Prediction

Model

extrange/qinxiang:9f927bc8

iy2tuv3bjmmh35pwkeuzok2cwm

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

66.7s

Created

about 2 years ago

Webhook

–

Input

prompt: profile of TOK, formal wear, professional photograph, DSLR
negative_prompt: Empty string
width: 1024
height: 1024
num_outputs: 4
scheduler: K_EULER
num_inference_steps: 50
guidance_scale: 7.5
prompt_strength: 0.8
refine: no_refiner
high_noise_frac: 0.8
apply_watermark: true
lora_scale: 0.6

{
  "apply_watermark": true,
  "guidance_scale": 7.5,
  "height": 1024,
  "high_noise_frac": 0.8,
  "lora_scale": 0.6,
  "negative_prompt": "",
  "num_inference_steps": 50,
  "num_outputs": 4,
  "prompt": "profile of TOK, formal wear, professional photograph, DSLR",
  "prompt_strength": 0.8,
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "width": 1024
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_PAL**********************************

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 extrange/qinxiang using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "extrange/qinxiang:9f927bc8a17986b557daa8e4f655ef7c09a112e4e0ec5ba6806f36e56ec9f56b",
  {
    input: {
      apply_watermark: true,
      guidance_scale: 7.5,
      height: 1024,
      high_noise_frac: 0.8,
      lora_scale: 0.6,
      negative_prompt: "",
      num_inference_steps: 50,
      num_outputs: 4,
      prompt: "profile of TOK, formal wear, professional photograph, DSLR",
      prompt_strength: 0.8,
      refine: "no_refiner",
      scheduler: "K_EULER",
      width: 1024
    }
  }
);

// 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_PAL**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run extrange/qinxiang using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "extrange/qinxiang:9f927bc8a17986b557daa8e4f655ef7c09a112e4e0ec5ba6806f36e56ec9f56b",
    input={
        "apply_watermark": True,
        "guidance_scale": 7.5,
        "height": 1024,
        "high_noise_frac": 0.8,
        "lora_scale": 0.6,
        "negative_prompt": "",
        "num_inference_steps": 50,
        "num_outputs": 4,
        "prompt": "profile of TOK, formal wear, professional photograph, DSLR",
        "prompt_strength": 0.8,
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "width": 1024
    }
)

# 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_PAL**********************************

This is your API token. Keep it to yourself.

Run extrange/qinxiang 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": "extrange/qinxiang:9f927bc8a17986b557daa8e4f655ef7c09a112e4e0ec5ba6806f36e56ec9f56b",
    "input": {
      "apply_watermark": true,
      "guidance_scale": 7.5,
      "height": 1024,
      "high_noise_frac": 0.8,
      "lora_scale": 0.6,
      "negative_prompt": "",
      "num_inference_steps": 50,
      "num_outputs": 4,
      "prompt": "profile of TOK, formal wear, professional photograph, DSLR",
      "prompt_strength": 0.8,
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "width": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "iy2tuv3bjmmh35pwkeuzok2cwm",
  "model": "extrange/qinxiang",
  "version": "9f927bc8a17986b557daa8e4f655ef7c09a112e4e0ec5ba6806f36e56ec9f56b",
  "input": {
    "apply_watermark": true,
    "guidance_scale": 7.5,
    "height": 1024,
    "high_noise_frac": 0.8,
    "lora_scale": 0.6,
    "negative_prompt": "",
    "num_inference_steps": 50,
    "num_outputs": 4,
    "prompt": "profile of TOK, formal wear, professional photograph, DSLR",
    "prompt_strength": 0.8,
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "width": 1024
  },
  "logs": "Using seed: 43767\nEnsuring enough disk space...\nFree disk space: 1553610043392\nDownloading weights: https://pbxt.replicate.delivery/kNaheb5WGz3TG6gDZgmEQBBe27HmyvXff2R9iyLa3UIckzHHB/trained_model.tar\nb'Downloaded 186 MB bytes in 4.320s (43 MB/s)\\nExtracted 186 MB in 0.078s (2.4 GB/s)\\n'\nDownloaded weights in 4.760844707489014 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: profile of <s0><s1>, formal wear, professional photograph, DSLR\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:01<00:51,  1.05s/it]\n  4%|▍         | 2/50 [00:02<00:50,  1.05s/it]\n  6%|▌         | 3/50 [00:03<00:49,  1.04s/it]\n  8%|▊         | 4/50 [00:04<00:48,  1.05s/it]\n 10%|█         | 5/50 [00:05<00:47,  1.05s/it]\n 12%|█▏        | 6/50 [00:06<00:46,  1.05s/it]\n 14%|█▍        | 7/50 [00:07<00:45,  1.05s/it]\n 16%|█▌        | 8/50 [00:08<00:44,  1.05s/it]\n 18%|█▊        | 9/50 [00:09<00:43,  1.05s/it]\n 20%|██        | 10/50 [00:10<00:41,  1.05s/it]\n 22%|██▏       | 11/50 [00:11<00:40,  1.05s/it]\n 24%|██▍       | 12/50 [00:12<00:39,  1.05s/it]\n 26%|██▌       | 13/50 [00:13<00:38,  1.05s/it]\n 28%|██▊       | 14/50 [00:14<00:37,  1.05s/it]\n 30%|███       | 15/50 [00:15<00:36,  1.05s/it]\n 32%|███▏      | 16/50 [00:16<00:35,  1.05s/it]\n 34%|███▍      | 17/50 [00:17<00:34,  1.05s/it]\n 36%|███▌      | 18/50 [00:18<00:33,  1.05s/it]\n 38%|███▊      | 19/50 [00:19<00:32,  1.05s/it]\n 40%|████      | 20/50 [00:20<00:31,  1.05s/it]\n 42%|████▏     | 21/50 [00:21<00:30,  1.05s/it]\n 44%|████▍     | 22/50 [00:23<00:29,  1.05s/it]\n 46%|████▌     | 23/50 [00:24<00:28,  1.05s/it]\n 48%|████▊     | 24/50 [00:25<00:27,  1.05s/it]\n 50%|█████     | 25/50 [00:26<00:26,  1.05s/it]\n 52%|█████▏    | 26/50 [00:27<00:25,  1.05s/it]\n 54%|█████▍    | 27/50 [00:28<00:24,  1.05s/it]\n 56%|█████▌    | 28/50 [00:29<00:23,  1.05s/it]\n 58%|█████▊    | 29/50 [00:30<00:22,  1.05s/it]\n 60%|██████    | 30/50 [00:31<00:20,  1.05s/it]\n 62%|██████▏   | 31/50 [00:32<00:19,  1.05s/it]\n 64%|██████▍   | 32/50 [00:33<00:18,  1.05s/it]\n 66%|██████▌   | 33/50 [00:34<00:17,  1.05s/it]\n 68%|██████▊   | 34/50 [00:35<00:16,  1.05s/it]\n 70%|███████   | 35/50 [00:36<00:15,  1.05s/it]\n 72%|███████▏  | 36/50 [00:37<00:14,  1.05s/it]\n 74%|███████▍  | 37/50 [00:38<00:13,  1.05s/it]\n 76%|███████▌  | 38/50 [00:39<00:12,  1.05s/it]\n 78%|███████▊  | 39/50 [00:40<00:11,  1.05s/it]\n 80%|████████  | 40/50 [00:41<00:10,  1.05s/it]\n 82%|████████▏ | 41/50 [00:42<00:09,  1.05s/it]\n 84%|████████▍ | 42/50 [00:44<00:08,  1.05s/it]\n 86%|████████▌ | 43/50 [00:45<00:07,  1.05s/it]\n 88%|████████▊ | 44/50 [00:46<00:06,  1.05s/it]\n 90%|█████████ | 45/50 [00:47<00:05,  1.05s/it]\n 92%|█████████▏| 46/50 [00:48<00:04,  1.05s/it]\n 94%|█████████▍| 47/50 [00:49<00:03,  1.05s/it]\n 96%|█████████▌| 48/50 [00:50<00:02,  1.05s/it]\n 98%|█████████▊| 49/50 [00:51<00:01,  1.05s/it]\n100%|██████████| 50/50 [00:52<00:00,  1.05s/it]\n100%|██████████| 50/50 [00:52<00:00,  1.05s/it]",
  "output": [
    "https://pbxt.replicate.delivery/2T2Ba6koW2bVEJkZdN1Xt8Xav3RvQqm7fqyW2s5KJRRktF5IA/out-0.png",
    "https://pbxt.replicate.delivery/QmL5otbAuOodC13yg0tT68iIflRHnQ8YFNSEnBhHJK0ktF5IA/out-1.png",
    "https://pbxt.replicate.delivery/mfa2OI4DCesU0U7UbvNIC1zy6d4xTZDscA41LXjqRW8JbLyRA/out-2.png",
    "https://pbxt.replicate.delivery/SJS8eGLCppXnVi2TIvZHoYMsRuNINZR5vssf4eFekjvmstIHB/out-3.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-10-27T00:29:59.384097Z",
  "started_at": "2023-10-27T00:30:02.542474Z",
  "completed_at": "2023-10-27T00:31:06.031361Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/iy2tuv3bjmmh35pwkeuzok2cwm/cancel",
    "get": "https://api.replicate.com/v1/predictions/iy2tuv3bjmmh35pwkeuzok2cwm"
  },
  "metrics": {
    "predict_time": 63.488887,
    "total_time": 66.647264
  }
}

Generated in

63.5 seconds

Tweak it Report

Using seed: 43767 Ensuring enough disk space... Free disk space: 1553610043392 Downloading weights: https://pbxt.replicate.delivery/kNaheb5WGz3TG6gDZgmEQBBe27HmyvXff2R9iyLa3UIckzHHB/trained_model.tar b'Downloaded 186 MB bytes in 4.320s (43 MB/s)\nExtracted 186 MB in 0.078s (2.4 GB/s)\n' Downloaded weights in 4.760844707489014 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: profile of <s0><s1>, formal wear, professional photograph, DSLR txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.04s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it] 20%|██ | 10/50 [00:10<00:41, 1.05s/it] 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it] 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it] 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it] 30%|███ | 15/50 [00:15<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it] 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it] 40%|████ | 20/50 [00:20<00:31, 1.05s/it] 42%|████▏ | 21/50 [00:21<00:30, 1.05s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it] 50%|█████ | 25/50 [00:26<00:26, 1.05s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.05s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.05s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.05s/it] 60%|██████ | 30/50 [00:31<00:20, 1.05s/it] 62%|██████▏ | 31/50 [00:32<00:19, 1.05s/it] 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it] 66%|██████▌ | 33/50 [00:34<00:17, 1.05s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.05s/it] 70%|███████ | 35/50 [00:36<00:15, 1.05s/it] 72%|███████▏ | 36/50 [00:37<00:14, 1.05s/it] 74%|███████▍ | 37/50 [00:38<00:13, 1.05s/it] 76%|███████▌ | 38/50 [00:39<00:12, 1.05s/it] 78%|███████▊ | 39/50 [00:40<00:11, 1.05s/it] 80%|████████ | 40/50 [00:41<00:10, 1.05s/it] 82%|████████▏ | 41/50 [00:42<00:09, 1.05s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.05s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.05s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.05s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.05s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.05s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.05s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.05s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.05s/it] 100%|██████████| 50/50 [00:52<00:00, 1.05s/it] 100%|██████████| 50/50 [00:52<00:00, 1.05s/it]