Readme
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Model description
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Intended use
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Ethical considerations
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Caveats and recommendations
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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 danjimenezm/food-gen-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"danjimenezm/food-gen-v1:235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443",
{
input: {
image: "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png",
width: 512,
height: 512,
prompt: "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1",
scheduler: "DPMSolverMultistep",
num_outputs: 1,
guidance_scale: 7.5,
prompt_strength: 0.9,
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 danjimenezm/food-gen-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"danjimenezm/food-gen-v1:235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443",
input={
"image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png",
"width": 512,
"height": 512,
"prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1",
"scheduler": "DPMSolverMultistep",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.9,
"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 danjimenezm/food-gen-v1 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": "235647b5791eedd2c58d9ccdf32328b8083143a2b0e68148da6ef30731453443",
"input": {
"image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png",
"width": 512,
"height": 512,
"prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1",
"scheduler": "DPMSolverMultistep",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.9,
"num_inference_steps": 100
}
}' \
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": "2023-04-10T23:35:48.340181Z",
"created_at": "2023-04-10T23:35:29.942182Z",
"data_removed": false,
"error": null,
"id": "6pk7uzqpnbecnd55ntnw5xwkvq",
"input": {
"image": "https://replicate.delivery/pbxt/Id7sB4syXLOODgSUcYmahgHUNispx6qePkO8PCTLbb51YUKf/test%20%284%29.png",
"width": 512,
"height": 512,
"prompt": "A professional food photo of a chicken pizza, on a marble table, overhead. centered. 50% negative space on all sides. minimalist style. centered: 1",
"scheduler": "DPMSolverMultistep",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.9,
"num_inference_steps": 100
},
"logs": "Using seed: 38955\n 0%| | 0/90 [00:00<?, ?it/s]\n 1%| | 1/90 [00:00<00:19, 4.56it/s]\n 2%|▏ | 2/90 [00:00<00:17, 5.01it/s]\n 3%|▎ | 3/90 [00:00<00:16, 5.18it/s]\n 4%|▍ | 4/90 [00:00<00:16, 5.28it/s]\n 6%|▌ | 5/90 [00:00<00:15, 5.34it/s]\n 7%|▋ | 6/90 [00:01<00:15, 5.32it/s]\n 8%|▊ | 7/90 [00:01<00:15, 5.31it/s]\n 9%|▉ | 8/90 [00:01<00:15, 5.33it/s]\n 10%|█ | 9/90 [00:01<00:15, 5.38it/s]\n 11%|█ | 10/90 [00:01<00:14, 5.41it/s]\n 12%|█▏ | 11/90 [00:02<00:14, 5.40it/s]\n 13%|█▎ | 12/90 [00:02<00:14, 5.39it/s]\n 14%|█▍ | 13/90 [00:02<00:14, 5.37it/s]\n 16%|█▌ | 14/90 [00:02<00:14, 5.36it/s]\n 17%|█▋ | 15/90 [00:02<00:13, 5.39it/s]\n 18%|█▊ | 16/90 [00:03<00:13, 5.40it/s]\n 19%|█▉ | 17/90 [00:03<00:13, 5.39it/s]\n 20%|██ | 18/90 [00:03<00:13, 5.38it/s]\n 21%|██ | 19/90 [00:03<00:13, 5.36it/s]\n 22%|██▏ | 20/90 [00:03<00:13, 5.37it/s]\n 23%|██▎ | 21/90 [00:03<00:12, 5.40it/s]\n 24%|██▍ | 22/90 [00:04<00:12, 5.40it/s]\n 26%|██▌ | 23/90 [00:04<00:12, 5.40it/s]\n 27%|██▋ | 24/90 [00:04<00:12, 5.37it/s]\n 28%|██▊ | 25/90 [00:04<00:12, 5.36it/s]\n 29%|██▉ | 26/90 [00:04<00:11, 5.38it/s]\n 30%|███ | 27/90 [00:05<00:11, 5.39it/s]\n 31%|███ | 28/90 [00:05<00:11, 5.41it/s]\n 32%|███▏ | 29/90 [00:05<00:11, 5.40it/s]\n 33%|███▎ | 30/90 [00:05<00:11, 5.36it/s]\n 34%|███▍ | 31/90 [00:05<00:11, 5.36it/s]\n 36%|███▌ | 32/90 [00:05<00:10, 5.37it/s]\n 37%|███▋ | 33/90 [00:06<00:10, 5.38it/s]\n 38%|███▊ | 34/90 [00:06<00:10, 5.38it/s]\n 39%|███▉ | 35/90 [00:06<00:10, 5.34it/s]\n 40%|████ | 36/90 [00:06<00:10, 5.36it/s]\n 41%|████ | 37/90 [00:06<00:09, 5.38it/s]\n 42%|████▏ | 38/90 [00:07<00:09, 5.38it/s]\n 43%|████▎ | 39/90 [00:07<00:09, 5.36it/s]\n 44%|████▍ | 40/90 [00:07<00:09, 5.33it/s]\n 46%|████▌ | 41/90 [00:07<00:09, 5.34it/s]\n 47%|████▋ | 42/90 [00:07<00:08, 5.33it/s]\n 48%|████▊ | 43/90 [00:08<00:08, 5.33it/s]\n 49%|████▉ | 44/90 [00:08<00:08, 5.36it/s]\n 50%|█████ | 45/90 [00:08<00:08, 5.36it/s]\n 51%|█████ | 46/90 [00:08<00:08, 5.36it/s]\n 52%|█████▏ | 47/90 [00:08<00:08, 5.34it/s]\n 53%|█████▎ | 48/90 [00:08<00:07, 5.35it/s]\n 54%|█████▍ | 49/90 [00:09<00:07, 5.38it/s]\n 56%|█████▌ | 50/90 [00:09<00:07, 5.39it/s]\n 57%|█████▋ | 51/90 [00:09<00:07, 5.38it/s]\n 58%|█████▊ | 52/90 [00:09<00:07, 5.34it/s]\n 59%|█████▉ | 53/90 [00:09<00:06, 5.33it/s]\n 60%|██████ | 54/90 [00:10<00:06, 5.35it/s]\n 61%|██████ | 55/90 [00:10<00:06, 5.34it/s]\n 62%|██████▏ | 56/90 [00:10<00:06, 5.32it/s]\n 63%|██████▎ | 57/90 [00:10<00:06, 5.33it/s]\n 64%|██████▍ | 58/90 [00:10<00:06, 5.32it/s]\n 66%|██████▌ | 59/90 [00:11<00:05, 5.31it/s]\n 67%|██████▋ | 60/90 [00:11<00:05, 5.31it/s]\n 68%|██████▊ | 61/90 [00:11<00:05, 5.32it/s]\n 69%|██████▉ | 62/90 [00:11<00:05, 5.30it/s]\n 70%|███████ | 63/90 [00:11<00:05, 5.32it/s]\n 71%|███████ | 64/90 [00:11<00:04, 5.32it/s]\n 72%|███████▏ | 65/90 [00:12<00:04, 5.30it/s]\n 73%|███████▎ | 66/90 [00:12<00:04, 5.31it/s]\n 74%|███████▍ | 67/90 [00:12<00:04, 5.33it/s]\n 76%|███████▌ | 68/90 [00:12<00:04, 5.33it/s]\n 77%|███████▋ | 69/90 [00:12<00:03, 5.31it/s]\n 78%|███████▊ | 70/90 [00:13<00:03, 5.32it/s]\n 79%|███████▉ | 71/90 [00:13<00:03, 5.31it/s]\n 80%|████████ | 72/90 [00:13<00:03, 5.30it/s]\n 81%|████████ | 73/90 [00:13<00:03, 5.32it/s]\n 82%|████████▏ | 74/90 [00:13<00:03, 5.30it/s]\n 83%|████████▎ | 75/90 [00:14<00:02, 5.28it/s]\n 84%|████████▍ | 76/90 [00:14<00:02, 5.30it/s]\n 86%|████████▌ | 77/90 [00:14<00:02, 5.31it/s]\n 87%|████████▋ | 78/90 [00:14<00:02, 5.28it/s]\n 88%|████████▊ | 79/90 [00:14<00:02, 5.32it/s]\n 89%|████████▉ | 80/90 [00:14<00:01, 5.30it/s]\n 90%|█████████ | 81/90 [00:15<00:01, 5.27it/s]\n 91%|█████████ | 82/90 [00:15<00:01, 5.29it/s]\n 92%|█████████▏| 83/90 [00:15<00:01, 5.27it/s]\n 93%|█████████▎| 84/90 [00:15<00:01, 5.29it/s]\n 94%|█████████▍| 85/90 [00:15<00:00, 5.31it/s]\n 96%|█████████▌| 86/90 [00:16<00:00, 5.28it/s]\n 97%|█████████▋| 87/90 [00:16<00:00, 5.30it/s]\n 98%|█████████▊| 88/90 [00:16<00:00, 5.31it/s]\n 99%|█████████▉| 89/90 [00:16<00:00, 5.29it/s]\n100%|██████████| 90/90 [00:16<00:00, 5.30it/s]\n100%|██████████| 90/90 [00:16<00:00, 5.33it/s]",
"metrics": {
"predict_time": 18.296322,
"total_time": 18.397999
},
"output": [
"https://replicate.delivery/pbxt/DSYuPHYKdLK7KdV5udy89jPoKJepgSbS0XF0VwgALRypekwQA/out-0.png"
],
"started_at": "2023-04-10T23:35:30.043859Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/6pk7uzqpnbecnd55ntnw5xwkvq",
"cancel": "https://api.replicate.com/v1/predictions/6pk7uzqpnbecnd55ntnw5xwkvq/cancel"
},
"version": "08bc07087eaabf26835dbb72e076fe1711fea2f451547f4ec0969473090831e9"
}
Using seed: 38955
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This example was created by a different version, danjimenezm/food-gen-v1:08bc0708.
This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.
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This model is cold. 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
Using seed: 38955
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