Readme
This model doesn't have a readme.
SDXL fine-tuned on Xmas sweaters
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-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 a TOK sweater, Christmas tree",
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 a TOK sweater, Christmas tree",
"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 a TOK sweater, Christmas tree",
"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 a TOK sweater, Christmas tree"' \
-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 a TOK sweater, Christmas tree", "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.
Each run costs approximately $0.011. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-12-03T21:31:33.530598Z",
"created_at": "2023-12-03T21:31:26.972342Z",
"data_removed": false,
"error": null,
"id": "wbor6vtb3gdqz5gv3mrsd3zg4e",
"input": {
"width": 768,
"height": 768,
"prompt": "A photo of a TOK sweater, Christmas tree",
"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: 27501\nskipping loading .. weights already loaded\nPrompt: A photo of a <s0><s1> sweater, Christmas tree\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:04, 6.15it/s]\n 7%|▋ | 2/27 [00:00<00:04, 6.09it/s]\n 11%|█ | 3/27 [00:00<00:03, 6.11it/s]\n 15%|█▍ | 4/27 [00:00<00:03, 6.11it/s]\n 19%|█▊ | 5/27 [00:00<00:03, 6.11it/s]\n 22%|██▏ | 6/27 [00:00<00:03, 6.10it/s]\n 26%|██▌ | 7/27 [00:01<00:03, 6.10it/s]\n 30%|██▉ | 8/27 [00:01<00:03, 6.10it/s]\n 33%|███▎ | 9/27 [00:01<00:02, 6.10it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 6.11it/s]\n 41%|████ | 11/27 [00:01<00:02, 6.10it/s]\n 44%|████▍ | 12/27 [00:01<00:02, 6.09it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 6.10it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 6.10it/s]\n 56%|█████▌ | 15/27 [00:02<00:01, 6.10it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 6.09it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 6.07it/s]\n 67%|██████▋ | 18/27 [00:02<00:01, 6.07it/s]\n 70%|███████ | 19/27 [00:03<00:01, 6.07it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 6.08it/s]\n 78%|███████▊ | 21/27 [00:03<00:00, 6.08it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 6.08it/s]\n 85%|████████▌ | 23/27 [00:03<00:00, 6.08it/s]\n 89%|████████▉ | 24/27 [00:03<00:00, 6.08it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 6.07it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 6.08it/s]\n100%|██████████| 27/27 [00:04<00:00, 6.08it/s]\n100%|██████████| 27/27 [00:04<00:00, 6.09it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 7.73it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 7.64it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.62it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.63it/s]",
"metrics": {
"predict_time": 6.518381,
"total_time": 6.558256
},
"output": [
"https://replicate.delivery/pbxt/qfRo9tfIr1nJc0uCgXmALHCb5zdpd2RaNbfqmrlpRn6ptU9jA/out-0.png"
],
"started_at": "2023-12-03T21:31:27.012217Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/wbor6vtb3gdqz5gv3mrsd3zg4e",
"cancel": "https://api.replicate.com/v1/predictions/wbor6vtb3gdqz5gv3mrsd3zg4e/cancel"
},
"version": "0690f6c650d0899dcdeaf789d33cf86bad646a00d5efe7b7600998f2950705db"
}
Using seed: 27501
skipping loading .. weights already loaded
Prompt: A photo of a <s0><s1> sweater, Christmas tree
txt2img mode
0%| | 0/27 [00:00<?, ?it/s]
4%|▎ | 1/27 [00:00<00:04, 6.15it/s]
7%|▋ | 2/27 [00:00<00:04, 6.09it/s]
11%|█ | 3/27 [00:00<00:03, 6.11it/s]
15%|█▍ | 4/27 [00:00<00:03, 6.11it/s]
19%|█▊ | 5/27 [00:00<00:03, 6.11it/s]
22%|██▏ | 6/27 [00:00<00:03, 6.10it/s]
26%|██▌ | 7/27 [00:01<00:03, 6.10it/s]
30%|██▉ | 8/27 [00:01<00:03, 6.10it/s]
33%|███▎ | 9/27 [00:01<00:02, 6.10it/s]
37%|███▋ | 10/27 [00:01<00:02, 6.11it/s]
41%|████ | 11/27 [00:01<00:02, 6.10it/s]
44%|████▍ | 12/27 [00:01<00:02, 6.09it/s]
48%|████▊ | 13/27 [00:02<00:02, 6.10it/s]
52%|█████▏ | 14/27 [00:02<00:02, 6.10it/s]
56%|█████▌ | 15/27 [00:02<00:01, 6.10it/s]
59%|█████▉ | 16/27 [00:02<00:01, 6.09it/s]
63%|██████▎ | 17/27 [00:02<00:01, 6.07it/s]
67%|██████▋ | 18/27 [00:02<00:01, 6.07it/s]
70%|███████ | 19/27 [00:03<00:01, 6.07it/s]
74%|███████▍ | 20/27 [00:03<00:01, 6.08it/s]
78%|███████▊ | 21/27 [00:03<00:00, 6.08it/s]
81%|████████▏ | 22/27 [00:03<00:00, 6.08it/s]
85%|████████▌ | 23/27 [00:03<00:00, 6.08it/s]
89%|████████▉ | 24/27 [00:03<00:00, 6.08it/s]
93%|█████████▎| 25/27 [00:04<00:00, 6.07it/s]
96%|█████████▋| 26/27 [00:04<00:00, 6.08it/s]
100%|██████████| 27/27 [00:04<00:00, 6.08it/s]
100%|██████████| 27/27 [00:04<00:00, 6.09it/s]
0%| | 0/3 [00:00<?, ?it/s]
33%|███▎ | 1/3 [00:00<00:00, 7.73it/s]
67%|██████▋ | 2/3 [00:00<00:00, 7.64it/s]
100%|██████████| 3/3 [00:00<00:00, 7.62it/s]
100%|██████████| 3/3 [00:00<00:00, 7.63it/s]
This model costs approximately $0.011 to run on Replicate, or 90 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 12 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: 27501
skipping loading .. weights already loaded
Prompt: A photo of a <s0><s1> sweater, Christmas tree
txt2img mode
0%| | 0/27 [00:00<?, ?it/s]
4%|▎ | 1/27 [00:00<00:04, 6.15it/s]
7%|▋ | 2/27 [00:00<00:04, 6.09it/s]
11%|█ | 3/27 [00:00<00:03, 6.11it/s]
15%|█▍ | 4/27 [00:00<00:03, 6.11it/s]
19%|█▊ | 5/27 [00:00<00:03, 6.11it/s]
22%|██▏ | 6/27 [00:00<00:03, 6.10it/s]
26%|██▌ | 7/27 [00:01<00:03, 6.10it/s]
30%|██▉ | 8/27 [00:01<00:03, 6.10it/s]
33%|███▎ | 9/27 [00:01<00:02, 6.10it/s]
37%|███▋ | 10/27 [00:01<00:02, 6.11it/s]
41%|████ | 11/27 [00:01<00:02, 6.10it/s]
44%|████▍ | 12/27 [00:01<00:02, 6.09it/s]
48%|████▊ | 13/27 [00:02<00:02, 6.10it/s]
52%|█████▏ | 14/27 [00:02<00:02, 6.10it/s]
56%|█████▌ | 15/27 [00:02<00:01, 6.10it/s]
59%|█████▉ | 16/27 [00:02<00:01, 6.09it/s]
63%|██████▎ | 17/27 [00:02<00:01, 6.07it/s]
67%|██████▋ | 18/27 [00:02<00:01, 6.07it/s]
70%|███████ | 19/27 [00:03<00:01, 6.07it/s]
74%|███████▍ | 20/27 [00:03<00:01, 6.08it/s]
78%|███████▊ | 21/27 [00:03<00:00, 6.08it/s]
81%|████████▏ | 22/27 [00:03<00:00, 6.08it/s]
85%|████████▌ | 23/27 [00:03<00:00, 6.08it/s]
89%|████████▉ | 24/27 [00:03<00:00, 6.08it/s]
93%|█████████▎| 25/27 [00:04<00:00, 6.07it/s]
96%|█████████▋| 26/27 [00:04<00:00, 6.08it/s]
100%|██████████| 27/27 [00:04<00:00, 6.08it/s]
100%|██████████| 27/27 [00:04<00:00, 6.09it/s]
0%| | 0/3 [00:00<?, ?it/s]
33%|███▎ | 1/3 [00:00<00:00, 7.73it/s]
67%|██████▋ | 2/3 [00:00<00:00, 7.64it/s]
100%|██████████| 3/3 [00:00<00:00, 7.62it/s]
100%|██████████| 3/3 [00:00<00:00, 7.63it/s]