Prediction

Model

fofr/sdxl-xmas-sweater:0690f6c6

jyk3zk3bnovkio7kfalkm6xlxi

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

11.3s

Created

about 2 years ago

Webhook

–

Input

prompt: A photo of santa wearing a TOK sweater
negative_prompt: Empty string
width: 768
height: 768
num_outputs: 1
scheduler: K_EULER
num_inference_steps: 30
guidance_scale: 7.5
prompt_strength: 0.8
refine: expert_ensemble_refiner
high_noise_frac: 0.9
apply_watermark: false
lora_scale: 0.6

{
  "apply_watermark": false,
  "guidance_scale": 7.5,
  "height": 768,
  "high_noise_frac": 0.9,
  "lora_scale": 0.6,
  "negative_prompt": "",
  "num_inference_steps": 30,
  "num_outputs": 1,
  "prompt": "A photo of santa wearing a TOK sweater",
  "prompt_strength": 0.8,
  "refine": "expert_ensemble_refiner",
  "scheduler": "K_EULER",
  "width": 768
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_64U**********************************

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 fofr/sdxl-xmas-sweater using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/sdxl-xmas-sweater:0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db",
  {
    input: {
      apply_watermark: false,
      guidance_scale: 7.5,
      height: 768,
      high_noise_frac: 0.9,
      lora_scale: 0.6,
      negative_prompt: "",
      num_inference_steps: 30,
      num_outputs: 1,
      prompt: "A photo of santa wearing a TOK sweater",
      prompt_strength: 0.8,
      refine: "expert_ensemble_refiner",
      scheduler: "K_EULER",
      width: 768
    }
  }
);

// 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_64U**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run fofr/sdxl-xmas-sweater using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/sdxl-xmas-sweater:0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db",
    input={
        "apply_watermark": False,
        "guidance_scale": 7.5,
        "height": 768,
        "high_noise_frac": 0.9,
        "lora_scale": 0.6,
        "negative_prompt": "",
        "num_inference_steps": 30,
        "num_outputs": 1,
        "prompt": "A photo of santa wearing a TOK sweater",
        "prompt_strength": 0.8,
        "refine": "expert_ensemble_refiner",
        "scheduler": "K_EULER",
        "width": 768
    }
)

# 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_64U**********************************

This is your API token. Keep it to yourself.

Run fofr/sdxl-xmas-sweater 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": "fofr/sdxl-xmas-sweater:0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db",
    "input": {
      "apply_watermark": false,
      "guidance_scale": 7.5,
      "height": 768,
      "high_noise_frac": 0.9,
      "lora_scale": 0.6,
      "negative_prompt": "",
      "num_inference_steps": 30,
      "num_outputs": 1,
      "prompt": "A photo of santa wearing a TOK sweater",
      "prompt_strength": 0.8,
      "refine": "expert_ensemble_refiner",
      "scheduler": "K_EULER",
      "width": 768
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "jyk3zk3bnovkio7kfalkm6xlxi",
  "model": "fofr/sdxl-xmas-sweater",
  "version": "0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db",
  "input": {
    "apply_watermark": false,
    "guidance_scale": 7.5,
    "height": 768,
    "high_noise_frac": 0.9,
    "lora_scale": 0.6,
    "negative_prompt": "",
    "num_inference_steps": 30,
    "num_outputs": 1,
    "prompt": "A photo of santa wearing a TOK sweater",
    "prompt_strength": 0.8,
    "refine": "expert_ensemble_refiner",
    "scheduler": "K_EULER",
    "width": 768
  },
  "logs": "Using seed: 19576\nEnsuring enough disk space...\nFree disk space: 2440310452224\nDownloading weights: https://replicate.delivery/pbxt/uSerlhyRLxz5fUKP2sXU3RhukmHeeFiUVM0WTB5mvNNlbF4HB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.239s (779 MB/s)\\nExtracted 186 MB in 0.062s (3.0 GB/s)\\n'\nDownloaded weights in 0.45714402198791504 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of santa wearing a <s0><s1> sweater\ntxt2img mode\n  0%|          | 0/27 [00:00<?, ?it/s]\n  4%|▎         | 1/27 [00:00<00:04,  6.16it/s]\n  7%|▋         | 2/27 [00:00<00:04,  6.13it/s]\n 11%|█         | 3/27 [00:00<00:03,  6.11it/s]\n 15%|█▍        | 4/27 [00:00<00:03,  6.10it/s]\n 19%|█▊        | 5/27 [00:00<00:03,  6.10it/s]\n 22%|██▏       | 6/27 [00:00<00:03,  6.09it/s]\n 26%|██▌       | 7/27 [00:01<00:03,  6.09it/s]\n 30%|██▉       | 8/27 [00:01<00:03,  6.09it/s]\n 33%|███▎      | 9/27 [00:01<00:02,  6.09it/s]\n 37%|███▋      | 10/27 [00:01<00:02,  6.08it/s]\n 41%|████      | 11/27 [00:01<00:02,  6.08it/s]\n 44%|████▍     | 12/27 [00:01<00:02,  6.09it/s]\n 48%|████▊     | 13/27 [00:02<00:02,  6.09it/s]\n 52%|█████▏    | 14/27 [00:02<00:02,  6.08it/s]\n 56%|█████▌    | 15/27 [00:02<00:01,  6.08it/s]\n 59%|█████▉    | 16/27 [00:02<00:01,  6.08it/s]\n 63%|██████▎   | 17/27 [00:02<00:01,  6.09it/s]\n 67%|██████▋   | 18/27 [00:02<00:01,  6.10it/s]\n 70%|███████   | 19/27 [00:03<00:01,  6.10it/s]\n 74%|███████▍  | 20/27 [00:03<00:01,  6.10it/s]\n 78%|███████▊  | 21/27 [00:03<00:00,  6.11it/s]\n 81%|████████▏ | 22/27 [00:03<00:00,  6.11it/s]\n 85%|████████▌ | 23/27 [00:03<00:00,  6.11it/s]\n 89%|████████▉ | 24/27 [00:03<00:00,  6.11it/s]\n 93%|█████████▎| 25/27 [00:04<00:00,  6.11it/s]\n 96%|█████████▋| 26/27 [00:04<00:00,  6.11it/s]\n100%|██████████| 27/27 [00:04<00:00,  6.11it/s]\n100%|██████████| 27/27 [00:04<00:00,  6.10it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.80it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  7.25it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.40it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.30it/s]",
  "output": [
    "https://replicate.delivery/pbxt/Dd9IAXSX2cJCANdbvYFBa7YI6518qoSXSz9UF6lDb8Q1lqfIA/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-12-03T21:31:54.922767Z",
  "started_at": "2023-12-03T21:31:59.426338Z",
  "completed_at": "2023-12-03T21:32:06.23207Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/jyk3zk3bnovkio7kfalkm6xlxi/cancel",
    "get": "https://api.replicate.com/v1/predictions/jyk3zk3bnovkio7kfalkm6xlxi"
  },
  "metrics": {
    "predict_time": 6.805732,
    "total_time": 11.309303
  }
}

Generated in

6.8 seconds

Tweak it Report

Using seed: 19576 Ensuring enough disk space... Free disk space: 2440310452224 Downloading weights: https://replicate.delivery/pbxt/uSerlhyRLxz5fUKP2sXU3RhukmHeeFiUVM0WTB5mvNNlbF4HB/trained_model.tar b'Downloaded 186 MB bytes in 0.239s (779 MB/s)\nExtracted 186 MB in 0.062s (3.0 GB/s)\n' Downloaded weights in 0.45714402198791504 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of santa wearing a <s0><s1> sweater txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:04, 6.16it/s] 7%|▋ | 2/27 [00:00<00:04, 6.13it/s] 11%|█ | 3/27 [00:00<00:03, 6.11it/s] 15%|█▍ | 4/27 [00:00<00:03, 6.10it/s] 19%|█▊ | 5/27 [00:00<00:03, 6.10it/s] 22%|██▏ | 6/27 [00:00<00:03, 6.09it/s] 26%|██▌ | 7/27 [00:01<00:03, 6.09it/s] 30%|██▉ | 8/27 [00:01<00:03, 6.09it/s] 33%|███▎ | 9/27 [00:01<00:02, 6.09it/s] 37%|███▋ | 10/27 [00:01<00:02, 6.08it/s] 41%|████ | 11/27 [00:01<00:02, 6.08it/s] 44%|████▍ | 12/27 [00:01<00:02, 6.09it/s] 48%|████▊ | 13/27 [00:02<00:02, 6.09it/s] 52%|█████▏ | 14/27 [00:02<00:02, 6.08it/s] 56%|█████▌ | 15/27 [00:02<00:01, 6.08it/s] 59%|█████▉ | 16/27 [00:02<00:01, 6.08it/s] 63%|██████▎ | 17/27 [00:02<00:01, 6.09it/s] 67%|██████▋ | 18/27 [00:02<00:01, 6.10it/s] 70%|███████ | 19/27 [00:03<00:01, 6.10it/s] 74%|███████▍ | 20/27 [00:03<00:01, 6.10it/s] 78%|███████▊ | 21/27 [00:03<00:00, 6.11it/s] 81%|████████▏ | 22/27 [00:03<00:00, 6.11it/s] 85%|████████▌ | 23/27 [00:03<00:00, 6.11it/s] 89%|████████▉ | 24/27 [00:03<00:00, 6.11it/s] 93%|█████████▎| 25/27 [00:04<00:00, 6.11it/s] 96%|█████████▋| 26/27 [00:04<00:00, 6.11it/s] 100%|██████████| 27/27 [00:04<00:00, 6.11it/s] 100%|██████████| 27/27 [00:04<00:00, 6.10it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.80it/s] 67%|██████▋ | 2/3 [00:00<00:00, 7.25it/s] 100%|██████████| 3/3 [00:00<00:00, 7.40it/s] 100%|██████████| 3/3 [00:00<00:00, 7.30it/s]