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anotherjesse
/
sdxl-recur
explore img2img zooming sdxl
- Public
- 1.1K runs
-
L40S
- GitHub
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=anotherjesse/sdxl-recur
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 anotherjesse/sdxl-recur using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"anotherjesse/sdxl-recur:dd9bc7346b840bc8902311579728f2af34f4745ee1d36b7afd76a228de1b341c",
{
input: {
zoom: 1.05,
image: "https://replicate.delivery/pbxt/JfxDhvZy41TsrWdGz4uO5YUpUQkjnndc57u6FQJuAVXl1RHF/moon.jpg",
steps: 10,
width: 1024,
frames: 20,
height: 1024,
prompt: "moon rising over clouds and mountains",
scheduler: "K_EULER",
guidance_scale: 7.5,
negative_prompt: "",
prompt_strength: 0.8
}
}
);
// 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 anotherjesse/sdxl-recur using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"anotherjesse/sdxl-recur:dd9bc7346b840bc8902311579728f2af34f4745ee1d36b7afd76a228de1b341c",
input={
"zoom": 1.05,
"image": "https://replicate.delivery/pbxt/JfxDhvZy41TsrWdGz4uO5YUpUQkjnndc57u6FQJuAVXl1RHF/moon.jpg",
"steps": 10,
"width": 1024,
"frames": 20,
"height": 1024,
"prompt": "moon rising over clouds and mountains",
"scheduler": "K_EULER",
"guidance_scale": 7.5,
"negative_prompt": "",
"prompt_strength": 0.8
}
)
# The anotherjesse/sdxl-recur model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
# https://replicate.com/anotherjesse/sdxl-recur/api#output-schema
print(item)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 anotherjesse/sdxl-recur 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": "anotherjesse/sdxl-recur:dd9bc7346b840bc8902311579728f2af34f4745ee1d36b7afd76a228de1b341c",
"input": {
"zoom": 1.05,
"image": "https://replicate.delivery/pbxt/JfxDhvZy41TsrWdGz4uO5YUpUQkjnndc57u6FQJuAVXl1RHF/moon.jpg",
"steps": 10,
"width": 1024,
"frames": 20,
"height": 1024,
"prompt": "moon rising over clouds and mountains",
"scheduler": "K_EULER",
"guidance_scale": 7.5,
"negative_prompt": "",
"prompt_strength": 0.8
}
}' \
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.
Sign in with GitHub
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2023-10-10T14:53:27.664546Z",
"created_at": "2023-10-10T14:52:51.473981Z",
"data_removed": false,
"error": null,
"id": "vv5vw2lbq63wr7hbdx72ow43ne",
"input": {
"zoom": 1.05,
"image": "https://replicate.delivery/pbxt/JfxDhvZy41TsrWdGz4uO5YUpUQkjnndc57u6FQJuAVXl1RHF/moon.jpg",
"steps": 10,
"width": 1024,
"frames": 20,
"height": 1024,
"prompt": "moon rising over clouds and mountains",
"scheduler": "K_EULER",
"guidance_scale": 7.5,
"negative_prompt": "",
"prompt_strength": 0.8
},
"logs": "Using seed: 37262\n/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 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:00<00:01, 6.01it/s]\n 25%|██▌ | 2/8 [00:00<00:00, 6.35it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 6.48it/s]\n 50%|█████ | 4/8 [00:00<00:00, 6.54it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.56it/s]\n 75%|███████▌ | 6/8 [00:00<00:00, 6.58it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 6.58it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.58it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.53it/s]\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:00<00:01, 6.65it/s]\n 25%|██▌ | 2/8 [00:00<00:00, 6.62it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 6.61it/s]\n 50%|█████ | 4/8 [00:00<00:00, 6.61it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.60it/s]\n 75%|███████▌ | 6/8 [00:00<00:00, 6.59it/s]\n 88%|████████▊ 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[00:00<00:00, 6.60it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.59it/s]\n 75%|███████▌ | 6/8 [00:00<00:00, 6.53it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 6.53it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.54it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.57it/s]\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:00<00:01, 6.64it/s]\n 25%|██▌ | 2/8 [00:00<00:00, 6.61it/s]\n 38%|███▊ | 3/8 [00:00<00:00, 6.59it/s]\n 50%|█████ | 4/8 [00:00<00:00, 6.59it/s]\n 62%|██████▎ | 5/8 [00:00<00:00, 6.59it/s]\n 75%|███████▌ | 6/8 [00:00<00:00, 6.53it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 6.53it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.54it/s]\n100%|██████████| 8/8 [00:01<00:00, 6.56it/s]",
"metrics": {
"predict_time": 35.052018,
"total_time": 36.190565
},
"output": [
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"https://replicate.delivery/pbxt/eWgNPUiLGkwATSioOKi0nsO2Qib1oisloxfZkBQeIRod6iZjA/0003.jpg",
"https://replicate.delivery/pbxt/jWfWsXr8i815HSkEqFbXs9ta0RLMCcnefFkpei28ufR4pLmNC/0004.jpg",
"https://replicate.delivery/pbxt/Gf6o3KmK3cTPNKrS3XYsM8WyZqgFRFRZZSn6lfs2uwLRdxsRA/0005.jpg",
"https://replicate.delivery/pbxt/kewn1fsgkgreYJlUed6NyCBlxeb2k41ktmF8pd8G4ATUqLmNC/0006.jpg",
"https://replicate.delivery/pbxt/rbCENhVAHF7qG9vbosynZPgHIYqAeIIsMQ5TkE0vnVAquY2IA/0007.jpg",
"https://replicate.delivery/pbxt/Ly2o5QBauSpKDZ6rUSOLxrYTHbMn881AMIHjy4X0DeLruY2IA/0008.jpg",
"https://replicate.delivery/pbxt/lREuQCqJmFIOIZpvsR77xlMX9efUGVwnI6m3g0XltNzXdxsRA/0009.jpg",
"https://replicate.delivery/pbxt/JoIpYgeEd0RgHy2FFBFudkzQ5qX9le4p10PIpOUkdq8ZdxsRA/0010.jpg",
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"https://replicate.delivery/pbxt/IiVxoYe4lMyXZSi0nCMXKevP3Y5mSAtM2zmxcBVMPpf56iZjA/0012.jpg",
"https://replicate.delivery/pbxt/ACh9vROTo9KSBVRgF7NH1Ame1vDkT08cjNGOnIwZfYcddxsRA/0013.jpg",
"https://replicate.delivery/pbxt/3APafV9f3NgQv0qcbHpff5ywbI4N9jLbEiQAk1tPyhdfrLmNC/0014.jpg",
"https://replicate.delivery/pbxt/Yoaj931WxYpFN9nQpmMsTrzOuMH7MX2gMQWRIoYSloRYXMbE/0015.jpg",
"https://replicate.delivery/pbxt/1cc8SmFsyq5cLV2Uo3YLahH1wXHkclHoZPczKA7zrBoYXMbE/0016.jpg",
"https://replicate.delivery/pbxt/PcV4xeVK21SCGCrfnyue8Ti8CqSUtU7P9YgqheHa4aTS2FzGB/0017.jpg",
"https://replicate.delivery/pbxt/OjkDX05tD7oAI1brzt35Tt2jx3X1OCWVFSDPHnBxJAbZXMbE/0018.jpg",
"https://replicate.delivery/pbxt/GaxoRfU77xXBGS4hZEFfYszIy5WOpHAzszZc5JyDUn4ndxsRA/0019.jpg"
],
"started_at": "2023-10-10T14:52:52.612528Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/vv5vw2lbq63wr7hbdx72ow43ne",
"cancel": "https://api.replicate.com/v1/predictions/vv5vw2lbq63wr7hbdx72ow43ne/cancel"
},
"version": "9cee5f979f55990d4acb49a9a19553de4d5cedcf0608d1d434e859e734ffb0a8"
}Generated in
Using seed: 37262
/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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100%|██████████| 8/8 [00:01<00:00, 6.56it/s]This output was created using a different version of the model, anotherjesse/sdxl-recur:9cee5f97.
Examples
Run time and cost
This model costs approximately $0.046 to run on Replicate, or 21 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 48 seconds. The predict time for this model varies significantly based on the inputs.