cristobalascencio
/
wirra
- Public
- 69 runs
-
H100
Prediction
cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88eID6eajhmgs65rm60chfxhafk1jy4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- schnell
- prompt
- AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA
- lora_scale
- 1.17
- num_outputs
- 2
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.64
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "schnell", "prompt": "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", "lora_scale": 1.17, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.64, "output_quality": 80, "num_inference_steps": 28 }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 cristobalascencio/wirra using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", { input: { model: "schnell", prompt: "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", lora_scale: 1.17, num_outputs: 2, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.64, output_quality: 80, num_inference_steps: 28 } } ); // 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 cristobalascencio/wirra using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", input={ "model": "schnell", "prompt": "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", "lora_scale": 1.17, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.64, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cristobalascencio/wirra 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": "6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", "input": { "model": "schnell", "prompt": "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", "lora_scale": 1.17, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.64, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-23T13:17:25.675600Z", "created_at": "2024-08-23T13:17:01.105000Z", "data_removed": false, "error": null, "id": "6eajhmgs65rm60chfxhafk1jy4", "input": { "model": "schnell", "prompt": "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", "lora_scale": 1.17, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.64, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 33110\nPrompt: AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA\ntxt2img mode\nUsing schnell model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:13, 2.00it/s]\n 7%|▋ | 2/28 [00:00<00:11, 2.26it/s]\n 11%|█ | 3/28 [00:01<00:11, 2.15it/s]\n 14%|█▍ | 4/28 [00:01<00:11, 2.10it/s]\n 18%|█▊ | 5/28 [00:02<00:11, 2.07it/s]\n 21%|██▏ | 6/28 [00:02<00:10, 2.06it/s]\n 25%|██▌ | 7/28 [00:03<00:10, 2.05it/s]\n 29%|██▊ | 8/28 [00:03<00:09, 2.05it/s]\n 32%|███▏ | 9/28 [00:04<00:09, 2.04it/s]\n 36%|███▌ | 10/28 [00:04<00:08, 2.04it/s]\n 39%|███▉ | 11/28 [00:05<00:08, 2.04it/s]\n 43%|████▎ | 12/28 [00:05<00:07, 2.03it/s]\n 46%|████▋ | 13/28 [00:06<00:07, 2.03it/s]\n 50%|█████ | 14/28 [00:06<00:06, 2.03it/s]\n 54%|█████▎ | 15/28 [00:07<00:06, 2.03it/s]\n 57%|█████▋ | 16/28 [00:07<00:05, 2.03it/s]\n 61%|██████ | 17/28 [00:08<00:05, 2.03it/s]\n 64%|██████▍ | 18/28 [00:08<00:04, 2.03it/s]\n 68%|██████▊ | 19/28 [00:09<00:04, 2.03it/s]\n 71%|███████▏ | 20/28 [00:09<00:03, 2.03it/s]\n 75%|███████▌ | 21/28 [00:10<00:03, 2.03it/s]\n 79%|███████▊ | 22/28 [00:10<00:02, 2.02it/s]\n 82%|████████▏ | 23/28 [00:11<00:02, 2.03it/s]\n 86%|████████▌ | 24/28 [00:11<00:01, 2.03it/s]\n 89%|████████▉ | 25/28 [00:12<00:01, 2.02it/s]\n 93%|█████████▎| 26/28 [00:12<00:00, 2.02it/s]\n 96%|█████████▋| 27/28 [00:13<00:00, 2.02it/s]\n100%|██████████| 28/28 [00:13<00:00, 2.02it/s]\n100%|██████████| 28/28 [00:13<00:00, 2.04it/s]", "metrics": { "predict_time": 24.56039867, "total_time": 24.5706 }, "output": [ "https://replicate.delivery/yhqm/98CnoTEiR8rIOtucMXVEzQ2cTiGv7EQzxZEHGaOOlvb59Y1E/out-0.png", "https://replicate.delivery/yhqm/qMgPNFocJFZKCZBFfbIo5EQeVqMK4pfynr7yRwI0qTALvHrmA/out-1.png" ], "started_at": "2024-08-23T13:17:01.115202Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6eajhmgs65rm60chfxhafk1jy4", "cancel": "https://api.replicate.com/v1/predictions/6eajhmgs65rm60chfxhafk1jy4/cancel" }, "version": "6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e" }Generated inUsing seed: 33110 Prompt: AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA txt2img mode Using schnell model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:13, 2.00it/s] 7%|▋ | 2/28 [00:00<00:11, 2.26it/s] 11%|█ | 3/28 [00:01<00:11, 2.15it/s] 14%|█▍ | 4/28 [00:01<00:11, 2.10it/s] 18%|█▊ | 5/28 [00:02<00:11, 2.07it/s] 21%|██▏ | 6/28 [00:02<00:10, 2.06it/s] 25%|██▌ | 7/28 [00:03<00:10, 2.05it/s] 29%|██▊ | 8/28 [00:03<00:09, 2.05it/s] 32%|███▏ | 9/28 [00:04<00:09, 2.04it/s] 36%|███▌ | 10/28 [00:04<00:08, 2.04it/s] 39%|███▉ | 11/28 [00:05<00:08, 2.04it/s] 43%|████▎ | 12/28 [00:05<00:07, 2.03it/s] 46%|████▋ | 13/28 [00:06<00:07, 2.03it/s] 50%|█████ | 14/28 [00:06<00:06, 2.03it/s] 54%|█████▎ | 15/28 [00:07<00:06, 2.03it/s] 57%|█████▋ | 16/28 [00:07<00:05, 2.03it/s] 61%|██████ | 17/28 [00:08<00:05, 2.03it/s] 64%|██████▍ | 18/28 [00:08<00:04, 2.03it/s] 68%|██████▊ | 19/28 [00:09<00:04, 2.03it/s] 71%|███████▏ | 20/28 [00:09<00:03, 2.03it/s] 75%|███████▌ | 21/28 [00:10<00:03, 2.03it/s] 79%|███████▊ | 22/28 [00:10<00:02, 2.02it/s] 82%|████████▏ | 23/28 [00:11<00:02, 2.03it/s] 86%|████████▌ | 24/28 [00:11<00:01, 2.03it/s] 89%|████████▉ | 25/28 [00:12<00:01, 2.02it/s] 93%|█████████▎| 26/28 [00:12<00:00, 2.02it/s] 96%|█████████▋| 27/28 [00:13<00:00, 2.02it/s] 100%|██████████| 28/28 [00:13<00:00, 2.02it/s] 100%|██████████| 28/28 [00:13<00:00, 2.04it/s]Prediction
cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88eIDf03tkmphndrm20chfh08mzt4mcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A pictogram of a data center in the middle of the composition, in the style of wirra
- lora_scale
- 1.3
- num_outputs
- 2
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A pictogram of a data center in the middle of the composition, in the style of wirra", "lora_scale": 1.3, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }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 cristobalascencio/wirra using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", { input: { model: "dev", prompt: "A pictogram of a data center in the middle of the composition, in the style of wirra", lora_scale: 1.3, num_outputs: 2, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // 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 cristobalascencio/wirra using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", input={ "model": "dev", "prompt": "A pictogram of a data center in the middle of the composition, in the style of wirra", "lora_scale": 1.3, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cristobalascencio/wirra 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": "6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", "input": { "model": "dev", "prompt": "A pictogram of a data center in the middle of the composition, in the style of wirra", "lora_scale": 1.3, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-22T22:42:16.847213Z", "created_at": "2024-08-22T22:41:48.459000Z", "data_removed": false, "error": null, "id": "f03tkmphndrm20chfh08mzt4mc", "input": { "model": "dev", "prompt": "A pictogram of a data center in the middle of the composition, in the style of wirra", "lora_scale": 1.3, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 23768\nPrompt: A pictogram of a data center in the middle of the composition, in the style of wirra\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9814201135104\nDownloading weights\n2024-08-22T22:41:49Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/1187d2c8e3e14db7 url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar\n2024-08-22T22:41:52Z | INFO | [ Complete ] dest=/src/weights-cache/1187d2c8e3e14db7 size=\"172 MB\" total_elapsed=3.181s url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar\nb''\nDownloaded weights in 3.2123372554779053 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:14, 1.87it/s]\n 7%|▋ | 2/28 [00:00<00:12, 2.13it/s]\n 11%|█ | 3/28 [00:01<00:12, 2.00it/s]\n 14%|█▍ | 4/28 [00:02<00:12, 1.95it/s]\n 18%|█▊ | 5/28 [00:02<00:11, 1.92it/s]\n 21%|██▏ | 6/28 [00:03<00:11, 1.90it/s]\n 25%|██▌ | 7/28 [00:03<00:11, 1.90it/s]\n 29%|██▊ | 8/28 [00:04<00:10, 1.89it/s]\n 32%|███▏ | 9/28 [00:04<00:10, 1.88it/s]\n 36%|███▌ | 10/28 [00:05<00:09, 1.88it/s]\n 39%|███▉ | 11/28 [00:05<00:09, 1.88it/s]\n 43%|████▎ | 12/28 [00:06<00:08, 1.87it/s]\n 46%|████▋ | 13/28 [00:06<00:08, 1.87it/s]\n 50%|█████ | 14/28 [00:07<00:07, 1.87it/s]\n 54%|█████▎ | 15/28 [00:07<00:06, 1.87it/s]\n 57%|█████▋ | 16/28 [00:08<00:06, 1.87it/s]\n 61%|██████ | 17/28 [00:08<00:05, 1.87it/s]\n 64%|██████▍ | 18/28 [00:09<00:05, 1.87it/s]\n 68%|██████▊ | 19/28 [00:10<00:04, 1.87it/s]\n 71%|███████▏ | 20/28 [00:10<00:04, 1.87it/s]\n 75%|███████▌ | 21/28 [00:11<00:03, 1.87it/s]\n 79%|███████▊ | 22/28 [00:11<00:03, 1.86it/s]\n 82%|████████▏ | 23/28 [00:12<00:02, 1.87it/s]\n 86%|████████▌ | 24/28 [00:12<00:02, 1.86it/s]\n 89%|████████▉ | 25/28 [00:13<00:01, 1.86it/s]\n 93%|█████████▎| 26/28 [00:13<00:01, 1.87it/s]\n 96%|█████████▋| 27/28 [00:14<00:00, 1.86it/s]\n100%|██████████| 28/28 [00:14<00:00, 1.87it/s]\n100%|██████████| 28/28 [00:14<00:00, 1.88it/s]", "metrics": { "predict_time": 27.372243066, "total_time": 28.388213 }, "output": [ "https://replicate.delivery/yhqm/lmqMRb1fJpzcJyXz7j3081vXNfZDzgTW5Ed2kqzsER1IDXVTA/out-0.webp", "https://replicate.delivery/yhqm/OvNzerg2qdWeQEewQyvRfqsPyPM9oEwdQrSd5IPHdtggMcVNB/out-1.webp" ], "started_at": "2024-08-22T22:41:49.474970Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f03tkmphndrm20chfh08mzt4mc", "cancel": "https://api.replicate.com/v1/predictions/f03tkmphndrm20chfh08mzt4mc/cancel" }, "version": "6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e" }Generated inUsing seed: 23768 Prompt: A pictogram of a data center in the middle of the composition, in the style of wirra txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9814201135104 Downloading weights 2024-08-22T22:41:49Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/1187d2c8e3e14db7 url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar 2024-08-22T22:41:52Z | INFO | [ Complete ] dest=/src/weights-cache/1187d2c8e3e14db7 size="172 MB" total_elapsed=3.181s url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar b'' Downloaded weights in 3.2123372554779053 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:14, 1.87it/s] 7%|▋ | 2/28 [00:00<00:12, 2.13it/s] 11%|█ | 3/28 [00:01<00:12, 2.00it/s] 14%|█▍ | 4/28 [00:02<00:12, 1.95it/s] 18%|█▊ | 5/28 [00:02<00:11, 1.92it/s] 21%|██▏ | 6/28 [00:03<00:11, 1.90it/s] 25%|██▌ | 7/28 [00:03<00:11, 1.90it/s] 29%|██▊ | 8/28 [00:04<00:10, 1.89it/s] 32%|███▏ | 9/28 [00:04<00:10, 1.88it/s] 36%|███▌ | 10/28 [00:05<00:09, 1.88it/s] 39%|███▉ | 11/28 [00:05<00:09, 1.88it/s] 43%|████▎ | 12/28 [00:06<00:08, 1.87it/s] 46%|████▋ | 13/28 [00:06<00:08, 1.87it/s] 50%|█████ | 14/28 [00:07<00:07, 1.87it/s] 54%|█████▎ | 15/28 [00:07<00:06, 1.87it/s] 57%|█████▋ | 16/28 [00:08<00:06, 1.87it/s] 61%|██████ | 17/28 [00:08<00:05, 1.87it/s] 64%|██████▍ | 18/28 [00:09<00:05, 1.87it/s] 68%|██████▊ | 19/28 [00:10<00:04, 1.87it/s] 71%|███████▏ | 20/28 [00:10<00:04, 1.87it/s] 75%|███████▌ | 21/28 [00:11<00:03, 1.87it/s] 79%|███████▊ | 22/28 [00:11<00:03, 1.86it/s] 82%|████████▏ | 23/28 [00:12<00:02, 1.87it/s] 86%|████████▌ | 24/28 [00:12<00:02, 1.86it/s] 89%|████████▉ | 25/28 [00:13<00:01, 1.86it/s] 93%|█████████▎| 26/28 [00:13<00:01, 1.87it/s] 96%|█████████▋| 27/28 [00:14<00:00, 1.86it/s] 100%|██████████| 28/28 [00:14<00:00, 1.87it/s] 100%|██████████| 28/28 [00:14<00:00, 1.88it/s]Prediction
cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88eIDwqpc7wjhw9rm60chrwvb9xarfrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- schnell
- prompt
- AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA
- lora_scale
- 1.17
- num_outputs
- 2
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.64
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "schnell", "prompt": "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", "lora_scale": 1.17, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.64, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 cristobalascencio/wirra using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", { input: { model: "schnell", prompt: "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", lora_scale: 1.17, num_outputs: 2, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.64, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 cristobalascencio/wirra using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", input={ "model": "schnell", "prompt": "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", "lora_scale": 1.17, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.64, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cristobalascencio/wirra 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": "6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", "input": { "model": "schnell", "prompt": "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", "lora_scale": 1.17, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.64, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-06T12:02:30.870717Z", "created_at": "2024-09-06T12:01:51.586000Z", "data_removed": false, "error": null, "id": "wqpc7wjhw9rm60chrwvb9xarfr", "input": { "model": "schnell", "prompt": "AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA", "lora_scale": 1.17, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.64, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 28617\nPrompt: AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA\n[!] txt2img mode\nUsing schnell model\nfree=8503225036800\nDownloading weights\n2024-09-06T12:02:04Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpffvs1nzs/weights url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar\n2024-09-06T12:02:06Z | INFO | [ Complete ] dest=/tmp/tmpffvs1nzs/weights size=\"172 MB\" total_elapsed=2.176s url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar\nDownloaded weights in 2.21s\nLoaded LoRAs in 10.45s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:14, 1.91it/s]\n 7%|▋ | 2/28 [00:00<00:12, 2.14it/s]\n 11%|█ | 3/28 [00:01<00:12, 2.03it/s]\n 14%|█▍ | 4/28 [00:01<00:12, 2.00it/s]\n 18%|█▊ | 5/28 [00:02<00:11, 1.97it/s]\n 21%|██▏ | 6/28 [00:03<00:11, 1.96it/s]\n 25%|██▌ | 7/28 [00:03<00:10, 1.95it/s]\n 29%|██▊ | 8/28 [00:04<00:10, 1.95it/s]\n 32%|███▏ | 9/28 [00:04<00:09, 1.95it/s]\n 36%|███▌ | 10/28 [00:05<00:09, 1.94it/s]\n 39%|███▉ | 11/28 [00:05<00:08, 1.94it/s]\n 43%|████▎ | 12/28 [00:06<00:08, 1.94it/s]\n 46%|████▋ | 13/28 [00:06<00:07, 1.94it/s]\n 50%|█████ | 14/28 [00:07<00:07, 1.94it/s]\n 54%|█████▎ | 15/28 [00:07<00:06, 1.94it/s]\n 57%|█████▋ | 16/28 [00:08<00:06, 1.94it/s]\n 61%|██████ | 17/28 [00:08<00:05, 1.94it/s]\n 64%|██████▍ | 18/28 [00:09<00:05, 1.94it/s]\n 68%|██████▊ | 19/28 [00:09<00:04, 1.94it/s]\n 71%|███████▏ | 20/28 [00:10<00:04, 1.94it/s]\n 75%|███████▌ | 21/28 [00:10<00:03, 1.94it/s]\n 79%|███████▊ | 22/28 [00:11<00:03, 1.94it/s]\n 82%|████████▏ | 23/28 [00:11<00:02, 1.94it/s]\n 86%|████████▌ | 24/28 [00:12<00:02, 1.94it/s]\n 89%|████████▉ | 25/28 [00:12<00:01, 1.94it/s]\n 93%|█████████▎| 26/28 [00:13<00:01, 1.94it/s]\n 96%|█████████▋| 27/28 [00:13<00:00, 1.93it/s]\n100%|██████████| 28/28 [00:14<00:00, 1.94it/s]\n100%|██████████| 28/28 [00:14<00:00, 1.95it/s]", "metrics": { "predict_time": 26.209548522, "total_time": 39.284717 }, "output": [ "https://replicate.delivery/yhqm/5T9BXryC5PLrGdyhTv4kCYSChfktPG5m7elcwK0ivkRWFKaTA/out-0.png", "https://replicate.delivery/yhqm/pIsitOlze8QvLKyZbqG4YVeZvr7TrPJFltebdhN3CvfZVooNB/out-1.png" ], "started_at": "2024-09-06T12:02:04.661168Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wqpc7wjhw9rm60chrwvb9xarfr", "cancel": "https://api.replicate.com/v1/predictions/wqpc7wjhw9rm60chrwvb9xarfr/cancel" }, "version": "6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e" }Generated inUsing seed: 28617 Prompt: AN IMAGE OF A LAPTOP IN THE STYLE OF WIRRA [!] txt2img mode Using schnell model free=8503225036800 Downloading weights 2024-09-06T12:02:04Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpffvs1nzs/weights url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar 2024-09-06T12:02:06Z | INFO | [ Complete ] dest=/tmp/tmpffvs1nzs/weights size="172 MB" total_elapsed=2.176s url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar Downloaded weights in 2.21s Loaded LoRAs in 10.45s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:14, 1.91it/s] 7%|▋ | 2/28 [00:00<00:12, 2.14it/s] 11%|█ | 3/28 [00:01<00:12, 2.03it/s] 14%|█▍ | 4/28 [00:01<00:12, 2.00it/s] 18%|█▊ | 5/28 [00:02<00:11, 1.97it/s] 21%|██▏ | 6/28 [00:03<00:11, 1.96it/s] 25%|██▌ | 7/28 [00:03<00:10, 1.95it/s] 29%|██▊ | 8/28 [00:04<00:10, 1.95it/s] 32%|███▏ | 9/28 [00:04<00:09, 1.95it/s] 36%|███▌ | 10/28 [00:05<00:09, 1.94it/s] 39%|███▉ | 11/28 [00:05<00:08, 1.94it/s] 43%|████▎ | 12/28 [00:06<00:08, 1.94it/s] 46%|████▋ | 13/28 [00:06<00:07, 1.94it/s] 50%|█████ | 14/28 [00:07<00:07, 1.94it/s] 54%|█████▎ | 15/28 [00:07<00:06, 1.94it/s] 57%|█████▋ | 16/28 [00:08<00:06, 1.94it/s] 61%|██████ | 17/28 [00:08<00:05, 1.94it/s] 64%|██████▍ | 18/28 [00:09<00:05, 1.94it/s] 68%|██████▊ | 19/28 [00:09<00:04, 1.94it/s] 71%|███████▏ | 20/28 [00:10<00:04, 1.94it/s] 75%|███████▌ | 21/28 [00:10<00:03, 1.94it/s] 79%|███████▊ | 22/28 [00:11<00:03, 1.94it/s] 82%|████████▏ | 23/28 [00:11<00:02, 1.94it/s] 86%|████████▌ | 24/28 [00:12<00:02, 1.94it/s] 89%|████████▉ | 25/28 [00:12<00:01, 1.94it/s] 93%|█████████▎| 26/28 [00:13<00:01, 1.94it/s] 96%|█████████▋| 27/28 [00:13<00:00, 1.93it/s] 100%|██████████| 28/28 [00:14<00:00, 1.94it/s] 100%|██████████| 28/28 [00:14<00:00, 1.95it/s]Prediction
cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88eIDh1hns6mbhsrm60chrx3ss7fpa0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- schnell
- prompt
- Create an image of a hand arranging a series of metal gears in the style of wirra
- lora_scale
- 1.13
- num_outputs
- 2
- aspect_ratio
- 1:1
- output_format
- jpg
- guidance_scale
- 3.64
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 30
{ "model": "schnell", "prompt": "Create an image of a hand arranging a series of metal gears in the style of wirra", "lora_scale": 1.13, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "jpg", "guidance_scale": 3.64, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 30 }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 cristobalascencio/wirra using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", { input: { model: "schnell", prompt: "Create an image of a hand arranging a series of metal gears in the style of wirra", lora_scale: 1.13, num_outputs: 2, aspect_ratio: "1:1", output_format: "jpg", guidance_scale: 3.64, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 30 } } ); // 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 cristobalascencio/wirra using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cristobalascencio/wirra:6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", input={ "model": "schnell", "prompt": "Create an image of a hand arranging a series of metal gears in the style of wirra", "lora_scale": 1.13, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "jpg", "guidance_scale": 3.64, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cristobalascencio/wirra 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": "6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e", "input": { "model": "schnell", "prompt": "Create an image of a hand arranging a series of metal gears in the style of wirra", "lora_scale": 1.13, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "jpg", "guidance_scale": 3.64, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-06T12:21:06.687856Z", "created_at": "2024-09-06T12:20:40.462000Z", "data_removed": false, "error": null, "id": "h1hns6mbhsrm60chrx3ss7fpa0", "input": { "model": "schnell", "prompt": "Create an image of a hand arranging a series of metal gears in the style of wirra", "lora_scale": 1.13, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "jpg", "guidance_scale": 3.64, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 30 }, "logs": "Using seed: 61846\nPrompt: Create an image of a hand arranging a series of metal gears in the style of wirra\n[!] txt2img mode\nUsing schnell model\nfree=8385749303296\nDownloading weights\n2024-09-06T12:20:40Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpyydi34i2/weights url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar\n2024-09-06T12:20:42Z | INFO | [ Complete ] dest=/tmp/tmpyydi34i2/weights size=\"172 MB\" total_elapsed=1.690s url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar\nDownloaded weights in 1.73s\nLoaded LoRAs in 10.12s\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:15, 1.92it/s]\n 7%|▋ | 2/30 [00:00<00:13, 2.15it/s]\n 10%|█ | 3/30 [00:01<00:13, 2.05it/s]\n 13%|█▎ | 4/30 [00:01<00:12, 2.01it/s]\n 17%|█▋ | 5/30 [00:02<00:12, 1.99it/s]\n 20%|██ | 6/30 [00:03<00:12, 1.97it/s]\n 23%|██▎ | 7/30 [00:03<00:11, 1.96it/s]\n 27%|██▋ | 8/30 [00:04<00:11, 1.96it/s]\n 30%|███ | 9/30 [00:04<00:10, 1.96it/s]\n 33%|███▎ | 10/30 [00:05<00:10, 1.95it/s]\n 37%|███▋ | 11/30 [00:05<00:09, 1.95it/s]\n 40%|████ | 12/30 [00:06<00:09, 1.95it/s]\n 43%|████▎ | 13/30 [00:06<00:08, 1.95it/s]\n 47%|████▋ | 14/30 [00:07<00:08, 1.95it/s]\n 50%|█████ | 15/30 [00:07<00:07, 1.94it/s]\n 53%|█████▎ | 16/30 [00:08<00:07, 1.94it/s]\n 57%|█████▋ | 17/30 [00:08<00:06, 1.95it/s]\n 60%|██████ | 18/30 [00:09<00:06, 1.95it/s]\n 63%|██████▎ | 19/30 [00:09<00:05, 1.95it/s]\n 67%|██████▋ | 20/30 [00:10<00:05, 1.94it/s]\n 70%|███████ | 21/30 [00:10<00:04, 1.95it/s]\n 73%|███████▎ | 22/30 [00:11<00:04, 1.95it/s]\n 77%|███████▋ | 23/30 [00:11<00:03, 1.95it/s]\n 80%|████████ | 24/30 [00:12<00:03, 1.94it/s]\n 83%|████████▎ | 25/30 [00:12<00:02, 1.95it/s]\n 87%|████████▋ | 26/30 [00:13<00:02, 1.94it/s]\n 90%|█████████ | 27/30 [00:13<00:01, 1.95it/s]\n 93%|█████████▎| 28/30 [00:14<00:01, 1.95it/s]\n 97%|█████████▋| 29/30 [00:14<00:00, 1.94it/s]\n100%|██████████| 30/30 [00:15<00:00, 1.95it/s]\n100%|██████████| 30/30 [00:15<00:00, 1.96it/s]", "metrics": { "predict_time": 26.217997645, "total_time": 26.225856 }, "output": [ "https://replicate.delivery/yhqm/RquUkDzhQJJpARjb06M4OjFaa1eejfQcn3QazLf1CNuLbpoNB/out-0.jpg", "https://replicate.delivery/yhqm/IQpYHvtl6A6wKd0HDdE0AX04xB8t6NPoyXiEkMLwXyisli2E/out-1.jpg" ], "started_at": "2024-09-06T12:20:40.469858Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/h1hns6mbhsrm60chrx3ss7fpa0", "cancel": "https://api.replicate.com/v1/predictions/h1hns6mbhsrm60chrx3ss7fpa0/cancel" }, "version": "6e1cf8d7882937eb157800d82d8e5575c27c2c5e28b1d1c663fa773627f8c88e" }Generated inUsing seed: 61846 Prompt: Create an image of a hand arranging a series of metal gears in the style of wirra [!] txt2img mode Using schnell model free=8385749303296 Downloading weights 2024-09-06T12:20:40Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpyydi34i2/weights url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar 2024-09-06T12:20:42Z | INFO | [ Complete ] dest=/tmp/tmpyydi34i2/weights size="172 MB" total_elapsed=1.690s url=https://replicate.delivery/yhqm/HEKMeQtqZLx0aSDuhy0eZUtvO0YqRNsbnoAxfCYtJ0AsUtqmA/trained_model.tar Downloaded weights in 1.73s Loaded LoRAs in 10.12s 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:15, 1.92it/s] 7%|▋ | 2/30 [00:00<00:13, 2.15it/s] 10%|█ | 3/30 [00:01<00:13, 2.05it/s] 13%|█▎ | 4/30 [00:01<00:12, 2.01it/s] 17%|█▋ | 5/30 [00:02<00:12, 1.99it/s] 20%|██ | 6/30 [00:03<00:12, 1.97it/s] 23%|██▎ | 7/30 [00:03<00:11, 1.96it/s] 27%|██▋ | 8/30 [00:04<00:11, 1.96it/s] 30%|███ | 9/30 [00:04<00:10, 1.96it/s] 33%|███▎ | 10/30 [00:05<00:10, 1.95it/s] 37%|███▋ | 11/30 [00:05<00:09, 1.95it/s] 40%|████ | 12/30 [00:06<00:09, 1.95it/s] 43%|████▎ | 13/30 [00:06<00:08, 1.95it/s] 47%|████▋ | 14/30 [00:07<00:08, 1.95it/s] 50%|█████ | 15/30 [00:07<00:07, 1.94it/s] 53%|█████▎ | 16/30 [00:08<00:07, 1.94it/s] 57%|█████▋ | 17/30 [00:08<00:06, 1.95it/s] 60%|██████ | 18/30 [00:09<00:06, 1.95it/s] 63%|██████▎ | 19/30 [00:09<00:05, 1.95it/s] 67%|██████▋ | 20/30 [00:10<00:05, 1.94it/s] 70%|███████ | 21/30 [00:10<00:04, 1.95it/s] 73%|███████▎ | 22/30 [00:11<00:04, 1.95it/s] 77%|███████▋ | 23/30 [00:11<00:03, 1.95it/s] 80%|████████ | 24/30 [00:12<00:03, 1.94it/s] 83%|████████▎ | 25/30 [00:12<00:02, 1.95it/s] 87%|████████▋ | 26/30 [00:13<00:02, 1.94it/s] 90%|█████████ | 27/30 [00:13<00:01, 1.95it/s] 93%|█████████▎| 28/30 [00:14<00:01, 1.95it/s] 97%|█████████▋| 29/30 [00:14<00:00, 1.94it/s] 100%|██████████| 30/30 [00:15<00:00, 1.95it/s] 100%|██████████| 30/30 [00:15<00:00, 1.96it/s]
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