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rinnakk /japanese-stable-diffusion:183d9142
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=rinnakk/japanese-stable-diffusion
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 rinnakk/japanese-stable-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"rinnakk/japanese-stable-diffusion:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be",
{
input: {
prompt: "サラリーマン 油絵",
num_outputs: 1,
guidance_scale: 7.5,
num_inference_steps: 50
}
}
);
console.log(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 rinnakk/japanese-stable-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"rinnakk/japanese-stable-diffusion:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be",
input={
"prompt": "サラリーマン 油絵",
"num_outputs": 1,
"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 rinnakk/japanese-stable-diffusion 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": "183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be",
"input": {
"prompt": "サラリーマン 油絵",
"num_outputs": 1,
"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.
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/rinnakk/japanese-stable-diffusion@sha256:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be \
-i 'prompt="サラリーマン 油絵"' \
-i 'num_outputs=1' \
-i 'guidance_scale=7.5' \
-i 'num_inference_steps=50'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/rinnakk/japanese-stable-diffusion@sha256:183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "prompt": "サラリーマン 油絵", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
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Output
{
"completed_at": "2022-09-17T22:25:55.638967Z",
"created_at": "2022-09-17T22:25:40.947972Z",
"data_removed": false,
"error": null,
"id": "6cswdgufubhlvpl3umwuzl2qpq",
"input": {
"prompt": "サラリーマン 油絵",
"num_outputs": 1,
"guidance_scale": 7.5,
"num_inference_steps": 50
},
"logs": "Using seed: 13560\nGlobal seed set to 13560\n\n 0%| | 0/51 [00:00<?, ?it/s]\n 2%|▏ | 1/51 [00:00<00:08, 5.81it/s]\n 4%|▍ | 2/51 [00:00<00:11, 4.33it/s]\n 6%|▌ | 3/51 [00:00<00:11, 4.12it/s]\n 8%|▊ | 4/51 [00:00<00:11, 4.01it/s]\n 10%|▉ | 5/51 [00:01<00:11, 3.95it/s]\n 12%|█▏ | 6/51 [00:01<00:11, 3.89it/s]\n 14%|█▎ | 7/51 [00:01<00:11, 3.88it/s]\n 16%|█▌ | 8/51 [00:02<00:11, 3.87it/s]\n 18%|█▊ | 9/51 [00:02<00:10, 3.87it/s]\n 20%|█▉ | 10/51 [00:02<00:10, 3.84it/s]\n 22%|██▏ | 11/51 [00:02<00:10, 3.83it/s]\n 24%|██▎ | 12/51 [00:03<00:10, 3.84it/s]\n 25%|██▌ | 13/51 [00:03<00:09, 3.85it/s]\n 27%|██▋ | 14/51 [00:03<00:09, 3.83it/s]\n 29%|██▉ | 15/51 [00:03<00:09, 3.83it/s]\n 31%|███▏ | 16/51 [00:04<00:09, 3.83it/s]\n 33%|███▎ | 17/51 [00:04<00:08, 3.84it/s]\n 35%|███▌ | 18/51 [00:04<00:08, 3.83it/s]\n 37%|███▋ | 19/51 [00:04<00:08, 3.82it/s]\n 39%|███▉ | 20/51 [00:05<00:08, 3.81it/s]\n 41%|████ | 21/51 [00:05<00:07, 3.81it/s]\n 43%|████▎ | 22/51 [00:05<00:07, 3.81it/s]\n 45%|████▌ | 23/51 [00:05<00:07, 3.80it/s]\n 47%|████▋ | 24/51 [00:06<00:07, 3.80it/s]\n 49%|████▉ | 25/51 [00:06<00:06, 3.81it/s]\n 51%|█████ | 26/51 [00:06<00:06, 3.81it/s]\n 53%|█████▎ | 27/51 [00:06<00:06, 3.80it/s]\n 55%|█████▍ | 28/51 [00:07<00:06, 3.81it/s]\n 57%|█████▋ | 29/51 [00:07<00:05, 3.82it/s]\n 59%|█████▉ | 30/51 [00:07<00:05, 3.82it/s]\n 61%|██████ | 31/51 [00:08<00:05, 3.82it/s]\n 63%|██████▎ | 32/51 [00:08<00:04, 3.83it/s]\n 65%|██████▍ | 33/51 [00:08<00:04, 3.82it/s]\n 67%|██████▋ | 34/51 [00:08<00:04, 3.81it/s]\n 69%|██████▊ | 35/51 [00:09<00:04, 3.82it/s]\n 71%|███████ | 36/51 [00:09<00:03, 3.82it/s]\n 73%|███████▎ | 37/51 [00:09<00:03, 3.82it/s]\n 75%|███████▍ | 38/51 [00:09<00:03, 3.81it/s]\n 76%|███████▋ | 39/51 [00:10<00:03, 3.81it/s]\n 78%|███████▊ | 40/51 [00:10<00:02, 3.80it/s]\n 80%|████████ | 41/51 [00:10<00:02, 3.81it/s]\n 82%|████████▏ | 42/51 [00:10<00:02, 3.81it/s]\n 84%|████████▍ | 43/51 [00:11<00:02, 3.82it/s]\n 86%|████████▋ | 44/51 [00:11<00:01, 3.81it/s]\n 88%|████████▊ | 45/51 [00:11<00:01, 3.81it/s]\n 90%|█████████ | 46/51 [00:11<00:01, 3.80it/s]\n 92%|█████████▏| 47/51 [00:12<00:01, 3.79it/s]\n 94%|█████████▍| 48/51 [00:12<00:00, 3.79it/s]\n 96%|█████████▌| 49/51 [00:12<00:00, 3.79it/s]\n 98%|█████████▊| 50/51 [00:13<00:00, 3.79it/s]\n100%|██████████| 51/51 [00:13<00:00, 3.79it/s]\n100%|██████████| 51/51 [00:13<00:00, 3.84it/s]",
"metrics": {
"predict_time": 14.481305,
"total_time": 14.690995
},
"output": [
"https://replicate.delivery/mgxm/fa193a69-05e6-471f-a8bf-96fe1ed65b1b/out-0.png"
],
"started_at": "2022-09-17T22:25:41.157662Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/6cswdgufubhlvpl3umwuzl2qpq",
"cancel": "https://api.replicate.com/v1/predictions/6cswdgufubhlvpl3umwuzl2qpq/cancel"
},
"version": "183d91429024984ac421394b65b2de4d0a51733a371d5dc38e18bac200bda4be"
}Generated in
Using seed: 13560
Global seed set to 13560
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