cbh123
/
iwan-baan-sdxl
Fine-tuned SDXL on my favorite architectural photographer, Iwan Baan
- Public
- 576 runs
-
L40S
Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDvzkob3lbnaogvj6evymmgbdxymStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:01:17.973159Z", "created_at": "2023-08-09T19:00:59.739479Z", "data_removed": false, "error": null, "id": "vzkob3lbnaogvj6evymmgbdxym", "input": { "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 3407\nPrompt: A photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:38, 1.29it/s]\n 4%|▍ | 2/50 [00:01<00:23, 2.09it/s]\n 6%|▌ | 3/50 [00:01<00:18, 2.61it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.94it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.17it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.33it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.44it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.51it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.56it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.60it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:05<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:08<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.69it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.69it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.69it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.69it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 3.69it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.69it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.69it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.55it/s]", "metrics": { "predict_time": 16.797472, "total_time": 18.23368 }, "output": [ "https://pbxt.replicate.delivery/0LDDWPXjRe0WMaI5DnxVUirTaaYGDyRxr0UKO8PIz23eRZYRA/out-0.png" ], "started_at": "2023-08-09T19:01:01.175687Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vzkob3lbnaogvj6evymmgbdxym", "cancel": "https://api.replicate.com/v1/predictions/vzkob3lbnaogvj6evymmgbdxym/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 3407 Prompt: A photo in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:38, 1.29it/s] 4%|▍ | 2/50 [00:01<00:23, 2.09it/s] 6%|▌ | 3/50 [00:01<00:18, 2.61it/s] 8%|▊ | 4/50 [00:01<00:15, 2.94it/s] 10%|█ | 5/50 [00:01<00:14, 3.17it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.33it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.44it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.51it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.56it/s] 20%|██ | 10/50 [00:03<00:11, 3.60it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:05<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.67it/s] 50%|█████ | 25/50 [00:07<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:08<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.69it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.69it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s] 70%|███████ | 35/50 [00:10<00:04, 3.69it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.69it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s] 78%|███████▊ | 39/50 [00:11<00:02, 3.69it/s] 80%|████████ | 40/50 [00:11<00:02, 3.69it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.69it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.69it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.69it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:14<00:00, 3.68it/s] 100%|██████████| 50/50 [00:14<00:00, 3.55it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDbypai3lbsm7yb7mz2cuzlfqiamStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of the moon in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of the moon in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of the moon in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of the moon in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of the moon in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:01:57.520045Z", "created_at": "2023-08-09T19:01:41.937315Z", "data_removed": false, "error": null, "id": "bypai3lbsm7yb7mz2cuzlfqiam", "input": { "width": 1024, "height": 1024, "prompt": "A photo of the moon in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 60706\nPrompt: A photo of the moon in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.616945, "total_time": 15.58273 }, "output": [ "https://pbxt.replicate.delivery/Epdd2qmuUU5FK1lnyl6AJ9MlDJj9XLKcfPupjOm0C0FSpMsIA/out-0.png" ], "started_at": "2023-08-09T19:01:41.903100Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bypai3lbsm7yb7mz2cuzlfqiam", "cancel": "https://api.replicate.com/v1/predictions/bypai3lbsm7yb7mz2cuzlfqiam/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 60706 Prompt: A photo of the moon in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDi7eaqxtbj6iard3haojrqlviduStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a hallway in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a hallway in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of a hallway in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of a hallway in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a hallway in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:04:16.459148Z", "created_at": "2023-08-09T19:04:00.517590Z", "data_removed": false, "error": null, "id": "i7eaqxtbj6iard3haojrqlvidu", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a hallway in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 33483\nPrompt: A photo of a hallway in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 15.96819, "total_time": 15.941558 }, "output": [ "https://pbxt.replicate.delivery/qV0r0QFAfWXFSaNAA2eAy8p1Pkyzv5OFCDQ0y9uWqXZvUZYRA/out-0.png" ], "started_at": "2023-08-09T19:04:00.490958Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/i7eaqxtbj6iard3haojrqlvidu", "cancel": "https://api.replicate.com/v1/predictions/i7eaqxtbj6iard3haojrqlvidu/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 33483 Prompt: A photo of a hallway in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDilmmkttb2bhejhemip7dtwilryStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a government building in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of a government building in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:06:12.959753Z", "created_at": "2023-08-09T19:05:57.269635Z", "data_removed": false, "error": null, "id": "ilmmkttb2bhejhemip7dtwilry", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 63009\nPrompt: A photo of a government building in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.689982, "total_time": 15.690118 }, "output": [ "https://pbxt.replicate.delivery/r2KBfZgGRN3ARKvPxigLl3NleKmgAwuNQIDHTRU9LDCkWZYRA/out-0.png" ], "started_at": "2023-08-09T19:05:57.269771Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ilmmkttb2bhejhemip7dtwilry", "cancel": "https://api.replicate.com/v1/predictions/ilmmkttb2bhejhemip7dtwilry/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 63009 Prompt: A photo of a government building in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDnint4klbab4pzisdz27kschbxiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:07:37.159292Z", "created_at": "2023-08-09T19:07:21.467469Z", "data_removed": false, "error": null, "id": "nint4klbab4pzisdz27kschbxi", "input": { "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 19254\nPrompt: A photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.689872, "total_time": 15.691823 }, "output": [ "https://pbxt.replicate.delivery/vfu6XU8ssNT5SKd0Dp5NWfdOzwKGytJJDP6JeZ9rU66wvywiA/out-0.png" ], "started_at": "2023-08-09T19:07:21.469420Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nint4klbab4pzisdz27kschbxi", "cancel": "https://api.replicate.com/v1/predictions/nint4klbab4pzisdz27kschbxi/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 19254 Prompt: A photo in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDdqgxollbxurmzdhbz6rmp3a4ruStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a house in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a house in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of a house in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of a house in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a house in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:08:47.884392Z", "created_at": "2023-08-09T19:08:32.309628Z", "data_removed": false, "error": null, "id": "dqgxollbxurmzdhbz6rmp3a4ru", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a house in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 2399\nPrompt: A photo of a house in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.613908, "total_time": 15.574764 }, "output": [ "https://pbxt.replicate.delivery/CKLN038na4KuIhwzC5VAFm21yXNbgCOkgRSycDvxw4mPWGWE/out-0.png" ], "started_at": "2023-08-09T19:08:32.270484Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dqgxollbxurmzdhbz6rmp3a4ru", "cancel": "https://api.replicate.com/v1/predictions/dqgxollbxurmzdhbz6rmp3a4ru/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 2399 Prompt: A photo of a house in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDvpzukxlbqonj7mj3odbodlvjaeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @cbh123Input
- width
- 1024
- height
- 1024
- prompt
- A landscape photo in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A landscape photo in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:09:29.173073Z", "created_at": "2023-08-09T19:09:13.610071Z", "data_removed": false, "error": null, "id": "vpzukxlbqonj7mj3odbodlvjae", "input": { "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 3439\nPrompt: A landscape photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.556481, "total_time": 15.563002 }, "output": [ "https://pbxt.replicate.delivery/NxVqv7OmpbZbD5KWiAvqGVZ78ntEiUbHJfFu2Nm7N6M0sMsIA/out-0.png" ], "started_at": "2023-08-09T19:09:13.616592Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vpzukxlbqonj7mj3odbodlvjae", "cancel": "https://api.replicate.com/v1/predictions/vpzukxlbqonj7mj3odbodlvjae/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 3439 Prompt: A landscape photo in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDv5vjtdlbdwduyfdxa5zvmjguc4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A landscape photo in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A landscape photo in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:10:12.206572Z", "created_at": "2023-08-09T19:09:56.652786Z", "data_removed": false, "error": null, "id": "v5vjtdlbdwduyfdxa5zvmjguc4", "input": { "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 37467\nPrompt: A landscape photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.70it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.70it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.70it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.69it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.69it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.555326, "total_time": 15.553786 }, "output": [ "https://pbxt.replicate.delivery/zYfX0G2NVw1DZ6i41IujxV9JmGhYfavsITlEbPaFLx3TaZYRA/out-0.png" ], "started_at": "2023-08-09T19:09:56.651246Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/v5vjtdlbdwduyfdxa5zvmjguc4", "cancel": "https://api.replicate.com/v1/predictions/v5vjtdlbdwduyfdxa5zvmjguc4/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 37467 Prompt: A landscape photo in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.70it/s] 10%|█ | 5/50 [00:01<00:12, 3.70it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.70it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s] 40%|████ | 20/50 [00:05<00:08, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDcz2ulxtb6ezztwuwshsr2zqcmmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a skyscraper in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a skyscraper in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of a skyscraper in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of a skyscraper in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a skyscraper in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:11:18.900305Z", "created_at": "2023-08-09T19:11:03.294290Z", "data_removed": false, "error": null, "id": "cz2ulxtb6ezztwuwshsr2zqcmm", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a skyscraper in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5130\nPrompt: A photo of a skyscraper in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.616362, "total_time": 15.606015 }, "output": [ "https://pbxt.replicate.delivery/ZfXKlu1WHfjPaU1A7p7W2ZTXlu9RUBEKOvxaRGhv24VVbZYRA/out-0.png" ], "started_at": "2023-08-09T19:11:03.283943Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cz2ulxtb6ezztwuwshsr2zqcmm", "cancel": "https://api.replicate.com/v1/predictions/cz2ulxtb6ezztwuwshsr2zqcmm/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 5130 Prompt: A photo of a skyscraper in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDwgk7ohtbq7bjedrczl5h2hzfsqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A landscape photo in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A landscape photo in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:14:18.924051Z", "created_at": "2023-08-09T19:13:22.463483Z", "data_removed": false, "error": null, "id": "wgk7ohtbq7bjedrczl5h2hzfsq", "input": { "width": 1024, "height": 1024, "prompt": "A landscape photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 60588\nPrompt: A landscape photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:48, 1.00it/s]\n 4%|▍ | 2/50 [00:01<00:47, 1.00it/s]\n 6%|▌ | 3/50 [00:02<00:46, 1.00it/s]\n 8%|▊ | 4/50 [00:03<00:45, 1.00it/s]\n 10%|█ | 5/50 [00:05<00:45, 1.00s/it]\n 12%|█▏ | 6/50 [00:05<00:43, 1.00it/s]\n 14%|█▍ | 7/50 [00:06<00:42, 1.00it/s]\n 16%|█▌ | 8/50 [00:07<00:41, 1.00it/s]\n 18%|█▊ | 9/50 [00:08<00:40, 1.00it/s]\n 20%|██ | 10/50 [00:09<00:39, 1.00it/s]\n 22%|██▏ | 11/50 [00:10<00:38, 1.00it/s]\n 24%|██▍ | 12/50 [00:11<00:37, 1.00it/s]\n 26%|██▌ | 13/50 [00:12<00:36, 1.00it/s]\n 28%|██▊ | 14/50 [00:13<00:35, 1.00it/s]\n 30%|███ | 15/50 [00:14<00:34, 1.00it/s]\n 32%|███▏ | 16/50 [00:15<00:33, 1.00it/s]\n 34%|███▍ | 17/50 [00:16<00:32, 1.00it/s]\n 36%|███▌ | 18/50 [00:17<00:32, 1.00s/it]\n 38%|███▊ | 19/50 [00:18<00:31, 1.00s/it]\n 40%|████ | 20/50 [00:19<00:30, 1.00s/it]\n 42%|████▏ | 21/50 [00:20<00:29, 1.00s/it]\n 44%|████▍ | 22/50 [00:21<00:28, 1.00s/it]\n 46%|████▌ | 23/50 [00:22<00:27, 1.00s/it]\n 48%|████▊ | 24/50 [00:23<00:26, 1.00s/it]\n 50%|█████ | 25/50 [00:24<00:25, 1.00s/it]\n 52%|█████▏ | 26/50 [00:26<00:24, 1.00s/it]\n 54%|█████▍ | 27/50 [00:27<00:23, 1.00s/it]\n 56%|█████▌ | 28/50 [00:28<00:22, 1.00s/it]\n 58%|█████▊ | 29/50 [00:29<00:21, 1.00s/it]\n 60%|██████ | 30/50 [00:30<00:20, 1.00s/it]\n 62%|██████▏ | 31/50 [00:31<00:19, 1.00s/it]\n 64%|██████▍ | 32/50 [00:32<00:18, 1.00s/it]\n 66%|██████▌ | 33/50 [00:33<00:17, 1.00s/it]\n 68%|██████▊ | 34/50 [00:34<00:16, 1.00s/it]\n 70%|███████ | 35/50 [00:35<00:15, 1.01s/it]\n 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it]\n 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it]\n 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it]\n 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it]\n 80%|████████ | 40/50 [00:40<00:10, 1.00s/it]\n 82%|████████▏ | 41/50 [00:41<00:09, 1.00s/it]\n 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it]\n 86%|████████▌ | 43/50 [00:43<00:07, 1.00s/it]\n 88%|████████▊ | 44/50 [00:44<00:06, 1.00s/it]\n 90%|█████████ | 45/50 [00:45<00:05, 1.00s/it]\n 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it]\n 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it]\n 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it]\n 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.00s/it]", "metrics": { "predict_time": 56.488437, "total_time": 56.460568 }, "output": [ "https://pbxt.replicate.delivery/FIUPocHdekRxIC80hhf6v6buapqJPuzbjrDSOuDftWpQ8ywiA/out-0.png", "https://pbxt.replicate.delivery/eXvu2P6nBwyxa6fTnpcquPF2PiPft9yNpmIAnP55wxiT8ywiA/out-1.png", "https://pbxt.replicate.delivery/VBf8zmKAyyXGKyCgNPEWgXOBuUJYTN5CGfuHc7d44FgJeywiA/out-2.png", "https://pbxt.replicate.delivery/7ghhqyyfv6SFeUscND7Mxj3MarF5LzG5FQld6Z00eGiU8ywiA/out-3.png" ], "started_at": "2023-08-09T19:13:22.435614Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wgk7ohtbq7bjedrczl5h2hzfsq", "cancel": "https://api.replicate.com/v1/predictions/wgk7ohtbq7bjedrczl5h2hzfsq/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 60588 Prompt: A landscape photo in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:48, 1.00it/s] 4%|▍ | 2/50 [00:01<00:47, 1.00it/s] 6%|▌ | 3/50 [00:02<00:46, 1.00it/s] 8%|▊ | 4/50 [00:03<00:45, 1.00it/s] 10%|█ | 5/50 [00:05<00:45, 1.00s/it] 12%|█▏ | 6/50 [00:05<00:43, 1.00it/s] 14%|█▍ | 7/50 [00:06<00:42, 1.00it/s] 16%|█▌ | 8/50 [00:07<00:41, 1.00it/s] 18%|█▊ | 9/50 [00:08<00:40, 1.00it/s] 20%|██ | 10/50 [00:09<00:39, 1.00it/s] 22%|██▏ | 11/50 [00:10<00:38, 1.00it/s] 24%|██▍ | 12/50 [00:11<00:37, 1.00it/s] 26%|██▌ | 13/50 [00:12<00:36, 1.00it/s] 28%|██▊ | 14/50 [00:13<00:35, 1.00it/s] 30%|███ | 15/50 [00:14<00:34, 1.00it/s] 32%|███▏ | 16/50 [00:15<00:33, 1.00it/s] 34%|███▍ | 17/50 [00:16<00:32, 1.00it/s] 36%|███▌ | 18/50 [00:17<00:32, 1.00s/it] 38%|███▊ | 19/50 [00:18<00:31, 1.00s/it] 40%|████ | 20/50 [00:19<00:30, 1.00s/it] 42%|████▏ | 21/50 [00:20<00:29, 1.00s/it] 44%|████▍ | 22/50 [00:21<00:28, 1.00s/it] 46%|████▌ | 23/50 [00:22<00:27, 1.00s/it] 48%|████▊ | 24/50 [00:23<00:26, 1.00s/it] 50%|█████ | 25/50 [00:24<00:25, 1.00s/it] 52%|█████▏ | 26/50 [00:26<00:24, 1.00s/it] 54%|█████▍ | 27/50 [00:27<00:23, 1.00s/it] 56%|█████▌ | 28/50 [00:28<00:22, 1.00s/it] 58%|█████▊ | 29/50 [00:29<00:21, 1.00s/it] 60%|██████ | 30/50 [00:30<00:20, 1.00s/it] 62%|██████▏ | 31/50 [00:31<00:19, 1.00s/it] 64%|██████▍ | 32/50 [00:32<00:18, 1.00s/it] 66%|██████▌ | 33/50 [00:33<00:17, 1.00s/it] 68%|██████▊ | 34/50 [00:34<00:16, 1.00s/it] 70%|███████ | 35/50 [00:35<00:15, 1.01s/it] 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it] 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it] 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it] 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it] 80%|████████ | 40/50 [00:40<00:10, 1.00s/it] 82%|████████▏ | 41/50 [00:41<00:09, 1.00s/it] 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it] 86%|████████▌ | 43/50 [00:43<00:07, 1.00s/it] 88%|████████▊ | 44/50 [00:44<00:06, 1.00s/it] 90%|█████████ | 45/50 [00:45<00:05, 1.00s/it] 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it] 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it] 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it] 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it] 100%|██████████| 50/50 [00:50<00:00, 1.01s/it] 100%|██████████| 50/50 [00:50<00:00, 1.00s/it]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDirusahlb3vt4buepuzrwxuvb2eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a government building in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of a government building in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:15:10.187146Z", "created_at": "2023-08-09T19:14:54.319540Z", "data_removed": false, "error": null, "id": "irusahlb3vt4buepuzrwxuvb2e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 59849\nPrompt: A photo of a government building in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.859826, "total_time": 15.867606 }, "output": [ "https://pbxt.replicate.delivery/pqWJbQwCf3UYCKAHbke27TYGl73lJd27F8KdSecvW6U69ywiA/out-0.png" ], "started_at": "2023-08-09T19:14:54.327320Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/irusahlb3vt4buepuzrwxuvb2e", "cancel": "https://api.replicate.com/v1/predictions/irusahlb3vt4buepuzrwxuvb2e/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 59849 Prompt: A photo of a government building in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eID4xuafcdbqsizi5c6bvtlgxwxuuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @cbh123Input
- width
- 1024
- height
- 1024
- prompt
- A photo of a car in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a car in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of a car in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of a car in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a car in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:16:10.807360Z", "created_at": "2023-08-09T19:15:55.123622Z", "data_removed": false, "error": null, "id": "4xuafcdbqsizi5c6bvtlgxwxuu", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a car in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 25381\nPrompt: A photo of a car in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.69it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.69it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.670337, "total_time": 15.683738 }, "output": [ "https://pbxt.replicate.delivery/TFD2D3gr6EbKOlF6nwiVMQf0x5xufWyfQsn1M2UKKpgzflhFB/out-0.png" ], "started_at": "2023-08-09T19:15:55.137023Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4xuafcdbqsizi5c6bvtlgxwxuu", "cancel": "https://api.replicate.com/v1/predictions/4xuafcdbqsizi5c6bvtlgxwxuu/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 25381 Prompt: A photo of a car in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s] 40%|████ | 20/50 [00:05<00:08, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDpeoji6lbmjxeplgqwtyqip5oz4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a government building in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of a government building in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:20:19.493476Z", "created_at": "2023-08-09T19:19:22.798599Z", "data_removed": false, "error": null, "id": "peoji6lbmjxeplgqwtyqip5oz4", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a government building in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 20273\nPrompt: A photo of a government building in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:48, 1.00it/s]\n 4%|▍ | 2/50 [00:01<00:47, 1.00it/s]\n 6%|▌ | 3/50 [00:02<00:46, 1.00it/s]\n 8%|▊ | 4/50 [00:03<00:45, 1.00it/s]\n 10%|█ | 5/50 [00:04<00:44, 1.00it/s]\n 12%|█▏ | 6/50 [00:05<00:44, 1.00s/it]\n 14%|█▍ | 7/50 [00:06<00:43, 1.00s/it]\n 16%|█▌ | 8/50 [00:07<00:41, 1.00it/s]\n 18%|█▊ | 9/50 [00:08<00:40, 1.00it/s]\n 20%|██ | 10/50 [00:09<00:39, 1.00it/s]\n 22%|██▏ | 11/50 [00:10<00:38, 1.00it/s]\n 24%|██▍ | 12/50 [00:11<00:37, 1.00it/s]\n 26%|██▌ | 13/50 [00:12<00:36, 1.00it/s]\n 28%|██▊ | 14/50 [00:13<00:35, 1.00it/s]\n 30%|███ | 15/50 [00:14<00:34, 1.00it/s]\n 32%|███▏ | 16/50 [00:15<00:33, 1.00it/s]\n 34%|███▍ | 17/50 [00:16<00:32, 1.00it/s]\n 36%|███▌ | 18/50 [00:17<00:31, 1.00it/s]\n 38%|███▊ | 19/50 [00:18<00:30, 1.00it/s]\n 40%|████ | 20/50 [00:19<00:29, 1.00it/s]\n 42%|████▏ | 21/50 [00:20<00:28, 1.00it/s]\n 44%|████▍ | 22/50 [00:21<00:27, 1.00it/s]\n 46%|████▌ | 23/50 [00:22<00:27, 1.00s/it]\n 48%|████▊ | 24/50 [00:23<00:26, 1.00s/it]\n 50%|█████ | 25/50 [00:24<00:25, 1.00s/it]\n 52%|█████▏ | 26/50 [00:25<00:24, 1.00s/it]\n 54%|█████▍ | 27/50 [00:26<00:23, 1.00s/it]\n 56%|█████▌ | 28/50 [00:27<00:22, 1.00s/it]\n 58%|█████▊ | 29/50 [00:28<00:21, 1.00s/it]\n 60%|██████ | 30/50 [00:29<00:20, 1.00s/it]\n 62%|██████▏ | 31/50 [00:30<00:19, 1.00s/it]\n 64%|██████▍ | 32/50 [00:31<00:18, 1.00s/it]\n 66%|██████▌ | 33/50 [00:32<00:17, 1.00s/it]\n 68%|██████▊ | 34/50 [00:33<00:16, 1.00s/it]\n 70%|███████ | 35/50 [00:34<00:15, 1.00s/it]\n 72%|███████▏ | 36/50 [00:35<00:14, 1.00s/it]\n 74%|███████▍ | 37/50 [00:36<00:13, 1.00s/it]\n 76%|███████▌ | 38/50 [00:37<00:12, 1.00s/it]\n 78%|███████▊ | 39/50 [00:38<00:11, 1.00s/it]\n 80%|████████ | 40/50 [00:39<00:10, 1.00s/it]\n 82%|████████▏ | 41/50 [00:40<00:09, 1.00s/it]\n 84%|████████▍ | 42/50 [00:41<00:08, 1.00s/it]\n 86%|████████▌ | 43/50 [00:42<00:07, 1.00s/it]\n 88%|████████▊ | 44/50 [00:43<00:06, 1.00s/it]\n 90%|█████████ | 45/50 [00:45<00:05, 1.00s/it]\n 92%|█████████▏| 46/50 [00:46<00:04, 1.00s/it]\n 94%|█████████▍| 47/50 [00:47<00:03, 1.00s/it]\n 96%|█████████▌| 48/50 [00:48<00:02, 1.00s/it]\n 98%|█████████▊| 49/50 [00:49<00:01, 1.00s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.00s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.00s/it]", "metrics": { "predict_time": 56.75433, "total_time": 56.694877 }, "output": [ "https://pbxt.replicate.delivery/9lPeJJkDcfjYj0zlmB4Wf9jgDB71shUTifUEVFr7BuWDPmhFB/out-0.png", "https://pbxt.replicate.delivery/grr98fGEwkR5Cq9LPtgIiANssROfswAzpMRizklxDYgyjZYRA/out-1.png", "https://pbxt.replicate.delivery/9cpamYWXhVbMF1nFlZ8qkfrz1w09zG5gTuRwhxktmVc5xMsIA/out-2.png", "https://pbxt.replicate.delivery/94lt2sfQs5SLMqiwtGC6C1QWl9zEIe0pfkxv4KHDIeHOPmhFB/out-3.png" ], "started_at": "2023-08-09T19:19:22.739146Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/peoji6lbmjxeplgqwtyqip5oz4", "cancel": "https://api.replicate.com/v1/predictions/peoji6lbmjxeplgqwtyqip5oz4/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 20273 Prompt: A photo of a government building in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:48, 1.00it/s] 4%|▍ | 2/50 [00:01<00:47, 1.00it/s] 6%|▌ | 3/50 [00:02<00:46, 1.00it/s] 8%|▊ | 4/50 [00:03<00:45, 1.00it/s] 10%|█ | 5/50 [00:04<00:44, 1.00it/s] 12%|█▏ | 6/50 [00:05<00:44, 1.00s/it] 14%|█▍ | 7/50 [00:06<00:43, 1.00s/it] 16%|█▌ | 8/50 [00:07<00:41, 1.00it/s] 18%|█▊ | 9/50 [00:08<00:40, 1.00it/s] 20%|██ | 10/50 [00:09<00:39, 1.00it/s] 22%|██▏ | 11/50 [00:10<00:38, 1.00it/s] 24%|██▍ | 12/50 [00:11<00:37, 1.00it/s] 26%|██▌ | 13/50 [00:12<00:36, 1.00it/s] 28%|██▊ | 14/50 [00:13<00:35, 1.00it/s] 30%|███ | 15/50 [00:14<00:34, 1.00it/s] 32%|███▏ | 16/50 [00:15<00:33, 1.00it/s] 34%|███▍ | 17/50 [00:16<00:32, 1.00it/s] 36%|███▌ | 18/50 [00:17<00:31, 1.00it/s] 38%|███▊ | 19/50 [00:18<00:30, 1.00it/s] 40%|████ | 20/50 [00:19<00:29, 1.00it/s] 42%|████▏ | 21/50 [00:20<00:28, 1.00it/s] 44%|████▍ | 22/50 [00:21<00:27, 1.00it/s] 46%|████▌ | 23/50 [00:22<00:27, 1.00s/it] 48%|████▊ | 24/50 [00:23<00:26, 1.00s/it] 50%|█████ | 25/50 [00:24<00:25, 1.00s/it] 52%|█████▏ | 26/50 [00:25<00:24, 1.00s/it] 54%|█████▍ | 27/50 [00:26<00:23, 1.00s/it] 56%|█████▌ | 28/50 [00:27<00:22, 1.00s/it] 58%|█████▊ | 29/50 [00:28<00:21, 1.00s/it] 60%|██████ | 30/50 [00:29<00:20, 1.00s/it] 62%|██████▏ | 31/50 [00:30<00:19, 1.00s/it] 64%|██████▍ | 32/50 [00:31<00:18, 1.00s/it] 66%|██████▌ | 33/50 [00:32<00:17, 1.00s/it] 68%|██████▊ | 34/50 [00:33<00:16, 1.00s/it] 70%|███████ | 35/50 [00:34<00:15, 1.00s/it] 72%|███████▏ | 36/50 [00:35<00:14, 1.00s/it] 74%|███████▍ | 37/50 [00:36<00:13, 1.00s/it] 76%|███████▌ | 38/50 [00:37<00:12, 1.00s/it] 78%|███████▊ | 39/50 [00:38<00:11, 1.00s/it] 80%|████████ | 40/50 [00:39<00:10, 1.00s/it] 82%|████████▏ | 41/50 [00:40<00:09, 1.00s/it] 84%|████████▍ | 42/50 [00:41<00:08, 1.00s/it] 86%|████████▌ | 43/50 [00:42<00:07, 1.00s/it] 88%|████████▊ | 44/50 [00:43<00:06, 1.00s/it] 90%|█████████ | 45/50 [00:45<00:05, 1.00s/it] 92%|█████████▏| 46/50 [00:46<00:04, 1.00s/it] 94%|█████████▍| 47/50 [00:47<00:03, 1.00s/it] 96%|█████████▌| 48/50 [00:48<00:02, 1.00s/it] 98%|█████████▊| 49/50 [00:49<00:01, 1.00s/it] 100%|██████████| 50/50 [00:50<00:00, 1.00s/it] 100%|██████████| 50/50 [00:50<00:00, 1.00s/it]Prediction
cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609eIDog2cxvlbcaglqu76zwgiwkrxnmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of ancient rome in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of ancient rome in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", { input: { width: 1024, height: 1024, prompt: "A photo of ancient rome in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", input={ "width": 1024, "height": 1024, "prompt": "A photo of ancient rome in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of ancient rome in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-09T19:21:46.273692Z", "created_at": "2023-08-09T19:21:31.469266Z", "data_removed": false, "error": null, "id": "og2cxvlbcaglqu76zwgiwkrxnm", "input": { "width": 1024, "height": 1024, "prompt": "A photo of ancient rome in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 64134\nPrompt: A photo of ancient rome in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.73it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.71it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.71it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.71it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.73it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.72it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.72it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.72it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.72it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.72it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.72it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.72it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.72it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.72it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.72it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.72it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.72it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.72it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.71it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.71it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.71it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.71it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.71it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.71it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.71it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.71it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.71it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.71it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.71it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.71it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.71it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.71it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.71it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.70it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.71it/s]", "metrics": { "predict_time": 14.836407, "total_time": 14.804426 }, "output": [ "https://pbxt.replicate.delivery/AofddZRc3ZXmAiVqdl30NxfL8X4BSwCkktT6zEeh5fukUmhFB/out-0.png" ], "started_at": "2023-08-09T19:21:31.437285Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/og2cxvlbcaglqu76zwgiwkrxnm", "cancel": "https://api.replicate.com/v1/predictions/og2cxvlbcaglqu76zwgiwkrxnm/cancel" }, "version": "bdf6bad7edcaf13aeedf68b625d19120a48a4658dfbef0713593c8736805609e" }Generated inUsing seed: 64134 Prompt: A photo of ancient rome in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.73it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.71it/s] 8%|▊ | 4/50 [00:01<00:12, 3.71it/s] 10%|█ | 5/50 [00:01<00:12, 3.71it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.73it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s] 30%|███ | 15/50 [00:04<00:09, 3.72it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.72it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.72it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.72it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.72it/s] 40%|████ | 20/50 [00:05<00:08, 3.72it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.72it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.72it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.72it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.72it/s] 50%|█████ | 25/50 [00:06<00:06, 3.72it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.72it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.72it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.71it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.71it/s] 60%|██████ | 30/50 [00:08<00:05, 3.71it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.71it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.71it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s] 70%|███████ | 35/50 [00:09<00:04, 3.71it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.71it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.71it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.71it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.71it/s] 80%|████████ | 40/50 [00:10<00:02, 3.71it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.71it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.71it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.71it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.71it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.70it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.71it/s]Prediction
cbh123/iwan-baan-sdxl:2904d308569e07141e158ebaefef6f2361f6f35d03a076af4a561cdeb5f6e913IDhmntvdlb2a7uqruzaanpd2qyjuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- An aerial shot of rome in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "An aerial shot of rome in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/iwan-baan-sdxl:2904d308569e07141e158ebaefef6f2361f6f35d03a076af4a561cdeb5f6e913", { input: { width: 1024, height: 1024, prompt: "An aerial shot of rome in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cbh123/iwan-baan-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/iwan-baan-sdxl:2904d308569e07141e158ebaefef6f2361f6f35d03a076af4a561cdeb5f6e913", input={ "width": 1024, "height": 1024, "prompt": "An aerial shot of rome in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/iwan-baan-sdxl 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": "2904d308569e07141e158ebaefef6f2361f6f35d03a076af4a561cdeb5f6e913", "input": { "width": 1024, "height": 1024, "prompt": "An aerial shot of rome in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-10T15:18:37.336631Z", "created_at": "2023-08-10T15:16:59.476222Z", "data_removed": false, "error": null, "id": "hmntvdlb2a7uqruzaanpd2qyju", "input": { "width": 1024, "height": 1024, "prompt": "An aerial shot of rome in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 56633\nPrompt: An aerial shot of rome in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<01:16, 1.55s/it]\n 4%|▍ | 2/50 [00:02<00:58, 1.23s/it]\n 6%|▌ | 3/50 [00:03<00:52, 1.13s/it]\n 8%|▊ | 4/50 [00:04<00:49, 1.07s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:45, 1.03s/it]\n 14%|█▍ | 7/50 [00:07<00:43, 1.02s/it]\n 16%|█▌ | 8/50 [00:08<00:42, 1.01s/it]\n 18%|█▊ | 9/50 [00:09<00:41, 1.01s/it]\n 20%|██ | 10/50 [00:10<00:40, 1.01s/it]\n 22%|██▏ | 11/50 [00:11<00:39, 1.00s/it]\n 24%|██▍ | 12/50 [00:12<00:38, 1.01s/it]\n 26%|██▌ | 13/50 [00:13<00:37, 1.01s/it]\n 28%|██▊ | 14/50 [00:14<00:36, 1.00s/it]\n 30%|███ | 15/50 [00:15<00:35, 1.00s/it]\n 32%|███▏ | 16/50 [00:16<00:34, 1.00s/it]\n 34%|███▍ | 17/50 [00:17<00:33, 1.00s/it]\n 36%|███▌ | 18/50 [00:18<00:32, 1.00s/it]\n 38%|███▊ | 19/50 [00:19<00:31, 1.00s/it]\n 40%|████ | 20/50 [00:20<00:30, 1.00s/it]\n 42%|████▏ | 21/50 [00:21<00:29, 1.00s/it]\n 44%|████▍ | 22/50 [00:22<00:28, 1.00s/it]\n 46%|████▌ | 23/50 [00:23<00:27, 1.00s/it]\n 48%|████▊ | 24/50 [00:24<00:26, 1.00s/it]\n 50%|█████ | 25/50 [00:25<00:25, 1.01s/it]\n 52%|█████▏ | 26/50 [00:26<00:24, 1.01s/it]\n 54%|█████▍ | 27/50 [00:27<00:23, 1.01s/it]\n 56%|█████▌ | 28/50 [00:28<00:22, 1.01s/it]\n 58%|█████▊ | 29/50 [00:29<00:21, 1.01s/it]\n 60%|██████ | 30/50 [00:30<00:20, 1.01s/it]\n 62%|██████▏ | 31/50 [00:31<00:19, 1.01s/it]\n 64%|██████▍ | 32/50 [00:32<00:18, 1.01s/it]\n 66%|██████▌ | 33/50 [00:33<00:17, 1.01s/it]\n 68%|██████▊ | 34/50 [00:34<00:16, 1.01s/it]\n 70%|███████ | 35/50 [00:35<00:15, 1.01s/it]\n 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it]\n 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it]\n 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it]\n 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it]\n 80%|████████ | 40/50 [00:40<00:10, 1.01s/it]\n 82%|████████▏ | 41/50 [00:41<00:09, 1.01s/it]\n 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it]\n 86%|████████▌ | 43/50 [00:43<00:07, 1.01s/it]\n 88%|████████▊ | 44/50 [00:44<00:06, 1.01s/it]\n 90%|█████████ | 45/50 [00:45<00:05, 1.01s/it]\n 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it]\n 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it]\n 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it]\n 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.02s/it]", "metrics": { "predict_time": 56.585381, "total_time": 97.860409 }, "output": [ "https://replicate.delivery/pbxt/kf3JAsAfRWosDkLdS4S49CkogF4hICXESuhJQnJmWrRLHrYRA/out-0.png", "https://replicate.delivery/pbxt/TyEWxCXsiuLdHNRwn9eGMafWXejZjGRqGpj9r5a3liRYOWxiA/out-1.png", "https://replicate.delivery/pbxt/ZLfBgUZx1kyNQKjvO0CoTj6YWIk2juQDJXWubEzeYJCMHrYRA/out-2.png", "https://replicate.delivery/pbxt/Vmy1gUP9ZroZLN39hNfzpWTa09fGu27Cez3pk1FFynlZOWxiA/out-3.png" ], "started_at": "2023-08-10T15:17:40.751250Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hmntvdlb2a7uqruzaanpd2qyju", "cancel": "https://api.replicate.com/v1/predictions/hmntvdlb2a7uqruzaanpd2qyju/cancel" }, "version": "2904d308569e07141e158ebaefef6f2361f6f35d03a076af4a561cdeb5f6e913" }Generated inUsing seed: 56633 Prompt: An aerial shot of rome in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<01:16, 1.55s/it] 4%|▍ | 2/50 [00:02<00:58, 1.23s/it] 6%|▌ | 3/50 [00:03<00:52, 1.13s/it] 8%|▊ | 4/50 [00:04<00:49, 1.07s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:45, 1.03s/it] 14%|█▍ | 7/50 [00:07<00:43, 1.02s/it] 16%|█▌ | 8/50 [00:08<00:42, 1.01s/it] 18%|█▊ | 9/50 [00:09<00:41, 1.01s/it] 20%|██ | 10/50 [00:10<00:40, 1.01s/it] 22%|██▏ | 11/50 [00:11<00:39, 1.00s/it] 24%|██▍ | 12/50 [00:12<00:38, 1.01s/it] 26%|██▌ | 13/50 [00:13<00:37, 1.01s/it] 28%|██▊ | 14/50 [00:14<00:36, 1.00s/it] 30%|███ | 15/50 [00:15<00:35, 1.00s/it] 32%|███▏ | 16/50 [00:16<00:34, 1.00s/it] 34%|███▍ | 17/50 [00:17<00:33, 1.00s/it] 36%|███▌ | 18/50 [00:18<00:32, 1.00s/it] 38%|███▊ | 19/50 [00:19<00:31, 1.00s/it] 40%|████ | 20/50 [00:20<00:30, 1.00s/it] 42%|████▏ | 21/50 [00:21<00:29, 1.00s/it] 44%|████▍ | 22/50 [00:22<00:28, 1.00s/it] 46%|████▌ | 23/50 [00:23<00:27, 1.00s/it] 48%|████▊ | 24/50 [00:24<00:26, 1.00s/it] 50%|█████ | 25/50 [00:25<00:25, 1.01s/it] 52%|█████▏ | 26/50 [00:26<00:24, 1.01s/it] 54%|█████▍ | 27/50 [00:27<00:23, 1.01s/it] 56%|█████▌ | 28/50 [00:28<00:22, 1.01s/it] 58%|█████▊ | 29/50 [00:29<00:21, 1.01s/it] 60%|██████ | 30/50 [00:30<00:20, 1.01s/it] 62%|██████▏ | 31/50 [00:31<00:19, 1.01s/it] 64%|██████▍ | 32/50 [00:32<00:18, 1.01s/it] 66%|██████▌ | 33/50 [00:33<00:17, 1.01s/it] 68%|██████▊ | 34/50 [00:34<00:16, 1.01s/it] 70%|███████ | 35/50 [00:35<00:15, 1.01s/it] 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it] 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it] 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it] 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it] 80%|████████ | 40/50 [00:40<00:10, 1.01s/it] 82%|████████▏ | 41/50 [00:41<00:09, 1.01s/it] 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it] 86%|████████▌ | 43/50 [00:43<00:07, 1.01s/it] 88%|████████▊ | 44/50 [00:44<00:06, 1.01s/it] 90%|█████████ | 45/50 [00:45<00:05, 1.01s/it] 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it] 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it] 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it] 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it] 100%|██████████| 50/50 [00:50<00:00, 1.01s/it] 100%|██████████| 50/50 [00:50<00:00, 1.02s/it]
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