Public

1.2K runs

Playground API Examples README Versions

Prediction

mdzor/weapons-items:da029eb60bea4362bb19c04b9d6039d225dd1a661242b71fa6b1866b6ae88c57

Model

mdzor/weapons-items:da029eb6

v22vdh0y5hrgp0cf67r9t42ctg

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

23.9s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, a chest, with gold coins inside
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, a chest, with gold coins inside",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:da029eb60bea4362bb19c04b9d6039d225dd1a661242b71fa6b1866b6ae88c57",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, a chest, with gold coins inside",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:da029eb60bea4362bb19c04b9d6039d225dd1a661242b71fa6b1866b6ae88c57",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, a chest, with gold coins inside",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:da029eb60bea4362bb19c04b9d6039d225dd1a661242b71fa6b1866b6ae88c57",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, a chest, with gold coins inside",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T02:16:34.897461Z",
  "created_at": "2024-05-01T02:16:11.052000Z",
  "data_removed": false,
  "error": null,
  "id": "v22vdh0y5hrgp0cf67r9t42ctg",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, a chest, with gold coins inside",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 34657\nEnsuring enough disk space...\nFree disk space: 1777953435648\nDownloading weights: https://replicate.delivery/pbxt/mNeiwD6PA9z5Y6nT9lWAuZPj1MpRdaRBJGQRDhwHl25Wu6XJA/trained_model.tar\n2024-05-01T02:16:14Z | INFO  | [ Initiating ] chunk_size=150M dest=/src/weights-cache/0056b97031f9baf3 url=https://replicate.delivery/pbxt/mNeiwD6PA9z5Y6nT9lWAuZPj1MpRdaRBJGQRDhwHl25Wu6XJA/trained_model.tar\n2024-05-01T02:16:19Z | INFO  | [ Complete ] dest=/src/weights-cache/0056b97031f9baf3 size=\"186 MB\" total_elapsed=4.378s url=https://replicate.delivery/pbxt/mNeiwD6PA9z5Y6nT9lWAuZPj1MpRdaRBJGQRDhwHl25Wu6XJA/trained_model.tar\nb''\nDownloaded weights in 4.528833627700806 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, a chest, with gold coins inside\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.67it/s]\n  4%|▍         | 2/50 [00:00<00:13,  3.66it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.67it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.67it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.67it/s]\n 12%|█▏        | 6/50 [00:01<00:11,  3.67it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.67it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.67it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.67it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.67it/s]\n 22%|██▏       | 11/50 [00:02<00:10,  3.67it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.67it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.67it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.67it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.67it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.67it/s]\n 34%|███▍      | 17/50 [00:04<00:09,  3.67it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.67it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.67it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.67it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.67it/s]\n 44%|████▍     | 22/50 [00:05<00:07,  3.66it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.66it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.66it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.66it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.66it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.66it/s]\n 56%|█████▌    | 28/50 [00:07<00:06,  3.66it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.66it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.65it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.65it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.65it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.65it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.65it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.65it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.65it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.65it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.65it/s]\n 78%|███████▊  | 39/50 [00:10<00:03,  3.65it/s]\n 80%|████████  | 40/50 [00:10<00:02,  3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.64it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.65it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.66it/s]",
  "metrics": {
    "predict_time": 20.194817,
    "total_time": 23.845461
  },
  "output": [
    "https://replicate.delivery/pbxt/OLEtrz1xt1YjLt3E0w7o2SK8IQdu9Z1oIj2ppQGI8TrAY9rE/out-0.png"
  ],
  "started_at": "2024-05-01T02:16:14.702644Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/v22vdh0y5hrgp0cf67r9t42ctg",
    "cancel": "https://api.replicate.com/v1/predictions/v22vdh0y5hrgp0cf67r9t42ctg/cancel"
  },
  "version": "da029eb60bea4362bb19c04b9d6039d225dd1a661242b71fa6b1866b6ae88c57"
}

Generated in

20.2 seconds

Tweak it Iterate in playground ShareReport

Using seed: 34657
Ensuring enough disk space...
Free disk space: 1777953435648
Downloading weights: https://replicate.delivery/pbxt/mNeiwD6PA9z5Y6nT9lWAuZPj1MpRdaRBJGQRDhwHl25Wu6XJA/trained_model.tar
2024-05-01T02:16:14Z | INFO  | [ Initiating ] chunk_size=150M dest=/src/weights-cache/0056b97031f9baf3 url=https://replicate.delivery/pbxt/mNeiwD6PA9z5Y6nT9lWAuZPj1MpRdaRBJGQRDhwHl25Wu6XJA/trained_model.tar
2024-05-01T02:16:19Z | INFO  | [ Complete ] dest=/src/weights-cache/0056b97031f9baf3 size="186 MB" total_elapsed=4.378s url=https://replicate.delivery/pbxt/mNeiwD6PA9z5Y6nT9lWAuZPj1MpRdaRBJGQRDhwHl25Wu6XJA/trained_model.tar
b''
Downloaded weights in 4.528833627700806 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: <s0><s1>, a chest, with gold coins inside
txt2img mode
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Prediction

mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04

Model

mdzor/weapons-items:4792efc4

c87s2baw51rgj0cf68gtpjhv6r

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

27.7s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, a japanese katana
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, a japanese katana",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, a japanese katana",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, a japanese katana",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, a japanese katana",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T03:10:25.894364Z",
  "created_at": "2024-05-01T03:09:58.184000Z",
  "data_removed": false,
  "error": null,
  "id": "c87s2baw51rgj0cf68gtpjhv6r",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, a japanese katana",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 17466\nEnsuring enough disk space...\nFree disk space: 2195480252416\nDownloading weights: https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\n2024-05-01T03:10:02Z | INFO  | [ Initiating ] chunk_size=150M dest=/src/weights-cache/95dc44853137c3e0 url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\n2024-05-01T03:10:08Z | INFO  | [ Complete ] dest=/src/weights-cache/95dc44853137c3e0 size=\"186 MB\" total_elapsed=6.431s url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\nb''\nDownloaded weights in 6.6116943359375 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, a japanese katana\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n  2%|▏         | 1/50 [00:00<00:21,  2.32it/s]\n  4%|▍         | 2/50 [00:00<00:16,  2.96it/s]\n  6%|▌         | 3/50 [00:00<00:14,  3.24it/s]\n  8%|▊         | 4/50 [00:01<00:13,  3.39it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.48it/s]\n 12%|█▏        | 6/50 [00:01<00:12,  3.54it/s]\n 14%|█▍        | 7/50 [00:02<00:12,  3.57it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.60it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.62it/s]\n 20%|██        | 10/50 [00:02<00:11,  3.63it/s]\n 22%|██▏       | 11/50 [00:03<00:10,  3.63it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.64it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.64it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.65it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.64it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.64it/s]\n 34%|███▍      | 17/50 [00:04<00:09,  3.65it/s]\n 36%|███▌      | 18/50 [00:05<00:08,  3.65it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.65it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.65it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.65it/s]\n 44%|████▍     | 22/50 [00:06<00:07,  3.66it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.66it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.67it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.67it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.67it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.67it/s]\n 56%|█████▌    | 28/50 [00:07<00:06,  3.67it/s]\n 58%|█████▊    | 29/50 [00:08<00:05,  3.67it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.66it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.66it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.66it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.66it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.66it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.66it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.66it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.66it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.66it/s]\n 78%|███████▊  | 39/50 [00:10<00:03,  3.66it/s]\n 80%|████████  | 40/50 [00:11<00:02,  3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.66it/s]\n 94%|█████████▍| 47/50 [00:13<00:00,  3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.67it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.66it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.62it/s]",
  "metrics": {
    "predict_time": 23.865447,
    "total_time": 27.710364
  },
  "output": [
    "https://replicate.delivery/pbxt/sKC7mHoBR4IuHtwwXPKUXd1I7afavK4Af5bQQAqLkJggS2vSA/out-0.png"
  ],
  "started_at": "2024-05-01T03:10:02.028917Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/c87s2baw51rgj0cf68gtpjhv6r",
    "cancel": "https://api.replicate.com/v1/predictions/c87s2baw51rgj0cf68gtpjhv6r/cancel"
  },
  "version": "4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04"
}

Generated in

23.9 seconds

Tweak it Iterate in playground ShareReport

Using seed: 17466
Ensuring enough disk space...
Free disk space: 2195480252416
Downloading weights: https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
2024-05-01T03:10:02Z | INFO  | [ Initiating ] chunk_size=150M dest=/src/weights-cache/95dc44853137c3e0 url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
2024-05-01T03:10:08Z | INFO  | [ Complete ] dest=/src/weights-cache/95dc44853137c3e0 size="186 MB" total_elapsed=6.431s url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
b''
Downloaded weights in 6.6116943359375 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: <s0><s1>, a japanese katana
txt2img mode
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return F.conv2d(input, weight, bias, self.stride,
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Prediction

mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04

Model

mdzor/weapons-items:4792efc4

37v9yz7j75rgp0cf68hbx1sqqc

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

16.2s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, a double sided axe
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, a double sided axe",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, a double sided axe",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, a double sided axe",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, a double sided axe",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T03:11:58.312081Z",
  "created_at": "2024-05-01T03:11:42.137000Z",
  "data_removed": false,
  "error": null,
  "id": "37v9yz7j75rgp0cf68hbx1sqqc",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, a double sided axe",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 13314\nskipping loading .. weights already loaded\nPrompt: <s0><s1>, a double sided axe\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.69it/s]\n  4%|▍         | 2/50 [00:00<00:13,  3.69it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.69it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.69it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.68it/s]\n 12%|█▏        | 6/50 [00:01<00:11,  3.68it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.68it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.68it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.68it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.67it/s]\n 22%|██▏       | 11/50 [00:02<00:10,  3.67it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.67it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.66it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.66it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.66it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.66it/s]\n 34%|███▍      | 17/50 [00:04<00:09,  3.66it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.66it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.66it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.66it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.66it/s]\n 44%|████▍     | 22/50 [00:05<00:07,  3.66it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.66it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.66it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.66it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.66it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.66it/s]\n 56%|█████▌    | 28/50 [00:07<00:06,  3.66it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.66it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.66it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.66it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.66it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.65it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.65it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.65it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.65it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.65it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.65it/s]\n 78%|███████▊  | 39/50 [00:10<00:03,  3.65it/s]\n 80%|████████  | 40/50 [00:10<00:02,  3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.65it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.65it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.66it/s]",
  "metrics": {
    "predict_time": 16.12066,
    "total_time": 16.175081
  },
  "output": [
    "https://replicate.delivery/pbxt/X7Jtpe84XJ1fgk6nh13nIeexKCLJmQ8EXggUrBP6Q78yPZfVC/out-0.png"
  ],
  "started_at": "2024-05-01T03:11:42.191421Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/37v9yz7j75rgp0cf68hbx1sqqc",
    "cancel": "https://api.replicate.com/v1/predictions/37v9yz7j75rgp0cf68hbx1sqqc/cancel"
  },
  "version": "4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04"
}

Generated in

16.1 seconds

Tweak it Iterate in playground ShareReport

Using seed: 13314
skipping loading .. weights already loaded
Prompt: <s0><s1>, a double sided axe
txt2img mode
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Prediction

mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04

Model

mdzor/weapons-items:4792efc4

7x5q5wbh01rgg0cf68j8hx45y4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

24.4s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, a simple pistol
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, a simple pistol",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, a simple pistol",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, a simple pistol",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, a simple pistol",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T03:13:44.550234Z",
  "created_at": "2024-05-01T03:13:20.128000Z",
  "data_removed": false,
  "error": null,
  "id": "7x5q5wbh01rgg0cf68j8hx45y4",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, a simple pistol",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 62820\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, a simple pistol\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.68it/s]\n  4%|▍         | 2/50 [00:00<00:13,  3.67it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.66it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.66it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.65it/s]\n 12%|█▏        | 6/50 [00:01<00:12,  3.65it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.65it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.65it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.65it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.65it/s]\n 22%|██▏       | 11/50 [00:03<00:10,  3.65it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.65it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.64it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.64it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.64it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.64it/s]\n 34%|███▍      | 17/50 [00:04<00:09,  3.64it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.64it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.64it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.64it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.64it/s]\n 44%|████▍     | 22/50 [00:06<00:07,  3.64it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.65it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.65it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.64it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.64it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.65it/s]\n 56%|█████▌    | 28/50 [00:07<00:06,  3.64it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.64it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.64it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.64it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.64it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.64it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.64it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.64it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.64it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.64it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.64it/s]\n 78%|███████▊  | 39/50 [00:10<00:03,  3.64it/s]\n 80%|████████  | 40/50 [00:10<00:02,  3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.64it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.64it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.64it/s]",
  "metrics": {
    "predict_time": 16.730906,
    "total_time": 24.422234
  },
  "output": [
    "https://replicate.delivery/pbxt/YpUpfv0uz8XpdibJxkCf6nHUnUEO0KVQC5PfCLTa2HvOrsfKB/out-0.png"
  ],
  "started_at": "2024-05-01T03:13:27.819328Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/7x5q5wbh01rgg0cf68j8hx45y4",
    "cancel": "https://api.replicate.com/v1/predictions/7x5q5wbh01rgg0cf68j8hx45y4/cancel"
  },
  "version": "4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04"
}

Generated in

16.7 seconds

Tweak it Iterate in playground ShareReport

Using seed: 62820
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: <s0><s1>, a simple pistol
txt2img mode
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Prediction

mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04

Model

mdzor/weapons-items:4792efc4

7rkgs6r2f1rgj0cf68jvxk0rm4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

17.4s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, a health potion
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, a health potion",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, a health potion",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, a health potion",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, a health potion",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T03:14:14.784825Z",
  "created_at": "2024-05-01T03:13:57.368000Z",
  "data_removed": false,
  "error": null,
  "id": "7rkgs6r2f1rgj0cf68jvxk0rm4",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, a health potion",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 37629\nEnsuring enough disk space...\nFree disk space: 1720380407808\nDownloading weights: https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\n2024-05-01T03:13:58Z | INFO  | [ Initiating ] dest=/src/weights-cache/95dc44853137c3e0 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\n2024-05-01T03:13:58Z | INFO  | [ Complete ] dest=/src/weights-cache/95dc44853137c3e0 size=\"186 MB\" total_elapsed=0.602s url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\nb''\nDownloaded weights in 0.7527902126312256 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, a health potion\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.72it/s]\n  4%|▍         | 2/50 [00:00<00:12,  3.70it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.70it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.70it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.69it/s]\n 12%|█▏        | 6/50 [00:01<00:11,  3.69it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.69it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.69it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.69it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.69it/s]\n 22%|██▏       | 11/50 [00:02<00:10,  3.68it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.68it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.68it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.68it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.68it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.68it/s]\n 34%|███▍      | 17/50 [00:04<00:08,  3.68it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.68it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.68it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.68it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.68it/s]\n 44%|████▍     | 22/50 [00:05<00:07,  3.68it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.67it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.67it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.67it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.67it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.67it/s]\n 56%|█████▌    | 28/50 [00:07<00:05,  3.67it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.67it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.67it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.67it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.67it/s]\n 66%|██████▌   | 33/50 [00:08<00:04,  3.67it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.67it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.67it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.67it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.67it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.68it/s]\n 78%|███████▊  | 39/50 [00:10<00:02,  3.68it/s]\n 80%|████████  | 40/50 [00:10<00:02,  3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01,  3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.69it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.69it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.69it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.68it/s]",
  "metrics": {
    "predict_time": 16.571043,
    "total_time": 17.416825
  },
  "output": [
    "https://replicate.delivery/pbxt/E1GIAgvqZtbtKN7Ms9xDOQhXIzoOfcYIYaZhOpOBD7NDL7XJA/out-0.png"
  ],
  "started_at": "2024-05-01T03:13:58.213782Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/7rkgs6r2f1rgj0cf68jvxk0rm4",
    "cancel": "https://api.replicate.com/v1/predictions/7rkgs6r2f1rgj0cf68jvxk0rm4/cancel"
  },
  "version": "4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04"
}

Generated in

16.6 seconds

Tweak it Iterate in playground ShareReport

Using seed: 37629
Ensuring enough disk space...
Free disk space: 1720380407808
Downloading weights: https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
2024-05-01T03:13:58Z | INFO  | [ Initiating ] dest=/src/weights-cache/95dc44853137c3e0 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
2024-05-01T03:13:58Z | INFO  | [ Complete ] dest=/src/weights-cache/95dc44853137c3e0 size="186 MB" total_elapsed=0.602s url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
b''
Downloaded weights in 0.7527902126312256 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: <s0><s1>, a health potion
txt2img mode
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Prediction

mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04

Model

mdzor/weapons-items:4792efc4

m6y3bsmg6drgg0cf68k9s7m7zg

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

16.4s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, a simple crate
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, a simple crate",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, a simple crate",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, a simple crate",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, a simple crate",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T03:15:55.527497Z",
  "created_at": "2024-05-01T03:15:39.187000Z",
  "data_removed": false,
  "error": null,
  "id": "m6y3bsmg6drgg0cf68k9s7m7zg",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, a simple crate",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 19298\nskipping loading .. weights already loaded\nPrompt: <s0><s1>, a simple crate\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.69it/s]\n  4%|▍         | 2/50 [00:00<00:12,  3.69it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.70it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.69it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.68it/s]\n 12%|█▏        | 6/50 [00:01<00:11,  3.68it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.68it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.68it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.68it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.68it/s]\n 22%|██▏       | 11/50 [00:02<00:10,  3.68it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.67it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.68it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.68it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.68it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.67it/s]\n 34%|███▍      | 17/50 [00:04<00:08,  3.68it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.68it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.67it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.67it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.67it/s]\n 44%|████▍     | 22/50 [00:05<00:07,  3.67it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.67it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.67it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.67it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.67it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.67it/s]\n 56%|█████▌    | 28/50 [00:07<00:05,  3.67it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.67it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.67it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.66it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.66it/s]\n 66%|██████▌   | 33/50 [00:08<00:04,  3.66it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.66it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.67it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.66it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.66it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.67it/s]\n 78%|███████▊  | 39/50 [00:10<00:02,  3.67it/s]\n 80%|████████  | 40/50 [00:10<00:02,  3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.66it/s]\n 88%|████████▊ | 44/50 [00:11<00:01,  3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.66it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.66it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.67it/s]",
  "metrics": {
    "predict_time": 16.303189,
    "total_time": 16.340497
  },
  "output": [
    "https://replicate.delivery/pbxt/ZR8NNeFbeVuk7UiUAfb1VFSCefcJHX2Uu1KGpDuBlgRQ9yerE/out-0.png"
  ],
  "started_at": "2024-05-01T03:15:39.224308Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/m6y3bsmg6drgg0cf68k9s7m7zg",
    "cancel": "https://api.replicate.com/v1/predictions/m6y3bsmg6drgg0cf68k9s7m7zg/cancel"
  },
  "version": "4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04"
}

Generated in

16.3 seconds

Tweak it Iterate in playground ShareReport

Using seed: 19298
skipping loading .. weights already loaded
Prompt: <s0><s1>, a simple crate
txt2img mode
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Prediction

mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04

Model

mdzor/weapons-items:4792efc4

gg051njymdrgj0cf68prg1ksww

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

16.4s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, a lowpoly sword, with green emerald, wooden handle, surrounded by fire
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, a lowpoly sword, with green emerald, wooden handle, surrounded by fire",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, a lowpoly sword, with green emerald, wooden handle, surrounded by fire",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, a lowpoly sword, with green emerald, wooden handle, surrounded by fire",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, a lowpoly sword, with green emerald, wooden handle, surrounded by fire",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T03:23:21.698760Z",
  "created_at": "2024-05-01T03:23:05.251000Z",
  "data_removed": false,
  "error": null,
  "id": "gg051njymdrgj0cf68prg1ksww",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, a lowpoly sword, with green emerald, wooden handle, surrounded by fire",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 36700\nskipping loading .. weights already loaded\nPrompt: <s0><s1>, a lowpoly sword, with green emerald, wooden handle, surrounded by fire\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.66it/s]\n  4%|▍         | 2/50 [00:00<00:13,  3.65it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.65it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.64it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.65it/s]\n 12%|█▏        | 6/50 [00:01<00:12,  3.64it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.64it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.64it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.64it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.64it/s]\n 22%|██▏       | 11/50 [00:03<00:10,  3.64it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.64it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.64it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.64it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.64it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.64it/s]\n 34%|███▍      | 17/50 [00:04<00:09,  3.64it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.64it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.63it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.63it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.63it/s]\n 44%|████▍     | 22/50 [00:06<00:07,  3.63it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.63it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.63it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.63it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.63it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.63it/s]\n 56%|█████▌    | 28/50 [00:07<00:06,  3.63it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.63it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.63it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.63it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.63it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.63it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.63it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.63it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.63it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.63it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.63it/s]\n 78%|███████▊  | 39/50 [00:10<00:03,  3.63it/s]\n 80%|████████  | 40/50 [00:11<00:02,  3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.62it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.62it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.62it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.62it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.62it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.62it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.63it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.63it/s]",
  "metrics": {
    "predict_time": 16.384155,
    "total_time": 16.44776
  },
  "output": [
    "https://replicate.delivery/pbxt/uHrkhSL8EBoICJkzKHXFwoL51MaPEdwvesBBDvpnmBFUP7XJA/out-0.png"
  ],
  "started_at": "2024-05-01T03:23:05.314605Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/gg051njymdrgj0cf68prg1ksww",
    "cancel": "https://api.replicate.com/v1/predictions/gg051njymdrgj0cf68prg1ksww/cancel"
  },
  "version": "4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04"
}

Generated in

16.4 seconds

Tweak it Iterate in playground ShareReport

Using seed: 36700
skipping loading .. weights already loaded
Prompt: <s0><s1>, a lowpoly sword, with green emerald, wooden handle, surrounded by fire
txt2img mode
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Prediction

mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04

Model

mdzor/weapons-items:4792efc4

wywx0mw0yxrgm0cf68qa2pr00r

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

37.4s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, a lowpoly knife, sharp, braided handle, embracing ice power
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, a lowpoly knife, sharp, braided handle, embracing ice power",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, a lowpoly knife, sharp, braided handle, embracing ice power",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, a lowpoly knife, sharp, braided handle, embracing ice power",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, a lowpoly knife, sharp, braided handle, embracing ice power",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T03:24:57.012669Z",
  "created_at": "2024-05-01T03:24:19.575000Z",
  "data_removed": false,
  "error": null,
  "id": "wywx0mw0yxrgm0cf68qa2pr00r",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, a lowpoly knife, sharp, braided handle, embracing ice power",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 56708\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, a lowpoly knife, sharp, braided handle, embracing ice power\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.68it/s]\n  4%|▍         | 2/50 [00:00<00:13,  3.67it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.67it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.66it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.66it/s]\n 12%|█▏        | 6/50 [00:01<00:12,  3.65it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.65it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.65it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.65it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.65it/s]\n 22%|██▏       | 11/50 [00:03<00:10,  3.65it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.63it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.62it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.63it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.64it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.64it/s]\n 34%|███▍      | 17/50 [00:04<00:09,  3.64it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.64it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.64it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.64it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.64it/s]\n 44%|████▍     | 22/50 [00:06<00:07,  3.64it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.64it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.63it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.64it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.64it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.64it/s]\n 56%|█████▌    | 28/50 [00:07<00:06,  3.64it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.64it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.64it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.63it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.63it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.63it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.64it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.63it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.63it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.63it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.63it/s]\n 78%|███████▊  | 39/50 [00:10<00:03,  3.63it/s]\n 80%|████████  | 40/50 [00:10<00:02,  3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.63it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.63it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.63it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.63it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.63it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.63it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.64it/s]",
  "metrics": {
    "predict_time": 16.695583,
    "total_time": 37.437669
  },
  "output": [
    "https://replicate.delivery/pbxt/tsn3H9hmklreLynJXTMroaW0C5cLSvjHFpJ6pEFqBk6DQ7XJA/out-0.png"
  ],
  "started_at": "2024-05-01T03:24:40.317086Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/wywx0mw0yxrgm0cf68qa2pr00r",
    "cancel": "https://api.replicate.com/v1/predictions/wywx0mw0yxrgm0cf68qa2pr00r/cancel"
  },
  "version": "4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04"
}

Generated in

16.7 seconds

Tweak it Iterate in playground ShareReport

Using seed: 56708
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: <s0><s1>, a lowpoly knife, sharp, braided handle, embracing ice power
txt2img mode
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Prediction

mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04

Model

mdzor/weapons-items:4792efc4

py4pwkvcgnrgj0cf68sbxmj004

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

20.1s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: TOK, half-opened chest, filled with jewels, shiny, simple details
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 1
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt: gold coins on the floor
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "TOK, half-opened chest, filled with jewels, shiny, simple details",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "gold coins on the floor",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "TOK, half-opened chest, filled with jewels, shiny, simple details",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "gold coins on the floor",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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

Import the client:

import replicate

Run mdzor/weapons-items using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "TOK, half-opened chest, filled with jewels, shiny, simple details",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "gold coins on the floor",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# 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 mdzor/weapons-items 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": "mdzor/weapons-items:4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "TOK, half-opened chest, filled with jewels, shiny, simple details",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "gold coins on the floor",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-01T03:28:56.587655Z",
  "created_at": "2024-05-01T03:28:36.485000Z",
  "data_removed": false,
  "error": null,
  "id": "py4pwkvcgnrgj0cf68sbxmj004",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "TOK, half-opened chest, filled with jewels, shiny, simple details",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "gold coins on the floor",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 62763\nEnsuring enough disk space...\nFree disk space: 1554384322560\nDownloading weights: https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\n2024-05-01T03:28:38Z | INFO  | [ Initiating ] dest=/src/weights-cache/95dc44853137c3e0 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\n2024-05-01T03:28:39Z | INFO  | [ Complete ] dest=/src/weights-cache/95dc44853137c3e0 size=\"186 MB\" total_elapsed=0.672s url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar\nb''\nDownloaded weights in 0.835094690322876 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, half-opened chest, filled with jewels, shiny, simple details\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:13,  3.63it/s]\n  4%|▍         | 2/50 [00:00<00:13,  3.62it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.63it/s]\n  8%|▊         | 4/50 [00:01<00:12,  3.63it/s]\n 10%|█         | 5/50 [00:01<00:12,  3.62it/s]\n 12%|█▏        | 6/50 [00:01<00:12,  3.62it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.62it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.62it/s]\n 18%|█▊        | 9/50 [00:02<00:11,  3.62it/s]\n 20%|██        | 10/50 [00:02<00:11,  3.62it/s]\n 22%|██▏       | 11/50 [00:03<00:10,  3.62it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.62it/s]\n 26%|██▌       | 13/50 [00:03<00:10,  3.61it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.61it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.61it/s]\n 32%|███▏      | 16/50 [00:04<00:09,  3.61it/s]\n 34%|███▍      | 17/50 [00:04<00:09,  3.61it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.61it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.61it/s]\n 40%|████      | 20/50 [00:05<00:08,  3.61it/s]\n 42%|████▏     | 21/50 [00:05<00:08,  3.61it/s]\n 44%|████▍     | 22/50 [00:06<00:07,  3.60it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.60it/s]\n 48%|████▊     | 24/50 [00:06<00:07,  3.60it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.61it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.61it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.61it/s]\n 56%|█████▌    | 28/50 [00:07<00:06,  3.61it/s]\n 58%|█████▊    | 29/50 [00:08<00:05,  3.60it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.60it/s]\n 62%|██████▏   | 31/50 [00:08<00:05,  3.60it/s]\n 64%|██████▍   | 32/50 [00:08<00:05,  3.60it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.60it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.59it/s]\n 70%|███████   | 35/50 [00:09<00:04,  3.60it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.56it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.57it/s]\n 76%|███████▌  | 38/50 [00:10<00:03,  3.58it/s]\n 78%|███████▊  | 39/50 [00:10<00:03,  3.58it/s]\n 80%|████████  | 40/50 [00:11<00:02,  3.58it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.59it/s]\n 84%|████████▍ | 42/50 [00:11<00:02,  3.59it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.59it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.59it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.59it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.59it/s]\n 94%|█████████▍| 47/50 [00:13<00:00,  3.59it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.59it/s]\n 98%|█████████▊| 49/50 [00:13<00:00,  3.59it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.59it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.60it/s]",
  "metrics": {
    "predict_time": 17.812367,
    "total_time": 20.102655
  },
  "output": [
    "https://replicate.delivery/pbxt/MxPFrxArKnr6Pp1JeewiHtMGuclitmuQRJgYrNfEIEyvHtfKB/out-0.png"
  ],
  "started_at": "2024-05-01T03:28:38.775288Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/py4pwkvcgnrgj0cf68sbxmj004",
    "cancel": "https://api.replicate.com/v1/predictions/py4pwkvcgnrgj0cf68sbxmj004/cancel"
  },
  "version": "4792efc4be50e9b63563a2cd82bea9016c90fcc03c510a5a7b79ee215d685c04"
}

Generated in

17.8 seconds

Tweak it Iterate in playground ShareReport

Using seed: 62763
Ensuring enough disk space...
Free disk space: 1554384322560
Downloading weights: https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
2024-05-01T03:28:38Z | INFO  | [ Initiating ] dest=/src/weights-cache/95dc44853137c3e0 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
2024-05-01T03:28:39Z | INFO  | [ Complete ] dest=/src/weights-cache/95dc44853137c3e0 size="186 MB" total_elapsed=0.672s url=https://replicate.delivery/pbxt/m23fOMRmFp1oKKMc4r664TodJ9FP42qZVHsU4C5t00A6H7XJA/trained_model.tar
b''
Downloaded weights in 0.835094690322876 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: <s0><s1>, half-opened chest, filled with jewels, shiny, simple details
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