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hudsongraeme /cybertruck:50ef505f
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=hudsongraeme/cybertruck
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run hudsongraeme/cybertruck using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"hudsongraeme/cybertruck:50ef505f835eb26967d7f3df96103ee0a90d51eeaea60bf7c2372e6ef70b0d06",
{
input: {
width: 1024,
height: 1024,
prompt: "A photo of TOK driving in deep water",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.6,
num_outputs: 4,
guidance_scale: 7.5,
apply_watermark: true,
high_noise_frac: 0.8,
negative_prompt: "",
prompt_strength: 0.8,
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.
Install Replicate’s Python client library:
pip install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun hudsongraeme/cybertruck using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"hudsongraeme/cybertruck:50ef505f835eb26967d7f3df96103ee0a90d51eeaea60bf7c2372e6ef70b0d06",
input={
"width": 1024,
"height": 1024,
"prompt": "A photo of TOK driving in deep water",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 4,
"guidance_scale": 7.5,
"apply_watermark": True,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
}
)
print(output)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=<paste-your-token-here>Find your API token in your account settings.
Run hudsongraeme/cybertruck 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": "50ef505f835eb26967d7f3df96103ee0a90d51eeaea60bf7c2372e6ef70b0d06",
"input": {
"width": 1024,
"height": 1024,
"prompt": "A photo of TOK driving in deep water",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 4,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
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terms of service and privacy policy
Output
{
"completed_at": "2023-10-14T18:12:42.279987Z",
"created_at": "2023-10-14T18:11:37.698719Z",
"data_removed": false,
"error": null,
"id": "tygkpdtbs7omzsxbsdzcmvuteq",
"input": {
"width": 1024,
"height": 1024,
"prompt": "A photo of TOK driving in deep water",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 4,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
},
"logs": "Using seed: 56998\nEnsuring enough disk space...\nFree disk space: 3072252948480\nDownloading weights: https://pbxt.replicate.delivery/4h6fsYXIdRXfrkTavb4PfmCshSz7LnHFKsOyVMJXHSWa1kOjA/trained_model.tar\nb'Downloaded 186 MB bytes in 3.357s (55 MB/s)\\nExtracted 186 MB in 0.069s (2.7 GB/s)\\n'\nDownloaded weights in 3.7890095710754395 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1> driving in deep water\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.06s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.06s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.06s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.06s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.06s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.06s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.06s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.06s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it]\n 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it]\n 30%|███ | 15/50 [00:15<00:37, 1.06s/it]\n 32%|███▏ | 16/50 [00:16<00:36, 1.06s/it]\n 34%|███▍ | 17/50 [00:18<00:35, 1.06s/it]\n 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:34<00:18, 1.06s/it]\n 68%|██████▊ | 34/50 [00:36<00:16, 1.06s/it]\n 70%|███████ | 35/50 [00:37<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.06s/it]",
"metrics": {
"predict_time": 62.753609,
"total_time": 64.581268
},
"output": [
"https://pbxt.replicate.delivery/oMpU4u6Gkia4OJX9gt241dNBHIf0ypqBpAZymtwZDhGMYE3IA/out-0.png",
"https://pbxt.replicate.delivery/MZASGPBfhuSydK3JAFfeWe3ctXqPbaUQ532o3BWHJdMmBj4GB/out-1.png",
"https://pbxt.replicate.delivery/1KyfqI3y0gXODKSNPU11zORefKBSKxhRleb7ap3Pg98kBj4GB/out-2.png",
"https://pbxt.replicate.delivery/JzVZ00lMEL6UIhlwqZLJNwhQRO8oHj0hezaL4z5TMtNNYE3IA/out-3.png"
],
"started_at": "2023-10-14T18:11:39.526378Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/tygkpdtbs7omzsxbsdzcmvuteq",
"cancel": "https://api.replicate.com/v1/predictions/tygkpdtbs7omzsxbsdzcmvuteq/cancel"
},
"version": "50ef505f835eb26967d7f3df96103ee0a90d51eeaea60bf7c2372e6ef70b0d06"
}Generated in
Using seed: 56998
Ensuring enough disk space...
Free disk space: 3072252948480
Downloading weights: https://pbxt.replicate.delivery/4h6fsYXIdRXfrkTavb4PfmCshSz7LnHFKsOyVMJXHSWa1kOjA/trained_model.tar
b'Downloaded 186 MB bytes in 3.357s (55 MB/s)\nExtracted 186 MB in 0.069s (2.7 GB/s)\n'
Downloaded weights in 3.7890095710754395 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: A photo of <s0><s1> driving in deep water
txt2img mode
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