Readme
This model doesn't have a readme.
A fine-tuned SDXL LoRA trained on cats being human like
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 hunterkamerman/sdxl-cats using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c",
{
input: {
width: 1024,
height: 1024,
prompt: "tok a cute kitten inspired by tracer",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.6,
num_outputs: 1,
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.
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 hunterkamerman/sdxl-cats using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c",
input={
"width": 1024,
"height": 1024,
"prompt": "tok a cute kitten inspired by tracer",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"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.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run hunterkamerman/sdxl-cats 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": "hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c",
"input": {
"width": 1024,
"height": 1024,
"prompt": "tok a cute kitten inspired by tracer",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"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/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": "2023-12-04T15:53:10.015529Z",
"created_at": "2023-12-04T15:52:45.485750Z",
"data_removed": false,
"error": null,
"id": "ckgxf3tbncsp23ihrx6l4nhgl4",
"input": {
"width": 1024,
"height": 1024,
"prompt": "tok a cute kitten inspired by tracer",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"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: 51681\nEnsuring enough disk space...\nFree disk space: 450182795264\nDownloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar\nb'Downloaded 186 MB bytes in 3.272s (57 MB/s)\\nExtracted 186 MB in 0.057s (3.2 GB/s)\\n'\nDownloaded weights in 3.816222906112671 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: tok a cute kitten inspired by tracer\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]",
"metrics": {
"predict_time": 20.318444,
"total_time": 24.529779
},
"output": [
"https://replicate.delivery/pbxt/d9Vz7f0DiegbZUFZhceftrx1wy1UmTdgnWfppEDC6hbs8T3PC/out-0.png"
],
"started_at": "2023-12-04T15:52:49.697085Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/ckgxf3tbncsp23ihrx6l4nhgl4",
"cancel": "https://api.replicate.com/v1/predictions/ckgxf3tbncsp23ihrx6l4nhgl4/cancel"
},
"version": "a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c"
}
Using seed: 51681
Ensuring enough disk space...
Free disk space: 450182795264
Downloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar
b'Downloaded 186 MB bytes in 3.272s (57 MB/s)\nExtracted 186 MB in 0.057s (3.2 GB/s)\n'
Downloaded weights in 3.816222906112671 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: tok a cute kitten inspired by tracer
txt2img mode
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This model costs approximately $0.024 to run on Replicate, or 41 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 25 seconds.
This model doesn't have a readme.
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: 51681
Ensuring enough disk space...
Free disk space: 450182795264
Downloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar
b'Downloaded 186 MB bytes in 3.272s (57 MB/s)\nExtracted 186 MB in 0.057s (3.2 GB/s)\n'
Downloaded weights in 3.816222906112671 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: tok a cute kitten inspired by tracer
txt2img mode
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