defaultAn astronaut riding a rainbow unicorn
typetext
{
"apply_watermark": false,
"controlnet_1": "soft_edge_hed",
"controlnet_1_conditioning_scale": 0.6,
"controlnet_1_end": 0.5,
"controlnet_1_image": "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
"controlnet_1_start": 0,
"controlnet_2": "none",
"controlnet_2_conditioning_scale": 0.75,
"controlnet_2_end": 1,
"controlnet_2_start": 0,
"controlnet_3": "none",
"controlnet_3_conditioning_scale": 0.75,
"controlnet_3_end": 1,
"controlnet_3_start": 0,
"guidance_scale": 1.1,
"height": 1024,
"lora_scale": 0.8,
"lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
"negative_prompt": "rainbow, soft, blurry",
"num_inference_steps": 8,
"num_outputs": 1,
"prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
"prompt_strength": 0.9,
"refine": "no_refiner",
"sizing_strategy": "width_height",
"width": 1024
}npm install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_M1L**********************************
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 fofr/sdxl-lcm-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(
"fofr/sdxl-lcm-multi-controlnet-lora:abf7d7a5c8a18aba0876166ad4f33d4a0a07dda68512c289f35611bae32ea8e8",
{
input: {
apply_watermark: false,
controlnet_1: "soft_edge_hed",
controlnet_1_conditioning_scale: 0.6,
controlnet_1_end: 0.5,
controlnet_1_image: "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
controlnet_1_start: 0,
controlnet_2: "none",
controlnet_2_conditioning_scale: 0.75,
controlnet_2_end: 1,
controlnet_2_start: 0,
controlnet_3: "none",
controlnet_3_conditioning_scale: 0.75,
controlnet_3_end: 1,
controlnet_3_start: 0,
guidance_scale: 1.1,
height: 1024,
lora_scale: 0.8,
lora_weights: "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
negative_prompt: "rainbow, soft, blurry",
num_inference_steps: 8,
num_outputs: 1,
prompt: "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
prompt_strength: 0.9,
refine: "no_refiner",
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_M1L**********************************
This is your API token. Keep it to yourself.
import replicate
Run fofr/sdxl-lcm-multi-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"fofr/sdxl-lcm-multi-controlnet-lora:abf7d7a5c8a18aba0876166ad4f33d4a0a07dda68512c289f35611bae32ea8e8",
input={
"apply_watermark": False,
"controlnet_1": "soft_edge_hed",
"controlnet_1_conditioning_scale": 0.6,
"controlnet_1_end": 0.5,
"controlnet_1_image": "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
"controlnet_1_start": 0,
"controlnet_2": "none",
"controlnet_2_conditioning_scale": 0.75,
"controlnet_2_end": 1,
"controlnet_2_start": 0,
"controlnet_3": "none",
"controlnet_3_conditioning_scale": 0.75,
"controlnet_3_end": 1,
"controlnet_3_start": 0,
"guidance_scale": 1.1,
"height": 1024,
"lora_scale": 0.8,
"lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
"negative_prompt": "rainbow, soft, blurry",
"num_inference_steps": 8,
"num_outputs": 1,
"prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
"prompt_strength": 0.9,
"refine": "no_refiner",
"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_M1L**********************************
This is your API token. Keep it to yourself.
Run fofr/sdxl-lcm-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": "fofr/sdxl-lcm-multi-controlnet-lora:abf7d7a5c8a18aba0876166ad4f33d4a0a07dda68512c289f35611bae32ea8e8",
"input": {
"apply_watermark": false,
"controlnet_1": "soft_edge_hed",
"controlnet_1_conditioning_scale": 0.6,
"controlnet_1_end": 0.5,
"controlnet_1_image": "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
"controlnet_1_start": 0,
"controlnet_2": "none",
"controlnet_2_conditioning_scale": 0.75,
"controlnet_2_end": 1,
"controlnet_2_start": 0,
"controlnet_3": "none",
"controlnet_3_conditioning_scale": 0.75,
"controlnet_3_end": 1,
"controlnet_3_start": 0,
"guidance_scale": 1.1,
"height": 1024,
"lora_scale": 0.8,
"lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
"negative_prompt": "rainbow, soft, blurry",
"num_inference_steps": 8,
"num_outputs": 1,
"prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
"prompt_strength": 0.9,
"refine": "no_refiner",
"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": "2ni5643b37sh5k2zbs6dy3rlkm",
"model": "fofr/sdxl-lcm-multi-controlnet-lora",
"version": "abf7d7a5c8a18aba0876166ad4f33d4a0a07dda68512c289f35611bae32ea8e8",
"input": {
"apply_watermark": false,
"controlnet_1": "soft_edge_hed",
"controlnet_1_conditioning_scale": 0.6,
"controlnet_1_end": 0.5,
"controlnet_1_image": "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
"controlnet_1_start": 0,
"controlnet_2": "none",
"controlnet_2_conditioning_scale": 0.75,
"controlnet_2_end": 1,
"controlnet_2_start": 0,
"controlnet_3": "none",
"controlnet_3_conditioning_scale": 0.75,
"controlnet_3_end": 1,
"controlnet_3_start": 0,
"guidance_scale": 1.1,
"height": 1024,
"lora_scale": 0.8,
"lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
"negative_prompt": "rainbow, soft, blurry",
"num_inference_steps": 8,
"num_outputs": 1,
"prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
"prompt_strength": 0.9,
"refine": "no_refiner",
"sizing_strategy": "width_height",
"width": 1024
},
"logs": "Using seed: 12353\nUsing given dimensions\nskipping loading .. weights already loaded\nPrompt: A <s0><s1> photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm\nProcessing image with soft_edge_hed\nLoading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 10512.04it/s]\nThe config attributes {'skip_prk_steps': True} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\nYou have 1 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:00<00:01, 4.80it/s]\n 25%|██▌ | 2/8 [00:00<00:01, 4.79it/s]\n 38%|███▊ | 3/8 [00:00<00:01, 4.78it/s]\n 50%|█████ | 4/8 [00:00<00:00, 4.78it/s]\n 62%|██████▎ | 5/8 [00:01<00:00, 4.78it/s]\n 75%|███████▌ | 6/8 [00:01<00:00, 4.78it/s]\n 88%|████████▊ | 7/8 [00:01<00:00, 4.78it/s]\n100%|██████████| 8/8 [00:01<00:00, 4.78it/s]\n100%|██████████| 8/8 [00:01<00:00, 4.78it/s]",
"output": [
"https://replicate.delivery/pbxt/J0wRPpT11M75Bdq6af3ssqiG0dReK64NPNMcRRgWqL8BDS6RA/control-0.png",
"https://replicate.delivery/pbxt/IDmeLjuK5txPbKzy7qzm6Ahrm1w77dYKwP9l1GsPlqwgBJ9IA/out-0.png"
],
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2023-11-20T14:35:42.095349Z",
"started_at": "2023-11-20T14:35:42.10848Z",
"completed_at": "2023-11-20T14:35:45.961164Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/2ni5643b37sh5k2zbs6dy3rlkm/cancel",
"get": "https://api.replicate.com/v1/predictions/2ni5643b37sh5k2zbs6dy3rlkm"
},
"metrics": {
"predict_time": 3.852684,
"total_time": 3.865815
}
}

