Prediction

Model

adirik/local-prompt-mixing:66a8d7d7

56rfuylbpukbee4wmu5oi5j3ye

Status

Succeeded

Source

Web

Hardware

A40

Total duration

2m 58s

Created

about 2 years ago

Webhook

–

Input

image: https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg
prompt: A vase with flowers on the table
object_of_interest: vase
proxy_words: Empty string
objects_to_preserve: flowers
number_of_variations: 2
steps: 50
start_prompt_range: 7
end_prompt_range: 17
guidance_scale: 7.5
seed: 10

{
  "end_prompt_range": 17,
  "guidance_scale": 7.5,
  "image": "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg",
  "number_of_variations": 2,
  "object_of_interest": "vase",
  "objects_to_preserve": "flowers",
  "prompt": "A vase with flowers on the table",
  "proxy_words": "",
  "seed": 10,
  "start_prompt_range": 7,
  "steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_Bqq**********************************

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 adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f",
  {
    input: {
      end_prompt_range: 17,
      guidance_scale: 7.5,
      image: "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg",
      number_of_variations: 2,
      object_of_interest: "vase",
      objects_to_preserve: "flowers",
      prompt: "A vase with flowers on the table",
      proxy_words: "",
      seed: 10,
      start_prompt_range: 7,
      steps: 50
    }
  }
);

// 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_Bqq**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run adirik/local-prompt-mixing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f",
    input={
        "end_prompt_range": 17,
        "guidance_scale": 7.5,
        "image": "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg",
        "number_of_variations": 2,
        "object_of_interest": "vase",
        "objects_to_preserve": "flowers",
        "prompt": "A vase with flowers on the table",
        "proxy_words": "",
        "seed": 10,
        "start_prompt_range": 7,
        "steps": 50
    }
)

# 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_Bqq**********************************

This is your API token. Keep it to yourself.

Run adirik/local-prompt-mixing 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": "adirik/local-prompt-mixing:66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f",
    "input": {
      "end_prompt_range": 17,
      "guidance_scale": 7.5,
      "image": "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg",
      "number_of_variations": 2,
      "object_of_interest": "vase",
      "objects_to_preserve": "flowers",
      "prompt": "A vase with flowers on the table",
      "proxy_words": "",
      "seed": 10,
      "start_prompt_range": 7,
      "steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "56rfuylbpukbee4wmu5oi5j3ye",
  "model": "adirik/local-prompt-mixing",
  "version": "66a8d7d7a76192ef7957a9230e379188564020ee2e3c2d6976ef9e088fa24e6f",
  "input": {
    "end_prompt_range": 17,
    "guidance_scale": 7.5,
    "image": "https://replicate.delivery/pbxt/K2CCi8IiDes9u7OWn2tQsZTtjatbfXsEapZMwWISUrtTMaOo/real.jpg",
    "number_of_variations": 2,
    "object_of_interest": "vase",
    "objects_to_preserve": "flowers",
    "prompt": "A vase with flowers on the table",
    "proxy_words": "",
    "seed": 10,
    "start_prompt_range": 7,
    "steps": 50
  },
  "logs": "0%|          | 0/15 [00:00<?, ?it/s]\n  7%|▋         | 1/15 [00:00<00:01,  7.23it/s]\n 13%|█▎        | 2/15 [00:01<00:08,  1.62it/s]\n 20%|██        | 3/15 [00:02<00:09,  1.31it/s]\n 27%|██▋       | 4/15 [00:02<00:09,  1.20it/s]\n 33%|███▎      | 5/15 [00:03<00:08,  1.14it/s]\n 40%|████      | 6/15 [00:04<00:08,  1.11it/s]\n 47%|████▋     | 7/15 [00:05<00:07,  1.09it/s]\n 53%|█████▎    | 8/15 [00:06<00:06,  1.08it/s]\n 60%|██████    | 9/15 [00:07<00:05,  1.07it/s]\n 67%|██████▋   | 10/15 [00:08<00:04,  1.07it/s]\n 73%|███████▎  | 11/15 [00:09<00:03,  1.06it/s]\n 80%|████████  | 12/15 [00:10<00:02,  1.06it/s]\n 87%|████████▋ | 13/15 [00:11<00:01,  1.05it/s]\n 93%|█████████▎| 14/15 [00:12<00:00,  1.05it/s]\n100%|██████████| 15/15 [00:13<00:00,  1.05it/s]\n100%|██████████| 15/15 [00:13<00:00,  1.12it/s]\nprompt: a photo of a {word}\ninitial word: vase\nTOP 50 SIMILAR WORDS:\n['jar', 'pot', 'plant', 'cup', 'flower', 'bottle', 'bowl', 'lamp', 'glass', 'mug', 'rose', 'trophy', 'decor', 'candle', 'acup', 'bucket', 'something', 'water', 'drink', 'table', 'grass', 'pipe', 'one', 'bloom', 'gift', 'scent', 'bone', 'globe', 'bell', 'stone', 'abstract', 'clock', 'rock', 'drum', 'present', 'ÅŁ', 'ÄŁ', 'âł', 'Ãł', 'phone', 've', 'milk', 'vege', 'wine', 'landscape', 'head', 'can', 'garden', 'grave', 'ðŁ']\n  0%|          | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:00<00:00, 62.06it/s]\nprompt: a {word} with flowers on the table\ninitial word: vase\nTOP 3 SIMILAR WORDS:\n['pot', 'jar', 'cup']\nThe config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.\n/root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler {\n\"_class_name\": \"DDIMScheduler\",\n\"_diffusers_version\": \"0.10.2\",\n\"beta_end\": 0.012,\n\"beta_schedule\": \"scaled_linear\",\n\"beta_start\": 0.00085,\n\"clip_sample\": false,\n\"num_train_timesteps\": 1000,\n\"prediction_type\": \"epsilon\",\n\"set_alpha_to_one\": false,\n\"steps_offset\": 0,\n\"trained_betas\": null\n}\nis outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\ndeprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\nStart null text inversion\nDDIM inversion...\n  0%|          | 0/50 [00:00<?, ?it/s]\n  4%|▍         | 2/50 [00:00<00:02, 16.13it/s]\n  8%|▊         | 4/50 [00:00<00:03, 12.51it/s]\n 12%|█▏        | 6/50 [00:00<00:03, 11.72it/s]\n 16%|█▌        | 8/50 [00:00<00:03, 11.37it/s]\n 20%|██        | 10/50 [00:00<00:03, 11.14it/s]\n 24%|██▍       | 12/50 [00:01<00:03, 11.04it/s]\n 28%|██▊       | 14/50 [00:01<00:03, 10.97it/s]\n 32%|███▏      | 16/50 [00:01<00:03, 10.92it/s]\n 36%|███▌      | 18/50 [00:01<00:02, 10.88it/s]\n 40%|████      | 20/50 [00:01<00:02, 10.88it/s]\n 44%|████▍     | 22/50 [00:01<00:02, 10.88it/s]\n 48%|████▊     | 24/50 [00:02<00:02, 10.87it/s]\n 52%|█████▏    | 26/50 [00:02<00:02, 10.85it/s]\n 56%|█████▌    | 28/50 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3.68it/s]\n 98%|█████████▊| 490/500 [01:25<00:02,  3.70it/s]\n 98%|█████████▊| 491/500 [01:25<00:03,  2.93it/s]\n 98%|█████████▊| 492/500 [01:26<00:02,  3.13it/s]\n 99%|█████████▊| 493/500 [01:26<00:02,  3.29it/s]\n 99%|█████████▉| 494/500 [01:26<00:01,  3.41it/s]\n 99%|█████████▉| 495/500 [01:26<00:01,  3.51it/s]\n 99%|█████████▉| 496/500 [01:27<00:01,  3.57it/s]\n 99%|█████████▉| 497/500 [01:27<00:00,  3.62it/s]\n100%|█████████▉| 498/500 [01:27<00:00,  3.65it/s]\n100%|█████████▉| 499/500 [01:27<00:00,  3.68it/s]\n100%|██████████| 500/500 [01:28<00:00,  3.70it/s]\n100%|██████████| 500/500 [01:28<00:00,  5.67it/s]\nEnd null text inversion\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:06,  7.48it/s]\n  4%|▍         | 2/50 [00:00<00:08,  5.71it/s]\n  6%|▌         | 3/50 [00:00<00:08,  5.31it/s]\n  8%|▊         | 4/50 [00:00<00:08,  5.15it/s]\n 10%|█         | 5/50 [00:00<00:08,  5.05it/s]\n 12%|█▏        | 6/50 [00:01<00:08,  5.00it/s]\n 14%|█▍        | 7/50 [00:01<00:08,  4.97it/s]\n 16%|█▌        | 8/50 [00:01<00:08,  4.95it/s]\n 18%|█▊        | 9/50 [00:01<00:08,  4.94it/s]\n 20%|██        | 10/50 [00:01<00:08,  4.87it/s]\n 22%|██▏       | 11/50 [00:02<00:07,  4.94it/s]\n 24%|██▍       | 12/50 [00:02<00:07,  4.93it/s]\n 26%|██▌       | 13/50 [00:02<00:07,  4.90it/s]\n 28%|██▊       | 14/50 [00:02<00:07,  4.91it/s]\n 30%|███       | 15/50 [00:02<00:07,  4.90it/s]\n 32%|███▏      | 16/50 [00:03<00:06,  4.93it/s]\n 34%|███▍      | 17/50 [00:03<00:06,  4.92it/s]\n 36%|███▌      | 18/50 [00:03<00:06,  4.91it/s]\n 38%|███▊      | 19/50 [00:03<00:06,  4.91it/s]\n 40%|████      | 20/50 [00:04<00:06,  4.90it/s]\n 42%|████▏     | 21/50 [00:04<00:05,  4.90it/s]\n 44%|████▍     | 22/50 [00:04<00:05,  4.90it/s]\n 46%|████▌     | 23/50 [00:04<00:05,  4.90it/s]\n 48%|████▊     | 24/50 [00:04<00:05,  4.90it/s]\n 50%|█████     | 25/50 [00:05<00:05,  4.89it/s]\n 52%|█████▏    | 26/50 [00:05<00:04,  4.89it/s]\n 54%|█████▍    | 27/50 [00:05<00:04,  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  "output": [
    "https://replicate.delivery/pbxt/tsoTLnv5VkZULFXeiP8fUXdaKfC6y3nRqb1D36cRjNThesFIB/pot.jpg",
    "https://replicate.delivery/pbxt/1yefI0hblIqbZE03OZUmVbM9N8j983qXeV4JLPUeelfS0zWgE/jar.jpg",
    "https://replicate.delivery/pbxt/tBAx4boguErsO5TqcpjipCUA8I6iwMVTaDIB13VaG2S0zWgE/cup.jpg"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-12-12T06:43:44.461587Z",
  "started_at": "2023-12-12T06:43:44.495686Z",
  "completed_at": "2023-12-12T06:46:42.089061Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/56rfuylbpukbee4wmu5oi5j3ye/cancel",
    "get": "https://api.replicate.com/v1/predictions/56rfuylbpukbee4wmu5oi5j3ye"
  },
  "metrics": {
    "predict_time": 177.593375,
    "total_time": 177.627474
  }
}

Generated in

2 minutes 58 seconds

Tweak it Report

0%| | 0/15 [00:00<?, ?it/s] 7%|▋ | 1/15 [00:00<00:01, 7.23it/s] 13%|█▎ | 2/15 [00:01<00:08, 1.62it/s] 20%|██ | 3/15 [00:02<00:09, 1.31it/s] 27%|██▋ | 4/15 [00:02<00:09, 1.20it/s] 33%|███▎ | 5/15 [00:03<00:08, 1.14it/s] 40%|████ | 6/15 [00:04<00:08, 1.11it/s] 47%|████▋ | 7/15 [00:05<00:07, 1.09it/s] 53%|█████▎ | 8/15 [00:06<00:06, 1.08it/s] 60%|██████ | 9/15 [00:07<00:05, 1.07it/s] 67%|██████▋ | 10/15 [00:08<00:04, 1.07it/s] 73%|███████▎ | 11/15 [00:09<00:03, 1.06it/s] 80%|████████ | 12/15 [00:10<00:02, 1.06it/s] 87%|████████▋ | 13/15 [00:11<00:01, 1.05it/s] 93%|█████████▎| 14/15 [00:12<00:00, 1.05it/s] 100%|██████████| 15/15 [00:13<00:00, 1.05it/s] 100%|██████████| 15/15 [00:13<00:00, 1.12it/s] prompt: a photo of a {word} initial word: vase TOP 50 SIMILAR WORDS: ['jar', 'pot', 'plant', 'cup', 'flower', 'bottle', 'bowl', 'lamp', 'glass', 'mug', 'rose', 'trophy', 'decor', 'candle', 'acup', 'bucket', 'something', 'water', 'drink', 'table', 'grass', 'pipe', 'one', 'bloom', 'gift', 'scent', 'bone', 'globe', 'bell', 'stone', 'abstract', 'clock', 'rock', 'drum', 'present', 'ÅŁ', 'ÄŁ', 'âł', 'Ãł', 'phone', 've', 'milk', 'vege', 'wine', 'landscape', 'head', 'can', 'garden', 'grave', 'ðŁ'] 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:00<00:00, 62.06it/s] prompt: a {word} with flowers on the table initial word: vase TOP 3 SIMILAR WORDS: ['pot', 'jar', 'cup'] The config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file. /root/.pyenv/versions/3.10.8/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:101: FutureWarning: The configuration file of this scheduler: DDIMScheduler { "_class_name": "DDIMScheduler", "_diffusers_version": "0.10.2", "beta_end": 0.012, "beta_schedule": "scaled_linear", "beta_start": 0.00085, "clip_sample": false, "num_train_timesteps": 1000, "prediction_type": "epsilon", "set_alpha_to_one": false, "steps_offset": 0, "trained_betas": null } is outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) Start null text inversion DDIM inversion... 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:02, 16.13it/s] 8%|▊ | 4/50 [00:00<00:03, 12.51it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.72it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.37it/s] 20%|██ | 10/50 [00:00<00:03, 11.14it/s] 24%|██▍ | 12/50 [00:01<00:03, 11.04it/s] 28%|██▊ | 14/50 [00:01<00:03, 10.97it/s] 32%|███▏ | 16/50 [00:01<00:03, 10.92it/s] 36%|███▌ | 18/50 [00:01<00:02, 10.88it/s] 40%|████ | 20/50 [00:01<00:02, 10.88it/s] 44%|████▍ | 22/50 [00:01<00:02, 10.88it/s] 48%|████▊ | 24/50 [00:02<00:02, 10.87it/s] 52%|█████▏ | 26/50 [00:02<00:02, 10.85it/s] 56%|█████▌ | 28/50 [00:02<00:02, 10.85it/s] 60%|██████ | 30/50 [00:02<00:01, 10.82it/s] 64%|██████▍ | 32/50 [00:02<00:01, 10.73it/s] 68%|██████▊ | 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