rileyhacks007 / sdxl-national-park-posters
Creates Images Based on the 1930's US National Park Posters
- Public
- 559 runs
-
L40S
- SDXL fine-tune
Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecID6jjkkstbgdexxsdipg35dj2t7iStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, mount Everest
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, mount Everest ", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, mount Everest ", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, mount Everest ", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, mount Everest ", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:15:37.646894Z", "created_at": "2023-09-12T23:15:22.540912Z", "data_removed": false, "error": null, "id": "6jjkkstbgdexxsdipg35dj2t7i", "input": { "width": 1024, "height": 1024, "prompt": "park style, mount Everest ", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 29526\nPrompt: park style, mount Everest\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.69it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.68it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.68it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.67it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s]\n 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.67it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.67it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.67it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.68it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.68it/s]\n 45%|████▌ | 18/40 [00:04<00:05, 3.68it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.68it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.68it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.67it/s]\n 55%|█████▌ | 22/40 [00:05<00:04, 3.67it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.67it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.67it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.67it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.67it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.67it/s]\n 72%|███████▎ | 29/40 [00:07<00:02, 3.67it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.67it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.67it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.67it/s]\n 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.67it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.67it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.34it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.32it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.32it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.31it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.30it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.31it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.31it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.31it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]", "metrics": { "predict_time": 15.125909, "total_time": 15.105982 }, "output": [ "https://replicate.delivery/pbxt/KsJiZoVnpWJgNtVhUDztQkrFITCbAnRwYOAIeNU8pfyYMqjRA/out-0.png" ], "started_at": "2023-09-12T23:15:22.520985Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6jjkkstbgdexxsdipg35dj2t7i", "cancel": "https://api.replicate.com/v1/predictions/6jjkkstbgdexxsdipg35dj2t7i/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 29526 Prompt: park style, mount Everest txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.69it/s] 5%|▌ | 2/40 [00:00<00:10, 3.68it/s] 8%|▊ | 3/40 [00:00<00:10, 3.68it/s] 10%|█ | 4/40 [00:01<00:09, 3.67it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s] 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s] 20%|██ | 8/40 [00:02<00:08, 3.67it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s] 30%|███ | 12/40 [00:03<00:07, 3.67it/s] 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90%|█████████ | 36/40 [00:09<00:01, 3.67it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.34it/s] 20%|██ | 2/10 [00:00<00:01, 4.32it/s] 30%|███ | 3/10 [00:00<00:01, 4.32it/s] 40%|████ | 4/10 [00:00<00:01, 4.31it/s] 50%|█████ | 5/10 [00:01<00:01, 4.30it/s] 60%|██████ | 6/10 [00:01<00:00, 4.31it/s] 70%|███████ | 7/10 [00:01<00:00, 4.31it/s] 80%|████████ | 8/10 [00:01<00:00, 4.31it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecID65s4233bfiu3mytxfutcfyzsuyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, Banff national park poster
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, Banff national park poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, Banff national park poster", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, Banff national park poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, Banff national park poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:20:09.751479Z", "created_at": "2023-09-12T23:19:54.499584Z", "data_removed": false, "error": null, "id": "65s4233bfiu3mytxfutcfyzsuy", "input": { "width": 1024, "height": 1024, "prompt": "park style, Banff national park poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 39776\nPrompt: park style, Banff national park poster\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.69it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.69it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.68it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.67it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.66it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.66it/s]\n 18%|█▊ | 7/40 [00:01<00:09, 3.66it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.66it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.66it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s]\n 28%|██▊ | 11/40 [00:03<00:07, 3.66it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.66it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.67it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.68it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.68it/s]\n 45%|████▌ | 18/40 [00:04<00:05, 3.68it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.68it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.68it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.68it/s]\n 55%|█████▌ | 22/40 [00:05<00:04, 3.68it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.68it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.68it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.68it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.68it/s]\n 72%|███████▎ | 29/40 [00:07<00:02, 3.68it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.68it/s]\n 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.68it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.67it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.34it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.31it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.31it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.31it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.31it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.31it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.30it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]", "metrics": { "predict_time": 15.286049, "total_time": 15.251895 }, "output": [ "https://replicate.delivery/pbxt/fRY6QdYMQy0hdiCIuvledbyftUvVxCN42J3F3OgXqwBQhUHjA/out-0.png" ], "started_at": "2023-09-12T23:19:54.465430Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/65s4233bfiu3mytxfutcfyzsuy", "cancel": "https://api.replicate.com/v1/predictions/65s4233bfiu3mytxfutcfyzsuy/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 39776 Prompt: park style, Banff national park poster txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.69it/s] 5%|▌ | 2/40 [00:00<00:10, 3.69it/s] 8%|▊ | 3/40 [00:00<00:10, 3.68it/s] 10%|█ | 4/40 [00:01<00:09, 3.67it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.66it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.66it/s] 18%|█▊ | 7/40 [00:01<00:09, 3.66it/s] 20%|██ | 8/40 [00:02<00:08, 3.66it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.66it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s] 28%|██▊ | 11/40 [00:03<00:07, 3.66it/s] 30%|███ | 12/40 [00:03<00:07, 3.66it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.67it/s] 40%|████ | 16/40 [00:04<00:06, 3.68it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.68it/s] 45%|████▌ | 18/40 [00:04<00:05, 3.68it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.68it/s] 50%|█████ | 20/40 [00:05<00:05, 3.68it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.68it/s] 55%|█████▌ | 22/40 [00:05<00:04, 3.68it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.68it/s] 60%|██████ | 24/40 [00:06<00:04, 3.68it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.68it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s] 70%|███████ | 28/40 [00:07<00:03, 3.68it/s] 72%|███████▎ | 29/40 [00:07<00:02, 3.68it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s] 80%|████████ | 32/40 [00:08<00:02, 3.68it/s] 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.68it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.67it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.34it/s] 20%|██ | 2/10 [00:00<00:01, 4.33it/s] 30%|███ | 3/10 [00:00<00:01, 4.31it/s] 40%|████ | 4/10 [00:00<00:01, 4.31it/s] 50%|█████ | 5/10 [00:01<00:01, 4.31it/s] 60%|██████ | 6/10 [00:01<00:00, 4.31it/s] 70%|███████ | 7/10 [00:01<00:00, 4.31it/s] 80%|████████ | 8/10 [00:01<00:00, 4.30it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecID2lfnh73bsomegm6yuk2mfnm3luStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, Yellowstone poster
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, Yellowstone poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, Yellowstone poster", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, Yellowstone poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, Yellowstone poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:21:02.279577Z", "created_at": "2023-09-12T23:20:47.081673Z", "data_removed": false, "error": null, "id": "2lfnh73bsomegm6yuk2mfnm3lu", "input": { "width": 1024, "height": 1024, "prompt": "park style, Yellowstone poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 18273\nPrompt: park style, Yellowstone poster\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.69it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.69it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.68it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.67it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s]\n 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.67it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.66it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.66it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.66it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s]\n 45%|████▌ | 18/40 [00:04<00:05, 3.67it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.67it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.68it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.67it/s]\n 55%|█████▌ | 22/40 [00:05<00:04, 3.68it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.68it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.68it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.68it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.68it/s]\n 72%|███████▎ | 29/40 [00:07<00:02, 3.68it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.68it/s]\n 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.68it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.68it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.34it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.32it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.31it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.31it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.31it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.31it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.31it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]", "metrics": { "predict_time": 15.193523, "total_time": 15.197904 }, "output": [ "https://replicate.delivery/pbxt/PtoCrCeqIenZG0e1EZFwVRyu1zzvnVenE34ziXrASz52FpOGB/out-0.png" ], "started_at": "2023-09-12T23:20:47.086054Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2lfnh73bsomegm6yuk2mfnm3lu", "cancel": "https://api.replicate.com/v1/predictions/2lfnh73bsomegm6yuk2mfnm3lu/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 18273 Prompt: park style, Yellowstone poster txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.69it/s] 5%|▌ | 2/40 [00:00<00:10, 3.69it/s] 8%|▊ | 3/40 [00:00<00:10, 3.68it/s] 10%|█ | 4/40 [00:01<00:09, 3.67it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s] 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s] 20%|██ | 8/40 [00:02<00:08, 3.67it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s] 30%|███ | 12/40 [00:03<00:07, 3.66it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.66it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s] 40%|████ | 16/40 [00:04<00:06, 3.66it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s] 45%|████▌ | 18/40 [00:04<00:05, 3.67it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.67it/s] 50%|█████ | 20/40 [00:05<00:05, 3.68it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.67it/s] 55%|█████▌ | 22/40 [00:05<00:04, 3.68it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.68it/s] 60%|██████ | 24/40 [00:06<00:04, 3.68it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.68it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s] 70%|███████ | 28/40 [00:07<00:03, 3.68it/s] 72%|███████▎ | 29/40 [00:07<00:02, 3.68it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s] 80%|████████ | 32/40 [00:08<00:02, 3.68it/s] 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.68it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.68it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.34it/s] 20%|██ | 2/10 [00:00<00:01, 4.33it/s] 30%|███ | 3/10 [00:00<00:01, 4.32it/s] 40%|████ | 4/10 [00:00<00:01, 4.31it/s] 50%|█████ | 5/10 [00:01<00:01, 4.31it/s] 60%|██████ | 6/10 [00:01<00:00, 4.31it/s] 70%|███████ | 7/10 [00:01<00:00, 4.31it/s] 80%|████████ | 8/10 [00:01<00:00, 4.31it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecIDfcqv6odbl22djctyw6vp6f3iyaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, denali mountain poster
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, denali mountain poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, denali mountain poster", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, denali mountain poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, denali mountain poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:21:43.990846Z", "created_at": "2023-09-12T23:21:28.881371Z", "data_removed": false, "error": null, "id": "fcqv6odbl22djctyw6vp6f3iya", "input": { "width": 1024, "height": 1024, "prompt": "park style, denali mountain poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 23417\nPrompt: park style, denali mountain poster\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.70it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.69it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.68it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.67it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s]\n 18%|█▊ | 7/40 [00:01<00:09, 3.66it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.66it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.66it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.66it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.67it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.67it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.67it/s]\n 45%|████▌ | 18/40 [00:04<00:05, 3.68it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.68it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.68it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.68it/s]\n 55%|█████▌ | 22/40 [00:05<00:04, 3.68it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.68it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.68it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.68it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.68it/s]\n 72%|███████▎ | 29/40 [00:07<00:02, 3.68it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.68it/s]\n 82%|████████▎ | 33/40 [00:08<00:01, 3.68it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.67it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.68it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.34it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.32it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.31it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.31it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.31it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.31it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.31it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]", "metrics": { "predict_time": 15.152987, "total_time": 15.109475 }, "output": [ "https://replicate.delivery/pbxt/4SHmFG02op4WA9xZgCKVtaRxRJY0fMEOzFLSC6HN3XeHSqjRA/out-0.png" ], "started_at": "2023-09-12T23:21:28.837859Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fcqv6odbl22djctyw6vp6f3iya", "cancel": "https://api.replicate.com/v1/predictions/fcqv6odbl22djctyw6vp6f3iya/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 23417 Prompt: park style, denali mountain poster txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.70it/s] 5%|▌ | 2/40 [00:00<00:10, 3.69it/s] 8%|▊ | 3/40 [00:00<00:10, 3.68it/s] 10%|█ | 4/40 [00:01<00:09, 3.67it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s] 18%|█▊ | 7/40 [00:01<00:09, 3.66it/s] 20%|██ | 8/40 [00:02<00:08, 3.66it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.66it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s] 30%|███ | 12/40 [00:03<00:07, 3.66it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.67it/s] 40%|████ | 16/40 [00:04<00:06, 3.67it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.67it/s] 45%|████▌ | 18/40 [00:04<00:05, 3.68it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.68it/s] 50%|█████ | 20/40 [00:05<00:05, 3.68it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.68it/s] 55%|█████▌ | 22/40 [00:05<00:04, 3.68it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.68it/s] 60%|██████ | 24/40 [00:06<00:04, 3.68it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.68it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s] 70%|███████ | 28/40 [00:07<00:03, 3.68it/s] 72%|███████▎ | 29/40 [00:07<00:02, 3.68it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s] 80%|████████ | 32/40 [00:08<00:02, 3.68it/s] 82%|████████▎ | 33/40 [00:08<00:01, 3.68it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.67it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.68it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.34it/s] 20%|██ | 2/10 [00:00<00:01, 4.33it/s] 30%|███ | 3/10 [00:00<00:01, 4.32it/s] 40%|████ | 4/10 [00:00<00:01, 4.31it/s] 50%|█████ | 5/10 [00:01<00:01, 4.31it/s] 60%|██████ | 6/10 [00:01<00:00, 4.31it/s] 70%|███████ | 7/10 [00:01<00:00, 4.31it/s] 80%|████████ | 8/10 [00:01<00:00, 4.31it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecID5dy746dbilx7zdmdqskvlsnkreStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, new york city poster
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, new york city poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, new york city poster", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, new york city poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, new york city poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:23:06.543294Z", "created_at": "2023-09-12T23:22:51.338980Z", "data_removed": false, "error": null, "id": "5dy746dbilx7zdmdqskvlsnkre", "input": { "width": 1024, "height": 1024, "prompt": "park style, new york city poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 30511\nPrompt: park style, new york city poster\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.71it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.69it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.69it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.68it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.68it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s]\n 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.67it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.67it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.67it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.67it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.66it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s]\n 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.66it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.66it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.66it/s]\n 55%|█████▌ | 22/40 [00:06<00:04, 3.66it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.66it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.66it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.66it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.67it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.67it/s]\n 72%|███████▎ | 29/40 [00:07<00:02, 3.67it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.68it/s]\n 82%|████████▎ | 33/40 [00:08<00:01, 3.68it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.68it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.68it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.68it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.68it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.68it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.68it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.68it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.35it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.32it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.32it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.31it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.31it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.32it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.31it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.31it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.32it/s]", "metrics": { "predict_time": 15.201471, "total_time": 15.204314 }, "output": [ "https://replicate.delivery/pbxt/EE4Z7eStSeprxUVfulu93lkdt8dw7BayhhFDfYiDJZHlNpOGB/out-0.png" ], "started_at": "2023-09-12T23:22:51.341823Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5dy746dbilx7zdmdqskvlsnkre", "cancel": "https://api.replicate.com/v1/predictions/5dy746dbilx7zdmdqskvlsnkre/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 30511 Prompt: park style, new york city poster txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.71it/s] 5%|▌ | 2/40 [00:00<00:10, 3.69it/s] 8%|▊ | 3/40 [00:00<00:10, 3.69it/s] 10%|█ | 4/40 [00:01<00:09, 3.68it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.68it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s] 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s] 20%|██ | 8/40 [00:02<00:08, 3.67it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.67it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.67it/s] 30%|███ | 12/40 [00:03<00:07, 3.67it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s] 40%|████ | 16/40 [00:04<00:06, 3.66it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s] 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.66it/s] 50%|█████ | 20/40 [00:05<00:05, 3.66it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.66it/s] 55%|█████▌ | 22/40 [00:06<00:04, 3.66it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s] 60%|██████ | 24/40 [00:06<00:04, 3.66it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.66it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.66it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.67it/s] 70%|███████ | 28/40 [00:07<00:03, 3.67it/s] 72%|███████▎ | 29/40 [00:07<00:02, 3.67it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s] 80%|████████ | 32/40 [00:08<00:02, 3.68it/s] 82%|████████▎ | 33/40 [00:08<00:01, 3.68it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.68it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.68it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.68it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.68it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.68it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.68it/s] 100%|██████████| 40/40 [00:10<00:00, 3.68it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.35it/s] 20%|██ | 2/10 [00:00<00:01, 4.33it/s] 30%|███ | 3/10 [00:00<00:01, 4.32it/s] 40%|████ | 4/10 [00:00<00:01, 4.32it/s] 50%|█████ | 5/10 [00:01<00:01, 4.31it/s] 60%|██████ | 6/10 [00:01<00:00, 4.31it/s] 70%|███████ | 7/10 [00:01<00:00, 4.32it/s] 80%|████████ | 8/10 [00:01<00:00, 4.31it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.31it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s] 100%|██████████| 10/10 [00:02<00:00, 4.32it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecIDdi6othtb26psmm6srjtkkhsh5mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, Tokyo poster
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, Tokyo poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, Tokyo poster", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, Tokyo poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, Tokyo poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:23:51.831921Z", "created_at": "2023-09-12T23:23:36.667228Z", "data_removed": false, "error": null, "id": "di6othtb26psmm6srjtkkhsh5m", "input": { "width": 1024, "height": 1024, "prompt": "park style, Tokyo poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 23411\nPrompt: park style, Tokyo poster\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.70it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.69it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.68it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.67it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s]\n 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.66it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.66it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.66it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.66it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.66it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s]\n 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.67it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.65it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.64it/s]\n 55%|█████▌ | 22/40 [00:06<00:04, 3.65it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.66it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.67it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.67it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.68it/s]\n 72%|███████▎ | 29/40 [00:07<00:02, 3.68it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.68it/s]\n 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.67it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.67it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.35it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.33it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.31it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.31it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.30it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.30it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.30it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]", "metrics": { "predict_time": 15.167596, "total_time": 15.164693 }, "output": [ "https://replicate.delivery/pbxt/03Pf33sFw4yzeEfdb36asv78RJU7NkIY9Y8zUHJmMxdMoUHjA/out-0.png" ], "started_at": "2023-09-12T23:23:36.664325Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/di6othtb26psmm6srjtkkhsh5m", "cancel": "https://api.replicate.com/v1/predictions/di6othtb26psmm6srjtkkhsh5m/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 23411 Prompt: park style, Tokyo poster txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.70it/s] 5%|▌ | 2/40 [00:00<00:10, 3.69it/s] 8%|▊ | 3/40 [00:00<00:10, 3.68it/s] 10%|█ | 4/40 [00:01<00:09, 3.67it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s] 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s] 20%|██ | 8/40 [00:02<00:08, 3.66it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.66it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s] 30%|███ | 12/40 [00:03<00:07, 3.66it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.66it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s] 40%|████ | 16/40 [00:04<00:06, 3.66it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s] 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.67it/s] 50%|█████ | 20/40 [00:05<00:05, 3.65it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.64it/s] 55%|█████▌ | 22/40 [00:06<00:04, 3.65it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s] 60%|██████ | 24/40 [00:06<00:04, 3.66it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.67it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.67it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s] 70%|███████ | 28/40 [00:07<00:03, 3.68it/s] 72%|███████▎ | 29/40 [00:07<00:02, 3.68it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s] 80%|████████ | 32/40 [00:08<00:02, 3.68it/s] 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.67it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.67it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.35it/s] 20%|██ | 2/10 [00:00<00:01, 4.33it/s] 30%|███ | 3/10 [00:00<00:01, 4.33it/s] 40%|████ | 4/10 [00:00<00:01, 4.31it/s] 50%|█████ | 5/10 [00:01<00:01, 4.31it/s] 60%|██████ | 6/10 [00:01<00:00, 4.30it/s] 70%|███████ | 7/10 [00:01<00:00, 4.30it/s] 80%|████████ | 8/10 [00:01<00:00, 4.30it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecIDiknzomtbeljxrxjsoxftq2d6eeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, san francisco poster
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, san francisco poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, san francisco poster", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, san francisco poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, san francisco poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:24:40.038472Z", "created_at": "2023-09-12T23:24:24.928152Z", "data_removed": false, "error": null, "id": "iknzomtbeljxrxjsoxftq2d6ee", "input": { "width": 1024, "height": 1024, "prompt": "park style, san francisco poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5281\nPrompt: park style, san francisco poster\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.70it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.69it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.68it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.67it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s]\n 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.67it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.66it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.66it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.66it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.66it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s]\n 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.67it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.67it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.67it/s]\n 55%|█████▌ | 22/40 [00:05<00:04, 3.67it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.68it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.67it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.68it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.68it/s]\n 72%|███████▎ | 29/40 [00:07<00:02, 3.67it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.68it/s]\n 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.67it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.67it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.35it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.32it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.31it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.30it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.30it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.31it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.31it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.30it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]", "metrics": { "predict_time": 15.128823, "total_time": 15.11032 }, "output": [ "https://replicate.delivery/pbxt/OEm9efZ51dnlCE5EijqhbfBQei3XlvbtM4xLP2seIeDzNl6YE/out-0.png" ], "started_at": "2023-09-12T23:24:24.909649Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iknzomtbeljxrxjsoxftq2d6ee", "cancel": "https://api.replicate.com/v1/predictions/iknzomtbeljxrxjsoxftq2d6ee/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 5281 Prompt: park style, san francisco poster txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.70it/s] 5%|▌ | 2/40 [00:00<00:10, 3.69it/s] 8%|▊ | 3/40 [00:00<00:10, 3.68it/s] 10%|█ | 4/40 [00:01<00:09, 3.67it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s] 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s] 20%|██ | 8/40 [00:02<00:08, 3.67it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.66it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.66it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s] 30%|███ | 12/40 [00:03<00:07, 3.66it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.66it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s] 40%|████ | 16/40 [00:04<00:06, 3.66it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s] 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.67it/s] 50%|█████ | 20/40 [00:05<00:05, 3.67it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.67it/s] 55%|█████▌ | 22/40 [00:05<00:04, 3.67it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.68it/s] 60%|██████ | 24/40 [00:06<00:04, 3.67it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.68it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.68it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.68it/s] 70%|███████ | 28/40 [00:07<00:03, 3.68it/s] 72%|███████▎ | 29/40 [00:07<00:02, 3.67it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.68it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.68it/s] 80%|████████ | 32/40 [00:08<00:02, 3.68it/s] 82%|████████▎ | 33/40 [00:08<00:01, 3.67it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.67it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.67it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.67it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.67it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.67it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.35it/s] 20%|██ | 2/10 [00:00<00:01, 4.33it/s] 30%|███ | 3/10 [00:00<00:01, 4.32it/s] 40%|████ | 4/10 [00:00<00:01, 4.31it/s] 50%|█████ | 5/10 [00:01<00:01, 4.30it/s] 60%|██████ | 6/10 [00:01<00:00, 4.30it/s] 70%|███████ | 7/10 [00:01<00:00, 4.31it/s] 80%|████████ | 8/10 [00:01<00:00, 4.31it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.30it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecIDngihkotb5xsgcdxbvzs66jxcmyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, the black forest poster
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, the black forest poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, the black forest poster", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, the black forest poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, the black forest poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:26:01.172974Z", "created_at": "2023-09-12T23:25:45.884672Z", "data_removed": false, "error": null, "id": "ngihkotb5xsgcdxbvzs66jxcmy", "input": { "width": 1024, "height": 1024, "prompt": "park style, the black forest poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 33019\nPrompt: park style, the black forest poster\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.71it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.70it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.69it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.68it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.68it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s]\n 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.66it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.67it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.66it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.66it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s]\n 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.66it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.66it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.66it/s]\n 55%|█████▌ | 22/40 [00:06<00:04, 3.66it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.66it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.66it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.66it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.66it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.67it/s]\n 72%|███████▎ | 29/40 [00:07<00:02, 3.67it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.67it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.67it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.68it/s]\n 82%|████████▎ | 33/40 [00:08<00:01, 3.68it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.68it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.68it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.68it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.68it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.68it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.68it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.68it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.36it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.32it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.31it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.31it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.31it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.32it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.32it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.31it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]", "metrics": { "predict_time": 15.31742, "total_time": 15.288302 }, "output": [ "https://replicate.delivery/pbxt/C4jLozEgalr6K9LmUOnMeBoEfketZZHPEsrdgplC8bmQsUHjA/out-0.png" ], "started_at": "2023-09-12T23:25:45.855554Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ngihkotb5xsgcdxbvzs66jxcmy", "cancel": "https://api.replicate.com/v1/predictions/ngihkotb5xsgcdxbvzs66jxcmy/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 33019 Prompt: park style, the black forest poster txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.71it/s] 5%|▌ | 2/40 [00:00<00:10, 3.70it/s] 8%|▊ | 3/40 [00:00<00:10, 3.69it/s] 10%|█ | 4/40 [00:01<00:09, 3.68it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.68it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s] 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s] 20%|██ | 8/40 [00:02<00:08, 3.66it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.67it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s] 30%|███ | 12/40 [00:03<00:07, 3.66it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s] 40%|████ | 16/40 [00:04<00:06, 3.66it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s] 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.66it/s] 50%|█████ | 20/40 [00:05<00:05, 3.66it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.66it/s] 55%|█████▌ | 22/40 [00:06<00:04, 3.66it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s] 60%|██████ | 24/40 [00:06<00:04, 3.66it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.66it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.66it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.66it/s] 70%|███████ | 28/40 [00:07<00:03, 3.67it/s] 72%|███████▎ | 29/40 [00:07<00:02, 3.67it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.67it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.67it/s] 80%|████████ | 32/40 [00:08<00:02, 3.68it/s] 82%|████████▎ | 33/40 [00:08<00:01, 3.68it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.68it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.68it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.68it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.68it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.68it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.68it/s] 100%|██████████| 40/40 [00:10<00:00, 3.68it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.36it/s] 20%|██ | 2/10 [00:00<00:01, 4.33it/s] 30%|███ | 3/10 [00:00<00:01, 4.32it/s] 40%|████ | 4/10 [00:00<00:01, 4.31it/s] 50%|█████ | 5/10 [00:01<00:01, 4.31it/s] 60%|██████ | 6/10 [00:01<00:00, 4.31it/s] 70%|███████ | 7/10 [00:01<00:00, 4.32it/s] 80%|████████ | 8/10 [00:01<00:00, 4.32it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.31it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecIDyd4bputbezq22dcro5htwxcbgmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, star wars poster
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, star wars poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, star wars poster", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, star wars poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "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 rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, star wars poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-12T23:28:22.119465Z", "created_at": "2023-09-12T23:28:06.803956Z", "data_removed": false, "error": null, "id": "yd4bputbezq22dcro5htwxcbgm", "input": { "width": 1024, "height": 1024, "prompt": "park style, star wars poster", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 16378\nPrompt: park style, star wars poster\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.71it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.69it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.69it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.69it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.68it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.68it/s]\n 18%|█▊ | 7/40 [00:01<00:08, 3.68it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.67it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.67it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.67it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.67it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.66it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s]\n 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.66it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.66it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.66it/s]\n 55%|█████▌ | 22/40 [00:05<00:04, 3.66it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.66it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.66it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.66it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.66it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.66it/s]\n 72%|███████▎ | 29/40 [00:07<00:03, 3.66it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.66it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.66it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.66it/s]\n 82%|████████▎ | 33/40 [00:09<00:01, 3.65it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.65it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.65it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.65it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.66it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.66it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.67it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.66it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.35it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.33it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.32it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.32it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.32it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.32it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.32it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.32it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.31it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.32it/s]", "metrics": { "predict_time": 15.3002, "total_time": 15.315509 }, "output": [ "https://replicate.delivery/pbxt/OOK8jLErBxYrJRUGZT6Czeuex2e16fHuvlG8ClIjbksXhpOGB/out-0.png" ], "started_at": "2023-09-12T23:28:06.819265Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yd4bputbezq22dcro5htwxcbgm", "cancel": "https://api.replicate.com/v1/predictions/yd4bputbezq22dcro5htwxcbgm/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 16378 Prompt: park style, star wars poster txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.71it/s] 5%|▌ | 2/40 [00:00<00:10, 3.69it/s] 8%|▊ | 3/40 [00:00<00:10, 3.69it/s] 10%|█ | 4/40 [00:01<00:09, 3.69it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.68it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.68it/s] 18%|█▊ | 7/40 [00:01<00:08, 3.68it/s] 20%|██ | 8/40 [00:02<00:08, 3.67it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.67it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.67it/s] 30%|███ | 12/40 [00:03<00:07, 3.67it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s] 40%|████ | 16/40 [00:04<00:06, 3.66it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s] 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.66it/s] 50%|█████ | 20/40 [00:05<00:05, 3.66it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.66it/s] 55%|█████▌ | 22/40 [00:05<00:04, 3.66it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s] 60%|██████ | 24/40 [00:06<00:04, 3.66it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.66it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.66it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.66it/s] 70%|███████ | 28/40 [00:07<00:03, 3.66it/s] 72%|███████▎ | 29/40 [00:07<00:03, 3.66it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.66it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.66it/s] 80%|████████ | 32/40 [00:08<00:02, 3.66it/s] 82%|████████▎ | 33/40 [00:09<00:01, 3.65it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.65it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.65it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.65it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.66it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.66it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.67it/s] 100%|██████████| 40/40 [00:10<00:00, 3.66it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.35it/s] 20%|██ | 2/10 [00:00<00:01, 4.33it/s] 30%|███ | 3/10 [00:00<00:01, 4.33it/s] 40%|████ | 4/10 [00:00<00:01, 4.32it/s] 50%|█████ | 5/10 [00:01<00:01, 4.32it/s] 60%|██████ | 6/10 [00:01<00:00, 4.32it/s] 70%|███████ | 7/10 [00:01<00:00, 4.32it/s] 80%|████████ | 8/10 [00:01<00:00, 4.32it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.32it/s] 100%|██████████| 10/10 [00:02<00:00, 4.31it/s] 100%|██████████| 10/10 [00:02<00:00, 4.32it/s]Prediction
rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eecIDs5prdpdbvpvzpx4ao3d4wqhcuqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- park style, mount fugi
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.85
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.81
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "park style, mount fugi", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.81, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", { input: { width: 1024, height: 1024, prompt: "park style, mount fugi", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.85, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.81, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun rileyhacks007/sdxl-national-park-posters using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", input={ "width": 1024, "height": 1024, "prompt": "park style, mount fugi", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.81, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run rileyhacks007/sdxl-national-park-posters 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": "rileyhacks007/sdxl-national-park-posters:f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec", "input": { "width": 1024, "height": 1024, "prompt": "park style, mount fugi", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.81, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-15T22:17:02.506482Z", "created_at": "2023-09-15T22:16:04.348572Z", "data_removed": false, "error": null, "id": "s5prdpdbvpvzpx4ao3d4wqhcuq", "input": { "width": 1024, "height": 1024, "prompt": "park style, mount fugi", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.81, "num_inference_steps": 50 }, "logs": "Using seed: 5343\nPrompt: park style, mount fugi\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:01<00:41, 1.07s/it]\n 5%|▌ | 2/40 [00:02<00:40, 1.08s/it]\n 8%|▊ | 3/40 [00:03<00:39, 1.08s/it]\n 10%|█ | 4/40 [00:04<00:38, 1.08s/it]\n 12%|█▎ | 5/40 [00:05<00:37, 1.08s/it]\n 15%|█▌ | 6/40 [00:06<00:36, 1.08s/it]\n 18%|█▊ | 7/40 [00:07<00:35, 1.08s/it]\n 20%|██ | 8/40 [00:08<00:34, 1.08s/it]\n 22%|██▎ | 9/40 [00:09<00:33, 1.08s/it]\n 25%|██▌ | 10/40 [00:10<00:32, 1.08s/it]\n 28%|██▊ | 11/40 [00:11<00:31, 1.08s/it]\n 30%|███ | 12/40 [00:12<00:30, 1.08s/it]\n 32%|███▎ | 13/40 [00:13<00:29, 1.08s/it]\n 35%|███▌ | 14/40 [00:15<00:27, 1.08s/it]\n 38%|███▊ | 15/40 [00:16<00:26, 1.08s/it]\n 40%|████ | 16/40 [00:17<00:25, 1.08s/it]\n 42%|████▎ | 17/40 [00:18<00:24, 1.08s/it]\n 45%|████▌ | 18/40 [00:19<00:23, 1.08s/it]\n 48%|████▊ | 19/40 [00:20<00:22, 1.08s/it]\n 50%|█████ | 20/40 [00:21<00:21, 1.08s/it]\n 52%|█████▎ | 21/40 [00:22<00:20, 1.08s/it]\n 55%|█████▌ | 22/40 [00:23<00:19, 1.08s/it]\n 57%|█████▊ | 23/40 [00:24<00:18, 1.08s/it]\n 60%|██████ | 24/40 [00:25<00:17, 1.08s/it]\n 62%|██████▎ | 25/40 [00:26<00:16, 1.08s/it]\n 65%|██████▌ | 26/40 [00:28<00:15, 1.08s/it]\n 68%|██████▊ | 27/40 [00:29<00:14, 1.08s/it]\n 70%|███████ | 28/40 [00:30<00:12, 1.08s/it]\n 72%|███████▎ | 29/40 [00:31<00:11, 1.08s/it]\n 75%|███████▌ | 30/40 [00:32<00:10, 1.08s/it]\n 78%|███████▊ | 31/40 [00:33<00:09, 1.08s/it]\n 80%|████████ | 32/40 [00:34<00:08, 1.08s/it]\n 82%|████████▎ | 33/40 [00:35<00:07, 1.08s/it]\n 85%|████████▌ | 34/40 [00:36<00:06, 1.08s/it]\n 88%|████████▊ | 35/40 [00:37<00:05, 1.08s/it]\n 90%|█████████ | 36/40 [00:38<00:04, 1.08s/it]\n 92%|█████████▎| 37/40 [00:39<00:03, 1.08s/it]\n 95%|█████████▌| 38/40 [00:40<00:02, 1.08s/it]\n 98%|█████████▊| 39/40 [00:42<00:01, 1.08s/it]\n100%|██████████| 40/40 [00:43<00:00, 1.08s/it]\n100%|██████████| 40/40 [00:43<00:00, 1.08s/it]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:07, 1.16it/s]\n 20%|██ | 2/10 [00:01<00:06, 1.15it/s]\n 30%|███ | 3/10 [00:02<00:06, 1.15it/s]\n 40%|████ | 4/10 [00:03<00:05, 1.15it/s]\n 50%|█████ | 5/10 [00:04<00:04, 1.15it/s]\n 60%|██████ | 6/10 [00:05<00:03, 1.15it/s]\n 70%|███████ | 7/10 [00:06<00:02, 1.15it/s]\n 80%|████████ | 8/10 [00:06<00:01, 1.15it/s]\n 90%|█████████ | 9/10 [00:07<00:00, 1.15it/s]\n100%|██████████| 10/10 [00:08<00:00, 1.15it/s]\n100%|██████████| 10/10 [00:08<00:00, 1.15it/s]", "metrics": { "predict_time": 58.171068, "total_time": 58.15791 }, "output": [ "https://replicate.delivery/pbxt/OkepTvX6kiXtEaQweR2Nj54eCPLDM5AmrSkq96VrWg32ORJjA/out-0.png", "https://replicate.delivery/pbxt/53IOVl6s3O6EGlTQH5K0XQ8k71GeuQeqlqia4FWBeq46ORJjA/out-1.png", "https://replicate.delivery/pbxt/X9LvQE5jGF6qMxQWQxYPwokAzUixOLvpDPk4vxv9qCZ3JKZE/out-2.png", "https://replicate.delivery/pbxt/9cL2kCvv3lIbMhglkvOlT7GVPD0Gn7XiWiW17nE1cpo3JKZE/out-3.png" ], "started_at": "2023-09-15T22:16:04.335414Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/s5prdpdbvpvzpx4ao3d4wqhcuq", "cancel": "https://api.replicate.com/v1/predictions/s5prdpdbvpvzpx4ao3d4wqhcuq/cancel" }, "version": "f0d21b0cb0410a4f4e5131233e3dcd08350b5d13adbc177f1d2076ddd7525eec" }Generated inUsing seed: 5343 Prompt: park style, mount fugi txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:01<00:41, 1.07s/it] 5%|▌ | 2/40 [00:02<00:40, 1.08s/it] 8%|▊ | 3/40 [00:03<00:39, 1.08s/it] 10%|█ | 4/40 [00:04<00:38, 1.08s/it] 12%|█▎ | 5/40 [00:05<00:37, 1.08s/it] 15%|█▌ | 6/40 [00:06<00:36, 1.08s/it] 18%|█▊ | 7/40 [00:07<00:35, 1.08s/it] 20%|██ | 8/40 [00:08<00:34, 1.08s/it] 22%|██▎ | 9/40 [00:09<00:33, 1.08s/it] 25%|██▌ | 10/40 [00:10<00:32, 1.08s/it] 28%|██▊ | 11/40 [00:11<00:31, 1.08s/it] 30%|███ | 12/40 [00:12<00:30, 1.08s/it] 32%|███▎ | 13/40 [00:13<00:29, 1.08s/it] 35%|███▌ | 14/40 [00:15<00:27, 1.08s/it] 38%|███▊ | 15/40 [00:16<00:26, 1.08s/it] 40%|████ | 16/40 [00:17<00:25, 1.08s/it] 42%|████▎ | 17/40 [00:18<00:24, 1.08s/it] 45%|████▌ | 18/40 [00:19<00:23, 1.08s/it] 48%|████▊ | 19/40 [00:20<00:22, 1.08s/it] 50%|█████ | 20/40 [00:21<00:21, 1.08s/it] 52%|█████▎ | 21/40 [00:22<00:20, 1.08s/it] 55%|█████▌ | 22/40 [00:23<00:19, 1.08s/it] 57%|█████▊ | 23/40 [00:24<00:18, 1.08s/it] 60%|██████ | 24/40 [00:25<00:17, 1.08s/it] 62%|██████▎ | 25/40 [00:26<00:16, 1.08s/it] 65%|██████▌ | 26/40 [00:28<00:15, 1.08s/it] 68%|██████▊ | 27/40 [00:29<00:14, 1.08s/it] 70%|███████ | 28/40 [00:30<00:12, 1.08s/it] 72%|███████▎ | 29/40 [00:31<00:11, 1.08s/it] 75%|███████▌ | 30/40 [00:32<00:10, 1.08s/it] 78%|███████▊ | 31/40 [00:33<00:09, 1.08s/it] 80%|████████ | 32/40 [00:34<00:08, 1.08s/it] 82%|████████▎ | 33/40 [00:35<00:07, 1.08s/it] 85%|████████▌ | 34/40 [00:36<00:06, 1.08s/it] 88%|████████▊ | 35/40 [00:37<00:05, 1.08s/it] 90%|█████████ | 36/40 [00:38<00:04, 1.08s/it] 92%|█████████▎| 37/40 [00:39<00:03, 1.08s/it] 95%|█████████▌| 38/40 [00:40<00:02, 1.08s/it] 98%|█████████▊| 39/40 [00:42<00:01, 1.08s/it] 100%|██████████| 40/40 [00:43<00:00, 1.08s/it] 100%|██████████| 40/40 [00:43<00:00, 1.08s/it] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:07, 1.16it/s] 20%|██ | 2/10 [00:01<00:06, 1.15it/s] 30%|███ | 3/10 [00:02<00:06, 1.15it/s] 40%|████ | 4/10 [00:03<00:05, 1.15it/s] 50%|█████ | 5/10 [00:04<00:04, 1.15it/s] 60%|██████ | 6/10 [00:05<00:03, 1.15it/s] 70%|███████ | 7/10 [00:06<00:02, 1.15it/s] 80%|████████ | 8/10 [00:06<00:01, 1.15it/s] 90%|█████████ | 9/10 [00:07<00:00, 1.15it/s] 100%|██████████| 10/10 [00:08<00:00, 1.15it/s] 100%|██████████| 10/10 [00:08<00:00, 1.15it/s]
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