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visualizevalue-dev /vv0:865ffd54
Input
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
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: {
width: 1024,
height: 1024,
prompt: "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
refine: "expert_ensemble_refiner",
scheduler: "K_EULER",
lora_scale: 0.7,
num_outputs: 1,
guidance_scale: 7.5,
apply_watermark: true,
high_noise_frac: 0.6,
negative_prompt: "colorful, color, grey, realistic, photo, white background, writing, words",
prompt_strength: 0.75,
num_inference_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.
pip install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
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={
"width": 1024,
"height": 1024,
"prompt": "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.7,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": True,
"high_noise_frac": 0.6,
"negative_prompt": "colorful, color, grey, realistic, photo, white background, writing, words",
"prompt_strength": 0.75,
"num_inference_steps": 50
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
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": {
"width": 1024,
"height": 1024,
"prompt": "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.7,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.6,
"negative_prompt": "colorful, color, grey, realistic, photo, white background, writing, words",
"prompt_strength": 0.75,
"num_inference_steps": 50
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
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Output
{
"completed_at": "2024-04-22T15:49:27.907820Z",
"created_at": "2024-04-22T15:49:09.857000Z",
"data_removed": false,
"error": null,
"id": "33ahryhgc5rgg0cf0szvmaeg1r",
"input": {
"width": 1024,
"height": 1024,
"prompt": "a globe visualized as a network of nodes, in the style of VV, minimal white graphic, black background",
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.7,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.6,
"negative_prompt": "colorful, color, grey, realistic, photo, white background, writing, words",
"prompt_strength": 0.75,
"num_inference_steps": 50
},
"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]",
"metrics": {
"predict_time": 15.026775,
"total_time": 18.05082
},
"output": [
"https://replicate.delivery/pbxt/7x6v7INMuoLtNF5PGpnFdR1kyZvJt5jbEHuvSmnWqqvB5QrE/out-0.png"
],
"started_at": "2024-04-22T15:49:12.881045Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/33ahryhgc5rgg0cf0szvmaeg1r",
"cancel": "https://api.replicate.com/v1/predictions/33ahryhgc5rgg0cf0szvmaeg1r/cancel"
},
"version": "865ffd5410c91b47596a8ed1f6398c1dcd8513137c220f88c2d9937029c4750e"
}
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
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