rinatkurmaev / flux-dev-lora-tatra-t3
A lora model for making photos of Tatra t3 tram
- Public
- 163 runs
-
H100
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2IDkh3hjqkss1rma0cmvmaav1nzewStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram TATRAT3 running a street in Florida. A vintage photo. 60s
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", { input: { model: "dev", width: 1024, height: 1024, prompt: "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", input={ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", "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.
Output
{ "completed_at": "2025-02-06T21:05:03.597897Z", "created_at": "2025-02-06T21:04:54.216000Z", "data_removed": false, "error": null, "id": "kh3hjqkss1rma0cmvmaav1nzew", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", "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": "2025-02-06 21:04:54.243 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-06 21:04:54.245 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s\nfree=28540482613248\nDownloading weights\n2025-02-06T21:04:54Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxq5pbphd/weights url=https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar\n2025-02-06T21:05:00Z | INFO | [ Complete ] dest=/tmp/tmpxq5pbphd/weights size=\"172 MB\" total_elapsed=5.808s url=https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar\nDownloaded weights in 5.84s\n2025-02-06 21:05:00.084 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/845a4e25b4cf9030\n2025-02-06 21:05:00.153 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-06 21:05:00.153 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-06 21:05:00.153 | 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: 41%|████ | 125/304 [00:00<00:00, 1233.37it/s]\nApplying LoRA: 82%|████████▏ | 249/304 [00:00<00:00, 982.48it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 974.10it/s]\n2025-02-06 21:05:00.466 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-06 21:05:00.466 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.38s\nrunning quantized prediction\nUsing seed: 3720543067\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.72it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 13.03it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 12.02it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.61it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.26it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.94it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.93it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.95it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.96it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.92it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.78it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.78it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.81it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.88it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.15it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 9.376099707, "total_time": 9.381897 }, "output": [ "https://replicate.delivery/xezq/HgjdU3lEEL6bC1tbJKba0XP1JfjMatftkOm4kqetLaGffqlhC/out-0.png" ], "started_at": "2025-02-06T21:04:54.221797Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-23h76m2ufbucevfarwy2vscwcr6aifgody6bmlonqhsbjr7nyx3q", "get": "https://api.replicate.com/v1/predictions/kh3hjqkss1rma0cmvmaav1nzew", "cancel": "https://api.replicate.com/v1/predictions/kh3hjqkss1rma0cmvmaav1nzew/cancel" }, "version": "bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2" }
Generated in2025-02-06 21:04:54.243 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-06 21:04:54.245 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s free=28540482613248 Downloading weights 2025-02-06T21:04:54Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxq5pbphd/weights url=https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar 2025-02-06T21:05:00Z | INFO | [ Complete ] dest=/tmp/tmpxq5pbphd/weights size="172 MB" total_elapsed=5.808s url=https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar Downloaded weights in 5.84s 2025-02-06 21:05:00.084 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/845a4e25b4cf9030 2025-02-06 21:05:00.153 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-06 21:05:00.153 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-06 21:05:00.153 | 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: 41%|████ | 125/304 [00:00<00:00, 1233.37it/s] Applying LoRA: 82%|████████▏ | 249/304 [00:00<00:00, 982.48it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 974.10it/s] 2025-02-06 21:05:00.466 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-06 21:05:00.466 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.38s running quantized prediction Using seed: 3720543067 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.72it/s] 14%|█▍ | 4/28 [00:00<00:01, 13.03it/s] 21%|██▏ | 6/28 [00:00<00:01, 12.02it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.61it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.26it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.94it/s] 50%|█████ | 14/28 [00:01<00:01, 10.93it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.95it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.96it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.92it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.78it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.78it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.81it/s] 100%|██████████| 28/28 [00:02<00:00, 10.88it/s] 100%|██████████| 28/28 [00:02<00:00, 11.15it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2IDkh3hjqkss1rma0cmvmaav1nzewStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram TATRAT3 running a street in Florida. A vintage photo. 60s
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", { input: { model: "dev", width: 1024, height: 1024, prompt: "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", input={ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", "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.
Output
{ "completed_at": "2025-02-06T21:05:03.597897Z", "created_at": "2025-02-06T21:04:54.216000Z", "data_removed": false, "error": null, "id": "kh3hjqkss1rma0cmvmaav1nzew", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida. A vintage photo. 60s", "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": "2025-02-06 21:04:54.243 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-06 21:04:54.245 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s\nfree=28540482613248\nDownloading weights\n2025-02-06T21:04:54Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxq5pbphd/weights url=https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar\n2025-02-06T21:05:00Z | INFO | [ Complete ] dest=/tmp/tmpxq5pbphd/weights size=\"172 MB\" total_elapsed=5.808s url=https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar\nDownloaded weights in 5.84s\n2025-02-06 21:05:00.084 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/845a4e25b4cf9030\n2025-02-06 21:05:00.153 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-06 21:05:00.153 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-06 21:05:00.153 | 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: 41%|████ | 125/304 [00:00<00:00, 1233.37it/s]\nApplying LoRA: 82%|████████▏ | 249/304 [00:00<00:00, 982.48it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 974.10it/s]\n2025-02-06 21:05:00.466 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-06 21:05:00.466 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.38s\nrunning quantized prediction\nUsing seed: 3720543067\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.72it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 13.03it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 12.02it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.61it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.26it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.94it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.93it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.95it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.96it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.92it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.78it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.78it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.81it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.88it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.15it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 9.376099707, "total_time": 9.381897 }, "output": [ "https://replicate.delivery/xezq/HgjdU3lEEL6bC1tbJKba0XP1JfjMatftkOm4kqetLaGffqlhC/out-0.png" ], "started_at": "2025-02-06T21:04:54.221797Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-23h76m2ufbucevfarwy2vscwcr6aifgody6bmlonqhsbjr7nyx3q", "get": "https://api.replicate.com/v1/predictions/kh3hjqkss1rma0cmvmaav1nzew", "cancel": "https://api.replicate.com/v1/predictions/kh3hjqkss1rma0cmvmaav1nzew/cancel" }, "version": "bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2" }
Generated in2025-02-06 21:04:54.243 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-06 21:04:54.245 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s free=28540482613248 Downloading weights 2025-02-06T21:04:54Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxq5pbphd/weights url=https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar 2025-02-06T21:05:00Z | INFO | [ Complete ] dest=/tmp/tmpxq5pbphd/weights size="172 MB" total_elapsed=5.808s url=https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar Downloaded weights in 5.84s 2025-02-06 21:05:00.084 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/845a4e25b4cf9030 2025-02-06 21:05:00.153 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-06 21:05:00.153 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-06 21:05:00.153 | 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: 41%|████ | 125/304 [00:00<00:00, 1233.37it/s] Applying LoRA: 82%|████████▏ | 249/304 [00:00<00:00, 982.48it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 974.10it/s] 2025-02-06 21:05:00.466 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-06 21:05:00.466 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.38s running quantized prediction Using seed: 3720543067 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.72it/s] 14%|█▍ | 4/28 [00:00<00:01, 13.03it/s] 21%|██▏ | 6/28 [00:00<00:01, 12.02it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.61it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.26it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.94it/s] 50%|█████ | 14/28 [00:01<00:01, 10.93it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.95it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.96it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.92it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.78it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.78it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.81it/s] 100%|██████████| 28/28 [00:02<00:00, 10.88it/s] 100%|██████████| 28/28 [00:02<00:00, 11.15it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2IDmw99pe5rcxrma0cmvmfr5yhz40StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram TATRAT3 running a street in Florida, Miami. A vintage photo. 60s
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida, Miami. A vintage photo. 60s", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", { input: { model: "dev", width: 1024, height: 1024, prompt: "A tram TATRAT3 running a street in Florida, Miami. A vintage photo. 60s", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", input={ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida, Miami. A vintage photo. 60s", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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:bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida, Miami. A vintage photo. 60s", "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.
Output
{ "completed_at": "2025-02-06T21:17:14.303710Z", "created_at": "2025-02-06T21:17:11.143000Z", "data_removed": false, "error": null, "id": "mw99pe5rcxrma0cmvmfr5yhz40", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street in Florida, Miami. A vintage photo. 60s", "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": "Lora https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 1035370845\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.35it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.82it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.83it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.39it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.15it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.78it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.76it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.77it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.77it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.78it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.62it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.58it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.64it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.66it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.96it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.125534473, "total_time": 3.16071 }, "output": [ "https://replicate.delivery/xezq/7CNp2VSrVuLXE9h2fwfaLoCy762Ai3fTCDjbrkRlmXP1GbZoA/out-0.png" ], "started_at": "2025-02-06T21:17:11.178176Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-3sdtuxu4eznxdsxodiwmoabz7cyb5xa52sgli4ue4x4jhl24xk5q", "get": "https://api.replicate.com/v1/predictions/mw99pe5rcxrma0cmvmfr5yhz40", "cancel": "https://api.replicate.com/v1/predictions/mw99pe5rcxrma0cmvmfr5yhz40/cancel" }, "version": "bda1c7e46622f83ee865df8d269db866874bfccafd42b9d0292f7d80250051b2" }
Generated inLora https://replicate.delivery/xezq/fRIYLNzQKXw5QCXaef0fN2rZusVW2Rd9BBVU4Deg99hyKmlhC/trained_model.tar already loaded running quantized prediction Using seed: 1035370845 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.35it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.82it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.83it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.39it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.15it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.78it/s] 50%|█████ | 14/28 [00:01<00:01, 10.76it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.77it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.77it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.78it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.62it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.58it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.64it/s] 100%|██████████| 28/28 [00:02<00:00, 10.66it/s] 100%|██████████| 28/28 [00:02<00:00, 10.96it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDkak13z0ar5rmc0cmwwpan28krcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram TATRAT3 running a street. A vintage photo. 60s
- go_fast
- 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
{ "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { mask: "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", image: "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", model: "dev", width: 1024, height: 1024, prompt: "A tram TATRAT3 running a street. A vintage photo. 60s", go_fast: false, 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": False, "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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.
Output
{ "completed_at": "2025-02-08T20:06:54.337767Z", "created_at": "2025-02-08T20:06:50.817000Z", "data_removed": false, "error": null, "id": "kak13z0ar5rmc0cmwwpan28krc", "input": { "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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": "Loaded LoRAs in 0.56s\nUsing seed: 47188\nPrompt: A tram TATRAT3 running a street. A vintage photo. 60s\n[!] Resizing input image from 720x480 to 720x480\n[!] inpaint mode\n 0%| | 0/23 [00:00<?, ?it/s]\n 4%|▍ | 1/23 [00:00<00:02, 7.93it/s]\n 9%|▊ | 2/23 [00:00<00:02, 8.46it/s]\n 13%|█▎ | 3/23 [00:00<00:02, 8.65it/s]\n 17%|█▋ | 4/23 [00:00<00:02, 8.74it/s]\n 22%|██▏ | 5/23 [00:00<00:02, 8.79it/s]\n 26%|██▌ | 6/23 [00:00<00:01, 8.81it/s]\n 30%|███ | 7/23 [00:00<00:01, 8.83it/s]\n 35%|███▍ | 8/23 [00:00<00:01, 8.85it/s]\n 39%|███▉ | 9/23 [00:01<00:01, 8.85it/s]\n 43%|████▎ | 10/23 [00:01<00:01, 8.85it/s]\n 48%|████▊ | 11/23 [00:01<00:01, 8.86it/s]\n 52%|█████▏ | 12/23 [00:01<00:01, 8.86it/s]\n 57%|█████▋ | 13/23 [00:01<00:01, 8.86it/s]\n 61%|██████ | 14/23 [00:01<00:01, 8.86it/s]\n 65%|██████▌ | 15/23 [00:01<00:00, 8.86it/s]\n 70%|██████▉ | 16/23 [00:01<00:00, 8.86it/s]\n 74%|███████▍ | 17/23 [00:01<00:00, 8.87it/s]\n 78%|███████▊ | 18/23 [00:02<00:00, 8.86it/s]\n 83%|████████▎ | 19/23 [00:02<00:00, 8.86it/s]\n 87%|████████▋ | 20/23 [00:02<00:00, 8.87it/s]\n 91%|█████████▏| 21/23 [00:02<00:00, 8.87it/s]\n 96%|█████████▌| 22/23 [00:02<00:00, 8.87it/s]\n100%|██████████| 23/23 [00:02<00:00, 8.89it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.491512022, "total_time": 3.520767 }, "output": [ "https://replicate.delivery/xezq/wQ99OQfR9TQ1GqXH9aTgJykB9SyIvrdY9gtgucXNo1RvWrGKA/out-0.png" ], "started_at": "2025-02-08T20:06:50.846255Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-sdsjfbzpskbo5qzc7gn33kkfvsnkbdkig25y42blaa6reyaedcmq", "get": "https://api.replicate.com/v1/predictions/kak13z0ar5rmc0cmwwpan28krc", "cancel": "https://api.replicate.com/v1/predictions/kak13z0ar5rmc0cmwwpan28krc/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLoaded LoRAs in 0.56s Using seed: 47188 Prompt: A tram TATRAT3 running a street. A vintage photo. 60s [!] Resizing input image from 720x480 to 720x480 [!] inpaint mode 0%| | 0/23 [00:00<?, ?it/s] 4%|▍ | 1/23 [00:00<00:02, 7.93it/s] 9%|▊ | 2/23 [00:00<00:02, 8.46it/s] 13%|█▎ | 3/23 [00:00<00:02, 8.65it/s] 17%|█▋ | 4/23 [00:00<00:02, 8.74it/s] 22%|██▏ | 5/23 [00:00<00:02, 8.79it/s] 26%|██▌ | 6/23 [00:00<00:01, 8.81it/s] 30%|███ | 7/23 [00:00<00:01, 8.83it/s] 35%|███▍ | 8/23 [00:00<00:01, 8.85it/s] 39%|███▉ | 9/23 [00:01<00:01, 8.85it/s] 43%|████▎ | 10/23 [00:01<00:01, 8.85it/s] 48%|████▊ | 11/23 [00:01<00:01, 8.86it/s] 52%|█████▏ | 12/23 [00:01<00:01, 8.86it/s] 57%|█████▋ | 13/23 [00:01<00:01, 8.86it/s] 61%|██████ | 14/23 [00:01<00:01, 8.86it/s] 65%|██████▌ | 15/23 [00:01<00:00, 8.86it/s] 70%|██████▉ | 16/23 [00:01<00:00, 8.86it/s] 74%|███████▍ | 17/23 [00:01<00:00, 8.87it/s] 78%|███████▊ | 18/23 [00:02<00:00, 8.86it/s] 83%|████████▎ | 19/23 [00:02<00:00, 8.86it/s] 87%|████████▋ | 20/23 [00:02<00:00, 8.87it/s] 91%|█████████▏| 21/23 [00:02<00:00, 8.87it/s] 96%|█████████▌| 22/23 [00:02<00:00, 8.87it/s] 100%|██████████| 23/23 [00:02<00:00, 8.89it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4ID3j3qjzc8dhrm80cmwwxrtymh6rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram TATRAT3 running a street. A vintage photo. 60s
- go_fast
- 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
{ "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { mask: "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", image: "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", model: "dev", width: 1024, height: 1024, prompt: "A tram TATRAT3 running a street. A vintage photo. 60s", go_fast: false, 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": False, "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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.
Output
{ "completed_at": "2025-02-08T20:23:49.638139Z", "created_at": "2025-02-08T20:23:46.028000Z", "data_removed": false, "error": null, "id": "3j3qjzc8dhrm80cmwwxrtymh6r", "input": { "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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": "Loaded LoRAs in 0.56s\nUsing seed: 62631\nPrompt: A tram TATRAT3 running a street. A vintage photo. 60s\n[!] Resizing input image from 720x480 to 720x480\n[!] inpaint mode\n 0%| | 0/23 [00:00<?, ?it/s]\n 4%|▍ | 1/23 [00:00<00:02, 7.94it/s]\n 9%|▊ | 2/23 [00:00<00:02, 8.48it/s]\n 13%|█▎ | 3/23 [00:00<00:02, 8.66it/s]\n 17%|█▋ | 4/23 [00:00<00:02, 8.75it/s]\n 22%|██▏ | 5/23 [00:00<00:02, 8.80it/s]\n 26%|██▌ | 6/23 [00:00<00:01, 8.84it/s]\n 30%|███ | 7/23 [00:00<00:01, 8.86it/s]\n 35%|███▍ | 8/23 [00:00<00:01, 8.88it/s]\n 39%|███▉ | 9/23 [00:01<00:01, 8.89it/s]\n 43%|████▎ | 10/23 [00:01<00:01, 8.90it/s]\n 48%|████▊ | 11/23 [00:01<00:01, 8.90it/s]\n 52%|█████▏ | 12/23 [00:01<00:01, 8.90it/s]\n 57%|█████▋ | 13/23 [00:01<00:01, 8.90it/s]\n 61%|██████ | 14/23 [00:01<00:01, 8.91it/s]\n 65%|██████▌ | 15/23 [00:01<00:00, 8.91it/s]\n 70%|██████▉ | 16/23 [00:01<00:00, 8.91it/s]\n 74%|███████▍ | 17/23 [00:01<00:00, 8.90it/s]\n 78%|███████▊ | 18/23 [00:02<00:00, 8.90it/s]\n 83%|████████▎ | 19/23 [00:02<00:00, 8.91it/s]\n 87%|████████▋ | 20/23 [00:02<00:00, 8.90it/s]\n 91%|█████████▏| 21/23 [00:02<00:00, 8.91it/s]\n 96%|█████████▌| 22/23 [00:02<00:00, 8.91it/s]\n100%|██████████| 23/23 [00:02<00:00, 8.93it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.490073311, "total_time": 3.610139 }, "output": [ "https://replicate.delivery/xezq/p5p0gdUry1ZRH1COZs7jre58KfNxfp8jS3iSzS2EyTHr6taoA/out-0.png" ], "started_at": "2025-02-08T20:23:46.148065Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-iz3emp4rtzh5ggmmziykvd2jm2cqq57zvfcpw32ttaqr6diaftxa", "get": "https://api.replicate.com/v1/predictions/3j3qjzc8dhrm80cmwwxrtymh6r", "cancel": "https://api.replicate.com/v1/predictions/3j3qjzc8dhrm80cmwwxrtymh6r/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLoaded LoRAs in 0.56s Using seed: 62631 Prompt: A tram TATRAT3 running a street. A vintage photo. 60s [!] Resizing input image from 720x480 to 720x480 [!] inpaint mode 0%| | 0/23 [00:00<?, ?it/s] 4%|▍ | 1/23 [00:00<00:02, 7.94it/s] 9%|▊ | 2/23 [00:00<00:02, 8.48it/s] 13%|█▎ | 3/23 [00:00<00:02, 8.66it/s] 17%|█▋ | 4/23 [00:00<00:02, 8.75it/s] 22%|██▏ | 5/23 [00:00<00:02, 8.80it/s] 26%|██▌ | 6/23 [00:00<00:01, 8.84it/s] 30%|███ | 7/23 [00:00<00:01, 8.86it/s] 35%|███▍ | 8/23 [00:00<00:01, 8.88it/s] 39%|███▉ | 9/23 [00:01<00:01, 8.89it/s] 43%|████▎ | 10/23 [00:01<00:01, 8.90it/s] 48%|████▊ | 11/23 [00:01<00:01, 8.90it/s] 52%|█████▏ | 12/23 [00:01<00:01, 8.90it/s] 57%|█████▋ | 13/23 [00:01<00:01, 8.90it/s] 61%|██████ | 14/23 [00:01<00:01, 8.91it/s] 65%|██████▌ | 15/23 [00:01<00:00, 8.91it/s] 70%|██████▉ | 16/23 [00:01<00:00, 8.91it/s] 74%|███████▍ | 17/23 [00:01<00:00, 8.90it/s] 78%|███████▊ | 18/23 [00:02<00:00, 8.90it/s] 83%|████████▎ | 19/23 [00:02<00:00, 8.91it/s] 87%|████████▋ | 20/23 [00:02<00:00, 8.90it/s] 91%|█████████▏| 21/23 [00:02<00:00, 8.91it/s] 96%|█████████▌| 22/23 [00:02<00:00, 8.91it/s] 100%|██████████| 23/23 [00:02<00:00, 8.93it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDbe7cm0m62hrmc0cmw9wajef080StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- 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
- 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
{ "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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.
Output
{ "completed_at": "2025-02-07T22:12:21.956565Z", "created_at": "2025-02-07T22:12:17.044000Z", "data_removed": false, "error": null, "id": "be7cm0m62hrmc0cmw9wajef080", "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 22:12:17.097 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 22:12:17.099 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s\nfree=28858181668864\nDownloading weights\n2025-02-07T22:12:17Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpravyvxrk/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T22:12:18Z | INFO | [ Complete ] dest=/tmp/tmpravyvxrk/weights size=\"215 MB\" total_elapsed=1.341s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 1.37s\n2025-02-07 22:12:18.468 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 22:12:18.548 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 22:12:18.548 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 22:12:18.548 | 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: 41%|████ | 124/304 [00:00<00:00, 1228.68it/s]\nApplying LoRA: 81%|████████▏ | 247/304 [00:00<00:00, 971.07it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 966.56it/s]\n2025-02-07 22:12:18.863 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 22:12:18.863 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s\nrunning quantized prediction\nUsing seed: 1681562567\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.54it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.95it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.93it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.52it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.21it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.89it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.86it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.86it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.87it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.85it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.70it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.71it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.75it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.79it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.07it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 4.880900741, "total_time": 4.912565 }, "output": [ "https://replicate.delivery/xezq/3otQzUKepu1URa4Y6mfpT7Q94uxwIcA6crceSt9Rg3vL6GaoA/out-0.png" ], "started_at": "2025-02-07T22:12:17.075664Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-fkekpce4jfjhfaqspkh4oywqqij3p6hisjbucjh4s254smkszgoa", "get": "https://api.replicate.com/v1/predictions/be7cm0m62hrmc0cmw9wajef080", "cancel": "https://api.replicate.com/v1/predictions/be7cm0m62hrmc0cmw9wajef080/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 22:12:17.097 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 22:12:17.099 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s free=28858181668864 Downloading weights 2025-02-07T22:12:17Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpravyvxrk/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T22:12:18Z | INFO | [ Complete ] dest=/tmp/tmpravyvxrk/weights size="215 MB" total_elapsed=1.341s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 1.37s 2025-02-07 22:12:18.468 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 22:12:18.548 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 22:12:18.548 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 22:12:18.548 | 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: 41%|████ | 124/304 [00:00<00:00, 1228.68it/s] Applying LoRA: 81%|████████▏ | 247/304 [00:00<00:00, 971.07it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 966.56it/s] 2025-02-07 22:12:18.863 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 22:12:18.863 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s running quantized prediction Using seed: 1681562567 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.54it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.95it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.93it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.52it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.21it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.89it/s] 50%|█████ | 14/28 [00:01<00:01, 10.86it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.86it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.87it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.85it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.70it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.71it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.75it/s] 100%|██████████| 28/28 [00:02<00:00, 10.79it/s] 100%|██████████| 28/28 [00:02<00:00, 11.07it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDt6r9mhz6tdrme0cmw9wbnf5xmrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- 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
- 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
{ "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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.
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" }
Generated in2025-02-07 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
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDwtzr93sfpsrmc0cmw9xbjfsy70StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram TATRAT3 running visible from front on a busy city street on a seafront. A cruise ship docker to a pier on a background
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running visible from front 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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 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.
Output
{ "completed_at": "2025-02-07T22:14:11.732849Z", "created_at": "2025-02-07T22:14:06.006000Z", "data_removed": false, "error": null, "id": "wtzr93sfpsrmc0cmw9xbjfsy70", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running visible from front 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 22:14:06.102 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 22:14:06.104 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s\nfree=29125187952640\nDownloading weights\n2025-02-07T22:14:06Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpcz2y_kc5/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T22:14:08Z | INFO | [ Complete ] dest=/tmp/tmpcz2y_kc5/weights size=\"215 MB\" total_elapsed=2.113s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 2.14s\n2025-02-07 22:14:08.242 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 22:14:08.318 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 22:14:08.318 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 22:14:08.318 | 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: 39%|███▉ | 120/304 [00:00<00:00, 1176.59it/s]\nApplying LoRA: 78%|███████▊ | 238/304 [00:00<00:00, 962.53it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 964.65it/s]\n2025-02-07 22:14:08.634 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 22:14:08.634 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.39s\nrunning quantized prediction\nUsing seed: 1029488391\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 16.97it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.74it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.77it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.37it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.11it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.81it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.79it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.78it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.78it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.76it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.66it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.64it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.68it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.71it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.98it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 5.652079976, "total_time": 5.726849 }, "output": [ "https://replicate.delivery/xezq/srADQ7YJf8QDYSS1fgxTmtShsXztIKAkTB8LNEtSWLgzeGaoA/out-0.png" ], "started_at": "2025-02-07T22:14:06.080769Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-kmwoig2v4akswbv2td3kopgi5z3shh6dvdlerxjbbijrmscl557a", "get": "https://api.replicate.com/v1/predictions/wtzr93sfpsrmc0cmw9xbjfsy70", "cancel": "https://api.replicate.com/v1/predictions/wtzr93sfpsrmc0cmw9xbjfsy70/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 22:14:06.102 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 22:14:06.104 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s free=29125187952640 Downloading weights 2025-02-07T22:14:06Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpcz2y_kc5/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T22:14:08Z | INFO | [ Complete ] dest=/tmp/tmpcz2y_kc5/weights size="215 MB" total_elapsed=2.113s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 2.14s 2025-02-07 22:14:08.242 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 22:14:08.318 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 22:14:08.318 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 22:14:08.318 | 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: 39%|███▉ | 120/304 [00:00<00:00, 1176.59it/s] Applying LoRA: 78%|███████▊ | 238/304 [00:00<00:00, 962.53it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 964.65it/s] 2025-02-07 22:14:08.634 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 22:14:08.634 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.39s running quantized prediction Using seed: 1029488391 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 16.97it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.74it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.77it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.37it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.11it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.81it/s] 50%|█████ | 14/28 [00:01<00:01, 10.79it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.78it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.78it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.76it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.66it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.64it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.68it/s] 100%|██████████| 28/28 [00:02<00:00, 10.71it/s] 100%|██████████| 28/28 [00:02<00:00, 10.98it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDqwj6y50zrsrme0cmwa1t02bdc4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and red. The snowy mountains are on the background
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and red.\\nThe snowy mountains are on the 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.
Output
{ "completed_at": "2025-02-07T22:23:57.813679Z", "created_at": "2025-02-07T22:23:51.750000Z", "data_removed": false, "error": null, "id": "qwj6y50zrsrme0cmwa1t02bdc4", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 22:23:51.796 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 22:23:51.797 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s\nfree=29389792985088\nDownloading weights\n2025-02-07T22:23:51Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmph51s9hmt/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T22:23:54Z | INFO | [ Complete ] dest=/tmp/tmph51s9hmt/weights size=\"215 MB\" total_elapsed=2.558s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 2.58s\n2025-02-07 22:23:54.382 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 22:23:54.459 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 22:23:54.459 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 22:23:54.459 | 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: 39%|███▉ | 120/304 [00:00<00:00, 1188.43it/s]\nApplying LoRA: 79%|███████▊ | 239/304 [00:00<00:00, 986.20it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 953.69it/s]\n2025-02-07 22:23:54.778 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 22:23:54.779 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s\nrunning quantized prediction\nUsing seed: 213122787\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.93it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 13.01it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.98it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.56it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.29it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.97it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.95it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.95it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.94it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.92it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.81it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.80it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.85it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.87it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.15it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 6.039796138, "total_time": 6.063679 }, "output": [ "https://replicate.delivery/xezq/1O1evWKSBoTiWaftq0S81NhHwvNLY8mbeRAxGvJEJSP7PHaoA/out-0.png" ], "started_at": "2025-02-07T22:23:51.773883Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-qh2skpg7reg7gcghgfro2asbezfrpziremvzdpxc62tnptxyz4xa", "get": "https://api.replicate.com/v1/predictions/qwj6y50zrsrme0cmwa1t02bdc4", "cancel": "https://api.replicate.com/v1/predictions/qwj6y50zrsrme0cmwa1t02bdc4/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 22:23:51.796 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 22:23:51.797 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s free=29389792985088 Downloading weights 2025-02-07T22:23:51Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmph51s9hmt/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T22:23:54Z | INFO | [ Complete ] dest=/tmp/tmph51s9hmt/weights size="215 MB" total_elapsed=2.558s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 2.58s 2025-02-07 22:23:54.382 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 22:23:54.459 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 22:23:54.459 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 22:23:54.459 | 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: 39%|███▉ | 120/304 [00:00<00:00, 1188.43it/s] Applying LoRA: 79%|███████▊ | 239/304 [00:00<00:00, 986.20it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 953.69it/s] 2025-02-07 22:23:54.778 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 22:23:54.779 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s running quantized prediction Using seed: 213122787 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.93it/s] 14%|█▍ | 4/28 [00:00<00:01, 13.01it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.98it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.56it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.29it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.97it/s] 50%|█████ | 14/28 [00:01<00:01, 10.95it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.95it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.94it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.92it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.81it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.80it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.85it/s] 100%|██████████| 28/28 [00:02<00:00, 10.87it/s] 100%|██████████| 28/28 [00:02<00:00, 11.15it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDvnxqg6hxnhrmc0cmwa3bdqece4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 256
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and red. The snowy mountains are on the background
- go_fast
- 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
{ "seed": 256, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { seed: 256, model: "dev", width: 1024, height: 1024, prompt: "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "seed": 256, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "seed": 256, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\\nThe snowy mountains are on the 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.
Output
{ "completed_at": "2025-02-07T22:27:19.095290Z", "created_at": "2025-02-07T22:27:16.012000Z", "data_removed": false, "error": null, "id": "vnxqg6hxnhrmc0cmwa3bdqece4", "input": { "seed": 256, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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": "Lora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 256\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.65it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.96it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.93it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.49it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.23it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.81it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.80it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.83it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.84it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.81it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.64it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.65it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.71it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.72it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.03it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.072471561, "total_time": 3.08329 }, "output": [ "https://replicate.delivery/xezq/FnBlPW6KWUrhCZ6YUE2xcXZRkvXy7nT80ffbCVaxoq7HrDNUA/out-0.png" ], "started_at": "2025-02-07T22:27:16.022818Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-my4olxqhoqtuoaoyz6dtc4jptdjzx7ezuxpwanvcdqqgtwtyta2q", "get": "https://api.replicate.com/v1/predictions/vnxqg6hxnhrmc0cmwa3bdqece4", "cancel": "https://api.replicate.com/v1/predictions/vnxqg6hxnhrmc0cmwa3bdqece4/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded running quantized prediction Using seed: 256 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.65it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.96it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.93it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.49it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.23it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.81it/s] 50%|█████ | 14/28 [00:01<00:01, 10.80it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.83it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.84it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.81it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.64it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.65it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.71it/s] 100%|██████████| 28/28 [00:02<00:00, 10.72it/s] 100%|██████████| 28/28 [00:02<00:00, 11.03it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDng0q524b89rm80cmwa397n1sdmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 256
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 on a city street. The tram is green and red. The snowy mountains are on the background
- go_fast
- 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
{ "seed": 256, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { seed: 256, model: "dev", width: 1024, height: 1024, prompt: "A tram model tatra t3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "seed": 256, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "seed": 256, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 on a city street. The tram is green and red.\\nThe snowy mountains are on the 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.
Output
{ "completed_at": "2025-02-07T22:27:42.481446Z", "created_at": "2025-02-07T22:27:35.874000Z", "data_removed": false, "error": null, "id": "ng0q524b89rm80cmwa397n1sdm", "input": { "seed": 256, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 22:27:35.906 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 22:27:35.907 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s\nfree=28471593234432\nDownloading weights\n2025-02-07T22:27:35Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpv57dbkor/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T22:27:38Z | INFO | [ Complete ] dest=/tmp/tmpv57dbkor/weights size=\"215 MB\" total_elapsed=3.046s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 3.07s\n2025-02-07 22:27:38.981 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 22:27:39.060 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 22:27:39.060 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 22:27:39.061 | 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: 39%|███▉ | 118/304 [00:00<00:00, 1177.22it/s]\nApplying LoRA: 78%|███████▊ | 236/304 [00:00<00:00, 981.72it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 961.15it/s]\n2025-02-07 22:27:39.377 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 22:27:39.377 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s\nrunning quantized prediction\nUsing seed: 256\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.58it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.77it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.74it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.30it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 10.98it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.70it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.68it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.69it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.66it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.61it/s]\n 79%|███████▊ | 22/28 [00:02<00:00, 10.51it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.52it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.58it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.60it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.88it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 6.596790403, "total_time": 6.607446 }, "output": [ "https://replicate.delivery/xezq/fMpYkC7rRoyGJKQ7LeoEpXIdewfbpZXI6evntmR28HpxbdohC/out-0.png" ], "started_at": "2025-02-07T22:27:35.884656Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-j6uvwyb5hw75c2je6apotpamhhjt5mpg3wbaw2ztvkvuqixgloca", "get": "https://api.replicate.com/v1/predictions/ng0q524b89rm80cmwa397n1sdm", "cancel": "https://api.replicate.com/v1/predictions/ng0q524b89rm80cmwa397n1sdm/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 22:27:35.906 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 22:27:35.907 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s free=28471593234432 Downloading weights 2025-02-07T22:27:35Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpv57dbkor/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T22:27:38Z | INFO | [ Complete ] dest=/tmp/tmpv57dbkor/weights size="215 MB" total_elapsed=3.046s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 3.07s 2025-02-07 22:27:38.981 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 22:27:39.060 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 22:27:39.060 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 22:27:39.061 | 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: 39%|███▉ | 118/304 [00:00<00:00, 1177.22it/s] Applying LoRA: 78%|███████▊ | 236/304 [00:00<00:00, 981.72it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 961.15it/s] 2025-02-07 22:27:39.377 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 22:27:39.377 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s running quantized prediction Using seed: 256 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.58it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.77it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.74it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.30it/s] 36%|███▌ | 10/28 [00:00<00:01, 10.98it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.70it/s] 50%|█████ | 14/28 [00:01<00:01, 10.68it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.69it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.66it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.61it/s] 79%|███████▊ | 22/28 [00:02<00:00, 10.51it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.52it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.58it/s] 100%|██████████| 28/28 [00:02<00:00, 10.60it/s] 100%|██████████| 28/28 [00:02<00:00, 10.88it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDe7vkvgp6q9rme0cmwa4bach08gStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 512
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and red. The snowy mountains are on the background
- go_fast
- 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
{ "seed": 512, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { seed: 512, model: "dev", width: 1024, height: 1024, prompt: "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "seed": 512, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "seed": 512, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\\nThe snowy mountains are on the 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.
Output
{ "completed_at": "2025-02-07T22:30:08.093380Z", "created_at": "2025-02-07T22:30:02.170000Z", "data_removed": false, "error": null, "id": "e7vkvgp6q9rme0cmwa4bach08g", "input": { "seed": 512, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and red.\nThe snowy mountains are on the 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 22:30:02.686 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 22:30:02.687 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s\nfree=28905335238656\nDownloading weights\n2025-02-07T22:30:02Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpcg_7_kbx/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T22:30:04Z | INFO | [ Complete ] dest=/tmp/tmpcg_7_kbx/weights size=\"215 MB\" total_elapsed=1.868s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 1.90s\n2025-02-07 22:30:04.584 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 22:30:04.666 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 22:30:04.666 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 22:30:04.666 | 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%|████ | 122/304 [00:00<00:00, 1206.48it/s]\nApplying LoRA: 80%|███████▉ | 243/304 [00:00<00:00, 973.12it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 958.04it/s]\n2025-02-07 22:30:04.984 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 22:30:04.984 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s\nrunning quantized prediction\nUsing seed: 512\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 16.84it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.70it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.75it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.37it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.05it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.72it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.72it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.76it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.77it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.71it/s]\n 79%|███████▊ | 22/28 [00:02<00:00, 10.56it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.59it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.62it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.68it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.93it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 5.429609817, "total_time": 5.92338 }, "output": [ "https://replicate.delivery/xezq/f8BJD10PgmT5J6hA0rjUw7pM1yUQp8KjMfrPtEo9y8hwtDNUA/out-0.png" ], "started_at": "2025-02-07T22:30:02.663770Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-uyyavcn5xtqaodq5m7yllluqngguzuaya2rr52hytwzzvpdopmuq", "get": "https://api.replicate.com/v1/predictions/e7vkvgp6q9rme0cmwa4bach08g", "cancel": "https://api.replicate.com/v1/predictions/e7vkvgp6q9rme0cmwa4bach08g/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 22:30:02.686 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 22:30:02.687 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s free=28905335238656 Downloading weights 2025-02-07T22:30:02Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpcg_7_kbx/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T22:30:04Z | INFO | [ Complete ] dest=/tmp/tmpcg_7_kbx/weights size="215 MB" total_elapsed=1.868s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 1.90s 2025-02-07 22:30:04.584 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 22:30:04.666 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 22:30:04.666 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 22:30:04.666 | 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%|████ | 122/304 [00:00<00:00, 1206.48it/s] Applying LoRA: 80%|███████▉ | 243/304 [00:00<00:00, 973.12it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 958.04it/s] 2025-02-07 22:30:04.984 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 22:30:04.984 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s running quantized prediction Using seed: 512 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 16.84it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.70it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.75it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.37it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.05it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.72it/s] 50%|█████ | 14/28 [00:01<00:01, 10.72it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.76it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.77it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.71it/s] 79%|███████▊ | 22/28 [00:02<00:00, 10.56it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.59it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.62it/s] 100%|██████████| 28/28 [00:02<00:00, 10.68it/s] 100%|██████████| 28/28 [00:02<00:00, 10.93it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDrd2nkeqnq1rme0cmwa98g0faemStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 1024
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.
- go_fast
- 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
{ "seed": 1024, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", "go_fast": false, "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { seed: 1024, model: "dev", width: 1024, height: 1024, prompt: "A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", go_fast: false, 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "seed": 1024, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", "go_fast": False, "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "seed": 1024, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", "go_fast": false, "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.
Output
{ "completed_at": "2025-02-07T22:41:17.704466Z", "created_at": "2025-02-07T22:41:09.560000Z", "data_removed": false, "error": null, "id": "rd2nkeqnq1rme0cmwa98g0faem", "input": { "seed": 1024, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", "go_fast": false, "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": "Loaded LoRAs in 0.58s\nUsing seed: 1024\nPrompt: A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 3.99it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.50it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.25it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.14it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.08it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.05it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.02it/s]\n 29%|██▊ | 8/28 [00:01<00:04, 4.01it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 4.00it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 4.00it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.00it/s]\n 43%|████▎ | 12/28 [00:02<00:04, 3.99it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.99it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.99it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.99it/s]\n 57%|█████▋ | 16/28 [00:03<00:03, 3.99it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.98it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.98it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.99it/s]\n 71%|███████▏ | 20/28 [00:04<00:02, 3.99it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.99it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.98it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.98it/s]\n 86%|████████▌ | 24/28 [00:05<00:01, 3.99it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.98it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.98it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 3.98it/s]\n100%|██████████| 28/28 [00:06<00:00, 3.98it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.01it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 8.061713238, "total_time": 8.144466 }, "output": [ "https://replicate.delivery/xezq/O77IAK4T7eTdd6zApQVbky8FPlOk6WB36IuOvvFVtDgG8hGKA/out-0.png" ], "started_at": "2025-02-07T22:41:09.642753Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-dvkb4bt5s4325qsevixwithnkthcyenn6cdabzsaoo2gwis4pwrq", "get": "https://api.replicate.com/v1/predictions/rd2nkeqnq1rme0cmwa98g0faem", "cancel": "https://api.replicate.com/v1/predictions/rd2nkeqnq1rme0cmwa98g0faem/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLoaded LoRAs in 0.58s Using seed: 1024 Prompt: A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side. [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 3.99it/s] 7%|▋ | 2/28 [00:00<00:05, 4.50it/s] 11%|█ | 3/28 [00:00<00:05, 4.25it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.14it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.08it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.05it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.02it/s] 29%|██▊ | 8/28 [00:01<00:04, 4.01it/s] 32%|███▏ | 9/28 [00:02<00:04, 4.00it/s] 36%|███▌ | 10/28 [00:02<00:04, 4.00it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.00it/s] 43%|████▎ | 12/28 [00:02<00:04, 3.99it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.99it/s] 50%|█████ | 14/28 [00:03<00:03, 3.99it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.99it/s] 57%|█████▋ | 16/28 [00:03<00:03, 3.99it/s] 61%|██████ | 17/28 [00:04<00:02, 3.98it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.98it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.99it/s] 71%|███████▏ | 20/28 [00:04<00:02, 3.99it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.99it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.98it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.98it/s] 86%|████████▌ | 24/28 [00:05<00:01, 3.99it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.98it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.98it/s] 96%|█████████▋| 27/28 [00:06<00:00, 3.98it/s] 100%|██████████| 28/28 [00:06<00:00, 3.98it/s] 100%|██████████| 28/28 [00:06<00:00, 4.01it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDth9j23hkt5rmc0cmwaat95a71wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", "go_fast": false, "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", go_fast: false, 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", "go_fast": False, "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", "go_fast": false, "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.
Output
{ "completed_at": "2025-02-07T22:43:44.592121Z", "created_at": "2025-02-07T22:43:36.529000Z", "data_removed": false, "error": null, "id": "th9j23hkt5rmc0cmwaat95a71w", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.", "go_fast": false, "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": "Loaded LoRAs in 0.57s\nUsing seed: 45568\nPrompt: A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side.\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 3.98it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.49it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.24it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.14it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.09it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.05it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.03it/s]\n 29%|██▊ | 8/28 [00:01<00:04, 4.02it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 4.01it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 4.00it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.00it/s]\n 43%|████▎ | 12/28 [00:02<00:04, 3.99it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.99it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.99it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.99it/s]\n 57%|█████▋ | 16/28 [00:03<00:03, 3.99it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.99it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.99it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.99it/s]\n 71%|███████▏ | 20/28 [00:04<00:02, 3.99it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.99it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.98it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.98it/s]\n 86%|████████▌ | 24/28 [00:05<00:01, 3.98it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.98it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.99it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 3.99it/s]\n100%|██████████| 28/28 [00:06<00:00, 3.99it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.01it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 8.04429792, "total_time": 8.063121 }, "output": [ "https://replicate.delivery/xezq/baAJwGAxzfRPS6T4dtdqke1WrolYHjlZFgE5zCovrbfA1HaoA/out-0.png" ], "started_at": "2025-02-07T22:43:36.547823Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-xlobaboxvhkpqxaxrqkdr5rz5i6zp4pkb3mjgdr4qvoj7e2xqmxq", "get": "https://api.replicate.com/v1/predictions/th9j23hkt5rmc0cmwaat95a71w", "cancel": "https://api.replicate.com/v1/predictions/th9j23hkt5rmc0cmwaat95a71w/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLoaded LoRAs in 0.57s Using seed: 45568 Prompt: A tram model tatra t3 TATRAT3 on a city street. The tram is green. The tram visible from front right side. [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 3.98it/s] 7%|▋ | 2/28 [00:00<00:05, 4.49it/s] 11%|█ | 3/28 [00:00<00:05, 4.24it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.14it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.09it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.05it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.03it/s] 29%|██▊ | 8/28 [00:01<00:04, 4.02it/s] 32%|███▏ | 9/28 [00:02<00:04, 4.01it/s] 36%|███▌ | 10/28 [00:02<00:04, 4.00it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.00it/s] 43%|████▎ | 12/28 [00:02<00:04, 3.99it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.99it/s] 50%|█████ | 14/28 [00:03<00:03, 3.99it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.99it/s] 57%|█████▋ | 16/28 [00:03<00:03, 3.99it/s] 61%|██████ | 17/28 [00:04<00:02, 3.99it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.99it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.99it/s] 71%|███████▏ | 20/28 [00:04<00:02, 3.99it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.99it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.98it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.98it/s] 86%|████████▌ | 24/28 [00:05<00:01, 3.98it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.98it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.99it/s] 96%|█████████▋| 27/28 [00:06<00:00, 3.99it/s] 100%|██████████| 28/28 [00:06<00:00, 3.99it/s] 100%|██████████| 28/28 [00:06<00:00, 4.01it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDy9ntxh1h55rma0cmwacsd1nm0wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green.
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green. ", "go_fast": false, "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green. ", go_fast: false, 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green. ", "go_fast": False, "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green. ", "go_fast": false, "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.
Output
{ "completed_at": "2025-02-07T22:48:08.489051Z", "created_at": "2025-02-07T22:47:57.993000Z", "data_removed": false, "error": null, "id": "y9ntxh1h55rma0cmwacsd1nm0w", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green. ", "go_fast": false, "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": "Loaded LoRAs in 0.57s\nUsing seed: 33780\nPrompt: A tram model tatra t3 TATRAT3 on a city street. The tram is green.\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 3.99it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.51it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.26it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.15it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.10it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.06it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.04it/s]\n 29%|██▊ | 8/28 [00:01<00:04, 4.03it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 4.02it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 4.01it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.01it/s]\n 43%|████▎ | 12/28 [00:02<00:03, 4.00it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 4.00it/s]\n 50%|█████ | 14/28 [00:03<00:03, 4.00it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 4.00it/s]\n 57%|█████▋ | 16/28 [00:03<00:02, 4.00it/s]\n 61%|██████ | 17/28 [00:04<00:02, 4.00it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 4.00it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 4.00it/s]\n 71%|███████▏ | 20/28 [00:04<00:02, 4.00it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 4.00it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 4.00it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 4.00it/s]\n 86%|████████▌ | 24/28 [00:05<00:01, 4.00it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 4.00it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 4.00it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 4.00it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.00it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.03it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 8.036268635, "total_time": 10.496051 }, "output": [ "https://replicate.delivery/xezq/2qAhFn4O35qXH9pShHUBWjNxn3HAPEt90j9sAMgjenWUfDNUA/out-0.png" ], "started_at": "2025-02-07T22:48:00.452782Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-pebrrua4c5su3sujtcliacowyppfasbpvfqsswzyvpxswaekpeja", "get": "https://api.replicate.com/v1/predictions/y9ntxh1h55rma0cmwacsd1nm0w", "cancel": "https://api.replicate.com/v1/predictions/y9ntxh1h55rma0cmwacsd1nm0w/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLoaded LoRAs in 0.57s Using seed: 33780 Prompt: A tram model tatra t3 TATRAT3 on a city street. The tram is green. [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 3.99it/s] 7%|▋ | 2/28 [00:00<00:05, 4.51it/s] 11%|█ | 3/28 [00:00<00:05, 4.26it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.15it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.10it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.06it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.04it/s] 29%|██▊ | 8/28 [00:01<00:04, 4.03it/s] 32%|███▏ | 9/28 [00:02<00:04, 4.02it/s] 36%|███▌ | 10/28 [00:02<00:04, 4.01it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.01it/s] 43%|████▎ | 12/28 [00:02<00:03, 4.00it/s] 46%|████▋ | 13/28 [00:03<00:03, 4.00it/s] 50%|█████ | 14/28 [00:03<00:03, 4.00it/s] 54%|█████▎ | 15/28 [00:03<00:03, 4.00it/s] 57%|█████▋ | 16/28 [00:03<00:02, 4.00it/s] 61%|██████ | 17/28 [00:04<00:02, 4.00it/s] 64%|██████▍ | 18/28 [00:04<00:02, 4.00it/s] 68%|██████▊ | 19/28 [00:04<00:02, 4.00it/s] 71%|███████▏ | 20/28 [00:04<00:02, 4.00it/s] 75%|███████▌ | 21/28 [00:05<00:01, 4.00it/s] 79%|███████▊ | 22/28 [00:05<00:01, 4.00it/s] 82%|████████▏ | 23/28 [00:05<00:01, 4.00it/s] 86%|████████▌ | 24/28 [00:05<00:01, 4.00it/s] 89%|████████▉ | 25/28 [00:06<00:00, 4.00it/s] 93%|█████████▎| 26/28 [00:06<00:00, 4.00it/s] 96%|█████████▋| 27/28 [00:06<00:00, 4.00it/s] 100%|██████████| 28/28 [00:06<00:00, 4.00it/s] 100%|██████████| 28/28 [00:06<00:00, 4.03it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDgkmh08b9d5rme0cmwahv1sy038StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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.
Output
{ "completed_at": "2025-02-07T22:59:10.797616Z", "created_at": "2025-02-07T22:59:07.753000Z", "data_removed": false, "error": null, "id": "gkmh08b9d5rme0cmwahv1sy038", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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": "Lora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 2035472808\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.64it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.93it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.97it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.58it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.38it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.99it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.93it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.94it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.94it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.95it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.84it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.83it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.81it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.84it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.15it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.032014158, "total_time": 3.044616 }, "output": [ "https://replicate.delivery/xezq/11hneg7vFejERkO1h0uaHkE8rvzYGEnoufNurAefmSUwHhohC/out-0.png" ], "started_at": "2025-02-07T22:59:07.765602Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-6rhoblcesayzhxhkvtis3bbkhneio3cwuepe3a7s4htdcbesrmjq", "get": "https://api.replicate.com/v1/predictions/gkmh08b9d5rme0cmwahv1sy038", "cancel": "https://api.replicate.com/v1/predictions/gkmh08b9d5rme0cmwahv1sy038/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded running quantized prediction Using seed: 2035472808 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.64it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.93it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.97it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.58it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.38it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.99it/s] 50%|█████ | 14/28 [00:01<00:01, 10.93it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.94it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.94it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.95it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.84it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.83it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.81it/s] 100%|██████████| 28/28 [00:02<00:00, 10.84it/s] 100%|██████████| 28/28 [00:02<00:00, 11.15it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDahkp2pymp1rme0cmwahshzq42cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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.
Output
{ "completed_at": "2025-02-07T22:59:38.431613Z", "created_at": "2025-02-07T22:59:35.216000Z", "data_removed": false, "error": null, "id": "ahkp2pymp1rme0cmwahshzq42c", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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": "Lora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 2861646718\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 16.72it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.62it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.68it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.31it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.00it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.63it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.61it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.63it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.65it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.61it/s]\n 79%|███████▊ | 22/28 [00:02<00:00, 10.45it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.45it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.51it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.57it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.83it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.094422848, "total_time": 3.215613 }, "output": [ "https://replicate.delivery/xezq/OBM4LqEVn9rzCdfU0l4wBfzvie1s26DDwYS1zWQBG731SIaoA/out-0.png" ], "started_at": "2025-02-07T22:59:35.337190Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-lxp4oghueydazxve4jjnqqyddl5qhmfbxkegxb57l23ejlnoed3q", "get": "https://api.replicate.com/v1/predictions/ahkp2pymp1rme0cmwahshzq42c", "cancel": "https://api.replicate.com/v1/predictions/ahkp2pymp1rme0cmwahshzq42c/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded running quantized prediction Using seed: 2861646718 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 16.72it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.62it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.68it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.31it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.00it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.63it/s] 50%|█████ | 14/28 [00:01<00:01, 10.61it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.63it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.65it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.61it/s] 79%|███████▊ | 22/28 [00:02<00:00, 10.45it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.45it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.51it/s] 100%|██████████| 28/28 [00:02<00:00, 10.57it/s] 100%|██████████| 28/28 [00:02<00:00, 10.83it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDcv13vw96ehrma0cmwajbg0h5qmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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.
Output
{ "completed_at": "2025-02-07T22:59:59.243552Z", "created_at": "2025-02-07T22:59:56.148000Z", "data_removed": false, "error": null, "id": "cv13vw96ehrma0cmwajbg0h5qm", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white ", "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": "Lora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 4136897473\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.67it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.84it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.81it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.39it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.12it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.64it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.63it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.67it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.68it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.64it/s]\n 79%|███████▊ | 22/28 [00:02<00:00, 10.49it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.47it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.52it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.57it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.87it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.088555885, "total_time": 3.095552 }, "output": [ "https://replicate.delivery/xezq/7tf4MDUio6S5IyP5gWva6i5ijfiYu1mbRk9QeJZYlYDemQ0QB/out-0.png" ], "started_at": "2025-02-07T22:59:56.154996Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-3mfn6mnxqzfsyaq2c6anentdha4vvuwvucjl7eana4jzgxrombmq", "get": "https://api.replicate.com/v1/predictions/cv13vw96ehrma0cmwajbg0h5qm", "cancel": "https://api.replicate.com/v1/predictions/cv13vw96ehrma0cmwajbg0h5qm/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded running quantized prediction Using seed: 4136897473 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.67it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.84it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.81it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.39it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.12it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.64it/s] 50%|█████ | 14/28 [00:01<00:01, 10.63it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.67it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.68it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.64it/s] 79%|███████▊ | 22/28 [00:02<00:00, 10.49it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.47it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.52it/s] 100%|██████████| 28/28 [00:02<00:00, 10.57it/s] 100%|██████████| 28/28 [00:02<00:00, 10.87it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDxxp8ehf4hnrma0cmwakbyrmh44StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", "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.
Output
{ "completed_at": "2025-02-07T23:03:08.123577Z", "created_at": "2025-02-07T23:02:55.885000Z", "data_removed": false, "error": null, "id": "xxp8ehf4hnrma0cmwakbyrmh44", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", "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 23:02:55.939 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 23:02:55.941 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s\nfree=28600137670656\nDownloading weights\n2025-02-07T23:02:55Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpomx3jb64/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T23:03:04Z | INFO | [ Complete ] dest=/tmp/tmpomx3jb64/weights size=\"215 MB\" total_elapsed=8.667s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 8.69s\n2025-02-07 23:03:04.635 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 23:03:04.715 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 23:03:04.715 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 23:03:04.715 | 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: 41%|████ | 124/304 [00:00<00:00, 1238.19it/s]\nApplying LoRA: 82%|████████▏ | 248/304 [00:00<00:00, 958.12it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 959.51it/s]\n2025-02-07 23:03:05.033 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 23:03:05.033 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s\nrunning quantized prediction\nUsing seed: 3769199527\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.28it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.82it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.82it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.42it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.09it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.83it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.80it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.80it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.80it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.73it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.63it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.60it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.67it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.70it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.98it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 12.204895797, "total_time": 12.238577 }, "output": [ "https://replicate.delivery/xezq/j0LfAfvWOCke2IWVLn4M5GlW1LGK3jy7ut186iSrpp1ZZIaoA/out-0.png" ], "started_at": "2025-02-07T23:02:55.918682Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-x7zz7wvbyancmwdzgfqqy5ksz57ha43w6sapagbi7nfabvstn5sa", "get": "https://api.replicate.com/v1/predictions/xxp8ehf4hnrma0cmwakbyrmh44", "cancel": "https://api.replicate.com/v1/predictions/xxp8ehf4hnrma0cmwakbyrmh44/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 23:02:55.939 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 23:02:55.941 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s free=28600137670656 Downloading weights 2025-02-07T23:02:55Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpomx3jb64/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T23:03:04Z | INFO | [ Complete ] dest=/tmp/tmpomx3jb64/weights size="215 MB" total_elapsed=8.667s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 8.69s 2025-02-07 23:03:04.635 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 23:03:04.715 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 23:03:04.715 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 23:03:04.715 | 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: 41%|████ | 124/304 [00:00<00:00, 1238.19it/s] Applying LoRA: 82%|████████▏ | 248/304 [00:00<00:00, 958.12it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 959.51it/s] 2025-02-07 23:03:05.033 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 23:03:05.033 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s running quantized prediction Using seed: 3769199527 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.28it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.82it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.82it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.42it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.09it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.83it/s] 50%|█████ | 14/28 [00:01<00:01, 10.80it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.80it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.80it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.73it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.63it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.60it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.67it/s] 100%|██████████| 28/28 [00:02<00:00, 10.70it/s] 100%|██████████| 28/28 [00:02<00:00, 10.98it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDa2sm2rys3drm80cmwam95qm34gStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", "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.
Output
{ "completed_at": "2025-02-07T23:05:07.085863Z", "created_at": "2025-02-07T23:05:04.027000Z", "data_removed": false, "error": null, "id": "a2sm2rys3drm80cmwam95qm34g", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white and visible from front right side", "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": "Lora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 51859889\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.52it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.83it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.85it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.45it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.21it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.82it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.78it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.77it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.79it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.80it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.70it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.64it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.66it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.72it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.01it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.048160813, "total_time": 3.058863 }, "output": [ "https://replicate.delivery/xezq/iNfPNIiUNF31c6UCExdAgpim3UeX5lTOD9vYArZIj71jOENUA/out-0.png" ], "started_at": "2025-02-07T23:05:04.037702Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-rstnhpr564b7sb5cjrg34z7ixvlrr52jryq4eelgk3fpmjdi7d5q", "get": "https://api.replicate.com/v1/predictions/a2sm2rys3drm80cmwam95qm34g", "cancel": "https://api.replicate.com/v1/predictions/a2sm2rys3drm80cmwam95qm34g/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded running quantized prediction Using seed: 51859889 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.52it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.83it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.85it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.45it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.21it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.82it/s] 50%|█████ | 14/28 [00:01<00:01, 10.78it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.77it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.79it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.80it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.70it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.64it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.66it/s] 100%|██████████| 28/28 [00:02<00:00, 10.72it/s] 100%|██████████| 28/28 [00:02<00:00, 11.01it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDqb5htf3gk5rm80cmwanbg9byzrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 1364272806
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white
- go_fast
- 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
{ "seed": 1364272806, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { seed: 1364272806, model: "dev", width: 1024, height: 1024, prompt: "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "seed": 1364272806, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "seed": 1364272806, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white", "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.
Output
{ "completed_at": "2025-02-07T23:06:51.453291Z", "created_at": "2025-02-07T23:06:48.345000Z", "data_removed": false, "error": null, "id": "qb5htf3gk5rm80cmwanbg9byzr", "input": { "seed": 1364272806, "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white", "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": "Lora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 1364272806\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.83it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.74it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.71it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.26it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 10.95it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.64it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.61it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.59it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.59it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.54it/s]\n 79%|███████▊ | 22/28 [00:02<00:00, 10.41it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.42it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.47it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.50it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.80it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.10140049, "total_time": 3.108291 }, "output": [ "https://replicate.delivery/xezq/ffamhr5DPbj5n0sTSmXAYqTfSEVJJPqotWKhXC2c6HmXgIaoA/out-0.png" ], "started_at": "2025-02-07T23:06:48.351890Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-743kzunswpbc64bkf6rfppscpixgvtwp7po556mvabh35qzsrs6q", "get": "https://api.replicate.com/v1/predictions/qb5htf3gk5rm80cmwanbg9byzr", "cancel": "https://api.replicate.com/v1/predictions/qb5htf3gk5rm80cmwanbg9byzr/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded running quantized prediction Using seed: 1364272806 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.83it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.74it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.71it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.26it/s] 36%|███▌ | 10/28 [00:00<00:01, 10.95it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.64it/s] 50%|█████ | 14/28 [00:01<00:01, 10.61it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.59it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.59it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.54it/s] 79%|███████▊ | 22/28 [00:02<00:00, 10.41it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.42it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.47it/s] 100%|██████████| 28/28 [00:02<00:00, 10.50it/s] 100%|██████████| 28/28 [00:02<00:00, 10.80it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDryv4j783jhrm80cmwap9ny88tcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", "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.
Output
{ "completed_at": "2025-02-07T23:08:36.807720Z", "created_at": "2025-02-07T23:08:31.508000Z", "data_removed": false, "error": null, "id": "ryv4j783jhrm80cmwap9ny88tc", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", "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 23:08:31.551 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 23:08:31.552 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s\nfree=28936303935488\nDownloading weights\n2025-02-07T23:08:31Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpbvovbnlz/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T23:08:33Z | INFO | [ Complete ] dest=/tmp/tmpbvovbnlz/weights size=\"215 MB\" total_elapsed=1.738s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 1.76s\n2025-02-07 23:08:33.317 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 23:08:33.396 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 23:08:33.396 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 23:08:33.396 | 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: 41%|████ | 124/304 [00:00<00:00, 1228.46it/s]\nApplying LoRA: 81%|████████▏ | 247/304 [00:00<00:00, 977.18it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 971.31it/s]\n2025-02-07 23:08:33.709 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 23:08:33.710 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.39s\nrunning quantized prediction\nUsing seed: 2192376279\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.78it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.93it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.88it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.44it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.08it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.76it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.77it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.77it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.79it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.76it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.63it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.64it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.69it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.72it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.99it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 5.277998297, "total_time": 5.29972 }, "output": [ "https://replicate.delivery/xezq/GwAYcCOzoIpJGlXIzzWO3kZo6UXHOgQJDuHHLoSElGHdERDF/out-0.png" ], "started_at": "2025-02-07T23:08:31.529721Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-7wwbpvnyjyrbuzt455gmliwwlksvzn4uy73kn57a54osg3veggpq", "get": "https://api.replicate.com/v1/predictions/ryv4j783jhrm80cmwap9ny88tc", "cancel": "https://api.replicate.com/v1/predictions/ryv4j783jhrm80cmwap9ny88tc/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 23:08:31.551 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 23:08:31.552 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.024s free=28936303935488 Downloading weights 2025-02-07T23:08:31Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpbvovbnlz/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T23:08:33Z | INFO | [ Complete ] dest=/tmp/tmpbvovbnlz/weights size="215 MB" total_elapsed=1.738s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 1.76s 2025-02-07 23:08:33.317 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 23:08:33.396 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 23:08:33.396 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 23:08:33.396 | 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: 41%|████ | 124/304 [00:00<00:00, 1228.46it/s] Applying LoRA: 81%|████████▏ | 247/304 [00:00<00:00, 977.18it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 971.31it/s] 2025-02-07 23:08:33.709 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 23:08:33.710 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.39s running quantized prediction Using seed: 2192376279 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.78it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.93it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.88it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.44it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.08it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.76it/s] 50%|█████ | 14/28 [00:01<00:01, 10.77it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.77it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.79it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.76it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.63it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.64it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.69it/s] 100%|██████████| 28/28 [00:02<00:00, 10.72it/s] 100%|██████████| 28/28 [00:02<00:00, 10.99it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4ID6y3tyvp1thrm80cmwapspcvf58StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", "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.
Output
{ "completed_at": "2025-02-07T23:10:38.538050Z", "created_at": "2025-02-07T23:10:25.748000Z", "data_removed": false, "error": null, "id": "6y3tyvp1thrm80cmwapspcvf58", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from front side", "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 23:10:25.833 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 23:10:25.834 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s\nfree=28920611713024\nDownloading weights\n2025-02-07T23:10:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw8387t1s/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T23:10:35Z | INFO | [ Complete ] dest=/tmp/tmpw8387t1s/weights size=\"215 MB\" total_elapsed=9.180s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 9.23s\n2025-02-07 23:10:35.061 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 23:10:35.143 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 23:10:35.143 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 23:10:35.143 | 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, 1224.57it/s]\nApplying LoRA: 81%|████████ | 246/304 [00:00<00:00, 951.82it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 966.91it/s]\n2025-02-07 23:10:35.458 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 23:10:35.458 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s\nrunning quantized prediction\nUsing seed: 1555528026\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.47it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.95it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.94it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.52it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.17it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.84it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.84it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.86it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.89it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.86it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.74it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.73it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.77it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.80it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.07it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 12.726937058, "total_time": 12.79005 }, "output": [ "https://replicate.delivery/xezq/509GiRoMeyRcMyD1rKu2vBZqSGkAWZyHN11iJlFVVDS3JiGKA/out-0.png" ], "started_at": "2025-02-07T23:10:25.811113Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-4aipjgd7puh2y2ybodata7rzgjt2gojffyrzfflaqfatkiax2qdq", "get": "https://api.replicate.com/v1/predictions/6y3tyvp1thrm80cmwapspcvf58", "cancel": "https://api.replicate.com/v1/predictions/6y3tyvp1thrm80cmwapspcvf58/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 23:10:25.833 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 23:10:25.834 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.025s free=28920611713024 Downloading weights 2025-02-07T23:10:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw8387t1s/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T23:10:35Z | INFO | [ Complete ] dest=/tmp/tmpw8387t1s/weights size="215 MB" total_elapsed=9.180s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 9.23s 2025-02-07 23:10:35.061 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 23:10:35.143 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 23:10:35.143 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 23:10:35.143 | 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, 1224.57it/s] Applying LoRA: 81%|████████ | 246/304 [00:00<00:00, 951.82it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 966.91it/s] 2025-02-07 23:10:35.458 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 23:10:35.458 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.4s running quantized prediction Using seed: 1555528026 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.47it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.95it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.94it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.52it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.17it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.84it/s] 50%|█████ | 14/28 [00:01<00:01, 10.84it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.86it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.89it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.86it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.74it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.73it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.77it/s] 100%|██████████| 28/28 [00:02<00:00, 10.80it/s] 100%|██████████| 28/28 [00:02<00:00, 11.07it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4ID3tteqx9pj1rm80cmwaq974zss0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the front side
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the front side", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the front side", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the front side", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the front side", "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.
Output
{ "completed_at": "2025-02-07T23:10:58.681306Z", "created_at": "2025-02-07T23:10:55.632000Z", "data_removed": false, "error": null, "id": "3tteqx9pj1rm80cmwaq974zss0", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the front side", "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": "Lora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 1664395363\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 18.24it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 13.11it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 12.00it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.55it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.31it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.89it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.85it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.86it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.87it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.88it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.76it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.75it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.79it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.80it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.11it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.043013098, "total_time": 3.049306 }, "output": [ "https://replicate.delivery/xezq/rVMMteZtqQ1hCSalzLXeQ8uDz7Dzfu2k7ExhcJffwqKVgiohC/out-0.png" ], "started_at": "2025-02-07T23:10:55.638293Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-lk72chhxdh6nzi7t5ego2mt4cqxht3teyvz4trzy5ispnbrbjdta", "get": "https://api.replicate.com/v1/predictions/3tteqx9pj1rm80cmwaq974zss0", "cancel": "https://api.replicate.com/v1/predictions/3tteqx9pj1rm80cmwaq974zss0/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded running quantized prediction Using seed: 1664395363 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 18.24it/s] 14%|█▍ | 4/28 [00:00<00:01, 13.11it/s] 21%|██▏ | 6/28 [00:00<00:01, 12.00it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.55it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.31it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.89it/s] 50%|█████ | 14/28 [00:01<00:01, 10.85it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.86it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.87it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.88it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.76it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.75it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.79it/s] 100%|██████████| 28/28 [00:02<00:00, 10.80it/s] 100%|██████████| 28/28 [00:02<00:00, 11.11it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4ID1v18fzt7j5rm80cmwaqt3w6h7mStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the back side
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the back side", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the back side", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the back side", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the back side", "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.
Output
{ "completed_at": "2025-02-07T23:12:08.538917Z", "created_at": "2025-02-07T23:12:05.521000Z", "data_removed": false, "error": null, "id": "1v18fzt7j5rm80cmwaqt3w6h7m", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a city street. The tram is green and white. The tram is visible from the back side", "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": "Lora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded\nrunning quantized prediction\nUsing seed: 3788670906\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 18.16it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 13.10it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 12.02it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.58it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.34it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.95it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.92it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.90it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.89it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.89it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.79it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.76it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.80it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.83it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.13it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 3.010437662, "total_time": 3.017917 }, "output": [ "https://replicate.delivery/xezq/LFIFuJIrNmbyFpTfjLbF0kuIzzUlkV2QrQHdYisfkNNIVENUA/out-0.png" ], "started_at": "2025-02-07T23:12:05.528479Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-i67r3s7wkp3deztsbv3v5f4anllc6pxpuacp6rzqoawccyn3z4xq", "get": "https://api.replicate.com/v1/predictions/1v18fzt7j5rm80cmwaqt3w6h7m", "cancel": "https://api.replicate.com/v1/predictions/1v18fzt7j5rm80cmwaqt3w6h7m/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inLora https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar already loaded running quantized prediction Using seed: 3788670906 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 18.16it/s] 14%|█▍ | 4/28 [00:00<00:01, 13.10it/s] 21%|██▏ | 6/28 [00:00<00:01, 12.02it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.58it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.34it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.95it/s] 50%|█████ | 14/28 [00:01<00:01, 10.92it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.90it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.89it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.89it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.79it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.76it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.80it/s] 100%|██████████| 28/28 [00:02<00:00, 10.83it/s] 100%|██████████| 28/28 [00:02<00:00, 11.13it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDj6eb7t2efdrmc0cmwb8vh7qce0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram model tatra t3 TATRAT3 on a Mediterranean city street. The tram is green and blue.
- go_fast
- 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
{ "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a Mediterranean city street. The tram is green and blue. ", "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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 model tatra t3 TATRAT3 on a Mediterranean city street. The tram is green and blue. ", 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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 model tatra t3 TATRAT3 on a Mediterranean city street. The tram is green and blue. ", "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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 model tatra t3 TATRAT3 on a Mediterranean city street. The tram is green and blue. ", "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.
Output
{ "completed_at": "2025-02-07T23:49:20.260083Z", "created_at": "2025-02-07T23:49:15.515000Z", "data_removed": false, "error": null, "id": "j6eb7t2efdrmc0cmwb8vh7qce0", "input": { "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram model tatra t3 TATRAT3 on a Mediterranean city street. The tram is green and blue. ", "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 23:49:15.621 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers\n2025-02-07 23:49:15.622 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.023s\nfree=5904957394944\nDownloading weights\n2025-02-07T23:49:15Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmprfcwoazm/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-07T23:49:16Z | INFO | [ Complete ] dest=/tmp/tmprfcwoazm/weights size=\"215 MB\" total_elapsed=1.132s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 1.16s\n2025-02-07 23:49:16.780 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad\n2025-02-07 23:49:16.858 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded\n2025-02-07 23:49:16.858 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys\n2025-02-07 23:49:16.859 | 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, 1214.25it/s]\nApplying LoRA: 81%|████████ | 245/304 [00:00<00:00, 964.44it/s] \nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 970.33it/s]\n2025-02-07 23:49:17.172 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8\n2025-02-07 23:49:17.172 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.39s\nrunning quantized prediction\nUsing seed: 1561267931\n 0%| | 0/28 [00:00<?, ?it/s]\n 7%|▋ | 2/28 [00:00<00:01, 17.26it/s]\n 14%|█▍ | 4/28 [00:00<00:01, 12.83it/s]\n 21%|██▏ | 6/28 [00:00<00:01, 11.86it/s]\n 29%|██▊ | 8/28 [00:00<00:01, 11.44it/s]\n 36%|███▌ | 10/28 [00:00<00:01, 11.13it/s]\n 43%|████▎ | 12/28 [00:01<00:01, 10.87it/s]\n 50%|█████ | 14/28 [00:01<00:01, 10.81it/s]\n 57%|█████▋ | 16/28 [00:01<00:01, 10.81it/s]\n 64%|██████▍ | 18/28 [00:01<00:00, 10.83it/s]\n 71%|███████▏ | 20/28 [00:01<00:00, 10.78it/s]\n 79%|███████▊ | 22/28 [00:01<00:00, 10.67it/s]\n 86%|████████▌ | 24/28 [00:02<00:00, 10.66it/s]\n 93%|█████████▎| 26/28 [00:02<00:00, 10.70it/s]\n100%|██████████| 28/28 [00:02<00:00, 10.72it/s]\n100%|██████████| 28/28 [00:02<00:00, 11.01it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 4.660416777, "total_time": 4.745083 }, "output": [ "https://replicate.delivery/xezq/S1Xy79mKNH4XBZinbey8AegL50ebMxw2eJ3zeAeed1rXAciGKA/out-0.png" ], "started_at": "2025-02-07T23:49:15.599667Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-cjti5ekclbg52gfrdzhyydfizeegk7n5wll37hivtveiv6jynuvq", "get": "https://api.replicate.com/v1/predictions/j6eb7t2efdrmc0cmwb8vh7qce0", "cancel": "https://api.replicate.com/v1/predictions/j6eb7t2efdrmc0cmwb8vh7qce0/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated in2025-02-07 23:49:15.621 | INFO | fp8.lora_loading:restore_clones:592 - Unloaded 304 layers 2025-02-07 23:49:15.622 | SUCCESS | fp8.lora_loading:unload_loras:563 - LoRAs unloaded in 0.023s free=5904957394944 Downloading weights 2025-02-07T23:49:15Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmprfcwoazm/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-07T23:49:16Z | INFO | [ Complete ] dest=/tmp/tmprfcwoazm/weights size="215 MB" total_elapsed=1.132s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 1.16s 2025-02-07 23:49:16.780 | INFO | fp8.lora_loading:convert_lora_weights:502 - Loading LoRA weights for /src/weights-cache/265dc36a7a34f2ad 2025-02-07 23:49:16.858 | INFO | fp8.lora_loading:convert_lora_weights:523 - LoRA weights loaded 2025-02-07 23:49:16.858 | DEBUG | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:602 - Extracting keys 2025-02-07 23:49:16.859 | 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, 1214.25it/s] Applying LoRA: 81%|████████ | 245/304 [00:00<00:00, 964.44it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 970.33it/s] 2025-02-07 23:49:17.172 | INFO | fp8.lora_loading:apply_lora_to_model_and_optionally_store_clones:661 - Loading LoRA in fp8 2025-02-07 23:49:17.172 | SUCCESS | fp8.lora_loading:load_lora:542 - LoRA applied in 0.39s running quantized prediction Using seed: 1561267931 0%| | 0/28 [00:00<?, ?it/s] 7%|▋ | 2/28 [00:00<00:01, 17.26it/s] 14%|█▍ | 4/28 [00:00<00:01, 12.83it/s] 21%|██▏ | 6/28 [00:00<00:01, 11.86it/s] 29%|██▊ | 8/28 [00:00<00:01, 11.44it/s] 36%|███▌ | 10/28 [00:00<00:01, 11.13it/s] 43%|████▎ | 12/28 [00:01<00:01, 10.87it/s] 50%|█████ | 14/28 [00:01<00:01, 10.81it/s] 57%|█████▋ | 16/28 [00:01<00:01, 10.81it/s] 64%|██████▍ | 18/28 [00:01<00:00, 10.83it/s] 71%|███████▏ | 20/28 [00:01<00:00, 10.78it/s] 79%|███████▊ | 22/28 [00:01<00:00, 10.67it/s] 86%|████████▌ | 24/28 [00:02<00:00, 10.66it/s] 93%|█████████▎| 26/28 [00:02<00:00, 10.70it/s] 100%|██████████| 28/28 [00:02<00:00, 10.72it/s] 100%|██████████| 28/28 [00:02<00:00, 11.01it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4ID11jh7179wxrme0cmwvs8s07sacStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram TATRAT3 running a street. A vintage photo. 60s
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3
- output_quality
- 80
- prompt_strength
- 0.5
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "image": "https://replicate.delivery/pbxt/MSscG4DTsvrlJML6XfFd1U3JbuCjDm3rOB42qijufEEmkDok/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "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": 0.5, "extra_lora_scale": 1, "num_inference_steps": 28 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { image: "https://replicate.delivery/pbxt/MSscG4DTsvrlJML6XfFd1U3JbuCjDm3rOB42qijufEEmkDok/1967-Centennial-3.jpg", model: "dev", width: 1024, height: 1024, prompt: "A tram TATRAT3 running a street. A vintage photo. 60s", 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: 0.5, 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "image": "https://replicate.delivery/pbxt/MSscG4DTsvrlJML6XfFd1U3JbuCjDm3rOB42qijufEEmkDok/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "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": 0.5, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "image": "https://replicate.delivery/pbxt/MSscG4DTsvrlJML6XfFd1U3JbuCjDm3rOB42qijufEEmkDok/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "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": 0.5, "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.
Output
{ "completed_at": "2025-02-08T19:04:34.624198Z", "created_at": "2025-02-08T19:04:26.855000Z", "data_removed": false, "error": null, "id": "11jh7179wxrme0cmwvs8s07sac", "input": { "image": "https://replicate.delivery/pbxt/MSscG4DTsvrlJML6XfFd1U3JbuCjDm3rOB42qijufEEmkDok/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "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": 0.5, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Img2img and inpainting not supported with fast fp8 inference; will run in bf16\nfree=28152046182400\nDownloading weights\n2025-02-08T19:04:28Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpc9zp4coz/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-08T19:04:31Z | INFO | [ Complete ] dest=/tmp/tmpc9zp4coz/weights size=\"215 MB\" total_elapsed=2.675s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 2.70s\nLoaded LoRAs in 3.27s\nUsing seed: 60731\nPrompt: A tram TATRAT3 running a street. A vintage photo. 60s\n[!] Resizing input image from 720x480 to 720x480\n[!] img2img mode\n 0%| | 0/14 [00:00<?, ?it/s]\n 7%|▋ | 1/14 [00:00<00:10, 1.26it/s]\n 14%|█▍ | 2/14 [00:00<00:04, 2.54it/s]\n 21%|██▏ | 3/14 [00:01<00:02, 3.78it/s]\n 29%|██▊ | 4/14 [00:01<00:02, 4.89it/s]\n 36%|███▌ | 5/14 [00:01<00:01, 5.85it/s]\n 43%|████▎ | 6/14 [00:01<00:01, 6.64it/s]\n 50%|█████ | 7/14 [00:01<00:00, 7.25it/s]\n 57%|█████▋ | 8/14 [00:01<00:00, 7.72it/s]\n 64%|██████▍ | 9/14 [00:01<00:00, 8.07it/s]\n 71%|███████▏ | 10/14 [00:01<00:00, 8.33it/s]\n 79%|███████▊ | 11/14 [00:01<00:00, 8.51it/s]\n 86%|████████▌ | 12/14 [00:02<00:00, 8.64it/s]\n 93%|█████████▎| 13/14 [00:02<00:00, 8.73it/s]\n100%|██████████| 14/14 [00:02<00:00, 8.80it/s]\n100%|██████████| 14/14 [00:02<00:00, 6.23it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 6.160816626, "total_time": 7.769198 }, "output": [ "https://replicate.delivery/xezq/0T2XbpF6XIozKFQVXuyK6SDJUCEylI3dbvRFNr9796twcVDF/out-0.png" ], "started_at": "2025-02-08T19:04:28.463381Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-wxzog3oitk6lzknsulpfsm5tasaj6v6tyiujja5iittmletpftra", "get": "https://api.replicate.com/v1/predictions/11jh7179wxrme0cmwvs8s07sac", "cancel": "https://api.replicate.com/v1/predictions/11jh7179wxrme0cmwvs8s07sac/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated inImg2img and inpainting not supported with fast fp8 inference; will run in bf16 free=28152046182400 Downloading weights 2025-02-08T19:04:28Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpc9zp4coz/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-08T19:04:31Z | INFO | [ Complete ] dest=/tmp/tmpc9zp4coz/weights size="215 MB" total_elapsed=2.675s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 2.70s Loaded LoRAs in 3.27s Using seed: 60731 Prompt: A tram TATRAT3 running a street. A vintage photo. 60s [!] Resizing input image from 720x480 to 720x480 [!] img2img mode 0%| | 0/14 [00:00<?, ?it/s] 7%|▋ | 1/14 [00:00<00:10, 1.26it/s] 14%|█▍ | 2/14 [00:00<00:04, 2.54it/s] 21%|██▏ | 3/14 [00:01<00:02, 3.78it/s] 29%|██▊ | 4/14 [00:01<00:02, 4.89it/s] 36%|███▌ | 5/14 [00:01<00:01, 5.85it/s] 43%|████▎ | 6/14 [00:01<00:01, 6.64it/s] 50%|█████ | 7/14 [00:01<00:00, 7.25it/s] 57%|█████▋ | 8/14 [00:01<00:00, 7.72it/s] 64%|██████▍ | 9/14 [00:01<00:00, 8.07it/s] 71%|███████▏ | 10/14 [00:01<00:00, 8.33it/s] 79%|███████▊ | 11/14 [00:01<00:00, 8.51it/s] 86%|████████▌ | 12/14 [00:02<00:00, 8.64it/s] 93%|█████████▎| 13/14 [00:02<00:00, 8.73it/s] 100%|██████████| 14/14 [00:02<00:00, 8.80it/s] 100%|██████████| 14/14 [00:02<00:00, 6.23it/s] Total safe images: 1 out of 1
Prediction
rinatkurmaev/flux-dev-lora-tatra-t3:25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4IDn253c60sp1rm80cmww49r929h4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 1024
- height
- 1024
- prompt
- A tram TATRAT3 running a street. A vintage photo. 60s
- go_fast
- 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
{ "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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 }
Install Replicate’s Node.js client library:npm install replicate
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 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: { mask: "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", image: "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", model: "dev", width: 1024, height: 1024, prompt: "A tram TATRAT3 running a street. A vintage photo. 60s", go_fast: false, 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.
Install Replicate’s Python client library:pip install replicate
Import the client: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={ "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": False, "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 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
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": { "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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.
Output
{ "completed_at": "2025-02-08T19:27:42.418015Z", "created_at": "2025-02-08T19:27:35.344000Z", "data_removed": false, "error": null, "id": "n253c60sp1rm80cmww49r929h4", "input": { "mask": "https://replicate.delivery/pbxt/MSsviUPBa1U7ABxnhOumqGNrH9nGysUDWUmeNVo7CnAV1rmH/mask.png", "image": "https://replicate.delivery/pbxt/MSsnc5WTWaMcrPp6zvQkCljV9V41JG104YdkMdHUfzHPSmV6/1967-Centennial-3.jpg", "model": "dev", "width": 1024, "height": 1024, "prompt": "A tram TATRAT3 running a street. A vintage photo. 60s", "go_fast": false, "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": "free=28302063566848\nDownloading weights\n2025-02-08T19:27:35Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpksqux536/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\n2025-02-08T19:27:38Z | INFO | [ Complete ] dest=/tmp/tmpksqux536/weights size=\"215 MB\" total_elapsed=2.220s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar\nDownloaded weights in 2.25s\nLoaded LoRAs in 2.81s\nUsing seed: 25700\nPrompt: A tram TATRAT3 running a street. A vintage photo. 60s\n[!] Resizing input image from 720x480 to 720x480\n[!] inpaint mode\n 0%| | 0/23 [00:00<?, ?it/s]\n 4%|▍ | 1/23 [00:00<00:17, 1.24it/s]\n 9%|▊ | 2/23 [00:00<00:08, 2.50it/s]\n 13%|█▎ | 3/23 [00:01<00:05, 3.72it/s]\n 17%|█▋ | 4/23 [00:01<00:03, 4.82it/s]\n 22%|██▏ | 5/23 [00:01<00:03, 5.76it/s]\n 26%|██▌ | 6/23 [00:01<00:02, 6.53it/s]\n 30%|███ | 7/23 [00:01<00:02, 7.13it/s]\n 35%|███▍ | 8/23 [00:01<00:01, 7.60it/s]\n 39%|███▉ | 9/23 [00:01<00:01, 7.94it/s]\n 43%|████▎ | 10/23 [00:01<00:01, 8.19it/s]\n 48%|████▊ | 11/23 [00:01<00:01, 8.37it/s]\n 52%|█████▏ | 12/23 [00:02<00:01, 8.50it/s]\n 57%|█████▋ | 13/23 [00:02<00:01, 8.60it/s]\n 61%|██████ | 14/23 [00:02<00:01, 8.66it/s]\n 65%|██████▌ | 15/23 [00:02<00:00, 8.70it/s]\n 70%|██████▉ | 16/23 [00:02<00:00, 8.73it/s]\n 74%|███████▍ | 17/23 [00:02<00:00, 8.76it/s]\n 78%|███████▊ | 18/23 [00:02<00:00, 8.77it/s]\n 83%|████████▎ | 19/23 [00:02<00:00, 8.79it/s]\n 87%|████████▋ | 20/23 [00:02<00:00, 8.80it/s]\n 91%|█████████▏| 21/23 [00:03<00:00, 8.80it/s]\n 96%|█████████▌| 22/23 [00:03<00:00, 8.81it/s]\n100%|██████████| 23/23 [00:03<00:00, 7.01it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 6.73930896, "total_time": 7.074015 }, "output": [ "https://replicate.delivery/xezq/1LVJIhQFAKZnP1hpEy7q4KKvIXa3YVp08VxYUbhNRjgLiVDF/out-0.png" ], "started_at": "2025-02-08T19:27:35.678706Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-mrqobmlqg6ncoyqditts7qy5xkm7o43x3wcccfv4ark5zi5gr34a", "get": "https://api.replicate.com/v1/predictions/n253c60sp1rm80cmww49r929h4", "cancel": "https://api.replicate.com/v1/predictions/n253c60sp1rm80cmww49r929h4/cancel" }, "version": "25b2bc71bd11c42a9e7ce2f91995ded93cc12f1f23d2cbb7fff77f3da1bb65c4" }
Generated infree=28302063566848 Downloading weights 2025-02-08T19:27:35Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpksqux536/weights url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar 2025-02-08T19:27:38Z | INFO | [ Complete ] dest=/tmp/tmpksqux536/weights size="215 MB" total_elapsed=2.220s url=https://replicate.delivery/xezq/5wg4NzY5IOIuMZ4bhf8BTSUhf8hq4vj2AWECTkhWETqANDNUA/trained_model.tar Downloaded weights in 2.25s Loaded LoRAs in 2.81s Using seed: 25700 Prompt: A tram TATRAT3 running a street. A vintage photo. 60s [!] Resizing input image from 720x480 to 720x480 [!] inpaint mode 0%| | 0/23 [00:00<?, ?it/s] 4%|▍ | 1/23 [00:00<00:17, 1.24it/s] 9%|▊ | 2/23 [00:00<00:08, 2.50it/s] 13%|█▎ | 3/23 [00:01<00:05, 3.72it/s] 17%|█▋ | 4/23 [00:01<00:03, 4.82it/s] 22%|██▏ | 5/23 [00:01<00:03, 5.76it/s] 26%|██▌ | 6/23 [00:01<00:02, 6.53it/s] 30%|███ | 7/23 [00:01<00:02, 7.13it/s] 35%|███▍ | 8/23 [00:01<00:01, 7.60it/s] 39%|███▉ | 9/23 [00:01<00:01, 7.94it/s] 43%|████▎ | 10/23 [00:01<00:01, 8.19it/s] 48%|████▊ | 11/23 [00:01<00:01, 8.37it/s] 52%|█████▏ | 12/23 [00:02<00:01, 8.50it/s] 57%|█████▋ | 13/23 [00:02<00:01, 8.60it/s] 61%|██████ | 14/23 [00:02<00:01, 8.66it/s] 65%|██████▌ | 15/23 [00:02<00:00, 8.70it/s] 70%|██████▉ | 16/23 [00:02<00:00, 8.73it/s] 74%|███████▍ | 17/23 [00:02<00:00, 8.76it/s] 78%|███████▊ | 18/23 [00:02<00:00, 8.77it/s] 83%|████████▎ | 19/23 [00:02<00:00, 8.79it/s] 87%|████████▋ | 20/23 [00:02<00:00, 8.80it/s] 91%|█████████▏| 21/23 [00:03<00:00, 8.80it/s] 96%|█████████▌| 22/23 [00:03<00:00, 8.81it/s] 100%|██████████| 23/23 [00:03<00:00, 7.01it/s] Total safe images: 1 out of 1
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