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laion-ai /deep-image-diffusion-prior:4ed232e4
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=laion-ai/deep-image-diffusion-prior
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 laion-ai/deep-image-diffusion-prior using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"laion-ai/deep-image-diffusion-prior:4ed232e4ebe82b3d70ee5e0526257aeeb07187f6727ee617795a7f94d2133e2d",
{
input: {
lr: 0.001,
seed: -1,
prompt: "An oil painting of mountains, in the style of monet",
lr_decay: 0.995,
iterations: 250,
num_scales: 6,
num_cutouts: 8,
offset_type: "none",
display_freq: 25,
offset_lr_fac: 1,
guidance_scale: 1,
input_noise_strength: 0,
param_noise_strength: 0,
num_samples_per_batch: 1
}
}
);
// 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 laion-ai/deep-image-diffusion-prior using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"laion-ai/deep-image-diffusion-prior:4ed232e4ebe82b3d70ee5e0526257aeeb07187f6727ee617795a7f94d2133e2d",
input={
"lr": 0.001,
"seed": -1,
"prompt": "An oil painting of mountains, in the style of monet",
"lr_decay": 0.995,
"iterations": 250,
"num_scales": 6,
"num_cutouts": 8,
"offset_type": "none",
"display_freq": 25,
"offset_lr_fac": 1,
"guidance_scale": 1,
"input_noise_strength": 0,
"param_noise_strength": 0,
"num_samples_per_batch": 1
}
)
# The laion-ai/deep-image-diffusion-prior 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/laion-ai/deep-image-diffusion-prior/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 laion-ai/deep-image-diffusion-prior 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": "laion-ai/deep-image-diffusion-prior:4ed232e4ebe82b3d70ee5e0526257aeeb07187f6727ee617795a7f94d2133e2d",
"input": {
"lr": 0.001,
"seed": -1,
"prompt": "An oil painting of mountains, in the style of monet",
"lr_decay": 0.995,
"iterations": 250,
"num_scales": 6,
"num_cutouts": 8,
"offset_type": "none",
"display_freq": 25,
"offset_lr_fac": 1,
"guidance_scale": 1,
"input_noise_strength": 0,
"param_noise_strength": 0,
"num_samples_per_batch": 1
}
}' \
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.
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Output
{
"completed_at": "2022-07-03T01:46:33.270423Z",
"created_at": "2022-07-03T01:42:59.952944Z",
"data_removed": false,
"error": null,
"id": "iz4anzmjzfgubakxieksaceysm",
"input": {
"lr": 0.001,
"seed": -1,
"prompt": "An oil painting of mountains, in the style of monet",
"lr_decay": 0.995,
"iterations": 250,
"num_scales": 6,
"num_cutouts": "8",
"offset_type": "none",
"display_freq": 25,
"offset_lr_fac": 1,
"guidance_scale": 1,
"num_samples_per_batch": "1"
},
"logs": "Initializing deep image prior net...\n\nsampling loop time step: 0%| | 0/1000 [00:00<?, ?it/s]\nsampling loop time step: 0%| | 2/1000 [00:00<01:02, 16.07it/s]\nsampling loop time step: 0%| | 4/1000 [00:00<00:58, 16.98it/s]\nsampling loop time step: 1%| | 6/1000 [00:00<00:57, 17.43it/s]\nsampling loop time step: 1%| | 8/1000 [00:00<00:55, 17.79it/s]\nsampling loop time step: 1%| | 10/1000 [00:00<00:55, 17.88it/s]\nsampling loop time step: 1%| | 12/1000 [00:00<00:55, 17.70it/s]\nsampling loop time step: 1%|▏ | 14/1000 [00:00<00:55, 17.65it/s]\nsampling loop time step: 2%|▏ | 16/1000 [00:00<00:55, 17.61it/s]\nsampling loop time step: 2%|▏ | 18/1000 [00:01<00:54, 17.92it/s]\nsampling loop time step: 2%|▏ | 20/1000 [00:01<00:53, 18.26it/s]\nsampling loop time step: 2%|▏ | 22/1000 [00:01<00:53, 18.37it/s]\nsampling loop time step: 2%|▏ | 24/1000 [00:01<00:51, 18.79it/s]\nsampling loop time step: 3%|▎ | 26/1000 [00:01<00:50, 19.13it/s]\nsampling loop time step: 3%|▎ | 29/1000 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deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n right_pads = (((out_sz - fw_arange(num_convs, fw, device) - 1) # (1)\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)\n return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n\n 0%| | 1/250 [00:00<02:41, 1.54it/s]\n 1%| | 2/250 [00:01<02:28, 1.67it/s]\n 1%| | 3/250 [00:01<02:18, 1.78it/s]\n 2%|▏ | 4/250 [00:02<02:15, 1.81it/s]\n 2%|▏ | 5/250 [00:02<02:12, 1.84it/s]\n 2%|▏ | 6/250 [00:03<02:10, 1.87it/s]\n 3%|▎ | 7/250 [00:03<02:08, 1.89it/s]\n 3%|▎ | 8/250 [00:04<02:08, 1.89it/s]\n 4%|▎ | 9/250 [00:04<02:08, 1.87it/s]\n 4%|▍ | 10/250 [00:05<02:09, 1.85it/s]\n 4%|▍ | 11/250 [00:05<02:08, 1.86it/s]\n 5%|▍ | 12/250 [00:06<02:07, 1.86it/s]\n 5%|▌ | 13/250 [00:07<02:08, 1.85it/s]\n 6%|▌ | 14/250 [00:07<02:08, 1.84it/s]\n 6%|▌ | 15/250 [00:08<02:06, 1.86it/s]\n 6%|▋ | 16/250 [00:08<02:04, 1.87it/s]\n 7%|▋ | 17/250 [00:09<02:04, 1.87it/s]\n 7%|▋ | 18/250 [00:09<02:02, 1.89it/s]\n 8%|▊ | 19/250 [00:10<02:02, 1.89it/s]\n 8%|▊ | 20/250 [00:10<02:02, 1.88it/s]\n 8%|▊ | 21/250 [00:11<02:00, 1.90it/s]\n 9%|▉ | 22/250 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1.83it/s]\n\n\n\n\ni: 200, loss: 0.0897339, losses: 0.0897339\n7it [01:50, 13.87s/it]\n 80%|███████▉ | 199/250 [01:49<00:27, 1.83it/s]\n\n\nSaved image to output/2022-07-03_01-46-05/out_00200.png\n\n\n7it [01:50, 13.87s/it]\n 80%|███████▉ | 199/250 [01:50<00:27, 1.83it/s]\n8it [01:50, 13.86s/it]\n 80%|████████ | 200/250 [01:50<00:28, 1.78it/s]\n 80%|████████ | 201/250 [01:50<00:27, 1.80it/s]\n 81%|████████ | 202/250 [01:51<00:26, 1.81it/s]\n 81%|████████ | 203/250 [01:51<00:26, 1.80it/s]\n 82%|████████▏ | 204/250 [01:52<00:25, 1.80it/s]\n 82%|████████▏ | 205/250 [01:52<00:25, 1.79it/s]\n 82%|████████▏ | 206/250 [01:53<00:24, 1.79it/s]\n 83%|████████▎ | 207/250 [01:53<00:23, 1.81it/s]\n 83%|████████▎ | 208/250 [01:54<00:23, 1.80it/s]\n 84%|████████▎ | 209/250 [01:54<00:22, 1.81it/s]\n 84%|████████▍ | 210/250 [01:55<00:21, 1.83it/s]\n 84%|████████▍ | 211/250 [01:56<00:21, 1.82it/s]\n 85%|████████▍ | 212/250 [01:56<00:20, 1.83it/s]\n 85%|████████▌ | 213/250 [01:57<00:20, 1.82it/s]\n 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99%|█████████▉| 248/250 [02:16<00:01, 1.82it/s]\n100%|█████████▉| 249/250 [02:17<00:00, 1.82it/s]\n\n\n\n\ni: 250, loss: 0.0951135, losses: 0.0951135\n9it [02:17, 13.84s/it]\n100%|█████████▉| 249/250 [02:17<00:00, 1.82it/s]\nSaved image to output/2022-07-03_01-46-32/out_00250.png\n\n\n\n\n9it [02:17, 13.84s/it]\n100%|█████████▉| 249/250 [02:17<00:00, 1.82it/s]\n10it [02:17, 13.84s/it]\n100%|██████████| 250/250 [02:17<00:00, 1.77it/s]\n100%|██████████| 250/250 [02:17<00:00, 1.82it/s]\n\n10it [02:17, 13.77s/it]",
"metrics": {
"predict_time": 189.116149,
"total_time": 213.317479
},
"output": [
"https://replicate.delivery/mgxm/8ccfd17c-8ed6-478f-89ee-8642a562b09a/out_00025.png",
"https://replicate.delivery/mgxm/860df9a9-1cec-4437-8c56-78ce77dae821/out_00050.png",
"https://replicate.delivery/mgxm/af52dbde-8b2d-481c-bb4d-d3ac7cef93e5/out_00075.png",
"https://replicate.delivery/mgxm/91c116d5-4013-4329-bbda-487a2cc0795f/out_00100.png",
"https://replicate.delivery/mgxm/b4c24c6b-9a37-436a-9611-5f917c52d59f/out_00125.png",
"https://replicate.delivery/mgxm/fa1d545a-12a8-43ff-8634-d10c998c88f6/out_00150.png",
"https://replicate.delivery/mgxm/95577f4b-fec0-4e29-8776-15dbff32d64d/out_00175.png",
"https://replicate.delivery/mgxm/2f40bd34-fa12-4551-84fe-208aa3a9c34a/out_00200.png",
"https://replicate.delivery/mgxm/1fed3af3-1247-447c-a662-d58361e54bbf/out_00225.png",
"https://replicate.delivery/mgxm/427d7301-e4d9-4893-a210-8cc91a48d188/out_00250.png"
],
"started_at": "2022-07-03T01:43:24.154274Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/iz4anzmjzfgubakxieksaceysm",
"cancel": "https://api.replicate.com/v1/predictions/iz4anzmjzfgubakxieksaceysm/cancel"
},
"version": "4ed232e4ebe82b3d70ee5e0526257aeeb07187f6727ee617795a7f94d2133e2d"
}Generated in
Initializing deep image prior net...
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right_pads = (((out_sz - fw_arange(num_convs, fw, device) - 1) # (1)
/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
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i: 175, loss: 0.0815946, losses: 0.0815946
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i: 200, loss: 0.0897339, losses: 0.0897339
7it [01:50, 13.87s/it]
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Saved image to output/2022-07-03_01-46-05/out_00200.png
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i: 225, loss: 0.0799063, losses: 0.0799063
8it [02:03, 13.86s/it]
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Saved image to output/2022-07-03_01-46-18/out_00225.png
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i: 250, loss: 0.0951135, losses: 0.0951135
9it [02:17, 13.84s/it]
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Saved image to output/2022-07-03_01-46-32/out_00250.png
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10it [02:17, 13.77s/it]