fofr
/
sdxl-mario-kart
- Public
- 189 runs
-
L40S
Prediction
fofr/sdxl-mario-kart:c46d88533b10df14e459bc18e2908c12943ab8ace0ae22d93db9a955bd7ed302IDzzslqvdbkhgb62rybgzte5r5z4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, gameplay, mario
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.95
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of TOK, gameplay, mario", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "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 fofr/sdxl-mario-kart using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-mario-kart:c46d88533b10df14e459bc18e2908c12943ab8ace0ae22d93db9a955bd7ed302", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, gameplay, mario", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.95, 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-mario-kart using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-mario-kart:c46d88533b10df14e459bc18e2908c12943ab8ace0ae22d93db9a955bd7ed302", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, gameplay, mario", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.95, "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-mario-kart 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-mario-kart:c46d88533b10df14e459bc18e2908c12943ab8ace0ae22d93db9a955bd7ed302", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, gameplay, mario", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "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": "2023-08-08T20:54:51.072481Z", "created_at": "2023-08-08T20:52:48.206648Z", "data_removed": false, "error": null, "id": "zzslqvdbkhgb62rybgzte5r5z4", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, gameplay, mario", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 59924\nPrompt: In the style of <s0><s1>, gameplay, mario\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<01:13, 1.51s/it]\n 4%|▍ | 2/50 [00:02<00:58, 1.21s/it]\n 6%|▌ | 3/50 [00:03<00:52, 1.11s/it]\n 8%|▊ | 4/50 [00:04<00:49, 1.07s/it]\n 10%|█ | 5/50 [00:05<00:46, 1.04s/it]\n 12%|█▏ | 6/50 [00:06<00:45, 1.03s/it]\n 14%|█▍ | 7/50 [00:07<00:43, 1.02s/it]\n 16%|█▌ | 8/50 [00:08<00:42, 1.01s/it]\n 18%|█▊ | 9/50 [00:09<00:41, 1.01s/it]\n 20%|██ | 10/50 [00:10<00:40, 1.01s/it]\n 22%|██▏ | 11/50 [00:11<00:39, 1.01s/it]\n 24%|██▍ | 12/50 [00:12<00:38, 1.01s/it]\n 26%|██▌ | 13/50 [00:13<00:37, 1.00s/it]\n 28%|██▊ | 14/50 [00:14<00:36, 1.00s/it]\n 30%|███ | 15/50 [00:15<00:35, 1.00s/it]\n 32%|███▏ | 16/50 [00:16<00:34, 1.00s/it]\n 34%|███▍ | 17/50 [00:17<00:33, 1.00s/it]\n 36%|███▌ | 18/50 [00:18<00:32, 1.00s/it]\n 38%|███▊ | 19/50 [00:19<00:31, 1.00s/it]\n 40%|████ | 20/50 [00:20<00:30, 1.00s/it]\n 42%|████▏ | 21/50 [00:21<00:29, 1.00s/it]\n 44%|████▍ | 22/50 [00:22<00:28, 1.00s/it]\n 46%|████▌ | 23/50 [00:23<00:27, 1.00s/it]\n 48%|████▊ | 24/50 [00:24<00:26, 1.01s/it]\n 50%|█████ | 25/50 [00:25<00:25, 1.01s/it]\n 52%|█████▏ | 26/50 [00:26<00:24, 1.01s/it]\n 54%|█████▍ | 27/50 [00:27<00:23, 1.01s/it]\n 56%|█████▌ | 28/50 [00:28<00:22, 1.01s/it]\n 58%|█████▊ | 29/50 [00:29<00:21, 1.01s/it]\n 60%|██████ | 30/50 [00:30<00:20, 1.01s/it]\n 62%|██████▏ | 31/50 [00:31<00:19, 1.01s/it]\n 64%|██████▍ | 32/50 [00:32<00:18, 1.01s/it]\n 66%|██████▌ | 33/50 [00:33<00:17, 1.01s/it]\n 68%|██████▊ | 34/50 [00:34<00:16, 1.01s/it]\n 70%|███████ | 35/50 [00:35<00:15, 1.01s/it]\n 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it]\n 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it]\n 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it]\n 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it]\n 80%|████████ | 40/50 [00:40<00:10, 1.01s/it]\n 82%|████████▏ | 41/50 [00:41<00:09, 1.01s/it]\n 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it]\n 86%|████████▌ | 43/50 [00:43<00:07, 1.01s/it]\n 88%|████████▊ | 44/50 [00:44<00:06, 1.01s/it]\n 90%|█████████ | 45/50 [00:45<00:05, 1.01s/it]\n 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it]\n 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it]\n 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it]\n 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.02s/it]", "metrics": { "predict_time": 57.46454, "total_time": 122.865833 }, "output": [ "https://replicate.delivery/pbxt/QEtd94M72jJXCZMvJ8Oi00gFAN0j3HN0zxifWCZh97RM7CsIA/out-0.png", "https://replicate.delivery/pbxt/jWdmHjhfTx0hC64A34jVpBhF9JnlMNNzH0ZxKDCIWXtM7CsIA/out-1.png", "https://replicate.delivery/pbxt/zt3RkGFO7S5XBlnKLwDhH4MaGeUriP8nlESxhscsZuON7CsIA/out-2.png", "https://replicate.delivery/pbxt/KRX2QVNcB6o7OFbwgjfdQLAX7HrfPHKD0C8LujMGEPsa2FYRA/out-3.png" ], "started_at": "2023-08-08T20:53:53.607941Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zzslqvdbkhgb62rybgzte5r5z4", "cancel": "https://api.replicate.com/v1/predictions/zzslqvdbkhgb62rybgzte5r5z4/cancel" }, "version": "c46d88533b10df14e459bc18e2908c12943ab8ace0ae22d93db9a955bd7ed302" }Generated inUsing seed: 59924 Prompt: In the style of <s0><s1>, gameplay, mario txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<01:13, 1.51s/it] 4%|▍ | 2/50 [00:02<00:58, 1.21s/it] 6%|▌ | 3/50 [00:03<00:52, 1.11s/it] 8%|▊ | 4/50 [00:04<00:49, 1.07s/it] 10%|█ | 5/50 [00:05<00:46, 1.04s/it] 12%|█▏ | 6/50 [00:06<00:45, 1.03s/it] 14%|█▍ | 7/50 [00:07<00:43, 1.02s/it] 16%|█▌ | 8/50 [00:08<00:42, 1.01s/it] 18%|█▊ | 9/50 [00:09<00:41, 1.01s/it] 20%|██ | 10/50 [00:10<00:40, 1.01s/it] 22%|██▏ | 11/50 [00:11<00:39, 1.01s/it] 24%|██▍ | 12/50 [00:12<00:38, 1.01s/it] 26%|██▌ | 13/50 [00:13<00:37, 1.00s/it] 28%|██▊ | 14/50 [00:14<00:36, 1.00s/it] 30%|███ | 15/50 [00:15<00:35, 1.00s/it] 32%|███▏ | 16/50 [00:16<00:34, 1.00s/it] 34%|███▍ | 17/50 [00:17<00:33, 1.00s/it] 36%|███▌ | 18/50 [00:18<00:32, 1.00s/it] 38%|███▊ | 19/50 [00:19<00:31, 1.00s/it] 40%|████ | 20/50 [00:20<00:30, 1.00s/it] 42%|████▏ | 21/50 [00:21<00:29, 1.00s/it] 44%|████▍ | 22/50 [00:22<00:28, 1.00s/it] 46%|████▌ | 23/50 [00:23<00:27, 1.00s/it] 48%|████▊ | 24/50 [00:24<00:26, 1.01s/it] 50%|█████ | 25/50 [00:25<00:25, 1.01s/it] 52%|█████▏ | 26/50 [00:26<00:24, 1.01s/it] 54%|█████▍ | 27/50 [00:27<00:23, 1.01s/it] 56%|█████▌ | 28/50 [00:28<00:22, 1.01s/it] 58%|█████▊ | 29/50 [00:29<00:21, 1.01s/it] 60%|██████ | 30/50 [00:30<00:20, 1.01s/it] 62%|██████▏ | 31/50 [00:31<00:19, 1.01s/it] 64%|██████▍ | 32/50 [00:32<00:18, 1.01s/it] 66%|██████▌ | 33/50 [00:33<00:17, 1.01s/it] 68%|██████▊ | 34/50 [00:34<00:16, 1.01s/it] 70%|███████ | 35/50 [00:35<00:15, 1.01s/it] 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it] 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it] 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it] 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it] 80%|████████ | 40/50 [00:40<00:10, 1.01s/it] 82%|████████▏ | 41/50 [00:41<00:09, 1.01s/it] 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it] 86%|████████▌ | 43/50 [00:43<00:07, 1.01s/it] 88%|████████▊ | 44/50 [00:44<00:06, 1.01s/it] 90%|█████████ | 45/50 [00:45<00:05, 1.01s/it] 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it] 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it] 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it] 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it] 100%|██████████| 50/50 [00:50<00:00, 1.01s/it] 100%|██████████| 50/50 [00:50<00:00, 1.02s/it]
Want to make some of these yourself?
Run this model