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fofr /sdxl-xmas-sweater:0690f6c6
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";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run fofr/sdxl-xmas-sweater using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"fofr/sdxl-xmas-sweater:0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db",
{
input: {
width: 768,
height: 768,
prompt: "A photo of santa wearing a TOK sweater",
refine: "expert_ensemble_refiner",
scheduler: "K_EULER",
lora_scale: 0.6,
num_outputs: 1,
guidance_scale: 7.5,
apply_watermark: false,
high_noise_frac: 0.9,
negative_prompt: "",
prompt_strength: 0.8,
num_inference_steps: 30
}
}
);
// 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-xmas-sweater using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"fofr/sdxl-xmas-sweater:0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db",
input={
"width": 768,
"height": 768,
"prompt": "A photo of santa wearing a TOK sweater",
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": False,
"high_noise_frac": 0.9,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 30
}
)
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-xmas-sweater 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": "fofr/sdxl-xmas-sweater:0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db",
"input": {
"width": 768,
"height": 768,
"prompt": "A photo of santa wearing a TOK sweater",
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": false,
"high_noise_frac": 0.9,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 30
}
}' \
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/fofr/sdxl-xmas-sweater@sha256:0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db \
-i 'width=768' \
-i 'height=768' \
-i 'prompt="A photo of santa wearing a TOK sweater"' \
-i 'refine="expert_ensemble_refiner"' \
-i 'scheduler="K_EULER"' \
-i 'lora_scale=0.6' \
-i 'num_outputs=1' \
-i 'guidance_scale=7.5' \
-i 'apply_watermark=false' \
-i 'high_noise_frac=0.9' \
-i 'negative_prompt=""' \
-i 'prompt_strength=0.8' \
-i 'num_inference_steps=30'
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/fofr/sdxl-xmas-sweater@sha256:0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 768, "height": 768, "prompt": "A photo of santa wearing a TOK sweater", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
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Output
{
"completed_at": "2023-12-03T21:32:06.232070Z",
"created_at": "2023-12-03T21:31:54.922767Z",
"data_removed": false,
"error": null,
"id": "jyk3zk3bnovkio7kfalkm6xlxi",
"input": {
"width": 768,
"height": 768,
"prompt": "A photo of santa wearing a TOK sweater",
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": false,
"high_noise_frac": 0.9,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 30
},
"logs": "Using seed: 19576\nEnsuring enough disk space...\nFree disk space: 2440310452224\nDownloading weights: https://replicate.delivery/pbxt/uSerlhyRLxz5fUKP2sXU3RhukmHeeFiUVM0WTB5mvNNlbF4HB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.239s (779 MB/s)\\nExtracted 186 MB in 0.062s (3.0 GB/s)\\n'\nDownloaded weights in 0.45714402198791504 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of santa wearing a <s0><s1> sweater\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:04, 6.16it/s]\n 7%|▋ | 2/27 [00:00<00:04, 6.13it/s]\n 11%|█ | 3/27 [00:00<00:03, 6.11it/s]\n 15%|█▍ | 4/27 [00:00<00:03, 6.10it/s]\n 19%|█▊ | 5/27 [00:00<00:03, 6.10it/s]\n 22%|██▏ | 6/27 [00:00<00:03, 6.09it/s]\n 26%|██▌ | 7/27 [00:01<00:03, 6.09it/s]\n 30%|██▉ | 8/27 [00:01<00:03, 6.09it/s]\n 33%|███▎ | 9/27 [00:01<00:02, 6.09it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 6.08it/s]\n 41%|████ | 11/27 [00:01<00:02, 6.08it/s]\n 44%|████▍ | 12/27 [00:01<00:02, 6.09it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 6.09it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 6.08it/s]\n 56%|█████▌ | 15/27 [00:02<00:01, 6.08it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 6.08it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 6.09it/s]\n 67%|██████▋ | 18/27 [00:02<00:01, 6.10it/s]\n 70%|███████ | 19/27 [00:03<00:01, 6.10it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 6.10it/s]\n 78%|███████▊ | 21/27 [00:03<00:00, 6.11it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 6.11it/s]\n 85%|████████▌ | 23/27 [00:03<00:00, 6.11it/s]\n 89%|████████▉ | 24/27 [00:03<00:00, 6.11it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 6.11it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 6.11it/s]\n100%|██████████| 27/27 [00:04<00:00, 6.11it/s]\n100%|██████████| 27/27 [00:04<00:00, 6.10it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.80it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 7.25it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.40it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.30it/s]",
"metrics": {
"predict_time": 6.805732,
"total_time": 11.309303
},
"output": [
"https://replicate.delivery/pbxt/Dd9IAXSX2cJCANdbvYFBa7YI6518qoSXSz9UF6lDb8Q1lqfIA/out-0.png"
],
"started_at": "2023-12-03T21:31:59.426338Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/jyk3zk3bnovkio7kfalkm6xlxi",
"cancel": "https://api.replicate.com/v1/predictions/jyk3zk3bnovkio7kfalkm6xlxi/cancel"
},
"version": "0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db"
}
Using seed: 19576
Ensuring enough disk space...
Free disk space: 2440310452224
Downloading weights: https://replicate.delivery/pbxt/uSerlhyRLxz5fUKP2sXU3RhukmHeeFiUVM0WTB5mvNNlbF4HB/trained_model.tar
b'Downloaded 186 MB bytes in 0.239s (779 MB/s)\nExtracted 186 MB in 0.062s (3.0 GB/s)\n'
Downloaded weights in 0.45714402198791504 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: A photo of santa wearing a <s0><s1> sweater
txt2img mode
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