Prediction

Model

fofr/0_1-webp:e927742a

sthe3aedg9rm00che0xbk475xw

Status

Succeeded

Source

Web

Hardware

H100

Total duration

20.4s

Created

over 1 year ago

Webhook

–

Input

prompt: a portrait photo of 0_1 as a charismatic female speaker at conference, captured gesturing mid-speech on stage. She is wearing a light grey sweater. She's also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text "Replicate". She has perfect eyes. Behind her, there is a blurred background with a white banner containing logos and text (including replicate), a professional conference setting.
aspect_ratio: 3:4
model: dev
num_outputs: 1
num_inference_steps: 28
guidance_scale: 2.5
output_format: webp
output_quality: 90
lora_scale: 0.9

{
  "aspect_ratio": "3:4",
  "guidance_scale": 2.5,
  "lora_scale": 0.9,
  "model": "dev",
  "num_inference_steps": 28,
  "num_outputs": 1,
  "output_format": "webp",
  "output_quality": 90,
  "prompt": "a portrait photo of 0_1 as a charismatic female speaker at conference, captured gesturing mid-speech on stage. She is wearing a light grey sweater. She's also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text \"Replicate\". She has perfect eyes.\n\nBehind her, there is a blurred background with a white banner containing logos and text (including replicate), a professional conference setting."
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_FcL**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run fofr/0_1-webp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/0_1-webp:e927742a5f430e7e36f3a646ced840cebb4c59e00e1bd1993e068a7f97a85fd7",
  {
    input: {
      aspect_ratio: "3:4",
      guidance_scale: 2.5,
      lora_scale: 0.9,
      model: "dev",
      num_inference_steps: 28,
      num_outputs: 1,
      output_format: "webp",
      output_quality: 90,
      prompt: "a portrait photo of 0_1 as a charismatic female speaker at conference, captured gesturing mid-speech on stage. She is wearing a light grey sweater. She's also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text \"Replicate\". She has perfect eyes.\n\nBehind her, there is a blurred background with a white banner containing logos and text (including replicate), a professional conference setting."
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_FcL**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run fofr/0_1-webp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/0_1-webp:e927742a5f430e7e36f3a646ced840cebb4c59e00e1bd1993e068a7f97a85fd7",
    input={
        "aspect_ratio": "3:4",
        "guidance_scale": 2.5,
        "lora_scale": 0.9,
        "model": "dev",
        "num_inference_steps": 28,
        "num_outputs": 1,
        "output_format": "webp",
        "output_quality": 90,
        "prompt": "a portrait photo of 0_1 as a charismatic female speaker at conference, captured gesturing mid-speech on stage. She is wearing a light grey sweater. She's also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text \"Replicate\". She has perfect eyes.\n\nBehind her, there is a blurred background with a white banner containing logos and text (including replicate), a professional conference setting."
    }
)

# 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.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_FcL**********************************

This is your API token. Keep it to yourself.

Run fofr/0_1-webp 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/0_1-webp:e927742a5f430e7e36f3a646ced840cebb4c59e00e1bd1993e068a7f97a85fd7",
    "input": {
      "aspect_ratio": "3:4",
      "guidance_scale": 2.5,
      "lora_scale": 0.9,
      "model": "dev",
      "num_inference_steps": 28,
      "num_outputs": 1,
      "output_format": "webp",
      "output_quality": 90,
      "prompt": "a portrait photo of 0_1 as a charismatic female speaker at conference, captured gesturing mid-speech on stage. She is wearing a light grey sweater. She\'s also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text \\"Replicate\\". She has perfect eyes.\\n\\nBehind her, there is a blurred background with a white banner containing logos and text (including replicate), a professional conference setting."
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "id": "sthe3aedg9rm00che0xbk475xw",
  "model": "fofr/0_1-webp",
  "version": "e927742a5f430e7e36f3a646ced840cebb4c59e00e1bd1993e068a7f97a85fd7",
  "input": {
    "aspect_ratio": "3:4",
    "guidance_scale": 2.5,
    "lora_scale": 0.9,
    "model": "dev",
    "num_inference_steps": 28,
    "num_outputs": 1,
    "output_format": "webp",
    "output_quality": 90,
    "prompt": "a portrait photo of 0_1 as a charismatic female speaker at conference, captured gesturing mid-speech on stage. She is wearing a light grey sweater. She's also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text \"Replicate\". She has perfect eyes.\n\nBehind her, there is a blurred background with a white banner containing logos and text (including replicate), a professional conference setting."
  },
  "logs": "Using seed: 2697\nPrompt: a portrait photo of 0_1 as a charismatic female speaker at conference, captured gesturing mid-speech on stage. She is wearing a light grey sweater. She's also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text \"Replicate\". She has perfect eyes.\nBehind her, there is a blurred background with a white banner containing logos and text (including replicate), a professional conference setting.\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['text ( including replicate ), a professional conference setting.']\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.55it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.97it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.77it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.68it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.64it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.61it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.59it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.58it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.58it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.57it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.57it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.56it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.57it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.56it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.56it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.56it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.56it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.55it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.55it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.56it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.56it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.56it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.56it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.56it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.56it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.56it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.56it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.56it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.58it/s]",
  "output": [
    "https://replicate.delivery/yhqm/zNGay29l8SqjGBbhaPhXxfW3txrcpUbFcEgGPws6eBXHzlUTA/out-0.webp"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-08-20T14:39:47.586Z",
  "started_at": "2024-08-20T14:39:51.77226Z",
  "completed_at": "2024-08-20T14:40:07.946202Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/sthe3aedg9rm00che0xbk475xw/cancel",
    "get": "https://api.replicate.com/v1/predictions/sthe3aedg9rm00che0xbk475xw",
    "web": "https://replicate.com/p/sthe3aedg9rm00che0xbk475xw"
  },
  "metrics": {
    "predict_time": 16.173941447,
    "total_time": 20.360202
  }
}

Generated in

16.2 seconds

Tweak it Report

Using seed: 2697 Prompt: a portrait photo of 0_1 as a charismatic female speaker at conference, captured gesturing mid-speech on stage. She is wearing a light grey sweater. She's also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text "Replicate". She has perfect eyes. Behind her, there is a blurred background with a white banner containing logos and text (including replicate), a professional conference setting. txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['text ( including replicate ), a professional conference setting.'] 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.55it/s] 7%|▋ | 2/28 [00:00<00:06, 3.97it/s] 11%|█ | 3/28 [00:00<00:06, 3.77it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.68it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.64it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.61it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.59it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.58it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.58it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.57it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.57it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.56it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.57it/s] 50%|█████ | 14/28 [00:03<00:03, 3.56it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.56it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.56it/s] 61%|██████ | 17/28 [00:04<00:03, 3.56it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.55it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.55it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.56it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.56it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.56it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.56it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.56it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.56it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.56it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.56it/s] 100%|██████████| 28/28 [00:07<00:00, 3.56it/s] 100%|██████████| 28/28 [00:07<00:00, 3.58it/s]