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SDXL fine-tuned on The Babadook trailer using YouTune.
SDXL fine-tuned on The Babadook trailer
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 cbh123/sdxl-the-babadook using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"cbh123/sdxl-the-babadook:13eaa7e5d6a92fa9630d3a029ad6b97b1562b84d9e80c3bfce3f25e963f10997",
{
input: {
seed: null,
width: 1024,
height: 1024,
prompt: "a photo of a book in the style of TOK",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.6,
num_outputs: 1,
refine_steps: null,
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 cbh123/sdxl-the-babadook using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"cbh123/sdxl-the-babadook:13eaa7e5d6a92fa9630d3a029ad6b97b1562b84d9e80c3bfce3f25e963f10997",
input={
"seed": null,
"width": 1024,
"height": 1024,
"prompt": "a photo of a book in the style of TOK",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"refine_steps": null,
"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 cbh123/sdxl-the-babadook 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": "13eaa7e5d6a92fa9630d3a029ad6b97b1562b84d9e80c3bfce3f25e963f10997",
"input": {
"seed": null,
"width": 1024,
"height": 1024,
"prompt": "a photo of a book in the style of TOK",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"refine_steps": null,
"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": "2023-10-30T16:21:34.962992Z",
"created_at": "2023-10-30T16:21:09.214860Z",
"data_removed": false,
"error": null,
"id": "3buzg53b2amfninumlzasqomya",
"input": {
"mask": null,
"seed": null,
"image": null,
"width": 1024,
"height": 1024,
"prompt": "a photo of a book in the style of TOK",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"refine_steps": null,
"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: 26281\nEnsuring enough disk space...\nFree disk space: 2282905907200\nDownloading weights: https://pbxt.replicate.delivery/U2tX93ZrXAJsOhsczlm7bX3dUWA7T3Xkj2gCb1jJZ5tEricE/trained_model.tar\nb'Downloaded 186 MB bytes in 5.523s (34 MB/s)\\nExtracted 186 MB in 0.074s (2.5 GB/s)\\n'\nDownloaded weights in 6.078482151031494 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a photo of a book in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/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, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/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, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/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, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/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, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]",
"metrics": {
"predict_time": 21.951246,
"total_time": 25.748132
},
"output": [
"https://pbxt.replicate.delivery/I7qupE4neDXen0TdtBl4eEvcfRsBX7EsZfR2QgMwppu2BFbOC/out-0.png"
],
"started_at": "2023-10-30T16:21:13.011746Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/3buzg53b2amfninumlzasqomya",
"cancel": "https://api.replicate.com/v1/predictions/3buzg53b2amfninumlzasqomya/cancel"
},
"version": "13eaa7e5d6a92fa9630d3a029ad6b97b1562b84d9e80c3bfce3f25e963f10997"
}
Using seed: 26281
Ensuring enough disk space...
Free disk space: 2282905907200
Downloading weights: https://pbxt.replicate.delivery/U2tX93ZrXAJsOhsczlm7bX3dUWA7T3Xkj2gCb1jJZ5tEricE/trained_model.tar
b'Downloaded 186 MB bytes in 5.523s (34 MB/s)\nExtracted 186 MB in 0.074s (2.5 GB/s)\n'
Downloaded weights in 6.078482151031494 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: a photo of a book in the style of <s0><s1>
txt2img mode
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This model costs approximately $0.021 to run on Replicate, or 47 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 22 seconds.
SDXL fine-tuned on The Babadook trailer using YouTune.
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: 26281
Ensuring enough disk space...
Free disk space: 2282905907200
Downloading weights: https://pbxt.replicate.delivery/U2tX93ZrXAJsOhsczlm7bX3dUWA7T3Xkj2gCb1jJZ5tEricE/trained_model.tar
b'Downloaded 186 MB bytes in 5.523s (34 MB/s)\nExtracted 186 MB in 0.074s (2.5 GB/s)\n'
Downloaded weights in 6.078482151031494 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: a photo of a book in the style of <s0><s1>
txt2img mode
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