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prompthero /lookbook:afd0956c
Input
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 prompthero/lookbook using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"prompthero/lookbook:afd0956c8dd6a67cbf163411fa9507475e92bd956d473d10751a49b67fb79522",
{
input: {
width: 512,
height: 640,
prompt: "a photography of a handsome fashion model wearing a beige jacket",
scheduler: "EULERa",
num_outputs: 1,
guidance_scale: 7,
num_inference_steps: 150
}
}
);
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 prompthero/lookbook using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"prompthero/lookbook:afd0956c8dd6a67cbf163411fa9507475e92bd956d473d10751a49b67fb79522",
input={
"width": 512,
"height": 640,
"prompt": "a photography of a handsome fashion model wearing a beige jacket",
"scheduler": "EULERa",
"num_outputs": 1,
"guidance_scale": 7,
"num_inference_steps": 150
}
)
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 prompthero/lookbook 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": "afd0956c8dd6a67cbf163411fa9507475e92bd956d473d10751a49b67fb79522",
"input": {
"width": 512,
"height": 640,
"prompt": "a photography of a handsome fashion model wearing a beige jacket",
"scheduler": "EULERa",
"num_outputs": 1,
"guidance_scale": 7,
"num_inference_steps": 150
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
brew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run prompthero/lookbook using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/prompthero/lookbook@sha256:afd0956c8dd6a67cbf163411fa9507475e92bd956d473d10751a49b67fb79522 \
-i 'width=512' \
-i 'height=640' \
-i 'prompt="a photography of a handsome fashion model wearing a beige jacket"' \
-i 'scheduler="EULERa"' \
-i 'num_outputs=1' \
-i 'guidance_scale=7' \
-i 'num_inference_steps=150'
To learn more, take a look at the Cog documentation.
Pull and run prompthero/lookbook using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/prompthero/lookbook@sha256:afd0956c8dd6a67cbf163411fa9507475e92bd956d473d10751a49b67fb79522
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 640, "prompt": "a photography of a handsome fashion model wearing a beige jacket", "scheduler": "EULERa", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 150 } }' \ http://localhost:5000/predictions
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Output
{
"completed_at": "2022-12-08T23:01:12.407294Z",
"created_at": "2022-12-08T23:00:59.553946Z",
"data_removed": false,
"error": null,
"id": "jc2xtzhqnvgb7dvxshg2v2rfji",
"input": {
"width": 512,
"height": "640",
"prompt": "a photography of a handsome fashion model wearing a beige jacket",
"scheduler": "EULERa",
"num_outputs": 1,
"guidance_scale": "7",
"num_inference_steps": "150"
},
"logs": "Using seed: 42838\nGlobal seed set to 42838\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%|▏ | 2/150 [00:00<00:11, 12.37it/s]\n 3%|▎ | 4/150 [00:00<00:11, 12.65it/s]\n 4%|▍ | 6/150 [00:00<00:11, 12.83it/s]\n 5%|▌ | 8/150 [00:00<00:11, 12.83it/s]\n 7%|▋ | 10/150 [00:00<00:10, 12.83it/s]\n 8%|▊ | 12/150 [00:00<00:10, 12.87it/s]\n 9%|▉ | 14/150 [00:01<00:10, 12.88it/s]\n 11%|█ | 16/150 [00:01<00:10, 12.91it/s]\n 12%|█▏ | 18/150 [00:01<00:10, 12.92it/s]\n 13%|█▎ | 20/150 [00:01<00:10, 12.84it/s]\n 15%|█▍ | 22/150 [00:01<00:10, 12.75it/s]\n 16%|█▌ | 24/150 [00:01<00:09, 12.78it/s]\n 17%|█▋ | 26/150 [00:02<00:09, 12.73it/s]\n 19%|█▊ | 28/150 [00:02<00:09, 12.78it/s]\n 20%|██ | 30/150 [00:02<00:09, 12.71it/s]\n 21%|██▏ | 32/150 [00:02<00:09, 12.63it/s]\n 23%|██▎ | 34/150 [00:02<00:09, 12.58it/s]\n 24%|██▍ | 36/150 [00:02<00:09, 12.66it/s]\n 25%|██▌ | 38/150 [00:02<00:08, 12.73it/s]\n 27%|██▋ | 40/150 [00:03<00:08, 12.78it/s]\n 28%|██▊ | 42/150 [00:03<00:08, 12.83it/s]\n 29%|██▉ | 44/150 [00:03<00:08, 12.86it/s]\n 31%|███ | 46/150 [00:03<00:08, 12.80it/s]\n 32%|███▏ | 48/150 [00:03<00:07, 12.86it/s]\n 33%|███▎ | 50/150 [00:03<00:08, 12.46it/s]\n 35%|███▍ | 52/150 [00:04<00:07, 12.57it/s]\n 36%|███▌ | 54/150 [00:04<00:07, 12.65it/s]\n 37%|███▋ | 56/150 [00:04<00:07, 12.76it/s]\n 39%|███▊ | 58/150 [00:04<00:07, 12.85it/s]\n 40%|████ | 60/150 [00:04<00:07, 12.83it/s]\n 41%|████▏ | 62/150 [00:04<00:06, 12.86it/s]\n 43%|████▎ | 64/150 [00:05<00:06, 12.69it/s]\n 44%|████▍ | 66/150 [00:05<00:06, 12.73it/s]\n 45%|████▌ | 68/150 [00:05<00:06, 12.76it/s]\n 47%|████▋ | 70/150 [00:05<00:06, 12.77it/s]\n 48%|████▊ | 72/150 [00:05<00:06, 12.84it/s]\n 49%|████▉ | 74/150 [00:05<00:05, 12.90it/s]\n 51%|█████ | 76/150 [00:05<00:05, 12.95it/s]\n 52%|█████▏ | 78/150 [00:06<00:05, 12.74it/s]\n 53%|█████▎ | 80/150 [00:06<00:05, 12.80it/s]\n 55%|█████▍ | 82/150 [00:06<00:05, 12.83it/s]\n 56%|█████▌ | 84/150 [00:06<00:05, 12.88it/s]\n 57%|█████▋ | 86/150 [00:06<00:04, 12.92it/s]\n 59%|█████▊ | 88/150 [00:06<00:04, 12.97it/s]\n 60%|██████ | 90/150 [00:07<00:04, 12.98it/s]\n 61%|██████▏ | 92/150 [00:07<00:04, 12.92it/s]\n 63%|██████▎ | 94/150 [00:07<00:04, 12.95it/s]\n 64%|██████▍ | 96/150 [00:07<00:04, 12.96it/s]\n 65%|██████▌ | 98/150 [00:07<00:04, 12.90it/s]\n 67%|██████▋ | 100/150 [00:07<00:03, 12.52it/s]\n 68%|██████▊ | 102/150 [00:07<00:03, 12.32it/s]\n 69%|██████▉ | 104/150 [00:08<00:03, 12.38it/s]\n 71%|███████ | 106/150 [00:08<00:03, 12.51it/s]\n 72%|███████▏ | 108/150 [00:08<00:03, 12.61it/s]\n 73%|███████▎ | 110/150 [00:08<00:03, 12.71it/s]\n 75%|███████▍ | 112/150 [00:08<00:02, 12.75it/s]\n 76%|███████▌ | 114/150 [00:08<00:02, 12.84it/s]\n 77%|███████▋ | 116/150 [00:09<00:02, 12.80it/s]\n 79%|███████▊ | 118/150 [00:09<00:02, 12.86it/s]\n 80%|████████ | 120/150 [00:09<00:02, 12.86it/s]\n 81%|████████▏ | 122/150 [00:09<00:02, 12.80it/s]\n 83%|████████▎ | 124/150 [00:09<00:02, 12.82it/s]\n 84%|████████▍ | 126/150 [00:09<00:01, 12.81it/s]\n 85%|████████▌ | 128/150 [00:10<00:01, 12.82it/s]\n 87%|████████▋ | 130/150 [00:10<00:01, 12.72it/s]\n 88%|████████▊ | 132/150 [00:10<00:01, 12.80it/s]\n 89%|████████▉ | 134/150 [00:10<00:01, 12.85it/s]\n 91%|█████████ | 136/150 [00:10<00:01, 12.79it/s]\n 92%|█████████▏| 138/150 [00:10<00:00, 12.89it/s]\n 93%|█████████▎| 140/150 [00:10<00:00, 12.91it/s]\n 95%|█████████▍| 142/150 [00:11<00:00, 12.92it/s]\n 96%|█████████▌| 144/150 [00:11<00:00, 12.75it/s]\n 97%|█████████▋| 146/150 [00:11<00:00, 12.64it/s]\n 99%|█████████▊| 148/150 [00:11<00:00, 12.51it/s]\n100%|██████████| 150/150 [00:11<00:00, 12.35it/s]\n100%|██████████| 150/150 [00:11<00:00, 12.75it/s]",
"metrics": {
"predict_time": 12.814997,
"total_time": 12.853348
},
"output": [
"https://replicate.delivery/pbxt/q6TBNBQeW1W4EyEBws3igeMYfVNi5xXQnZG84DTkeVAeWPABC/out-0.png"
],
"started_at": "2022-12-08T23:00:59.592297Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/jc2xtzhqnvgb7dvxshg2v2rfji",
"cancel": "https://api.replicate.com/v1/predictions/jc2xtzhqnvgb7dvxshg2v2rfji/cancel"
},
"version": "afd0956c8dd6a67cbf163411fa9507475e92bd956d473d10751a49b67fb79522"
}
Using seed: 42838
Global seed set to 42838
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