Readme
This model doesn't have a readme.
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run workroomprds/sdxl-james using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"workroomprds/sdxl-james:0b619747d8438f53732cf914ffcc4f02321b1d05069e477135aaf15c92cad084",
{
input: {
width: 1024,
height: 1024,
prompt: "a photo portrait of TOK in Bulgarian costume with a black kalpac and a red sleeveless jacket",
refine: "no_refiner",
scheduler: "KarrasDPM",
lora_scale: 0.6,
num_outputs: 4,
guidance_scale: 7.5,
apply_watermark: true,
high_noise_frac: 0.8,
negative_prompt: "",
prompt_strength: 0.8,
num_inference_steps: 37
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run workroomprds/sdxl-james using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"workroomprds/sdxl-james:0b619747d8438f53732cf914ffcc4f02321b1d05069e477135aaf15c92cad084",
input={
"width": 1024,
"height": 1024,
"prompt": "a photo portrait of TOK in Bulgarian costume with a black kalpac and a red sleeveless jacket",
"refine": "no_refiner",
"scheduler": "KarrasDPM",
"lora_scale": 0.6,
"num_outputs": 4,
"guidance_scale": 7.5,
"apply_watermark": True,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 37
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run workroomprds/sdxl-james 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": "0b619747d8438f53732cf914ffcc4f02321b1d05069e477135aaf15c92cad084",
"input": {
"width": 1024,
"height": 1024,
"prompt": "a photo portrait of TOK in Bulgarian costume with a black kalpac and a red sleeveless jacket",
"refine": "no_refiner",
"scheduler": "KarrasDPM",
"lora_scale": 0.6,
"num_outputs": 4,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 37
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-11-16T00:21:15.837568Z",
"created_at": "2023-11-16T00:20:01.265223Z",
"data_removed": false,
"error": null,
"id": "rr2e2otbuwjwu6rz6cp2qmhkfm",
"input": {
"width": 1024,
"height": 1024,
"prompt": "a photo portrait of TOK in Bulgarian costume with a black kalpac and a red sleeveless jacket",
"refine": "no_refiner",
"scheduler": "KarrasDPM",
"lora_scale": 0.6,
"num_outputs": 4,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 37
},
"logs": "Using seed: 64047\nEnsuring enough disk space...\nFree disk space: 1790269693952\nDownloading weights: https://replicate.delivery/pbxt/8r23TLG4vAruFdfo1GPdTqMOsGapeuhvIVbh4FOAeKm9nhxjA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.353s (526 MB/s)\\nExtracted 186 MB in 0.054s (3.5 GB/s)\\n'\nDownloaded weights in 0.4770321846008301 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a photo portrait of <s0><s1> in Bulgarian costume with a black kalpac and a red sleeveless jacket\ntxt2img mode\n 0%| | 0/37 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 3%|▎ | 1/37 [00:01<00:57, 1.60s/it]\n 5%|▌ | 2/37 [00:02<00:44, 1.28s/it]\n 8%|▊ | 3/37 [00:03<00:40, 1.18s/it]\n 11%|█ | 4/37 [00:04<00:37, 1.13s/it]\n 14%|█▎ | 5/37 [00:05<00:35, 1.10s/it]\n 16%|█▌ | 6/37 [00:06<00:33, 1.09s/it]\n 19%|█▉ | 7/37 [00:07<00:32, 1.08s/it]\n 22%|██▏ | 8/37 [00:08<00:31, 1.07s/it]\n 24%|██▍ | 9/37 [00:10<00:29, 1.07s/it]\n 27%|██▋ | 10/37 [00:11<00:28, 1.06s/it]\n 30%|██▉ | 11/37 [00:12<00:27, 1.06s/it]\n 32%|███▏ | 12/37 [00:13<00:26, 1.06s/it]\n 35%|███▌ | 13/37 [00:14<00:25, 1.06s/it]\n 38%|███▊ | 14/37 [00:15<00:24, 1.06s/it]\n 41%|████ | 15/37 [00:16<00:23, 1.06s/it]\n 43%|████▎ | 16/37 [00:17<00:22, 1.06s/it]\n 46%|████▌ | 17/37 [00:18<00:21, 1.06s/it]\n 49%|████▊ | 18/37 [00:19<00:20, 1.06s/it]\n 51%|█████▏ | 19/37 [00:20<00:19, 1.06s/it]\n 54%|█████▍ | 20/37 [00:21<00:18, 1.06s/it]\n 57%|█████▋ | 21/37 [00:22<00:17, 1.06s/it]\n 59%|█████▉ | 22/37 [00:23<00:15, 1.06s/it]\n 62%|██████▏ | 23/37 [00:24<00:14, 1.06s/it]\n 65%|██████▍ | 24/37 [00:25<00:13, 1.06s/it]\n 68%|██████▊ | 25/37 [00:27<00:12, 1.06s/it]\n 70%|███████ | 26/37 [00:28<00:11, 1.06s/it]\n 73%|███████▎ | 27/37 [00:29<00:10, 1.06s/it]\n 76%|███████▌ | 28/37 [00:30<00:09, 1.06s/it]\n 78%|███████▊ | 29/37 [00:31<00:08, 1.06s/it]\n 81%|████████ | 30/37 [00:32<00:07, 1.06s/it]\n 84%|████████▍ | 31/37 [00:33<00:06, 1.06s/it]\n 86%|████████▋ | 32/37 [00:34<00:05, 1.06s/it]\n 89%|████████▉ | 33/37 [00:35<00:04, 1.06s/it]\n 92%|█████████▏| 34/37 [00:36<00:03, 1.06s/it]\n 95%|█████████▍| 35/37 [00:37<00:02, 1.06s/it]\n 97%|█████████▋| 36/37 [00:38<00:01, 1.06s/it]\n100%|██████████| 37/37 [00:39<00:00, 1.07s/it]\n100%|██████████| 37/37 [00:39<00:00, 1.08s/it]",
"metrics": {
"predict_time": 49.397552,
"total_time": 74.572345
},
"output": [
"https://replicate.delivery/pbxt/BSQlTS9eQyQDYatcXQ2DhF6sMyMFFw7bfB1RRPu7R5b5Jx4RA/out-0.png",
"https://replicate.delivery/pbxt/QFjYLfkOUeuejIzlpcdDsi6ptyZkXDvT5BCpvxmap8b1TixjA/out-1.png",
"https://replicate.delivery/pbxt/LQbh3MbtTIYNJdT3vfVX4hHlNg6P1ha2fVeFumSS6qU1TixjA/out-2.png",
"https://replicate.delivery/pbxt/cfPNnshZC7RxYiD36Arj2yfsYii7XhkLwX5OtrffNPFtnEjHB/out-3.png"
],
"started_at": "2023-11-16T00:20:26.440016Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/rr2e2otbuwjwu6rz6cp2qmhkfm",
"cancel": "https://api.replicate.com/v1/predictions/rr2e2otbuwjwu6rz6cp2qmhkfm/cancel"
},
"version": "0b619747d8438f53732cf914ffcc4f02321b1d05069e477135aaf15c92cad084"
}
Using seed: 64047
Ensuring enough disk space...
Free disk space: 1790269693952
Downloading weights: https://replicate.delivery/pbxt/8r23TLG4vAruFdfo1GPdTqMOsGapeuhvIVbh4FOAeKm9nhxjA/trained_model.tar
b'Downloaded 186 MB bytes in 0.353s (526 MB/s)\nExtracted 186 MB in 0.054s (3.5 GB/s)\n'
Downloaded weights in 0.4770321846008301 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: a photo portrait of <s0><s1> in Bulgarian costume with a black kalpac and a red sleeveless jacket
txt2img mode
0%| | 0/37 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)
return F.conv2d(input, weight, bias, self.stride,
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This model costs approximately $0.050 to run on Replicate, or 20 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 52 seconds.
This model doesn't have a readme.
This model is warm. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
Choose a file from your machine
Hint: you can also drag files onto the input
Choose a file from your machine
Hint: you can also drag files onto the input
Using seed: 64047
Ensuring enough disk space...
Free disk space: 1790269693952
Downloading weights: https://replicate.delivery/pbxt/8r23TLG4vAruFdfo1GPdTqMOsGapeuhvIVbh4FOAeKm9nhxjA/trained_model.tar
b'Downloaded 186 MB bytes in 0.353s (526 MB/s)\nExtracted 186 MB in 0.054s (3.5 GB/s)\n'
Downloaded weights in 0.4770321846008301 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: a photo portrait of <s0><s1> in Bulgarian costume with a black kalpac and a red sleeveless jacket
txt2img mode
0%| | 0/37 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)
return F.conv2d(input, weight, bias, self.stride,
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