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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: 512,
prompt: "a close up of a person wearing a brown shirt",
scheduler: "EULERa",
num_outputs: 1,
guidance_scale: 7,
num_inference_steps: 100
}
}
);
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": 512,
"prompt": "a close up of a person wearing a brown shirt",
"scheduler": "EULERa",
"num_outputs": 1,
"guidance_scale": 7,
"num_inference_steps": 100
}
)
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": 512,
"prompt": "a close up of a person wearing a brown shirt",
"scheduler": "EULERa",
"num_outputs": 1,
"guidance_scale": 7,
"num_inference_steps": 100
}
}' \
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=512' \
-i 'prompt="a close up of a person wearing a brown shirt"' \
-i 'scheduler="EULERa"' \
-i 'num_outputs=1' \
-i 'guidance_scale=7' \
-i 'num_inference_steps=100'
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": 512, "prompt": "a close up of a person wearing a brown shirt", "scheduler": "EULERa", "num_outputs": 1, "guidance_scale": 7, "num_inference_steps": 100 } }' \ http://localhost:5000/predictions
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Output
{
"completed_at": "2022-12-08T11:47:47.758268Z",
"created_at": "2022-12-08T11:46:46.526357Z",
"data_removed": false,
"error": null,
"id": "aoejgdjelvfb3lzotxr6oslqzq",
"input": {
"width": 512,
"height": 512,
"prompt": "a close up of a person wearing a brown shirt",
"scheduler": "EULERa",
"num_outputs": 1,
"guidance_scale": "7",
"num_inference_steps": "100"
},
"logs": "Using seed: 34339\nGlobal seed set to 34339\n 0%| | 0/100 [00:00<?, ?it/s]\n 2%|▏ | 2/100 [00:00<00:06, 14.48it/s]\n 4%|▍ | 4/100 [00:00<00:06, 14.65it/s]\n 6%|▌ | 6/100 [00:00<00:06, 14.66it/s]\n 8%|▊ | 8/100 [00:00<00:06, 13.97it/s]\n 10%|█ | 10/100 [00:00<00:06, 14.27it/s]\n 12%|█▏ | 12/100 [00:00<00:06, 14.47it/s]\n 14%|█▍ | 14/100 [00:00<00:05, 14.60it/s]\n 16%|█▌ | 16/100 [00:01<00:05, 14.58it/s]\n 18%|█▊ | 18/100 [00:01<00:05, 14.68it/s]\n 20%|██ | 20/100 [00:01<00:05, 14.70it/s]\n 22%|██▏ | 22/100 [00:01<00:05, 14.68it/s]\n 24%|██▍ | 24/100 [00:01<00:05, 14.23it/s]\n 26%|██▌ | 26/100 [00:01<00:05, 14.43it/s]\n 28%|██▊ | 28/100 [00:01<00:04, 14.44it/s]\n 30%|███ | 30/100 [00:02<00:04, 14.49it/s]\n 32%|███▏ | 32/100 [00:02<00:04, 14.59it/s]\n 34%|███▍ | 34/100 [00:02<00:04, 14.64it/s]\n 36%|███▌ | 36/100 [00:02<00:04, 14.62it/s]\n 38%|███▊ | 38/100 [00:02<00:04, 13.84it/s]\n 40%|████ | 40/100 [00:02<00:04, 13.95it/s]\n 42%|████▏ | 42/100 [00:02<00:04, 14.05it/s]\n 44%|████▍ | 44/100 [00:03<00:03, 14.11it/s]\n 46%|████▌ | 46/100 [00:03<00:03, 14.33it/s]\n 48%|████▊ | 48/100 [00:03<00:03, 14.46it/s]\n 50%|█████ | 50/100 [00:03<00:03, 14.48it/s]\n 52%|█████▏ | 52/100 [00:03<00:03, 14.45it/s]\n 54%|█████▍ | 54/100 [00:03<00:03, 14.49it/s]\n 56%|█████▌ | 56/100 [00:03<00:03, 14.35it/s]\n 58%|█████▊ | 58/100 [00:04<00:02, 14.31it/s]\n 60%|██████ | 60/100 [00:04<00:02, 14.45it/s]\n 62%|██████▏ | 62/100 [00:04<00:02, 14.54it/s]\n 64%|██████▍ | 64/100 [00:04<00:02, 14.56it/s]\n 66%|██████▌ | 66/100 [00:04<00:02, 14.59it/s]\n 68%|██████▊ | 68/100 [00:04<00:02, 14.22it/s]\n 70%|███████ | 70/100 [00:04<00:02, 14.10it/s]\n 72%|███████▏ | 72/100 [00:05<00:01, 14.32it/s]\n 74%|███████▍ | 74/100 [00:05<00:01, 14.43it/s]\n 76%|███████▌ | 76/100 [00:05<00:01, 14.59it/s]\n 78%|███████▊ | 78/100 [00:05<00:01, 14.66it/s]\n 80%|████████ | 80/100 [00:05<00:01, 14.71it/s]\n 82%|████████▏ | 82/100 [00:05<00:01, 14.28it/s]\n 84%|████████▍ | 84/100 [00:05<00:01, 14.41it/s]\n 86%|████████▌ | 86/100 [00:05<00:00, 14.48it/s]\n 88%|████████▊ | 88/100 [00:06<00:00, 14.41it/s]\n 90%|█████████ | 90/100 [00:06<00:00, 14.02it/s]\n 92%|█████████▏| 92/100 [00:06<00:00, 14.20it/s]\n 94%|█████████▍| 94/100 [00:06<00:00, 14.37it/s]\n 96%|█████████▌| 96/100 [00:06<00:00, 14.51it/s]\n 98%|█████████▊| 98/100 [00:06<00:00, 13.76it/s]\n100%|██████████| 100/100 [00:06<00:00, 13.97it/s]\n100%|██████████| 100/100 [00:06<00:00, 14.36it/s]",
"metrics": {
"predict_time": 7.589341,
"total_time": 61.231911
},
"output": [
"https://replicate.delivery/pbxt/pemLRGGnxq1dIKFZ8SG0SczfeEEbEuU2frVb5fw7Hq1e4AeDIA/out-0.png"
],
"started_at": "2022-12-08T11:47:40.168927Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/aoejgdjelvfb3lzotxr6oslqzq",
"cancel": "https://api.replicate.com/v1/predictions/aoejgdjelvfb3lzotxr6oslqzq/cancel"
},
"version": "afd0956c8dd6a67cbf163411fa9507475e92bd956d473d10751a49b67fb79522"
}
Using seed: 34339
Global seed set to 34339
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