Prediction

g579vqhce1rm40chxf18m3y1y0

Status

Succeeded

Source

Web

Hardware

H100

Total duration

26.5s

Created

10 months ago

Input

model: dev
prompt: Amsterdam, the Netherlands in the style of STL
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "Amsterdam, the Netherlands in the style of STL",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
  {
    input: {
      model: "dev",
      prompt: "Amsterdam, the Netherlands in the style of STL",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
    input={
        "model": "dev",
        "prompt": "Amsterdam, the Netherlands in the style of STL",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
    "input": {
      "model": "dev",
      "prompt": "Amsterdam, the Netherlands in the style of STL",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T14:21:23.270184Z",
  "created_at": "2024-09-13T14:20:56.816000Z",
  "data_removed": false,
  "error": null,
  "id": "g579vqhce1rm40chxf18m3y1y0",
  "input": {
    "model": "dev",
    "prompt": "Amsterdam, the Netherlands in the style of STL",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 63839\nPrompt: Amsterdam, the Netherlands in the style of STL\n[!] txt2img mode\nUsing dev model\nfree=7995863048192\nDownloading weights\n2024-09-13T14:21:01Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpdprqlgou/weights url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar\n2024-09-13T14:21:02Z | INFO  | [ Complete ] dest=/tmp/tmpdprqlgou/weights size=\"172 MB\" total_elapsed=1.124s url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar\nDownloaded weights in 1.15s\nLoaded LoRAs in 13.87s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.53it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.99it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.77it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.68it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.62it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.59it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.58it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.56it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.56it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.55it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.55it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.55it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.54it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.54it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.54it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.54it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.54it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.54it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.54it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.54it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.54it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.54it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.54it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.54it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.54it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.54it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.54it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.53it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.56it/s]",
  "metrics": {
    "predict_time": 22.094252972,
    "total_time": 26.454184
  },
  "output": [
    "https://replicate.delivery/yhqm/Tb3Kn1lM5oogPd7xYYPz4hON0pfBYaJCCkCQbwLPYG3x4PuJA/out-0.webp"
  ],
  "started_at": "2024-09-13T14:21:01.175931Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/g579vqhce1rm40chxf18m3y1y0",
    "cancel": "https://api.replicate.com/v1/predictions/g579vqhce1rm40chxf18m3y1y0/cancel"
  },
  "version": "48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839"
}

Generated in

22.1 seconds

levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839

Using seed: 63839
Prompt: Amsterdam, the Netherlands in the style of STL
[!] txt2img mode
Using dev model
free=7995863048192
Downloading weights
2024-09-13T14:21:01Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpdprqlgou/weights url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar
2024-09-13T14:21:02Z | INFO  | [ Complete ] dest=/tmp/tmpdprqlgou/weights size="172 MB" total_elapsed=1.124s url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar
Downloaded weights in 1.15s
Loaded LoRAs in 13.87s
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Prediction

Model

6f8ejdpbedrm60chxf3b0sph6m

Status

Succeeded

Source

Web

Hardware

H100

Total duration

50.8s

Created

10 months ago by @levelsio

Input

model: dev
prompt: man posing in front of neighbourhood in the style of STL with a circular road layout at sunset
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
  {
    input: {
      model: "dev",
      prompt: "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
    input={
        "model": "dev",
        "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
    "input": {
      "model": "dev",
      "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T14:26:50.423827Z",
  "created_at": "2024-09-13T14:25:59.667000Z",
  "data_removed": false,
  "error": null,
  "id": "6f8ejdpbedrm60chxf3b0sph6m",
  "input": {
    "model": "dev",
    "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 54761\nPrompt: man posing in front of neighbourhood in the style of STL with a circular road layout at sunset\n[!] txt2img mode\nUsing dev model\nfree=7828227280896\nDownloading weights\n2024-09-13T14:26:25Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpi9xcj5iq/weights url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar\n2024-09-13T14:26:26Z | INFO  | [ Complete ] dest=/tmp/tmpi9xcj5iq/weights size=\"172 MB\" total_elapsed=1.274s url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar\nDownloaded weights in 1.31s\nLoaded LoRAs in 15.94s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.83it/s]\n  7%|▋         | 2/28 [00:00<00:07,  3.59it/s]\n 11%|█         | 3/28 [00:00<00:07,  3.57it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.56it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.56it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.55it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.55it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.55it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.55it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.55it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.55it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.55it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.55it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.55it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.55it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.55it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.55it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.55it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.55it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.55it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.55it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.55it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.55it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.55it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.55it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.55it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.55it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.55it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.54it/s]",
  "metrics": {
    "predict_time": 25.099139663,
    "total_time": 50.756827
  },
  "output": [
    "https://replicate.delivery/yhqm/NgHcc6GOdjYZD1QFM5pVAnqtiF6p8nJBnjJsulGXVebV7PuJA/out-0.webp"
  ],
  "started_at": "2024-09-13T14:26:25.324688Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/6f8ejdpbedrm60chxf3b0sph6m",
    "cancel": "https://api.replicate.com/v1/predictions/6f8ejdpbedrm60chxf3b0sph6m/cancel"
  },
  "version": "48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839"
}

Generated in

25.1 seconds

levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a

Using seed: 54761
Prompt: man posing in front of neighbourhood in the style of STL with a circular road layout at sunset
[!] txt2img mode
Using dev model
free=7828227280896
Downloading weights
2024-09-13T14:26:25Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpi9xcj5iq/weights url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar
2024-09-13T14:26:26Z | INFO  | [ Complete ] dest=/tmp/tmpi9xcj5iq/weights size="172 MB" total_elapsed=1.274s url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar
Downloaded weights in 1.31s
Loaded LoRAs in 15.94s
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Prediction

Model

rd9zyddja5rm40chxh1s4e9ng8

Status

Succeeded

Source

Web

Hardware

H100

Total duration

17.9s

Created

10 months ago

Input

model: dev
prompt: man posing in front of neighbourhood in the style of STL with a circular road layout at sunset
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
  {
    input: {
      model: "dev",
      prompt: "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    input={
        "model": "dev",
        "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    "input": {
      "model": "dev",
      "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T16:42:43.092532Z",
  "created_at": "2024-09-13T16:42:25.233000Z",
  "data_removed": false,
  "error": null,
  "id": "rd9zyddja5rm40chxh1s4e9ng8",
  "input": {
    "model": "dev",
    "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 24888\nPrompt: man posing in front of neighbourhood in the style of STL with a circular road layout at sunset\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 9.63s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.53it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.99it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.76it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.67it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.61it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.58it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.57it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.56it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.55it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.54it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.54it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.54it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.54it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.54it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.54it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.54it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.53it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.53it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.53it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.53it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.53it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.53it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.53it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.53it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.53it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.53it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.53it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.53it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.56it/s]",
  "metrics": {
    "predict_time": 17.849874111,
    "total_time": 17.859532
  },
  "output": [
    "https://replicate.delivery/yhqm/lyEfXqNVLdwqWiUvyhOFPucDfwmrhtZf9efAj8oG4BLdwOkbC/out-0.webp"
  ],
  "started_at": "2024-09-13T16:42:25.242658Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/rd9zyddja5rm40chxh1s4e9ng8",
    "cancel": "https://api.replicate.com/v1/predictions/rd9zyddja5rm40chxh1s4e9ng8/cancel"
  },
  "version": "b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a"
}

Generated in

17.9 seconds

levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a

Prediction

Model

475zpw2hc1rm60chxh2813t0jc

Status

Succeeded

Source

Web

Hardware

H100

Total duration

8.3s

Created

10 months ago

Input

model: dev
prompt: blonde man posing in front of neighbourhood in the style of STL with a circular road layout at sunset
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "blonde man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
  {
    input: {
      model: "dev",
      prompt: "blonde man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    input={
        "model": "dev",
        "prompt": "blonde man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    "input": {
      "model": "dev",
      "prompt": "blonde man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T16:43:14.229439Z",
  "created_at": "2024-09-13T16:43:05.952000Z",
  "data_removed": false,
  "error": null,
  "id": "475zpw2hc1rm60chxh2813t0jc",
  "input": {
    "model": "dev",
    "prompt": "blonde man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 14994\nPrompt: blonde man posing in front of neighbourhood in the style of STL with a circular road layout at sunset\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.53it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.99it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.76it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.66it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.61it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.58it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.56it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.54it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.53it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.53it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.52it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.52it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.52it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.52it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.52it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.52it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.52it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.52it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.52it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.52it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.52it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.52it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.52it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.52it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.52it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.52it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.52it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.52it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.54it/s]",
  "metrics": {
    "predict_time": 8.26921725,
    "total_time": 8.277439
  },
  "output": [
    "https://replicate.delivery/yhqm/R5en65fXPFrgi00o75XGdZqPs0x5b4EnBcBx4HyxooPi2hcTA/out-0.webp"
  ],
  "started_at": "2024-09-13T16:43:05.960222Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/475zpw2hc1rm60chxh2813t0jc",
    "cancel": "https://api.replicate.com/v1/predictions/475zpw2hc1rm60chxh2813t0jc/cancel"
  },
  "version": "b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a"
}

Generated in

8.3 seconds

levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a

Prediction

Model

8nnxc9y9jxrm60chxh2vz4z1yr

Status

Succeeded

Source

Web

Hardware

H100

Total duration

18.8s

Created

10 months ago

Input

model: dev
prompt: blonde woman posing in front of neighbourhood in the style of STL with a circular road layout at sunset
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "blonde woman posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
  {
    input: {
      model: "dev",
      prompt: "blonde woman posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    input={
        "model": "dev",
        "prompt": "blonde woman posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    "input": {
      "model": "dev",
      "prompt": "blonde woman posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T16:45:01.050782Z",
  "created_at": "2024-09-13T16:44:42.263000Z",
  "data_removed": false,
  "error": null,
  "id": "8nnxc9y9jxrm60chxh2vz4z1yr",
  "input": {
    "model": "dev",
    "prompt": "blonde woman posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 36836\nPrompt: blonde woman posing in front of neighbourhood in the style of STL with a circular road layout at sunset\n[!] txt2img mode\nUsing dev model\nfree=7274291617792\nDownloading weights\n2024-09-13T16:44:42Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpxrdqoost/weights url=https://replicate.delivery/yhqm/vufmKxRU5Q11GK9mVFtlje6Q4pEpSZGdjef84baSV1xfNJkbC/trained_model.tar\n2024-09-13T16:44:43Z | INFO  | [ Complete ] dest=/tmp/tmpxrdqoost/weights size=\"172 MB\" total_elapsed=1.152s url=https://replicate.delivery/yhqm/vufmKxRU5Q11GK9mVFtlje6Q4pEpSZGdjef84baSV1xfNJkbC/trained_model.tar\nDownloaded weights in 1.18s\nLoaded LoRAs in 10.59s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.54it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.00it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.78it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.68it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.63it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.59it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.57it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.56it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.55it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.55it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.54it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.54it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.54it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.54it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.54it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.54it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.54it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.54it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.54it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.53it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.53it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.52it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.54it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.54it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.54it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.54it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.54it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.54it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.56it/s]",
  "metrics": {
    "predict_time": 18.778944717999998,
    "total_time": 18.787782
  },
  "output": [
    "https://replicate.delivery/yhqm/TsjgNhCTNVpeDijqMcP2RPAXfflW9J5WQQWkYclYXf31gHyNB/out-0.webp"
  ],
  "started_at": "2024-09-13T16:44:42.271837Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/8nnxc9y9jxrm60chxh2vz4z1yr",
    "cancel": "https://api.replicate.com/v1/predictions/8nnxc9y9jxrm60chxh2vz4z1yr/cancel"
  },
  "version": "b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a"
}

Generated in

18.8 seconds

levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a

Using seed: 36836
Prompt: blonde woman posing in front of neighbourhood in the style of STL with a circular road layout at sunset
[!] txt2img mode
Using dev model
free=7274291617792
Downloading weights
2024-09-13T16:44:42Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpxrdqoost/weights url=https://replicate.delivery/yhqm/vufmKxRU5Q11GK9mVFtlje6Q4pEpSZGdjef84baSV1xfNJkbC/trained_model.tar
2024-09-13T16:44:43Z | INFO  | [ Complete ] dest=/tmp/tmpxrdqoost/weights size="172 MB" total_elapsed=1.152s url=https://replicate.delivery/yhqm/vufmKxRU5Q11GK9mVFtlje6Q4pEpSZGdjef84baSV1xfNJkbC/trained_model.tar
Downloaded weights in 1.18s
Loaded LoRAs in 10.59s
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Prediction

Model

10a716e74srm00chxh38v54fer

Status

Succeeded

Source

Web

Hardware

H100

Total duration

17.9s

Created

10 months ago

Input

model: dev
prompt: man driving BMX in a street the style of STL with a circular road layout, view from the back
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "man driving BMX in a street the style of STL with a circular road layout, view from the back",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
  {
    input: {
      model: "dev",
      prompt: "man driving BMX in a street the style of STL with a circular road layout, view from the back",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    input={
        "model": "dev",
        "prompt": "man driving BMX in a street the style of STL with a circular road layout, view from the back",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    "input": {
      "model": "dev",
      "prompt": "man driving BMX in a street the style of STL with a circular road layout, view from the back",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T16:46:05.024204Z",
  "created_at": "2024-09-13T16:45:47.174000Z",
  "data_removed": false,
  "error": null,
  "id": "10a716e74srm00chxh38v54fer",
  "input": {
    "model": "dev",
    "prompt": "man driving BMX in a street the style of STL with a circular road layout, view from the back",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 41392\nPrompt: man driving BMX in a street the style of STL with a circular road layout, view from the back\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 9.58s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.52it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.98it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.75it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.66it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.61it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.58it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.56it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.55it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.54it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.53it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.53it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.52it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.52it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.52it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.52it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.52it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.52it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.52it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.52it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.52it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.52it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.52it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.52it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.52it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.52it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.52it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.52it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.52it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.54it/s]",
  "metrics": {
    "predict_time": 17.8427018,
    "total_time": 17.850204
  },
  "output": [
    "https://replicate.delivery/yhqm/RaXorc9UMpLeTag0zk6yK7TeQOdZhlKqzwFRkq3aWn8N5hcTA/out-0.webp"
  ],
  "started_at": "2024-09-13T16:45:47.181502Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/10a716e74srm00chxh38v54fer",
    "cancel": "https://api.replicate.com/v1/predictions/10a716e74srm00chxh38v54fer/cancel"
  },
  "version": "b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a"
}

Generated in

17.9 seconds

levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a

Prediction

Model

ygsjdg9tg1rm40chxh3rg7amcg

Status

Succeeded

Source

Web

Hardware

H100

Total duration

16.9s

Created

10 months ago

Input

model: dev
prompt: blonde woman driving BMX in a street the style of STL with a circular road layout, view from the back
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "blonde woman driving BMX in a street the style of STL with a circular road layout, view from the back",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
  {
    input: {
      model: "dev",
      prompt: "blonde woman driving BMX in a street the style of STL with a circular road layout, view from the back",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    input={
        "model": "dev",
        "prompt": "blonde woman driving BMX in a street the style of STL with a circular road layout, view from the back",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    "input": {
      "model": "dev",
      "prompt": "blonde woman driving BMX in a street the style of STL with a circular road layout, view from the back",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T16:46:33.574369Z",
  "created_at": "2024-09-13T16:46:16.704000Z",
  "data_removed": false,
  "error": null,
  "id": "ygsjdg9tg1rm40chxh3rg7amcg",
  "input": {
    "model": "dev",
    "prompt": "blonde woman driving BMX in a street the style of STL with a circular road layout, view from the back",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 41978\nPrompt: blonde woman driving BMX in a street the style of STL with a circular road layout, view from the back\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 8.59s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.52it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.99it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.75it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.65it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.61it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.57it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.56it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.54it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.53it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.53it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.52it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.52it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.52it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.52it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.52it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.52it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.52it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.52it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.51it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.51it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.51it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.52it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.51it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.51it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.51it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.51it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.51it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.51it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.54it/s]",
  "metrics": {
    "predict_time": 16.859620906,
    "total_time": 16.870369
  },
  "output": [
    "https://replicate.delivery/yhqm/lFgUo9Gx12YpLRQvf0DRnto5iUrXessmcofEDlXndSHSzD5mA/out-0.webp"
  ],
  "started_at": "2024-09-13T16:46:16.714748Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ygsjdg9tg1rm40chxh3rg7amcg",
    "cancel": "https://api.replicate.com/v1/predictions/ygsjdg9tg1rm40chxh3rg7amcg/cancel"
  },
  "version": "b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a"
}

Generated in

16.9 seconds

levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a

Prediction

Model

5fp43xf2snrm60chxh7a93ecj0

Status

Succeeded

Source

Web

Hardware

H100

Total duration

19.7s

Created

10 months ago

Input

model: dev
prompt: beach village from the sky in Portugal in the style of STL. houses have orange roofs
lora_scale: 1.05
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "beach village from the sky in Portugal in the style of STL. houses have orange roofs",
  "lora_scale": 1.05,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
  {
    input: {
      model: "dev",
      prompt: "beach village from the sky in Portugal in the style of STL. houses have orange roofs",
      lora_scale: 1.05,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    input={
        "model": "dev",
        "prompt": "beach village from the sky in Portugal in the style of STL. houses have orange roofs",
        "lora_scale": 1.05,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a",
    "input": {
      "model": "dev",
      "prompt": "beach village from the sky in Portugal in the style of STL. houses have orange roofs",
      "lora_scale": 1.05,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T16:54:58.245807Z",
  "created_at": "2024-09-13T16:54:38.541000Z",
  "data_removed": false,
  "error": null,
  "id": "5fp43xf2snrm60chxh7a93ecj0",
  "input": {
    "model": "dev",
    "prompt": "beach village from the sky in Portugal in the style of STL. houses have orange roofs",
    "lora_scale": 1.05,
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37731\nPrompt: beach village from the sky in Portugal in the style of STL. houses have orange roofs\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 7.41s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.44it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.81it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.68it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.62it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.59it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.56it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.56it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.55it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.54it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.54it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.53it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.54it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.54it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.54it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.53it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.54it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.54it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.53it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.53it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.53it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.53it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.53it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.53it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.53it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.53it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.53it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.53it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.53it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.55it/s]",
  "metrics": {
    "predict_time": 15.650811618,
    "total_time": 19.704807
  },
  "output": [
    "https://replicate.delivery/yhqm/ymM1zHctQSY0F9Cfsd34RpCaKrYlZUveUfiiegr37biIGIyNB/out-0.webp"
  ],
  "started_at": "2024-09-13T16:54:42.594995Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/5fp43xf2snrm60chxh7a93ecj0",
    "cancel": "https://api.replicate.com/v1/predictions/5fp43xf2snrm60chxh7a93ecj0/cancel"
  },
  "version": "b70fb777ee533a9fda6a842c88b96d7b5a34b5bfb1d7fa15bf63a72bc40ceb6a"
}

Generated in

15.7 seconds

levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839

Prediction

Model

effdzc96bnrm20chxeysx81nj4

Status

Succeeded

Source

Web

Hardware

H100

Total duration

24.2s

Created

10 months ago

Input

model: dev
prompt: New York City in the style of STL
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "New York City in the style of STL",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
  {
    input: {
      model: "dev",
      prompt: "New York City in the style of STL",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
    input={
        "model": "dev",
        "prompt": "New York City in the style of STL",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
    "input": {
      "model": "dev",
      "prompt": "New York City in the style of STL",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T14:15:51.820837Z",
  "created_at": "2024-09-13T14:15:27.581000Z",
  "data_removed": false,
  "error": null,
  "id": "effdzc96bnrm20chxeysx81nj4",
  "input": {
    "model": "dev",
    "prompt": "New York City in the style of STL",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 62609\nPrompt: New York City in the style of STL\n[!] txt2img mode\nUsing dev model\nfree=7244219662336\nDownloading weights\n2024-09-13T14:15:33Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp2ktwfxvv/weights url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar\n2024-09-13T14:15:35Z | INFO  | [ Complete ] dest=/tmp/tmp2ktwfxvv/weights size=\"172 MB\" total_elapsed=1.773s url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar\nDownloaded weights in 1.80s\nLoaded LoRAs in 10.14s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.56it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.03it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.80it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.70it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.65it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.62it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.60it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.59it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.58it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.57it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.57it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.57it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.57it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.56it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.56it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.56it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.56it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.56it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.56it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.56it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.56it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.56it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.56it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.56it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.56it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.56it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.56it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.56it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.58it/s]",
  "metrics": {
    "predict_time": 18.324876414,
    "total_time": 24.239837
  },
  "output": [
    "https://replicate.delivery/yhqm/6weKx319oDRcIa3m79x6BWKsR00xB1Sd083M4IvbVu2L2PuJA/out-0.webp"
  ],
  "started_at": "2024-09-13T14:15:33.495961Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/effdzc96bnrm20chxeysx81nj4",
    "cancel": "https://api.replicate.com/v1/predictions/effdzc96bnrm20chxeysx81nj4/cancel"
  },
  "version": "48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839"
}

Generated in

18.3 seconds

levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839

Using seed: 62609
Prompt: New York City in the style of STL
[!] txt2img mode
Using dev model
free=7244219662336
Downloading weights
2024-09-13T14:15:33Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp2ktwfxvv/weights url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar
2024-09-13T14:15:35Z | INFO  | [ Complete ] dest=/tmp/tmp2ktwfxvv/weights size="172 MB" total_elapsed=1.773s url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar
Downloaded weights in 1.80s
Loaded LoRAs in 10.14s
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Prediction

Model

ttfvh7dp8drm00chxf0vt1phvw

Status

Succeeded

Source

Web

Hardware

H100

Total duration

18.5s

Created

10 months ago

Input

model: dev
prompt: San Francisco bridge in the style of STL
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "San Francisco bridge in the style of STL",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
  {
    input: {
      model: "dev",
      prompt: "San Francisco bridge in the style of STL",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
    input={
        "model": "dev",
        "prompt": "San Francisco bridge in the style of STL",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839",
    "input": {
      "model": "dev",
      "prompt": "San Francisco bridge in the style of STL",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-13T14:20:45.095563Z",
  "created_at": "2024-09-13T14:20:26.563000Z",
  "data_removed": false,
  "error": null,
  "id": "ttfvh7dp8drm00chxf0vt1phvw",
  "input": {
    "model": "dev",
    "prompt": "San Francisco bridge in the style of STL",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 16152\nPrompt: San Francisco bridge in the style of STL\n[!] txt2img mode\nUsing dev model\nfree=6881752465408\nDownloading weights\n2024-09-13T14:20:26Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp_a7uq672/weights url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar\n2024-09-13T14:20:28Z | INFO  | [ Complete ] dest=/tmp/tmp_a7uq672/weights size=\"172 MB\" total_elapsed=1.639s url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar\nDownloaded weights in 1.67s\nLoaded LoRAs in 10.37s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.56it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.03it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.80it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.70it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.65it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.62it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.60it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.58it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.58it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.57it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.57it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.56it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.56it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.56it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.56it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.55it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.56it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.56it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.56it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.56it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.55it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.55it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.55it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.55it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.56it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.54it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.54it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.54it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.58it/s]",
  "metrics": {
    "predict_time": 18.524824246,
    "total_time": 18.532563
  },
  "output": [
    "https://replicate.delivery/yhqm/T49uBPQNiWYDMt0hUEIu4fKjAusYDNw9nXkMEtnpnfu9wf4mA/out-0.webp"
  ],
  "started_at": "2024-09-13T14:20:26.570739Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ttfvh7dp8drm00chxf0vt1phvw",
    "cancel": "https://api.replicate.com/v1/predictions/ttfvh7dp8drm00chxf0vt1phvw/cancel"
  },
  "version": "48ec7052507119a3d8e4ab31299faf1246275e39d4727ab3542c6a38e22ae839"
}

Generated in

18.5 seconds

levelsio/san-andreas:61cdb2f6a8f234ea9ca3cce88d5454f9b951f93619f5f353a331407f4a05a314

Using seed: 16152
Prompt: San Francisco bridge in the style of STL
[!] txt2img mode
Using dev model
free=6881752465408
Downloading weights
2024-09-13T14:20:26Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp_a7uq672/weights url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar
2024-09-13T14:20:28Z | INFO  | [ Complete ] dest=/tmp/tmp_a7uq672/weights size="172 MB" total_elapsed=1.639s url=https://replicate.delivery/yhqm/E000pz6pSZKcKxV3MyPTb57Zf3AJTXeQD199lp2Mms3fL64mA/trained_model.tar
Downloaded weights in 1.67s
Loaded LoRAs in 10.37s
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Prediction

Model

levelsio/san-andreas:61cdb2f6

7tznw8cmqnrm20chy4xvh66a5c

Status

Succeeded

Source

Web

Hardware

H100

Total duration

20.3s

Created

10 months ago

Input

model: dev
prompt: man posing in front of neighbourhood in the style of STL with a circular road layout at sunset
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

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 levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "levelsio/san-andreas:61cdb2f6a8f234ea9ca3cce88d5454f9b951f93619f5f353a331407f4a05a314",
  {
    input: {
      model: "dev",
      prompt: "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run levelsio/san-andreas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "levelsio/san-andreas:61cdb2f6a8f234ea9ca3cce88d5454f9b951f93619f5f353a331407f4a05a314",
    input={
        "model": "dev",
        "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run levelsio/san-andreas 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": "levelsio/san-andreas:61cdb2f6a8f234ea9ca3cce88d5454f9b951f93619f5f353a331407f4a05a314",
    "input": {
      "model": "dev",
      "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-09-14T15:51:59.762689Z",
  "created_at": "2024-09-14T15:51:39.453000Z",
  "data_removed": false,
  "error": null,
  "id": "7tznw8cmqnrm20chy4xvh66a5c",
  "input": {
    "model": "dev",
    "prompt": "man posing in front of neighbourhood in the style of STL with a circular road layout at sunset",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 65281\nPrompt: man posing in front of neighbourhood in the style of STL with a circular road layout at sunset\n[!] txt2img mode\nUsing dev model\nfree=7381671841792\nDownloading weights\n2024-09-14T15:51:39Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpxezi2aaf/weights url=https://replicate.delivery/yhqm/EUjFpsIS93ahDR7oSQ2yxAZgdUsa4MZ4W7ex1L3b2fXb4mcTA/trained_model.tar\n2024-09-14T15:51:43Z | INFO  | [ Complete ] dest=/tmp/tmpxezi2aaf/weights size=\"172 MB\" total_elapsed=3.243s url=https://replicate.delivery/yhqm/EUjFpsIS93ahDR7oSQ2yxAZgdUsa4MZ4W7ex1L3b2fXb4mcTA/trained_model.tar\nDownloaded weights in 3.34s\nLoaded LoRAs in 11.86s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.54it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.01it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.79it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.69it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.63it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.60it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.58it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.57it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.56it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.56it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.55it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.55it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.55it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.55it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.55it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.55it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.54it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.54it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.54it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.54it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.54it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.54it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.54it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.54it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.54it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.54it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.54it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.54it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.57it/s]",
  "metrics": {
    "predict_time": 20.037383925,
    "total_time": 20.309689
  },
  "output": [
    "https://replicate.delivery/yhqm/RSfL5oqrZxUYBy23swyH6DbOkfBVV4tlc2qbwREQe8fejxmbC/out-0.webp"
  ],
  "started_at": "2024-09-14T15:51:39.725305Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/7tznw8cmqnrm20chy4xvh66a5c",
    "cancel": "https://api.replicate.com/v1/predictions/7tznw8cmqnrm20chy4xvh66a5c/cancel"
  },
  "version": "61cdb2f6a8f234ea9ca3cce88d5454f9b951f93619f5f353a331407f4a05a314"
}

Generated in

20.0 seconds