Readme
You should add “overexposed” to the negative prompt
A SDXL LoRA inspired by Tomb Raider (1996)
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 jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67",
{
input: {
width: 1024,
height: 1024,
prompt: "Lara driving a car in Paris, in the style of TOK",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.85,
num_outputs: 1,
guidance_scale: 18.25,
apply_watermark: true,
high_noise_frac: 0.8,
negative_prompt: "overexposed",
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 jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67",
input={
"width": 1024,
"height": 1024,
"prompt": "Lara driving a car in Paris, in the style of TOK",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.85,
"num_outputs": 1,
"guidance_scale": 18.25,
"apply_watermark": True,
"high_noise_frac": 0.8,
"negative_prompt": "overexposed",
"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 jbilcke/sdxl-tombraider 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": "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67",
"input": {
"width": 1024,
"height": 1024,
"prompt": "Lara driving a car in Paris, in the style of TOK",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.85,
"num_outputs": 1,
"guidance_scale": 18.25,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "overexposed",
"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.
brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/jbilcke/sdxl-tombraider@sha256:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67 \
-i 'width=1024' \
-i 'height=1024' \
-i 'prompt="Lara driving a car in Paris, in the style of TOK"' \
-i 'refine="no_refiner"' \
-i 'scheduler="K_EULER"' \
-i 'lora_scale=0.85' \
-i 'num_outputs=1' \
-i 'guidance_scale=18.25' \
-i 'apply_watermark=true' \
-i 'high_noise_frac=0.8' \
-i 'negative_prompt="overexposed"' \
-i 'prompt_strength=0.8' \
-i 'num_inference_steps=50'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/jbilcke/sdxl-tombraider@sha256:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "Lara driving a car in Paris, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Each run costs approximately $0.015. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-08-31T12:10:39.802590Z",
"created_at": "2023-08-31T12:09:16.700285Z",
"data_removed": false,
"error": null,
"id": "lqvrt6dbfrw5jq4cazc6ojd4xm",
"input": {
"width": 1024,
"height": 1024,
"prompt": "Lara driving a car in Paris, in the style of TOK",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.85,
"num_outputs": 1,
"guidance_scale": 18.25,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "overexposed",
"prompt_strength": 0.8,
"num_inference_steps": 50
},
"logs": "Using seed: 7689\nPrompt: Lara driving a car in Paris, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:41, 1.19it/s]\n 4%|▍ | 2/50 [00:01<00:24, 1.98it/s]\n 6%|▌ | 3/50 [00:01<00:18, 2.52it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.88it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.13it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.30it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.42it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.50it/s]\n 18%|█▊ | 9/50 [00:03<00:11, 3.56it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.60it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:05<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:08<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.67it/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 [00:12<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.53it/s]",
"metrics": {
"predict_time": 16.775731,
"total_time": 83.102305
},
"output": [
"https://replicate.delivery/pbxt/JbOE0IDcYx5eDaBJTIHWn4soaEbqtDxdeEatnD45dCmepGfFB/out-0.png"
],
"started_at": "2023-08-31T12:10:23.026859Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/lqvrt6dbfrw5jq4cazc6ojd4xm",
"cancel": "https://api.replicate.com/v1/predictions/lqvrt6dbfrw5jq4cazc6ojd4xm/cancel"
},
"version": "5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67"
}
Using seed: 7689
Prompt: Lara driving a car in Paris, in the style of <s0><s1>
txt2img mode
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This model costs approximately $0.015 to run on Replicate, or 66 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 16 seconds. The predict time for this model varies significantly based on the inputs.
You should add “overexposed” to the negative prompt
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: 7689
Prompt: Lara driving a car in Paris, in the style of <s0><s1>
txt2img mode
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