@maorefrati's maorefrati/maor

Model

maorefrati/maor:b3e37199

dnkzea0405rmc0cm4bsssgvfzc

Status

Succeeded

Source

Web

Hardware

H100

Total duration

11.8s

Created

12 months ago

Webhook

–

Input

prompt: Maor is a guitar player with an Orange les-Paul guitar on an black crane in a construction site. He has an orange construction vest, black T-shirt, orange trucks, grey walls, sand
aspect_ratio: 1:1
width: 737
prompt_strength: 0.8
model: dev
num_outputs: 1
num_inference_steps: 28
guidance_scale: 3
output_format: webp
output_quality: 80
go_fast: false
megapixels: 1
lora_scale: 1
extra_lora_scale: 1

{
  "aspect_ratio": "1:1",
  "extra_lora_scale": 1,
  "go_fast": false,
  "guidance_scale": 3,
  "lora_scale": 1,
  "megapixels": "1",
  "model": "dev",
  "num_inference_steps": 28,
  "num_outputs": 1,
  "output_format": "webp",
  "output_quality": 80,
  "prompt": "Maor is a guitar player with an Orange les-Paul guitar on an black crane in a construction site. He has an orange construction vest, black T-shirt, orange trucks, grey walls, sand",
  "prompt_strength": 0.8,
  "width": 737
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_WOb**********************************

This is your API token. Keep it to yourself.

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 maorefrati/maor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "maorefrati/maor:b3e371998332a55e71b854bd14cfb59ed993d7c377e00187ddd32189db127257",
  {
    input: {
      aspect_ratio: "1:1",
      extra_lora_scale: 1,
      go_fast: false,
      guidance_scale: 3,
      lora_scale: 1,
      megapixels: "1",
      model: "dev",
      num_inference_steps: 28,
      num_outputs: 1,
      output_format: "webp",
      output_quality: 80,
      prompt: "Maor is a guitar player with an Orange les-Paul guitar on an black crane in a construction site. He has an orange construction vest, black T-shirt, orange trucks, grey walls, sand",
      prompt_strength: 0.8,
      width: 737
    }
  }
);

// 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.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_WOb**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run maorefrati/maor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "maorefrati/maor:b3e371998332a55e71b854bd14cfb59ed993d7c377e00187ddd32189db127257",
    input={
        "aspect_ratio": "1:1",
        "extra_lora_scale": 1,
        "go_fast": False,
        "guidance_scale": 3,
        "lora_scale": 1,
        "megapixels": "1",
        "model": "dev",
        "num_inference_steps": 28,
        "num_outputs": 1,
        "output_format": "webp",
        "output_quality": 80,
        "prompt": "Maor is a guitar player with an Orange les-Paul guitar on an black crane in a construction site. He has an orange construction vest, black T-shirt, orange trucks, grey walls, sand",
        "prompt_strength": 0.8,
        "width": 737
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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=r8_WOb**********************************

This is your API token. Keep it to yourself.

Run maorefrati/maor 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": "maorefrati/maor:b3e371998332a55e71b854bd14cfb59ed993d7c377e00187ddd32189db127257",
    "input": {
      "aspect_ratio": "1:1",
      "extra_lora_scale": 1,
      "go_fast": false,
      "guidance_scale": 3,
      "lora_scale": 1,
      "megapixels": "1",
      "model": "dev",
      "num_inference_steps": 28,
      "num_outputs": 1,
      "output_format": "webp",
      "output_quality": 80,
      "prompt": "Maor is a guitar player with an Orange les-Paul guitar on an black crane in a construction site. He has an orange construction vest, black T-shirt, orange trucks, grey walls, sand",
      "prompt_strength": 0.8,
      "width": 737
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "dnkzea0405rmc0cm4bsssgvfzc",
  "model": "maorefrati/maor",
  "version": "b3e371998332a55e71b854bd14cfb59ed993d7c377e00187ddd32189db127257",
  "input": {
    "aspect_ratio": "1:1",
    "extra_lora_scale": 1,
    "go_fast": false,
    "guidance_scale": 3,
    "lora_scale": 1,
    "megapixels": "1",
    "model": "dev",
    "num_inference_steps": 28,
    "num_outputs": 1,
    "output_format": "webp",
    "output_quality": 80,
    "prompt": "Maor is a guitar player with an Orange les-Paul guitar on an black crane in a construction site. He has an orange construction vest, black T-shirt, orange trucks, grey walls, sand",
    "prompt_strength": 0.8,
    "width": 737
  },
  "logs": "2025-01-01 17:38:59.274 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-01 17:38:59.275 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  90%|█████████ | 275/304 [00:00<00:00, 2739.83it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2540.85it/s]\n2025-01-01 17:38:59.395 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s\nfree=29693021175808\nDownloading weights\n2025-01-01T17:38:59Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpswd3sacs/weights url=https://replicate.delivery/xezq/asftcqR1YcX8B6TLVqpw30gM2mcuXUV9OLy2EoRPex0kOvAUA/trained_model.tar\n2025-01-01T17:39:04Z | INFO  | [ Complete ] dest=/tmp/tmpswd3sacs/weights size=\"344 MB\" total_elapsed=5.320s url=https://replicate.delivery/xezq/asftcqR1YcX8B6TLVqpw30gM2mcuXUV9OLy2EoRPex0kOvAUA/trained_model.tar\nDownloaded weights in 5.35s\n2025-01-01 17:39:04.746 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/e7272a8305dc8d4a\n2025-01-01 17:39:04.854 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-01-01 17:39:04.854 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-01 17:39:04.854 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  90%|█████████ | 275/304 [00:00<00:00, 2742.33it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2541.92it/s]\n2025-01-01 17:39:04.974 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.23s\nUsing seed: 18173\n0it [00:00, ?it/s]\n1it [00:00,  8.32it/s]\n2it [00:00,  5.85it/s]\n3it [00:00,  5.36it/s]\n4it [00:00,  5.15it/s]\n5it [00:00,  5.04it/s]\n6it [00:01,  4.96it/s]\n7it [00:01,  4.92it/s]\n8it [00:01,  4.90it/s]\n9it [00:01,  4.89it/s]\n10it [00:01,  4.88it/s]\n11it [00:02,  4.87it/s]\n12it [00:02,  4.86it/s]\n13it [00:02,  4.84it/s]\n14it [00:02,  4.84it/s]\n15it [00:03,  4.84it/s]\n16it [00:03,  4.84it/s]\n17it [00:03,  4.84it/s]\n18it [00:03,  4.84it/s]\n19it [00:03,  4.84it/s]\n20it [00:04,  4.83it/s]\n21it [00:04,  4.84it/s]\n22it [00:04,  4.84it/s]\n23it [00:04,  4.85it/s]\n24it [00:04,  4.85it/s]\n25it [00:05,  4.84it/s]\n26it [00:05,  4.84it/s]\n27it [00:05,  4.83it/s]\n28it [00:05,  4.84it/s]\n28it [00:05,  4.91it/s]\nTotal safe images: 1 out of 1",
  "output": [
    "https://replicate.delivery/xezq/VL5AYDFP0MJLJdnbJKJ9C0AjTtOe49jItYVRpzCSwQ2felBoA/out-0.webp"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2025-01-01T17:38:59.201Z",
  "started_at": "2025-01-01T17:38:59.275813Z",
  "completed_at": "2025-01-01T17:39:11.03149Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/dnkzea0405rmc0cm4bsssgvfzc/cancel",
    "get": "https://api.replicate.com/v1/predictions/dnkzea0405rmc0cm4bsssgvfzc",
    "stream": "https://stream.replicate.com/v1/files/bcwr-vhlyp5uhhwqogq7ehsx5myjs6bl43jwb5et75n2ia23hu3fgwn2a",
    "web": "https://replicate.com/p/dnkzea0405rmc0cm4bsssgvfzc"
  },
  "metrics": {
    "predict_time": 11.755677037,
    "total_time": 11.83049
  }
}

Generated in

11.8 seconds

Tweak it Report

2025-01-01 17:38:59.274 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-01 17:38:59.275 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2739.83it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2540.85it/s] 2025-01-01 17:38:59.395 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s free=29693021175808 Downloading weights 2025-01-01T17:38:59Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpswd3sacs/weights url=https://replicate.delivery/xezq/asftcqR1YcX8B6TLVqpw30gM2mcuXUV9OLy2EoRPex0kOvAUA/trained_model.tar 2025-01-01T17:39:04Z | INFO | [ Complete ] dest=/tmp/tmpswd3sacs/weights size="344 MB" total_elapsed=5.320s url=https://replicate.delivery/xezq/asftcqR1YcX8B6TLVqpw30gM2mcuXUV9OLy2EoRPex0kOvAUA/trained_model.tar Downloaded weights in 5.35s 2025-01-01 17:39:04.746 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/e7272a8305dc8d4a 2025-01-01 17:39:04.854 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-01-01 17:39:04.854 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-01 17:39:04.854 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2742.33it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2541.92it/s] 2025-01-01 17:39:04.974 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.23s Using seed: 18173 0it [00:00, ?it/s] 1it [00:00, 8.32it/s] 2it [00:00, 5.85it/s] 3it [00:00, 5.36it/s] 4it [00:00, 5.15it/s] 5it [00:00, 5.04it/s] 6it [00:01, 4.96it/s] 7it [00:01, 4.92it/s] 8it [00:01, 4.90it/s] 9it [00:01, 4.89it/s] 10it [00:01, 4.88it/s] 11it [00:02, 4.87it/s] 12it [00:02, 4.86it/s] 13it [00:02, 4.84it/s] 14it [00:02, 4.84it/s] 15it [00:03, 4.84it/s] 16it [00:03, 4.84it/s] 17it [00:03, 4.84it/s] 18it [00:03, 4.84it/s] 19it [00:03, 4.84it/s] 20it [00:04, 4.83it/s] 21it [00:04, 4.84it/s] 22it [00:04, 4.84it/s] 23it [00:04, 4.85it/s] 24it [00:04, 4.85it/s] 25it [00:05, 4.84it/s] 26it [00:05, 4.84it/s] 27it [00:05, 4.83it/s] 28it [00:05, 4.84it/s] 28it [00:05, 4.91it/s] Total safe images: 1 out of 1