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tommoore515 /material_stable_diffusion:3b5c0242
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=tommoore515/material_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 tommoore515/material_stable_diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"tommoore515/material_stable_diffusion:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449",
{
input: {
width: 512,
height: 512,
prompt: "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
num_outputs: 1,
guidance_scale: 7.5,
prompt_strength: 0.8,
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 tommoore515/material_stable_diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"tommoore515/material_stable_diffusion:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449",
input={
"width": 512,
"height": 512,
"prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.8,
"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 tommoore515/material_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": "3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449",
"input": {
"width": 512,
"height": 512,
"prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.8,
"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/tommoore515/material_stable_diffusion@sha256:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449 \
-i 'width=512' \
-i 'height=512' \
-i 'prompt="Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k"' \
-i 'num_outputs=1' \
-i 'guidance_scale=7.5' \
-i 'prompt_strength=0.8' \
-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/tommoore515/material_stable_diffusion@sha256:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
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Output
{
"completed_at": "2022-09-07T01:14:14.906612Z",
"created_at": "2022-09-07T01:13:59.677073Z",
"data_removed": false,
"error": null,
"id": "6o5jjvornrcwtadmvv7qkty2z4",
"input": {
"width": 512,
"height": 512,
"prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
"num_outputs": 1,
"guidance_scale": 7.5,
"prompt_strength": 0.8,
"num_inference_steps": 50
},
"logs": "Using seed: 46183\n\n0it [00:00, ?it/s]\n1it [00:00, 5.73it/s]\n2it [00:00, 4.17it/s]\n3it [00:00, 3.93it/s]\n4it [00:01, 3.83it/s]\n5it [00:01, 3.77it/s]\n6it [00:01, 3.73it/s]\n7it [00:01, 3.70it/s]\n8it [00:02, 3.71it/s]\n9it [00:02, 3.68it/s]\n10it [00:02, 3.68it/s]\n11it [00:02, 3.67it/s]\n12it [00:03, 3.68it/s]\n13it [00:03, 3.66it/s]\n14it [00:03, 3.66it/s]\n15it [00:04, 3.65it/s]\n16it [00:04, 3.65it/s]\n17it [00:04, 3.65it/s]\n18it [00:04, 3.65it/s]\n19it [00:05, 3.65it/s]\n20it [00:05, 3.63it/s]\n21it [00:05, 3.64it/s]\n22it [00:05, 3.63it/s]\n23it [00:06, 3.64it/s]\n24it [00:06, 3.64it/s]\n25it [00:06, 3.65it/s]\n26it [00:07, 3.64it/s]\n27it [00:07, 3.63it/s]\n28it [00:07, 3.63it/s]\n29it [00:07, 3.63it/s]\n30it [00:08, 3.63it/s]\n31it [00:08, 3.63it/s]\n32it [00:08, 3.62it/s]\n33it [00:08, 3.62it/s]\n34it [00:09, 3.62it/s]\n35it [00:09, 3.62it/s]\n36it [00:09, 3.62it/s]\n37it [00:10, 3.61it/s]\n38it [00:10, 3.61it/s]\n39it [00:10, 3.62it/s]\n40it [00:10, 3.62it/s]\n41it [00:11, 3.61it/s]\n42it [00:11, 3.61it/s]\n43it [00:11, 3.61it/s]\n44it [00:12, 3.61it/s]\n45it [00:12, 3.61it/s]\n46it [00:12, 3.62it/s]\n47it [00:12, 3.62it/s]\n48it [00:13, 3.62it/s]\n49it [00:13, 3.61it/s]\n50it [00:13, 3.61it/s]\n50it [00:13, 3.66it/s]",
"metrics": {
"predict_time": 15.037458,
"total_time": 15.229539
},
"output": [
"https://replicate.delivery/mgxm/a5f7f356-99b4-4b7c-85b9-b084960f7552/out-0.png"
],
"started_at": "2022-09-07T01:13:59.869154Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/6o5jjvornrcwtadmvv7qkty2z4",
"cancel": "https://api.replicate.com/v1/predictions/6o5jjvornrcwtadmvv7qkty2z4/cancel"
},
"version": "56f26876a159c10b429c382f66ccda648c1d5678d7ce15ed010734b715be5ab9"
}Generated in
Using seed: 46183
0it [00:00, ?it/s]
1it [00:00, 5.73it/s]
2it [00:00, 4.17it/s]
3it [00:00, 3.93it/s]
4it [00:01, 3.83it/s]
5it [00:01, 3.77it/s]
6it [00:01, 3.73it/s]
7it [00:01, 3.70it/s]
8it [00:02, 3.71it/s]
9it [00:02, 3.68it/s]
10it [00:02, 3.68it/s]
11it [00:02, 3.67it/s]
12it [00:03, 3.68it/s]
13it [00:03, 3.66it/s]
14it [00:03, 3.66it/s]
15it [00:04, 3.65it/s]
16it [00:04, 3.65it/s]
17it [00:04, 3.65it/s]
18it [00:04, 3.65it/s]
19it [00:05, 3.65it/s]
20it [00:05, 3.63it/s]
21it [00:05, 3.64it/s]
22it [00:05, 3.63it/s]
23it [00:06, 3.64it/s]
24it [00:06, 3.64it/s]
25it [00:06, 3.65it/s]
26it [00:07, 3.64it/s]
27it [00:07, 3.63it/s]
28it [00:07, 3.63it/s]
29it [00:07, 3.63it/s]
30it [00:08, 3.63it/s]
31it [00:08, 3.63it/s]
32it [00:08, 3.62it/s]
33it [00:08, 3.62it/s]
34it [00:09, 3.62it/s]
35it [00:09, 3.62it/s]
36it [00:09, 3.62it/s]
37it [00:10, 3.61it/s]
38it [00:10, 3.61it/s]
39it [00:10, 3.62it/s]
40it [00:10, 3.62it/s]
41it [00:11, 3.61it/s]
42it [00:11, 3.61it/s]
43it [00:11, 3.61it/s]
44it [00:12, 3.61it/s]
45it [00:12, 3.61it/s]
46it [00:12, 3.62it/s]
47it [00:12, 3.62it/s]
48it [00:13, 3.62it/s]
49it [00:13, 3.61it/s]
50it [00:13, 3.61it/s]
50it [00:13, 3.66it/s]This example was created by a different version, tommoore515/material_stable_diffusion:56f26876.