Readme
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Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variableexport 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 ghostofpokemon/sdxl-bluey using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"ghostofpokemon/sdxl-bluey:d810c838247a981f4d19fa6f91561fa336ab36fed7a452a94fd0ced523ad7bd6",
{
input: {
width: 1024,
height: 1024,
prompt: "A bluey cat riding a rainbow dog in the style of Bluey",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.84,
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
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run ghostofpokemon/sdxl-bluey using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"ghostofpokemon/sdxl-bluey:d810c838247a981f4d19fa6f91561fa336ab36fed7a452a94fd0ced523ad7bd6",
input={
"width": 1024,
"height": 1024,
"prompt": "A bluey cat riding a rainbow dog in the style of Bluey",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.84,
"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 variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run ghostofpokemon/sdxl-bluey 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": "d810c838247a981f4d19fa6f91561fa336ab36fed7a452a94fd0ced523ad7bd6",
"input": {
"width": 1024,
"height": 1024,
"prompt": "A bluey cat riding a rainbow dog in the style of Bluey",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.84,
"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": "2023-11-15T15:50:32.032566Z",
"created_at": "2023-11-15T15:50:04.459770Z",
"data_removed": false,
"error": null,
"id": "p5zwx2db3ywwont6skuloqsoam",
"input": {
"width": 1024,
"height": 1024,
"prompt": "A bluey cat riding a rainbow dog in the style of Bluey",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.84,
"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: 23002\nEnsuring enough disk space...\nFree disk space: 1423516946432\nDownloading weights: https://replicate.delivery/pbxt/jR9pmxxLslZ1JprqQ28efdOCp5DuefEgkJMP5QRvLiLopvgHB/trained_model.tar\nb'Downloaded 186 MB bytes in 5.319s (35 MB/s)\\nExtracted 186 MB in 0.054s (3.5 GB/s)\\n'\nDownloaded weights in 5.8463029861450195 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A bluey cat riding a rainbow dog in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.63it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.62it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.62it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.62it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.62it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.61it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.61it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.61it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/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, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.62it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.62it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.62it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]",
"metrics": {
"predict_time": 22.305557,
"total_time": 27.572796
},
"output": [
"https://replicate.delivery/pbxt/v5FJTsrQ0Wq4JZfydaWr7kANUJetJKgKvVfBCLfRSjIasmiHB/out-0.png"
],
"started_at": "2023-11-15T15:50:09.727009Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/p5zwx2db3ywwont6skuloqsoam",
"cancel": "https://api.replicate.com/v1/predictions/p5zwx2db3ywwont6skuloqsoam/cancel"
},
"version": "d810c838247a981f4d19fa6f91561fa336ab36fed7a452a94fd0ced523ad7bd6"
}
Using seed: 23002
Ensuring enough disk space...
Free disk space: 1423516946432
Downloading weights: https://replicate.delivery/pbxt/jR9pmxxLslZ1JprqQ28efdOCp5DuefEgkJMP5QRvLiLopvgHB/trained_model.tar
b'Downloaded 186 MB bytes in 5.319s (35 MB/s)\nExtracted 186 MB in 0.054s (3.5 GB/s)\n'
Downloaded weights in 5.8463029861450195 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: A bluey cat riding a rainbow dog in the style of <s0><s1>
txt2img mode
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This model costs approximately $0.025 to run on Replicate, or 40 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 26 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: 23002
Ensuring enough disk space...
Free disk space: 1423516946432
Downloading weights: https://replicate.delivery/pbxt/jR9pmxxLslZ1JprqQ28efdOCp5DuefEgkJMP5QRvLiLopvgHB/trained_model.tar
b'Downloaded 186 MB bytes in 5.319s (35 MB/s)\nExtracted 186 MB in 0.054s (3.5 GB/s)\n'
Downloaded weights in 5.8463029861450195 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: A bluey cat riding a rainbow dog in the style of <s0><s1>
txt2img mode
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