Readme
token_string
isVV
caption_prefix
isin the style of VV
Trained on VV instagram posts.
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";
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.
By signing in, you agree to our
terms of service and privacy policy
{
"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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This model costs approximately $0.019 to run on Replicate, or 52 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 20 seconds.
token_string
is VV
caption_prefix
is in the style of VV
This model is warm. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
Choose a file from your machine
Hint: you can also drag files onto the input
Choose a file from your machine
Hint: you can also drag files onto the input
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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