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fofr /sdxl-googly-eyes:3239d84b
Input
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 fofr/sdxl-googly-eyes using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f",
{
input: {
width: 768,
height: 768,
prompt: "A photo of TOK eyes",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.8,
num_outputs: 1,
guidance_scale: 7.5,
apply_watermark: false,
high_noise_frac: 0.8,
negative_prompt: "",
prompt_strength: 0.8,
num_inference_steps: 25
}
}
);
// 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 fofr/sdxl-googly-eyes using Replicateβs API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f",
input={
"width": 768,
"height": 768,
"prompt": "A photo of TOK eyes",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.8,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": False,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 25
}
)
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 fofr/sdxl-googly-eyes using Replicateβs 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": "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f",
"input": {
"width": 768,
"height": 768,
"prompt": "A photo of TOK eyes",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.8,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": false,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 25
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicateβs HTTP API reference docs.
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Output
{
"completed_at": "2023-12-09T14:07:20.465650Z",
"created_at": "2023-12-09T14:07:13.799636Z",
"data_removed": false,
"error": null,
"id": "qa5l53lbyucqp7e2b62w3vtuau",
"input": {
"width": 768,
"height": 768,
"prompt": "A photo of TOK eyes",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.8,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": false,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 25
},
"logs": "Using seed: 22560\nEnsuring enough disk space...\nFree disk space: 2262383398912\nDownloading weights: https://replicate.delivery/pbxt/5ecT4YGgj3wjFCOxsHhKWwwsSP1rvaEsKFqOkoCIWDzaJRAJA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.434s (428 MB/s)\\nExtracted 186 MB in 0.061s (3.0 GB/s)\\n'\nDownloaded weights in 0.5974154472351074 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1> eyes\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|β | 1/25 [00:00<00:03, 6.19it/s]\n 8%|β | 2/25 [00:00<00:03, 6.16it/s]\n 12%|ββ | 3/25 [00:00<00:03, 6.16it/s]\n 16%|ββ | 4/25 [00:00<00:03, 6.15it/s]\n 20%|ββ | 5/25 [00:00<00:03, 6.12it/s]\n 24%|βββ | 6/25 [00:00<00:03, 6.12it/s]\n 28%|βββ | 7/25 [00:01<00:02, 6.12it/s]\n 32%|ββββ | 8/25 [00:01<00:02, 6.12it/s]\n 36%|ββββ | 9/25 [00:01<00:02, 6.12it/s]\n 40%|ββββ | 10/25 [00:01<00:02, 6.12it/s]\n 44%|βββββ | 11/25 [00:01<00:02, 6.11it/s]\n 48%|βββββ | 12/25 [00:01<00:02, 6.11it/s]\n 52%|ββββββ | 13/25 [00:02<00:01, 6.11it/s]\n 56%|ββββββ | 14/25 [00:02<00:01, 6.11it/s]\n 60%|ββββββ | 15/25 [00:02<00:01, 6.11it/s]\n 64%|βββββββ | 16/25 [00:02<00:01, 6.13it/s]\n 68%|βββββββ | 17/25 [00:02<00:01, 6.13it/s]\n 72%|ββββββββ | 18/25 [00:02<00:01, 6.14it/s]\n 76%|ββββββββ | 19/25 [00:03<00:00, 6.14it/s]\n 80%|ββββββββ | 20/25 [00:03<00:00, 6.15it/s]\n 84%|βββββββββ | 21/25 [00:03<00:00, 6.15it/s]\n 88%|βββββββββ | 22/25 [00:03<00:00, 6.16it/s]\n 92%|ββββββββββ| 23/25 [00:03<00:00, 6.15it/s]\n 96%|ββββββββββ| 24/25 [00:03<00:00, 6.15it/s]\n100%|ββββββββββ| 25/25 [00:04<00:00, 6.15it/s]\n100%|ββββββββββ| 25/25 [00:04<00:00, 6.14it/s]",
"metrics": {
"predict_time": 6.628585,
"total_time": 6.666014
},
"output": [
"https://replicate.delivery/pbxt/5l25VS08DYq6CVjWQNTGSK28ScSWJ8bxbPP5EDe2f1bXaiASA/out-0.png"
],
"started_at": "2023-12-09T14:07:13.837065Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/qa5l53lbyucqp7e2b62w3vtuau",
"cancel": "https://api.replicate.com/v1/predictions/qa5l53lbyucqp7e2b62w3vtuau/cancel"
},
"version": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f"
}
Using seed: 22560
Ensuring enough disk space...
Free disk space: 2262383398912
Downloading weights: https://replicate.delivery/pbxt/5ecT4YGgj3wjFCOxsHhKWwwsSP1rvaEsKFqOkoCIWDzaJRAJA/trained_model.tar
b'Downloaded 186 MB bytes in 0.434s (428 MB/s)\nExtracted 186 MB in 0.061s (3.0 GB/s)\n'
Downloaded weights in 0.5974154472351074 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: A photo of <s0><s1> eyes
txt2img mode
0%| | 0/25 [00:00<?, ?it/s]
4%|β | 1/25 [00:00<00:03, 6.19it/s]
8%|β | 2/25 [00:00<00:03, 6.16it/s]
12%|ββ | 3/25 [00:00<00:03, 6.16it/s]
16%|ββ | 4/25 [00:00<00:03, 6.15it/s]
20%|ββ | 5/25 [00:00<00:03, 6.12it/s]
24%|βββ | 6/25 [00:00<00:03, 6.12it/s]
28%|βββ | 7/25 [00:01<00:02, 6.12it/s]
32%|ββββ | 8/25 [00:01<00:02, 6.12it/s]
36%|ββββ | 9/25 [00:01<00:02, 6.12it/s]
40%|ββββ | 10/25 [00:01<00:02, 6.12it/s]
44%|βββββ | 11/25 [00:01<00:02, 6.11it/s]
48%|βββββ | 12/25 [00:01<00:02, 6.11it/s]
52%|ββββββ | 13/25 [00:02<00:01, 6.11it/s]
56%|ββββββ | 14/25 [00:02<00:01, 6.11it/s]
60%|ββββββ | 15/25 [00:02<00:01, 6.11it/s]
64%|βββββββ | 16/25 [00:02<00:01, 6.13it/s]
68%|βββββββ | 17/25 [00:02<00:01, 6.13it/s]
72%|ββββββββ | 18/25 [00:02<00:01, 6.14it/s]
76%|ββββββββ | 19/25 [00:03<00:00, 6.14it/s]
80%|ββββββββ | 20/25 [00:03<00:00, 6.15it/s]
84%|βββββββββ | 21/25 [00:03<00:00, 6.15it/s]
88%|βββββββββ | 22/25 [00:03<00:00, 6.16it/s]
92%|ββββββββββ| 23/25 [00:03<00:00, 6.15it/s]
96%|ββββββββββ| 24/25 [00:03<00:00, 6.15it/s]
100%|ββββββββββ| 25/25 [00:04<00:00, 6.15it/s]
100%|ββββββββββ| 25/25 [00:04<00:00, 6.14it/s]