fofr / flux-handwriting-and-sketch
(Updated 6 months ago)
- Public
- 829 runs
Prediction
fofr/flux-handwriting-and-sketch:d592f6972aadf30aadf04272cabb1698eb80feb669cbc356e8a3610d4fb2fb39IDxannsaw3gsrme0cn0vq8mjnm7gStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a HWRIT sketch
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a HWRIT sketch", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "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 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 fofr/flux-handwriting-and-sketch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-handwriting-and-sketch:d592f6972aadf30aadf04272cabb1698eb80feb669cbc356e8a3610d4fb2fb39", { input: { model: "dev", prompt: "a HWRIT sketch", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", 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 replicateImport the client:import replicateRun fofr/flux-handwriting-and-sketch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-handwriting-and-sketch:d592f6972aadf30aadf04272cabb1698eb80feb669cbc356e8a3610d4fb2fb39", input={ "model": "dev", "prompt": "a HWRIT sketch", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "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 fofr/flux-handwriting-and-sketch 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": "fofr/flux-handwriting-and-sketch:d592f6972aadf30aadf04272cabb1698eb80feb669cbc356e8a3610d4fb2fb39", "input": { "model": "dev", "prompt": "a HWRIT sketch", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "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.
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/fofr/flux-handwriting-and-sketch@sha256:d592f6972aadf30aadf04272cabb1698eb80feb669cbc356e8a3610d4fb2fb39 \ -i 'model="dev"' \ -i 'prompt="a HWRIT sketch"' \ -i 'go_fast=false' \ -i 'lora_scale=1' \ -i 'megapixels="1"' \ -i 'num_outputs=1' \ -i 'aspect_ratio="1:1"' \ -i 'output_format="webp"' \ -i 'guidance_scale=3' \ -i 'output_quality=80' \ -i 'prompt_strength=0.8' \ -i 'extra_lora_scale=1' \ -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/fofr/flux-handwriting-and-sketch@sha256:d592f6972aadf30aadf04272cabb1698eb80feb669cbc356e8a3610d4fb2fb39curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "a HWRIT sketch", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2025-02-15T00:07:40.652314Z", "created_at": "2025-02-15T00:07:29.414000Z", "data_removed": false, "error": null, "id": "xannsaw3gsrme0cn0vq8mjnm7g", "input": { "model": "dev", "prompt": "a HWRIT sketch", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "free=28191497994240\nDownloading weights\n2025-02-15T00:07:29Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpzzllwywg/weights url=https://replicate.delivery/xezq/JMDGg2KffFrVSkFMEGAKm0Hswyxct10C7MxVSSUlyBeVIl1nA/trained_model.tar\n2025-02-15T00:07:32Z | INFO | [ Complete ] dest=/tmp/tmpzzllwywg/weights size=\"172 MB\" total_elapsed=3.460s url=https://replicate.delivery/xezq/JMDGg2KffFrVSkFMEGAKm0Hswyxct10C7MxVSSUlyBeVIl1nA/trained_model.tar\nDownloaded weights in 3.48s\nLoaded LoRAs in 4.04s\nUsing seed: 6577\nPrompt: a HWRIT sketch\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 4.09it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.60it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.35it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.24it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.18it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.15it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 4.13it/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, 4.10it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 4.09it/s]\n 43%|████▎ | 12/28 [00:02<00:03, 4.09it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 4.09it/s]\n 50%|█████ | 14/28 [00:03<00:03, 4.09it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 4.08it/s]\n 57%|█████▋ | 16/28 [00:03<00:02, 4.08it/s]\n 61%|██████ | 17/28 [00:04<00:02, 4.08it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 4.08it/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, 4.08it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 4.08it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 4.08it/s]\n 86%|████████▌ | 24/28 [00:05<00:00, 4.07it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 4.08it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 4.08it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 4.08it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.08it/s]\n100%|██████████| 28/28 [00:06<00:00, 4.11it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 11.190425504, "total_time": 11.238314 }, "output": [ "https://replicate.delivery/xezq/IFmM4hDGfbTCcqq1vylWyo7DQlOWqS22OCdQv2HIyoWmZsHKA/out-0.webp" ], "started_at": "2025-02-15T00:07:29.461889Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-nauoh3ty7y7bbqbd7rtwl2u3xvkz5bmurclsnukql5amngz4fsra", "get": "https://api.replicate.com/v1/predictions/xannsaw3gsrme0cn0vq8mjnm7g", "cancel": "https://api.replicate.com/v1/predictions/xannsaw3gsrme0cn0vq8mjnm7g/cancel" }, "version": "d592f6972aadf30aadf04272cabb1698eb80feb669cbc356e8a3610d4fb2fb39" }Generated infree=28191497994240 Downloading weights 2025-02-15T00:07:29Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpzzllwywg/weights url=https://replicate.delivery/xezq/JMDGg2KffFrVSkFMEGAKm0Hswyxct10C7MxVSSUlyBeVIl1nA/trained_model.tar 2025-02-15T00:07:32Z | INFO | [ Complete ] dest=/tmp/tmpzzllwywg/weights size="172 MB" total_elapsed=3.460s url=https://replicate.delivery/xezq/JMDGg2KffFrVSkFMEGAKm0Hswyxct10C7MxVSSUlyBeVIl1nA/trained_model.tar Downloaded weights in 3.48s Loaded LoRAs in 4.04s Using seed: 6577 Prompt: a HWRIT sketch [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 4.09it/s] 7%|▋ | 2/28 [00:00<00:05, 4.60it/s] 11%|█ | 3/28 [00:00<00:05, 4.35it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.24it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.18it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.15it/s] 25%|██▌ | 7/28 [00:01<00:05, 4.13it/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, 4.10it/s] 39%|███▉ | 11/28 [00:02<00:04, 4.09it/s] 43%|████▎ | 12/28 [00:02<00:03, 4.09it/s] 46%|████▋ | 13/28 [00:03<00:03, 4.09it/s] 50%|█████ | 14/28 [00:03<00:03, 4.09it/s] 54%|█████▎ | 15/28 [00:03<00:03, 4.08it/s] 57%|█████▋ | 16/28 [00:03<00:02, 4.08it/s] 61%|██████ | 17/28 [00:04<00:02, 4.08it/s] 64%|██████▍ | 18/28 [00:04<00:02, 4.08it/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, 4.08it/s] 79%|███████▊ | 22/28 [00:05<00:01, 4.08it/s] 82%|████████▏ | 23/28 [00:05<00:01, 4.08it/s] 86%|████████▌ | 24/28 [00:05<00:00, 4.07it/s] 89%|████████▉ | 25/28 [00:06<00:00, 4.08it/s] 93%|█████████▎| 26/28 [00:06<00:00, 4.08it/s] 96%|█████████▋| 27/28 [00:06<00:00, 4.08it/s] 100%|██████████| 28/28 [00:06<00:00, 4.08it/s] 100%|██████████| 28/28 [00:06<00:00, 4.11it/s] Total safe images: 1 out of 1
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