defaultAn astronaut riding a rainbow unicorn
typetext
{
"apply_watermark": true,
"guidance_scale": 7.5,
"height": 1024,
"high_noise_frac": 0.8,
"lora_scale": 0.6,
"negative_prompt": "ugly, blurry, realistic",
"num_inference_steps": 50,
"num_outputs": 1,
"prompt": "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
"prompt_strength": 0.8,
"prompt_weighting": true,
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"seed": 12345,
"width": 1024
}npm install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_d54**********************************
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-weighting-prompts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"fermatresearch/sdxl-weighting-prompts:66175a2993706e1721076d5c7f92f0c81ec6d065ec20717527f05dd8528a1fc7",
{
input: {
apply_watermark: true,
guidance_scale: 7.5,
height: 1024,
high_noise_frac: 0.8,
lora_scale: 0.6,
negative_prompt: "ugly, blurry, realistic",
num_inference_steps: 50,
num_outputs: 1,
prompt: "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
prompt_strength: 0.8,
prompt_weighting: true,
refine: "expert_ensemble_refiner",
scheduler: "K_EULER",
seed: 12345,
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_d54**********************************
This is your API token. Keep it to yourself.
import replicate
Run fermatresearch/sdxl-weighting-prompts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"fermatresearch/sdxl-weighting-prompts:66175a2993706e1721076d5c7f92f0c81ec6d065ec20717527f05dd8528a1fc7",
input={
"apply_watermark": True,
"guidance_scale": 7.5,
"height": 1024,
"high_noise_frac": 0.8,
"lora_scale": 0.6,
"negative_prompt": "ugly, blurry, realistic",
"num_inference_steps": 50,
"num_outputs": 1,
"prompt": "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
"prompt_strength": 0.8,
"prompt_weighting": True,
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"seed": 12345,
"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_d54**********************************
This is your API token. Keep it to yourself.
Run fermatresearch/sdxl-weighting-prompts 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-weighting-prompts:66175a2993706e1721076d5c7f92f0c81ec6d065ec20717527f05dd8528a1fc7",
"input": {
"apply_watermark": true,
"guidance_scale": 7.5,
"height": 1024,
"high_noise_frac": 0.8,
"lora_scale": 0.6,
"negative_prompt": "ugly, blurry, realistic",
"num_inference_steps": 50,
"num_outputs": 1,
"prompt": "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
"prompt_strength": 0.8,
"prompt_weighting": true,
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"seed": 12345,
"width": 1024
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
{
"id": "us5s7izbrsgk35tbt7nj3hgqji",
"model": "fermatresearch/sdxl-weighting-prompts",
"version": "66175a2993706e1721076d5c7f92f0c81ec6d065ec20717527f05dd8528a1fc7",
"input": {
"apply_watermark": true,
"guidance_scale": 7.5,
"height": 1024,
"high_noise_frac": 0.8,
"lora_scale": 0.6,
"negative_prompt": "ugly, blurry, realistic",
"num_inference_steps": 50,
"num_outputs": 1,
"prompt": "a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4",
"prompt_strength": 0.8,
"prompt_weighting": true,
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"seed": 12345,
"width": 1024
},
"logs": "Using seed: 12345\nPrompt: a legendary bird is flying under the sea water staring at a multicolor sky, wallpaper, (corals and fish are swimming around)0.4\nUsing Compel for prompt embeddings\ntxt2img mode\nPrompt embeddings calculated by Compel\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:05, 6.87it/s]\n 5%|▌ | 2/40 [00:00<00:04, 7.66it/s]\n 8%|▊ | 3/40 [00:00<00:04, 8.07it/s]\n 10%|█ | 4/40 [00:00<00:04, 8.27it/s]\n 12%|█▎ | 5/40 [00:00<00:04, 8.38it/s]\n 15%|█▌ | 6/40 [00:00<00:04, 8.45it/s]\n 18%|█▊ | 7/40 [00:00<00:03, 8.50it/s]\n 20%|██ | 8/40 [00:00<00:03, 8.42it/s]\n 22%|██▎ | 9/40 [00:01<00:03, 8.47it/s]\n 25%|██▌ | 10/40 [00:01<00:03, 8.50it/s]\n 28%|██▊ | 11/40 [00:01<00:03, 8.52it/s]\n 30%|███ | 12/40 [00:01<00:03, 8.52it/s]\n 32%|███▎ | 13/40 [00:01<00:03, 8.53it/s]\n 35%|███▌ | 14/40 [00:01<00:03, 8.54it/s]\n 38%|███▊ | 15/40 [00:01<00:02, 8.42it/s]\n 40%|████ | 16/40 [00:01<00:02, 8.46it/s]\n 42%|████▎ | 17/40 [00:02<00:02, 8.25it/s]\n 45%|████▌ | 18/40 [00:02<00:02, 8.35it/s]\n 48%|████▊ | 19/40 [00:02<00:02, 8.41it/s]\n 50%|█████ | 20/40 [00:02<00:02, 8.45it/s]\n 52%|█████▎ | 21/40 [00:02<00:02, 8.48it/s]\n 55%|█████▌ | 22/40 [00:02<00:02, 8.51it/s]\n 57%|█████▊ | 23/40 [00:02<00:02, 8.50it/s]\n 60%|██████ | 24/40 [00:02<00:01, 8.52it/s]\n 62%|██████▎ | 25/40 [00:02<00:01, 8.50it/s]\n 65%|██████▌ | 26/40 [00:03<00:01, 8.51it/s]\n 68%|██████▊ | 27/40 [00:03<00:01, 8.53it/s]\n 70%|███████ | 28/40 [00:03<00:01, 8.54it/s]\n 72%|███████▎ | 29/40 [00:03<00:01, 8.55it/s]\n 75%|███████▌ | 30/40 [00:03<00:01, 8.55it/s]\n 78%|███████▊ | 31/40 [00:03<00:01, 8.55it/s]\n 80%|████████ | 32/40 [00:03<00:00, 8.53it/s]\n 82%|████████▎ | 33/40 [00:03<00:00, 8.53it/s]\n 85%|████████▌ | 34/40 [00:04<00:00, 8.50it/s]\n 88%|████████▊ | 35/40 [00:04<00:00, 8.34it/s]\n 90%|█████████ | 36/40 [00:04<00:00, 8.40it/s]\n 92%|█████████▎| 37/40 [00:04<00:00, 8.44it/s]\n 95%|█████████▌| 38/40 [00:04<00:00, 8.47it/s]\n 98%|█████████▊| 39/40 [00:04<00:00, 8.49it/s]\n100%|██████████| 40/40 [00:04<00:00, 8.44it/s]\n100%|██████████| 40/40 [00:04<00:00, 8.43it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:01, 7.72it/s]\n 20%|██ | 2/10 [00:00<00:01, 7.67it/s]\n 30%|███ | 3/10 [00:00<00:00, 7.67it/s]\n 40%|████ | 4/10 [00:00<00:00, 7.67it/s]\n 50%|█████ | 5/10 [00:00<00:00, 7.67it/s]\n 60%|██████ | 6/10 [00:00<00:00, 7.68it/s]\n 70%|███████ | 7/10 [00:00<00:00, 7.69it/s]\n 80%|████████ | 8/10 [00:01<00:00, 7.68it/s]\n 90%|█████████ | 9/10 [00:01<00:00, 7.67it/s]\n100%|██████████| 10/10 [00:01<00:00, 7.68it/s]\n100%|██████████| 10/10 [00:01<00:00, 7.68it/s]",
"output": [
"https://replicate.delivery/pbxt/SvBffoE1nwjKpkDVCxAJIqutrTs2znWCyvY2bzfIrBQ5w71iA/out-0.png"
],
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2023-08-17T14:17:56.886546Z",
"started_at": "2023-08-17T14:17:56.845114Z",
"completed_at": "2023-08-17T14:18:05.208362Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/us5s7izbrsgk35tbt7nj3hgqji/cancel",
"get": "https://api.replicate.com/v1/predictions/us5s7izbrsgk35tbt7nj3hgqji"
},
"metrics": {
"predict_time": 8.363248,
"total_time": 8.321816
}
}