jbilcke / sdxl-panorama
First version of my panorama LoRA (use 1024x512)
- Public
- 2.1K runs
-
L40S
- SDXL fine-tune
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDppyufjlb4gzq3qhyk6b2ogtav4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, a nice house in London, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, a nice house in London, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, a nice house in London, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, a nice house in London, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, a nice house in London, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-06T16:36:42.757078Z", "created_at": "2023-09-06T16:36:34.829030Z", "data_removed": false, "error": null, "id": "ppyufjlb4gzq3qhyk6b2ogtav4", "input": { "width": 1024, "height": 512, "prompt": "hdri view, a nice house in London, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 19131\nPrompt: hdri view, a nice house in London, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.18it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.17it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.15it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.14it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.13it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.13it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.13it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.03it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.05it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.07it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.08it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.09it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.09it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.09it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.10it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.11it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.11it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.12it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.13it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.13it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.12it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.13it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.13it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.14it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.14it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.14it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.14it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.14it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.14it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.14it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.14it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.14it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.14it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.14it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.14it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.15it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.15it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.14it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.14it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.14it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.14it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.14it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.15it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.14it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.15it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.15it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.14it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.14it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.14it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.14it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.13it/s]", "metrics": { "predict_time": 8.002077, "total_time": 7.928048 }, "output": [ "https://pbxt.replicate.delivery/EWNP6WvWfP3FaK8EFrBHWpgOt0u437fF0ojXjwWDrtZaylhRA/out-0.png" ], "started_at": "2023-09-06T16:36:34.755001Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ppyufjlb4gzq3qhyk6b2ogtav4", "cancel": "https://api.replicate.com/v1/predictions/ppyufjlb4gzq3qhyk6b2ogtav4/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 19131 Prompt: hdri view, a nice house in London, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.18it/s] 4%|▍ | 2/50 [00:00<00:06, 7.17it/s] 6%|▌ | 3/50 [00:00<00:06, 7.15it/s] 8%|▊ | 4/50 [00:00<00:06, 7.14it/s] 10%|█ | 5/50 [00:00<00:06, 7.13it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.13it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.13it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.03it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.05it/s] 20%|██ | 10/50 [00:01<00:05, 7.07it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.08it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.09it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.09it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.09it/s] 30%|███ | 15/50 [00:02<00:04, 7.10it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.11it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.11it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.12it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.13it/s] 40%|████ | 20/50 [00:02<00:04, 7.13it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.12it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.13it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.13it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.14it/s] 50%|█████ | 25/50 [00:03<00:03, 7.14it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.14it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.14it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.14it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.14it/s] 60%|██████ | 30/50 [00:04<00:02, 7.14it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.14it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.14it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.14it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.14it/s] 70%|███████ | 35/50 [00:04<00:02, 7.14it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.15it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.15it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.14it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.14it/s] 80%|████████ | 40/50 [00:05<00:01, 7.14it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.14it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.14it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.15it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.14it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.15it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.15it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.14it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.14it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.14it/s] 100%|██████████| 50/50 [00:07<00:00, 7.14it/s] 100%|██████████| 50/50 [00:07<00:00, 7.13it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDjsakgrtblvwsuoo2g67fg7dobiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, paris center, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, paris center, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, paris center, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, paris center, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, paris center, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-07T09:34:54.182731Z", "created_at": "2023-09-07T09:34:04.011123Z", "data_removed": false, "error": null, "id": "jsakgrtblvwsuoo2g67fg7dobi", "input": { "width": 1024, "height": 512, "prompt": "hdri view, paris center, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5795\nPrompt: hdri view, paris center, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:36, 1.35it/s]\n 4%|▍ | 2/50 [00:00<00:18, 2.58it/s]\n 6%|▌ | 3/50 [00:01<00:12, 3.64it/s]\n 8%|▊ | 4/50 [00:01<00:10, 4.51it/s]\n 10%|█ | 5/50 [00:01<00:08, 5.19it/s]\n 12%|█▏ | 6/50 [00:01<00:07, 5.72it/s]\n 14%|█▍ | 7/50 [00:01<00:07, 6.11it/s]\n 16%|█▌ | 8/50 [00:01<00:06, 6.40it/s]\n 18%|█▊ | 9/50 [00:01<00:06, 6.60it/s]\n 20%|██ | 10/50 [00:02<00:05, 6.75it/s]\n 22%|██▏ | 11/50 [00:02<00:05, 6.86it/s]\n 24%|██▍ | 12/50 [00:02<00:05, 6.93it/s]\n 26%|██▌ | 13/50 [00:02<00:05, 6.99it/s]\n 28%|██▊ | 14/50 [00:02<00:05, 7.02it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.05it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.07it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.08it/s]\n 36%|███▌ | 18/50 [00:03<00:04, 7.09it/s]\n 38%|███▊ | 19/50 [00:03<00:04, 7.10it/s]\n 40%|████ | 20/50 [00:03<00:04, 7.11it/s]\n 42%|████▏ | 21/50 [00:03<00:04, 7.11it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.10it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.09it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.09it/s]\n 50%|█████ | 25/50 [00:04<00:03, 7.08it/s]\n 52%|█████▏ | 26/50 [00:04<00:03, 7.08it/s]\n 54%|█████▍ | 27/50 [00:04<00:03, 7.08it/s]\n 56%|█████▌ | 28/50 [00:04<00:03, 7.08it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.07it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.07it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.07it/s]\n 64%|██████▍ | 32/50 [00:05<00:02, 7.08it/s]\n 66%|██████▌ | 33/50 [00:05<00:02, 7.07it/s]\n 68%|██████▊ | 34/50 [00:05<00:02, 7.07it/s]\n 70%|███████ | 35/50 [00:05<00:02, 7.07it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.08it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.07it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.07it/s]\n 78%|███████▊ | 39/50 [00:06<00:01, 7.06it/s]\n 80%|████████ | 40/50 [00:06<00:01, 7.06it/s]\n 82%|████████▏ | 41/50 [00:06<00:01, 7.05it/s]\n 84%|████████▍ | 42/50 [00:06<00:01, 7.05it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.05it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.06it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.06it/s]\n 92%|█████████▏| 46/50 [00:07<00:00, 7.06it/s]\n 94%|█████████▍| 47/50 [00:07<00:00, 7.06it/s]\n 96%|█████████▌| 48/50 [00:07<00:00, 7.06it/s]\n 98%|█████████▊| 49/50 [00:07<00:00, 7.06it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.06it/s]\n100%|██████████| 50/50 [00:07<00:00, 6.53it/s]", "metrics": { "predict_time": 9.39887, "total_time": 50.171608 }, "output": [ "https://pbxt.replicate.delivery/uKIKwGi32kJVE9PFDARf3hsARR4qGTgXgOYz7igKNjges0hRA/out-0.png" ], "started_at": "2023-09-07T09:34:44.783861Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jsakgrtblvwsuoo2g67fg7dobi", "cancel": "https://api.replicate.com/v1/predictions/jsakgrtblvwsuoo2g67fg7dobi/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 5795 Prompt: hdri view, paris center, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:36, 1.35it/s] 4%|▍ | 2/50 [00:00<00:18, 2.58it/s] 6%|▌ | 3/50 [00:01<00:12, 3.64it/s] 8%|▊ | 4/50 [00:01<00:10, 4.51it/s] 10%|█ | 5/50 [00:01<00:08, 5.19it/s] 12%|█▏ | 6/50 [00:01<00:07, 5.72it/s] 14%|█▍ | 7/50 [00:01<00:07, 6.11it/s] 16%|█▌ | 8/50 [00:01<00:06, 6.40it/s] 18%|█▊ | 9/50 [00:01<00:06, 6.60it/s] 20%|██ | 10/50 [00:02<00:05, 6.75it/s] 22%|██▏ | 11/50 [00:02<00:05, 6.86it/s] 24%|██▍ | 12/50 [00:02<00:05, 6.93it/s] 26%|██▌ | 13/50 [00:02<00:05, 6.99it/s] 28%|██▊ | 14/50 [00:02<00:05, 7.02it/s] 30%|███ | 15/50 [00:02<00:04, 7.05it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.07it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.08it/s] 36%|███▌ | 18/50 [00:03<00:04, 7.09it/s] 38%|███▊ | 19/50 [00:03<00:04, 7.10it/s] 40%|████ | 20/50 [00:03<00:04, 7.11it/s] 42%|████▏ | 21/50 [00:03<00:04, 7.11it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.10it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.09it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.09it/s] 50%|█████ | 25/50 [00:04<00:03, 7.08it/s] 52%|█████▏ | 26/50 [00:04<00:03, 7.08it/s] 54%|█████▍ | 27/50 [00:04<00:03, 7.08it/s] 56%|█████▌ | 28/50 [00:04<00:03, 7.08it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.07it/s] 60%|██████ | 30/50 [00:04<00:02, 7.07it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.07it/s] 64%|██████▍ | 32/50 [00:05<00:02, 7.08it/s] 66%|██████▌ | 33/50 [00:05<00:02, 7.07it/s] 68%|██████▊ | 34/50 [00:05<00:02, 7.07it/s] 70%|███████ | 35/50 [00:05<00:02, 7.07it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.08it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.07it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.07it/s] 78%|███████▊ | 39/50 [00:06<00:01, 7.06it/s] 80%|████████ | 40/50 [00:06<00:01, 7.06it/s] 82%|████████▏ | 41/50 [00:06<00:01, 7.05it/s] 84%|████████▍ | 42/50 [00:06<00:01, 7.05it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.05it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.06it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.06it/s] 92%|█████████▏| 46/50 [00:07<00:00, 7.06it/s] 94%|█████████▍| 47/50 [00:07<00:00, 7.06it/s] 96%|█████████▌| 48/50 [00:07<00:00, 7.06it/s] 98%|█████████▊| 49/50 [00:07<00:00, 7.06it/s] 100%|██████████| 50/50 [00:07<00:00, 7.06it/s] 100%|██████████| 50/50 [00:07<00:00, 6.53it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDuxcieftbwt7l3uwwy57qyar3jiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, pompeii, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, pompeii, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, pompeii, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, pompeii, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, pompeii, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-07T09:38:23.100764Z", "created_at": "2023-09-07T09:38:15.136812Z", "data_removed": false, "error": null, "id": "uxcieftbwt7l3uwwy57qyar3ji", "input": { "width": 1024, "height": 512, "prompt": "hdri view, pompeii, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 59911\nPrompt: hdri view, pompeii, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.13it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.10it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.09it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.09it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.09it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.08it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.08it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.09it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.09it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.09it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.09it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.10it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.12it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.12it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.12it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.13it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.13it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.13it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.13it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.13it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.13it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.13it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.13it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.13it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.13it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.13it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.13it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.13it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.13it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.13it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.12it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.12it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.11it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.12it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.12it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.11it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.11it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.11it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.11it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.11it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.11it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.11it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.11it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.11it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.11it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.11it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.11it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.12it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.11it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.12it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.11it/s]", "metrics": { "predict_time": 8.000564, "total_time": 7.963952 }, "output": [ "https://pbxt.replicate.delivery/RXBz8pwf6tVhekGt1OPbKcm6sfXewnaHL7ebJQe8CguiDMdYE/out-0.png" ], "started_at": "2023-09-07T09:38:15.100200Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uxcieftbwt7l3uwwy57qyar3ji", "cancel": "https://api.replicate.com/v1/predictions/uxcieftbwt7l3uwwy57qyar3ji/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 59911 Prompt: hdri view, pompeii, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.13it/s] 4%|▍ | 2/50 [00:00<00:06, 7.10it/s] 6%|▌ | 3/50 [00:00<00:06, 7.09it/s] 8%|▊ | 4/50 [00:00<00:06, 7.09it/s] 10%|█ | 5/50 [00:00<00:06, 7.09it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.08it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.08it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.09it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.09it/s] 20%|██ | 10/50 [00:01<00:05, 7.09it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.09it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.10it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.12it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.12it/s] 30%|███ | 15/50 [00:02<00:04, 7.12it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.13it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.13it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.13it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.13it/s] 40%|████ | 20/50 [00:02<00:04, 7.13it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.13it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.13it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.13it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.13it/s] 50%|█████ | 25/50 [00:03<00:03, 7.13it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.13it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.13it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.13it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.13it/s] 60%|██████ | 30/50 [00:04<00:02, 7.13it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.12it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.12it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.11it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.12it/s] 70%|███████ | 35/50 [00:04<00:02, 7.12it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.11it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.11it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.11it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.11it/s] 80%|████████ | 40/50 [00:05<00:01, 7.11it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.11it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.11it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.11it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.11it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.11it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.11it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.11it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.12it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.11it/s] 100%|██████████| 50/50 [00:07<00:00, 7.12it/s] 100%|██████████| 50/50 [00:07<00:00, 7.11it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDt3sbbttbawuc4dngsxxkxp2c34StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, futurist, science fiction, spaceship, Martial canyon, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, futurist, science fiction, spaceship, Martial canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, futurist, science fiction, spaceship, Martial canyon, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, futurist, science fiction, spaceship, Martial canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, futurist, science fiction, spaceship, Martial canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-07T09:41:10.647493Z", "created_at": "2023-09-07T09:41:02.639964Z", "data_removed": false, "error": null, "id": "t3sbbttbawuc4dngsxxkxp2c34", "input": { "width": 1024, "height": 512, "prompt": "hdri view, futurist, science fiction, spaceship, Martial canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 15631\nPrompt: hdri view, futurist, science fiction, spaceship, Martial canyon, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.13it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.11it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.10it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.08it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.07it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.08it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.08it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.08it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.08it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.08it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.07it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.07it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.07it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.07it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.07it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.06it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.06it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.06it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.06it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.07it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.09it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.10it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.11it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.11it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.11it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.11it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.11it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.11it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.11it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.12it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.12it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.12it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.12it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.12it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.12it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.13it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.12it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.12it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.11it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.10it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.10it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.10it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.10it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.09it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.09it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.09it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.09it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.10it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 6.77it/s]\n100%|██████████| 50/50 [00:07<00:00, 6.87it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.07it/s]", "metrics": { "predict_time": 8.061723, "total_time": 8.007529 }, "output": [ "https://pbxt.replicate.delivery/7VyCkC3CFQo5A1yDkTEGmDOFl21z7SRfaTMssbXWOoabZ6wIA/out-0.png" ], "started_at": "2023-09-07T09:41:02.585770Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/t3sbbttbawuc4dngsxxkxp2c34", "cancel": "https://api.replicate.com/v1/predictions/t3sbbttbawuc4dngsxxkxp2c34/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 15631 Prompt: hdri view, futurist, science fiction, spaceship, Martial canyon, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.13it/s] 4%|▍ | 2/50 [00:00<00:06, 7.11it/s] 6%|▌ | 3/50 [00:00<00:06, 7.10it/s] 8%|▊ | 4/50 [00:00<00:06, 7.08it/s] 10%|█ | 5/50 [00:00<00:06, 7.07it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.08it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.08it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.08it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.08it/s] 20%|██ | 10/50 [00:01<00:05, 7.08it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.07it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.07it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.07it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.07it/s] 30%|███ | 15/50 [00:02<00:04, 7.07it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.06it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.06it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.06it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.06it/s] 40%|████ | 20/50 [00:02<00:04, 7.07it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.09it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.10it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.11it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.11it/s] 50%|█████ | 25/50 [00:03<00:03, 7.11it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.11it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.11it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.11it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.11it/s] 60%|██████ | 30/50 [00:04<00:02, 7.12it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.12it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.12it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.12it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.12it/s] 70%|███████ | 35/50 [00:04<00:02, 7.12it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.13it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.12it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.12it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.11it/s] 80%|████████ | 40/50 [00:05<00:01, 7.10it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.10it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.10it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.10it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.09it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.09it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.09it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.09it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.10it/s] 98%|█████████▊| 49/50 [00:06<00:00, 6.77it/s] 100%|██████████| 50/50 [00:07<00:00, 6.87it/s] 100%|██████████| 50/50 [00:07<00:00, 7.07it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDmmguxw3bawqhbmgx27usxxd2y4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, inside the cantina in star wars, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, inside the cantina in star wars, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, inside the cantina in star wars, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, inside the cantina in star wars, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, inside the cantina in star wars, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-07T09:42:36.106243Z", "created_at": "2023-09-07T09:42:27.951052Z", "data_removed": false, "error": null, "id": "mmguxw3bawqhbmgx27usxxd2y4", "input": { "width": 1024, "height": 512, "prompt": "hdri view, inside the cantina in star wars, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 38864\nPrompt: hdri view, inside the cantina in star wars, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.13it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.10it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.08it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.08it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.08it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.07it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.07it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.07it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.07it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.07it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.06it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.06it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.06it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.06it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.06it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.07it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.09it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.10it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.10it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.10it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.10it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.10it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.10it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.11it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.11it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.11it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.11it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.11it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.11it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.10it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.10it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.11it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.10it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.09it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.09it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.10it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.09it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.08it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.05it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.01it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 6.96it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.00it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.03it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.05it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.06it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.06it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.08it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.09it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.09it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.09it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.08it/s]", "metrics": { "predict_time": 8.187582, "total_time": 8.155191 }, "output": [ "https://pbxt.replicate.delivery/9Vgk74LVGxpxHlIsuzUARZkSxPPcEWLsgUnvwTrjgekFa6wIA/out-0.png" ], "started_at": "2023-09-07T09:42:27.918661Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mmguxw3bawqhbmgx27usxxd2y4", "cancel": "https://api.replicate.com/v1/predictions/mmguxw3bawqhbmgx27usxxd2y4/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 38864 Prompt: hdri view, inside the cantina in star wars, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.13it/s] 4%|▍ | 2/50 [00:00<00:06, 7.10it/s] 6%|▌ | 3/50 [00:00<00:06, 7.08it/s] 8%|▊ | 4/50 [00:00<00:06, 7.08it/s] 10%|█ | 5/50 [00:00<00:06, 7.08it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.07it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.07it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.07it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.07it/s] 20%|██ | 10/50 [00:01<00:05, 7.07it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.06it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.06it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.06it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.06it/s] 30%|███ | 15/50 [00:02<00:04, 7.06it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.07it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.09it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.10it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.10it/s] 40%|████ | 20/50 [00:02<00:04, 7.10it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.10it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.10it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.10it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.11it/s] 50%|█████ | 25/50 [00:03<00:03, 7.11it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.11it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.11it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.11it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.11it/s] 60%|██████ | 30/50 [00:04<00:02, 7.10it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.10it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.11it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.10it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.09it/s] 70%|███████ | 35/50 [00:04<00:02, 7.09it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.10it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.09it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.08it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.05it/s] 80%|████████ | 40/50 [00:05<00:01, 7.01it/s] 82%|████████▏ | 41/50 [00:05<00:01, 6.96it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.00it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.03it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.05it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.06it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.06it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.08it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.09it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.09it/s] 100%|██████████| 50/50 [00:07<00:00, 7.09it/s] 100%|██████████| 50/50 [00:07<00:00, 7.08it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDtcejen3bnvvzsuf3yhyhwouk7uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, inside mos eisley, in star wars, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, inside mos eisley, in star wars, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, inside mos eisley, in star wars, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, inside mos eisley, in star wars, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, inside mos eisley, in star wars, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-07T09:43:17.392589Z", "created_at": "2023-09-07T09:43:09.380930Z", "data_removed": false, "error": null, "id": "tcejen3bnvvzsuf3yhyhwouk7u", "input": { "width": 1024, "height": 512, "prompt": "hdri view, inside mos eisley, in star wars, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5116\nPrompt: hdri view, inside mos eisley, in star wars, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.14it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.12it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.10it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.09it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.09it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.08it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.07it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.07it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.07it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.07it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.07it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.07it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.07it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.07it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.07it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.07it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.07it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.07it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.07it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.06it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.06it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.07it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.08it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.09it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.09it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.10it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.10it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.10it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.11it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.11it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.11it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.12it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.12it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.12it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.12it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.12it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.12it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.11it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.11it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.11it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.08it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.09it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.10it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.10it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.10it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.10it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.10it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.10it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.09it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.09it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.09it/s]", "metrics": { "predict_time": 8.053214, "total_time": 8.011659 }, "output": [ "https://pbxt.replicate.delivery/GCv1Mg4ZBpa8EtE8Tt9QxT2wetbBH4xd567SbIA9gnSaa6wIA/out-0.png" ], "started_at": "2023-09-07T09:43:09.339375Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tcejen3bnvvzsuf3yhyhwouk7u", "cancel": "https://api.replicate.com/v1/predictions/tcejen3bnvvzsuf3yhyhwouk7u/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 5116 Prompt: hdri view, inside mos eisley, in star wars, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.14it/s] 4%|▍ | 2/50 [00:00<00:06, 7.12it/s] 6%|▌ | 3/50 [00:00<00:06, 7.10it/s] 8%|▊ | 4/50 [00:00<00:06, 7.09it/s] 10%|█ | 5/50 [00:00<00:06, 7.09it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.08it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.07it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.07it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.07it/s] 20%|██ | 10/50 [00:01<00:05, 7.07it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.07it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.07it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.07it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.07it/s] 30%|███ | 15/50 [00:02<00:04, 7.07it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.07it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.07it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.07it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.07it/s] 40%|████ | 20/50 [00:02<00:04, 7.06it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.06it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.07it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.08it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.09it/s] 50%|█████ | 25/50 [00:03<00:03, 7.09it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.10it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.10it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.10it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.11it/s] 60%|██████ | 30/50 [00:04<00:02, 7.11it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.11it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.12it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.12it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.12it/s] 70%|███████ | 35/50 [00:04<00:02, 7.12it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.12it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.12it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.11it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.11it/s] 80%|████████ | 40/50 [00:05<00:01, 7.11it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.08it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.09it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.10it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.10it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.10it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.10it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.10it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.10it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.09it/s] 100%|██████████| 50/50 [00:07<00:00, 7.09it/s] 100%|██████████| 50/50 [00:07<00:00, 7.09it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDyzqvualbul3dvvtkuwsaggc37qStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, panorama of new york, from a rooftop, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, panorama of new york, from a rooftop, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, panorama of new york, from a rooftop, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, panorama of new york, from a rooftop, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, panorama of new york, from a rooftop, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-07T14:28:19.205017Z", "created_at": "2023-09-07T14:28:11.232414Z", "data_removed": false, "error": null, "id": "yzqvualbul3dvvtkuwsaggc37q", "input": { "width": 1024, "height": 512, "prompt": "hdri view, panorama of new york, from a rooftop, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 43973\nPrompt: hdri view, panorama of new york, from a rooftop, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.06it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.07it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.06it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.06it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.05it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.05it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.06it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.07it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.07it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.06it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.06it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.06it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.06it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.05it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.05it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.05it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.04it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.04it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.05it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.05it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.04it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.04it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.03it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.03it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.02it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.02it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.02it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.02it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.02it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.03it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.02it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.02it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.02it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.01it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.02it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.02it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.02it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.02it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.01it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.01it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.00it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.00it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.00it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.00it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.00it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.00it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.00it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.00it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.00it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.01it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.03it/s]", "metrics": { "predict_time": 8.118276, "total_time": 7.972603 }, "output": [ "https://pbxt.replicate.delivery/i3lX7nbRI6bkGp31S8TS7hRZkV1Mqgdo7hMLzw63bmsAQewIA/out-0.png" ], "started_at": "2023-09-07T14:28:11.086741Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yzqvualbul3dvvtkuwsaggc37q", "cancel": "https://api.replicate.com/v1/predictions/yzqvualbul3dvvtkuwsaggc37q/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 43973 Prompt: hdri view, panorama of new york, from a rooftop, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.06it/s] 4%|▍ | 2/50 [00:00<00:06, 7.07it/s] 6%|▌ | 3/50 [00:00<00:06, 7.06it/s] 8%|▊ | 4/50 [00:00<00:06, 7.06it/s] 10%|█ | 5/50 [00:00<00:06, 7.05it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.05it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.06it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.07it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.07it/s] 20%|██ | 10/50 [00:01<00:05, 7.06it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.06it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.06it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.06it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.05it/s] 30%|███ | 15/50 [00:02<00:04, 7.05it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.05it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.04it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.04it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.05it/s] 40%|████ | 20/50 [00:02<00:04, 7.05it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.04it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.04it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.03it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.03it/s] 50%|█████ | 25/50 [00:03<00:03, 7.02it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.02it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.02it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.02it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.02it/s] 60%|██████ | 30/50 [00:04<00:02, 7.03it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.02it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.02it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.02it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.01it/s] 70%|███████ | 35/50 [00:04<00:02, 7.02it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.02it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.02it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.02it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.01it/s] 80%|████████ | 40/50 [00:05<00:01, 7.01it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.00it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.00it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.00it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.00it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.00it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.00it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.00it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.00it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.00it/s] 100%|██████████| 50/50 [00:07<00:00, 7.01it/s] 100%|██████████| 50/50 [00:07<00:00, 7.03it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDl5g47utbpleinnfgut42bccwuuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, panorama of busy street, in new york, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, panorama of busy street, in new york, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, panorama of busy street, in new york, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, panorama of busy street, in new york, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, panorama of busy street, in new york, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-07T14:29:48.232966Z", "created_at": "2023-09-07T14:29:40.147225Z", "data_removed": false, "error": null, "id": "l5g47utbpleinnfgut42bccwuu", "input": { "width": 1024, "height": 512, "prompt": "hdri view, panorama of busy street, in new york, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 15114\nPrompt: hdri view, panorama of busy street, in new york, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.10it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.09it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.07it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.07it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.07it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.06it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.06it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.06it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.05it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.05it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.05it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.05it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.04it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.04it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.05it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.05it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.04it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.03it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.02it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.02it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.03it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.04it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.04it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.04it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.03it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.02it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.02it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.02it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.02it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.02it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.03it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.02it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.02it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.01it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.01it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.01it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.02it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.02it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.03it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.03it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.02it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.02it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.02it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.01it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.01it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.01it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.01it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.01it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.01it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.01it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.03it/s]", "metrics": { "predict_time": 8.138818, "total_time": 8.085741 }, "output": [ "https://pbxt.replicate.delivery/QCnQ3lJ8O8ZyHdfIyJuDGvRUMHlUzIwWifrwX9QrpgvbB5hRA/out-0.png" ], "started_at": "2023-09-07T14:29:40.094148Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/l5g47utbpleinnfgut42bccwuu", "cancel": "https://api.replicate.com/v1/predictions/l5g47utbpleinnfgut42bccwuu/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 15114 Prompt: hdri view, panorama of busy street, in new york, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.10it/s] 4%|▍ | 2/50 [00:00<00:06, 7.09it/s] 6%|▌ | 3/50 [00:00<00:06, 7.07it/s] 8%|▊ | 4/50 [00:00<00:06, 7.07it/s] 10%|█ | 5/50 [00:00<00:06, 7.07it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.06it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.06it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.06it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.05it/s] 20%|██ | 10/50 [00:01<00:05, 7.05it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.05it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.05it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.04it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.04it/s] 30%|███ | 15/50 [00:02<00:04, 7.05it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.05it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.04it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.03it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.02it/s] 40%|████ | 20/50 [00:02<00:04, 7.02it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.03it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.04it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.04it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.04it/s] 50%|█████ | 25/50 [00:03<00:03, 7.03it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.02it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.02it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.02it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.02it/s] 60%|██████ | 30/50 [00:04<00:02, 7.02it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.03it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.02it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.02it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.01it/s] 70%|███████ | 35/50 [00:04<00:02, 7.01it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.01it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.02it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.02it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.03it/s] 80%|████████ | 40/50 [00:05<00:01, 7.03it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.02it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.02it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.02it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.01it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.01it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.01it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.01it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.01it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.01it/s] 100%|██████████| 50/50 [00:07<00:00, 7.01it/s] 100%|██████████| 50/50 [00:07<00:00, 7.03it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDogwbphlb6kwwb2pl4yyau376bmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri view, panorama of new york city in 1880, street view, pedestrians in old clothes, horses, carriages, victorian, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred, 3D 3D render, videogame, CGI
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri view, panorama of new york city in 1880, street view, pedestrians in old clothes, horses, carriages, victorian, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred, 3D 3D render, videogame, CGI", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri view, panorama of new york city in 1880, street view, pedestrians in old clothes, horses, carriages, victorian, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred, 3D 3D render, videogame, CGI", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri view, panorama of new york city in 1880, street view, pedestrians in old clothes, horses, carriages, victorian, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred, 3D 3D render, videogame, CGI", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri view, panorama of new york city in 1880, street view, pedestrians in old clothes, horses, carriages, victorian, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred, 3D 3D render, videogame, CGI", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-07T14:42:44.611086Z", "created_at": "2023-09-07T14:42:36.517015Z", "data_removed": false, "error": null, "id": "ogwbphlb6kwwb2pl4yyau376bm", "input": { "width": 1024, "height": 512, "prompt": "hdri view, panorama of new york city in 1880, street view, pedestrians in old clothes, horses, carriages, victorian, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred, 3D 3D render, videogame, CGI", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 44392\nPrompt: hdri view, panorama of new york city in 1880, street view, pedestrians in old clothes, horses, carriages, victorian, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.08it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.06it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.04it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.04it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.03it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.03it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.03it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.02it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.02it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.02it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.02it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.02it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.02it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.02it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.01it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.01it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.01it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.01it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.01it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.02it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.02it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.02it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.02it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.01it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.00it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.01it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.01it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.01it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.01it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.01it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.02it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.03it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.04it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.04it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.05it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.05it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.05it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.05it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.05it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.05it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.05it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.04it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.04it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.04it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.04it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.04it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.04it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.04it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.04it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.04it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.03it/s]", "metrics": { "predict_time": 8.135932, "total_time": 8.094071 }, "output": [ "https://pbxt.replicate.delivery/B5BDeYbUVc12KimOvfOWfw7AxBO4zteQBiRBwb4dzCtS2kHGB/out-0.png" ], "started_at": "2023-09-07T14:42:36.475154Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ogwbphlb6kwwb2pl4yyau376bm", "cancel": "https://api.replicate.com/v1/predictions/ogwbphlb6kwwb2pl4yyau376bm/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 44392 Prompt: hdri view, panorama of new york city in 1880, street view, pedestrians in old clothes, horses, carriages, victorian, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.08it/s] 4%|▍ | 2/50 [00:00<00:06, 7.06it/s] 6%|▌ | 3/50 [00:00<00:06, 7.04it/s] 8%|▊ | 4/50 [00:00<00:06, 7.04it/s] 10%|█ | 5/50 [00:00<00:06, 7.03it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.03it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.03it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.02it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.02it/s] 20%|██ | 10/50 [00:01<00:05, 7.02it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.02it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.02it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.02it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.02it/s] 30%|███ | 15/50 [00:02<00:04, 7.01it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.01it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.01it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.01it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.01it/s] 40%|████ | 20/50 [00:02<00:04, 7.02it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.02it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.02it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.02it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.01it/s] 50%|█████ | 25/50 [00:03<00:03, 7.00it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.01it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.01it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.01it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.01it/s] 60%|██████ | 30/50 [00:04<00:02, 7.01it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.02it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.03it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.04it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.04it/s] 70%|███████ | 35/50 [00:04<00:02, 7.05it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.05it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.05it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.05it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.05it/s] 80%|████████ | 40/50 [00:05<00:01, 7.05it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.05it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.04it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.04it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.04it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.04it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.04it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.04it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.04it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.04it/s] 100%|██████████| 50/50 [00:07<00:00, 7.04it/s] 100%|██████████| 50/50 [00:07<00:00, 7.03it/s]
Prediction
jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8faIDvqtufalbxzcrqwl5y6xuy4up4aStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 512
- prompt
- hdri panorama image a xwing landing on tatooine, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed, blur, blurry, blurred
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 512, "prompt": "hdri panorama image a xwing landing on tatooine, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", { input: { width: 1024, height: 512, prompt: "hdri panorama image a xwing landing on tatooine, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed, blur, blurry, blurred", 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 replicate
Import the client:import replicate
Run jbilcke/sdxl-panorama using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", input={ "width": 1024, "height": 512, "prompt": "hdri panorama image a xwing landing on tatooine, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "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 jbilcke/sdxl-panorama 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": "jbilcke/sdxl-panorama:fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa", "input": { "width": 1024, "height": 512, "prompt": "hdri panorama image a xwing landing on tatooine, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-08T14:50:38.947091Z", "created_at": "2023-09-08T14:50:30.275259Z", "data_removed": false, "error": null, "id": "vqtufalbxzcrqwl5y6xuy4up4a", "input": { "width": 1024, "height": 512, "prompt": "hdri panorama image a xwing landing on tatooine, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed, blur, blurry, blurred", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 8448\nPrompt: hdri panorama image a xwing landing on tatooine, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.09it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.08it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.06it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.05it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.05it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 7.04it/s]\n 14%|█▍ | 7/50 [00:00<00:06, 7.04it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.05it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.05it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.05it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 7.05it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 7.05it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 7.04it/s]\n 28%|██▊ | 14/50 [00:01<00:05, 7.04it/s]\n 30%|███ | 15/50 [00:02<00:04, 7.06it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.06it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.06it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.08it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 7.08it/s]\n 40%|████ | 20/50 [00:02<00:04, 7.07it/s]\n 42%|████▏ | 21/50 [00:02<00:04, 7.07it/s]\n 44%|████▍ | 22/50 [00:03<00:03, 7.08it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 7.07it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.07it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.07it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.07it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.06it/s]\n 56%|█████▌ | 28/50 [00:03<00:03, 7.06it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 7.06it/s]\n 60%|██████ | 30/50 [00:04<00:02, 7.06it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 7.06it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.06it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.06it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.07it/s]\n 70%|███████ | 35/50 [00:04<00:02, 7.07it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 7.07it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 7.06it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 7.06it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 7.06it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.05it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.05it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.05it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.06it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.06it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.06it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 7.06it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 7.06it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.06it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.05it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.05it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.06it/s]", "metrics": { "predict_time": 8.704183, "total_time": 8.671832 }, "output": [ "https://pbxt.replicate.delivery/er55YtVeBGsFbEvixIOQiQKjBCnHnDELDpd8jH7fisW81cEjA/out-0.png" ], "started_at": "2023-09-08T14:50:30.242908Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vqtufalbxzcrqwl5y6xuy4up4a", "cancel": "https://api.replicate.com/v1/predictions/vqtufalbxzcrqwl5y6xuy4up4a/cancel" }, "version": "fd1f53277a424000168dfceeef58934c43bb3c27a3843a75e7b6d72c46cfc8fa" }
Generated inUsing seed: 8448 Prompt: hdri panorama image a xwing landing on tatooine, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.09it/s] 4%|▍ | 2/50 [00:00<00:06, 7.08it/s] 6%|▌ | 3/50 [00:00<00:06, 7.06it/s] 8%|▊ | 4/50 [00:00<00:06, 7.05it/s] 10%|█ | 5/50 [00:00<00:06, 7.05it/s] 12%|█▏ | 6/50 [00:00<00:06, 7.04it/s] 14%|█▍ | 7/50 [00:00<00:06, 7.04it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.05it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.05it/s] 20%|██ | 10/50 [00:01<00:05, 7.05it/s] 22%|██▏ | 11/50 [00:01<00:05, 7.05it/s] 24%|██▍ | 12/50 [00:01<00:05, 7.05it/s] 26%|██▌ | 13/50 [00:01<00:05, 7.04it/s] 28%|██▊ | 14/50 [00:01<00:05, 7.04it/s] 30%|███ | 15/50 [00:02<00:04, 7.06it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.06it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.06it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.08it/s] 38%|███▊ | 19/50 [00:02<00:04, 7.08it/s] 40%|████ | 20/50 [00:02<00:04, 7.07it/s] 42%|████▏ | 21/50 [00:02<00:04, 7.07it/s] 44%|████▍ | 22/50 [00:03<00:03, 7.08it/s] 46%|████▌ | 23/50 [00:03<00:03, 7.07it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.07it/s] 50%|█████ | 25/50 [00:03<00:03, 7.07it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.07it/s] 54%|█████▍ | 27/50 [00:03<00:03, 7.06it/s] 56%|█████▌ | 28/50 [00:03<00:03, 7.06it/s] 58%|█████▊ | 29/50 [00:04<00:02, 7.06it/s] 60%|██████ | 30/50 [00:04<00:02, 7.06it/s] 62%|██████▏ | 31/50 [00:04<00:02, 7.06it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.06it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.06it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.07it/s] 70%|███████ | 35/50 [00:04<00:02, 7.07it/s] 72%|███████▏ | 36/50 [00:05<00:01, 7.07it/s] 74%|███████▍ | 37/50 [00:05<00:01, 7.06it/s] 76%|███████▌ | 38/50 [00:05<00:01, 7.06it/s] 78%|███████▊ | 39/50 [00:05<00:01, 7.06it/s] 80%|████████ | 40/50 [00:05<00:01, 7.05it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.05it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.05it/s] 86%|████████▌ | 43/50 [00:06<00:00, 7.06it/s] 88%|████████▊ | 44/50 [00:06<00:00, 7.06it/s] 90%|█████████ | 45/50 [00:06<00:00, 7.06it/s] 92%|█████████▏| 46/50 [00:06<00:00, 7.06it/s] 94%|█████████▍| 47/50 [00:06<00:00, 7.06it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.06it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.05it/s] 100%|██████████| 50/50 [00:07<00:00, 7.05it/s] 100%|██████████| 50/50 [00:07<00:00, 7.06it/s]
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