8197-9fa7-44ad-975f-7800fd635a64/out.png" } ], "started_at": "2021-12-20T23:05:08.980615Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zyf5456hx5h7lhgokhaiomzvma", "cancel": "https://api.replicate.com/v1/predictions/zyf5456hx5h7lhgokhaiomzvma/cancel" }, "version": "6c4d732882f90d0fe29ab2f8124f854828e547ecd590f45602621d8f4dcba8b9" }Generated in0%| | 0/256 [00:00<?, ?it/s] 0%| | 1/256 [00:00<01:19, 3.20it/s] 1%| | 3/256 [00:00<01:00, 4.20it/s] 2%|▏ | 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saehoonkim/mindall-e:a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256IDdwuutlqnozdspcjlxsry3ceai4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "0"
- prompt
- Eiffel tower on a desert
- num_samples
- "6"
{ "seed": "0", "prompt": "Eiffel tower on a desert", "num_samples": "6" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "saehoonkim/mindall-e:a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256", { input: { seed: "0", prompt: "Eiffel tower on a desert", num_samples: "6" } } ); 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 replicateImport the client:import replicateRun saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "saehoonkim/mindall-e:a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256", input={ "seed": "0", "prompt": "Eiffel tower on a desert", "num_samples": "6" } ) # The saehoonkim/mindall-e 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/saehoonkim/mindall-e/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run saehoonkim/mindall-e 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": "saehoonkim/mindall-e:a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256", "input": { "seed": "0", "prompt": "Eiffel tower on a desert", "num_samples": "6" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-20T23:56:54.716775Z", "created_at": "2021-12-20T23:56:35.576307Z", "data_removed": false, "error": null, "id": "dwuutlqnozdspcjlxsry3ceai4", "input": { "seed": "0", "prompt": "Eiffel tower on a desert", "num_samples": "6" }, "logs": "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n\n 0%| | 0/256 [00:00<?, ?it/s]\n 0%| | 1/256 [00:00<00:49, 5.17it/s]\n 1%| | 3/256 [00:00<00:38, 6.57it/s]\n 2%|▏ | 5/256 [00:00<00:30, 8.13it/s]\n 3%|▎ | 7/256 [00:00<00:25, 9.75it/s]\n 4%|▎ | 9/256 [00:00<00:21, 11.33it/s]\n 4%|▍ | 11/256 [00:00<00:19, 12.78it/s]\n 5%|▌ | 13/256 [00:00<00:17, 14.02it/s]\n 6%|▌ | 15/256 [00:00<00:16, 15.03it/s]\n 7%|▋ | 17/256 [00:01<00:15, 15.82it/s]\n 7%|▋ | 19/256 [00:01<00:14, 16.37it/s]\n 8%|▊ | 21/256 [00:01<00:14, 16.74it/s]\n 9%|▉ | 23/256 [00:01<00:13, 17.03it/s]\n 10%|▉ | 25/256 [00:01<00:13, 17.25it/s]\n 11%|█ | 27/256 [00:01<00:13, 17.40it/s]\n 11%|█▏ | 29/256 [00:01<00:12, 17.51it/s]\n 12%|█▏ | 31/256 [00:01<00:12, 17.57it/s]\n 13%|█▎ | 33/256 [00:01<00:12, 17.59it/s]\n 14%|█▎ | 35/256 [00:02<00:12, 17.58it/s]\n 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13.78it/s]\n 96%|█████████▋| 247/256 [00:15<00:00, 13.77it/s]\n 97%|█████████▋| 249/256 [00:16<00:00, 13.73it/s]\n 98%|█████████▊| 251/256 [00:16<00:00, 13.71it/s]\n 99%|█████████▉| 253/256 [00:16<00:00, 13.67it/s]\n100%|█████████▉| 255/256 [00:16<00:00, 13.65it/s]\n100%|██████████| 256/256 [00:16<00:00, 15.47it/s]", "metrics": { "predict_time": 18.917878, "total_time": 19.140468 }, "output": [ { "file": "https://replicate.delivery/mgxm/b511d2b3-53bb-4eb7-a6ef-c833a53a9315/out.png" } ], "started_at": "2021-12-20T23:56:35.798897Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dwuutlqnozdspcjlxsry3ceai4", "cancel": "https://api.replicate.com/v1/predictions/dwuutlqnozdspcjlxsry3ceai4/cancel" }, "version": "a8c0116d5009a4a7034b6c26cb9bbf460185150e8479968d93e2a01ffb139256" }Generated inhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) 0%| | 0/256 [00:00<?, ?it/s] 0%| | 1/256 [00:00<00:49, 5.17it/s] 1%| | 3/256 [00:00<00:38, 6.57it/s] 2%|▏ | 5/256 [00:00<00:30, 8.13it/s] 3%|▎ | 7/256 [00:00<00:25, 9.75it/s] 4%|▎ | 9/256 [00:00<00:21, 11.33it/s] 4%|▍ | 11/256 [00:00<00:19, 12.78it/s] 5%|▌ | 13/256 [00:00<00:17, 14.02it/s] 6%|▌ | 15/256 [00:00<00:16, 15.03it/s] 7%|▋ | 17/256 [00:01<00:15, 15.82it/s] 7%|▋ | 19/256 [00:01<00:14, 16.37it/s] 8%|▊ | 21/256 [00:01<00:14, 16.74it/s] 9%|▉ | 23/256 [00:01<00:13, 17.03it/s] 10%|▉ | 25/256 [00:01<00:13, 17.25it/s] 11%|█ | 27/256 [00:01<00:13, 17.40it/s] 11%|█▏ | 29/256 [00:01<00:12, 17.51it/s] 12%|█▏ | 31/256 [00:01<00:12, 17.57it/s] 13%|█▎ | 33/256 [00:01<00:12, 17.59it/s] 14%|█▎ | 35/256 [00:02<00:12, 17.58it/s] 14%|█▍ | 37/256 [00:02<00:12, 17.53it/s] 15%|█▌ | 39/256 [00:02<00:12, 17.47it/s] 16%|█▌ | 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15.47it/s]Prediction
saehoonkim/mindall-e:be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643aID7kjnsjxulndzjjl7zrwmjri35eStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "0"
- prompt
- A painting of a cat with sunglasses in the frame
- num_samples
- "4"
{ "seed": "0", "prompt": "A painting of a cat with sunglasses in the frame", "num_samples": "4" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "saehoonkim/mindall-e:be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643a", { input: { seed: "0", prompt: "A painting of a cat with sunglasses in the frame", num_samples: "4" } } ); 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 replicateImport the client:import replicateRun saehoonkim/mindall-e using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "saehoonkim/mindall-e:be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643a", input={ "seed": "0", "prompt": "A painting of a cat with sunglasses in the frame", "num_samples": "4" } ) # The saehoonkim/mindall-e 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/saehoonkim/mindall-e/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run saehoonkim/mindall-e 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": "saehoonkim/mindall-e:be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643a", "input": { "seed": "0", "prompt": "A painting of a cat with sunglasses in the frame", "num_samples": "4" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-21T00:17:14.073557Z", "created_at": "2021-12-21T00:14:31.814809Z", "data_removed": false, "error": null, "id": "7kjnsjxulndzjjl7zrwmjri35e", "input": { "seed": "0", "prompt": "A painting of a cat with sunglasses in the frame", "num_samples": "4" }, "logs": "\n 0%| | 0/256 [00:00<?, ?it/s]\n 0%| | 1/256 [00:00<02:03, 2.06it/s]\n 1%| | 3/256 [00:00<01:31, 2.77it/s]\n 2%|▏ | 5/256 [00:00<01:08, 3.65it/s]\n 3%|▎ | 7/256 [00:00<00:53, 4.70it/s]\n 4%|▎ | 9/256 [00:01<00:42, 5.87it/s]\n 4%|▍ | 11/256 [00:01<00:40, 6.05it/s]\n 5%|▌ | 13/256 [00:01<00:33, 7.28it/s]\n 6%|▌ | 15/256 [00:01<00:28, 8.48it/s]\n 7%|▋ | 17/256 [00:01<00:24, 9.57it/s]\n 7%|▋ | 19/256 [00:02<00:27, 8.49it/s]\n 8%|▊ | 21/256 [00:02<00:24, 9.56it/s]\n 9%|▉ | 23/256 [00:02<00:22, 10.47it/s]\n 10%|▉ | 25/256 [00:02<00:26, 8.85it/s]\n 11%|█ | 27/256 [00:02<00:23, 9.83it/s]\n 11%|█▏ | 29/256 [00:02<00:21, 10.64it/s]\n 12%|█▏ | 31/256 [00:03<00:20, 11.23it/s]\n 13%|█▎ | 33/256 [00:03<00:24, 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"succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7kjnsjxulndzjjl7zrwmjri35e", "cancel": "https://api.replicate.com/v1/predictions/7kjnsjxulndzjjl7zrwmjri35e/cancel" }, "version": "be9816fa6f8177ec9304981d70d7f08d151b650f5073b58a240b62f9d36a643a" }Generated in0%| | 0/256 [00:00<?, ?it/s] 0%| | 1/256 [00:00<02:03, 2.06it/s] 1%| | 3/256 [00:00<01:31, 2.77it/s] 2%|▏ | 5/256 [00:00<01:08, 3.65it/s] 3%|▎ | 7/256 [00:00<00:53, 4.70it/s] 4%|▎ | 9/256 [00:01<00:42, 5.87it/s] 4%|▍ | 11/256 [00:01<00:40, 6.05it/s] 5%|▌ | 13/256 [00:01<00:33, 7.28it/s] 6%|▌ | 15/256 [00:01<00:28, 8.48it/s] 7%|▋ | 17/256 [00:01<00:24, 9.57it/s] 7%|▋ | 19/256 [00:02<00:27, 8.49it/s] 8%|▊ | 21/256 [00:02<00:24, 9.56it/s] 9%|▉ | 23/256 [00:02<00:22, 10.47it/s] 10%|▉ | 25/256 [00:02<00:26, 8.85it/s] 11%|█ | 27/256 [00:02<00:23, 9.83it/s] 11%|█▏ | 29/256 [00:02<00:21, 10.64it/s] 12%|█▏ | 31/256 [00:03<00:20, 11.23it/s] 13%|█▎ | 33/256 [00:03<00:24, 9.29it/s] 14%|█▎ | 35/256 [00:03<00:21, 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