Prediction

tommoore515/material_stable_diffusion:3b5c0242

Model

tommoore515/material_stable_diffusion:56f26876

ID

6o5jjvornrcwtadmvv7qkty2z4

Status

Succeeded

Source

Web

Hardware

–

Total duration

15.2s

Created

over 2 years ago by @tommoore515

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

{
  "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
}

Install Replicate’s Node.js client library:

npm install replicate

Set 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 replicate

Set 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 replicate

Run 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/predictions

To 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 cog

If 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@sha256:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449
curl -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.

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

15.0 seconds

Tweak it ShareReport

Prediction

tommoore515/material_stable_diffusion:3b5c0242

Model

tommoore515/material_stable_diffusion:56f26876

ID

i3dfvgcnezhsra5paq25ucqsn4

Status

Succeeded

Source

Web

Hardware

–

Total duration

15.6s

Created

over 2 years ago by @tommoore515

Input

width: 512
height: 512
prompt: Muddy ground with autumn leaves seamless texture, trending on artstation, base color, albedo, 4k
num_outputs: 1
guidance_scale: 7.5
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 512,
  "height": 512,
  "prompt": "Muddy ground with autumn leaves seamless texture, trending on artstation, base color, albedo, 4k",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Set 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: "Muddy ground with autumn leaves seamless texture, trending on artstation, 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 replicate

Set 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 replicate

Run 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": "Muddy ground with autumn leaves seamless texture, trending on artstation, 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": "Muddy ground with autumn leaves seamless texture, trending on artstation, base color, albedo, 4k",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To 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 cog

If 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="Muddy ground with autumn leaves seamless texture, trending on artstation, 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@sha256:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Muddy ground with autumn leaves seamless texture, trending on artstation, 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.

Output

{
  "completed_at": "2022-09-07T01:16:21.696452Z",
  "created_at": "2022-09-07T01:16:06.098825Z",
  "data_removed": false,
  "error": null,
  "id": "i3dfvgcnezhsra5paq25ucqsn4",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "Muddy ground with autumn leaves seamless texture, trending on artstation, base color, albedo, 4k",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 51368\n\n0it [00:00, ?it/s]\n1it [00:00,  6.29it/s]\n2it [00:00,  4.19it/s]\n3it [00:00,  3.88it/s]\n4it [00:01,  3.75it/s]\n5it [00:01,  3.67it/s]\n6it [00:01,  3.64it/s]\n7it [00:01,  3.62it/s]\n8it [00:02,  3.60it/s]\n9it [00:02,  3.58it/s]\n10it [00:02,  3.58it/s]\n11it [00:02,  3.57it/s]\n12it [00:03,  3.57it/s]\n13it [00:03,  3.56it/s]\n14it [00:03,  3.55it/s]\n15it [00:04,  3.55it/s]\n16it [00:04,  3.54it/s]\n17it [00:04,  3.53it/s]\n18it [00:04,  3.54it/s]\n19it [00:05,  3.54it/s]\n20it [00:05,  3.54it/s]\n21it [00:05,  3.55it/s]\n22it [00:06,  3.55it/s]\n23it [00:06,  3.54it/s]\n24it [00:06,  3.55it/s]\n25it [00:06,  3.54it/s]\n26it [00:07,  3.54it/s]\n27it [00:07,  3.54it/s]\n28it [00:07,  3.53it/s]\n29it [00:08,  3.53it/s]\n30it [00:08,  3.53it/s]\n31it [00:08,  3.53it/s]\n32it [00:08,  3.52it/s]\n33it [00:09,  3.52it/s]\n34it [00:09,  3.51it/s]\n35it [00:09,  3.51it/s]\n36it [00:10,  3.51it/s]\n37it [00:10,  3.51it/s]\n38it [00:10,  3.51it/s]\n39it [00:10,  3.51it/s]\n40it [00:11,  3.51it/s]\n41it [00:11,  3.51it/s]\n42it [00:11,  3.51it/s]\n43it [00:12,  3.50it/s]\n44it [00:12,  3.50it/s]\n45it [00:12,  3.50it/s]\n46it [00:12,  3.50it/s]\n47it [00:13,  3.50it/s]\n48it [00:13,  3.50it/s]\n49it [00:13,  3.50it/s]\n50it [00:14,  3.49it/s]\n50it [00:14,  3.56it/s]",
  "metrics": {
    "predict_time": 15.398979,
    "total_time": 15.597627
  },
  "output": [
    "https://replicate.delivery/mgxm/9b8f4ec9-eef0-437f-a27a-cbd233d22407/out-0.png"
  ],
  "started_at": "2022-09-07T01:16:06.297473Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/i3dfvgcnezhsra5paq25ucqsn4",
    "cancel": "https://api.replicate.com/v1/predictions/i3dfvgcnezhsra5paq25ucqsn4/cancel"
  },
  "version": "56f26876a159c10b429c382f66ccda648c1d5678d7ce15ed010734b715be5ab9"
}

Generated in

15.4 seconds

Tweak it ShareReport

Prediction

tommoore515/material_stable_diffusion:3b5c0242

Model

tommoore515/material_stable_diffusion:56f26876

ID

bsfkncbtt5fdhoswrh7uy64r2m

Status

Succeeded

Source

Web

Hardware

–

Total duration

16.4s

Created

over 2 years ago

Input

width: 512
height: 512
prompt: Ancient carvings trim sheet texture, trending on artstation, sandstone, base color, albedo, 4k
num_outputs: 1
guidance_scale: 7.5
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 512,
  "height": 512,
  "prompt": "Ancient carvings trim sheet texture, trending on artstation, sandstone, base color, albedo, 4k",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Set 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: "Ancient carvings trim sheet texture, trending on artstation, sandstone, 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 replicate

Set 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 replicate

Run 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": "Ancient carvings trim sheet texture, trending on artstation, sandstone, 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": "Ancient carvings trim sheet texture, trending on artstation, sandstone, base color, albedo, 4k",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To 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 cog

If 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="Ancient carvings trim sheet texture, trending on artstation, sandstone, 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@sha256:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Ancient carvings trim sheet texture, trending on artstation, sandstone, 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.

Output

{
  "completed_at": "2022-09-07T01:18:36.223262Z",
  "created_at": "2022-09-07T01:18:19.794958Z",
  "data_removed": false,
  "error": null,
  "id": "bsfkncbtt5fdhoswrh7uy64r2m",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "Ancient carvings trim sheet texture, trending on artstation, sandstone, base color, albedo, 4k",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 50474\n\n0it [00:00, ?it/s]\n1it [00:00,  5.77it/s]\n2it [00:00,  4.14it/s]\n3it [00:00,  3.79it/s]\n4it [00:01,  3.63it/s]\n5it [00:01,  3.50it/s]\n6it [00:01,  3.48it/s]\n7it [00:01,  3.46it/s]\n8it [00:02,  3.42it/s]\n9it [00:02,  3.42it/s]\n10it [00:02,  3.42it/s]\n11it [00:03,  3.40it/s]\n12it [00:03,  3.39it/s]\n13it [00:03,  3.39it/s]\n14it [00:04,  3.39it/s]\n15it [00:04,  3.37it/s]\n16it [00:04,  3.38it/s]\n17it [00:04,  3.38it/s]\n18it [00:05,  3.38it/s]\n19it [00:05,  3.37it/s]\n20it [00:05,  3.37it/s]\n21it [00:06,  3.37it/s]\n22it [00:06,  3.37it/s]\n23it [00:06,  3.37it/s]\n24it [00:06,  3.35it/s]\n25it [00:07,  3.36it/s]\n26it [00:07,  3.35it/s]\n27it [00:07,  3.35it/s]\n28it [00:08,  3.34it/s]\n29it [00:08,  3.33it/s]\n30it [00:08,  3.33it/s]\n31it [00:09,  3.32it/s]\n32it [00:09,  3.31it/s]\n33it [00:09,  3.31it/s]\n34it [00:09,  3.32it/s]\n35it [00:10,  3.32it/s]\n36it [00:10,  3.31it/s]\n37it [00:10,  3.31it/s]\n38it [00:11,  3.31it/s]\n39it [00:11,  3.31it/s]\n40it [00:11,  3.30it/s]\n41it [00:12,  3.31it/s]\n42it [00:12,  3.30it/s]\n43it [00:12,  3.29it/s]\n44it [00:13,  3.30it/s]\n45it [00:13,  3.30it/s]\n46it [00:13,  3.30it/s]\n47it [00:13,  3.29it/s]\n48it [00:14,  3.28it/s]\n49it [00:14,  3.28it/s]\n50it [00:14,  3.28it/s]\n50it [00:14,  3.37it/s]",
  "metrics": {
    "predict_time": 16.268535,
    "total_time": 16.428304
  },
  "output": [
    "https://replicate.delivery/mgxm/147f2329-db56-4a6a-a950-7a358f731fb7/out-0.png"
  ],
  "started_at": "2022-09-07T01:18:19.954727Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/bsfkncbtt5fdhoswrh7uy64r2m",
    "cancel": "https://api.replicate.com/v1/predictions/bsfkncbtt5fdhoswrh7uy64r2m/cancel"
  },
  "version": "56f26876a159c10b429c382f66ccda648c1d5678d7ce15ed010734b715be5ab9"
}

Generated in

16.3 seconds

Tweak it ShareReport

Prediction

tommoore515/material_stable_diffusion:3b5c0242

Model

tommoore515/material_stable_diffusion:56f26876

ID

7z63mz2dzfdajg3wbbtailuehm

Status

Succeeded

Source

Web

Hardware

–

Total duration

16.0s

Created

over 2 years ago

Input

width: 512
height: 512
prompt: Lunar surface seamless texture, trending on artstation, base color, albedo, 4k
num_outputs: 1
guidance_scale: 7.5
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 512,
  "height": 512,
  "prompt": "Lunar surface seamless texture, trending on artstation, base color, albedo, 4k",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Set 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: "Lunar surface seamless texture, trending on artstation, 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 replicate

Set 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 replicate

Run 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": "Lunar surface seamless texture, trending on artstation, 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": "Lunar surface seamless texture, trending on artstation, base color, albedo, 4k",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To 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 cog

If 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="Lunar surface seamless texture, trending on artstation, 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@sha256:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Lunar surface seamless texture, trending on artstation, 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.

Output

{
  "completed_at": "2022-09-07T01:17:26.190848Z",
  "created_at": "2022-09-07T01:17:10.170479Z",
  "data_removed": false,
  "error": null,
  "id": "7z63mz2dzfdajg3wbbtailuehm",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "Lunar surface seamless texture, trending on artstation, base color, albedo, 4k",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 7061\n\n0it [00:00, ?it/s]\n1it [00:00,  5.65it/s]\n2it [00:00,  4.04it/s]\n3it [00:00,  3.77it/s]\n4it [00:01,  3.64it/s]\n5it [00:01,  3.57it/s]\n6it [00:01,  3.54it/s]\n7it [00:01,  3.51it/s]\n8it [00:02,  3.49it/s]\n9it [00:02,  3.49it/s]\n10it [00:02,  3.48it/s]\n11it [00:03,  3.48it/s]\n12it [00:03,  3.47it/s]\n13it [00:03,  3.47it/s]\n14it [00:03,  3.46it/s]\n15it [00:04,  3.45it/s]\n16it [00:04,  3.44it/s]\n17it [00:04,  3.45it/s]\n18it [00:05,  3.45it/s]\n19it [00:05,  3.45it/s]\n20it [00:05,  3.44it/s]\n21it [00:05,  3.44it/s]\n22it [00:06,  3.45it/s]\n23it [00:06,  3.43it/s]\n24it [00:06,  3.44it/s]\n25it [00:07,  3.44it/s]\n26it [00:07,  3.43it/s]\n27it [00:07,  3.43it/s]\n28it [00:08,  3.42it/s]\n29it [00:08,  3.42it/s]\n30it [00:08,  3.43it/s]\n31it [00:08,  3.43it/s]\n32it [00:09,  3.42it/s]\n33it [00:09,  3.43it/s]\n34it [00:09,  3.43it/s]\n35it [00:10,  3.43it/s]\n36it [00:10,  3.43it/s]\n37it [00:10,  3.42it/s]\n38it [00:10,  3.41it/s]\n39it [00:11,  3.41it/s]\n40it [00:11,  3.41it/s]\n41it [00:11,  3.41it/s]\n42it [00:12,  3.41it/s]\n43it [00:12,  3.41it/s]\n44it [00:12,  3.41it/s]\n45it [00:12,  3.41it/s]\n46it [00:13,  3.40it/s]\n47it [00:13,  3.41it/s]\n48it [00:13,  3.39it/s]\n49it [00:14,  3.40it/s]\n50it [00:14,  3.39it/s]\n50it [00:14,  3.46it/s]",
  "metrics": {
    "predict_time": 15.802695,
    "total_time": 16.020369
  },
  "output": [
    "https://replicate.delivery/mgxm/8f75db20-72d9-4917-bc86-db4ca5d73c35/out-0.png"
  ],
  "started_at": "2022-09-07T01:17:10.388153Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/7z63mz2dzfdajg3wbbtailuehm",
    "cancel": "https://api.replicate.com/v1/predictions/7z63mz2dzfdajg3wbbtailuehm/cancel"
  },
  "version": "56f26876a159c10b429c382f66ccda648c1d5678d7ce15ed010734b715be5ab9"
}

Generated in

15.8 seconds

Tweak it ShareReport

Prediction

tommoore515/material_stable_diffusion:3b5c0242

Model

tommoore515/material_stable_diffusion:56f26876

ID

v2dsko5lc5aglm4osf4tvvsfsm

Status

Succeeded

Source

Web

Hardware

–

Total duration

15.8s

Created

over 2 years ago

Input

width: 512
height: 512
prompt: Tree bark seamless photoscan texture, trending on artstation, base color, albedo, 4k
num_outputs: 1
guidance_scale: 7.5
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 512,
  "height": 512,
  "prompt": "Tree bark seamless photoscan texture, trending on artstation, base color, albedo, 4k",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Set 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: "Tree bark seamless photoscan texture, trending on artstation, 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 replicate

Set 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 replicate

Run 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": "Tree bark seamless photoscan texture, trending on artstation, 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": "Tree bark seamless photoscan texture, trending on artstation, base color, albedo, 4k",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To 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 cog

If 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="Tree bark seamless photoscan texture, trending on artstation, 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@sha256:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Tree bark seamless photoscan texture, trending on artstation, 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.

Output

{
  "completed_at": "2022-09-07T01:20:57.808362Z",
  "created_at": "2022-09-07T01:20:41.996288Z",
  "data_removed": false,
  "error": null,
  "id": "v2dsko5lc5aglm4osf4tvvsfsm",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "Tree bark seamless photoscan texture, trending on artstation, base color, albedo, 4k",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 62103\n\n0it [00:00, ?it/s]\n1it [00:00,  5.73it/s]\n2it [00:00,  4.03it/s]\n3it [00:00,  3.76it/s]\n4it [00:01,  3.64it/s]\n5it [00:01,  3.56it/s]\n6it [00:01,  3.54it/s]\n7it [00:01,  3.52it/s]\n8it [00:02,  3.50it/s]\n9it [00:02,  3.49it/s]\n10it [00:02,  3.49it/s]\n11it [00:03,  3.49it/s]\n12it [00:03,  3.47it/s]\n13it [00:03,  3.48it/s]\n14it [00:03,  3.47it/s]\n15it [00:04,  3.47it/s]\n16it [00:04,  3.47it/s]\n17it [00:04,  3.47it/s]\n18it [00:05,  3.46it/s]\n19it [00:05,  3.45it/s]\n20it [00:05,  3.45it/s]\n21it [00:05,  3.45it/s]\n22it [00:06,  3.45it/s]\n23it [00:06,  3.44it/s]\n24it [00:06,  3.44it/s]\n25it [00:07,  3.45it/s]\n26it [00:07,  3.44it/s]\n27it [00:07,  3.44it/s]\n28it [00:07,  3.44it/s]\n29it [00:08,  3.43it/s]\n30it [00:08,  3.43it/s]\n31it [00:08,  3.42it/s]\n32it [00:09,  3.43it/s]\n33it [00:09,  3.42it/s]\n34it [00:09,  3.42it/s]\n35it [00:10,  3.42it/s]\n36it [00:10,  3.43it/s]\n37it [00:10,  3.42it/s]\n38it [00:10,  3.42it/s]\n39it [00:11,  3.42it/s]\n40it [00:11,  3.42it/s]\n41it [00:11,  3.42it/s]\n42it [00:12,  3.41it/s]\n43it [00:12,  3.41it/s]\n44it [00:12,  3.41it/s]\n45it [00:12,  3.41it/s]\n46it [00:13,  3.41it/s]\n47it [00:13,  3.41it/s]\n48it [00:13,  3.40it/s]\n49it [00:14,  3.40it/s]\n50it [00:14,  3.40it/s]\n50it [00:14,  3.46it/s]",
  "metrics": {
    "predict_time": 15.611519,
    "total_time": 15.812074
  },
  "output": [
    "https://replicate.delivery/mgxm/7d3bc46c-612f-42cb-9347-317b2db1d3d6/out-0.png"
  ],
  "started_at": "2022-09-07T01:20:42.196843Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/v2dsko5lc5aglm4osf4tvvsfsm",
    "cancel": "https://api.replicate.com/v1/predictions/v2dsko5lc5aglm4osf4tvvsfsm/cancel"
  },
  "version": "56f26876a159c10b429c382f66ccda648c1d5678d7ce15ed010734b715be5ab9"
}

Generated in

15.6 seconds

Tweak it ShareReport

Prediction

tommoore515/material_stable_diffusion:3b5c0242

Model

tommoore515/material_stable_diffusion:56f26876

ID

umjv6lbdvjaeldlcghpyjebquu

Status

Succeeded

Source

Web

Hardware

–

Total duration

62.3s

Created

over 2 years ago by @tommoore515

Input

width: 512
height: 512
prompt: Wall made from chocolate bars seamless texture, trending on artstation, tasty, base color, albedo, 4k
num_outputs: 1
guidance_scale: 7.5
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 512,
  "height": 512,
  "prompt": "Wall made from chocolate bars seamless texture, trending on artstation, tasty, base color, albedo, 4k",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Set 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: "Wall made from chocolate bars seamless texture, trending on artstation, tasty, 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 replicate

Set 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 replicate

Run 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": "Wall made from chocolate bars seamless texture, trending on artstation, tasty, 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": "Wall made from chocolate bars seamless texture, trending on artstation, tasty, base color, albedo, 4k",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To 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 cog

If 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="Wall made from chocolate bars seamless texture, trending on artstation, tasty, 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@sha256:3b5c0242f8925a4ab6c79b4c51e9b4ce6374e9b07b5e8461d89e692fd0faa449
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Wall made from chocolate bars seamless texture, trending on artstation, tasty, 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.

Output

{
  "completed_at": "2022-09-07T01:43:25.543516Z",
  "created_at": "2022-09-07T01:42:23.220340Z",
  "data_removed": false,
  "error": null,
  "id": "umjv6lbdvjaeldlcghpyjebquu",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "Wall made from chocolate bars seamless texture, trending on artstation, tasty, base color, albedo, 4k",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 61281\n\n0it [00:00, ?it/s]\n1it [00:03,  3.12s/it]\n2it [00:03,  1.45s/it]\n3it [00:03,  1.09it/s]\n4it [00:03,  1.51it/s]\n5it [00:04,  1.91it/s]\n6it [00:04,  2.27it/s]\n7it [00:04,  2.60it/s]\n8it [00:05,  2.85it/s]\n9it [00:05,  3.04it/s]\n10it [00:05,  3.20it/s]\n11it [00:05,  3.32it/s]\n12it [00:06,  3.39it/s]\n13it [00:06,  3.46it/s]\n14it [00:06,  3.50it/s]\n15it [00:07,  3.53it/s]\n16it [00:07,  3.55it/s]\n17it [00:07,  3.57it/s]\n18it [00:07,  3.58it/s]\n19it [00:08,  3.58it/s]\n20it [00:08,  3.59it/s]\n21it [00:08,  3.59it/s]\n22it [00:08,  3.59it/s]\n23it [00:09,  3.58it/s]\n24it [00:09,  3.58it/s]\n25it [00:09,  3.59it/s]\n26it [00:10,  3.59it/s]\n27it [00:10,  3.57it/s]\n28it [00:10,  3.58it/s]\n29it [00:10,  3.58it/s]\n30it [00:11,  3.57it/s]\n31it [00:11,  3.56it/s]\n32it [00:11,  3.57it/s]\n33it [00:12,  3.57it/s]\n34it [00:12,  3.57it/s]\n35it [00:12,  3.57it/s]\n36it [00:12,  3.56it/s]\n37it [00:13,  3.57it/s]\n38it [00:13,  3.56it/s]\n39it [00:13,  3.56it/s]\n40it [00:13,  3.56it/s]\n41it [00:14,  3.56it/s]\n42it [00:14,  3.55it/s]\n43it [00:14,  3.55it/s]\n44it [00:15,  3.56it/s]\n45it [00:15,  3.55it/s]\n46it [00:15,  3.55it/s]\n47it [00:15,  3.55it/s]\n48it [00:16,  3.55it/s]\n49it [00:16,  3.54it/s]\n50it [00:16,  3.54it/s]\n50it [00:16,  2.97it/s]",
  "metrics": {
    "predict_time": 20.87728,
    "total_time": 62.323176
  },
  "output": [
    "https://replicate.delivery/mgxm/9c645c58-82e8-4d88-bb7d-972472978698/out-0.png"
  ],
  "started_at": "2022-09-07T01:43:04.666236Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/umjv6lbdvjaeldlcghpyjebquu",
    "cancel": "https://api.replicate.com/v1/predictions/umjv6lbdvjaeldlcghpyjebquu/cancel"
  },
  "version": "56f26876a159c10b429c382f66ccda648c1d5678d7ce15ed010734b715be5ab9"
}

Generated in

20.9 seconds

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tommoore515 / material_stable_diffusion

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