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qunash /circassian-culture-flux-3000-steps:bf772d87
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=qunash/circassian-culture-flux-3000-steps
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";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run qunash/circassian-culture-flux-3000-steps using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"qunash/circassian-culture-flux-3000-steps:bf772d87d563c647335dd4bdf50ca56ee5ced4fe55bf0e8ed3d7f874303ae688",
{
input: {
model: "dev",
prompt: "A Circassian female in a traditional Circassian dress fasha with visible eyebrows, hazel eyes, small lips, a bit broader short nose, almond-shaped eyes, a broad chin, and a prominent jawline. She has shoulder-length hair, a slight smile, high cheekbones, and is in a portrait against a white background. The photo was taken with Kodak Portra 800 film.",
go_fast: false,
lora_scale: 1,
megapixels: "1",
num_outputs: 4,
aspect_ratio: "1:1",
output_format: "webp",
guidance_scale: 3.5,
output_quality: 80,
prompt_strength: 0.8,
extra_lora_scale: 1,
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 qunash/circassian-culture-flux-3000-steps using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"qunash/circassian-culture-flux-3000-steps:bf772d87d563c647335dd4bdf50ca56ee5ced4fe55bf0e8ed3d7f874303ae688",
input={
"model": "dev",
"prompt": "A Circassian female in a traditional Circassian dress fasha with visible eyebrows, hazel eyes, small lips, a bit broader short nose, almond-shaped eyes, a broad chin, and a prominent jawline. She has shoulder-length hair, a slight smile, high cheekbones, and is in a portrait against a white background. The photo was taken with Kodak Portra 800 film.",
"go_fast": False,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 4,
"aspect_ratio": "1:1",
"output_format": "webp",
"guidance_scale": 3.5,
"output_quality": 80,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 50
}
)
print(output)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 qunash/circassian-culture-flux-3000-steps 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": "qunash/circassian-culture-flux-3000-steps:bf772d87d563c647335dd4bdf50ca56ee5ced4fe55bf0e8ed3d7f874303ae688",
"input": {
"model": "dev",
"prompt": "A Circassian female in a traditional Circassian dress fasha with visible eyebrows, hazel eyes, small lips, a bit broader short nose, almond-shaped eyes, a broad chin, and a prominent jawline. She has shoulder-length hair, a slight smile, high cheekbones, and is in a portrait against a white background. The photo was taken with Kodak Portra 800 film.",
"go_fast": false,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 4,
"aspect_ratio": "1:1",
"output_format": "webp",
"guidance_scale": 3.5,
"output_quality": 80,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 50
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{
"completed_at": "2024-08-18T08:41:43.354769Z",
"created_at": "2024-08-18T08:40:50.190000Z",
"data_removed": false,
"error": null,
"id": "1221wd3k1srm20chcjjrbdbbmg",
"input": {
"model": "dev",
"prompt": "A Circassian female in a traditional Circassian dress fasha with visible eyebrows, hazel eyes, small lips, a bit broader short nose, almond-shaped eyes, a broad chin, and a prominent jawline. She has shoulder-length hair, a slight smile, high cheekbones, and is in a portrait against a white background. The photo was taken with Kodak Portra 800 film.",
"lora_scale": 1,
"num_outputs": 4,
"aspect_ratio": "1:1",
"output_format": "webp",
"guidance_scale": 3.5,
"output_quality": 80,
"num_inference_steps": 50
},
"logs": "Using seed: 9905\nPrompt: A Circassian female in a traditional Circassian dress fasha with visible eyebrows, hazel eyes, small lips, a bit broader short nose, almond-shaped eyes, a broad chin, and a prominent jawline. She has shoulder-length hair, a slight smile, high cheekbones, and is in a portrait against a white background. The photo was taken with Kodak Portra 800 film.\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nweights already loaded!\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['kodak portra 8 0 0 film.', 'kodak portra 8 0 0 film.', 'kodak portra 8 0 0 film.', 'kodak portra 8 0 0 film.']\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:50, 1.03s/it]\n 4%|▍ | 2/50 [00:01<00:43, 1.11it/s]\n 6%|▌ | 3/50 [00:02<00:45, 1.04it/s]\n 8%|▊ | 4/50 [00:03<00:45, 1.02it/s]\n 10%|█ | 5/50 [00:04<00:44, 1.00it/s]\n 12%|█▏ | 6/50 [00:05<00:44, 1.01s/it]\n 14%|█▍ | 7/50 [00:06<00:43, 1.01s/it]\n 16%|█▌ | 8/50 [00:07<00:42, 1.02s/it]\n 18%|█▊ | 9/50 [00:09<00:41, 1.02s/it]\n 20%|██ | 10/50 [00:10<00:41, 1.03s/it]\n 22%|██▏ | 11/50 [00:11<00:40, 1.03s/it]\n 24%|██▍ | 12/50 [00:12<00:39, 1.03s/it]\n 26%|██▌ | 13/50 [00:13<00:38, 1.03s/it]\n 28%|██▊ | 14/50 [00:14<00:37, 1.03s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.03s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.03s/it]\n 34%|███▍ | 17/50 [00:17<00:33, 1.03s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.03s/it]\n 38%|███▊ | 19/50 [00:19<00:31, 1.03s/it]\n 40%|████ | 20/50 [00:20<00:30, 1.03s/it]\n 42%|████▏ | 21/50 [00:21<00:29, 1.03s/it]\n 44%|████▍ | 22/50 [00:22<00:28, 1.04s/it]\n 46%|████▌ | 23/50 [00:23<00:27, 1.04s/it]\n 48%|████▊ | 24/50 [00:24<00:26, 1.03s/it]\n 50%|█████ | 25/50 [00:25<00:25, 1.03s/it]\n 52%|█████▏ | 26/50 [00:26<00:24, 1.03s/it]\n 54%|█████▍ | 27/50 [00:27<00:23, 1.03s/it]\n 56%|█████▌ | 28/50 [00:28<00:22, 1.04s/it]\n 58%|█████▊ | 29/50 [00:29<00:21, 1.04s/it]\n 60%|██████ | 30/50 [00:30<00:20, 1.04s/it]\n 62%|██████▏ | 31/50 [00:31<00:19, 1.04s/it]\n 64%|██████▍ | 32/50 [00:32<00:18, 1.04s/it]\n 66%|██████▌ | 33/50 [00:33<00:17, 1.04s/it]\n 68%|██████▊ | 34/50 [00:34<00:16, 1.04s/it]\n 70%|███████ | 35/50 [00:35<00:15, 1.04s/it]\n 72%|███████▏ | 36/50 [00:36<00:14, 1.04s/it]\n 74%|███████▍ | 37/50 [00:38<00:13, 1.04s/it]\n 76%|███████▌ | 38/50 [00:39<00:12, 1.04s/it]\n 78%|███████▊ | 39/50 [00:40<00:11, 1.04s/it]\n 80%|████████ | 40/50 [00:41<00:10, 1.04s/it]\n 82%|████████▏ | 41/50 [00:42<00:09, 1.04s/it]\n 84%|████████▍ | 42/50 [00:43<00:08, 1.04s/it]\n 86%|████████▌ | 43/50 [00:44<00:07, 1.04s/it]\n 88%|████████▊ | 44/50 [00:45<00:06, 1.04s/it]\n 90%|█████████ | 45/50 [00:46<00:05, 1.04s/it]\n 92%|█████████▏| 46/50 [00:47<00:04, 1.04s/it]\n 94%|█████████▍| 47/50 [00:48<00:03, 1.04s/it]\n 96%|█████████▌| 48/50 [00:49<00:02, 1.04s/it]\n 98%|█████████▊| 49/50 [00:50<00:01, 1.04s/it]\n100%|██████████| 50/50 [00:51<00:00, 1.04s/it]\n100%|██████████| 50/50 [00:51<00:00, 1.03s/it]",
"metrics": {
"predict_time": 53.131398988,
"total_time": 53.164769
},
"output": [
"https://replicate.delivery/yhqm/wu5fxnAZ8pWdXiuGpQ8yV9Py01GjhZJxRmicTHfLec0NusnmA/out-0.webp",
"https://replicate.delivery/yhqm/T2kepfBPS6q6Mkj6XLysoaOdfvHptgf7rs3TxVuDHZ6ccZPNB/out-1.webp",
"https://replicate.delivery/yhqm/ePBAA0vKPmVSd6fGlNf7AVvofD0x8ZbqUbmrhfjP87Q44ye0E/out-2.webp",
"https://replicate.delivery/yhqm/SFj2brHc8jI8LxgKU8HcU3DoekQaoqhSeiPOImc4kaXHX2TTA/out-3.webp"
],
"started_at": "2024-08-18T08:40:50.223370Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/1221wd3k1srm20chcjjrbdbbmg",
"cancel": "https://api.replicate.com/v1/predictions/1221wd3k1srm20chcjjrbdbbmg/cancel"
},
"version": "bf772d87d563c647335dd4bdf50ca56ee5ced4fe55bf0e8ed3d7f874303ae688"
}Generated in
Using seed: 9905
Prompt: A Circassian female in a traditional Circassian dress fasha with visible eyebrows, hazel eyes, small lips, a bit broader short nose, almond-shaped eyes, a broad chin, and a prominent jawline. She has shoulder-length hair, a slight smile, high cheekbones, and is in a portrait against a white background. The photo was taken with Kodak Portra 800 film.
txt2img mode
Using dev model
Loading LoRA weights
weights already loaded!
The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['kodak portra 8 0 0 film.', 'kodak portra 8 0 0 film.', 'kodak portra 8 0 0 film.', 'kodak portra 8 0 0 film.']
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