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camenduru /visual-style-prompting-controlnet:bfd1dedb
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=camenduru/visual-style-prompting-controlnet
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 camenduru/visual-style-prompting-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"camenduru/visual-style-prompting-controlnet:bfd1dedbb46545a191b3a42b5f3778d6c97fa6b6c0283de7d099403c05560b65",
{
input: {
prompt: "",
style_name: "fire",
depth_image: "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png",
style_image: "https://replicate.delivery/pbxt/KZccYM9O5deD8ojgR7f0jQct4suKTXCo6Xqomx4f5qxFqoac/ref_fire_fire.png",
diffusion_steps: 50,
controlnet_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 camenduru/visual-style-prompting-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"camenduru/visual-style-prompting-controlnet:bfd1dedbb46545a191b3a42b5f3778d6c97fa6b6c0283de7d099403c05560b65",
input={
"prompt": "",
"style_name": "fire",
"depth_image": "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png",
"style_image": "https://replicate.delivery/pbxt/KZccYM9O5deD8ojgR7f0jQct4suKTXCo6Xqomx4f5qxFqoac/ref_fire_fire.png",
"diffusion_steps": 50,
"controlnet_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 camenduru/visual-style-prompting-controlnet 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": "camenduru/visual-style-prompting-controlnet:bfd1dedbb46545a191b3a42b5f3778d6c97fa6b6c0283de7d099403c05560b65",
"input": {
"prompt": "",
"style_name": "fire",
"depth_image": "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png",
"style_image": "https://replicate.delivery/pbxt/KZccYM9O5deD8ojgR7f0jQct4suKTXCo6Xqomx4f5qxFqoac/ref_fire_fire.png",
"diffusion_steps": 50,
"controlnet_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/camenduru/visual-style-prompting-controlnet@sha256:bfd1dedbb46545a191b3a42b5f3778d6c97fa6b6c0283de7d099403c05560b65 \
-i 'prompt=""' \
-i 'style_name="fire"' \
-i 'depth_image="https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png"' \
-i 'style_image="https://replicate.delivery/pbxt/KZccYM9O5deD8ojgR7f0jQct4suKTXCo6Xqomx4f5qxFqoac/ref_fire_fire.png"' \
-i 'diffusion_steps=50' \
-i 'controlnet_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 r8.im/camenduru/visual-style-prompting-controlnet@sha256:bfd1dedbb46545a191b3a42b5f3778d6c97fa6b6c0283de7d099403c05560b65curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "prompt": "", "style_name": "fire", "depth_image": "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png", "style_image": "https://replicate.delivery/pbxt/KZccYM9O5deD8ojgR7f0jQct4suKTXCo6Xqomx4f5qxFqoac/ref_fire_fire.png", "diffusion_steps": 50, "controlnet_scale": 0.5 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
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": "2024-03-15T11:09:36.469647Z",
"created_at": "2024-03-15T11:04:42.611455Z",
"data_removed": false,
"error": null,
"id": "xwdipjzbfqyqggd5673qp2v6qy",
"input": {
"prompt": "",
"style_name": "fire",
"depth_image": "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png",
"style_image": "https://replicate.delivery/pbxt/KZccYM9O5deD8ojgR7f0jQct4suKTXCo6Xqomx4f5qxFqoac/ref_fire_fire.png",
"diffusion_steps": 50,
"controlnet_scale": 0.5
},
"logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<02:18, 2.82s/it]\n 4%|▍ | 2/50 [00:03<01:05, 1.37s/it]\n 6%|▌ | 3/50 [00:03<00:43, 1.09it/s]\n 8%|▊ | 4/50 [00:03<00:32, 1.42it/s]\n 10%|█ | 5/50 [00:04<00:26, 1.71it/s]\n 12%|█▏ | 6/50 [00:04<00:22, 1.94it/s]\n 14%|█▍ | 7/50 [00:05<00:20, 2.13it/s]\n 16%|█▌ | 8/50 [00:05<00:18, 2.27it/s]\n 18%|█▊ | 9/50 [00:05<00:17, 2.38it/s]\n 20%|██ | 10/50 [00:06<00:16, 2.46it/s]\n 22%|██▏ | 11/50 [00:06<00:15, 2.51it/s]\n 24%|██▍ | 12/50 [00:06<00:14, 2.55it/s]\n 26%|██▌ | 13/50 [00:07<00:14, 2.58it/s]\n 28%|██▊ | 14/50 [00:07<00:13, 2.60it/s]\n 30%|███ | 15/50 [00:08<00:13, 2.61it/s]\n 32%|███▏ | 16/50 [00:08<00:12, 2.62it/s]\n 34%|███▍ | 17/50 [00:08<00:12, 2.59it/s]\n 36%|███▌ | 18/50 [00:09<00:12, 2.61it/s]\n 38%|███▊ | 19/50 [00:09<00:11, 2.62it/s]\n 40%|████ | 20/50 [00:09<00:11, 2.63it/s]\n 42%|████▏ | 21/50 [00:10<00:11, 2.63it/s]\n 44%|████▍ | 22/50 [00:10<00:10, 2.61it/s]\n 46%|████▌ | 23/50 [00:11<00:10, 2.46it/s]\n 48%|████▊ | 24/50 [00:11<00:10, 2.52it/s]\n 50%|█████ | 25/50 [00:11<00:09, 2.55it/s]\n 52%|█████▏ | 26/50 [00:12<00:09, 2.58it/s]\n 54%|█████▍ | 27/50 [00:12<00:08, 2.60it/s]\n 56%|█████▌ | 28/50 [00:13<00:08, 2.61it/s]\n 58%|█████▊ | 29/50 [00:13<00:08, 2.62it/s]\n 60%|██████ | 30/50 [00:13<00:07, 2.62it/s]\n 62%|██████▏ | 31/50 [00:14<00:07, 2.63it/s]\n 64%|██████▍ | 32/50 [00:14<00:06, 2.63it/s]\n 66%|██████▌ | 33/50 [00:15<00:06, 2.62it/s]\n 68%|██████▊ | 34/50 [00:15<00:06, 2.63it/s]\n 70%|███████ | 35/50 [00:15<00:05, 2.63it/s]\n 72%|███████▏ | 36/50 [00:16<00:05, 2.62it/s]\n 74%|███████▍ | 37/50 [00:16<00:05, 2.53it/s]\n 76%|███████▌ | 38/50 [00:16<00:04, 2.52it/s]\n 78%|███████▊ | 39/50 [00:17<00:04, 2.56it/s]\n 80%|████████ | 40/50 [00:17<00:03, 2.58it/s]\n 82%|████████▏ | 41/50 [00:18<00:03, 2.59it/s]\n 84%|████████▍ | 42/50 [00:18<00:03, 2.61it/s]\n 86%|████████▌ | 43/50 [00:18<00:02, 2.61it/s]\n 88%|████████▊ | 44/50 [00:19<00:02, 2.62it/s]\n 90%|█████████ | 45/50 [00:19<00:01, 2.62it/s]\n 92%|█████████▏| 46/50 [00:20<00:01, 2.63it/s]\n 94%|█████████▍| 47/50 [00:20<00:01, 2.63it/s]\n 96%|█████████▌| 48/50 [00:20<00:00, 2.63it/s]\n 98%|█████████▊| 49/50 [00:21<00:00, 2.61it/s]\n100%|██████████| 50/50 [00:21<00:00, 2.62it/s]\n100%|██████████| 50/50 [00:21<00:00, 2.32it/s]",
"metrics": {
"predict_time": 30.800651,
"total_time": 293.858192
},
"output": "https://replicate.delivery/pbxt/eehLBvNw7WsyU0NNkCZ6ULlffufnTfvyctI2HUyF6K0MceOQJA/output_image.png",
"started_at": "2024-03-15T11:09:05.668996Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/xwdipjzbfqyqggd5673qp2v6qy",
"cancel": "https://api.replicate.com/v1/predictions/xwdipjzbfqyqggd5673qp2v6qy/cancel"
},
"version": "bfd1dedbb46545a191b3a42b5f3778d6c97fa6b6c0283de7d099403c05560b65"
}Generated in
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