lucataco / flux-vlta-layer
Flux finetune of Violeta - specific layer training
- Public
- 443 runs
-
H100
Prediction
lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6ID73bxhk6y2drm20cjd1284w6ws8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- portrait photo of VLTA with purple hair
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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 lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", { input: { model: "dev", prompt: "portrait photo of VLTA with purple hair", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", input={ "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run lucataco/flux-vlta-layer 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": "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", "input": { "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-10-07T18:36:44.379510Z", "created_at": "2024-10-07T18:36:36.755000Z", "data_removed": false, "error": null, "id": "73bxhk6y2drm20cjd1284w6ws8", "input": { "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 44675\nPrompt: portrait photo of VLTA with purple hair\n[!] txt2img mode\nUsing dev model\nfree=6713653563392\nDownloading weights\n2024-10-07T18:36:36Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpgoj603ls/weights url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar\n2024-10-07T18:36:36Z | INFO | [ Complete ] dest=/tmp/tmpgoj603ls/weights size=\"614 kB\" total_elapsed=0.137s url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar\nDownloaded weights in 0.16s\nLoaded LoRAs in 0.23s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 4.10it/s]\n 7%|▋ | 2/28 [00:00<00:05, 5.01it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.54it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.34it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.23it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.18it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.14it/s]\n 29%|██▊ | 8/28 [00:01<00:04, 4.11it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 4.10it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 4.09it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.08it/s]\n 43%|████▎ | 12/28 [00:02<00:03, 4.08it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 4.08it/s]\n 50%|█████ | 14/28 [00:03<00:03, 4.07it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 4.07it/s]\n 57%|█████▋ | 16/28 [00:03<00:02, 4.07it/s]\n 61%|██████ | 17/28 [00:04<00:02, 4.07it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 4.06it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 4.06it/s]\n 71%|███████▏ | 20/28 [00:04<00:01, 4.06it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 4.06it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 4.06it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 4.06it/s]\n 86%|████████▌ | 24/28 [00:05<00:00, 4.06it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 4.06it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 4.06it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 4.06it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.06it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.11it/s]", "metrics": { "predict_time": 7.618202998, "total_time": 7.62451 }, "output": [ "https://replicate.delivery/yhqm/iHuXyImETcJlNl8cUly3TZ2aViYDj8MuGBgl9lQxhRIPcH5E/out-0.webp" ], "started_at": "2024-10-07T18:36:36.761307Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/73bxhk6y2drm20cjd1284w6ws8", "cancel": "https://api.replicate.com/v1/predictions/73bxhk6y2drm20cjd1284w6ws8/cancel" }, "version": "f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6" }Generated inUsing seed: 44675 Prompt: portrait photo of VLTA with purple hair [!] txt2img mode Using dev model free=6713653563392 Downloading weights 2024-10-07T18:36:36Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpgoj603ls/weights url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar 2024-10-07T18:36:36Z | INFO | [ Complete ] dest=/tmp/tmpgoj603ls/weights size="614 kB" total_elapsed=0.137s url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar Downloaded weights in 0.16s Loaded LoRAs in 0.23s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 4.10it/s] 7%|▋ | 2/28 [00:00<00:05, 5.01it/s] 11%|█ | 3/28 [00:00<00:05, 4.54it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.34it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.23it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.18it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.14it/s] 29%|██▊ | 8/28 [00:01<00:04, 4.11it/s] 32%|███▏ | 9/28 [00:02<00:04, 4.10it/s] 36%|███▌ | 10/28 [00:02<00:04, 4.09it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.08it/s] 43%|████▎ | 12/28 [00:02<00:03, 4.08it/s] 46%|████▋ | 13/28 [00:03<00:03, 4.08it/s] 50%|█████ | 14/28 [00:03<00:03, 4.07it/s] 54%|█████▎ | 15/28 [00:03<00:03, 4.07it/s] 57%|█████▋ | 16/28 [00:03<00:02, 4.07it/s] 61%|██████ | 17/28 [00:04<00:02, 4.07it/s] 64%|██████▍ | 18/28 [00:04<00:02, 4.06it/s] 68%|██████▊ | 19/28 [00:04<00:02, 4.06it/s] 71%|███████▏ | 20/28 [00:04<00:01, 4.06it/s] 75%|███████▌ | 21/28 [00:05<00:01, 4.06it/s] 79%|███████▊ | 22/28 [00:05<00:01, 4.06it/s] 82%|████████▏ | 23/28 [00:05<00:01, 4.06it/s] 86%|████████▌ | 24/28 [00:05<00:00, 4.06it/s] 89%|████████▉ | 25/28 [00:06<00:00, 4.06it/s] 93%|█████████▎| 26/28 [00:06<00:00, 4.06it/s] 96%|█████████▋| 27/28 [00:06<00:00, 4.06it/s] 100%|██████████| 28/28 [00:06<00:00, 4.06it/s] 100%|██████████| 28/28 [00:06<00:00, 4.11it/s]Prediction
lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6ID73wfm5rk7srm00cjd13syn2wm8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- portrait photo of VLTA with purple hair
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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 lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", { input: { model: "dev", prompt: "portrait photo of VLTA with purple hair", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", input={ "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run lucataco/flux-vlta-layer 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": "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", "input": { "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-10-07T18:39:32.937546Z", "created_at": "2024-10-07T18:39:01.438000Z", "data_removed": false, "error": null, "id": "73wfm5rk7srm00cjd13syn2wm8", "input": { "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 33270\nPrompt: portrait photo of VLTA with purple hair\n[!] txt2img mode\nUsing dev model\nfree=7480087928832\nDownloading weights\n2024-10-07T18:39:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpl8wl5pn8/weights url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar\n2024-10-07T18:39:25Z | INFO | [ Complete ] dest=/tmp/tmpl8wl5pn8/weights size=\"614 kB\" total_elapsed=0.163s url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar\nDownloaded weights in 0.25s\nLoaded LoRAs in 0.33s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 4.12it/s]\n 7%|▋ | 2/28 [00:00<00:05, 5.03it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.57it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.38it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.27it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.21it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.18it/s]\n 29%|██▊ | 8/28 [00:01<00:04, 4.15it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 4.13it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 4.12it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.11it/s]\n 43%|████▎ | 12/28 [00:02<00:03, 4.10it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 4.11it/s]\n 50%|█████ | 14/28 [00:03<00:03, 4.10it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 4.10it/s]\n 57%|█████▋ | 16/28 [00:03<00:02, 4.10it/s]\n 61%|██████ | 17/28 [00:04<00:02, 4.10it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 4.10it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 4.10it/s]\n 71%|███████▏ | 20/28 [00:04<00:01, 4.10it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 4.10it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 4.10it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 4.10it/s]\n 86%|████████▌ | 24/28 [00:05<00:00, 4.10it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 4.10it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 4.10it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 4.10it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.11it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.15it/s]", "metrics": { "predict_time": 7.443578779, "total_time": 31.499546 }, "output": [ "https://replicate.delivery/yhqm/86MJAcaMmc4mCpHBgs0JivvueMEDXXW8yfoctiw59uZkzdkTA/out-0.webp" ], "started_at": "2024-10-07T18:39:25.493967Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/73wfm5rk7srm00cjd13syn2wm8", "cancel": "https://api.replicate.com/v1/predictions/73wfm5rk7srm00cjd13syn2wm8/cancel" }, "version": "f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6" }Generated inUsing seed: 33270 Prompt: portrait photo of VLTA with purple hair [!] txt2img mode Using dev model free=7480087928832 Downloading weights 2024-10-07T18:39:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpl8wl5pn8/weights url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar 2024-10-07T18:39:25Z | INFO | [ Complete ] dest=/tmp/tmpl8wl5pn8/weights size="614 kB" total_elapsed=0.163s url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar Downloaded weights in 0.25s Loaded LoRAs in 0.33s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 4.12it/s] 7%|▋ | 2/28 [00:00<00:05, 5.03it/s] 11%|█ | 3/28 [00:00<00:05, 4.57it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.38it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.27it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.21it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.18it/s] 29%|██▊ | 8/28 [00:01<00:04, 4.15it/s] 32%|███▏ | 9/28 [00:02<00:04, 4.13it/s] 36%|███▌ | 10/28 [00:02<00:04, 4.12it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.11it/s] 43%|████▎ | 12/28 [00:02<00:03, 4.10it/s] 46%|████▋ | 13/28 [00:03<00:03, 4.11it/s] 50%|█████ | 14/28 [00:03<00:03, 4.10it/s] 54%|█████▎ | 15/28 [00:03<00:03, 4.10it/s] 57%|█████▋ | 16/28 [00:03<00:02, 4.10it/s] 61%|██████ | 17/28 [00:04<00:02, 4.10it/s] 64%|██████▍ | 18/28 [00:04<00:02, 4.10it/s] 68%|██████▊ | 19/28 [00:04<00:02, 4.10it/s] 71%|███████▏ | 20/28 [00:04<00:01, 4.10it/s] 75%|███████▌ | 21/28 [00:05<00:01, 4.10it/s] 79%|███████▊ | 22/28 [00:05<00:01, 4.10it/s] 82%|████████▏ | 23/28 [00:05<00:01, 4.10it/s] 86%|████████▌ | 24/28 [00:05<00:00, 4.10it/s] 89%|████████▉ | 25/28 [00:06<00:00, 4.10it/s] 93%|█████████▎| 26/28 [00:06<00:00, 4.10it/s] 96%|█████████▋| 27/28 [00:06<00:00, 4.10it/s] 100%|██████████| 28/28 [00:06<00:00, 4.11it/s] 100%|██████████| 28/28 [00:06<00:00, 4.15it/s]Prediction
lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6ID5akghgtw99rm20cjd1wtxgqykgStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 32190
- model
- dev
- prompt
- portrait photo of VLTA with purple hair
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "seed": 32190, "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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 lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", { input: { seed: 32190, model: "dev", prompt: "portrait photo of VLTA with purple hair", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", input={ "seed": 32190, "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run lucataco/flux-vlta-layer 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": "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", "input": { "seed": 32190, "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-10-07T19:34:06.851155Z", "created_at": "2024-10-07T19:33:56.938000Z", "data_removed": false, "error": null, "id": "5akghgtw99rm20cjd1wtxgqykg", "input": { "seed": 32190, "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 32190\nPrompt: portrait photo of VLTA with purple hair\n[!] txt2img mode\nUsing dev model\nfree=6361538617344\nDownloading weights\n2024-10-07T19:33:59Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp7tb8md4h/weights url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar\n2024-10-07T19:33:59Z | INFO | [ Complete ] dest=/tmp/tmp7tb8md4h/weights size=\"614 kB\" total_elapsed=0.127s url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar\nDownloaded weights in 0.15s\nLoaded LoRAs in 0.23s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 4.11it/s]\n 7%|▋ | 2/28 [00:00<00:05, 5.02it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.55it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.36it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.25it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.19it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.16it/s]\n 29%|██▊ | 8/28 [00:01<00:04, 4.13it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 4.13it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 4.11it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.09it/s]\n 43%|████▎ | 12/28 [00:02<00:03, 4.08it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 4.07it/s]\n 50%|█████ | 14/28 [00:03<00:03, 4.08it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 4.08it/s]\n 57%|█████▋ | 16/28 [00:03<00:02, 4.07it/s]\n 61%|██████ | 17/28 [00:04<00:02, 4.06it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 4.06it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 4.06it/s]\n 71%|███████▏ | 20/28 [00:04<00:01, 4.06it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 4.05it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 4.05it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 4.06it/s]\n 86%|████████▌ | 24/28 [00:05<00:00, 4.06it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 4.06it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 4.06it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 4.06it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.07it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.12it/s]", "metrics": { "predict_time": 7.390135199, "total_time": 9.913155 }, "output": [ "https://replicate.delivery/yhqm/bDmfzBd0cLyBOCAGqbuAmETUJVSchZ04s3OmHb90UIZXTPyJA/out-0.webp" ], "started_at": "2024-10-07T19:33:59.461020Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5akghgtw99rm20cjd1wtxgqykg", "cancel": "https://api.replicate.com/v1/predictions/5akghgtw99rm20cjd1wtxgqykg/cancel" }, "version": "f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6" }Generated inUsing seed: 32190 Prompt: portrait photo of VLTA with purple hair [!] txt2img mode Using dev model free=6361538617344 Downloading weights 2024-10-07T19:33:59Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp7tb8md4h/weights url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar 2024-10-07T19:33:59Z | INFO | [ Complete ] dest=/tmp/tmp7tb8md4h/weights size="614 kB" total_elapsed=0.127s url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar Downloaded weights in 0.15s Loaded LoRAs in 0.23s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 4.11it/s] 7%|▋ | 2/28 [00:00<00:05, 5.02it/s] 11%|█ | 3/28 [00:00<00:05, 4.55it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.36it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.25it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.19it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.16it/s] 29%|██▊ | 8/28 [00:01<00:04, 4.13it/s] 32%|███▏ | 9/28 [00:02<00:04, 4.13it/s] 36%|███▌ | 10/28 [00:02<00:04, 4.11it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.09it/s] 43%|████▎ | 12/28 [00:02<00:03, 4.08it/s] 46%|████▋ | 13/28 [00:03<00:03, 4.07it/s] 50%|█████ | 14/28 [00:03<00:03, 4.08it/s] 54%|█████▎ | 15/28 [00:03<00:03, 4.08it/s] 57%|█████▋ | 16/28 [00:03<00:02, 4.07it/s] 61%|██████ | 17/28 [00:04<00:02, 4.06it/s] 64%|██████▍ | 18/28 [00:04<00:02, 4.06it/s] 68%|██████▊ | 19/28 [00:04<00:02, 4.06it/s] 71%|███████▏ | 20/28 [00:04<00:01, 4.06it/s] 75%|███████▌ | 21/28 [00:05<00:01, 4.05it/s] 79%|███████▊ | 22/28 [00:05<00:01, 4.05it/s] 82%|████████▏ | 23/28 [00:05<00:01, 4.06it/s] 86%|████████▌ | 24/28 [00:05<00:00, 4.06it/s] 89%|████████▉ | 25/28 [00:06<00:00, 4.06it/s] 93%|█████████▎| 26/28 [00:06<00:00, 4.06it/s] 96%|█████████▋| 27/28 [00:06<00:00, 4.06it/s] 100%|██████████| 28/28 [00:06<00:00, 4.07it/s] 100%|██████████| 28/28 [00:06<00:00, 4.12it/s]Prediction
lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6IDjz8werwm4srm00cjd12tbg374cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- portrait photo of VLTA with purple hair
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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 lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", { input: { model: "dev", prompt: "portrait photo of VLTA with purple hair", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", input={ "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run lucataco/flux-vlta-layer 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": "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", "input": { "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-10-07T18:37:30.675958Z", "created_at": "2024-10-07T18:37:23.366000Z", "data_removed": false, "error": null, "id": "jz8werwm4srm00cjd12tbg374c", "input": { "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 17755\nPrompt: portrait photo of VLTA with purple hair\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.01s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 4.13it/s]\n 7%|▋ | 2/28 [00:00<00:05, 5.05it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.58it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.38it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.28it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.22it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.18it/s]\n 29%|██▊ | 8/28 [00:01<00:04, 4.16it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 4.14it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 4.13it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.12it/s]\n 43%|████▎ | 12/28 [00:02<00:03, 4.11it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 4.10it/s]\n 50%|█████ | 14/28 [00:03<00:03, 4.11it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 4.11it/s]\n 57%|█████▋ | 16/28 [00:03<00:02, 4.10it/s]\n 61%|██████ | 17/28 [00:04<00:02, 4.10it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 4.09it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 4.10it/s]\n 71%|███████▏ | 20/28 [00:04<00:01, 4.11it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 4.10it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 4.10it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 4.09it/s]\n 86%|████████▌ | 24/28 [00:05<00:00, 4.09it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 4.09it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 4.10it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 4.10it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.10it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.15it/s]", "metrics": { "predict_time": 7.302493211, "total_time": 7.309958 }, "output": [ "https://replicate.delivery/yhqm/BkOxP0wilf2wdSbDfdZnfSYTlBOCFraBY4rxAbxWtnvUj7InA/out-0.webp" ], "started_at": "2024-10-07T18:37:23.373464Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jz8werwm4srm00cjd12tbg374c", "cancel": "https://api.replicate.com/v1/predictions/jz8werwm4srm00cjd12tbg374c/cancel" }, "version": "f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6" }Generated inUsing seed: 17755 Prompt: portrait photo of VLTA with purple hair [!] txt2img mode Using dev model Weights already loaded Loaded LoRAs in 0.01s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 4.13it/s] 7%|▋ | 2/28 [00:00<00:05, 5.05it/s] 11%|█ | 3/28 [00:00<00:05, 4.58it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.38it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.28it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.22it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.18it/s] 29%|██▊ | 8/28 [00:01<00:04, 4.16it/s] 32%|███▏ | 9/28 [00:02<00:04, 4.14it/s] 36%|███▌ | 10/28 [00:02<00:04, 4.13it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.12it/s] 43%|████▎ | 12/28 [00:02<00:03, 4.11it/s] 46%|████▋ | 13/28 [00:03<00:03, 4.10it/s] 50%|█████ | 14/28 [00:03<00:03, 4.11it/s] 54%|█████▎ | 15/28 [00:03<00:03, 4.11it/s] 57%|█████▋ | 16/28 [00:03<00:02, 4.10it/s] 61%|██████ | 17/28 [00:04<00:02, 4.10it/s] 64%|██████▍ | 18/28 [00:04<00:02, 4.09it/s] 68%|██████▊ | 19/28 [00:04<00:02, 4.10it/s] 71%|███████▏ | 20/28 [00:04<00:01, 4.11it/s] 75%|███████▌ | 21/28 [00:05<00:01, 4.10it/s] 79%|███████▊ | 22/28 [00:05<00:01, 4.10it/s] 82%|████████▏ | 23/28 [00:05<00:01, 4.09it/s] 86%|████████▌ | 24/28 [00:05<00:00, 4.09it/s] 89%|████████▉ | 25/28 [00:06<00:00, 4.09it/s] 93%|█████████▎| 26/28 [00:06<00:00, 4.10it/s] 96%|█████████▋| 27/28 [00:06<00:00, 4.10it/s] 100%|██████████| 28/28 [00:06<00:00, 4.10it/s] 100%|██████████| 28/28 [00:06<00:00, 4.15it/s]Prediction
lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6ID9e34e54m2srm60cjd129shasamStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- portrait photo of VLTA with purple hair
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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 lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", { input: { model: "dev", prompt: "portrait photo of VLTA with purple hair", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lucataco/flux-vlta-layer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", input={ "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run lucataco/flux-vlta-layer 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": "lucataco/flux-vlta-layer:f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6", "input": { "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-10-07T18:36:31.237018Z", "created_at": "2024-10-07T18:36:17.814000Z", "data_removed": false, "error": null, "id": "9e34e54m2srm60cjd129shasam", "input": { "model": "dev", "prompt": "portrait photo of VLTA with purple hair", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 56842\nPrompt: portrait photo of VLTA with purple hair\n[!] txt2img mode\nUsing dev model\nfree=7472369057792\nDownloading weights\n2024-10-07T18:36:23Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxxh6lgi2/weights url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar\n2024-10-07T18:36:24Z | INFO | [ Complete ] dest=/tmp/tmpxxh6lgi2/weights size=\"614 kB\" total_elapsed=0.110s url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar\nDownloaded weights in 0.13s\nLoaded LoRAs in 0.21s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 4.12it/s]\n 7%|▋ | 2/28 [00:00<00:05, 5.03it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.55it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.36it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.25it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.19it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.16it/s]\n 29%|██▊ | 8/28 [00:01<00:04, 4.14it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 4.12it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 4.12it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.10it/s]\n 43%|████▎ | 12/28 [00:02<00:03, 4.08it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 4.07it/s]\n 50%|█████ | 14/28 [00:03<00:03, 4.08it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 4.08it/s]\n 57%|█████▋ | 16/28 [00:03<00:02, 4.07it/s]\n 61%|██████ | 17/28 [00:04<00:02, 4.07it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 4.07it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 4.08it/s]\n 71%|███████▏ | 20/28 [00:04<00:01, 4.08it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 4.07it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 4.07it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 4.07it/s]\n 86%|████████▌ | 24/28 [00:05<00:00, 4.07it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 4.07it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 4.07it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 4.07it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.08it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.12it/s]", "metrics": { "predict_time": 7.361963791, "total_time": 13.423018 }, "output": [ "https://replicate.delivery/yhqm/wAvL6kCPJfVKCCaC2AAygykyQQsfcoUpm1DTaesOAe68C3ROB/out-0.webp" ], "started_at": "2024-10-07T18:36:23.875055Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/9e34e54m2srm60cjd129shasam", "cancel": "https://api.replicate.com/v1/predictions/9e34e54m2srm60cjd129shasam/cancel" }, "version": "f8f977857adf7cc8efb3a53eb44be614c521cc26972996be93f771bc8ce1a0d6" }Generated inUsing seed: 56842 Prompt: portrait photo of VLTA with purple hair [!] txt2img mode Using dev model free=7472369057792 Downloading weights 2024-10-07T18:36:23Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxxh6lgi2/weights url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar 2024-10-07T18:36:24Z | INFO | [ Complete ] dest=/tmp/tmpxxh6lgi2/weights size="614 kB" total_elapsed=0.110s url=https://replicate.delivery/yhqm/4YzMFDejcdxHNC9QXDNmkEzQYLly5EScyzD3luzhrF2eEgjTA/trained_model.tar Downloaded weights in 0.13s Loaded LoRAs in 0.21s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 4.12it/s] 7%|▋ | 2/28 [00:00<00:05, 5.03it/s] 11%|█ | 3/28 [00:00<00:05, 4.55it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.36it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.25it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.19it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.16it/s] 29%|██▊ | 8/28 [00:01<00:04, 4.14it/s] 32%|███▏ | 9/28 [00:02<00:04, 4.12it/s] 36%|███▌ | 10/28 [00:02<00:04, 4.12it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.10it/s] 43%|████▎ | 12/28 [00:02<00:03, 4.08it/s] 46%|████▋ | 13/28 [00:03<00:03, 4.07it/s] 50%|█████ | 14/28 [00:03<00:03, 4.08it/s] 54%|█████▎ | 15/28 [00:03<00:03, 4.08it/s] 57%|█████▋ | 16/28 [00:03<00:02, 4.07it/s] 61%|██████ | 17/28 [00:04<00:02, 4.07it/s] 64%|██████▍ | 18/28 [00:04<00:02, 4.07it/s] 68%|██████▊ | 19/28 [00:04<00:02, 4.08it/s] 71%|███████▏ | 20/28 [00:04<00:01, 4.08it/s] 75%|███████▌ | 21/28 [00:05<00:01, 4.07it/s] 79%|███████▊ | 22/28 [00:05<00:01, 4.07it/s] 82%|████████▏ | 23/28 [00:05<00:01, 4.07it/s] 86%|████████▌ | 24/28 [00:05<00:00, 4.07it/s] 89%|████████▉ | 25/28 [00:06<00:00, 4.07it/s] 93%|█████████▎| 26/28 [00:06<00:00, 4.07it/s] 96%|█████████▋| 27/28 [00:06<00:00, 4.07it/s] 100%|██████████| 28/28 [00:06<00:00, 4.08it/s] 100%|██████████| 28/28 [00:06<00:00, 4.12it/s]
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