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wcarle /stable-diffusion-videos-ghibli:c0a42f58
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=wcarle/stable-diffusion-videos-ghibli
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 wcarle/stable-diffusion-videos-ghibli using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"wcarle/stable-diffusion-videos-ghibli:c0a42f58b44674d3188b9932bf2a62acab5ee4a9f2359faa8ce2bed44a67cd16",
{
input: {
fps: 15,
seeds: "155 | 688",
prompts: "ghibli style open field with clouds (sunset) | ghibli style open field with clouds (night)",
num_steps: 50,
scheduler: "klms",
guidance_scale: 7.5,
num_inference_steps: 50
}
}
);
// 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 wcarle/stable-diffusion-videos-ghibli using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"wcarle/stable-diffusion-videos-ghibli:c0a42f58b44674d3188b9932bf2a62acab5ee4a9f2359faa8ce2bed44a67cd16",
input={
"fps": 15,
"seeds": "155 | 688",
"prompts": "ghibli style open field with clouds (sunset) | ghibli style open field with clouds (night)",
"num_steps": 50,
"scheduler": "klms",
"guidance_scale": 7.5,
"num_inference_steps": 50
}
)
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 wcarle/stable-diffusion-videos-ghibli 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": "wcarle/stable-diffusion-videos-ghibli:c0a42f58b44674d3188b9932bf2a62acab5ee4a9f2359faa8ce2bed44a67cd16",
"input": {
"fps": 15,
"seeds": "155 | 688",
"prompts": "ghibli style open field with clouds (sunset) | ghibli style open field with clouds (night)",
"num_steps": 50,
"scheduler": "klms",
"guidance_scale": 7.5,
"num_inference_steps": 50
}
}' \
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": "2023-04-03T01:20:36.934315Z",
"created_at": "2023-04-03T01:12:36.286981Z",
"data_removed": false,
"error": null,
"id": "pg7gqgamffb3hmsnyvtywf5xyy",
"input": {
"fps": 15,
"seeds": "155 | 688",
"prompts": "ghibli style open field with clouds (sunset) | ghibli style open field with clouds (night)",
"num_steps": 50,
"scheduler": "klms",
"guidance_scale": 7.5,
"num_inference_steps": 50
},
"logs": "Setting Random seeds.\nSeeds used for prompts are:\nghibli style open field with clouds (sunset): 155\nghibli style open field with clouds (night): 688\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<01:23, 1.70s/it]\n 6%|▌ | 3/50 [00:01<00:23, 2.01it/s]\n 10%|█ | 5/50 [00:02<00:12, 3.57it/s]\n 14%|█▍ | 7/50 [00:02<00:08, 5.13it/s]\n 18%|█▊ | 9/50 [00:02<00:06, 6.47it/s]\n 22%|██▏ | 11/50 [00:02<00:05, 7.72it/s]\n 26%|██▌ | 13/50 [00:02<00:04, 8.63it/s]\n 30%|███ | 15/50 [00:02<00:03, 9.74it/s]\n 34%|███▍ | 17/50 [00:02<00:03, 10.56it/s]\n 38%|███▊ | 19/50 [00:03<00:02, 11.22it/s]\n 42%|████▏ | 21/50 [00:03<00:02, 11.71it/s]\n 46%|████▌ | 23/50 [00:03<00:02, 12.01it/s]\n 50%|█████ | 25/50 [00:03<00:02, 12.26it/s]\n 54%|█████▍ | 27/50 [00:03<00:01, 12.31it/s]\n 58%|█████▊ | 29/50 [00:03<00:01, 12.62it/s]\n 62%|██████▏ | 31/50 [00:04<00:01, 12.83it/s]\n 66%|██████▌ | 33/50 [00:04<00:01, 12.95it/s]\n 70%|███████ | 35/50 [00:04<00:01, 13.13it/s]\n 74%|███████▍ | 37/50 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88%|████████▊ | 44/50 [00:03<00:00, 12.52it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 12.24it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 12.05it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.00it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.03it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.29it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.24it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 12.46it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 12.54it/s]\n 20%|██ | 10/50 [00:00<00:03, 12.40it/s]\n 24%|██▍ | 12/50 [00:00<00:03, 12.61it/s]\n 28%|██▊ | 14/50 [00:01<00:02, 12.32it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 12.43it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 12.50it/s]\n 40%|████ | 20/50 [00:01<00:02, 12.32it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 12.45it/s]\n 48%|████▊ | 24/50 [00:01<00:02, 12.24it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 11.98it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 12.05it/s]\n 60%|██████ | 30/50 [00:02<00:01, 11.98it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 12.13it/s]\n 68%|██████▊ | 34/50 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32%|███▏ | 16/50 [00:01<00:02, 11.95it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 11.86it/s]\n 40%|████ | 20/50 [00:01<00:02, 12.00it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 12.09it/s]\n 48%|████▊ | 24/50 [00:01<00:02, 12.33it/s]\n 52%|█████▏ | 26/50 [00:02<00:01, 12.68it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 12.84it/s]\n 60%|██████ | 30/50 [00:02<00:01, 12.87it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 13.08it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 13.21it/s]\n 72%|███████▏ | 36/50 [00:02<00:01, 13.25it/s]\n 76%|███████▌ | 38/50 [00:03<00:00, 13.25it/s]\n 80%|████████ | 40/50 [00:03<00:00, 13.30it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 13.33it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 13.12it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 13.26it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 13.32it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.31it/s]\n100%|██████████| 50/50 [00:03<00:00, 12.73it/s]",
"metrics": {
"predict_time": 221.415975,
"total_time": 480.647334
},
"output": "https://replicate.delivery/pbxt/oQfNlQeCEfFfVQMKPenYgtT9Ro4ihGFJPw3zrzSbgDVm8tvFC/out.mp4",
"started_at": "2023-04-03T01:16:55.518340Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/pg7gqgamffb3hmsnyvtywf5xyy",
"cancel": "https://api.replicate.com/v1/predictions/pg7gqgamffb3hmsnyvtywf5xyy/cancel"
},
"version": "c0a42f58b44674d3188b9932bf2a62acab5ee4a9f2359faa8ce2bed44a67cd16"
}Generated in
Setting Random seeds.
Seeds used for prompts are:
ghibli style open field with clouds (sunset): 155
ghibli style open field with clouds (night): 688
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