{
"apply_watermark": false,
"controlnet_1": "soft_edge_hed",
"controlnet_1_conditioning_scale": 0.5,
"controlnet_1_end": 0.8,
"controlnet_1_image": "https://replicate.delivery/pbxt/JsfCQE8k1lsCinW1yo76yKMQe6R5MRt9WLL3H5T5Ypc5wasq/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
"controlnet_1_start": 0,
"controlnet_2": "illusion",
"controlnet_2_conditioning_scale": 1,
"controlnet_2_end": 0.65,
"controlnet_2_image": "https://replicate.delivery/pbxt/KAqPeqxKt8MVPNru3R9YaIiKHVuRAoOxKIk1ICtvrUpQ2XXY/spiral_black_transparent-2513050263.jpg",
"controlnet_2_start": 0.08,
"controlnet_3": "none",
"controlnet_3_conditioning_scale": 0.75,
"controlnet_3_end": 1,
"controlnet_3_start": 0,
"guidance_scale": 7.5,
"height": 1024,
"lora_scale": 0.8,
"negative_prompt": "low quality, worst quality, ugly, soft, blurry",
"num_inference_steps": 50,
"num_outputs": 1,
"prompt": "A detailed photo of an astronaut riding a unicorn through a field of flowers",
"prompt_strength": 0.8,
"refine": "no_refiner",
"scheduler": "K_EULER",
"sizing_strategy": "width_height",
"width": 1024
}npm install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_RYu**********************************
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 sdxl-based/realvisxl-v3-multi-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"sdxl-based/realvisxl-v3-multi-controlnet-lora:90a4a3604cd637cb9f1a2bdae1cfa9ed869362ca028814cdce310a78e27daade",
{
input: {
apply_watermark: false,
controlnet_1: "soft_edge_hed",
controlnet_1_conditioning_scale: 0.5,
controlnet_1_end: 0.8,
controlnet_1_image: "https://replicate.delivery/pbxt/JsfCQE8k1lsCinW1yo76yKMQe6R5MRt9WLL3H5T5Ypc5wasq/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
controlnet_1_start: 0,
controlnet_2: "illusion",
controlnet_2_conditioning_scale: 1,
controlnet_2_end: 0.65,
controlnet_2_image: "https://replicate.delivery/pbxt/KAqPeqxKt8MVPNru3R9YaIiKHVuRAoOxKIk1ICtvrUpQ2XXY/spiral_black_transparent-2513050263.jpg",
controlnet_2_start: 0.08,
controlnet_3: "none",
controlnet_3_conditioning_scale: 0.75,
controlnet_3_end: 1,
controlnet_3_start: 0,
guidance_scale: 7.5,
height: 1024,
lora_scale: 0.8,
negative_prompt: "low quality, worst quality, ugly, soft, blurry",
num_inference_steps: 50,
num_outputs: 1,
prompt: "A detailed photo of an astronaut riding a unicorn through a field of flowers",
prompt_strength: 0.8,
refine: "no_refiner",
scheduler: "K_EULER",
sizing_strategy: "width_height",
width: 1024
}
}
);
// 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_RYu**********************************
This is your API token. Keep it to yourself.
import replicate
Run sdxl-based/realvisxl-v3-multi-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"sdxl-based/realvisxl-v3-multi-controlnet-lora:90a4a3604cd637cb9f1a2bdae1cfa9ed869362ca028814cdce310a78e27daade",
input={
"apply_watermark": False,
"controlnet_1": "soft_edge_hed",
"controlnet_1_conditioning_scale": 0.5,
"controlnet_1_end": 0.8,
"controlnet_1_image": "https://replicate.delivery/pbxt/JsfCQE8k1lsCinW1yo76yKMQe6R5MRt9WLL3H5T5Ypc5wasq/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
"controlnet_1_start": 0,
"controlnet_2": "illusion",
"controlnet_2_conditioning_scale": 1,
"controlnet_2_end": 0.65,
"controlnet_2_image": "https://replicate.delivery/pbxt/KAqPeqxKt8MVPNru3R9YaIiKHVuRAoOxKIk1ICtvrUpQ2XXY/spiral_black_transparent-2513050263.jpg",
"controlnet_2_start": 0.08,
"controlnet_3": "none",
"controlnet_3_conditioning_scale": 0.75,
"controlnet_3_end": 1,
"controlnet_3_start": 0,
"guidance_scale": 7.5,
"height": 1024,
"lora_scale": 0.8,
"negative_prompt": "low quality, worst quality, ugly, soft, blurry",
"num_inference_steps": 50,
"num_outputs": 1,
"prompt": "A detailed photo of an astronaut riding a unicorn through a field of flowers",
"prompt_strength": 0.8,
"refine": "no_refiner",
"scheduler": "K_EULER",
"sizing_strategy": "width_height",
"width": 1024
}
)
# 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_RYu**********************************
This is your API token. Keep it to yourself.
Run sdxl-based/realvisxl-v3-multi-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": "sdxl-based/realvisxl-v3-multi-controlnet-lora:90a4a3604cd637cb9f1a2bdae1cfa9ed869362ca028814cdce310a78e27daade",
"input": {
"apply_watermark": false,
"controlnet_1": "soft_edge_hed",
"controlnet_1_conditioning_scale": 0.5,
"controlnet_1_end": 0.8,
"controlnet_1_image": "https://replicate.delivery/pbxt/JsfCQE8k1lsCinW1yo76yKMQe6R5MRt9WLL3H5T5Ypc5wasq/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
"controlnet_1_start": 0,
"controlnet_2": "illusion",
"controlnet_2_conditioning_scale": 1,
"controlnet_2_end": 0.65,
"controlnet_2_image": "https://replicate.delivery/pbxt/KAqPeqxKt8MVPNru3R9YaIiKHVuRAoOxKIk1ICtvrUpQ2XXY/spiral_black_transparent-2513050263.jpg",
"controlnet_2_start": 0.08,
"controlnet_3": "none",
"controlnet_3_conditioning_scale": 0.75,
"controlnet_3_end": 1,
"controlnet_3_start": 0,
"guidance_scale": 7.5,
"height": 1024,
"lora_scale": 0.8,
"negative_prompt": "low quality, worst quality, ugly, soft, blurry",
"num_inference_steps": 50,
"num_outputs": 1,
"prompt": "A detailed photo of an astronaut riding a unicorn through a field of flowers",
"prompt_strength": 0.8,
"refine": "no_refiner",
"scheduler": "K_EULER",
"sizing_strategy": "width_height",
"width": 1024
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
{
"id": "fkcqnklbo35y3aig7mgfe526qq",
"model": "sdxl-based/realvisxl-v3-multi-controlnet-lora",
"version": "90a4a3604cd637cb9f1a2bdae1cfa9ed869362ca028814cdce310a78e27daade",
"input": {
"apply_watermark": false,
"controlnet_1": "soft_edge_hed",
"controlnet_1_conditioning_scale": 0.5,
"controlnet_1_end": 0.8,
"controlnet_1_image": "https://replicate.delivery/pbxt/JsfCQE8k1lsCinW1yo76yKMQe6R5MRt9WLL3H5T5Ypc5wasq/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
"controlnet_1_start": 0,
"controlnet_2": "illusion",
"controlnet_2_conditioning_scale": 1,
"controlnet_2_end": 0.65,
"controlnet_2_image": "https://replicate.delivery/pbxt/KAqPeqxKt8MVPNru3R9YaIiKHVuRAoOxKIk1ICtvrUpQ2XXY/spiral_black_transparent-2513050263.jpg",
"controlnet_2_start": 0.08,
"controlnet_3": "none",
"controlnet_3_conditioning_scale": 0.75,
"controlnet_3_end": 1,
"controlnet_3_start": 0,
"guidance_scale": 7.5,
"height": 1024,
"lora_scale": 0.8,
"negative_prompt": "low quality, worst quality, ugly, soft, blurry",
"num_inference_steps": 50,
"num_outputs": 1,
"prompt": "A detailed photo of an astronaut riding a unicorn through a field of flowers",
"prompt_strength": 0.8,
"refine": "no_refiner",
"scheduler": "K_EULER",
"sizing_strategy": "width_height",
"width": 1024
},
"logs": "Using seed: 50913\nUsing given dimensions\nresize took: 0.04s\nPrompt: A detailed photo of an astronaut riding a unicorn through a field of flowers\nProcessing image with soft_edge_hed\ncontrolnet preprocess took: 0.42s\nUsing txt2img + controlnet pipeline\nLoading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 13751.82it/s]\nYou have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:22, 2.17it/s]\n 4%|▍ | 2/50 [00:00<00:22, 2.17it/s]\n 6%|▌ | 3/50 [00:01<00:21, 2.17it/s]\n 8%|▊ | 4/50 [00:01<00:21, 2.17it/s]\n 10%|█ | 5/50 [00:02<00:20, 2.17it/s]\n 12%|█▏ | 6/50 [00:02<00:20, 2.17it/s]\n 14%|█▍ | 7/50 [00:03<00:19, 2.17it/s]\n 16%|█▌ | 8/50 [00:03<00:19, 2.17it/s]\n 18%|█▊ | 9/50 [00:04<00:18, 2.17it/s]\n 20%|██ | 10/50 [00:04<00:18, 2.16it/s]\n 22%|██▏ | 11/50 [00:05<00:18, 2.16it/s]\n 24%|██▍ | 12/50 [00:05<00:17, 2.16it/s]\n 26%|██▌ | 13/50 [00:06<00:17, 2.16it/s]\n 28%|██▊ | 14/50 [00:06<00:16, 2.16it/s]\n 30%|███ | 15/50 [00:06<00:16, 2.16it/s]\n 32%|███▏ | 16/50 [00:07<00:15, 2.16it/s]\n 34%|███▍ | 17/50 [00:07<00:15, 2.16it/s]\n 36%|███▌ | 18/50 [00:08<00:14, 2.16it/s]\n 38%|███▊ | 19/50 [00:08<00:14, 2.16it/s]\n 40%|████ | 20/50 [00:09<00:13, 2.16it/s]\n 42%|████▏ | 21/50 [00:09<00:13, 2.16it/s]\n 44%|████▍ | 22/50 [00:10<00:12, 2.16it/s]\n 46%|████▌ | 23/50 [00:10<00:12, 2.16it/s]\n 48%|████▊ | 24/50 [00:11<00:12, 2.15it/s]\n 50%|█████ | 25/50 [00:11<00:11, 2.15it/s]\n 52%|█████▏ | 26/50 [00:12<00:11, 2.15it/s]\n 54%|█████▍ | 27/50 [00:12<00:10, 2.15it/s]\n 56%|█████▌ | 28/50 [00:12<00:10, 2.15it/s]\n 58%|█████▊ | 29/50 [00:13<00:09, 2.15it/s]\n 60%|██████ | 30/50 [00:13<00:09, 2.15it/s]\n 62%|██████▏ | 31/50 [00:14<00:08, 2.15it/s]\n 64%|██████▍ | 32/50 [00:14<00:08, 2.15it/s]\n 66%|██████▌ | 33/50 [00:15<00:07, 2.15it/s]\n 68%|██████▊ | 34/50 [00:15<00:07, 2.15it/s]\n 70%|███████ | 35/50 [00:16<00:06, 2.15it/s]\n 72%|███████▏ | 36/50 [00:16<00:06, 2.15it/s]\n 74%|███████▍ | 37/50 [00:17<00:06, 2.15it/s]\n 76%|███████▌ | 38/50 [00:17<00:05, 2.15it/s]\n 78%|███████▊ | 39/50 [00:18<00:05, 2.15it/s]\n 80%|████████ | 40/50 [00:18<00:04, 2.15it/s]\n 82%|████████▏ | 41/50 [00:19<00:04, 2.15it/s]\n 84%|████████▍ | 42/50 [00:19<00:03, 2.15it/s]\n 86%|████████▌ | 43/50 [00:19<00:03, 2.15it/s]\n 88%|████████▊ | 44/50 [00:20<00:02, 2.15it/s]\n 90%|█████████ | 45/50 [00:20<00:02, 2.15it/s]\n 92%|█████████▏| 46/50 [00:21<00:01, 2.15it/s]\n 94%|█████████▍| 47/50 [00:21<00:01, 2.14it/s]\n 96%|█████████▌| 48/50 [00:22<00:00, 2.15it/s]\n 98%|█████████▊| 49/50 [00:22<00:00, 2.15it/s]\n100%|██████████| 50/50 [00:23<00:00, 2.15it/s]\n100%|██████████| 50/50 [00:23<00:00, 2.16it/s]\ninference took: 23.66s\nprediction took: 24.44s",
"output": [
"https://replicate.delivery/pbxt/L6Goc02N7fXcfEoirX3vE1TUpqJpUFXf0lrtUuzIaPsYT6SkA/control-0.png",
"https://replicate.delivery/pbxt/vpb5gbXNqtpTBJgfwkU8EMTtCbfs4YuUZkTLbh579SOtJdJSA/control-1.png",
"https://replicate.delivery/pbxt/Pjd50izNdzJDFJlfczALREnyfGIedK4HufhEVV3tVCK4m0lIB/out-0.png"
],
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2024-01-05T15:29:23.467941Z",
"started_at": "2024-01-05T15:29:23.505689Z",
"completed_at": "2024-01-05T15:29:50.723676Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/fkcqnklbo35y3aig7mgfe526qq/cancel",
"get": "https://api.replicate.com/v1/predictions/fkcqnklbo35y3aig7mgfe526qq"
},
"metrics": {
"predict_time": 27.217987,
"total_time": 27.255735
}
}



