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prakharsaxena24 /masked-upscaler:0e864cd4
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=prakharsaxena24/masked-upscaler
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run prakharsaxena24/masked-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"prakharsaxena24/masked-upscaler:0e864cd4844ac63d862efd3468e4c55219066351009db73833ad67f98c5eaefb",
{
input: {
mask: "https://replicate.delivery/pbxt/L293tY1UNaSlq01zA1VCCkNyv49jD4Ab3QrMau376xUON56q/inverse_image_mask.png",
seed: 42,
image: "https://replicate.delivery/pbxt/L293tzfx8WiFQQrLRxPMwRwMZHzi9Bs5a1mUOgwySqf77men/img5a21b4bd1c924b0ba6d04f1c75ced25d.png",
prompt: "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>",
scale_factor: 2,
num_inference_steps: 20
}
}
);
// 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 replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun prakharsaxena24/masked-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"prakharsaxena24/masked-upscaler:0e864cd4844ac63d862efd3468e4c55219066351009db73833ad67f98c5eaefb",
input={
"mask": "https://replicate.delivery/pbxt/L293tY1UNaSlq01zA1VCCkNyv49jD4Ab3QrMau376xUON56q/inverse_image_mask.png",
"seed": 42,
"image": "https://replicate.delivery/pbxt/L293tzfx8WiFQQrLRxPMwRwMZHzi9Bs5a1mUOgwySqf77men/img5a21b4bd1c924b0ba6d04f1c75ced25d.png",
"prompt": "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>",
"scale_factor": 2,
"num_inference_steps": 20
}
)
print(output)To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run prakharsaxena24/masked-upscaler 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": "prakharsaxena24/masked-upscaler:0e864cd4844ac63d862efd3468e4c55219066351009db73833ad67f98c5eaefb",
"input": {
"mask": "https://replicate.delivery/pbxt/L293tY1UNaSlq01zA1VCCkNyv49jD4Ab3QrMau376xUON56q/inverse_image_mask.png",
"seed": 42,
"image": "https://replicate.delivery/pbxt/L293tzfx8WiFQQrLRxPMwRwMZHzi9Bs5a1mUOgwySqf77men/img5a21b4bd1c924b0ba6d04f1c75ced25d.png",
"prompt": "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>",
"scale_factor": 2,
"num_inference_steps": 20
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
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Output
{
"completed_at": "2024-06-03T19:24:15.121352Z",
"created_at": "2024-06-03T19:22:02.672000Z",
"data_removed": false,
"error": null,
"id": "y9q613he61rgp0cfvy6sqcwjkc",
"input": {
"mask": "https://replicate.delivery/pbxt/L293tY1UNaSlq01zA1VCCkNyv49jD4Ab3QrMau376xUON56q/inverse_image_mask.png",
"seed": 42,
"image": "https://replicate.delivery/pbxt/L293tzfx8WiFQQrLRxPMwRwMZHzi9Bs5a1mUOgwySqf77men/img5a21b4bd1c924b0ba6d04f1c75ced25d.png",
"prompt": "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>",
"scale_factor": 2,
"num_inference_steps": 20
},
"logs": "Running prediction\nUpscaling with scale_factor: 2.0\n[Tiled Diffusion] upscaling image with 4x-UltraSharp...\n[Tiled Diffusion] ControlNet found, support is enabled.\n2024-06-03 19:24:07,997 - ControlNet - \u001b[0;32mINFO\u001b[0m - unit_separate = False, style_align = False\n2024-06-03 19:24:07,997 - ControlNet - \u001b[0;32mINFO\u001b[0m - Loading model from cache: control_v11f1e_sd15_tile\n2024-06-03 19:24:08,011 - ControlNet - \u001b[0;32mINFO\u001b[0m - Using preprocessor: tile_resample\n2024-06-03 19:24:08,011 - ControlNet - \u001b[0;32mINFO\u001b[0m - preprocessor resolution = 950\n2024-06-03 19:24:08,092 - ControlNet - \u001b[0;32mINFO\u001b[0m - ControlNet Hooked - Time = 0.1022803783416748\nMultiDiffusion hooked into 'DPM++ 3M SDE Karras' sampler, Tile size: 118x112, Tile count: 3, Batch size: 3, Tile batches: 1 (ext: ContrlNet)\n[Tiled VAE]: the input size is tiny and unnecessary to tile.\nMultiDiffusion Sampling: 0%| | 0/1 [00:00<?, ?it/s]\n 0%| | 0/8 [00:00<?, ?it/s]\u001b[A\nTotal progress: 0%| | 0/8 [00:00<?, ?it/s]\u001b[A\n 12%|█▎ | 1/8 [00:01<00:07, 1.01s/it]\u001b[A\nTotal progress: 25%|██▌ | 2/8 [00:00<00:00, 6.32it/s]\u001b[A\n 25%|██▌ | 2/8 [00:01<00:03, 1.66it/s]\u001b[A\nTotal progress: 38%|███▊ | 3/8 [00:00<00:01, 4.51it/s]\u001b[A\n 38%|███▊ | 3/8 [00:01<00:02, 2.13it/s]\u001b[A\nTotal progress: 50%|█████ | 4/8 [00:00<00:01, 3.91it/s]\u001b[A\n 50%|█████ | 4/8 [00:01<00:01, 2.45it/s]\u001b[A\nTotal progress: 62%|██████▎ | 5/8 [00:01<00:00, 3.63it/s]\u001b[A\n 62%|██████▎ | 5/8 [00:02<00:01, 2.68it/s]\u001b[A\nTotal progress: 75%|███████▌ | 6/8 [00:01<00:00, 3.48it/s]\u001b[A\n 75%|███████▌ | 6/8 [00:02<00:00, 2.84it/s]\u001b[A\nTotal progress: 88%|████████▊ | 7/8 [00:01<00:00, 3.38it/s]\u001b[A\n 88%|████████▊ | 7/8 [00:02<00:00, 2.96it/s]\u001b[A\n100%|██████████| 8/8 [00:03<00:00, 3.05it/s]\u001b[A\n100%|██████████| 8/8 [00:03<00:00, 2.51it/s]\nTotal progress: 100%|██████████| 8/8 [00:02<00:00, 3.33it/s]\u001b[A[Tiled VAE]: input_size: torch.Size([1, 4, 118, 250]), tile_size: 128, padding: 11\n[Tiled VAE]: split to 1x2 = 2 tiles. Optimal tile size 128x96, original tile size 128x128\n[Tiled VAE]: Fast mode enabled, estimating group norm parameters on 128 x 60 image\n[Tiled VAE]: Executing Decoder Task Queue: 0%| | 0/246 [00:00<?, ?it/s]\u001b[A\u001b[A\n[Tiled VAE]: Executing Decoder Task Queue: 50%|█████ | 124/246 [00:00<00:00, 672.41it/s]\u001b[A\u001b[A\n[Tiled VAE]: Executing Decoder Task Queue: 100%|██████████| 246/246 [00:00<00:00, 744.19it/s]\n[Tiled VAE]: Done in 0.965s, max VRAM alloc 5375.187 MB\nTotal progress: 100%|██████████| 8/8 [00:03<00:00, 3.33it/s]\u001b[A\nTotal progress: 100%|██████████| 8/8 [00:03<00:00, 2.30it/s]\nPrediction took 8.56 seconds",
"metrics": {
"predict_time": 9.202251,
"total_time": 132.449352
},
"output": [
"https://replicate.delivery/pbxt/4n6tIgWqfnV3EyQRdqGfyYjkhye5sG4xyr3Ab20EQp99S51lA/42-dd0112c8-21de-11ef-b285-1e1adbf3739f.png"
],
"started_at": "2024-06-03T19:24:05.919101Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/y9q613he61rgp0cfvy6sqcwjkc",
"cancel": "https://api.replicate.com/v1/predictions/y9q613he61rgp0cfvy6sqcwjkc/cancel"
},
"version": "0e864cd4844ac63d862efd3468e4c55219066351009db73833ad67f98c5eaefb"
}Generated in
Running prediction
Upscaling with scale_factor: 2.0
[Tiled Diffusion] upscaling image with 4x-UltraSharp...
[Tiled Diffusion] ControlNet found, support is enabled.
2024-06-03 19:24:07,997 - ControlNet - INFO - unit_separate = False, style_align = False
2024-06-03 19:24:07,997 - ControlNet - INFO - Loading model from cache: control_v11f1e_sd15_tile
2024-06-03 19:24:08,011 - ControlNet - INFO - Using preprocessor: tile_resample
2024-06-03 19:24:08,011 - ControlNet - INFO - preprocessor resolution = 950
2024-06-03 19:24:08,092 - ControlNet - INFO - ControlNet Hooked - Time = 0.1022803783416748
MultiDiffusion hooked into 'DPM++ 3M SDE Karras' sampler, Tile size: 118x112, Tile count: 3, Batch size: 3, Tile batches: 1 (ext: ContrlNet)
[Tiled VAE]: the input size is tiny and unnecessary to tile.
MultiDiffusion Sampling: 0%| | 0/1 [00:00<?, ?it/s]
0%| | 0/8 [00:00<?, ?it/s]
Total progress: 0%| | 0/8 [00:00<?, ?it/s]
12%|█▎ | 1/8 [00:01<00:07, 1.01s/it]
Total progress: 25%|██▌ | 2/8 [00:00<00:00, 6.32it/s]
25%|██▌ | 2/8 [00:01<00:03, 1.66it/s]
Total progress: 38%|███▊ | 3/8 [00:00<00:01, 4.51it/s]
38%|███▊ | 3/8 [00:01<00:02, 2.13it/s]
Total progress: 50%|█████ | 4/8 [00:00<00:01, 3.91it/s]
50%|█████ | 4/8 [00:01<00:01, 2.45it/s]
Total progress: 62%|██████▎ | 5/8 [00:01<00:00, 3.63it/s]
62%|██████▎ | 5/8 [00:02<00:01, 2.68it/s]
Total progress: 75%|███████▌ | 6/8 [00:01<00:00, 3.48it/s]
75%|███████▌ | 6/8 [00:02<00:00, 2.84it/s]
Total progress: 88%|████████▊ | 7/8 [00:01<00:00, 3.38it/s]
88%|████████▊ | 7/8 [00:02<00:00, 2.96it/s]
100%|██████████| 8/8 [00:03<00:00, 3.05it/s]
100%|██████████| 8/8 [00:03<00:00, 2.51it/s]
Total progress: 100%|██████████| 8/8 [00:02<00:00, 3.33it/s][Tiled VAE]: input_size: torch.Size([1, 4, 118, 250]), tile_size: 128, padding: 11
[Tiled VAE]: split to 1x2 = 2 tiles. Optimal tile size 128x96, original tile size 128x128
[Tiled VAE]: Fast mode enabled, estimating group norm parameters on 128 x 60 image
[Tiled VAE]: Executing Decoder Task Queue: 0%| | 0/246 [00:00<?, ?it/s]
[Tiled VAE]: Executing Decoder Task Queue: 50%|█████ | 124/246 [00:00<00:00, 672.41it/s]
[Tiled VAE]: Executing Decoder Task Queue: 100%|██████████| 246/246 [00:00<00:00, 744.19it/s]
[Tiled VAE]: Done in 0.965s, max VRAM alloc 5375.187 MB
Total progress: 100%|██████████| 8/8 [00:03<00:00, 3.33it/s]
Total progress: 100%|██████████| 8/8 [00:03<00:00, 2.30it/s]
Prediction took 8.56 seconds