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imagesoutofcontrol/stanidani
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=imagesoutofcontrol/stanidani
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run imagesoutofcontrol/stanidani using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"imagesoutofcontrol/stanidani:1d406563f35fe7d09f507d6b6fe1e5a785f2eda42a97a36730b7fafb92159f99",
{
input: {
width: 1824,
height: 1312,
prompt: "a portrait of StaniDani",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.6,
num_outputs: 1,
guidance_scale: 7.5,
apply_watermark: true,
high_noise_frac: 0.8,
negative_prompt: "",
prompt_strength: 0.8,
num_inference_steps: 50
}
}
);
// 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.
Install Replicate’s Python client library:
pip install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun imagesoutofcontrol/stanidani using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"imagesoutofcontrol/stanidani:1d406563f35fe7d09f507d6b6fe1e5a785f2eda42a97a36730b7fafb92159f99",
input={
"width": 1824,
"height": 1312,
"prompt": "a portrait of StaniDani",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": True,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
}
)
# 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.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run imagesoutofcontrol/stanidani 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": "imagesoutofcontrol/stanidani:1d406563f35fe7d09f507d6b6fe1e5a785f2eda42a97a36730b7fafb92159f99",
"input": {
"width": 1824,
"height": 1312,
"prompt": "a portrait of StaniDani",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/imagesoutofcontrol/stanidani@sha256:1d406563f35fe7d09f507d6b6fe1e5a785f2eda42a97a36730b7fafb92159f99 \
-i 'width=1824' \
-i 'height=1312' \
-i 'prompt="a portrait of StaniDani"' \
-i 'refine="no_refiner"' \
-i 'scheduler="K_EULER"' \
-i 'lora_scale=0.6' \
-i 'num_outputs=1' \
-i 'guidance_scale=7.5' \
-i 'apply_watermark=true' \
-i 'high_noise_frac=0.8' \
-i 'negative_prompt=""' \
-i 'prompt_strength=0.8' \
-i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/imagesoutofcontrol/stanidani@sha256:1d406563f35fe7d09f507d6b6fe1e5a785f2eda42a97a36730b7fafb92159f99curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1824, "height": 1312, "prompt": "a portrait of StaniDani", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Sign in with GitHub
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terms of service and privacy policy
Output
{
"completed_at": "2024-05-03T13:11:16.053541Z",
"created_at": "2024-05-03T13:10:31.961000Z",
"data_removed": false,
"error": null,
"id": "y2gs7gq935rgj0cf7t9vc4r390",
"input": {
"width": 1824,
"height": 1312,
"prompt": "a portrait of StaniDani",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
},
"logs": "Using seed: 25717\nEnsuring enough disk space...\nFree disk space: 1725506011136\nDownloading weights: https://replicate.delivery/pbxt/h4LKIt4cRIqEEdctVQMZPelQdiSl8csmmHBVMDx3qGm0nUYJA/trained_model.tar\n2024-05-03T13:10:34Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/104b8b2c47a7f3fd url=https://replicate.delivery/pbxt/h4LKIt4cRIqEEdctVQMZPelQdiSl8csmmHBVMDx3qGm0nUYJA/trained_model.tar\n2024-05-03T13:10:37Z | INFO | [ Complete ] dest=/src/weights-cache/104b8b2c47a7f3fd size=\"186 MB\" total_elapsed=2.501s url=https://replicate.delivery/pbxt/h4LKIt4cRIqEEdctVQMZPelQdiSl8csmmHBVMDx3qGm0nUYJA/trained_model.tar\nb''\nDownloaded weights in 2.576716423034668 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a portrait of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:34, 1.42it/s]\n 4%|▍ | 2/50 [00:01<00:33, 1.43it/s]\n 6%|▌ | 3/50 [00:02<00:32, 1.43it/s]\n 8%|▊ | 4/50 [00:02<00:32, 1.43it/s]\n 10%|█ | 5/50 [00:03<00:31, 1.43it/s]\n 12%|█▏ | 6/50 [00:04<00:30, 1.43it/s]\n 14%|█▍ | 7/50 [00:04<00:30, 1.43it/s]\n 16%|█▌ | 8/50 [00:05<00:29, 1.43it/s]\n 18%|█▊ | 9/50 [00:06<00:28, 1.43it/s]\n 20%|██ | 10/50 [00:07<00:28, 1.43it/s]\n 22%|██▏ | 11/50 [00:07<00:27, 1.43it/s]\n 24%|██▍ | 12/50 [00:08<00:26, 1.42it/s]\n 26%|██▌ | 13/50 [00:09<00:25, 1.42it/s]\n 28%|██▊ | 14/50 [00:09<00:25, 1.42it/s]\n 30%|███ | 15/50 [00:10<00:24, 1.42it/s]\n 32%|███▏ | 16/50 [00:11<00:23, 1.42it/s]\n 34%|███▍ | 17/50 [00:11<00:23, 1.42it/s]\n 36%|███▌ | 18/50 [00:12<00:22, 1.42it/s]\n 38%|███▊ | 19/50 [00:13<00:21, 1.42it/s]\n 40%|████ | 20/50 [00:14<00:21, 1.42it/s]\n 42%|████▏ | 21/50 [00:14<00:20, 1.42it/s]\n 44%|████▍ | 22/50 [00:15<00:19, 1.42it/s]\n 46%|████▌ | 23/50 [00:16<00:18, 1.42it/s]\n 48%|████▊ | 24/50 [00:16<00:18, 1.42it/s]\n 50%|█████ | 25/50 [00:17<00:17, 1.42it/s]\n 52%|█████▏ | 26/50 [00:18<00:16, 1.42it/s]\n 54%|█████▍ | 27/50 [00:18<00:16, 1.42it/s]\n 56%|█████▌ | 28/50 [00:19<00:15, 1.42it/s]\n 58%|█████▊ | 29/50 [00:20<00:14, 1.42it/s]\n 60%|██████ | 30/50 [00:21<00:14, 1.42it/s]\n 62%|██████▏ | 31/50 [00:21<00:13, 1.42it/s]\n 64%|██████▍ | 32/50 [00:22<00:12, 1.42it/s]\n 66%|██████▌ | 33/50 [00:23<00:11, 1.42it/s]\n 68%|██████▊ | 34/50 [00:23<00:11, 1.42it/s]\n 70%|███████ | 35/50 [00:24<00:10, 1.42it/s]\n 72%|███████▏ | 36/50 [00:25<00:09, 1.42it/s]\n 74%|███████▍ | 37/50 [00:26<00:09, 1.42it/s]\n 76%|███████▌ | 38/50 [00:26<00:08, 1.42it/s]\n 78%|███████▊ | 39/50 [00:27<00:07, 1.42it/s]\n 80%|████████ | 40/50 [00:28<00:07, 1.42it/s]\n 82%|████████▏ | 41/50 [00:28<00:06, 1.42it/s]\n 84%|████████▍ | 42/50 [00:29<00:05, 1.42it/s]\n 86%|████████▌ | 43/50 [00:30<00:04, 1.42it/s]\n 88%|████████▊ | 44/50 [00:30<00:04, 1.41it/s]\n 90%|█████████ | 45/50 [00:31<00:03, 1.42it/s]\n 92%|█████████▏| 46/50 [00:32<00:02, 1.42it/s]\n 94%|█████████▍| 47/50 [00:33<00:02, 1.42it/s]\n 96%|█████████▌| 48/50 [00:33<00:01, 1.42it/s]\n 98%|█████████▊| 49/50 [00:34<00:00, 1.42it/s]\n100%|██████████| 50/50 [00:35<00:00, 1.42it/s]\n100%|██████████| 50/50 [00:35<00:00, 1.42it/s]",
"metrics": {
"predict_time": 41.391319,
"total_time": 44.092541
},
"output": [
"https://replicate.delivery/pbxt/osrqhJro5e3jRSuXoXswhBeTBHzGdGaf9KRAaINAo2eNHlCLB/out-0.png"
],
"started_at": "2024-05-03T13:10:34.662222Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/y2gs7gq935rgj0cf7t9vc4r390",
"cancel": "https://api.replicate.com/v1/predictions/y2gs7gq935rgj0cf7t9vc4r390/cancel"
},
"version": "1d406563f35fe7d09f507d6b6fe1e5a785f2eda42a97a36730b7fafb92159f99"
}Generated in
Using seed: 25717
Ensuring enough disk space...
Free disk space: 1725506011136
Downloading weights: https://replicate.delivery/pbxt/h4LKIt4cRIqEEdctVQMZPelQdiSl8csmmHBVMDx3qGm0nUYJA/trained_model.tar
2024-05-03T13:10:34Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/104b8b2c47a7f3fd url=https://replicate.delivery/pbxt/h4LKIt4cRIqEEdctVQMZPelQdiSl8csmmHBVMDx3qGm0nUYJA/trained_model.tar
2024-05-03T13:10:37Z | INFO | [ Complete ] dest=/src/weights-cache/104b8b2c47a7f3fd size="186 MB" total_elapsed=2.501s url=https://replicate.delivery/pbxt/h4LKIt4cRIqEEdctVQMZPelQdiSl8csmmHBVMDx3qGm0nUYJA/trained_model.tar
b''
Downloaded weights in 2.576716423034668 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: a portrait of <s0><s1>
txt2img mode
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Run time and cost
This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.