fofr / sdxl-simpsons
(Updated 1 year, 10 months ago)
- Public
- 796 runs
Prediction
fofr/sdxl-simpsons:e080bb23696e6b8b1a354d672927d4131c2ad8dd138081528fc6298d91b94f88IDziosavlboxniaohmrxeltphlimStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1360
- height
- 768
- prompt
- A picture of a house in the style of TOK, clean, simple
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.95
- negative_prompt
- underexposed, ugly, broken
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1360, "height": 768, "prompt": "A picture of a house in the style of TOK, clean, simple", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.95, "negative_prompt": "underexposed, ugly, broken", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-simpsons using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-simpsons:e080bb23696e6b8b1a354d672927d4131c2ad8dd138081528fc6298d91b94f88", { input: { width: 1360, height: 768, prompt: "A picture of a house in the style of TOK, clean, simple", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.95, negative_prompt: "underexposed, ugly, broken", 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 fofr/sdxl-simpsons using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-simpsons:e080bb23696e6b8b1a354d672927d4131c2ad8dd138081528fc6298d91b94f88", input={ "width": 1360, "height": 768, "prompt": "A picture of a house in the style of TOK, clean, simple", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.95, "negative_prompt": "underexposed, ugly, broken", "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 fofr/sdxl-simpsons 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": "fofr/sdxl-simpsons:e080bb23696e6b8b1a354d672927d4131c2ad8dd138081528fc6298d91b94f88", "input": { "width": 1360, "height": 768, "prompt": "A picture of a house in the style of TOK, clean, simple", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.95, "negative_prompt": "underexposed, ugly, broken", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-12T15:41:37.835940Z", "created_at": "2023-08-12T15:40:42.724110Z", "data_removed": false, "error": null, "id": "ziosavlboxniaohmrxeltphlim", "input": { "width": 1360, "height": 768, "prompt": "A picture of a house in the style of TOK, clean, simple", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.95, "negative_prompt": "underexposed, ugly, broken", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 1404\nPrompt: A picture of a house in the style of <s0><s1>, clean, simple\ntxt2img mode\n 0%| | 0/47 [00:00<?, ?it/s]\n 2%|▏ | 1/47 [00:01<00:46, 1.01s/it]\n 4%|▍ | 2/47 [00:02<00:45, 1.01s/it]\n 6%|▋ | 3/47 [00:03<00:44, 1.01s/it]\n 9%|▊ | 4/47 [00:04<00:43, 1.01s/it]\n 11%|█ | 5/47 [00:05<00:42, 1.01s/it]\n 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it]\n 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it]\n 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it]\n 19%|█▉ | 9/47 [00:09<00:38, 1.01s/it]\n 21%|██▏ | 10/47 [00:10<00:37, 1.01s/it]\n 23%|██▎ | 11/47 [00:11<00:36, 1.01s/it]\n 26%|██▌ | 12/47 [00:12<00:35, 1.01s/it]\n 28%|██▊ | 13/47 [00:13<00:34, 1.01s/it]\n 30%|██▉ | 14/47 [00:14<00:33, 1.01s/it]\n 32%|███▏ | 15/47 [00:15<00:32, 1.01s/it]\n 34%|███▍ | 16/47 [00:16<00:31, 1.01s/it]\n 36%|███▌ | 17/47 [00:17<00:30, 1.01s/it]\n 38%|███▊ | 18/47 [00:18<00:29, 1.02s/it]\n 40%|████ | 19/47 [00:19<00:28, 1.02s/it]\n 43%|████▎ | 20/47 [00:20<00:27, 1.02s/it]\n 45%|████▍ | 21/47 [00:21<00:26, 1.02s/it]\n 47%|████▋ | 22/47 [00:22<00:25, 1.02s/it]\n 49%|████▉ | 23/47 [00:23<00:24, 1.02s/it]\n 51%|█████ | 24/47 [00:24<00:23, 1.02s/it]\n 53%|█████▎ | 25/47 [00:25<00:22, 1.02s/it]\n 55%|█████▌ | 26/47 [00:26<00:21, 1.02s/it]\n 57%|█████▋ | 27/47 [00:27<00:20, 1.02s/it]\n 60%|█████▉ | 28/47 [00:28<00:19, 1.02s/it]\n 62%|██████▏ | 29/47 [00:29<00:18, 1.02s/it]\n 64%|██████▍ | 30/47 [00:30<00:17, 1.02s/it]\n 66%|██████▌ | 31/47 [00:31<00:16, 1.02s/it]\n 68%|██████▊ | 32/47 [00:32<00:15, 1.02s/it]\n 70%|███████ | 33/47 [00:33<00:14, 1.02s/it]\n 72%|███████▏ | 34/47 [00:34<00:13, 1.02s/it]\n 74%|███████▍ | 35/47 [00:35<00:12, 1.02s/it]\n 77%|███████▋ | 36/47 [00:36<00:11, 1.02s/it]\n 79%|███████▊ | 37/47 [00:37<00:10, 1.02s/it]\n 81%|████████ | 38/47 [00:38<00:09, 1.02s/it]\n 83%|████████▎ | 39/47 [00:39<00:08, 1.02s/it]\n 85%|████████▌ | 40/47 [00:40<00:07, 1.02s/it]\n 87%|████████▋ | 41/47 [00:41<00:06, 1.02s/it]\n 89%|████████▉ | 42/47 [00:42<00:05, 1.02s/it]\n 91%|█████████▏| 43/47 [00:43<00:04, 1.02s/it]\n 94%|█████████▎| 44/47 [00:44<00:03, 1.02s/it]\n 96%|█████████▌| 45/47 [00:45<00:02, 1.02s/it]\n 98%|█████████▊| 46/47 [00:46<00:01, 1.02s/it]\n100%|██████████| 47/47 [00:47<00:00, 1.02s/it]\n100%|██████████| 47/47 [00:47<00:00, 1.02s/it]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.21it/s]\n 67%|██████▋ | 2/3 [00:01<00:00, 1.21it/s]\n100%|██████████| 3/3 [00:02<00:00, 1.21it/s]\n100%|██████████| 3/3 [00:02<00:00, 1.21it/s]", "metrics": { "predict_time": 55.15423, "total_time": 55.11183 }, "output": [ "https://replicate.delivery/pbxt/EDy8fGFvMO2PZSGw2lQCqfNjd8w7ff18gDM909aXgAnCjWlFB/out-0.png", "https://replicate.delivery/pbxt/RlzfO1jzjY2sVKijxf9fuXNTLTfvA5mnEiLS5pHUp89DjWlFB/out-1.png", "https://replicate.delivery/pbxt/booR6M056W7JLxftWWpJX9wa8eMKaA0dlGsaJ4MxnSlxoVZRA/out-2.png", "https://replicate.delivery/pbxt/7kmmN4bhfFXYU6lFGUw3LMp5gmAMiF74qo0BceMEhL7xoVZRA/out-3.png" ], "started_at": "2023-08-12T15:40:42.681710Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ziosavlboxniaohmrxeltphlim", "cancel": "https://api.replicate.com/v1/predictions/ziosavlboxniaohmrxeltphlim/cancel" }, "version": "e080bb23696e6b8b1a354d672927d4131c2ad8dd138081528fc6298d91b94f88" }Generated inUsing seed: 1404 Prompt: A picture of a house in the style of <s0><s1>, clean, simple txt2img mode 0%| | 0/47 [00:00<?, ?it/s] 2%|▏ | 1/47 [00:01<00:46, 1.01s/it] 4%|▍ | 2/47 [00:02<00:45, 1.01s/it] 6%|▋ | 3/47 [00:03<00:44, 1.01s/it] 9%|▊ | 4/47 [00:04<00:43, 1.01s/it] 11%|█ | 5/47 [00:05<00:42, 1.01s/it] 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it] 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it] 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it] 19%|█▉ | 9/47 [00:09<00:38, 1.01s/it] 21%|██▏ | 10/47 [00:10<00:37, 1.01s/it] 23%|██▎ | 11/47 [00:11<00:36, 1.01s/it] 26%|██▌ | 12/47 [00:12<00:35, 1.01s/it] 28%|██▊ | 13/47 [00:13<00:34, 1.01s/it] 30%|██▉ | 14/47 [00:14<00:33, 1.01s/it] 32%|███▏ | 15/47 [00:15<00:32, 1.01s/it] 34%|███▍ | 16/47 [00:16<00:31, 1.01s/it] 36%|███▌ | 17/47 [00:17<00:30, 1.01s/it] 38%|███▊ | 18/47 [00:18<00:29, 1.02s/it] 40%|████ | 19/47 [00:19<00:28, 1.02s/it] 43%|████▎ | 20/47 [00:20<00:27, 1.02s/it] 45%|████▍ | 21/47 [00:21<00:26, 1.02s/it] 47%|████▋ | 22/47 [00:22<00:25, 1.02s/it] 49%|████▉ | 23/47 [00:23<00:24, 1.02s/it] 51%|█████ | 24/47 [00:24<00:23, 1.02s/it] 53%|█████▎ | 25/47 [00:25<00:22, 1.02s/it] 55%|█████▌ | 26/47 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