defaultAn astronaut riding a rainbow unicorn
typetext
{
"apply_watermark": true,
"condition_scale": 0.5,
"guidance_scale": 7.5,
"image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
"lora_scale": 0.95,
"lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
"negative_prompt": "",
"num_inference_steps": 40,
"num_outputs": 1,
"prompt": "shot in the style of sksfer, a woman in alaska",
"refine": "base_image_refiner",
"refine_steps": 20,
"scheduler": "K_EULER",
"seed": null
}npm install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_Tdr**********************************
This is your API token. Keep it to yourself.
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"fermatresearch/sdxl-controlnet-lora:a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4",
{
input: {
apply_watermark: true,
condition_scale: 0.5,
guidance_scale: 7.5,
image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
lora_scale: 0.95,
lora_weights: "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
negative_prompt: "",
num_inference_steps: 40,
num_outputs: 1,
prompt: "shot in the style of sksfer, a woman in alaska",
refine: "base_image_refiner",
refine_steps: 20,
scheduler: "K_EULER"
}
}
);
// 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=r8_Tdr**********************************
This is your API token. Keep it to yourself.
import replicate
Run fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"fermatresearch/sdxl-controlnet-lora:a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4",
input={
"apply_watermark": True,
"condition_scale": 0.5,
"guidance_scale": 7.5,
"image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
"lora_scale": 0.95,
"lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
"negative_prompt": "",
"num_inference_steps": 40,
"num_outputs": 1,
"prompt": "shot in the style of sksfer, a woman in alaska",
"refine": "base_image_refiner",
"refine_steps": 20,
"scheduler": "K_EULER"
}
)
# To access the file URL:
print(output[0].url())
#=> "http://example.com"
# To write the file to disk:
with open("my-image.png", "wb") as file:
file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_Tdr**********************************
This is your API token. Keep it to yourself.
Run fermatresearch/sdxl-controlnet-lora 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": "fermatresearch/sdxl-controlnet-lora:a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4",
"input": {
"apply_watermark": true,
"condition_scale": 0.5,
"guidance_scale": 7.5,
"image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
"lora_scale": 0.95,
"lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
"negative_prompt": "",
"num_inference_steps": 40,
"num_outputs": 1,
"prompt": "shot in the style of sksfer, a woman in alaska",
"refine": "base_image_refiner",
"refine_steps": 20,
"scheduler": "K_EULER"
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
{
"id": "ruzsnmtb7idrwecy2ue5ig6gli",
"model": "fermatresearch/sdxl-controlnet-lora",
"version": "a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4",
"input": {
"apply_watermark": true,
"condition_scale": 0.5,
"guidance_scale": 7.5,
"image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png",
"lora_scale": 0.95,
"lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
"negative_prompt": "",
"num_inference_steps": 40,
"num_outputs": 1,
"prompt": "shot in the style of sksfer, a woman in alaska",
"refine": "base_image_refiner",
"refine_steps": 20,
"scheduler": "K_EULER",
"seed": null
},
"logs": "Using seed: 44186\nloading custom weights\nweights already in cache\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: shot in the style of <s0><s1>, a woman in alaska\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:14, 2.72it/s]\n 5%|▌ | 2/40 [00:00<00:13, 2.73it/s]\n 8%|▊ | 3/40 [00:01<00:13, 2.73it/s]\n 10%|█ | 4/40 [00:01<00:13, 2.73it/s]\n 12%|█▎ | 5/40 [00:01<00:12, 2.73it/s]\n 15%|█▌ | 6/40 [00:02<00:12, 2.73it/s]\n 18%|█▊ | 7/40 [00:02<00:12, 2.73it/s]\n 20%|██ | 8/40 [00:02<00:11, 2.73it/s]\n 22%|██▎ | 9/40 [00:03<00:11, 2.73it/s]\n 25%|██▌ | 10/40 [00:03<00:11, 2.72it/s]\n 28%|██▊ | 11/40 [00:04<00:10, 2.72it/s]\n 30%|███ | 12/40 [00:04<00:10, 2.72it/s]\n 32%|███▎ | 13/40 [00:04<00:09, 2.72it/s]\n 35%|███▌ | 14/40 [00:05<00:09, 2.72it/s]\n 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s]\n 40%|████ | 16/40 [00:05<00:08, 2.72it/s]\n 42%|████▎ | 17/40 [00:06<00:08, 2.71it/s]\n 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s]\n 48%|████▊ | 19/40 [00:06<00:07, 2.71it/s]\n 50%|█████ | 20/40 [00:07<00:07, 2.71it/s]\n 52%|█████▎ | 21/40 [00:07<00:06, 2.71it/s]\n 55%|█████▌ | 22/40 [00:08<00:06, 2.72it/s]\n 57%|█████▊ | 23/40 [00:08<00:06, 2.71it/s]\n 60%|██████ | 24/40 [00:08<00:05, 2.71it/s]\n 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s]\n 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s]\n 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s]\n 70%|███████ | 28/40 [00:10<00:04, 2.72it/s]\n 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s]\n 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s]\n 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s]\n 80%|████████ | 32/40 [00:11<00:02, 2.71it/s]\n 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s]\n 85%|████████▌ | 34/40 [00:12<00:02, 2.71it/s]\n 88%|████████▊ | 35/40 [00:12<00:01, 2.71it/s]\n 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s]\n 92%|█████████▎| 37/40 [00:13<00:01, 2.71it/s]\n 95%|█████████▌| 38/40 [00:13<00:00, 2.71it/s]\n 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.72it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.32it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.30it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.28it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.29it/s]",
"output": [
"https://replicate.delivery/pbxt/oDtYIK2lDoaKMtdE4E5ozQSa61BU3gc4aRvGF3xmFpdwCxbE/out-0.png"
],
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2023-10-17T13:48:30.378425Z",
"started_at": "2023-10-17T13:48:30.34826Z",
"completed_at": "2023-10-17T13:48:50.759475Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/ruzsnmtb7idrwecy2ue5ig6gli/cancel",
"get": "https://api.replicate.com/v1/predictions/ruzsnmtb7idrwecy2ue5ig6gli"
},
"metrics": {
"predict_time": 20.411215,
"total_time": 20.38105
}
}
