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jagilley /controlnet-depth2img:922c7bb6
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=jagilley/controlnet-depth2img
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 jagilley/controlnet-depth2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"jagilley/controlnet-depth2img:922c7bb67b87ec32cbc2fd11b1d5f94f0ba4f5519c4dbd02856376444127cc60",
{
input: {
eta: 0,
image: "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
scale: 9,
prompt: "a stormtrooper giving a lecture at a university",
a_prompt: "best quality, extremely detailed",
n_prompt: "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
ddim_steps: 20,
num_samples: "1",
image_resolution: "512",
detect_resolution: 512
}
}
);
// 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 jagilley/controlnet-depth2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"jagilley/controlnet-depth2img:922c7bb67b87ec32cbc2fd11b1d5f94f0ba4f5519c4dbd02856376444127cc60",
input={
"eta": 0,
"image": "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
"scale": 9,
"prompt": "a stormtrooper giving a lecture at a university",
"a_prompt": "best quality, extremely detailed",
"n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"ddim_steps": 20,
"num_samples": "1",
"image_resolution": "512",
"detect_resolution": 512
}
)
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 jagilley/controlnet-depth2img 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": "jagilley/controlnet-depth2img:922c7bb67b87ec32cbc2fd11b1d5f94f0ba4f5519c4dbd02856376444127cc60",
"input": {
"eta": 0,
"image": "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
"scale": 9,
"prompt": "a stormtrooper giving a lecture at a university",
"a_prompt": "best quality, extremely detailed",
"n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"ddim_steps": 20,
"num_samples": "1",
"image_resolution": "512",
"detect_resolution": 512
}
}' \
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": "2023-02-16T19:44:54.954858Z",
"created_at": "2023-02-16T19:42:23.645033Z",
"data_removed": false,
"error": null,
"id": "vuqyln4f3ncxjkmrxofxph7woy",
"input": {
"image": "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
"scale": 9,
"prompt": "a stormtrooper giving a lecture at a university",
"a_prompt": "best quality, extremely detailed",
"n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"ddim_steps": 20,
"num_samples": "1",
"image_resolution": "512",
"detect_resolution": 512
},
"logs": "Global seed set to 625452\nData shape for DDIM sampling is (1, 4, 64, 64), eta 0.0\nRunning DDIM Sampling with 20 timesteps\nDDIM Sampler: 0%| | 0/20 [00:00<?, ?it/s]\nDDIM Sampler: 5%|▌ | 1/20 [00:00<00:17, 1.10it/s]\nDDIM Sampler: 10%|█ | 2/20 [00:01<00:14, 1.26it/s]\nDDIM Sampler: 15%|█▌ | 3/20 [00:02<00:12, 1.32it/s]\nDDIM Sampler: 20%|██ | 4/20 [00:03<00:11, 1.35it/s]\nDDIM Sampler: 25%|██▌ | 5/20 [00:03<00:10, 1.37it/s]\nDDIM Sampler: 30%|███ | 6/20 [00:04<00:10, 1.38it/s]\nDDIM Sampler: 35%|███▌ | 7/20 [00:05<00:09, 1.39it/s]\nDDIM Sampler: 40%|████ | 8/20 [00:05<00:08, 1.39it/s]\nDDIM Sampler: 45%|████▌ | 9/20 [00:06<00:07, 1.39it/s]\nDDIM Sampler: 50%|█████ | 10/20 [00:07<00:07, 1.39it/s]\nDDIM Sampler: 55%|█████▌ | 11/20 [00:08<00:06, 1.39it/s]\nDDIM Sampler: 60%|██████ | 12/20 [00:08<00:05, 1.39it/s]\nDDIM Sampler: 65%|██████▌ | 13/20 [00:09<00:05, 1.39it/s]\nDDIM Sampler: 70%|███████ | 14/20 [00:10<00:04, 1.39it/s]\nDDIM Sampler: 75%|███████▌ | 15/20 [00:10<00:03, 1.38it/s]\nDDIM Sampler: 80%|████████ | 16/20 [00:11<00:02, 1.38it/s]\nDDIM Sampler: 85%|████████▌ | 17/20 [00:12<00:02, 1.38it/s]\nDDIM Sampler: 90%|█████████ | 18/20 [00:13<00:01, 1.38it/s]\nDDIM Sampler: 95%|█████████▌| 19/20 [00:13<00:00, 1.38it/s]\nDDIM Sampler: 100%|██████████| 20/20 [00:14<00:00, 1.38it/s]\nDDIM Sampler: 100%|██████████| 20/20 [00:14<00:00, 1.37it/s]",
"metrics": {
"predict_time": 20.895252,
"total_time": 151.309825
},
"output": [
"https://replicate.delivery/pbxt/tLbfGg8jnegaSEokEekC2x7lZeC6kenBJQ4r3B3kPZHo2c4DC/output_0.png",
"https://replicate.delivery/pbxt/CCPUFqXwc2LZF5CVU13C8yPE03ENz9gMdnuvXf7Kb2abzhPIA/output_1.png"
],
"started_at": "2023-02-16T19:44:34.059606Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/vuqyln4f3ncxjkmrxofxph7woy",
"cancel": "https://api.replicate.com/v1/predictions/vuqyln4f3ncxjkmrxofxph7woy/cancel"
},
"version": "922c7bb67b87ec32cbc2fd11b1d5f94f0ba4f5519c4dbd02856376444127cc60"
}Generated in
Global seed set to 625452
Data shape for DDIM sampling is (1, 4, 64, 64), eta 0.0
Running DDIM Sampling with 20 timesteps
DDIM Sampler: 0%| | 0/20 [00:00<?, ?it/s]
DDIM Sampler: 5%|▌ | 1/20 [00:00<00:17, 1.10it/s]
DDIM Sampler: 10%|█ | 2/20 [00:01<00:14, 1.26it/s]
DDIM Sampler: 15%|█▌ | 3/20 [00:02<00:12, 1.32it/s]
DDIM Sampler: 20%|██ | 4/20 [00:03<00:11, 1.35it/s]
DDIM Sampler: 25%|██▌ | 5/20 [00:03<00:10, 1.37it/s]
DDIM Sampler: 30%|███ | 6/20 [00:04<00:10, 1.38it/s]
DDIM Sampler: 35%|███▌ | 7/20 [00:05<00:09, 1.39it/s]
DDIM Sampler: 40%|████ | 8/20 [00:05<00:08, 1.39it/s]
DDIM Sampler: 45%|████▌ | 9/20 [00:06<00:07, 1.39it/s]
DDIM Sampler: 50%|█████ | 10/20 [00:07<00:07, 1.39it/s]
DDIM Sampler: 55%|█████▌ | 11/20 [00:08<00:06, 1.39it/s]
DDIM Sampler: 60%|██████ | 12/20 [00:08<00:05, 1.39it/s]
DDIM Sampler: 65%|██████▌ | 13/20 [00:09<00:05, 1.39it/s]
DDIM Sampler: 70%|███████ | 14/20 [00:10<00:04, 1.39it/s]
DDIM Sampler: 75%|███████▌ | 15/20 [00:10<00:03, 1.38it/s]
DDIM Sampler: 80%|████████ | 16/20 [00:11<00:02, 1.38it/s]
DDIM Sampler: 85%|████████▌ | 17/20 [00:12<00:02, 1.38it/s]
DDIM Sampler: 90%|█████████ | 18/20 [00:13<00:01, 1.38it/s]
DDIM Sampler: 95%|█████████▌| 19/20 [00:13<00:00, 1.38it/s]
DDIM Sampler: 100%|██████████| 20/20 [00:14<00:00, 1.38it/s]
DDIM Sampler: 100%|██████████| 20/20 [00:14<00:00, 1.37it/s]