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lucataco /diffusionlight:958d6f03
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=lucataco/diffusionlight
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";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run lucataco/diffusionlight using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
{
input: {
image: "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
prompt: "a perfect mirrored reflective chrome ball sphere",
negative_prompt: "matte, diffuse, flat, dull",
num_inference_steps: 30,
controlnet_conditioning_scale: 0.5
}
}
);
// To access the file URL:
console.log(output.url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output);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 lucataco/diffusionlight using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
input={
"image": "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
"prompt": "a perfect mirrored reflective chrome ball sphere",
"negative_prompt": "matte, diffuse, flat, dull",
"num_inference_steps": 30,
"controlnet_conditioning_scale": 0.5
}
)
print(output)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 lucataco/diffusionlight 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": "lucataco/diffusionlight:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef",
"input": {
"image": "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
"prompt": "a perfect mirrored reflective chrome ball sphere",
"negative_prompt": "matte, diffuse, flat, dull",
"num_inference_steps": 30,
"controlnet_conditioning_scale": 0.5
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/lucataco/diffusionlight@sha256:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef \
-i 'image="https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg"' \
-i 'prompt="a perfect mirrored reflective chrome ball sphere"' \
-i 'negative_prompt="matte, diffuse, flat, dull"' \
-i 'num_inference_steps=30' \
-i 'controlnet_conditioning_scale=0.5'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/lucataco/diffusionlight@sha256:958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5efcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg", "prompt": "a perfect mirrored reflective chrome ball sphere", "negative_prompt": "matte, diffuse, flat, dull", "num_inference_steps": 30, "controlnet_conditioning_scale": 0.5 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
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Output
{
"completed_at": "2024-01-30T21:51:51.655582Z",
"created_at": "2024-01-30T21:49:10.599328Z",
"data_removed": false,
"error": null,
"id": "c3twa5lb3dphdu5rhzrf5lqngm",
"input": {
"image": "https://replicate.delivery/pbxt/KJoKQAifOmk7YFHsSVzSabOJdgjb6QXrgyjSCh3rskrUK32a/ski.jpeg",
"prompt": "a perfect mirrored reflective chrome ball sphere",
"negative_prompt": "matte, diffuse, flat, dull",
"num_inference_steps": 30,
"controlnet_conditioning_scale": 0.5
},
"logs": "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/functional.py:504: 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:3483.)\nreturn _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n 0%| | 0/29 [00:00<?, ?it/s]\n 3%|▎ | 1/29 [00:00<00:10, 2.72it/s]\n 7%|▋ | 2/29 [00:00<00:08, 3.09it/s]\n 10%|█ | 3/29 [00:00<00:08, 3.19it/s]\n 14%|█▍ | 4/29 [00:01<00:07, 3.27it/s]\n 17%|█▋ | 5/29 [00:01<00:07, 3.32it/s]\n 21%|██ | 6/29 [00:01<00:06, 3.34it/s]\n 24%|██▍ | 7/29 [00:02<00:06, 3.36it/s]\n 28%|██▊ | 8/29 [00:02<00:06, 3.37it/s]\n 31%|███ | 9/29 [00:02<00:05, 3.38it/s]\n 34%|███▍ | 10/29 [00:03<00:05, 3.38it/s]\n 38%|███▊ | 11/29 [00:03<00:05, 3.38it/s]\n 41%|████▏ | 12/29 [00:03<00:05, 3.39it/s]\n 45%|████▍ | 13/29 [00:03<00:04, 3.39it/s]\n 48%|████▊ | 14/29 [00:04<00:04, 3.39it/s]\n 52%|█████▏ | 15/29 [00:04<00:04, 3.39it/s]\n 55%|█████▌ | 16/29 [00:04<00:03, 3.39it/s]\n 59%|█████▊ | 17/29 [00:05<00:03, 3.38it/s]\n 62%|██████▏ | 18/29 [00:05<00:03, 3.38it/s]\n 66%|██████▌ | 19/29 [00:05<00:02, 3.38it/s]\n 69%|██████▉ | 20/29 [00:05<00:02, 3.38it/s]\n 72%|███████▏ | 21/29 [00:06<00:02, 3.38it/s]\n 76%|███████▌ | 22/29 [00:06<00:02, 3.38it/s]\n 79%|███████▉ | 23/29 [00:06<00:01, 3.38it/s]\n 83%|████████▎ | 24/29 [00:07<00:01, 3.38it/s]\n 86%|████████▌ | 25/29 [00:07<00:01, 3.38it/s]\n 90%|████████▉ | 26/29 [00:07<00:00, 3.38it/s]\n 93%|█████████▎| 27/29 [00:08<00:00, 3.38it/s]\n 97%|█████████▋| 28/29 [00:08<00:00, 3.37it/s]\n100%|██████████| 29/29 [00:08<00:00, 3.37it/s]\n100%|██████████| 29/29 [00:08<00:00, 3.35it/s]",
"metrics": {
"predict_time": 12.652394,
"total_time": 161.056254
},
"output": "https://replicate.delivery/pbxt/Vkrkqo4CIxqVGNHcZy6EGY11W5KaBuu6ggRYeFsJ5fN3FyRSA/output.png",
"started_at": "2024-01-30T21:51:39.003188Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/c3twa5lb3dphdu5rhzrf5lqngm",
"cancel": "https://api.replicate.com/v1/predictions/c3twa5lb3dphdu5rhzrf5lqngm/cancel"
},
"version": "958d6f0315da9b86f74ca6a67b8cafa1b44b4b6330f0385d956da171ec02e5ef"
}Generated in
/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/functional.py:504: 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:3483.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
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