lucataco / style-aligned
GoogleAI: Style Aligned Image Generation via Shared Attention
Prediction
lucataco/style-aligned:4ae87bee984bc5346c5ec8d6f8573825d0c602b5f5f82fc15b12460b997f90c8IDavdn7nrbhnzisx2twbi7xxoe74StatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 7998
- image
- width
- 768
- height
- 768
- prompt
- A man working on a laptop A man eats pizza A woman playing on saxophone
- style_prompt
- medieval painting
- image_subject
- None
- guidance_scale
- 7
- negative_prompt
- low-resolution
- shared_score_scale
- 1
- shared_score_shift
- 2
- num_inference_steps
- 50
{ "seed": 7998, "image": "https://replicate.delivery/pbxt/K23WY2plxah7r8TASoTXvBGH6nZVZmnrLXBlRNt3cP7DyoK3/medieval-bed.jpeg", "width": 768, "height": 768, "prompt": "A man working on a laptop\nA man eats pizza\nA woman playing on saxophone", "style_prompt": "medieval painting", "image_subject": "None", "guidance_scale": 7, "negative_prompt": "low-resolution", "shared_score_scale": 1, "shared_score_shift": 2, "num_inference_steps": 50 }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 lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/style-aligned:4ae87bee984bc5346c5ec8d6f8573825d0c602b5f5f82fc15b12460b997f90c8", { input: { seed: 7998, image: "https://replicate.delivery/pbxt/K23WY2plxah7r8TASoTXvBGH6nZVZmnrLXBlRNt3cP7DyoK3/medieval-bed.jpeg", width: 768, height: 768, prompt: "A man working on a laptop\nA man eats pizza\nA woman playing on saxophone", style_prompt: "medieval painting", image_subject: "None", guidance_scale: 7, negative_prompt: "low-resolution", shared_score_scale: 1, shared_score_shift: 2, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/style-aligned:4ae87bee984bc5346c5ec8d6f8573825d0c602b5f5f82fc15b12460b997f90c8", input={ "seed": 7998, "image": "https://replicate.delivery/pbxt/K23WY2plxah7r8TASoTXvBGH6nZVZmnrLXBlRNt3cP7DyoK3/medieval-bed.jpeg", "width": 768, "height": 768, "prompt": "A man working on a laptop\nA man eats pizza\nA woman playing on saxophone", "style_prompt": "medieval painting", "image_subject": "None", "guidance_scale": 7, "negative_prompt": "low-resolution", "shared_score_scale": 1, "shared_score_shift": 2, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/style-aligned 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": "lucataco/style-aligned:4ae87bee984bc5346c5ec8d6f8573825d0c602b5f5f82fc15b12460b997f90c8", "input": { "seed": 7998, "image": "https://replicate.delivery/pbxt/K23WY2plxah7r8TASoTXvBGH6nZVZmnrLXBlRNt3cP7DyoK3/medieval-bed.jpeg", "width": 768, "height": 768, "prompt": "A man working on a laptop\\nA man eats pizza\\nA woman playing on saxophone", "style_prompt": "medieval painting", "image_subject": "None", "guidance_scale": 7, "negative_prompt": "low-resolution", "shared_score_scale": 1, "shared_score_shift": 2, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-11T21:16:26.510044Z", "created_at": "2023-12-11T21:15:45.036722Z", "data_removed": false, "error": null, "id": "avdn7nrbhnzisx2twbi7xxoe74", "input": { "seed": 7998, "image": "https://replicate.delivery/pbxt/K23WY2plxah7r8TASoTXvBGH6nZVZmnrLXBlRNt3cP7DyoK3/medieval-bed.jpeg", "width": 768, "height": 768, "prompt": "A man working on a laptop\nA man eats pizza\nA woman playing on saxophone", "style_prompt": "medieval painting", "image_subject": "None", "guidance_scale": 7, "negative_prompt": "low-resolution", "shared_score_scale": 1, "shared_score_shift": 2, "num_inference_steps": 50 }, "logs": "Using seed: 7998\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:10, 4.57it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.53it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.47it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.50it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.53it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.56it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.58it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.58it/s]\n 18%|█▊ | 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94%|█████████▍| 47/50 [00:24<00:01, 1.88it/s]\n 96%|█████████▌| 48/50 [00:25<00:01, 1.88it/s]\n 98%|█████████▊| 49/50 [00:25<00:00, 1.88it/s]\n100%|██████████| 50/50 [00:26<00:00, 1.88it/s]\n100%|██████████| 50/50 [00:26<00:00, 1.88it/s]", "metrics": { "predict_time": 41.460926, "total_time": 41.473322 }, "output": [ "https://replicate.delivery/pbxt/KleYIJ8ZZ3yHAKndOeGg4bpHWjIbfbu3MZ7yMSrxffPGFXKQC/output-1.png", "https://replicate.delivery/pbxt/8n0uU9xHHfzGXyJIkAOu4HOagJilKNaDCotUpIo8K27UcpAJA/output-2.png", "https://replicate.delivery/pbxt/g23clZinoZIzANqxp1iUDjneJu2lfGeYGAQVCY3PRMdSxlCkA/output-3.png" ], "started_at": "2023-12-11T21:15:45.049118Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/avdn7nrbhnzisx2twbi7xxoe74", "cancel": "https://api.replicate.com/v1/predictions/avdn7nrbhnzisx2twbi7xxoe74/cancel" }, "version": "4ae87bee984bc5346c5ec8d6f8573825d0c602b5f5f82fc15b12460b997f90c8" }Generated inUsing seed: 7998 0%| | 0/50 [00:00<?, ?it/s] 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lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2eIDp5xbnxbb5i4u6j7s5p644d6x2uStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 62967
- width
- 768
- height
- 768
- prompt
- Toy train colorful, macro photo. Toy airplane colorful, macro photo. Toy car colorful, macro photo. Toy boat colorful, macro photo.
- negative_prompt
- low-resolution
- num_inference_steps
- 50
{ "seed": 62967, "width": 768, "height": 768, "prompt": "Toy train colorful, macro photo.\nToy airplane colorful, macro photo.\nToy car colorful, macro photo.\nToy boat colorful, macro photo.", "negative_prompt": "low-resolution", "num_inference_steps": 50 }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 lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", { input: { seed: 62967, width: 768, height: 768, prompt: "Toy train colorful, macro photo.\nToy airplane colorful, macro photo.\nToy car colorful, macro photo.\nToy boat colorful, macro photo.", negative_prompt: "low-resolution", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", input={ "seed": 62967, "width": 768, "height": 768, "prompt": "Toy train colorful, macro photo.\nToy airplane colorful, macro photo.\nToy car colorful, macro photo.\nToy boat colorful, macro photo.", "negative_prompt": "low-resolution", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/style-aligned 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": "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", "input": { "seed": 62967, "width": 768, "height": 768, "prompt": "Toy train colorful, macro photo.\\nToy airplane colorful, macro photo.\\nToy car colorful, macro photo.\\nToy boat colorful, macro photo.", "negative_prompt": "low-resolution", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-05T22:18:12.354683Z", "created_at": "2023-12-05T22:14:05.156174Z", "data_removed": false, "error": null, "id": "p5xbnxbb5i4u6j7s5p644d6x2u", "input": { "seed": 62967, "width": 768, "height": 768, "prompt": "Toy train colorful, macro photo.\nToy airplane colorful, macro photo.\nToy car colorful, macro photo.\nToy boat colorful, macro photo.", "negative_prompt": "low-resolution", "num_inference_steps": 50 }, "logs": "Using seed: 62967\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:05<04:43, 5.78s/it]\n 4%|▍ | 2/50 [00:06<02:02, 2.56s/it]\n 6%|▌ | 3/50 [00:06<01:11, 1.53s/it]\n 8%|▊ | 4/50 [00:06<00:47, 1.04s/it]\n 10%|█ | 5/50 [00:06<00:34, 1.29it/s]\n 12%|█▏ | 6/50 [00:07<00:27, 1.63it/s]\n 14%|█▍ | 7/50 [00:07<00:22, 1.95it/s]\n 16%|█▌ | 8/50 [00:07<00:18, 2.25it/s]\n 18%|█▊ | 9/50 [00:08<00:16, 2.50it/s]\n 20%|██ | 10/50 [00:08<00:14, 2.71it/s]\n 22%|██▏ | 11/50 [00:08<00:13, 2.87it/s]\n 24%|██▍ | 12/50 [00:09<00:12, 3.00it/s]\n 26%|██▌ | 13/50 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72%|███████▏ | 36/50 [00:16<00:04, 3.32it/s]\n 74%|███████▍ | 37/50 [00:16<00:03, 3.31it/s]\n 76%|███████▌ | 38/50 [00:16<00:03, 3.31it/s]\n 78%|███████▊ | 39/50 [00:17<00:03, 3.31it/s]\n 80%|████████ | 40/50 [00:17<00:03, 3.31it/s]\n 82%|████████▏ | 41/50 [00:17<00:02, 3.30it/s]\n 84%|████████▍ | 42/50 [00:18<00:02, 3.31it/s]\n 86%|████████▌ | 43/50 [00:18<00:02, 3.31it/s]\n 88%|████████▊ | 44/50 [00:18<00:01, 3.31it/s]\n 90%|█████████ | 45/50 [00:19<00:01, 3.31it/s]\n 92%|█████████▏| 46/50 [00:19<00:01, 3.31it/s]\n 94%|█████████▍| 47/50 [00:19<00:00, 3.32it/s]\n 96%|█████████▌| 48/50 [00:19<00:00, 3.32it/s]\n 98%|█████████▊| 49/50 [00:20<00:00, 3.32it/s]\n100%|██████████| 50/50 [00:20<00:00, 3.32it/s]\n100%|██████████| 50/50 [00:20<00:00, 2.43it/s]", "metrics": { "predict_time": 27.773339, "total_time": 247.198509 }, "output": [ "https://replicate.delivery/pbxt/BXpfqgS1ErTfRUmI9fxcxBY5inzWYayPXIpKNayZKnqDdqeHB/output-0.png", "https://replicate.delivery/pbxt/E7e2jdEllnT7Z6hlXWuCIuj6fi5lrJh99kjeYNnwdw9FdqeHB/output-1.png", "https://replicate.delivery/pbxt/oNpi9q3SY26cO1kzjqL7eJqv3G9aGUTMlhPPVOU8if8jOVfjA/output-2.png", "https://replicate.delivery/pbxt/pkolzSOMs6JrBpdfaQUrGICCa9gYE2GsumJnFlf54tljOVfjA/output-3.png" ], "started_at": "2023-12-05T22:17:44.581344Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/p5xbnxbb5i4u6j7s5p644d6x2u", "cancel": "https://api.replicate.com/v1/predictions/p5xbnxbb5i4u6j7s5p644d6x2u/cancel" }, "version": "a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e" }Generated inUsing seed: 62967 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:05<04:43, 5.78s/it] 4%|▍ | 2/50 [00:06<02:02, 2.56s/it] 6%|▌ | 3/50 [00:06<01:11, 1.53s/it] 8%|▊ | 4/50 [00:06<00:47, 1.04s/it] 10%|█ | 5/50 [00:06<00:34, 1.29it/s] 12%|█▏ | 6/50 [00:07<00:27, 1.63it/s] 14%|█▍ | 7/50 [00:07<00:22, 1.95it/s] 16%|█▌ | 8/50 [00:07<00:18, 2.25it/s] 18%|█▊ | 9/50 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lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2eIDuxxityrbas4wqz5sbkhxphm5jqStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 34079
- width
- 768
- height
- 768
- prompt
- Toy train BW logo, high contrast. Toy airplane BW logo, high contrast. Toy car BW logo, high contrast. Toy boat BW logo, high contrast.
- negative_prompt
- low-resolution
- num_inference_steps
- 50
{ "seed": 34079, "width": 768, "height": 768, "prompt": "Toy train BW logo, high contrast.\nToy airplane BW logo, high contrast.\nToy car BW logo, high contrast.\nToy boat BW logo, high contrast.", "negative_prompt": "low-resolution", "num_inference_steps": 50 }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 lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", { input: { seed: 34079, width: 768, height: 768, prompt: "Toy train BW logo, high contrast.\nToy airplane BW logo, high contrast.\nToy car BW logo, high contrast.\nToy boat BW logo, high contrast.", negative_prompt: "low-resolution", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", input={ "seed": 34079, "width": 768, "height": 768, "prompt": "Toy train BW logo, high contrast.\nToy airplane BW logo, high contrast.\nToy car BW logo, high contrast.\nToy boat BW logo, high contrast.", "negative_prompt": "low-resolution", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/style-aligned 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": "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", "input": { "seed": 34079, "width": 768, "height": 768, "prompt": "Toy train BW logo, high contrast.\\nToy airplane BW logo, high contrast.\\nToy car BW logo, high contrast.\\nToy boat BW logo, high contrast.", "negative_prompt": "low-resolution", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-05T22:19:44.312925Z", "created_at": "2023-12-05T22:19:24.798899Z", "data_removed": false, "error": null, "id": "uxxityrbas4wqz5sbkhxphm5jq", "input": { "seed": 34079, "width": 768, "height": 768, "prompt": "Toy train BW logo, high contrast.\nToy airplane BW logo, high contrast.\nToy car BW logo, high contrast.\nToy boat BW logo, high contrast.", "negative_prompt": "low-resolution", "num_inference_steps": 50 }, "logs": "Using seed: 34079\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:15, 3.14it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.24it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.28it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.30it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.31it/s]\n 12%|█▏ | 6/50 [00:01<00:13, 3.31it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.32it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.32it/s]\n 18%|█▊ | 9/50 [00:02<00:12, 3.32it/s]\n 20%|██ | 10/50 [00:03<00:12, 3.31it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.32it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.32it/s]\n 26%|██▌ | 13/50 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72%|███████▏ | 36/50 [00:10<00:04, 3.32it/s]\n 74%|███████▍ | 37/50 [00:11<00:03, 3.32it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.32it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.32it/s]\n 80%|████████ | 40/50 [00:12<00:03, 3.32it/s]\n 82%|████████▏ | 41/50 [00:12<00:02, 3.32it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.32it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.32it/s]\n 88%|████████▊ | 44/50 [00:13<00:01, 3.31it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.31it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.31it/s]\n 94%|█████████▍| 47/50 [00:14<00:00, 3.31it/s]\n 96%|█████████▌| 48/50 [00:14<00:00, 3.31it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.31it/s]\n100%|██████████| 50/50 [00:15<00:00, 3.31it/s]\n100%|██████████| 50/50 [00:15<00:00, 3.31it/s]", "metrics": { "predict_time": 19.501057, "total_time": 19.514026 }, "output": [ "https://replicate.delivery/pbxt/nNFxEU3cL17NFlu1yUcc7uljTLHEQGWeULvc7P3iPQxePVfjA/output-0.png", "https://replicate.delivery/pbxt/uRIGFJsflpXRZKRdZ91sr0QDXuWzRxSgLfev6odrZ969fU9HB/output-1.png", "https://replicate.delivery/pbxt/JiopYsURHJ7sFVKGIEs38Yyrf8oOf6fjckkYy0uecNb6fp6PC/output-2.png", "https://replicate.delivery/pbxt/oNOUan87I16lFNfCnyADDrvxrGzrNfX1G36NVEDe40Uffp6PC/output-3.png" ], "started_at": "2023-12-05T22:19:24.811868Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uxxityrbas4wqz5sbkhxphm5jq", "cancel": "https://api.replicate.com/v1/predictions/uxxityrbas4wqz5sbkhxphm5jq/cancel" }, "version": "a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e" }Generated inUsing seed: 34079 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:15, 3.14it/s] 4%|▍ | 2/50 [00:00<00:14, 3.24it/s] 6%|▌ | 3/50 [00:00<00:14, 3.28it/s] 8%|▊ | 4/50 [00:01<00:13, 3.30it/s] 10%|█ | 5/50 [00:01<00:13, 3.31it/s] 12%|█▏ | 6/50 [00:01<00:13, 3.31it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.32it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.32it/s] 18%|█▊ | 9/50 [00:02<00:12, 3.32it/s] 20%|██ | 10/50 [00:03<00:12, 3.31it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.32it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.32it/s] 26%|██▌ | 13/50 [00:03<00:11, 3.32it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.32it/s] 30%|███ | 15/50 [00:04<00:10, 3.32it/s] 32%|███▏ | 16/50 [00:04<00:10, 3.32it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.32it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.32it/s] 38%|███▊ | 19/50 [00:05<00:09, 3.32it/s] 40%|████ | 20/50 [00:06<00:09, 3.32it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.32it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.31it/s] 46%|████▌ | 23/50 [00:06<00:08, 3.31it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.31it/s] 50%|█████ | 25/50 [00:07<00:07, 3.31it/s] 52%|█████▏ | 26/50 [00:07<00:07, 3.31it/s] 54%|█████▍ | 27/50 [00:08<00:06, 3.32it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.31it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.32it/s] 60%|██████ | 30/50 [00:09<00:06, 3.32it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.32it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.32it/s] 66%|██████▌ | 33/50 [00:09<00:05, 3.32it/s] 68%|██████▊ | 34/50 [00:10<00:04, 3.32it/s] 70%|███████ | 35/50 [00:10<00:04, 3.32it/s] 72%|███████▏ | 36/50 [00:10<00:04, 3.32it/s] 74%|███████▍ | 37/50 [00:11<00:03, 3.32it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.32it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.32it/s] 80%|████████ | 40/50 [00:12<00:03, 3.32it/s] 82%|████████▏ | 41/50 [00:12<00:02, 3.32it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.32it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.32it/s] 88%|████████▊ | 44/50 [00:13<00:01, 3.31it/s] 90%|█████████ | 45/50 [00:13<00:01, 3.31it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.31it/s] 94%|█████████▍| 47/50 [00:14<00:00, 3.31it/s] 96%|█████████▌| 48/50 [00:14<00:00, 3.31it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.31it/s] 100%|██████████| 50/50 [00:15<00:00, 3.31it/s] 100%|██████████| 50/50 [00:15<00:00, 3.31it/s]Prediction
lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2eID2ewwexbbxjdr5duyskytbcfzzuStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- width
- 768
- height
- 768
- prompt
- a cat, origami a dog, origami a bear, origami
- negative_prompt
- low-resolution
- num_inference_steps
- 50
{ "width": 768, "height": 768, "prompt": "a cat, origami\na dog, origami\na bear, origami", "negative_prompt": "low-resolution", "num_inference_steps": 50 }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 lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", { input: { width: 768, height: 768, prompt: "a cat, origami\na dog, origami\na bear, origami", negative_prompt: "low-resolution", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", input={ "width": 768, "height": 768, "prompt": "a cat, origami\na dog, origami\na bear, origami", "negative_prompt": "low-resolution", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/style-aligned 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": "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", "input": { "width": 768, "height": 768, "prompt": "a cat, origami\\na dog, origami\\na bear, origami", "negative_prompt": "low-resolution", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-05T22:23:52.786686Z", "created_at": "2023-12-05T22:23:37.128703Z", "data_removed": false, "error": null, "id": "2ewwexbbxjdr5duyskytbcfzzu", "input": { "width": 768, "height": 768, "prompt": "a cat, origami\na dog, origami\na bear, origami", "negative_prompt": "low-resolution", "num_inference_steps": 50 }, "logs": "Using seed: 14299\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:17, 2.83it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.54it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.78it/s]\n 8%|▊ | 4/50 [00:01<00:11, 3.97it/s]\n 10%|█ | 5/50 [00:01<00:11, 4.08it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.12it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.11it/s]\n 16%|█▌ | 8/50 [00:02<00:10, 4.06it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 4.05it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.12it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.17it/s]\n 24%|██▍ | 12/50 [00:02<00:09, 4.21it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.26it/s]\n 30%|███ | 15/50 [00:03<00:08, 4.27it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.28it/s]\n 34%|███▍ | 17/50 [00:04<00:07, 4.29it/s]\n 36%|███▌ | 18/50 [00:04<00:07, 4.29it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 4.30it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.30it/s]\n 42%|████▏ | 21/50 [00:05<00:06, 4.30it/s]\n 44%|████▍ | 22/50 [00:05<00:06, 4.30it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 4.30it/s]\n 48%|████▊ | 24/50 [00:05<00:06, 4.30it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.30it/s]\n 52%|█████▏ | 26/50 [00:06<00:05, 4.30it/s]\n 54%|█████▍ | 27/50 [00:06<00:05, 4.30it/s]\n 56%|█████▌ | 28/50 [00:06<00:05, 4.30it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.30it/s]\n 60%|██████ | 30/50 [00:07<00:04, 4.30it/s]\n 62%|██████▏ | 31/50 [00:07<00:04, 4.12it/s]\n 64%|██████▍ | 32/50 [00:07<00:04, 4.00it/s]\n 66%|██████▌ | 33/50 [00:07<00:04, 3.91it/s]\n 68%|██████▊ | 34/50 [00:08<00:03, 4.02it/s]\n 70%|███████ | 35/50 [00:08<00:03, 4.05it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.08it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.14it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 4.19it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.22it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.24it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.26it/s]\n 84%|████████▍ | 42/50 [00:10<00:01, 4.27it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.18it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.14it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.17it/s]\n 92%|█████████▏| 46/50 [00:11<00:00, 4.21it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.23it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.26it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.27it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.18it/s]", "metrics": { "predict_time": 15.634704, "total_time": 15.657983 }, "output": [ "https://replicate.delivery/pbxt/SoEpPfCcF7VncS67TxbkYn1bos4rRnsmd9JKQF02GFR7pqfRA/output-0.png", "https://replicate.delivery/pbxt/I42ItFlAbvZVN5EhvArafqtHfMzO0hoLHGL61EFoOQO3TVfjA/output-1.png", "https://replicate.delivery/pbxt/bhUrXn3KsxbaNhQ6c5HFfPJpWXYRz4FF2BbcikGamBS8pqfRA/output-2.png" ], "started_at": "2023-12-05T22:23:37.151982Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2ewwexbbxjdr5duyskytbcfzzu", "cancel": "https://api.replicate.com/v1/predictions/2ewwexbbxjdr5duyskytbcfzzu/cancel" }, "version": "a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e" }Generated inUsing seed: 14299 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:17, 2.83it/s] 4%|▍ | 2/50 [00:00<00:13, 3.54it/s] 6%|▌ | 3/50 [00:00<00:12, 3.78it/s] 8%|▊ | 4/50 [00:01<00:11, 3.97it/s] 10%|█ | 5/50 [00:01<00:11, 4.08it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.12it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.11it/s] 16%|█▌ | 8/50 [00:02<00:10, 4.06it/s] 18%|█▊ | 9/50 [00:02<00:10, 4.05it/s] 20%|██ | 10/50 [00:02<00:09, 4.12it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.17it/s] 24%|██▍ | 12/50 [00:02<00:09, 4.21it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.26it/s] 30%|███ | 15/50 [00:03<00:08, 4.27it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.28it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.29it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.29it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.30it/s] 40%|████ | 20/50 [00:04<00:06, 4.30it/s] 42%|████▏ | 21/50 [00:05<00:06, 4.30it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.30it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.30it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.30it/s] 50%|█████ | 25/50 [00:05<00:05, 4.30it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.30it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.30it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.30it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.30it/s] 60%|██████ | 30/50 [00:07<00:04, 4.30it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.12it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.00it/s] 66%|██████▌ | 33/50 [00:07<00:04, 3.91it/s] 68%|██████▊ | 34/50 [00:08<00:03, 4.02it/s] 70%|███████ | 35/50 [00:08<00:03, 4.05it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.08it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.14it/s] 76%|███████▌ | 38/50 [00:09<00:02, 4.19it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.22it/s] 80%|████████ | 40/50 [00:09<00:02, 4.24it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.26it/s] 84%|████████▍ | 42/50 [00:10<00:01, 4.27it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.18it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.14it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.17it/s] 92%|█████████▏| 46/50 [00:11<00:00, 4.21it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.23it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.26it/s] 100%|██████████| 50/50 [00:11<00:00, 4.27it/s] 100%|██████████| 50/50 [00:11<00:00, 4.18it/s]Prediction
lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2eID24sqzwrbyl4qbndrbtk47dnujeStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 15502
- width
- 768
- height
- 768
- prompt
- Firewoman made of claymation, stop motion animation Scientist made of claymation, stop motion animation Painter made of claymation, stop motion animation Policeman made of claymation, stop motion animation
- negative_prompt
- low-resolution
- num_inference_steps
- 50
{ "seed": 15502, "width": 768, "height": 768, "prompt": "Firewoman made of claymation, stop motion animation\nScientist made of claymation, stop motion animation\nPainter made of claymation, stop motion animation\nPoliceman made of claymation, stop motion animation", "negative_prompt": "low-resolution", "num_inference_steps": 50 }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 lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", { input: { seed: 15502, width: 768, height: 768, prompt: "Firewoman made of claymation, stop motion animation\nScientist made of claymation, stop motion animation\nPainter made of claymation, stop motion animation\nPoliceman made of claymation, stop motion animation", negative_prompt: "low-resolution", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", input={ "seed": 15502, "width": 768, "height": 768, "prompt": "Firewoman made of claymation, stop motion animation\nScientist made of claymation, stop motion animation\nPainter made of claymation, stop motion animation\nPoliceman made of claymation, stop motion animation", "negative_prompt": "low-resolution", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/style-aligned 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": "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", "input": { "seed": 15502, "width": 768, "height": 768, "prompt": "Firewoman made of claymation, stop motion animation\\nScientist made of claymation, stop motion animation\\nPainter made of claymation, stop motion animation\\nPoliceman made of claymation, stop motion animation", "negative_prompt": "low-resolution", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-05T22:26:33.260534Z", "created_at": "2023-12-05T22:26:13.654949Z", "data_removed": false, "error": null, "id": "24sqzwrbyl4qbndrbtk47dnuje", "input": { "seed": 15502, "width": 768, "height": 768, "prompt": "Firewoman made of claymation, stop motion animation\nScientist made of claymation, stop motion animation\nPainter made of claymation, stop motion animation\nPoliceman made of claymation, stop motion animation", "negative_prompt": "low-resolution", "num_inference_steps": 50 }, "logs": "Using seed: 15502\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 2.99it/s]\n 4%|▍ | 2/50 [00:00<00:15, 3.17it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.24it/s]\n 8%|▊ | 4/50 [00:01<00:14, 3.27it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.28it/s]\n 12%|█▏ | 6/50 [00:01<00:13, 3.30it/s]\n 14%|█▍ | 7/50 [00:02<00:13, 3.30it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.31it/s]\n 18%|█▊ | 9/50 [00:02<00:12, 3.31it/s]\n 20%|██ | 10/50 [00:03<00:12, 3.32it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 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[00:10<00:04, 3.32it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.32it/s]\n 72%|███████▏ | 36/50 [00:10<00:04, 3.32it/s]\n 74%|███████▍ | 37/50 [00:11<00:03, 3.32it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.32it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.32it/s]\n 80%|████████ | 40/50 [00:12<00:03, 3.32it/s]\n 82%|████████▏ | 41/50 [00:12<00:02, 3.32it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.32it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.32it/s]\n 88%|████████▊ | 44/50 [00:13<00:01, 3.32it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.32it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.31it/s]\n 94%|█████████▍| 47/50 [00:14<00:00, 3.31it/s]\n 96%|█████████▌| 48/50 [00:14<00:00, 3.31it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.31it/s]\n100%|██████████| 50/50 [00:15<00:00, 3.31it/s]\n100%|██████████| 50/50 [00:15<00:00, 3.31it/s]", "metrics": { "predict_time": 19.593468, "total_time": 19.605585 }, "output": [ "https://replicate.delivery/pbxt/wkfSoNiqTXSft0OakcZzCfohUakCZTlou44CrNP7XfwbZV9HB/output-0.png", "https://replicate.delivery/pbxt/qfiUogoteSu2wEXo3SqMbgE7EeSW2i60am54jr5QtjcusqeHB/output-1.png", "https://replicate.delivery/pbxt/mjsS97tiej0LMio2b0txhcKEeWxpG2WcRjLQFUYXJKQXWVfjA/output-2.png", "https://replicate.delivery/pbxt/qerpMIWJ6vRqfkoBFfZF0XReWlJfBuU4Fms1XfNqa74PmV1fIA/output-3.png" ], "started_at": "2023-12-05T22:26:13.667066Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/24sqzwrbyl4qbndrbtk47dnuje", "cancel": "https://api.replicate.com/v1/predictions/24sqzwrbyl4qbndrbtk47dnuje/cancel" }, "version": "a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e" }Generated inUsing seed: 15502 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 2.99it/s] 4%|▍ | 2/50 [00:00<00:15, 3.17it/s] 6%|▌ | 3/50 [00:00<00:14, 3.24it/s] 8%|▊ | 4/50 [00:01<00:14, 3.27it/s] 10%|█ | 5/50 [00:01<00:13, 3.28it/s] 12%|█▏ | 6/50 [00:01<00:13, 3.30it/s] 14%|█▍ | 7/50 [00:02<00:13, 3.30it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.31it/s] 18%|█▊ | 9/50 [00:02<00:12, 3.31it/s] 20%|██ | 10/50 [00:03<00:12, 3.32it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.32it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.32it/s] 26%|██▌ | 13/50 [00:03<00:11, 3.32it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.32it/s] 30%|███ | 15/50 [00:04<00:10, 3.32it/s] 32%|███▏ | 16/50 [00:04<00:10, 3.32it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.32it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.32it/s] 38%|███▊ | 19/50 [00:05<00:09, 3.32it/s] 40%|████ | 20/50 [00:06<00:09, 3.32it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.32it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.32it/s] 46%|████▌ | 23/50 [00:06<00:08, 3.32it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.32it/s] 50%|█████ | 25/50 [00:07<00:07, 3.32it/s] 52%|█████▏ | 26/50 [00:07<00:07, 3.32it/s] 54%|█████▍ | 27/50 [00:08<00:06, 3.31it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.31it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.31it/s] 60%|██████ | 30/50 [00:09<00:06, 3.31it/s] 62%|██████▏ | 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lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2eIDg4epswrblqwjuscs2eqmrosdumStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 27041
- width
- 768
- height
- 768
- prompt
- Fox logo, minimal flat design illustration Cat logo, minimal flat design illustration Avocado logo, minimal flat design illustration Violin logo, minimal flat design illustration
- num_inference_steps
- 50
{ "seed": 27041, "width": 768, "height": 768, "prompt": "Fox logo, minimal flat design illustration\nCat logo, minimal flat design illustration\nAvocado logo, minimal flat design illustration\nViolin logo, minimal flat design illustration", "num_inference_steps": 50 }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 lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", { input: { seed: 27041, width: 768, height: 768, prompt: "Fox logo, minimal flat design illustration\nCat logo, minimal flat design illustration\nAvocado logo, minimal flat design illustration\nViolin logo, minimal flat design illustration", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/style-aligned using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", input={ "seed": 27041, "width": 768, "height": 768, "prompt": "Fox logo, minimal flat design illustration\nCat logo, minimal flat design illustration\nAvocado logo, minimal flat design illustration\nViolin logo, minimal flat design illustration", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lucataco/style-aligned 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": "lucataco/style-aligned:a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e", "input": { "seed": 27041, "width": 768, "height": 768, "prompt": "Fox logo, minimal flat design illustration\\nCat logo, minimal flat design illustration\\nAvocado logo, minimal flat design illustration\\nViolin logo, minimal flat design illustration", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-05T22:30:35.755087Z", "created_at": "2023-12-05T22:30:12.909097Z", "data_removed": false, "error": null, "id": "g4epswrblqwjuscs2eqmrosdum", "input": { "seed": 27041, "width": 768, "height": 768, "prompt": "Fox logo, minimal flat design illustration\nCat logo, minimal flat design illustration\nAvocado logo, minimal flat design illustration\nViolin logo, minimal flat design illustration", "num_inference_steps": 50 }, "logs": "Using seed: 27041\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 3.00it/s]\n 4%|▍ | 2/50 [00:00<00:15, 3.18it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.24it/s]\n 8%|▊ | 4/50 [00:01<00:14, 3.27it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.29it/s]\n 12%|█▏ | 6/50 [00:01<00:13, 3.30it/s]\n 14%|█▍ | 7/50 [00:02<00:13, 3.30it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.31it/s]\n 18%|█▊ | 9/50 [00:02<00:12, 3.31it/s]\n 20%|██ | 10/50 [00:03<00:12, 3.31it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.32it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.32it/s]\n 26%|██▌ | 13/50 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72%|███████▏ | 36/50 [00:10<00:04, 3.32it/s]\n 74%|███████▍ | 37/50 [00:11<00:03, 3.32it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.32it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.32it/s]\n 80%|████████ | 40/50 [00:12<00:03, 3.32it/s]\n 82%|████████▏ | 41/50 [00:12<00:02, 3.31it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.32it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.32it/s]\n 88%|████████▊ | 44/50 [00:13<00:01, 3.32it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.31it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.31it/s]\n 94%|█████████▍| 47/50 [00:14<00:00, 3.31it/s]\n 96%|█████████▌| 48/50 [00:14<00:00, 3.31it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.32it/s]\n100%|██████████| 50/50 [00:15<00:00, 3.32it/s]\n100%|██████████| 50/50 [00:15<00:00, 3.31it/s]", "metrics": { "predict_time": 22.833193, "total_time": 22.84599 }, "output": [ "https://replicate.delivery/pbxt/QiJMh86cjuJbK9DZaS4r9FciBdeC2AlsxXbxv9OxCNmEtqfRA/output-0.png", "https://replicate.delivery/pbxt/6Txfx8erQtllfIvy7mDJNfoGvuAfxpvlBjzXoglWkiMORr6PC/output-1.png", "https://replicate.delivery/pbxt/cXzwGqk3Sdp2KJrjtbaf0W3ebgawSGi9jhy5bnjGeK5V0qeHB/output-2.png", "https://replicate.delivery/pbxt/vmF9vM9luk6QPprFGVXfxcyAQ13RLG7WMHzVK75os05FtqfRA/output-3.png" ], "started_at": "2023-12-05T22:30:12.921894Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/g4epswrblqwjuscs2eqmrosdum", "cancel": "https://api.replicate.com/v1/predictions/g4epswrblqwjuscs2eqmrosdum/cancel" }, "version": "a9086389a701a88fd63e6739e5699d3f1b7ba3433bb2ec60dd3fb6be81ee8a2e" }Generated inUsing seed: 27041 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 3.00it/s] 4%|▍ | 2/50 [00:00<00:15, 3.18it/s] 6%|▌ | 3/50 [00:00<00:14, 3.24it/s] 8%|▊ | 4/50 [00:01<00:14, 3.27it/s] 10%|█ | 5/50 [00:01<00:13, 3.29it/s] 12%|█▏ | 6/50 [00:01<00:13, 3.30it/s] 14%|█▍ | 7/50 [00:02<00:13, 3.30it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.31it/s] 18%|█▊ | 9/50 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