fofr / flux-weird
- Public
- 649 runs
-
H100
Want to make some of these yourself?
Run this model
{
"model": "dev",
"prompt": "a vibrant photo in the style of WRD",
"lora_scale": 0.9,
"num_outputs": 1,
"aspect_ratio": "3:4",
"output_format": "webp",
"guidance_scale": 2.5,
"output_quality": 90,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 28
}
npm install replicate
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run fofr/flux-weird using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"fofr/flux-weird:a731522059fe08264e0403847198ed5fa29973e0a0a594b45d7e0244f943f3ee",
{
input: {
model: "dev",
prompt: "a vibrant photo in the style of WRD",
lora_scale: 0.9,
num_outputs: 1,
aspect_ratio: "3:4",
output_format: "webp",
guidance_scale: 2.5,
output_quality: 90,
prompt_strength: 0.8,
extra_lora_scale: 1,
num_inference_steps: 28
}
}
);
// 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
import replicate
Run fofr/flux-weird using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"fofr/flux-weird:a731522059fe08264e0403847198ed5fa29973e0a0a594b45d7e0244f943f3ee",
input={
"model": "dev",
"prompt": "a vibrant photo in the style of WRD",
"lora_scale": 0.9,
"num_outputs": 1,
"aspect_ratio": "3:4",
"output_format": "webp",
"guidance_scale": 2.5,
"output_quality": 90,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 28
}
)
# 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.
Run fofr/flux-weird 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/flux-weird:a731522059fe08264e0403847198ed5fa29973e0a0a594b45d7e0244f943f3ee",
"input": {
"model": "dev",
"prompt": "a vibrant photo in the style of WRD",
"lora_scale": 0.9,
"num_outputs": 1,
"aspect_ratio": "3:4",
"output_format": "webp",
"guidance_scale": 2.5,
"output_quality": 90,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 28
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
{
"completed_at": "2024-09-16T16:52:57.180889Z",
"created_at": "2024-09-16T16:52:13.200000Z",
"data_removed": false,
"error": null,
"id": "kj1x5xdz21rm20chzeztm0a19c",
"input": {
"model": "dev",
"prompt": "a vibrant photo in the style of WRD",
"lora_scale": 0.9,
"num_outputs": 1,
"aspect_ratio": "3:4",
"output_format": "webp",
"guidance_scale": 2.5,
"output_quality": 90,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 28
},
"logs": "Using seed: 58797\nPrompt: a vibrant photo in the style of WRD\n[!] txt2img mode\nUsing dev model\nfree=7885243183104\nDownloading weights\n2024-09-16T16:52:13Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpyq339ed_/weights url=https://replicate.delivery/yhqm/empYgTZRxy1IKCgP5cmuDa4caLVXblvEFYJH0USdss2vef6mA/trained_model.tar\n2024-09-16T16:52:15Z | INFO | [ Complete ] dest=/tmp/tmpyq339ed_/weights size=\"344 MB\" total_elapsed=2.170s url=https://replicate.delivery/yhqm/empYgTZRxy1IKCgP5cmuDa4caLVXblvEFYJH0USdss2vef6mA/trained_model.tar\nDownloaded weights in 2.21s\nLoaded LoRAs in 35.74s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.44it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.82it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.69it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.60it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.59it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.58it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.57it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.56it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.56it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.55it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.55it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.55it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.55it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.55it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.55it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.55it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.55it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.55it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.55it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.55it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.55it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.55it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.55it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.55it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.55it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.55it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.56it/s]",
"metrics": {
"predict_time": 43.972205456,
"total_time": 43.980889
},
"output": [
"https://replicate.delivery/yhqm/i0RM9Rx1ym5xMBjDurOBKusPgCJceU9sjfiTuD6c1l9pRhdTA/out-0.webp"
],
"started_at": "2024-09-16T16:52:13.208683Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/kj1x5xdz21rm20chzeztm0a19c",
"cancel": "https://api.replicate.com/v1/predictions/kj1x5xdz21rm20chzeztm0a19c/cancel"
},
"version": "a731522059fe08264e0403847198ed5fa29973e0a0a594b45d7e0244f943f3ee"
}
Using seed: 58797
Prompt: a vibrant photo in the style of WRD
[!] txt2img mode
Using dev model
free=7885243183104
Downloading weights
2024-09-16T16:52:13Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpyq339ed_/weights url=https://replicate.delivery/yhqm/empYgTZRxy1IKCgP5cmuDa4caLVXblvEFYJH0USdss2vef6mA/trained_model.tar
2024-09-16T16:52:15Z | INFO | [ Complete ] dest=/tmp/tmpyq339ed_/weights size="344 MB" total_elapsed=2.170s url=https://replicate.delivery/yhqm/empYgTZRxy1IKCgP5cmuDa4caLVXblvEFYJH0USdss2vef6mA/trained_model.tar
Downloaded weights in 2.21s
Loaded LoRAs in 35.74s
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Want to make some of these yourself?
Run this modelThis model is warm. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
This model runs on H100. View more.
Using seed: 58797
Prompt: a vibrant photo in the style of WRD
[!] txt2img mode
Using dev model
free=7885243183104
Downloading weights
2024-09-16T16:52:13Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpyq339ed_/weights url=https://replicate.delivery/yhqm/empYgTZRxy1IKCgP5cmuDa4caLVXblvEFYJH0USdss2vef6mA/trained_model.tar
2024-09-16T16:52:15Z | INFO | [ Complete ] dest=/tmp/tmpyq339ed_/weights size="344 MB" total_elapsed=2.170s url=https://replicate.delivery/yhqm/empYgTZRxy1IKCgP5cmuDa4caLVXblvEFYJH0USdss2vef6mA/trained_model.tar
Downloaded weights in 2.21s
Loaded LoRAs in 35.74s
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