sundai-club / ytahk13
A fine-tuned FLUX.1 model
- Public
- 29 runs
-
H100
Prediction
sundai-club/ytahk13:19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5IDb7xddygke5rma0cmgnvtn77hvrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- tahktuah standing in in front of the golden gate bridge
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 4.57
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "tahktuah standing in in front of the golden gate bridge", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 4.57, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport 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 sundai-club/ytahk13 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sundai-club/ytahk13:19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5", { input: { model: "dev", prompt: "tahktuah standing in in front of the golden gate bridge", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 4.57, 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 replicateImport the client:import replicateRun sundai-club/ytahk13 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sundai-club/ytahk13:19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5", input={ "model": "dev", "prompt": "tahktuah standing in in front of the golden gate bridge", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 4.57, "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 sundai-club/ytahk13 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": "sundai-club/ytahk13:19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5", "input": { "model": "dev", "prompt": "tahktuah standing in in front of the golden gate bridge", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 4.57, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-01-20T20:46:09.296946Z", "created_at": "2025-01-20T20:46:01.073000Z", "data_removed": false, "error": null, "id": "b7xddygke5rma0cmgnvtn77hvr", "input": { "model": "dev", "prompt": "tahktuah standing in in front of the golden gate bridge", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 4.57, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "2025-01-20 20:46:02.790 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-20 20:46:02.790 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2797.64it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2701.19it/s]\n2025-01-20 20:46:02.903 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s\n2025-01-20 20:46:02.904 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/d72345d095504252\n2025-01-20 20:46:03.018 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-01-20 20:46:03.018 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-20 20:46:03.018 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2806.30it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2708.84it/s]\n2025-01-20 20:46:03.131 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.23s\nUsing seed: 52650\n0it [00:00, ?it/s]\n1it [00:00, 8.33it/s]\n2it [00:00, 5.82it/s]\n3it [00:00, 5.30it/s]\n4it [00:00, 5.08it/s]\n5it [00:00, 4.94it/s]\n6it [00:01, 4.85it/s]\n7it [00:01, 4.83it/s]\n8it [00:01, 4.80it/s]\n9it [00:01, 4.78it/s]\n10it [00:02, 4.76it/s]\n11it [00:02, 4.75it/s]\n12it [00:02, 4.74it/s]\n13it [00:02, 4.75it/s]\n14it [00:02, 4.76it/s]\n15it [00:03, 4.75it/s]\n16it [00:03, 4.75it/s]\n17it [00:03, 4.74it/s]\n18it [00:03, 4.74it/s]\n19it [00:03, 4.75it/s]\n20it [00:04, 4.74it/s]\n21it [00:04, 4.75it/s]\n22it [00:04, 4.75it/s]\n23it [00:04, 4.75it/s]\n24it [00:04, 4.75it/s]\n25it [00:05, 4.75it/s]\n26it [00:05, 4.75it/s]\n27it [00:05, 4.74it/s]\n28it [00:05, 4.75it/s]\n28it [00:05, 4.82it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 6.505956063, "total_time": 8.223946 }, "output": [ "https://replicate.delivery/xezq/15resGr0Rt0FdScVaRQz0IzFWB1xYy8xSZIw8cqf3atRgGHUA/out-0.webp" ], "started_at": "2025-01-20T20:46:02.790990Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-ymto5kfp245a7pl4pe6ge3is5dnmi5tersg4vqu5udwcxodmn2ta", "get": "https://api.replicate.com/v1/predictions/b7xddygke5rma0cmgnvtn77hvr", "cancel": "https://api.replicate.com/v1/predictions/b7xddygke5rma0cmgnvtn77hvr/cancel" }, "version": "19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5" }Generated in2025-01-20 20:46:02.790 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-20 20:46:02.790 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2797.64it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2701.19it/s] 2025-01-20 20:46:02.903 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s 2025-01-20 20:46:02.904 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/d72345d095504252 2025-01-20 20:46:03.018 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-01-20 20:46:03.018 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-20 20:46:03.018 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2806.30it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2708.84it/s] 2025-01-20 20:46:03.131 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.23s Using seed: 52650 0it [00:00, ?it/s] 1it [00:00, 8.33it/s] 2it [00:00, 5.82it/s] 3it [00:00, 5.30it/s] 4it [00:00, 5.08it/s] 5it [00:00, 4.94it/s] 6it [00:01, 4.85it/s] 7it [00:01, 4.83it/s] 8it [00:01, 4.80it/s] 9it [00:01, 4.78it/s] 10it [00:02, 4.76it/s] 11it [00:02, 4.75it/s] 12it [00:02, 4.74it/s] 13it [00:02, 4.75it/s] 14it [00:02, 4.76it/s] 15it [00:03, 4.75it/s] 16it [00:03, 4.75it/s] 17it [00:03, 4.74it/s] 18it [00:03, 4.74it/s] 19it [00:03, 4.75it/s] 20it [00:04, 4.74it/s] 21it [00:04, 4.75it/s] 22it [00:04, 4.75it/s] 23it [00:04, 4.75it/s] 24it [00:04, 4.75it/s] 25it [00:05, 4.75it/s] 26it [00:05, 4.75it/s] 27it [00:05, 4.74it/s] 28it [00:05, 4.75it/s] 28it [00:05, 4.82it/s] Total safe images: 1 out of 1Prediction
sundai-club/ytahk13:19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5ID530rtsd1ddrme0cmgnwbnvkpswStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- tahktuah playing tennis at the us open in front of a crowd
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.56
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "tahktuah playing tennis at the us open in front of a crowd", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.56, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport 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 sundai-club/ytahk13 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sundai-club/ytahk13:19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5", { input: { model: "dev", prompt: "tahktuah playing tennis at the us open in front of a crowd", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.56, 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 replicateImport the client:import replicateRun sundai-club/ytahk13 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sundai-club/ytahk13:19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5", input={ "model": "dev", "prompt": "tahktuah playing tennis at the us open in front of a crowd", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.56, "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 sundai-club/ytahk13 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": "sundai-club/ytahk13:19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5", "input": { "model": "dev", "prompt": "tahktuah playing tennis at the us open in front of a crowd", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.56, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-01-20T20:47:51.584727Z", "created_at": "2025-01-20T20:47:42.955000Z", "data_removed": false, "error": null, "id": "530rtsd1ddrme0cmgnwbnvkpsw", "input": { "model": "dev", "prompt": "tahktuah playing tennis at the us open in front of a crowd", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.56, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "2025-01-20 20:47:43.125 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-20 20:47:43.125 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 91%|█████████▏| 278/304 [00:00<00:00, 2747.03it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2657.95it/s]\n2025-01-20 20:47:43.240 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s\nfree=29886214578176\nDownloading weights\n2025-01-20T20:47:43Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp50dt9pff/weights url=https://replicate.delivery/xezq/Qz6S3kLtMtKxEVrzV1kY4KLJY93AkGXonUfw9UiELE4PGjDKA/trained_model.tar\n2025-01-20T20:47:45Z | INFO | [ Complete ] dest=/tmp/tmp50dt9pff/weights size=\"172 MB\" total_elapsed=2.052s url=https://replicate.delivery/xezq/Qz6S3kLtMtKxEVrzV1kY4KLJY93AkGXonUfw9UiELE4PGjDKA/trained_model.tar\nDownloaded weights in 2.07s\n2025-01-20 20:47:45.316 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/d72345d095504252\n2025-01-20 20:47:45.385 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-01-20 20:47:45.385 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-20 20:47:45.385 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 91%|█████████▏| 278/304 [00:00<00:00, 2751.20it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2661.10it/s]\n2025-01-20 20:47:45.500 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.18s\nUsing seed: 19393\n0it [00:00, ?it/s]\n1it [00:00, 8.36it/s]\n2it [00:00, 5.76it/s]\n3it [00:00, 5.30it/s]\n4it [00:00, 5.12it/s]\n5it [00:00, 5.02it/s]\n6it [00:01, 4.94it/s]\n7it [00:01, 4.89it/s]\n8it [00:01, 4.87it/s]\n9it [00:01, 4.86it/s]\n10it [00:01, 4.86it/s]\n11it [00:02, 4.85it/s]\n12it [00:02, 4.84it/s]\n13it [00:02, 4.84it/s]\n14it [00:02, 4.84it/s]\n15it [00:03, 4.84it/s]\n16it [00:03, 4.84it/s]\n17it [00:03, 4.83it/s]\n18it [00:03, 4.83it/s]\n19it [00:03, 4.82it/s]\n20it [00:04, 4.83it/s]\n21it [00:04, 4.82it/s]\n22it [00:04, 4.82it/s]\n23it [00:04, 4.83it/s]\n24it [00:04, 4.83it/s]\n25it [00:05, 4.83it/s]\n26it [00:05, 4.83it/s]\n27it [00:05, 4.82it/s]\n28it [00:05, 4.82it/s]\n28it [00:05, 4.90it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 8.45904965, "total_time": 8.629727 }, "output": [ "https://replicate.delivery/xezq/4UQ9qCxGePXzQiRziWCjEpwG37nz8xvDwWpnuUVz4f93hGHUA/out-0.webp" ], "started_at": "2025-01-20T20:47:43.125677Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-dy34mvxjh6xh2jh6tsakjdo65uw6qjli6e2hpmoj6g7efxzf7kxq", "get": "https://api.replicate.com/v1/predictions/530rtsd1ddrme0cmgnwbnvkpsw", "cancel": "https://api.replicate.com/v1/predictions/530rtsd1ddrme0cmgnwbnvkpsw/cancel" }, "version": "19d33d2b70ef80e45cb831c8ed72f65083971e211a1ea7b8947cc54cf8d031f5" }Generated in2025-01-20 20:47:43.125 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-20 20:47:43.125 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 91%|█████████▏| 278/304 [00:00<00:00, 2747.03it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2657.95it/s] 2025-01-20 20:47:43.240 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s free=29886214578176 Downloading weights 2025-01-20T20:47:43Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp50dt9pff/weights url=https://replicate.delivery/xezq/Qz6S3kLtMtKxEVrzV1kY4KLJY93AkGXonUfw9UiELE4PGjDKA/trained_model.tar 2025-01-20T20:47:45Z | INFO | [ Complete ] dest=/tmp/tmp50dt9pff/weights size="172 MB" total_elapsed=2.052s url=https://replicate.delivery/xezq/Qz6S3kLtMtKxEVrzV1kY4KLJY93AkGXonUfw9UiELE4PGjDKA/trained_model.tar Downloaded weights in 2.07s 2025-01-20 20:47:45.316 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/d72345d095504252 2025-01-20 20:47:45.385 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-01-20 20:47:45.385 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-20 20:47:45.385 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 91%|█████████▏| 278/304 [00:00<00:00, 2751.20it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2661.10it/s] 2025-01-20 20:47:45.500 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.18s Using seed: 19393 0it [00:00, ?it/s] 1it [00:00, 8.36it/s] 2it [00:00, 5.76it/s] 3it [00:00, 5.30it/s] 4it [00:00, 5.12it/s] 5it [00:00, 5.02it/s] 6it [00:01, 4.94it/s] 7it [00:01, 4.89it/s] 8it [00:01, 4.87it/s] 9it [00:01, 4.86it/s] 10it [00:01, 4.86it/s] 11it [00:02, 4.85it/s] 12it [00:02, 4.84it/s] 13it [00:02, 4.84it/s] 14it [00:02, 4.84it/s] 15it [00:03, 4.84it/s] 16it [00:03, 4.84it/s] 17it [00:03, 4.83it/s] 18it [00:03, 4.83it/s] 19it [00:03, 4.82it/s] 20it [00:04, 4.83it/s] 21it [00:04, 4.82it/s] 22it [00:04, 4.82it/s] 23it [00:04, 4.83it/s] 24it [00:04, 4.83it/s] 25it [00:05, 4.83it/s] 26it [00:05, 4.83it/s] 27it [00:05, 4.82it/s] 28it [00:05, 4.82it/s] 28it [00:05, 4.90it/s] Total safe images: 1 out of 1
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