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melgor /stabledesign_interiordesign:5e13482e
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=melgor/stabledesign_interiordesign
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 melgor/stabledesign_interiordesign using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"melgor/stabledesign_interiordesign:5e13482ea317670bfc797bb18bace359860a721a39b5bbcaa1ffcd241d62bca0",
{
input: {
seed: 35853,
prompt: "A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury.",
img_size: 640,
strength: 0.9,
num_steps: 50,
image_base: "https://replicate.delivery/pbxt/L71nWgMUQvH7zKHG3U9YYdfREv1ybOfO1Q5ntak3YmUJizSR/image_0.jpg",
guidance_scale: 10
}
}
);
// 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 melgor/stabledesign_interiordesign using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"melgor/stabledesign_interiordesign:5e13482ea317670bfc797bb18bace359860a721a39b5bbcaa1ffcd241d62bca0",
input={
"seed": 35853,
"prompt": "A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury.",
"img_size": 640,
"strength": 0.9,
"num_steps": 50,
"image_base": "https://replicate.delivery/pbxt/L71nWgMUQvH7zKHG3U9YYdfREv1ybOfO1Q5ntak3YmUJizSR/image_0.jpg",
"guidance_scale": 10
}
)
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 melgor/stabledesign_interiordesign 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": "5e13482ea317670bfc797bb18bace359860a721a39b5bbcaa1ffcd241d62bca0",
"input": {
"seed": 35853,
"prompt": "A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury.",
"img_size": 640,
"strength": 0.9,
"num_steps": 50,
"image_base": "https://replicate.delivery/pbxt/L71nWgMUQvH7zKHG3U9YYdfREv1ybOfO1Q5ntak3YmUJizSR/image_0.jpg",
"guidance_scale": 10
}
}' \
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/melgor/stabledesign_interiordesign@sha256:5e13482ea317670bfc797bb18bace359860a721a39b5bbcaa1ffcd241d62bca0 \
-i 'seed=35853' \
-i 'prompt="A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury."' \
-i 'img_size=640' \
-i 'strength=0.9' \
-i 'num_steps=50' \
-i 'image_base="https://replicate.delivery/pbxt/L71nWgMUQvH7zKHG3U9YYdfREv1ybOfO1Q5ntak3YmUJizSR/image_0.jpg"' \
-i 'guidance_scale=10'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/melgor/stabledesign_interiordesign@sha256:5e13482ea317670bfc797bb18bace359860a721a39b5bbcaa1ffcd241d62bca0curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 35853, "prompt": "A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury.", "img_size": 640, "strength": 0.9, "num_steps": 50, "image_base": "https://replicate.delivery/pbxt/L71nWgMUQvH7zKHG3U9YYdfREv1ybOfO1Q5ntak3YmUJizSR/image_0.jpg", "guidance_scale": 10 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Each run costs approximately $0.0077. Alternatively, try out our featured models for free.
Sign in with GitHub
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Output
{
"completed_at": "2024-06-17T13:37:22.508770Z",
"created_at": "2024-06-17T13:30:54.561000Z",
"data_removed": false,
"error": null,
"id": "z6b9q5dww5rgp0cg4sj91cnv1r",
"input": {
"seed": 35853,
"prompt": "A glamorous master bedroom in Hollywood Regency style, boasting a plush tufted headboard, mirrored furniture reflecting elegance, luxurious fabrics in rich textures, and opulent gold accents for a touch of luxury.",
"img_size": 640,
"strength": 0.9,
"num_steps": 50,
"image_base": "https://replicate.delivery/pbxt/L71nWgMUQvH7zKHG3U9YYdfREv1ybOfO1Q5ntak3YmUJizSR/image_0.jpg",
"guidance_scale": 10
},
"logs": "/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:07, 6.68it/s]\n 8%|▊ | 4/50 [00:00<00:02, 15.39it/s]\n 14%|█▍ | 7/50 [00:00<00:02, 18.52it/s]\n 20%|██ | 10/50 [00:00<00:02, 19.84it/s]\n 26%|██▌ | 13/50 [00:00<00:01, 20.74it/s]\n 32%|███▏ | 16/50 [00:00<00:01, 21.31it/s]\n 38%|███▊ | 19/50 [00:00<00:01, 21.66it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 21.90it/s]\n 50%|█████ | 25/50 [00:01<00:01, 22.06it/s]\n 56%|█████▌ | 28/50 [00:01<00:00, 22.17it/s]\n 62%|██████▏ | 31/50 [00:01<00:00, 22.22it/s]\n 68%|██████▊ | 34/50 [00:01<00:00, 22.03it/s]\n 74%|███████▍ | 37/50 [00:01<00:00, 22.10it/s]\n 80%|████████ | 40/50 [00:01<00:00, 22.11it/s]\n 86%|████████▌ | 43/50 [00:02<00:00, 22.17it/s]\n 92%|█████████▏| 46/50 [00:02<00:00, 22.21it/s]\n 98%|█████████▊| 49/50 [00:02<00:00, 22.19it/s]\n100%|██████████| 50/50 [00:02<00:00, 21.25it/s]\n 0%| | 0/45 [00:00<?, ?it/s]\n 2%|▏ | 1/45 [00:00<00:07, 6.14it/s]\n 7%|▋ | 3/45 [00:00<00:04, 9.30it/s]\n 11%|█ | 5/45 [00:00<00:03, 10.74it/s]\n 16%|█▌ | 7/45 [00:00<00:03, 11.46it/s]\n 20%|██ | 9/45 [00:00<00:03, 11.86it/s]\n 24%|██▍ | 11/45 [00:00<00:02, 12.11it/s]\n 29%|██▉ | 13/45 [00:01<00:02, 12.25it/s]\n 33%|███▎ | 15/45 [00:01<00:02, 12.35it/s]\n 38%|███▊ | 17/45 [00:01<00:02, 12.42it/s]\n 42%|████▏ | 19/45 [00:01<00:02, 12.45it/s]\n 47%|████▋ | 21/45 [00:01<00:01, 12.48it/s]\n 51%|█████ | 23/45 [00:01<00:01, 12.50it/s]\n 56%|█████▌ | 25/45 [00:02<00:01, 12.52it/s]\n 60%|██████ | 27/45 [00:02<00:01, 12.54it/s]\n 64%|██████▍ | 29/45 [00:02<00:01, 12.55it/s]\n 69%|██████▉ | 31/45 [00:02<00:01, 12.56it/s]\n 73%|███████▎ | 33/45 [00:02<00:00, 12.56it/s]\n 78%|███████▊ | 35/45 [00:02<00:00, 12.57it/s]\n 82%|████████▏ | 37/45 [00:03<00:00, 12.58it/s]\n 87%|████████▋ | 39/45 [00:03<00:00, 12.57it/s]\n 91%|█████████ | 41/45 [00:03<00:00, 12.58it/s]\n 96%|█████████▌| 43/45 [00:03<00:00, 12.61it/s]\n100%|██████████| 45/45 [00:03<00:00, 12.62it/s]\n100%|██████████| 45/45 [00:03<00:00, 12.24it/s]",
"metrics": {
"predict_time": 11.483450089,
"total_time": 387.94777
},
"output": "https://replicate.delivery/pbxt/xyCGKiWjs04hKRFZaGOfSqgTCsO1spe53eMr5OBbcenHh79LB/design.png",
"started_at": "2024-06-17T13:37:11.025319Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/z6b9q5dww5rgp0cg4sj91cnv1r",
"cancel": "https://api.replicate.com/v1/predictions/z6b9q5dww5rgp0cg4sj91cnv1r/cancel"
},
"version": "5e13482ea317670bfc797bb18bace359860a721a39b5bbcaa1ffcd241d62bca0"
}Generated in
/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)
return F.conv2d(input, weight, bias, self.stride,
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