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rinatkurmaev /flux-dev-lora-tatra-t3:25b2bc71
Input
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run rinatkurmaev/flux-dev-lora-tatra-t3 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4",
{
input: {
model: "dev",
width: 1024,
height: 1024,
prompt: "A tram TATRAT3 running visible from front right on a busy city street on a seafront. A cruise ship docker to a pier on a background ",
go_fast: true,
lora_scale: 1,
megapixels: "1",
num_outputs: 1,
aspect_ratio: "1:1",
output_format: "png",
guidance_scale: 3,
output_quality: 80,
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
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run rinatkurmaev/flux-dev-lora-tatra-t3 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4",
input={
"model": "dev",
"width": 1024,
"height": 1024,
"prompt": "A tram TATRAT3 running visible from front right on a busy city street on a seafront. A cruise ship docker to a pier on a background ",
"go_fast": True,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 1,
"aspect_ratio": "1:1",
"output_format": "png",
"guidance_scale": 3,
"output_quality": 80,
"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.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run rinatkurmaev/flux-dev-lora-tatra-t3 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": "rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4",
"input": {
"model": "dev",
"width": 1024,
"height": 1024,
"prompt": "A tram TATRAT3 running visible from front right on a busy city street on a seafront. A cruise ship docker to a pier on a background ",
"go_fast": true,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 1,
"aspect_ratio": "1:1",
"output_format": "png",
"guidance_scale": 3,
"output_quality": 80,
"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.
Add a payment method to run this model.
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Output
{
"completed_at": "2025-02-07T22:12:48.904581Z",
"created_at": "2025-02-07T22:12:41.811000Z",
"data_removed": false,
"error": null,
"id": "t6r9mhz6tdrme0cmw9wbnf5xmr",
"input": {
"model": "dev",
"width": 1024,
"height": 1024,
"prompt": "A tram TATRAT3 running visible from front right on a busy city street on a seafront. A cruise ship docker to a pier on a background ",
"go_fast": true,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 1,
"aspect_ratio": "1:1",
"output_format": "png",
"guidance_scale": 3,
"output_quality": 80,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 28
},
"logs": "2025-02-07 22:12:42.260 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 22:12:42.261 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s\nfree=29142539214848\nDownloading weights\n2025-02-07T22:12:42Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpuij78so6/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T22:12:45Z | INFO | [ Complete ] dest=/tmp/tmpuij78so6/weights size=\"215 MB\" total_elapsed=3.066s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 3.09s\n2025-02-07 22:12:45.356 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 22:12:45.499 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 22:12:45.499 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 22:12:45.499 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:609 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 40%|████ | 123/304 [00:00<00:00, 1222.61it/s]\nApplying LoRA: 81%|████████ | 246/304 [00:00<00:00, 968.70it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 977.04it/s]\n2025-02-07 22:12:45.811 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 22:12:45.811 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.45s\nrunning quantized prediction\nUsing seed: 918440467\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.49it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.96it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 12.02it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.60it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.27it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.92it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.91it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.92it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.93it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.91it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.77it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.75it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.77it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.81it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.12it/s]\nTotal safe images: 1 out of 1",
"metrics": {
"predict_time": 6.666143294,
"total_time": 7.093581
},
"output": [
"https://replicate.delivery/xezq/9nOvkD18oV7dF9QHTBTYoDJHLcvpd4NXVyVGgqTe0uMwuhGKA/out-0.png"
],
"started_at": "2025-02-07T22:12:42.238438Z",
"status": "succeeded",
"urls": {
"stream": "https://stream.replicate.com/v1/files/bcwr-hkuw4wiuta3r7utieteqt4mlgad25qvdrlo2tnmlmio5aa2zazpa",
"get": "https://api.replicate.com/v1/predictions/t6r9mhz6tdrme0cmw9wbnf5xmr",
"cancel": "https://api.replicate.com/v1/predictions/t6r9mhz6tdrme0cmw9wbnf5xmr/cancel"
},
"version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4"
}
2025-02-07 22:12:42.260 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers
2025-02-07 22:12:42.261 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s
free=29142539214848
Downloading weights
2025-02-07T22:12:42Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpuij78so6/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar
2025-02-07T22:12:45Z | INFO | [ Complete ] dest=/tmp/tmpuij78so6/weights size="215 MB" total_elapsed=3.066s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar
Downloaded weights in 3.09s
2025-02-07 22:12:45.356 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad
2025-02-07 22:12:45.499 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded
2025-02-07 22:12:45.499 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys
2025-02-07 22:12:45.499 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:609 - Keys extracted
Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s]
Applying LoRA: 40%|████ | 123/304 [00:00<00:00, 1222.61it/s]
Applying LoRA: 81%|████████ | 246/304 [00:00<00:00, 968.70it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 977.04it/s]
2025-02-07 22:12:45.811 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8
2025-02-07 22:12:45.811 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.45s
running quantized prediction
Using seed: 918440467
0%| | 0/28 [00:00<?, ?it/s]
7%|▋ | 2/28 [00:00<00:01, 17.49it/s]
14%|█▍ | 4/28 [00:00<00:01, 12.96it/s]
21%|██▏ | 6/28 [00:00<00:01, 12.02it/s]
29%|██▊ | 8/28 [00:00<00:01, 11.60it/s]
36%|███▌ | 10/28 [00:00<00:01, 11.27it/s]
43%|████▎ | 12/28 [00:01<00:01, 10.92it/s]
50%|█████ | 14/28 [00:01<00:01, 10.91it/s]
57%|█████▋ | 16/28 [00:01<00:01, 10.92it/s]
64%|██████▍ | 18/28 [00:01<00:00, 10.93it/s]
71%|███████▏ | 20/28 [00:01<00:00, 10.91it/s]
79%|███████▊ | 22/28 [00:01<00:00, 10.77it/s]
86%|████████▌ | 24/28 [00:02<00:00, 10.75it/s]
93%|█████████▎| 26/28 [00:02<00:00, 10.77it/s]
100%|██████████| 28/28 [00:02<00:00, 10.81it/s]
100%|██████████| 28/28 [00:02<00:00, 11.12it/s]
Total safe images: 1 out of 1