igorriti / flux-fileteado
Fileteado porteño style for Flux (Updated 10 months ago)
- Public
- 191 runs
-
H100
Prediction
igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915ID6cxc7wpwe5rm40chb3pb7xb3egStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a sign with the text "Nacho" in the TOK style
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a sign with the text \"Nacho\" in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 igorriti/flux-fileteado using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", { input: { model: "dev", prompt: "a sign with the text \"Nacho\" in the TOK style", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, 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 igorriti/flux-fileteado using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", input={ "model": "dev", "prompt": "a sign with the text \"Nacho\" in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run igorriti/flux-fileteado 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": "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", "input": { "model": "dev", "prompt": "a sign with the text \\"Nacho\\" in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/igorriti/flux-fileteado@sha256:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915 \ -i 'model="dev"' \ -i $'prompt="a sign with the text \\"Nacho\\" in the TOK style"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="1:1"' \ -i 'output_format="webp"' \ -i 'guidance_scale=3.5' \ -i 'output_quality=80' \ -i 'num_inference_steps=28'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/igorriti/flux-fileteado@sha256:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "a sign with the text \\"Nacho\\" in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-16T02:03:43.984070Z", "created_at": "2024-08-16T02:03:23.633000Z", "data_removed": false, "error": null, "id": "6cxc7wpwe5rm40chb3pb7xb3eg", "input": { "model": "dev", "prompt": "a sign with the text \"Nacho\" in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 44898\nPrompt: a sign with the text \"Nacho\" in the TOK style\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9582453587968\nDownloading weights: https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar\n2024-08-16T02:03:25Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/308793ec9f86c6f5 url=https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar\n2024-08-16T02:03:27Z | INFO | [ Complete ] dest=/src/weights-cache/308793ec9f86c6f5 size=\"172 MB\" total_elapsed=2.021s url=https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar\nb''\nDownloaded weights in 2.0490853786468506 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.67it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.23it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.96it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.78it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.68it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.67it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 18.953292708, "total_time": 20.35107 }, "output": [ "https://replicate.delivery/yhqm/jY1yltaYyZ7ePSB97eJQ6hcZngnmaNoVfdJD1UtKMOpeXZMNB/out-0.webp" ], "started_at": "2024-08-16T02:03:25.030777Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6cxc7wpwe5rm40chb3pb7xb3eg", "cancel": "https://api.replicate.com/v1/predictions/6cxc7wpwe5rm40chb3pb7xb3eg/cancel" }, "version": "6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915" }Generated inUsing seed: 44898 Prompt: a sign with the text "Nacho" in the TOK style txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9582453587968 Downloading weights: https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar 2024-08-16T02:03:25Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/308793ec9f86c6f5 url=https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar 2024-08-16T02:03:27Z | INFO | [ Complete ] dest=/src/weights-cache/308793ec9f86c6f5 size="172 MB" total_elapsed=2.021s url=https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar b'' Downloaded weights in 2.0490853786468506 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.67it/s] 7%|▋ | 2/28 [00:00<00:06, 4.23it/s] 11%|█ | 3/28 [00:00<00:06, 3.96it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.78it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s] 50%|█████ | 14/28 [00:03<00:03, 3.68it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s] 61%|██████ | 17/28 [00:04<00:02, 3.67it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]Prediction
igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915ID8f5wyygy9drm00chb3q83cnw6wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a sign of buenos aires in the TOK style
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a sign of buenos aires in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 igorriti/flux-fileteado using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", { input: { model: "dev", prompt: "a sign of buenos aires in the TOK style", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 80, 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 igorriti/flux-fileteado using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", input={ "model": "dev", "prompt": "a sign of buenos aires in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run igorriti/flux-fileteado 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": "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", "input": { "model": "dev", "prompt": "a sign of buenos aires in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/igorriti/flux-fileteado@sha256:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915 \ -i 'model="dev"' \ -i 'prompt="a sign of buenos aires in the TOK style"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="16:9"' \ -i 'output_format="webp"' \ -i 'guidance_scale=3.5' \ -i 'output_quality=80' \ -i 'num_inference_steps=28'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/igorriti/flux-fileteado@sha256:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "a sign of buenos aires in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-16T02:05:09.751603Z", "created_at": "2024-08-16T02:04:46.027000Z", "data_removed": false, "error": null, "id": "8f5wyygy9drm00chb3q83cnw6w", "input": { "model": "dev", "prompt": "a sign of buenos aires in the TOK style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 26810\nPrompt: a sign of buenos aires in the TOK style\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9723637444608\nDownloading weights: https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar\n2024-08-16T02:04:48Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/308793ec9f86c6f5 url=https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar\n2024-08-16T02:04:52Z | INFO | [ Complete ] dest=/src/weights-cache/308793ec9f86c6f5 size=\"172 MB\" total_elapsed=3.607s url=https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar\nb''\nDownloaded weights in 3.7124156951904297 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:09, 2.93it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.77it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.73it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.72it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.71it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.70it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.70it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.70it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.70it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]", "metrics": { "predict_time": 20.934572022, "total_time": 23.724603 }, "output": [ "https://replicate.delivery/yhqm/ricB7W2jYdpxLFR0sVfbVfT7QQ7tiYMrU2CtvaYHzTQVXGTTA/out-0.webp" ], "started_at": "2024-08-16T02:04:48.817031Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/8f5wyygy9drm00chb3q83cnw6w", "cancel": "https://api.replicate.com/v1/predictions/8f5wyygy9drm00chb3q83cnw6w/cancel" }, "version": "6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915" }Generated inUsing seed: 26810 Prompt: a sign of buenos aires in the TOK style txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9723637444608 Downloading weights: https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar 2024-08-16T02:04:48Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/308793ec9f86c6f5 url=https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar 2024-08-16T02:04:52Z | INFO | [ Complete ] dest=/src/weights-cache/308793ec9f86c6f5 size="172 MB" total_elapsed=3.607s url=https://replicate.delivery/yhqm/qK6DpvdIIRaWBREhd1D5oeX6Rt8vRR6zoVr3obNB6RzgJjpJA/trained_model.tar b'' Downloaded weights in 3.7124156951904297 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.93it/s] 7%|▋ | 2/28 [00:00<00:06, 3.77it/s] 11%|█ | 3/28 [00:00<00:06, 3.73it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.72it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.71it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.70it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.70it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s] 61%|██████ | 17/28 [00:04<00:02, 3.70it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.70it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s]Prediction
igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915IDn69jbfqjcxrm00chb429d3fyxcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of a women with his face painted, TOK style makeup
- lora_scale
- 1.22
- num_outputs
- 3
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of a women with his face painted, TOK style makeup", "lora_scale": 1.22, "num_outputs": 3, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 igorriti/flux-fileteado using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", { input: { model: "dev", prompt: "a photo of a women with his face painted, TOK style makeup", lora_scale: 1.22, num_outputs: 3, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 80, 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 igorriti/flux-fileteado using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", input={ "model": "dev", "prompt": "a photo of a women with his face painted, TOK style makeup", "lora_scale": 1.22, "num_outputs": 3, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run igorriti/flux-fileteado 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": "igorriti/flux-fileteado:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915", "input": { "model": "dev", "prompt": "a photo of a women with his face painted, TOK style makeup", "lora_scale": 1.22, "num_outputs": 3, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/igorriti/flux-fileteado@sha256:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915 \ -i 'model="dev"' \ -i 'prompt="a photo of a women with his face painted, TOK style makeup"' \ -i 'lora_scale=1.22' \ -i 'num_outputs=3' \ -i 'aspect_ratio="16:9"' \ -i 'output_format="webp"' \ -i 'guidance_scale=3.5' \ -i 'output_quality=80' \ -i 'num_inference_steps=28'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/igorriti/flux-fileteado@sha256:6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "a photo of a women with his face painted, TOK style makeup", "lora_scale": 1.22, "num_outputs": 3, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-16T02:30:14.269239Z", "created_at": "2024-08-16T02:29:42.119000Z", "data_removed": false, "error": null, "id": "n69jbfqjcxrm00chb429d3fyxc", "input": { "model": "dev", "prompt": "a photo of a women with his face painted, TOK style makeup", "lora_scale": 1.22, "num_outputs": 3, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 44001\nPrompt: a photo of a women with his face painted, TOK style makeup\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:20, 1.31it/s]\n 7%|▋ | 2/28 [00:01<00:17, 1.50it/s]\n 11%|█ | 3/28 [00:02<00:17, 1.41it/s]\n 14%|█▍ | 4/28 [00:02<00:17, 1.38it/s]\n 18%|█▊ | 5/28 [00:03<00:16, 1.36it/s]\n 21%|██▏ | 6/28 [00:04<00:16, 1.35it/s]\n 25%|██▌ | 7/28 [00:05<00:15, 1.34it/s]\n 29%|██▊ | 8/28 [00:05<00:14, 1.33it/s]\n 32%|███▏ | 9/28 [00:06<00:14, 1.33it/s]\n 36%|███▌ | 10/28 [00:07<00:13, 1.33it/s]\n 39%|███▉ | 11/28 [00:08<00:12, 1.33it/s]\n 43%|████▎ | 12/28 [00:08<00:12, 1.32it/s]\n 46%|████▋ | 13/28 [00:09<00:11, 1.32it/s]\n 50%|█████ | 14/28 [00:10<00:10, 1.32it/s]\n 54%|█████▎ | 15/28 [00:11<00:09, 1.32it/s]\n 57%|█████▋ | 16/28 [00:11<00:09, 1.32it/s]\n 61%|██████ | 17/28 [00:12<00:08, 1.32it/s]\n 64%|██████▍ | 18/28 [00:13<00:07, 1.32it/s]\n 68%|██████▊ | 19/28 [00:14<00:06, 1.32it/s]\n 71%|███████▏ | 20/28 [00:14<00:06, 1.32it/s]\n 75%|███████▌ | 21/28 [00:15<00:05, 1.32it/s]\n 79%|███████▊ | 22/28 [00:16<00:04, 1.32it/s]\n 82%|████████▏ | 23/28 [00:17<00:03, 1.32it/s]\n 86%|████████▌ | 24/28 [00:17<00:03, 1.32it/s]\n 89%|████████▉ | 25/28 [00:18<00:02, 1.32it/s]\n 93%|█████████▎| 26/28 [00:19<00:01, 1.32it/s]\n 96%|█████████▋| 27/28 [00:20<00:00, 1.32it/s]\n100%|██████████| 28/28 [00:21<00:00, 1.32it/s]\n100%|██████████| 28/28 [00:21<00:00, 1.33it/s]", "metrics": { "predict_time": 30.348115891, "total_time": 32.150239 }, "output": [ "https://replicate.delivery/yhqm/pJFZsN5a1QqFMx0KOv9qDQGF69hzLWnoxAV6bTDlCFXtrx0E/out-0.webp", "https://replicate.delivery/yhqm/3BCKsPH029bjNFB80fxw4gd6hHrfdOiHghWfWCksDGosdNmmA/out-1.webp", "https://replicate.delivery/yhqm/eteqFdA6qLtzckFooSdFU3DieeA6EanQBA97DCE1WukZ7aMNB/out-2.webp" ], "started_at": "2024-08-16T02:29:43.921123Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/n69jbfqjcxrm00chb429d3fyxc", "cancel": "https://api.replicate.com/v1/predictions/n69jbfqjcxrm00chb429d3fyxc/cancel" }, "version": "6186ef6882ff2c1764686f32fe3b27e562a27edfc62eef5c7223c06ab1689915" }Generated inUsing seed: 44001 Prompt: a photo of a women with his face painted, TOK style makeup txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:20, 1.31it/s] 7%|▋ | 2/28 [00:01<00:17, 1.50it/s] 11%|█ | 3/28 [00:02<00:17, 1.41it/s] 14%|█▍ | 4/28 [00:02<00:17, 1.38it/s] 18%|█▊ | 5/28 [00:03<00:16, 1.36it/s] 21%|██▏ | 6/28 [00:04<00:16, 1.35it/s] 25%|██▌ | 7/28 [00:05<00:15, 1.34it/s] 29%|██▊ | 8/28 [00:05<00:14, 1.33it/s] 32%|███▏ | 9/28 [00:06<00:14, 1.33it/s] 36%|███▌ | 10/28 [00:07<00:13, 1.33it/s] 39%|███▉ | 11/28 [00:08<00:12, 1.33it/s] 43%|████▎ | 12/28 [00:08<00:12, 1.32it/s] 46%|████▋ | 13/28 [00:09<00:11, 1.32it/s] 50%|█████ | 14/28 [00:10<00:10, 1.32it/s] 54%|█████▎ | 15/28 [00:11<00:09, 1.32it/s] 57%|█████▋ | 16/28 [00:11<00:09, 1.32it/s] 61%|██████ | 17/28 [00:12<00:08, 1.32it/s] 64%|██████▍ | 18/28 [00:13<00:07, 1.32it/s] 68%|██████▊ | 19/28 [00:14<00:06, 1.32it/s] 71%|███████▏ | 20/28 [00:14<00:06, 1.32it/s] 75%|███████▌ | 21/28 [00:15<00:05, 1.32it/s] 79%|███████▊ | 22/28 [00:16<00:04, 1.32it/s] 82%|████████▏ | 23/28 [00:17<00:03, 1.32it/s] 86%|████████▌ | 24/28 [00:17<00:03, 1.32it/s] 89%|████████▉ | 25/28 [00:18<00:02, 1.32it/s] 93%|█████████▎| 26/28 [00:19<00:01, 1.32it/s] 96%|█████████▋| 27/28 [00:20<00:00, 1.32it/s] 100%|██████████| 28/28 [00:21<00:00, 1.32it/s] 100%|██████████| 28/28 [00:21<00:00, 1.33it/s]
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