mcgregory99 / goyofacebooth
- Public
- 75 runs
-
T4
Prediction
mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963IDtincyhjbwyg7okjhwiefxgr2viStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- seed
- 1312
- width
- 512
- height
- 512
- prompt
- Capture the essence of professionalism with an 8k, meticulously detailed corporate portrait headshot photograph. This stunning image features a distinguished gycn person in a suit, radiating confidence and elegance. This hyperrealistic masterpiece is hailed as the best corporate photo winner, showcasing an unrivaled level of perfection.
- scheduler
- KLMS
- num_outputs
- 3
- guidance_scale
- 7.5
- prompt_strength
- 0.7
- num_inference_steps
- 80
- disable_safety_check
{ "seed": 1312, "width": 512, "height": 512, "prompt": "Capture the essence of professionalism with an 8k, meticulously detailed corporate portrait headshot photograph. This stunning image features a distinguished gycn person in a suit, radiating confidence and elegance. This hyperrealistic masterpiece is hailed as the best corporate photo winner, showcasing an unrivaled level of perfection.", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }
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 mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", { input: { seed: 1312, width: 512, height: 512, prompt: "Capture the essence of professionalism with an 8k, meticulously detailed corporate portrait headshot photograph. This stunning image features a distinguished gycn person in a suit, radiating confidence and elegance. This hyperrealistic masterpiece is hailed as the best corporate photo winner, showcasing an unrivaled level of perfection.", scheduler: "KLMS", num_outputs: 3, guidance_scale: 7.5, prompt_strength: 0.7, num_inference_steps: 80, disable_safety_check: false } } ); // 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 replicate
Import the client:import replicate
Run mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", input={ "seed": 1312, "width": 512, "height": 512, "prompt": "Capture the essence of professionalism with an 8k, meticulously detailed corporate portrait headshot photograph. This stunning image features a distinguished gycn person in a suit, radiating confidence and elegance. This hyperrealistic masterpiece is hailed as the best corporate photo winner, showcasing an unrivaled level of perfection.", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": False } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run mcgregory99/goyofacebooth 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": "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", "input": { "seed": 1312, "width": 512, "height": 512, "prompt": "Capture the essence of professionalism with an 8k, meticulously detailed corporate portrait headshot photograph. This stunning image features a distinguished gycn person in a suit, radiating confidence and elegance. This hyperrealistic masterpiece is hailed as the best corporate photo winner, showcasing an unrivaled level of perfection.", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-02-20T08:43:25.614884Z", "created_at": "2024-02-20T08:42:33.326915Z", "data_removed": false, "error": null, "id": "tincyhjbwyg7okjhwiefxgr2vi", "input": { "seed": 1312, "width": 512, "height": 512, "prompt": "Capture the essence of professionalism with an 8k, meticulously detailed corporate portrait headshot photograph. This stunning image features a distinguished gycn person in a suit, radiating confidence and elegance. This hyperrealistic masterpiece is hailed as the best corporate photo winner, showcasing an unrivaled level of perfection.", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }, "logs": "Using seed: 1312\nusing txt2img\n 0%| | 0/80 [00:00<?, ?it/s]\n 1%|▏ | 1/80 [00:00<00:47, 1.65it/s]\n 2%|▎ | 2/80 [00:01<00:47, 1.66it/s]\n 4%|▍ | 3/80 [00:01<00:45, 1.68it/s]\n 5%|▌ | 4/80 [00:02<00:45, 1.66it/s]\n 6%|▋ | 5/80 [00:02<00:44, 1.67it/s]\n 8%|▊ | 6/80 [00:03<00:44, 1.67it/s]\n 9%|▉ | 7/80 [00:04<00:43, 1.67it/s]\n 10%|█ | 8/80 [00:04<00:43, 1.66it/s]\n 11%|█▏ | 9/80 [00:05<00:42, 1.66it/s]\n 12%|█▎ | 10/80 [00:06<00:42, 1.66it/s]\n 14%|█▍ | 11/80 [00:06<00:41, 1.65it/s]\n 15%|█▌ | 12/80 [00:07<00:41, 1.65it/s]\n 16%|█▋ | 13/80 [00:07<00:40, 1.65it/s]\n 18%|█▊ | 14/80 [00:08<00:40, 1.65it/s]\n 19%|█▉ | 15/80 [00:09<00:39, 1.64it/s]\n 20%|██ | 16/80 [00:09<00:39, 1.63it/s]\n 21%|██▏ | 17/80 [00:10<00:38, 1.63it/s]\n 22%|██▎ | 18/80 [00:10<00:38, 1.63it/s]\n 24%|██▍ | 19/80 [00:11<00:37, 1.62it/s]\n 25%|██▌ | 20/80 [00:12<00:37, 1.62it/s]\n 26%|██▋ | 21/80 [00:12<00:36, 1.62it/s]\n 28%|██▊ | 22/80 [00:13<00:36, 1.60it/s]\n 29%|██▉ | 23/80 [00:14<00:35, 1.60it/s]\n 30%|███ | 24/80 [00:14<00:35, 1.59it/s]\n 31%|███▏ | 25/80 [00:15<00:34, 1.59it/s]\n 32%|███▎ | 26/80 [00:15<00:33, 1.59it/s]\n 34%|███▍ | 27/80 [00:16<00:33, 1.59it/s]\n 35%|███▌ | 28/80 [00:17<00:32, 1.59it/s]\n 36%|███▋ | 29/80 [00:17<00:32, 1.58it/s]\n 38%|███▊ | 30/80 [00:18<00:31, 1.58it/s]\n 39%|███▉ | 31/80 [00:19<00:31, 1.57it/s]\n 40%|████ | 32/80 [00:19<00:30, 1.56it/s]\n 41%|████▏ | 33/80 [00:20<00:30, 1.56it/s]\n 42%|████▎ | 34/80 [00:21<00:29, 1.55it/s]\n 44%|████▍ | 35/80 [00:21<00:28, 1.55it/s]\n 45%|████▌ | 36/80 [00:22<00:28, 1.55it/s]\n 46%|████▋ | 37/80 [00:22<00:27, 1.55it/s]\n 48%|████▊ | 38/80 [00:23<00:27, 1.54it/s]\n 49%|████▉ | 39/80 [00:24<00:26, 1.54it/s]\n 50%|█████ | 40/80 [00:24<00:25, 1.54it/s]\n 51%|█████▏ | 41/80 [00:25<00:25, 1.54it/s]\n 52%|█████▎ | 42/80 [00:26<00:24, 1.54it/s]\n 54%|█████▍ | 43/80 [00:26<00:24, 1.53it/s]\n 55%|█████▌ | 44/80 [00:27<00:23, 1.52it/s]\n 56%|█████▋ | 45/80 [00:28<00:22, 1.52it/s]\n 57%|█████▊ | 46/80 [00:28<00:22, 1.52it/s]\n 59%|█████▉ | 47/80 [00:29<00:21, 1.52it/s]\n 60%|██████ | 48/80 [00:30<00:21, 1.52it/s]\n 61%|██████▏ | 49/80 [00:30<00:20, 1.52it/s]\n 62%|██████▎ | 50/80 [00:31<00:19, 1.53it/s]\n 64%|██████▍ | 51/80 [00:32<00:18, 1.53it/s]\n 65%|██████▌ | 52/80 [00:32<00:18, 1.53it/s]\n 66%|██████▋ | 53/80 [00:33<00:17, 1.53it/s]\n 68%|██████▊ | 54/80 [00:34<00:16, 1.54it/s]\n 69%|██████▉ | 55/80 [00:34<00:16, 1.54it/s]\n 70%|███████ | 56/80 [00:35<00:15, 1.54it/s]\n 71%|███████▏ | 57/80 [00:36<00:14, 1.55it/s]\n 72%|███████▎ | 58/80 [00:36<00:14, 1.55it/s]\n 74%|███████▍ | 59/80 [00:37<00:13, 1.56it/s]\n 75%|███████▌ | 60/80 [00:37<00:12, 1.56it/s]\n 76%|███████▋ | 61/80 [00:38<00:12, 1.56it/s]\n 78%|███████▊ | 62/80 [00:39<00:11, 1.57it/s]\n 79%|███████▉ | 63/80 [00:39<00:10, 1.57it/s]\n 80%|████████ | 64/80 [00:40<00:10, 1.58it/s]\n 81%|████████▏ | 65/80 [00:41<00:09, 1.59it/s]\n 82%|████████▎ | 66/80 [00:41<00:08, 1.58it/s]\n 84%|████████▍ | 67/80 [00:42<00:08, 1.59it/s]\n 85%|████████▌ | 68/80 [00:42<00:07, 1.59it/s]\n 86%|████████▋ | 69/80 [00:43<00:06, 1.60it/s]\n 88%|████████▊ | 70/80 [00:44<00:06, 1.60it/s]\n 89%|████████▉ | 71/80 [00:44<00:05, 1.60it/s]\n 90%|█████████ | 72/80 [00:45<00:04, 1.61it/s]\n 91%|█████████▏| 73/80 [00:46<00:04, 1.61it/s]\n 92%|█████████▎| 74/80 [00:46<00:03, 1.62it/s]\n 94%|█████████▍| 75/80 [00:47<00:03, 1.62it/s]\n 95%|█████████▌| 76/80 [00:47<00:02, 1.62it/s]\n 96%|█████████▋| 77/80 [00:48<00:01, 1.63it/s]\n 98%|█████████▊| 78/80 [00:49<00:01, 1.61it/s]\n 99%|█████████▉| 79/80 [00:49<00:00, 1.62it/s]\n100%|██████████| 80/80 [00:50<00:00, 1.63it/s]\n100%|██████████| 80/80 [00:50<00:00, 1.59it/s]", "metrics": { "predict_time": 52.274183, "total_time": 52.287969 }, "output": [ "https://replicate.delivery/pbxt/xqrWe2FwskwwHqJxABYuqdthVgyFNF3sg3CF1SYNrR0WwQMJA/out-0.png", "https://replicate.delivery/pbxt/pufpuEo8TYSAf0AtlDEN3VOpBJENv3AsidcelLQRicNaBDxkA/out-1.png", "https://replicate.delivery/pbxt/GI3dZwB7xWJYIJrLnAXhRdTLqaJ7kT5p84KVjqRQN4TLYImE/out-2.png" ], "started_at": "2024-02-20T08:42:33.340701Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tincyhjbwyg7okjhwiefxgr2vi", "cancel": "https://api.replicate.com/v1/predictions/tincyhjbwyg7okjhwiefxgr2vi/cancel" }, "version": "6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963" }
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Prediction
mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963IDwltry7zbsj2pguoulpi6u4k5t4StatusSucceededSourceWebHardwareT4Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- Portrait full body of a gycn person in military uniform, with strong, directional lighting, UHD 8k
- scheduler
- KLMS
- num_outputs
- 3
- guidance_scale
- 7.5
- prompt_strength
- 0.7
- num_inference_steps
- 80
- disable_safety_check
{ "width": 512, "height": 512, "prompt": "Portrait full body of a gycn person in military uniform, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }
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 mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", { input: { width: 512, height: 512, prompt: "Portrait full body of a gycn person in military uniform, with strong, directional lighting, UHD 8k", scheduler: "KLMS", num_outputs: 3, guidance_scale: 7.5, prompt_strength: 0.7, num_inference_steps: 80, disable_safety_check: false } } ); // 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 replicate
Import the client:import replicate
Run mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", input={ "width": 512, "height": 512, "prompt": "Portrait full body of a gycn person in military uniform, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": False } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run mcgregory99/goyofacebooth 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": "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", "input": { "width": 512, "height": 512, "prompt": "Portrait full body of a gycn person in military uniform, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-02-20T10:07:40.988635Z", "created_at": "2024-02-20T10:06:53.356328Z", "data_removed": false, "error": null, "id": "wltry7zbsj2pguoulpi6u4k5t4", "input": { "width": 512, "height": 512, "prompt": "Portrait full body of a gycn person in military uniform, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }, "logs": "Using seed: 37497\nusing txt2img\n 0%| | 0/80 [00:00<?, ?it/s]\n 1%|▏ | 1/80 [00:00<00:44, 1.79it/s]\n 2%|▎ | 2/80 [00:01<00:42, 1.84it/s]\n 4%|▍ | 3/80 [00:01<00:41, 1.85it/s]\n 5%|▌ | 4/80 [00:02<00:41, 1.84it/s]\n 6%|▋ | 5/80 [00:02<00:40, 1.85it/s]\n 8%|▊ | 6/80 [00:03<00:40, 1.84it/s]\n 9%|▉ | 7/80 [00:03<00:39, 1.85it/s]\n 10%|█ | 8/80 [00:04<00:38, 1.85it/s]\n 11%|█▏ | 9/80 [00:04<00:38, 1.85it/s]\n 12%|█▎ | 10/80 [00:05<00:38, 1.83it/s]\n 14%|█▍ | 11/80 [00:05<00:37, 1.84it/s]\n 15%|█▌ | 12/80 [00:06<00:37, 1.82it/s]\n 16%|█▋ | 13/80 [00:07<00:36, 1.83it/s]\n 18%|█▊ | 14/80 [00:07<00:36, 1.83it/s]\n 19%|█▉ | 15/80 [00:08<00:35, 1.82it/s]\n 20%|██ | 16/80 [00:08<00:35, 1.82it/s]\n 21%|██▏ | 17/80 [00:09<00:34, 1.81it/s]\n 22%|██▎ | 18/80 [00:09<00:34, 1.82it/s]\n 24%|██▍ | 19/80 [00:10<00:33, 1.81it/s]\n 25%|██▌ | 20/80 [00:10<00:33, 1.81it/s]\n 26%|██▋ | 21/80 [00:11<00:32, 1.81it/s]\n 28%|██▊ | 22/80 [00:12<00:32, 1.81it/s]\n 29%|██▉ | 23/80 [00:12<00:31, 1.79it/s]\n 30%|███ | 24/80 [00:13<00:31, 1.79it/s]\n 31%|███▏ | 25/80 [00:13<00:30, 1.79it/s]\n 32%|███▎ | 26/80 [00:14<00:30, 1.78it/s]\n 34%|███▍ | 27/80 [00:14<00:29, 1.79it/s]\n 35%|███▌ | 28/80 [00:15<00:29, 1.77it/s]\n 36%|███▋ | 29/80 [00:16<00:28, 1.77it/s]\n 38%|███▊ | 30/80 [00:16<00:28, 1.77it/s]\n 39%|███▉ | 31/80 [00:17<00:27, 1.77it/s]\n 40%|████ | 32/80 [00:17<00:27, 1.76it/s]\n 41%|████▏ | 33/80 [00:18<00:26, 1.76it/s]\n 42%|████▎ | 34/80 [00:18<00:26, 1.76it/s]\n 44%|████▍ | 35/80 [00:19<00:25, 1.76it/s]\n 45%|████▌ | 36/80 [00:19<00:24, 1.77it/s]\n 46%|████▋ | 37/80 [00:20<00:24, 1.76it/s]\n 48%|████▊ | 38/80 [00:21<00:23, 1.76it/s]\n 49%|████▉ | 39/80 [00:21<00:23, 1.76it/s]\n 50%|█████ | 40/80 [00:22<00:22, 1.76it/s]\n 51%|█████▏ | 41/80 [00:22<00:22, 1.75it/s]\n 52%|█████▎ | 42/80 [00:23<00:21, 1.74it/s]\n 54%|█████▍ | 43/80 [00:23<00:21, 1.74it/s]\n 55%|█████▌ | 44/80 [00:24<00:20, 1.74it/s]\n 56%|█████▋ | 45/80 [00:25<00:20, 1.74it/s]\n 57%|█████▊ | 46/80 [00:25<00:19, 1.73it/s]\n 59%|█████▉ | 47/80 [00:26<00:19, 1.73it/s]\n 60%|██████ | 48/80 [00:26<00:18, 1.72it/s]\n 61%|██████▏ | 49/80 [00:27<00:18, 1.72it/s]\n 62%|██████▎ | 50/80 [00:28<00:17, 1.71it/s]\n 64%|██████▍ | 51/80 [00:28<00:17, 1.70it/s]\n 65%|██████▌ | 52/80 [00:29<00:16, 1.70it/s]\n 66%|██████▋ | 53/80 [00:29<00:15, 1.70it/s]\n 68%|██████▊ | 54/80 [00:30<00:15, 1.70it/s]\n 69%|██████▉ | 55/80 [00:31<00:14, 1.70it/s]\n 70%|███████ | 56/80 [00:31<00:14, 1.70it/s]\n 71%|███████▏ | 57/80 [00:32<00:13, 1.70it/s]\n 72%|███████▎ | 58/80 [00:32<00:12, 1.70it/s]\n 74%|███████▍ | 59/80 [00:33<00:12, 1.70it/s]\n 75%|███████▌ | 60/80 [00:33<00:11, 1.69it/s]\n 76%|███████▋ | 61/80 [00:34<00:11, 1.69it/s]\n 78%|███████▊ | 62/80 [00:35<00:10, 1.70it/s]\n 79%|███████▉ | 63/80 [00:35<00:10, 1.70it/s]\n 80%|████████ | 64/80 [00:36<00:09, 1.69it/s]\n 81%|████████▏ | 65/80 [00:36<00:08, 1.70it/s]\n 82%|████████▎ | 66/80 [00:37<00:08, 1.70it/s]\n 84%|████████▍ | 67/80 [00:38<00:07, 1.70it/s]\n 85%|████████▌ | 68/80 [00:38<00:07, 1.71it/s]\n 86%|████████▋ | 69/80 [00:39<00:06, 1.71it/s]\n 88%|████████▊ | 70/80 [00:39<00:05, 1.72it/s]\n 89%|████████▉ | 71/80 [00:40<00:05, 1.71it/s]\n 90%|█████████ | 72/80 [00:40<00:04, 1.71it/s]\n 91%|█████████▏| 73/80 [00:41<00:04, 1.71it/s]\n 92%|█████████▎| 74/80 [00:42<00:03, 1.72it/s]\n 94%|█████████▍| 75/80 [00:42<00:02, 1.73it/s]\n 95%|█████████▌| 76/80 [00:43<00:02, 1.72it/s]\n 96%|█████████▋| 77/80 [00:43<00:01, 1.73it/s]\n 98%|█████████▊| 78/80 [00:44<00:01, 1.72it/s]\n 99%|█████████▉| 79/80 [00:45<00:00, 1.73it/s]\n100%|██████████| 80/80 [00:45<00:00, 1.73it/s]\n100%|██████████| 80/80 [00:45<00:00, 1.75it/s]", "metrics": { "predict_time": 47.620308, "total_time": 47.632307 }, "output": [ "https://replicate.delivery/pbxt/YLRcsA8WDJqRMZj0AoSx3J0M7UFMf8gmsG8w47bj53Q2XRMJA/out-0.png", "https://replicate.delivery/pbxt/euieMg9H4bhS1EQjLOE43n8qrbsX4hrgK3ABpWEVzWhsviYSA/out-1.png", "https://replicate.delivery/pbxt/tDRxviVQf82ufUhN5LjUUv9cSjvJfRMMMl2CssS5IM6YfKiJB/out-2.png" ], "started_at": "2024-02-20T10:06:53.368327Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wltry7zbsj2pguoulpi6u4k5t4", "cancel": "https://api.replicate.com/v1/predictions/wltry7zbsj2pguoulpi6u4k5t4/cancel" }, "version": "6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963" }
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Prediction
mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963ID4fipgtjbeywyrgn6fkt235qsmqStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- Portrait full body of a gycn person in futuristic military uniform, with strong, directional lighting, UHD 8k
- scheduler
- KLMS
- num_outputs
- 3
- guidance_scale
- 7.5
- prompt_strength
- 0.7
- num_inference_steps
- 80
- disable_safety_check
{ "width": 512, "height": 512, "prompt": "Portrait full body of a gycn person in futuristic military uniform, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }
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 mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", { input: { width: 512, height: 512, prompt: "Portrait full body of a gycn person in futuristic military uniform, with strong, directional lighting, UHD 8k", scheduler: "KLMS", num_outputs: 3, guidance_scale: 7.5, prompt_strength: 0.7, num_inference_steps: 80, disable_safety_check: false } } ); // 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 replicate
Import the client:import replicate
Run mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", input={ "width": 512, "height": 512, "prompt": "Portrait full body of a gycn person in futuristic military uniform, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": False } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run mcgregory99/goyofacebooth 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": "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", "input": { "width": 512, "height": 512, "prompt": "Portrait full body of a gycn person in futuristic military uniform, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-02-20T10:11:58.031007Z", "created_at": "2024-02-20T10:11:09.682786Z", "data_removed": false, "error": null, "id": "4fipgtjbeywyrgn6fkt235qsmq", "input": { "width": 512, "height": 512, "prompt": "Portrait full body of a gycn person in futuristic military uniform, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }, "logs": "Using seed: 2855\nusing txt2img\n 0%| | 0/80 [00:00<?, ?it/s]\n 1%|▏ | 1/80 [00:00<00:45, 1.74it/s]\n 2%|▎ | 2/80 [00:01<00:42, 1.84it/s]\n 4%|▍ | 3/80 [00:01<00:41, 1.85it/s]\n 5%|▌ | 4/80 [00:02<00:40, 1.86it/s]\n 6%|▋ | 5/80 [00:02<00:40, 1.85it/s]\n 8%|▊ | 6/80 [00:03<00:39, 1.86it/s]\n 9%|▉ | 7/80 [00:03<00:39, 1.86it/s]\n 10%|█ | 8/80 [00:04<00:38, 1.86it/s]\n 11%|█▏ | 9/80 [00:04<00:38, 1.85it/s]\n 12%|█▎ | 10/80 [00:05<00:37, 1.86it/s]\n 14%|█▍ | 11/80 [00:05<00:37, 1.84it/s]\n 15%|█▌ | 12/80 [00:06<00:36, 1.84it/s]\n 16%|█▋ | 13/80 [00:07<00:36, 1.83it/s]\n 18%|█▊ | 14/80 [00:07<00:35, 1.84it/s]\n 19%|█▉ | 15/80 [00:08<00:35, 1.83it/s]\n 20%|██ | 16/80 [00:08<00:34, 1.84it/s]\n 21%|██▏ | 17/80 [00:09<00:34, 1.83it/s]\n 22%|██▎ | 18/80 [00:09<00:33, 1.83it/s]\n 24%|██▍ | 19/80 [00:10<00:33, 1.82it/s]\n 25%|██▌ | 20/80 [00:10<00:32, 1.83it/s]\n 26%|██▋ | 21/80 [00:11<00:32, 1.81it/s]\n 28%|██▊ | 22/80 [00:11<00:31, 1.82it/s]\n 29%|██▉ | 23/80 [00:12<00:31, 1.80it/s]\n 30%|███ | 24/80 [00:13<00:31, 1.81it/s]\n 31%|███▏ | 25/80 [00:13<00:30, 1.79it/s]\n 32%|███▎ | 26/80 [00:14<00:30, 1.80it/s]\n 34%|███▍ | 27/80 [00:14<00:29, 1.79it/s]\n 35%|███▌ | 28/80 [00:15<00:29, 1.79it/s]\n 36%|███▋ | 29/80 [00:15<00:28, 1.78it/s]\n 38%|███▊ | 30/80 [00:16<00:27, 1.79it/s]\n 39%|███▉ | 31/80 [00:17<00:27, 1.78it/s]\n 40%|████ | 32/80 [00:17<00:27, 1.77it/s]\n 41%|████▏ | 33/80 [00:18<00:26, 1.77it/s]\n 42%|████▎ | 34/80 [00:18<00:26, 1.77it/s]\n 44%|████▍ | 35/80 [00:19<00:25, 1.77it/s]\n 45%|████▌ | 36/80 [00:19<00:24, 1.76it/s]\n 46%|████▋ | 37/80 [00:20<00:24, 1.76it/s]\n 48%|████▊ | 38/80 [00:21<00:23, 1.76it/s]\n 49%|████▉ | 39/80 [00:21<00:23, 1.77it/s]\n 50%|█████ | 40/80 [00:22<00:22, 1.76it/s]\n 51%|█████▏ | 41/80 [00:22<00:22, 1.77it/s]\n 52%|█████▎ | 42/80 [00:23<00:21, 1.75it/s]\n 54%|█████▍ | 43/80 [00:23<00:21, 1.76it/s]\n 55%|█████▌ | 44/80 [00:24<00:20, 1.75it/s]\n 56%|█████▋ | 45/80 [00:25<00:20, 1.74it/s]\n 57%|█████▊ | 46/80 [00:25<00:19, 1.74it/s]\n 59%|█████▉ | 47/80 [00:26<00:19, 1.74it/s]\n 60%|██████ | 48/80 [00:26<00:18, 1.74it/s]\n 61%|██████▏ | 49/80 [00:27<00:17, 1.74it/s]\n 62%|██████▎ | 50/80 [00:27<00:17, 1.73it/s]\n 64%|██████▍ | 51/80 [00:28<00:16, 1.73it/s]\n 65%|██████▌ | 52/80 [00:29<00:16, 1.72it/s]\n 66%|██████▋ | 53/80 [00:29<00:15, 1.72it/s]\n 68%|██████▊ | 54/80 [00:30<00:15, 1.72it/s]\n 69%|██████▉ | 55/80 [00:30<00:14, 1.72it/s]\n 70%|███████ | 56/80 [00:31<00:13, 1.72it/s]\n 71%|███████▏ | 57/80 [00:31<00:13, 1.71it/s]\n 72%|███████▎ | 58/80 [00:32<00:12, 1.70it/s]\n 74%|███████▍ | 59/80 [00:33<00:12, 1.71it/s]\n 75%|███████▌ | 60/80 [00:33<00:11, 1.71it/s]\n 76%|███████▋ | 61/80 [00:34<00:11, 1.71it/s]\n 78%|███████▊ | 62/80 [00:34<00:10, 1.71it/s]\n 79%|███████▉ | 63/80 [00:35<00:09, 1.71it/s]\n 80%|████████ | 64/80 [00:36<00:09, 1.71it/s]\n 81%|████████▏ | 65/80 [00:36<00:08, 1.70it/s]\n 82%|████████▎ | 66/80 [00:37<00:08, 1.70it/s]\n 84%|████████▍ | 67/80 [00:37<00:07, 1.70it/s]\n 85%|████████▌ | 68/80 [00:38<00:07, 1.70it/s]\n 86%|████████▋ | 69/80 [00:39<00:06, 1.70it/s]\n 88%|████████▊ | 70/80 [00:39<00:05, 1.70it/s]\n 89%|████████▉ | 71/80 [00:40<00:05, 1.70it/s]\n 90%|█████████ | 72/80 [00:40<00:04, 1.70it/s]\n 91%|█████████▏| 73/80 [00:41<00:04, 1.71it/s]\n 92%|█████████▎| 74/80 [00:41<00:03, 1.70it/s]\n 94%|█████████▍| 75/80 [00:42<00:02, 1.71it/s]\n 95%|█████████▌| 76/80 [00:43<00:02, 1.71it/s]\n 96%|█████████▋| 77/80 [00:43<00:01, 1.72it/s]\n 98%|█████████▊| 78/80 [00:44<00:01, 1.72it/s]\n 99%|█████████▉| 79/80 [00:44<00:00, 1.72it/s]\n100%|██████████| 80/80 [00:45<00:00, 1.73it/s]\n100%|██████████| 80/80 [00:45<00:00, 1.76it/s]", "metrics": { "predict_time": 48.329283, "total_time": 48.348221 }, "output": [ "https://replicate.delivery/pbxt/vQGsvaP1VtohABd8Kpedb8mvqOQeG5w8EEMXWfeqHFL0OLiJB/out-0.png", "https://replicate.delivery/pbxt/eJ3TGUZNJtwVX6WeqUAIIg4w0Ph3G7he5RkhllfQLLK3OLiJB/out-1.png", "https://replicate.delivery/pbxt/zAmkZLmpfk3LA60mtLPVMD54lrGHGrkkjLgbxyEYnJ92ZRMJA/out-2.png" ], "started_at": "2024-02-20T10:11:09.701724Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4fipgtjbeywyrgn6fkt235qsmq", "cancel": "https://api.replicate.com/v1/predictions/4fipgtjbeywyrgn6fkt235qsmq/cancel" }, "version": "6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963" }
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Prediction
mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963IDuhwlszjbuo5pkm7c4ofm4pouseStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- seed
- 1312
- width
- 512
- height
- 512
- prompt
- Close portrait of elegant gycn person in tailored suit- futurist style, intricate baroque detial, elegant, glowing lights, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by wlop, mars ravelo and greg rutkowski
- scheduler
- KLMS
- num_outputs
- 3
- guidance_scale
- 7.5
- prompt_strength
- 0.7
- num_inference_steps
- 80
- disable_safety_check
{ "seed": 1312, "width": 512, "height": 512, "prompt": "Close portrait of elegant gycn person in tailored suit- futurist style, intricate baroque detial, elegant, glowing lights, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by wlop, mars ravelo and greg rutkowski", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }
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 mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", { input: { seed: 1312, width: 512, height: 512, prompt: "Close portrait of elegant gycn person in tailored suit- futurist style, intricate baroque detial, elegant, glowing lights, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by wlop, mars ravelo and greg rutkowski", scheduler: "KLMS", num_outputs: 3, guidance_scale: 7.5, prompt_strength: 0.7, num_inference_steps: 80, disable_safety_check: false } } ); // 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 replicate
Import the client:import replicate
Run mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", input={ "seed": 1312, "width": 512, "height": 512, "prompt": "Close portrait of elegant gycn person in tailored suit- futurist style, intricate baroque detial, elegant, glowing lights, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by wlop, mars ravelo and greg rutkowski", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": False } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run mcgregory99/goyofacebooth 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": "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", "input": { "seed": 1312, "width": 512, "height": 512, "prompt": "Close portrait of elegant gycn person in tailored suit- futurist style, intricate baroque detial, elegant, glowing lights, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by wlop, mars ravelo and greg rutkowski", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-02-20T08:46:08.177093Z", "created_at": "2024-02-20T08:45:15.785818Z", "data_removed": false, "error": null, "id": "uhwlszjbuo5pkm7c4ofm4pouse", "input": { "seed": 1312, "width": 512, "height": 512, "prompt": "Close portrait of elegant gycn person in tailored suit- futurist style, intricate baroque detial, elegant, glowing lights, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by wlop, mars ravelo and greg rutkowski", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }, "logs": "Using seed: 1312\nusing txt2img\n 0%| | 0/80 [00:00<?, ?it/s]\n 1%|▏ | 1/80 [00:00<00:48, 1.62it/s]\n 2%|▎ | 2/80 [00:01<00:47, 1.65it/s]\n 4%|▍ | 3/80 [00:01<00:46, 1.67it/s]\n 5%|▌ | 4/80 [00:02<00:46, 1.65it/s]\n 6%|▋ | 5/80 [00:03<00:45, 1.66it/s]\n 8%|▊ | 6/80 [00:03<00:44, 1.65it/s]\n 9%|▉ | 7/80 [00:04<00:44, 1.65it/s]\n 10%|█ | 8/80 [00:04<00:43, 1.64it/s]\n 11%|█▏ | 9/80 [00:05<00:43, 1.65it/s]\n 12%|█▎ | 10/80 [00:06<00:42, 1.64it/s]\n 14%|█▍ | 11/80 [00:06<00:42, 1.64it/s]\n 15%|█▌ | 12/80 [00:07<00:41, 1.64it/s]\n 16%|█▋ | 13/80 [00:07<00:40, 1.65it/s]\n 18%|█▊ | 14/80 [00:08<00:40, 1.64it/s]\n 19%|█▉ | 15/80 [00:09<00:39, 1.64it/s]\n 20%|██ | 16/80 [00:09<00:39, 1.63it/s]\n 21%|██▏ | 17/80 [00:10<00:38, 1.62it/s]\n 22%|██▎ | 18/80 [00:10<00:38, 1.62it/s]\n 24%|██▍ | 19/80 [00:11<00:38, 1.60it/s]\n 25%|██▌ | 20/80 [00:12<00:37, 1.60it/s]\n 26%|██▋ | 21/80 [00:12<00:36, 1.60it/s]\n 28%|██▊ | 22/80 [00:13<00:36, 1.59it/s]\n 29%|██▉ | 23/80 [00:14<00:35, 1.59it/s]\n 30%|███ | 24/80 [00:14<00:35, 1.58it/s]\n 31%|███▏ | 25/80 [00:15<00:34, 1.58it/s]\n 32%|███▎ | 26/80 [00:16<00:34, 1.58it/s]\n 34%|███▍ | 27/80 [00:16<00:33, 1.58it/s]\n 35%|███▌ | 28/80 [00:17<00:32, 1.58it/s]\n 36%|███▋ | 29/80 [00:17<00:32, 1.57it/s]\n 38%|███▊ | 30/80 [00:18<00:31, 1.57it/s]\n 39%|███▉ | 31/80 [00:19<00:31, 1.56it/s]\n 40%|████ | 32/80 [00:19<00:30, 1.56it/s]\n 41%|████▏ | 33/80 [00:20<00:30, 1.55it/s]\n 42%|████▎ | 34/80 [00:21<00:29, 1.55it/s]\n 44%|████▍ | 35/80 [00:21<00:29, 1.55it/s]\n 45%|████▌ | 36/80 [00:22<00:28, 1.55it/s]\n 46%|████▋ | 37/80 [00:23<00:27, 1.54it/s]\n 48%|████▊ | 38/80 [00:23<00:27, 1.54it/s]\n 49%|████▉ | 39/80 [00:24<00:26, 1.54it/s]\n 50%|█████ | 40/80 [00:25<00:25, 1.54it/s]\n 51%|█████▏ | 41/80 [00:25<00:25, 1.54it/s]\n 52%|█████▎ | 42/80 [00:26<00:24, 1.54it/s]\n 54%|█████▍ | 43/80 [00:27<00:24, 1.53it/s]\n 55%|█████▌ | 44/80 [00:27<00:23, 1.53it/s]\n 56%|█████▋ | 45/80 [00:28<00:22, 1.54it/s]\n 57%|█████▊ | 46/80 [00:28<00:22, 1.54it/s]\n 59%|█████▉ | 47/80 [00:29<00:21, 1.54it/s]\n 60%|██████ | 48/80 [00:30<00:20, 1.54it/s]\n 61%|██████▏ | 49/80 [00:30<00:20, 1.54it/s]\n 62%|██████▎ | 50/80 [00:31<00:19, 1.54it/s]\n 64%|██████▍ | 51/80 [00:32<00:18, 1.54it/s]\n 65%|██████▌ | 52/80 [00:32<00:18, 1.55it/s]\n 66%|██████▋ | 53/80 [00:33<00:17, 1.55it/s]\n 68%|██████▊ | 54/80 [00:34<00:16, 1.56it/s]\n 69%|██████▉ | 55/80 [00:34<00:16, 1.55it/s]\n 70%|███████ | 56/80 [00:35<00:15, 1.56it/s]\n 71%|███████▏ | 57/80 [00:36<00:14, 1.56it/s]\n 72%|███████▎ | 58/80 [00:36<00:14, 1.56it/s]\n 74%|███████▍ | 59/80 [00:37<00:13, 1.57it/s]\n 75%|███████▌ | 60/80 [00:37<00:12, 1.57it/s]\n 76%|███████▋ | 61/80 [00:38<00:12, 1.57it/s]\n 78%|███████▊ | 62/80 [00:39<00:11, 1.58it/s]\n 79%|███████▉ | 63/80 [00:39<00:10, 1.58it/s]\n 80%|████████ | 64/80 [00:40<00:10, 1.58it/s]\n 81%|████████▏ | 65/80 [00:41<00:09, 1.59it/s]\n 82%|████████▎ | 66/80 [00:41<00:08, 1.59it/s]\n 84%|████████▍ | 67/80 [00:42<00:08, 1.59it/s]\n 85%|████████▌ | 68/80 [00:42<00:07, 1.60it/s]\n 86%|████████▋ | 69/80 [00:43<00:06, 1.60it/s]\n 88%|████████▊ | 70/80 [00:44<00:06, 1.60it/s]\n 89%|████████▉ | 71/80 [00:44<00:05, 1.61it/s]\n 90%|█████████ | 72/80 [00:45<00:04, 1.61it/s]\n 91%|█████████▏| 73/80 [00:46<00:04, 1.61it/s]\n 92%|█████████▎| 74/80 [00:46<00:03, 1.61it/s]\n 94%|█████████▍| 75/80 [00:47<00:03, 1.61it/s]\n 95%|█████████▌| 76/80 [00:47<00:02, 1.62it/s]\n 96%|█████████▋| 77/80 [00:48<00:01, 1.62it/s]\n 98%|█████████▊| 78/80 [00:49<00:01, 1.62it/s]\n 99%|█████████▉| 79/80 [00:49<00:00, 1.62it/s]\n100%|██████████| 80/80 [00:50<00:00, 1.63it/s]\n100%|██████████| 80/80 [00:50<00:00, 1.59it/s]", "metrics": { "predict_time": 52.371498, "total_time": 52.391275 }, "output": [ "https://replicate.delivery/pbxt/xuWP0e82SB0EfE5J1zVZt0S8uDlD7qfXGgPheJgasfe4zYImE/out-0.png", "https://replicate.delivery/pbxt/ZZReYqGbYoSUPCFmF0XV9RA5VD51GjBOa1yIkLIHFm0nxQMJA/out-1.png", "https://replicate.delivery/pbxt/55hTxghL0t74DZ2StnqOedBd3YcynwBGPvafjgbEhB7QjhYSA/out-2.png" ], "started_at": "2024-02-20T08:45:15.805595Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uhwlszjbuo5pkm7c4ofm4pouse", "cancel": "https://api.replicate.com/v1/predictions/uhwlszjbuo5pkm7c4ofm4pouse/cancel" }, "version": "6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963" }
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Prediction
mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963IDdzgahmrb7celaz6xlmrhsarcqyStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- Hyperrealistic portrait upper body of a gycn person in german military uniform from SS, with strong, directional lighting, UHD 8k
- scheduler
- KLMS
- num_outputs
- 3
- guidance_scale
- 7.5
- prompt_strength
- 0.7
- num_inference_steps
- 80
- disable_safety_check
{ "width": 512, "height": 512, "prompt": "Hyperrealistic portrait upper body of a gycn person in german military uniform from SS, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }
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 mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", { input: { width: 512, height: 512, prompt: "Hyperrealistic portrait upper body of a gycn person in german military uniform from SS, with strong, directional lighting, UHD 8k", scheduler: "KLMS", num_outputs: 3, guidance_scale: 7.5, prompt_strength: 0.7, num_inference_steps: 80, disable_safety_check: false } } ); // 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 replicate
Import the client:import replicate
Run mcgregory99/goyofacebooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", input={ "width": 512, "height": 512, "prompt": "Hyperrealistic portrait upper body of a gycn person in german military uniform from SS, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": False } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run mcgregory99/goyofacebooth 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": "mcgregory99/goyofacebooth:6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963", "input": { "width": 512, "height": 512, "prompt": "Hyperrealistic portrait upper body of a gycn person in german military uniform from SS, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-02-21T11:16:07.164724Z", "created_at": "2024-02-21T11:11:11.988667Z", "data_removed": false, "error": null, "id": "dzgahmrb7celaz6xlmrhsarcqy", "input": { "width": 512, "height": 512, "prompt": "Hyperrealistic portrait upper body of a gycn person in german military uniform from SS, with strong, directional lighting, UHD 8k", "scheduler": "KLMS", "num_outputs": 3, "guidance_scale": 7.5, "prompt_strength": 0.7, "num_inference_steps": 80, "disable_safety_check": false }, "logs": "Using seed: 35860\nusing txt2img\n 0%| | 0/80 [00:00<?, ?it/s]\n 1%|▏ | 1/80 [00:03<04:16, 3.25s/it]\n 2%|▎ | 2/80 [00:03<02:07, 1.63s/it]\n 4%|▍ | 3/80 [00:04<01:26, 1.13s/it]\n 5%|▌ | 4/80 [00:04<01:07, 1.13it/s]\n 6%|▋ | 5/80 [00:05<00:56, 1.33it/s]\n 8%|▊ | 6/80 [00:05<00:49, 1.49it/s]\n 9%|▉ | 7/80 [00:06<00:45, 1.61it/s]\n 10%|█ | 8/80 [00:06<00:42, 1.70it/s]\n 11%|█▏ | 9/80 [00:07<00:40, 1.77it/s]\n 12%|█▎ | 10/80 [00:07<00:38, 1.81it/s]\n 14%|█▍ | 11/80 [00:08<00:37, 1.85it/s]\n 15%|█▌ | 12/80 [00:08<00:36, 1.87it/s]\n 16%|█▋ | 13/80 [00:09<00:35, 1.87it/s]\n 18%|█▊ | 14/80 [00:09<00:34, 1.90it/s]\n 19%|█▉ | 15/80 [00:10<00:34, 1.91it/s]\n 20%|██ | 16/80 [00:10<00:33, 1.93it/s]\n 21%|██▏ | 17/80 [00:11<00:32, 1.92it/s]\n 22%|██▎ | 18/80 [00:12<00:32, 1.93it/s]\n 24%|██▍ | 19/80 [00:12<00:31, 1.93it/s]\n 25%|██▌ | 20/80 [00:13<00:30, 1.94it/s]\n 26%|██▋ | 21/80 [00:13<00:30, 1.93it/s]\n 28%|██▊ | 22/80 [00:14<00:30, 1.92it/s]\n 29%|██▉ | 23/80 [00:14<00:29, 1.92it/s]\n 30%|███ | 24/80 [00:15<00:29, 1.92it/s]\n 31%|███▏ | 25/80 [00:15<00:28, 1.92it/s]\n 32%|███▎ | 26/80 [00:16<00:28, 1.91it/s]\n 34%|███▍ | 27/80 [00:16<00:27, 1.92it/s]\n 35%|███▌ | 28/80 [00:17<00:27, 1.91it/s]\n 36%|███▋ | 29/80 [00:17<00:26, 1.91it/s]\n 38%|███▊ | 30/80 [00:18<00:26, 1.91it/s]\n 39%|███▉ | 31/80 [00:18<00:25, 1.91it/s]\n 40%|████ | 32/80 [00:19<00:25, 1.90it/s]\n 41%|████▏ | 33/80 [00:19<00:24, 1.90it/s]\n 42%|████▎ | 34/80 [00:20<00:24, 1.90it/s]\n 44%|████▍ | 35/80 [00:20<00:23, 1.88it/s]\n 45%|████▌ | 36/80 [00:21<00:23, 1.88it/s]\n 46%|████▋ | 37/80 [00:22<00:22, 1.89it/s]\n 48%|████▊ | 38/80 [00:22<00:22, 1.89it/s]\n 49%|████▉ | 39/80 [00:23<00:21, 1.90it/s]\n 50%|█████ | 40/80 [00:23<00:21, 1.90it/s]\n 51%|█████▏ | 41/80 [00:24<00:20, 1.90it/s]\n 52%|█████▎ | 42/80 [00:24<00:20, 1.90it/s]\n 54%|█████▍ | 43/80 [00:25<00:19, 1.89it/s]\n 55%|█████▌ | 44/80 [00:25<00:19, 1.89it/s]\n 56%|█████▋ | 45/80 [00:26<00:18, 1.88it/s]\n 57%|█████▊ | 46/80 [00:26<00:18, 1.88it/s]\n 59%|█████▉ | 47/80 [00:27<00:17, 1.87it/s]\n 60%|██████ | 48/80 [00:27<00:17, 1.87it/s]\n 61%|██████▏ | 49/80 [00:28<00:16, 1.86it/s]\n 62%|██████▎ | 50/80 [00:28<00:16, 1.87it/s]\n 64%|██████▍ | 51/80 [00:29<00:15, 1.86it/s]\n 65%|██████▌ | 52/80 [00:30<00:15, 1.86it/s]\n 66%|██████▋ | 53/80 [00:30<00:14, 1.85it/s]\n 68%|██████▊ | 54/80 [00:31<00:14, 1.86it/s]\n 69%|██████▉ | 55/80 [00:31<00:13, 1.85it/s]\n 70%|███████ | 56/80 [00:32<00:12, 1.85it/s]\n 71%|███████▏ | 57/80 [00:32<00:12, 1.85it/s]\n 72%|███████▎ | 58/80 [00:33<00:11, 1.85it/s]\n 74%|███████▍ | 59/80 [00:33<00:11, 1.84it/s]\n 75%|███████▌ | 60/80 [00:34<00:10, 1.84it/s]\n 76%|███████▋ | 61/80 [00:34<00:10, 1.84it/s]\n 78%|███████▊ | 62/80 [00:35<00:09, 1.84it/s]\n 79%|███████▉ | 63/80 [00:35<00:09, 1.83it/s]\n 80%|████████ | 64/80 [00:36<00:08, 1.83it/s]\n 81%|████████▏ | 65/80 [00:37<00:08, 1.83it/s]\n 82%|████████▎ | 66/80 [00:37<00:07, 1.83it/s]\n 84%|████████▍ | 67/80 [00:38<00:07, 1.83it/s]\n 85%|████████▌ | 68/80 [00:38<00:06, 1.83it/s]\n 86%|████████▋ | 69/80 [00:39<00:06, 1.82it/s]\n 88%|████████▊ | 70/80 [00:39<00:05, 1.82it/s]\n 89%|████████▉ | 71/80 [00:40<00:04, 1.82it/s]\n 90%|█████████ | 72/80 [00:40<00:04, 1.82it/s]\n 91%|█████████▏| 73/80 [00:41<00:03, 1.81it/s]\n 92%|█████████▎| 74/80 [00:42<00:03, 1.81it/s]\n 94%|█████████▍| 75/80 [00:42<00:02, 1.80it/s]\n 95%|█████████▌| 76/80 [00:43<00:02, 1.79it/s]\n 96%|█████████▋| 77/80 [00:43<00:01, 1.79it/s]\n 98%|█████████▊| 78/80 [00:44<00:01, 1.78it/s]\n 99%|█████████▉| 79/80 [00:44<00:00, 1.78it/s]\n100%|██████████| 80/80 [00:45<00:00, 1.78it/s]\n100%|██████████| 80/80 [00:45<00:00, 1.76it/s]", "metrics": { "predict_time": 48.885516, "total_time": 295.176057 }, "output": [ "https://replicate.delivery/pbxt/xw09zyJAv4YmMpVBvTcyyDy1Aqup8mZbeGLbeyXLfhWsrxxkA/out-0.png", "https://replicate.delivery/pbxt/RDGs5DUl5Iq8B13PRoJjek4p6KHBKk2yISndMfqRIxetrxxkA/out-1.png", "https://replicate.delivery/pbxt/VJ8OyVKU7OZHKZf1rRuqseZujsIzTf4u8dKCczPOArZurxxkA/out-2.png" ], "started_at": "2024-02-21T11:15:18.279208Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dzgahmrb7celaz6xlmrhsarcqy", "cancel": "https://api.replicate.com/v1/predictions/dzgahmrb7celaz6xlmrhsarcqy/cancel" }, "version": "6e688d1ea46cb252b0334b3842ccad3ead53ecd2d0ffac2e21ffd2800bb30963" }
Generated inUsing seed: 35860 using txt2img 0%| | 0/80 [00:00<?, ?it/s] 1%|▏ | 1/80 [00:03<04:16, 3.25s/it] 2%|▎ | 2/80 [00:03<02:07, 1.63s/it] 4%|▍ | 3/80 [00:04<01:26, 1.13s/it] 5%|▌ | 4/80 [00:04<01:07, 1.13it/s] 6%|▋ | 5/80 [00:05<00:56, 1.33it/s] 8%|▊ | 6/80 [00:05<00:49, 1.49it/s] 9%|▉ | 7/80 [00:06<00:45, 1.61it/s] 10%|█ | 8/80 [00:06<00:42, 1.70it/s] 11%|█▏ | 9/80 [00:07<00:40, 1.77it/s] 12%|█▎ | 10/80 [00:07<00:38, 1.81it/s] 14%|█▍ | 11/80 [00:08<00:37, 1.85it/s] 15%|█▌ | 12/80 [00:08<00:36, 1.87it/s] 16%|█▋ | 13/80 [00:09<00:35, 1.87it/s] 18%|█▊ | 14/80 [00:09<00:34, 1.90it/s] 19%|█▉ | 15/80 [00:10<00:34, 1.91it/s] 20%|██ | 16/80 [00:10<00:33, 1.93it/s] 21%|██▏ | 17/80 [00:11<00:32, 1.92it/s] 22%|██▎ | 18/80 [00:12<00:32, 1.93it/s] 24%|██▍ | 19/80 [00:12<00:31, 1.93it/s] 25%|██▌ | 20/80 [00:13<00:30, 1.94it/s] 26%|██▋ | 21/80 [00:13<00:30, 1.93it/s] 28%|██▊ | 22/80 [00:14<00:30, 1.92it/s] 29%|██▉ | 23/80 [00:14<00:29, 1.92it/s] 30%|███ | 24/80 [00:15<00:29, 1.92it/s] 31%|███▏ | 25/80 [00:15<00:28, 1.92it/s] 32%|███▎ | 26/80 [00:16<00:28, 1.91it/s] 34%|███▍ | 27/80 [00:16<00:27, 1.92it/s] 35%|███▌ | 28/80 [00:17<00:27, 1.91it/s] 36%|███▋ | 29/80 [00:17<00:26, 1.91it/s] 38%|███▊ | 30/80 [00:18<00:26, 1.91it/s] 39%|███▉ | 31/80 [00:18<00:25, 1.91it/s] 40%|████ | 32/80 [00:19<00:25, 1.90it/s] 41%|████▏ | 33/80 [00:19<00:24, 1.90it/s] 42%|████▎ | 34/80 [00:20<00:24, 1.90it/s] 44%|████▍ | 35/80 [00:20<00:23, 1.88it/s] 45%|████▌ | 36/80 [00:21<00:23, 1.88it/s] 46%|████▋ | 37/80 [00:22<00:22, 1.89it/s] 48%|████▊ | 38/80 [00:22<00:22, 1.89it/s] 49%|████▉ | 39/80 [00:23<00:21, 1.90it/s] 50%|█████ | 40/80 [00:23<00:21, 1.90it/s] 51%|█████▏ | 41/80 [00:24<00:20, 1.90it/s] 52%|█████▎ | 42/80 [00:24<00:20, 1.90it/s] 54%|█████▍ | 43/80 [00:25<00:19, 1.89it/s] 55%|█████▌ | 44/80 [00:25<00:19, 1.89it/s] 56%|█████▋ | 45/80 [00:26<00:18, 1.88it/s] 57%|█████▊ | 46/80 [00:26<00:18, 1.88it/s] 59%|█████▉ | 47/80 [00:27<00:17, 1.87it/s] 60%|██████ | 48/80 [00:27<00:17, 1.87it/s] 61%|██████▏ | 49/80 [00:28<00:16, 1.86it/s] 62%|██████▎ | 50/80 [00:28<00:16, 1.87it/s] 64%|██████▍ | 51/80 [00:29<00:15, 1.86it/s] 65%|██████▌ | 52/80 [00:30<00:15, 1.86it/s] 66%|██████▋ | 53/80 [00:30<00:14, 1.85it/s] 68%|██████▊ | 54/80 [00:31<00:14, 1.86it/s] 69%|██████▉ | 55/80 [00:31<00:13, 1.85it/s] 70%|███████ | 56/80 [00:32<00:12, 1.85it/s] 71%|███████▏ | 57/80 [00:32<00:12, 1.85it/s] 72%|███████▎ | 58/80 [00:33<00:11, 1.85it/s] 74%|███████▍ | 59/80 [00:33<00:11, 1.84it/s] 75%|███████▌ | 60/80 [00:34<00:10, 1.84it/s] 76%|███████▋ | 61/80 [00:34<00:10, 1.84it/s] 78%|███████▊ | 62/80 [00:35<00:09, 1.84it/s] 79%|███████▉ | 63/80 [00:35<00:09, 1.83it/s] 80%|████████ | 64/80 [00:36<00:08, 1.83it/s] 81%|████████▏ | 65/80 [00:37<00:08, 1.83it/s] 82%|████████▎ | 66/80 [00:37<00:07, 1.83it/s] 84%|████████▍ | 67/80 [00:38<00:07, 1.83it/s] 85%|████████▌ | 68/80 [00:38<00:06, 1.83it/s] 86%|████████▋ | 69/80 [00:39<00:06, 1.82it/s] 88%|████████▊ | 70/80 [00:39<00:05, 1.82it/s] 89%|████████▉ | 71/80 [00:40<00:04, 1.82it/s] 90%|█████████ | 72/80 [00:40<00:04, 1.82it/s] 91%|█████████▏| 73/80 [00:41<00:03, 1.81it/s] 92%|█████████▎| 74/80 [00:42<00:03, 1.81it/s] 94%|█████████▍| 75/80 [00:42<00:02, 1.80it/s] 95%|█████████▌| 76/80 [00:43<00:02, 1.79it/s] 96%|█████████▋| 77/80 [00:43<00:01, 1.79it/s] 98%|█████████▊| 78/80 [00:44<00:01, 1.78it/s] 99%|█████████▉| 79/80 [00:44<00:00, 1.78it/s] 100%|██████████| 80/80 [00:45<00:00, 1.78it/s] 100%|██████████| 80/80 [00:45<00:00, 1.76it/s]
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