@visualizevalue-dev's visualizevalue-dev/vv0

Model

visualizevalue-dev/vv0:865ffd54

33ahryhgc5rgg0cf0szvmaeg1r

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

18.1s

Created

over 1 year ago

Webhook

–

Input

prompt: a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background
negative_prompt: colorful, color, grey, realistic, photo, white background, writing, words
width: 1024
height: 1024
num_outputs: 1
scheduler: K_EULER
num_inference_steps: 50
guidance_scale: 7.5
prompt_strength: 0.75
refine: expert_ensemble_refiner
high_noise_frac: 0.6
apply_watermark: true
lora_scale: 0.7

{
  "apply_watermark": true,
  "guidance_scale": 7.5,
  "height": 1024,
  "high_noise_frac": 0.6,
  "lora_scale": 0.7,
  "negative_prompt": "colorful, color, grey, realistic, photo, white background, writing, words",
  "num_inference_steps": 50,
  "num_outputs": 1,
  "prompt": "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
  "prompt_strength": 0.75,
  "refine": "expert_ensemble_refiner",
  "scheduler": "K_EULER",
  "width": 1024
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_0QP**********************************

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 visualizevalue-dev/vv0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "visualizevalue-dev/vv0:865ffd5410c91b47596a8ed1f6398c1dcd8513137c220f88c2d9937029c4750e",
  {
    input: {
      apply_watermark: true,
      guidance_scale: 7.5,
      height: 1024,
      high_noise_frac: 0.6,
      lora_scale: 0.7,
      negative_prompt: "colorful, color, grey, realistic, photo, white background, writing, words",
      num_inference_steps: 50,
      num_outputs: 1,
      prompt: "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
      prompt_strength: 0.75,
      refine: "expert_ensemble_refiner",
      scheduler: "K_EULER",
      width: 1024
    }
  }
);

// 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_0QP**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run visualizevalue-dev/vv0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "visualizevalue-dev/vv0:865ffd5410c91b47596a8ed1f6398c1dcd8513137c220f88c2d9937029c4750e",
    input={
        "apply_watermark": True,
        "guidance_scale": 7.5,
        "height": 1024,
        "high_noise_frac": 0.6,
        "lora_scale": 0.7,
        "negative_prompt": "colorful, color, grey, realistic, photo, white background, writing, words",
        "num_inference_steps": 50,
        "num_outputs": 1,
        "prompt": "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
        "prompt_strength": 0.75,
        "refine": "expert_ensemble_refiner",
        "scheduler": "K_EULER",
        "width": 1024
    }
)

# 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_0QP**********************************

This is your API token. Keep it to yourself.

Run visualizevalue-dev/vv0 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": "visualizevalue-dev/vv0:865ffd5410c91b47596a8ed1f6398c1dcd8513137c220f88c2d9937029c4750e",
    "input": {
      "apply_watermark": true,
      "guidance_scale": 7.5,
      "height": 1024,
      "high_noise_frac": 0.6,
      "lora_scale": 0.7,
      "negative_prompt": "colorful, color, grey, realistic, photo, white background, writing, words",
      "num_inference_steps": 50,
      "num_outputs": 1,
      "prompt": "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
      "prompt_strength": 0.75,
      "refine": "expert_ensemble_refiner",
      "scheduler": "K_EULER",
      "width": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "33ahryhgc5rgg0cf0szvmaeg1r",
  "model": "visualizevalue-dev/vv0",
  "version": "865ffd5410c91b47596a8ed1f6398c1dcd8513137c220f88c2d9937029c4750e",
  "input": {
    "apply_watermark": true,
    "guidance_scale": 7.5,
    "height": 1024,
    "high_noise_frac": 0.6,
    "lora_scale": 0.7,
    "negative_prompt": "colorful, color, grey, realistic, photo, white background, writing, words",
    "num_inference_steps": 50,
    "num_outputs": 1,
    "prompt": "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
    "prompt_strength": 0.75,
    "refine": "expert_ensemble_refiner",
    "scheduler": "K_EULER",
    "width": 1024
  },
  "logs": "Using seed: 46639\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a globe visualized as a network of nodes, in the style of <s0><s1>, minimal white graphic, black background\ntxt2img mode\n  0%|          | 0/26 [00:00<?, ?it/s]\n  4%|▍         | 1/26 [00:00<00:06,  3.65it/s]\n  8%|▊         | 2/26 [00:00<00:06,  3.64it/s]\n 12%|█▏        | 3/26 [00:00<00:06,  3.64it/s]\n 15%|█▌        | 4/26 [00:01<00:06,  3.63it/s]\n 19%|█▉        | 5/26 [00:01<00:05,  3.63it/s]\n 23%|██▎       | 6/26 [00:01<00:05,  3.63it/s]\n 27%|██▋       | 7/26 [00:01<00:05,  3.63it/s]\n 31%|███       | 8/26 [00:02<00:04,  3.62it/s]\n 35%|███▍      | 9/26 [00:02<00:04,  3.62it/s]\n 38%|███▊      | 10/26 [00:02<00:04,  3.62it/s]\n 42%|████▏     | 11/26 [00:03<00:04,  3.62it/s]\n 46%|████▌     | 12/26 [00:03<00:03,  3.62it/s]\n 50%|█████     | 13/26 [00:03<00:03,  3.62it/s]\n 54%|█████▍    | 14/26 [00:03<00:03,  3.62it/s]\n 58%|█████▊    | 15/26 [00:04<00:03,  3.62it/s]\n 62%|██████▏   | 16/26 [00:04<00:02,  3.62it/s]\n 65%|██████▌   | 17/26 [00:04<00:02,  3.62it/s]\n 69%|██████▉   | 18/26 [00:04<00:02,  3.62it/s]\n 73%|███████▎  | 19/26 [00:05<00:01,  3.62it/s]\n 77%|███████▋  | 20/26 [00:05<00:01,  3.62it/s]\n 81%|████████  | 21/26 [00:05<00:01,  3.61it/s]\n 85%|████████▍ | 22/26 [00:06<00:01,  3.61it/s]\n 88%|████████▊ | 23/26 [00:06<00:00,  3.61it/s]\n 92%|█████████▏| 24/26 [00:06<00:00,  3.61it/s]\n 96%|█████████▌| 25/26 [00:06<00:00,  3.61it/s]\n100%|██████████| 26/26 [00:07<00:00,  3.61it/s]\n100%|██████████| 26/26 [00:07<00:00,  3.62it/s]\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:04,  4.19it/s]\n 10%|█         | 2/20 [00:00<00:04,  4.18it/s]\n 15%|█▌        | 3/20 [00:00<00:04,  4.16it/s]\n 20%|██        | 4/20 [00:00<00:03,  4.15it/s]\n 25%|██▌       | 5/20 [00:01<00:03,  4.15it/s]\n 30%|███       | 6/20 [00:01<00:03,  4.15it/s]\n 35%|███▌      | 7/20 [00:01<00:03,  4.15it/s]\n 40%|████      | 8/20 [00:01<00:02,  4.15it/s]\n 45%|████▌     | 9/20 [00:02<00:02,  4.15it/s]\n 50%|█████     | 10/20 [00:02<00:02,  4.16it/s]\n 55%|█████▌    | 11/20 [00:02<00:02,  4.16it/s]\n 60%|██████    | 12/20 [00:02<00:01,  4.16it/s]\n 65%|██████▌   | 13/20 [00:03<00:01,  4.16it/s]\n 70%|███████   | 14/20 [00:03<00:01,  4.15it/s]\n 75%|███████▌  | 15/20 [00:03<00:01,  4.15it/s]\n 80%|████████  | 16/20 [00:03<00:00,  4.15it/s]\n 85%|████████▌ | 17/20 [00:04<00:00,  4.15it/s]\n 90%|█████████ | 18/20 [00:04<00:00,  4.15it/s]\n 95%|█████████▌| 19/20 [00:04<00:00,  4.15it/s]\n100%|██████████| 20/20 [00:04<00:00,  4.15it/s]\n100%|██████████| 20/20 [00:04<00:00,  4.15it/s]",
  "output": [
    "https://replicate.delivery/pbxt/7x6v7INMuoLtNF5PGpnFdR1kyZvJt5jbEHuvSmnWqqvB5QrE/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-04-22T15:49:09.857Z",
  "started_at": "2024-04-22T15:49:12.881045Z",
  "completed_at": "2024-04-22T15:49:27.90782Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/33ahryhgc5rgg0cf0szvmaeg1r/cancel",
    "get": "https://api.replicate.com/v1/predictions/33ahryhgc5rgg0cf0szvmaeg1r",
    "web": "https://replicate.com/p/33ahryhgc5rgg0cf0szvmaeg1r"
  },
  "metrics": {
    "predict_time": 15.026775,
    "total_time": 18.05082
  }
}

Generated in

15.0 seconds

Tweak it Report

Using seed: 46639 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a globe visualized as a network of nodes, in the style of <s0><s1>, minimal white graphic, black background txt2img mode 0%| | 0/26 [00:00<?, ?it/s] 4%|▍ | 1/26 [00:00<00:06, 3.65it/s] 8%|▊ | 2/26 [00:00<00:06, 3.64it/s] 12%|█▏ | 3/26 [00:00<00:06, 3.64it/s] 15%|█▌ | 4/26 [00:01<00:06, 3.63it/s] 19%|█▉ | 5/26 [00:01<00:05, 3.63it/s] 23%|██▎ | 6/26 [00:01<00:05, 3.63it/s] 27%|██▋ | 7/26 [00:01<00:05, 3.63it/s] 31%|███ | 8/26 [00:02<00:04, 3.62it/s] 35%|███▍ | 9/26 [00:02<00:04, 3.62it/s] 38%|███▊ | 10/26 [00:02<00:04, 3.62it/s] 42%|████▏ | 11/26 [00:03<00:04, 3.62it/s] 46%|████▌ | 12/26 [00:03<00:03, 3.62it/s] 50%|█████ | 13/26 [00:03<00:03, 3.62it/s] 54%|█████▍ | 14/26 [00:03<00:03, 3.62it/s] 58%|█████▊ | 15/26 [00:04<00:03, 3.62it/s] 62%|██████▏ | 16/26 [00:04<00:02, 3.62it/s] 65%|██████▌ | 17/26 [00:04<00:02, 3.62it/s] 69%|██████▉ | 18/26 [00:04<00:02, 3.62it/s] 73%|███████▎ | 19/26 [00:05<00:01, 3.62it/s] 77%|███████▋ | 20/26 [00:05<00:01, 3.62it/s] 81%|████████ | 21/26 [00:05<00:01, 3.61it/s] 85%|████████▍ | 22/26 [00:06<00:01, 3.61it/s] 88%|████████▊ | 23/26 [00:06<00:00, 3.61it/s] 92%|█████████▏| 24/26 [00:06<00:00, 3.61it/s] 96%|█████████▌| 25/26 [00:06<00:00, 3.61it/s] 100%|██████████| 26/26 [00:07<00:00, 3.61it/s] 100%|██████████| 26/26 [00:07<00:00, 3.62it/s] 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:04, 4.19it/s] 10%|█ | 2/20 [00:00<00:04, 4.18it/s] 15%|█▌ | 3/20 [00:00<00:04, 4.16it/s] 20%|██ | 4/20 [00:00<00:03, 4.15it/s] 25%|██▌ | 5/20 [00:01<00:03, 4.15it/s] 30%|███ | 6/20 [00:01<00:03, 4.15it/s] 35%|███▌ | 7/20 [00:01<00:03, 4.15it/s] 40%|████ | 8/20 [00:01<00:02, 4.15it/s] 45%|████▌ | 9/20 [00:02<00:02, 4.15it/s] 50%|█████ | 10/20 [00:02<00:02, 4.16it/s] 55%|█████▌ | 11/20 [00:02<00:02, 4.16it/s] 60%|██████ | 12/20 [00:02<00:01, 4.16it/s] 65%|██████▌ | 13/20 [00:03<00:01, 4.16it/s] 70%|███████ | 14/20 [00:03<00:01, 4.15it/s] 75%|███████▌ | 15/20 [00:03<00:01, 4.15it/s] 80%|████████ | 16/20 [00:03<00:00, 4.15it/s] 85%|████████▌ | 17/20 [00:04<00:00, 4.15it/s] 90%|█████████ | 18/20 [00:04<00:00, 4.15it/s] 95%|█████████▌| 19/20 [00:04<00:00, 4.15it/s] 100%|██████████| 20/20 [00:04<00:00, 4.15it/s] 100%|██████████| 20/20 [00:04<00:00, 4.15it/s]