nicolas7894
/
flux-undraw
Undraw Illustration Generator
- Public
- 475 runs
-
H100
Prediction
nicolas7894/flux-undraw:a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20IDkcs0j0xktnrm20chc35r03289mStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Person holding a large card with the text "Follow @nicolas_tch" untokoldr illustration
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Person holding a large card with the text \"Follow @nicolas_tch\" untokoldr illustration", "lora_scale": 1, "num_outputs": 1, "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 nicolas7894/flux-undraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nicolas7894/flux-undraw:a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20", { input: { model: "dev", prompt: "Person holding a large card with the text \"Follow @nicolas_tch\" untokoldr illustration", lora_scale: 1, num_outputs: 1, 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 nicolas7894/flux-undraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nicolas7894/flux-undraw:a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20", input={ "model": "dev", "prompt": "Person holding a large card with the text \"Follow @nicolas_tch\" untokoldr illustration", "lora_scale": 1, "num_outputs": 1, "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 nicolas7894/flux-undraw 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": "a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20", "input": { "model": "dev", "prompt": "Person holding a large card with the text \\"Follow @nicolas_tch\\" untokoldr illustration", "lora_scale": 1, "num_outputs": 1, "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": "2024-08-17T14:47:16.381717Z", "created_at": "2024-08-17T14:44:08.277000Z", "data_removed": false, "error": null, "id": "kcs0j0xktnrm20chc35r03289m", "input": { "model": "dev", "prompt": "Person holding a large card with the text \"Follow @nicolas_tch\" untokoldr illustration", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 14270\nPrompt: Person holding a large card with the text \"Follow @nicolas_tch\" untokoldr illustration\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9315138420736\nDownloading weights: https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar\n2024-08-17T14:46:44Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/1787d8148963af3e url=https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar\n2024-08-17T14:46:45Z | INFO | [ Complete ] dest=/src/weights-cache/1787d8148963af3e size=\"172 MB\" total_elapsed=1.464s url=https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar\nb''\nDownloaded weights in 1.5000555515289307 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.67it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.22it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.94it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.74it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.71it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.67it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.66it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.66it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.66it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.66it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.66it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.67it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.66it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.66it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.66it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.66it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.66it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.66it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]", "metrics": { "predict_time": 32.180946444, "total_time": 188.104717 }, "output": [ "https://replicate.delivery/yhqm/70iJuvSeCFW8ESoFzr7eKMLgYLc6YnksIrXb1Ues1RqoPNnmA/out-0.webp" ], "started_at": "2024-08-17T14:46:44.200771Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kcs0j0xktnrm20chc35r03289m", "cancel": "https://api.replicate.com/v1/predictions/kcs0j0xktnrm20chc35r03289m/cancel" }, "version": "a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20" }Generated inUsing seed: 14270 Prompt: Person holding a large card with the text "Follow @nicolas_tch" untokoldr illustration txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9315138420736 Downloading weights: https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar 2024-08-17T14:46:44Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/1787d8148963af3e url=https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar 2024-08-17T14:46:45Z | INFO | [ Complete ] dest=/src/weights-cache/1787d8148963af3e size="172 MB" total_elapsed=1.464s url=https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar b'' Downloaded weights in 1.5000555515289307 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.67it/s] 7%|▋ | 2/28 [00:00<00:06, 4.22it/s] 11%|█ | 3/28 [00:00<00:06, 3.94it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.74it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.71it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.67it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.66it/s] 50%|█████ | 14/28 [00:03<00:03, 3.66it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.66it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.66it/s] 61%|██████ | 17/28 [00:04<00:03, 3.66it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.67it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.66it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.66it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.66it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.66it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s]Prediction
nicolas7894/flux-undraw:a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20IDfj5exwjnr9rm00chc3bs61a6qcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Tesla Model X car, untokoldr illustration
- lora_scale
- 1
- num_outputs
- 2
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Tesla Model X car, untokoldr illustration", "lora_scale": 1, "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 nicolas7894/flux-undraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nicolas7894/flux-undraw:a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20", { input: { model: "dev", prompt: "Tesla Model X car, untokoldr illustration", lora_scale: 1, 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 nicolas7894/flux-undraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nicolas7894/flux-undraw:a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20", input={ "model": "dev", "prompt": "Tesla Model X car, untokoldr illustration", "lora_scale": 1, "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 nicolas7894/flux-undraw 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": "a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20", "input": { "model": "dev", "prompt": "Tesla Model X car, untokoldr illustration", "lora_scale": 1, "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": "2024-08-17T14:59:42.678624Z", "created_at": "2024-08-17T14:56:50.626000Z", "data_removed": false, "error": null, "id": "fj5exwjnr9rm00chc3bs61a6qc", "input": { "model": "dev", "prompt": "Tesla Model X car, untokoldr illustration", "lora_scale": 1, "num_outputs": 2, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 62107\nPrompt: Tesla Model X car, untokoldr illustration\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9723612024832\nDownloading weights: https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar\n2024-08-17T14:59:15Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/1787d8148963af3e url=https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar\n2024-08-17T14:59:17Z | INFO | [ Complete ] dest=/src/weights-cache/1787d8148963af3e size=\"172 MB\" total_elapsed=1.923s url=https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar\nb''\nDownloaded weights in 1.9507231712341309 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:15, 1.76it/s]\n 7%|▋ | 2/28 [00:00<00:12, 2.12it/s]\n 11%|█ | 3/28 [00:01<00:12, 2.04it/s]\n 14%|█▍ | 4/28 [00:02<00:12, 2.00it/s]\n 18%|█▊ | 5/28 [00:02<00:11, 1.98it/s]\n 21%|██▏ | 6/28 [00:03<00:11, 1.97it/s]\n 25%|██▌ | 7/28 [00:03<00:10, 1.96it/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, 1.94it/s]\n100%|██████████| 28/28 [00:14<00:00, 1.94it/s]\n100%|██████████| 28/28 [00:14<00:00, 1.95it/s]", "metrics": { "predict_time": 27.100707485, "total_time": 172.052624 }, "output": [ "https://replicate.delivery/yhqm/fZ6L4WXQ4ZRIekDasiiqQKzSKomkImjqtMF1Wqvjjc2emNnmA/out-0.webp", "https://replicate.delivery/yhqm/Y5PfFWFThfjYP0HjwnnEMwVreIiVJXvBCFJXZbOGG4z8mNnmA/out-1.webp" ], "started_at": "2024-08-17T14:59:15.577916Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fj5exwjnr9rm00chc3bs61a6qc", "cancel": "https://api.replicate.com/v1/predictions/fj5exwjnr9rm00chc3bs61a6qc/cancel" }, "version": "a1c8efd083f978ab08ae9260cf2770e76e6ae959793bfe63c8d1afdaebbbcb20" }Generated inUsing seed: 62107 Prompt: Tesla Model X car, untokoldr illustration txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9723612024832 Downloading weights: https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar 2024-08-17T14:59:15Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/1787d8148963af3e url=https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar 2024-08-17T14:59:17Z | INFO | [ Complete ] dest=/src/weights-cache/1787d8148963af3e size="172 MB" total_elapsed=1.923s url=https://replicate.delivery/yhqm/qfhW9ucR070ZPSZuHRqCKnCMfjxZvCTH4rgPPOKSiC6yetmmA/trained_model.tar b'' Downloaded weights in 1.9507231712341309 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:15, 1.76it/s] 7%|▋ | 2/28 [00:00<00:12, 2.12it/s] 11%|█ | 3/28 [00:01<00:12, 2.04it/s] 14%|█▍ | 4/28 [00:02<00:12, 2.00it/s] 18%|█▊ | 5/28 [00:02<00:11, 1.98it/s] 21%|██▏ | 6/28 [00:03<00:11, 1.97it/s] 25%|██▌ | 7/28 [00:03<00:10, 1.96it/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, 1.94it/s] 100%|██████████| 28/28 [00:14<00:00, 1.94it/s] 100%|██████████| 28/28 [00:14<00:00, 1.95it/s]
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