fofr/sdxl-demo
sdxl-demo
Prediction
fofr/sdxl-demo:d70462b94de165b98d8e1467fc323e091d09ac04bee2bd5427df53ec7c266d1bIDpzi7bxtblioztxgvbnhppbm5piStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK VR headset, a man is dancing
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.7
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.95
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK VR headset, a man is dancing", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-demo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-demo:d70462b94de165b98d8e1467fc323e091d09ac04bee2bd5427df53ec7c266d1b", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK VR headset, a man is dancing", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.7, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, 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-demo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-demo:d70462b94de165b98d8e1467fc323e091d09ac04bee2bd5427df53ec7c266d1b", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK VR headset, a man is dancing", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.95, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-demo 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-demo:d70462b94de165b98d8e1467fc323e091d09ac04bee2bd5427df53ec7c266d1b", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK VR headset, a man is dancing", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "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": "2023-08-14T12:14:57.018688Z", "created_at": "2023-08-14T12:14:42.326562Z", "data_removed": false, "error": null, "id": "pzi7bxtblioztxgvbnhppbm5pi", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK VR headset, a man is dancing", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.95, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 13861\nPrompt: A photo of a <s0><s1> VR headset, a man is dancing\ntxt2img mode\n 0%| | 0/47 [00:00<?, ?it/s]\n 2%|▏ | 1/47 [00:00<00:12, 3.72it/s]\n 4%|▍ | 2/47 [00:00<00:12, 3.71it/s]\n 6%|▋ | 3/47 [00:00<00:11, 3.71it/s]\n 9%|▊ | 4/47 [00:01<00:11, 3.70it/s]\n 11%|█ | 5/47 [00:01<00:11, 3.69it/s]\n 13%|█▎ | 6/47 [00:01<00:11, 3.69it/s]\n 15%|█▍ | 7/47 [00:01<00:10, 3.69it/s]\n 17%|█▋ | 8/47 [00:02<00:10, 3.69it/s]\n 19%|█▉ | 9/47 [00:02<00:10, 3.68it/s]\n 21%|██▏ | 10/47 [00:02<00:10, 3.69it/s]\n 23%|██▎ | 11/47 [00:02<00:09, 3.68it/s]\n 26%|██▌ | 12/47 [00:03<00:09, 3.68it/s]\n 28%|██▊ | 13/47 [00:03<00:09, 3.68it/s]\n 30%|██▉ | 14/47 [00:03<00:08, 3.68it/s]\n 32%|███▏ | 15/47 [00:04<00:08, 3.68it/s]\n 34%|███▍ | 16/47 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 17/47 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 18/47 [00:04<00:07, 3.69it/s]\n 40%|████ | 19/47 [00:05<00:07, 3.69it/s]\n 43%|████▎ | 20/47 [00:05<00:07, 3.70it/s]\n 45%|████▍ | 21/47 [00:05<00:07, 3.70it/s]\n 47%|████▋ | 22/47 [00:05<00:06, 3.70it/s]\n 49%|████▉ | 23/47 [00:06<00:06, 3.70it/s]\n 51%|█████ | 24/47 [00:06<00:06, 3.70it/s]\n 53%|█████▎ | 25/47 [00:06<00:05, 3.70it/s]\n 55%|█████▌ | 26/47 [00:07<00:05, 3.70it/s]\n 57%|█████▋ | 27/47 [00:07<00:05, 3.70it/s]\n 60%|█████▉ | 28/47 [00:07<00:05, 3.70it/s]\n 62%|██████▏ | 29/47 [00:07<00:04, 3.70it/s]\n 64%|██████▍ | 30/47 [00:08<00:04, 3.70it/s]\n 66%|██████▌ | 31/47 [00:08<00:04, 3.70it/s]\n 68%|██████▊ | 32/47 [00:08<00:04, 3.70it/s]\n 70%|███████ | 33/47 [00:08<00:03, 3.70it/s]\n 72%|███████▏ | 34/47 [00:09<00:03, 3.70it/s]\n 74%|███████▍ | 35/47 [00:09<00:03, 3.70it/s]\n 77%|███████▋ | 36/47 [00:09<00:02, 3.70it/s]\n 79%|███████▊ | 37/47 [00:10<00:02, 3.70it/s]\n 81%|████████ | 38/47 [00:10<00:02, 3.69it/s]\n 83%|████████▎ | 39/47 [00:10<00:02, 3.70it/s]\n 85%|████████▌ | 40/47 [00:10<00:01, 3.70it/s]\n 87%|████████▋ | 41/47 [00:11<00:01, 3.70it/s]\n 89%|████████▉ | 42/47 [00:11<00:01, 3.69it/s]\n 91%|█████████▏| 43/47 [00:11<00:01, 3.69it/s]\n 94%|█████████▎| 44/47 [00:11<00:00, 3.69it/s]\n 96%|█████████▌| 45/47 [00:12<00:00, 3.69it/s]\n 98%|█████████▊| 46/47 [00:12<00:00, 3.69it/s]\n100%|██████████| 47/47 [00:12<00:00, 3.69it/s]\n100%|██████████| 47/47 [00:12<00:00, 3.69it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.21it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.29it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.31it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.30it/s]", "metrics": { "predict_time": 14.757468, "total_time": 14.692126 }, "output": [ "https://replicate.delivery/pbxt/YSJutYCfxc0PYaNKI3fW084lebzV2NeiS57PNt5eIiqAYmPLC/out-0.png" ], "started_at": "2023-08-14T12:14:42.261220Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pzi7bxtblioztxgvbnhppbm5pi", "cancel": "https://api.replicate.com/v1/predictions/pzi7bxtblioztxgvbnhppbm5pi/cancel" }, "version": "d70462b94de165b98d8e1467fc323e091d09ac04bee2bd5427df53ec7c266d1b" }Generated inUsing seed: 13861 Prompt: A photo of a <s0><s1> VR headset, a man is dancing txt2img mode 0%| | 0/47 [00:00<?, ?it/s] 2%|▏ | 1/47 [00:00<00:12, 3.72it/s] 4%|▍ | 2/47 [00:00<00:12, 3.71it/s] 6%|▋ | 3/47 [00:00<00:11, 3.71it/s] 9%|▊ | 4/47 [00:01<00:11, 3.70it/s] 11%|█ | 5/47 [00:01<00:11, 3.69it/s] 13%|█▎ | 6/47 [00:01<00:11, 3.69it/s] 15%|█▍ | 7/47 [00:01<00:10, 3.69it/s] 17%|█▋ | 8/47 [00:02<00:10, 3.69it/s] 19%|█▉ | 9/47 [00:02<00:10, 3.68it/s] 21%|██▏ | 10/47 [00:02<00:10, 3.69it/s] 23%|██▎ | 11/47 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