{
"creativity": 0.4,
"format": "jpg",
"guess_mode": false,
"guidance_scale": 0,
"hdr": 0,
"image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
"lora_details_strength": 0.75,
"lora_sharpness_strength": 1,
"negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
"prompt": "a woman wearing a colorful suit",
"resemblance": 0.85,
"resolution": 2048,
"scheduler": "DDIM",
"steps": 8,
"tile_size": 512
}npm install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_cxf**********************************
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/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
{
input: {
creativity: 0.4,
format: "jpg",
guess_mode: false,
guidance_scale: 0,
hdr: 0,
image: "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
lora_details_strength: 0.75,
lora_sharpness_strength: 1,
negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
prompt: "a woman wearing a colorful suit",
resemblance: 0.85,
resolution: 2048,
scheduler: "DDIM",
steps: 8,
tile_size: 512
}
}
);
// To access the file URL:
console.log(output.url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output);
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_cxf**********************************
This is your API token. Keep it to yourself.
import replicate
Run fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
input={
"creativity": 0.4,
"format": "jpg",
"guess_mode": False,
"guidance_scale": 0,
"hdr": 0,
"image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
"lora_details_strength": 0.75,
"lora_sharpness_strength": 1,
"negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
"prompt": "a woman wearing a colorful suit",
"resemblance": 0.85,
"resolution": 2048,
"scheduler": "DDIM",
"steps": 8,
"tile_size": 512
}
)
# To access the file URL:
print(output.url())
#=> "http://example.com"
# To write the file to disk:
with open("my-image.png", "wb") as file:
file.write(output.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_cxf**********************************
This is your API token. Keep it to yourself.
Run fermatresearch/high-resolution-controlnet-tile 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/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
"input": {
"creativity": 0.4,
"format": "jpg",
"guess_mode": false,
"guidance_scale": 0,
"hdr": 0,
"image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
"lora_details_strength": 0.75,
"lora_sharpness_strength": 1,
"negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
"prompt": "a woman wearing a colorful suit",
"resemblance": 0.85,
"resolution": 2048,
"scheduler": "DDIM",
"steps": 8,
"tile_size": 512
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Loading...
{
"id": "4nt04qw3exrgm0cgx9stm0nek8",
"model": "fermatresearch/high-resolution-controlnet-tile",
"version": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
"input": {
"creativity": 0.4,
"format": "jpg",
"guess_mode": false,
"guidance_scale": 0,
"hdr": 0,
"image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
"lora_details_strength": 0.75,
"lora_sharpness_strength": 1,
"negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
"prompt": "a woman wearing a colorful suit",
"resemblance": 0.85,
"resolution": 2048,
"scheduler": "DDIM",
"steps": 8,
"tile_size": 512
},
"logs": "Using seed: 33864\nThe config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.64it/s]\n100%|██████████| 3/3 [00:00<00:00, 9.96it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.80it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.21it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.66it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.88it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.80it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.23it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.72it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.08it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.54it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.72it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.08it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.54it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.87it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.78it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.21it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.77it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.16it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.61it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.38it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.61it/s]\n100%|██████████| 3/3 [00:00<00:00, 9.94it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.78it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.15it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.61it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.34it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.58it/s]\n100%|██████████| 3/3 [00:00<00:00, 9.91it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.71it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.04it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.51it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.86it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.76it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.20it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.27it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.86it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.75it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.19it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.70it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.04it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.50it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.59it/s]\n100%|██████████| 3/3 [00:00<00:00, 9.92it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.77it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.14it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.60it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:01<00:01, 1.95s/it]\n100%|██████████| 2/2 [00:02<00:00, 1.39s/it]\n100%|██████████| 2/2 [00:02<00:00, 1.47s/it]",
"output": "https://replicate.delivery/pbxt/uVYXPohfE3U5WC67kSr4TahCjwuu1hJJZz3f633PfMwQtDYmA/output.jpg",
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2024-07-25T15:12:37.239Z",
"started_at": "2024-07-25T15:13:11.319777Z",
"completed_at": "2024-07-25T15:13:45.687896Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/4nt04qw3exrgm0cgx9stm0nek8/cancel",
"get": "https://api.replicate.com/v1/predictions/4nt04qw3exrgm0cgx9stm0nek8",
"web": "https://replicate.com/p/4nt04qw3exrgm0cgx9stm0nek8"
},
"metrics": {
"predict_time": 34.368119126,
"total_time": 68.448896
}
}