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fofr /sdxl-multi-controlnet-lora:a7e9ded4
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-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-multi-controlnet-lora:a7e9ded4a0bf23d05e7bdf65cd53a2bc0549b802cf7740f03351487371e53f18",
{
input: {
image: "https://replicate.delivery/pbxt/JsfNMvUfNDY4hh03Joa4yZXGgWqkk6myOWEncVJdjImv5y1d/out-3-14.png",
width: 768,
height: 768,
prompt: "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 18mm",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.8,
num_outputs: 1,
controlnet_1: "soft_edge_hed",
controlnet_2: "none",
controlnet_3: "none",
lora_weights: "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
guidance_scale: 7.5,
apply_watermark: false,
high_noise_frac: 0.8,
negative_prompt: "soft, rainbow",
prompt_strength: 0.8,
sizing_strategy: "width_height",
controlnet_1_end: 1,
controlnet_2_end: 1,
controlnet_3_end: 1,
controlnet_1_image: "https://replicate.delivery/pbxt/JsfNNRrcxL39mumIcKPKRsPf75Bm1sDhU0eOHr8ukiZqczQk/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
controlnet_1_start: 0,
controlnet_2_start: 0,
controlnet_3_start: 0,
num_inference_steps: 30,
controlnet_1_conditioning_scale: 0.8,
controlnet_2_conditioning_scale: 0.8,
controlnet_3_conditioning_scale: 0.75
}
}
);
// 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-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-multi-controlnet-lora:a7e9ded4a0bf23d05e7bdf65cd53a2bc0549b802cf7740f03351487371e53f18",
input={
"image": "https://replicate.delivery/pbxt/JsfNMvUfNDY4hh03Joa4yZXGgWqkk6myOWEncVJdjImv5y1d/out-3-14.png",
"width": 768,
"height": 768,
"prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 18mm",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.8,
"num_outputs": 1,
"controlnet_1": "soft_edge_hed",
"controlnet_2": "none",
"controlnet_3": "none",
"lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
"guidance_scale": 7.5,
"apply_watermark": False,
"high_noise_frac": 0.8,
"negative_prompt": "soft, rainbow",
"prompt_strength": 0.8,
"sizing_strategy": "width_height",
"controlnet_1_end": 1,
"controlnet_2_end": 1,
"controlnet_3_end": 1,
"controlnet_1_image": "https://replicate.delivery/pbxt/JsfNNRrcxL39mumIcKPKRsPf75Bm1sDhU0eOHr8ukiZqczQk/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
"controlnet_1_start": 0,
"controlnet_2_start": 0,
"controlnet_3_start": 0,
"num_inference_steps": 30,
"controlnet_1_conditioning_scale": 0.8,
"controlnet_2_conditioning_scale": 0.8,
"controlnet_3_conditioning_scale": 0.75
}
)
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-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-multi-controlnet-lora:a7e9ded4a0bf23d05e7bdf65cd53a2bc0549b802cf7740f03351487371e53f18",
"input": {
"image": "https://replicate.delivery/pbxt/JsfNMvUfNDY4hh03Joa4yZXGgWqkk6myOWEncVJdjImv5y1d/out-3-14.png",
"width": 768,
"height": 768,
"prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 18mm",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.8,
"num_outputs": 1,
"controlnet_1": "soft_edge_hed",
"controlnet_2": "none",
"controlnet_3": "none",
"lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
"guidance_scale": 7.5,
"apply_watermark": false,
"high_noise_frac": 0.8,
"negative_prompt": "soft, rainbow",
"prompt_strength": 0.8,
"sizing_strategy": "width_height",
"controlnet_1_end": 1,
"controlnet_2_end": 1,
"controlnet_3_end": 1,
"controlnet_1_image": "https://replicate.delivery/pbxt/JsfNNRrcxL39mumIcKPKRsPf75Bm1sDhU0eOHr8ukiZqczQk/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
"controlnet_1_start": 0,
"controlnet_2_start": 0,
"controlnet_3_start": 0,
"num_inference_steps": 30,
"controlnet_1_conditioning_scale": 0.8,
"controlnet_2_conditioning_scale": 0.8,
"controlnet_3_conditioning_scale": 0.75
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
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Output
{
"completed_at": "2023-11-15T10:24:18.612369Z",
"created_at": "2023-11-15T10:24:09.646918Z",
"data_removed": false,
"error": null,
"id": "unv4n4tbrwdybd4xah6msqf66a",
"input": {
"image": "https://replicate.delivery/pbxt/JsfNMvUfNDY4hh03Joa4yZXGgWqkk6myOWEncVJdjImv5y1d/out-3-14.png",
"width": 768,
"height": 768,
"prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 18mm",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.8,
"num_outputs": 1,
"controlnet_1": "soft_edge_hed",
"controlnet_2": "none",
"controlnet_3": "none",
"lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
"guidance_scale": 7.5,
"apply_watermark": false,
"high_noise_frac": 0.8,
"negative_prompt": "soft, rainbow",
"prompt_strength": 0.8,
"sizing_strategy": "width_height",
"controlnet_1_end": 1,
"controlnet_2_end": 1,
"controlnet_3_end": 1,
"controlnet_1_image": "https://replicate.delivery/pbxt/JsfNNRrcxL39mumIcKPKRsPf75Bm1sDhU0eOHr8ukiZqczQk/020e656d-0c71-45c3-a7f5-1facf7d52d4f.png",
"controlnet_1_start": 0,
"controlnet_2_start": 0,
"controlnet_3_start": 0,
"num_inference_steps": 30,
"controlnet_1_conditioning_scale": 0.8,
"controlnet_2_conditioning_scale": 0.8,
"controlnet_3_conditioning_scale": 0.75
},
"logs": "Using seed: 61519\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, 18mm\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, 15567.41it/s]\nYou have 1 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n 0%| | 0/24 [00:00<?, ?it/s]\n 4%|▍ | 1/24 [00:00<00:04, 4.61it/s]\n 8%|▊ | 2/24 [00:00<00:04, 4.60it/s]\n 12%|█▎ | 3/24 [00:00<00:04, 4.60it/s]\n 17%|█▋ | 4/24 [00:00<00:04, 4.60it/s]\n 21%|██ | 5/24 [00:01<00:04, 4.62it/s]\n 25%|██▌ | 6/24 [00:01<00:03, 4.62it/s]\n 29%|██▉ | 7/24 [00:01<00:03, 4.63it/s]\n 33%|███▎ | 8/24 [00:01<00:03, 4.63it/s]\n 38%|███▊ | 9/24 [00:01<00:03, 4.63it/s]\n 42%|████▏ | 10/24 [00:02<00:03, 4.63it/s]\n 46%|████▌ | 11/24 [00:02<00:02, 4.63it/s]\n 50%|█████ | 12/24 [00:02<00:02, 4.63it/s]\n 54%|█████▍ | 13/24 [00:02<00:02, 4.62it/s]\n 58%|█████▊ | 14/24 [00:03<00:02, 4.62it/s]\n 62%|██████▎ | 15/24 [00:03<00:01, 4.62it/s]\n 67%|██████▋ | 16/24 [00:03<00:01, 4.63it/s]\n 71%|███████ | 17/24 [00:03<00:01, 4.63it/s]\n 75%|███████▌ | 18/24 [00:03<00:01, 4.62it/s]\n 79%|███████▉ | 19/24 [00:04<00:01, 4.62it/s]\n 83%|████████▎ | 20/24 [00:04<00:00, 4.62it/s]\n 88%|████████▊ | 21/24 [00:04<00:00, 4.62it/s]\n 92%|█████████▏| 22/24 [00:04<00:00, 4.62it/s]\n 96%|█████████▌| 23/24 [00:04<00:00, 4.62it/s]\n100%|██████████| 24/24 [00:05<00:00, 4.62it/s]\n100%|██████████| 24/24 [00:05<00:00, 4.62it/s]",
"metrics": {
"predict_time": 8.914106,
"total_time": 8.965451
},
"output": [
"https://replicate.delivery/pbxt/XKhUYwvpoppzNJkUYhJ9pGsxflUJChmVBOay9KzeF6zR5k4RA/control-0.png",
"https://replicate.delivery/pbxt/tcEOrqX3qXL9CdCtgpkv5gTzEaAXAOMzlYpDqScfDdQpcS8IA/out-0.png"
],
"started_at": "2023-11-15T10:24:09.698263Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/unv4n4tbrwdybd4xah6msqf66a",
"cancel": "https://api.replicate.com/v1/predictions/unv4n4tbrwdybd4xah6msqf66a/cancel"
},
"version": "a7e9ded4a0bf23d05e7bdf65cd53a2bc0549b802cf7740f03351487371e53f18"
}
Using seed: 61519
Using given dimensions
skipping loading .. weights already loaded
Prompt: A <s0><s1> photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 18mm
Processing image with soft_edge_hed
Loading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]
Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 15567.41it/s]
You have 1 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.
0%| | 0/24 [00:00<?, ?it/s]
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