Prediction

czse4e6k4hrme0cm180rzr04hw

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.0s

Created

4 months ago

Input

model: dev
prompt: a DALGONA cookie in a tin, with the word "REPLICATE" on top
go_fast
lora_scale: 1
megapixels: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "a DALGONA cookie in a tin, with the word \"REPLICATE\" on top",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
  {
    input: {
      model: "dev",
      prompt: "a DALGONA cookie in a tin, with the word \"REPLICATE\" on top",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

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

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

pip install replicate

Import the client:

import replicate

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    input={
        "model": "dev",
        "prompt": "a DALGONA cookie in a tin, with the word \"REPLICATE\" on top",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run lucataco/flux-dalgona 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": "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    "input": {
      "model": "dev",
      "prompt": "a DALGONA cookie in a tin, with the word \\"REPLICATE\\" on top",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-12-27T21:24:49.324095Z",
  "created_at": "2024-12-27T21:24:39.332000Z",
  "data_removed": false,
  "error": null,
  "id": "czse4e6k4hrme0cm180rzr04hw",
  "input": {
    "model": "dev",
    "prompt": "a DALGONA cookie in a tin, with the word \"REPLICATE\" on top",
    "go_fast": false,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "2024-12-27 21:24:40.686 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-27 21:24:40.686 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  92%|█████████▏| 280/304 [00:00<00:00, 2788.41it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2652.37it/s]\n2024-12-27 21:24:40.801 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s\nfree=30037206175744\nDownloading weights\n2024-12-27T21:24:40Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp94g30dmg/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\n2024-12-27T21:24:42Z | INFO  | [ Complete ] dest=/tmp/tmp94g30dmg/weights size=\"172 MB\" total_elapsed=2.124s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\nDownloaded weights in 2.15s\n2024-12-27 21:24:42.950 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1\n2024-12-27 21:24:43.021 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2024-12-27 21:24:43.021 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-27 21:24:43.022 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  92%|█████████▏| 281/304 [00:00<00:00, 2805.58it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2671.72it/s]\n2024-12-27 21:24:43.136 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s\nUsing seed: 59768\n0it [00:00, ?it/s]\n1it [00:00,  8.38it/s]\n2it [00:00,  5.81it/s]\n3it [00:00,  5.31it/s]\n4it [00:00,  5.10it/s]\n5it [00:00,  4.92it/s]\n6it [00:01,  4.84it/s]\n7it [00:01,  4.82it/s]\n8it [00:01,  4.81it/s]\n9it [00:01,  4.79it/s]\n10it [00:02,  4.76it/s]\n11it [00:02,  4.75it/s]\n12it [00:02,  4.75it/s]\n13it [00:02,  4.76it/s]\n14it [00:02,  4.76it/s]\n15it [00:03,  4.73it/s]\n16it [00:03,  4.73it/s]\n17it [00:03,  4.73it/s]\n18it [00:03,  4.74it/s]\n19it [00:03,  4.74it/s]\n20it [00:04,  4.74it/s]\n21it [00:04,  4.74it/s]\n22it [00:04,  4.74it/s]\n23it [00:04,  4.74it/s]\n24it [00:04,  4.74it/s]\n25it [00:05,  4.74it/s]\n26it [00:05,  4.74it/s]\n27it [00:05,  4.73it/s]\n28it [00:05,  4.74it/s]\n28it [00:05,  4.82it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 8.637053994,
    "total_time": 9.992095
  },
  "output": [
    "https://replicate.delivery/xezq/qNIp3nrIVuYfdyosBlQfbCMQtU1MmGO0WjXExT8gxXYh0MfnA/out-0.webp"
  ],
  "started_at": "2024-12-27T21:24:40.687041Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-bzyqt5mgmxxeea4lrgwynnb4ohdlb3s62or3cxgki774xcdc5lya",
    "get": "https://api.replicate.com/v1/predictions/czse4e6k4hrme0cm180rzr04hw",
    "cancel": "https://api.replicate.com/v1/predictions/czse4e6k4hrme0cm180rzr04hw/cancel"
  },
  "version": "491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1"
}

Generated in

8.6 seconds

lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1

2024-12-27 21:24:40.686 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-27 21:24:40.686 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  92%|█████████▏| 280/304 [00:00<00:00, 2788.41it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2652.37it/s]
2024-12-27 21:24:40.801 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s
free=30037206175744
Downloading weights
2024-12-27T21:24:40Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp94g30dmg/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar
2024-12-27T21:24:42Z | INFO  | [ Complete ] dest=/tmp/tmp94g30dmg/weights size="172 MB" total_elapsed=2.124s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar
Downloaded weights in 2.15s
2024-12-27 21:24:42.950 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1
2024-12-27 21:24:43.021 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded
2024-12-27 21:24:43.021 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-27 21:24:43.022 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  92%|█████████▏| 281/304 [00:00<00:00, 2805.58it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2671.72it/s]
2024-12-27 21:24:43.136 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s
Using seed: 59768
0it [00:00, ?it/s]
1it [00:00,  8.38it/s]
2it [00:00,  5.81it/s]
3it [00:00,  5.31it/s]
4it [00:00,  5.10it/s]
5it [00:00,  4.92it/s]
6it [00:01,  4.84it/s]
7it [00:01,  4.82it/s]
8it [00:01,  4.81it/s]
9it [00:01,  4.79it/s]
10it [00:02,  4.76it/s]
11it [00:02,  4.75it/s]
12it [00:02,  4.75it/s]
13it [00:02,  4.76it/s]
14it [00:02,  4.76it/s]
15it [00:03,  4.73it/s]
16it [00:03,  4.73it/s]
17it [00:03,  4.73it/s]
18it [00:03,  4.74it/s]
19it [00:03,  4.74it/s]
20it [00:04,  4.74it/s]
21it [00:04,  4.74it/s]
22it [00:04,  4.74it/s]
23it [00:04,  4.74it/s]
24it [00:04,  4.74it/s]
25it [00:05,  4.74it/s]
26it [00:05,  4.74it/s]
27it [00:05,  4.73it/s]
28it [00:05,  4.74it/s]
28it [00:05,  4.82it/s]
Total safe images: 1 out of 1

Prediction

Model

xg9qeana4srm80cm181tdqscg4

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.4s

Created

4 months ago

Input

model: dev
prompt: a DALGONA cookie in a tin, with an elephant shape on top
go_fast
lora_scale: 1
megapixels: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "a DALGONA cookie in a tin, with an elephant shape on top",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
  {
    input: {
      model: "dev",
      prompt: "a DALGONA cookie in a tin, with an elephant shape on top",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

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

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

pip install replicate

Import the client:

import replicate

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    input={
        "model": "dev",
        "prompt": "a DALGONA cookie in a tin, with an elephant shape on top",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run lucataco/flux-dalgona 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": "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    "input": {
      "model": "dev",
      "prompt": "a DALGONA cookie in a tin, with an elephant shape on top",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-12-27T21:26:50.343849Z",
  "created_at": "2024-12-27T21:26:39.910000Z",
  "data_removed": false,
  "error": null,
  "id": "xg9qeana4srm80cm181tdqscg4",
  "input": {
    "model": "dev",
    "prompt": "a DALGONA cookie in a tin, with an elephant shape on top",
    "go_fast": false,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "2024-12-27 21:26:40.497 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-27 21:26:40.497 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  90%|█████████ | 275/304 [00:00<00:00, 2736.32it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2596.52it/s]\n2024-12-27 21:26:40.614 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s\nfree=30183013171200\nDownloading weights\n2024-12-27T21:26:40Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpt1y6cv27/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\n2024-12-27T21:26:43Z | INFO  | [ Complete ] dest=/tmp/tmpt1y6cv27/weights size=\"172 MB\" total_elapsed=3.345s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\nDownloaded weights in 3.37s\n2024-12-27 21:26:43.983 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1\n2024-12-27 21:26:44.051 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2024-12-27 21:26:44.052 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-27 21:26:44.052 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  91%|█████████ | 276/304 [00:00<00:00, 2732.52it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2609.85it/s]\n2024-12-27 21:26:44.169 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s\nUsing seed: 58848\n0it [00:00, ?it/s]\n1it [00:00,  8.34it/s]\n2it [00:00,  5.79it/s]\n3it [00:00,  5.28it/s]\n4it [00:00,  5.08it/s]\n5it [00:00,  4.94it/s]\n6it [00:01,  4.86it/s]\n7it [00:01,  4.82it/s]\n8it [00:01,  4.81it/s]\n9it [00:01,  4.80it/s]\n10it [00:02,  4.77it/s]\n11it [00:02,  4.76it/s]\n12it [00:02,  4.75it/s]\n13it [00:02,  4.75it/s]\n14it [00:02,  4.75it/s]\n15it [00:03,  4.75it/s]\n16it [00:03,  4.74it/s]\n17it [00:03,  4.75it/s]\n18it [00:03,  4.75it/s]\n19it [00:03,  4.74it/s]\n20it [00:04,  4.75it/s]\n21it [00:04,  4.75it/s]\n22it [00:04,  4.74it/s]\n23it [00:04,  4.74it/s]\n24it [00:04,  4.73it/s]\n25it [00:05,  4.73it/s]\n26it [00:05,  4.73it/s]\n27it [00:05,  4.74it/s]\n28it [00:05,  4.74it/s]\n28it [00:05,  4.82it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 9.846495991,
    "total_time": 10.433849
  },
  "output": [
    "https://replicate.delivery/xezq/4eCIqMA4NAQkOC3kqa6dSGfrcitYeK57YOKVEhpQk9u1sZePB/out-0.webp"
  ],
  "started_at": "2024-12-27T21:26:40.497353Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-vow6eqq6d42yqdz7pqegzbjempdehwx5zeq5633jxbpkfb36xdfa",
    "get": "https://api.replicate.com/v1/predictions/xg9qeana4srm80cm181tdqscg4",
    "cancel": "https://api.replicate.com/v1/predictions/xg9qeana4srm80cm181tdqscg4/cancel"
  },
  "version": "491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1"
}

Generated in

9.8 seconds

lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1

2024-12-27 21:26:40.497 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-27 21:26:40.497 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  90%|█████████ | 275/304 [00:00<00:00, 2736.32it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2596.52it/s]
2024-12-27 21:26:40.614 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s
free=30183013171200
Downloading weights
2024-12-27T21:26:40Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpt1y6cv27/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar
2024-12-27T21:26:43Z | INFO  | [ Complete ] dest=/tmp/tmpt1y6cv27/weights size="172 MB" total_elapsed=3.345s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar
Downloaded weights in 3.37s
2024-12-27 21:26:43.983 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1
2024-12-27 21:26:44.051 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded
2024-12-27 21:26:44.052 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-27 21:26:44.052 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  91%|█████████ | 276/304 [00:00<00:00, 2732.52it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2609.85it/s]
2024-12-27 21:26:44.169 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s
Using seed: 58848
0it [00:00, ?it/s]
1it [00:00,  8.34it/s]
2it [00:00,  5.79it/s]
3it [00:00,  5.28it/s]
4it [00:00,  5.08it/s]
5it [00:00,  4.94it/s]
6it [00:01,  4.86it/s]
7it [00:01,  4.82it/s]
8it [00:01,  4.81it/s]
9it [00:01,  4.80it/s]
10it [00:02,  4.77it/s]
11it [00:02,  4.76it/s]
12it [00:02,  4.75it/s]
13it [00:02,  4.75it/s]
14it [00:02,  4.75it/s]
15it [00:03,  4.75it/s]
16it [00:03,  4.74it/s]
17it [00:03,  4.75it/s]
18it [00:03,  4.75it/s]
19it [00:03,  4.74it/s]
20it [00:04,  4.75it/s]
21it [00:04,  4.75it/s]
22it [00:04,  4.74it/s]
23it [00:04,  4.74it/s]
24it [00:04,  4.73it/s]
25it [00:05,  4.73it/s]
26it [00:05,  4.73it/s]
27it [00:05,  4.74it/s]
28it [00:05,  4.74it/s]
28it [00:05,  4.82it/s]
Total safe images: 1 out of 1

Prediction

Model

bmr223998drmc0cm182b3jv82c

Status

Succeeded

Source

Web

Hardware

H100

Total duration

9.7s

Created

4 months ago

Input

model: dev
prompt: a DALGONA cookie in a tin, with a lion shape on top
go_fast
lora_scale: 1
megapixels: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "a DALGONA cookie in a tin, with a lion shape on top",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
  {
    input: {
      model: "dev",
      prompt: "a DALGONA cookie in a tin, with a lion shape on top",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

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

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

pip install replicate

Import the client:

import replicate

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    input={
        "model": "dev",
        "prompt": "a DALGONA cookie in a tin, with a lion shape on top",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run lucataco/flux-dalgona 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": "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    "input": {
      "model": "dev",
      "prompt": "a DALGONA cookie in a tin, with a lion shape on top",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-12-27T21:27:22.125661Z",
  "created_at": "2024-12-27T21:27:12.451000Z",
  "data_removed": false,
  "error": null,
  "id": "bmr223998drmc0cm182b3jv82c",
  "input": {
    "model": "dev",
    "prompt": "a DALGONA cookie in a tin, with a lion shape on top",
    "go_fast": false,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "2024-12-27 21:27:14.463 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-27 21:27:14.463 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  96%|█████████▌| 291/304 [00:00<00:00, 2895.24it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2848.61it/s]\n2024-12-27 21:27:14.570 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s\nfree=30283274035200\nDownloading weights\n2024-12-27T21:27:14Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpyezkpxx6/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\n2024-12-27T21:27:15Z | INFO  | [ Complete ] dest=/tmp/tmpyezkpxx6/weights size=\"172 MB\" total_elapsed=1.281s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\nDownloaded weights in 1.30s\n2024-12-27 21:27:15.875 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1\n2024-12-27 21:27:15.944 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2024-12-27 21:27:15.944 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-27 21:27:15.944 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  93%|█████████▎| 282/304 [00:00<00:00, 2792.79it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2707.61it/s]\n2024-12-27 21:27:16.057 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.18s\nUsing seed: 27540\n0it [00:00, ?it/s]\n1it [00:00,  8.42it/s]\n2it [00:00,  5.90it/s]\n3it [00:00,  5.38it/s]\n4it [00:00,  5.16it/s]\n5it [00:00,  5.04it/s]\n6it [00:01,  4.95it/s]\n7it [00:01,  4.91it/s]\n8it [00:01,  4.89it/s]\n9it [00:01,  4.88it/s]\n10it [00:01,  4.86it/s]\n11it [00:02,  4.84it/s]\n12it [00:02,  4.84it/s]\n13it [00:02,  4.84it/s]\n14it [00:02,  4.84it/s]\n15it [00:03,  4.83it/s]\n16it [00:03,  4.83it/s]\n17it [00:03,  4.83it/s]\n18it [00:03,  4.83it/s]\n19it [00:03,  4.83it/s]\n20it [00:04,  4.83it/s]\n21it [00:04,  4.82it/s]\n22it [00:04,  4.81it/s]\n23it [00:04,  4.82it/s]\n24it [00:04,  4.81it/s]\n25it [00:05,  4.82it/s]\n26it [00:05,  4.82it/s]\n27it [00:05,  4.82it/s]\n28it [00:05,  4.83it/s]\n28it [00:05,  4.90it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 7.661395959,
    "total_time": 9.674661
  },
  "output": [
    "https://replicate.delivery/xezq/QIA9iGUIAF50CRok5RBfJuYrnMW2ErtlGwqn8GsC3JEdbmfTA/out-0.webp"
  ],
  "started_at": "2024-12-27T21:27:14.464265Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-jljozeih7xlv66h7rmjijp6nl654pxrdxxbsxtxumca64ubyo4tq",
    "get": "https://api.replicate.com/v1/predictions/bmr223998drmc0cm182b3jv82c",
    "cancel": "https://api.replicate.com/v1/predictions/bmr223998drmc0cm182b3jv82c/cancel"
  },
  "version": "491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1"
}

Generated in

7.7 seconds

lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1

2024-12-27 21:27:14.463 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-27 21:27:14.463 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  96%|█████████▌| 291/304 [00:00<00:00, 2895.24it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2848.61it/s]
2024-12-27 21:27:14.570 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s
free=30283274035200
Downloading weights
2024-12-27T21:27:14Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpyezkpxx6/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar
2024-12-27T21:27:15Z | INFO  | [ Complete ] dest=/tmp/tmpyezkpxx6/weights size="172 MB" total_elapsed=1.281s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar
Downloaded weights in 1.30s
2024-12-27 21:27:15.875 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1
2024-12-27 21:27:15.944 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded
2024-12-27 21:27:15.944 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-27 21:27:15.944 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  93%|█████████▎| 282/304 [00:00<00:00, 2792.79it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2707.61it/s]
2024-12-27 21:27:16.057 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.18s
Using seed: 27540
0it [00:00, ?it/s]
1it [00:00,  8.42it/s]
2it [00:00,  5.90it/s]
3it [00:00,  5.38it/s]
4it [00:00,  5.16it/s]
5it [00:00,  5.04it/s]
6it [00:01,  4.95it/s]
7it [00:01,  4.91it/s]
8it [00:01,  4.89it/s]
9it [00:01,  4.88it/s]
10it [00:01,  4.86it/s]
11it [00:02,  4.84it/s]
12it [00:02,  4.84it/s]
13it [00:02,  4.84it/s]
14it [00:02,  4.84it/s]
15it [00:03,  4.83it/s]
16it [00:03,  4.83it/s]
17it [00:03,  4.83it/s]
18it [00:03,  4.83it/s]
19it [00:03,  4.83it/s]
20it [00:04,  4.83it/s]
21it [00:04,  4.82it/s]
22it [00:04,  4.81it/s]
23it [00:04,  4.82it/s]
24it [00:04,  4.81it/s]
25it [00:05,  4.82it/s]
26it [00:05,  4.82it/s]
27it [00:05,  4.82it/s]
28it [00:05,  4.83it/s]
28it [00:05,  4.90it/s]
Total safe images: 1 out of 1

Prediction

Model

v5pr37g7hsrme0cm183ap3wr00

Status

Succeeded

Source

Web

Hardware

H100

Total duration

8.8s

Created

4 months ago

Input

model: dev
prompt: a DALGONA cookie in a tin, with a penguin shape on top
go_fast
lora_scale: 1
megapixels: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "a DALGONA cookie in a tin, with a penguin shape on top",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
  {
    input: {
      model: "dev",
      prompt: "a DALGONA cookie in a tin, with a penguin shape on top",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

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

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

pip install replicate

Import the client:

import replicate

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    input={
        "model": "dev",
        "prompt": "a DALGONA cookie in a tin, with a penguin shape on top",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run lucataco/flux-dalgona 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": "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    "input": {
      "model": "dev",
      "prompt": "a DALGONA cookie in a tin, with a penguin shape on top",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-12-27T21:29:23.694613Z",
  "created_at": "2024-12-27T21:29:14.894000Z",
  "data_removed": false,
  "error": null,
  "id": "v5pr37g7hsrme0cm183ap3wr00",
  "input": {
    "model": "dev",
    "prompt": "a DALGONA cookie in a tin, with a penguin shape on top",
    "go_fast": false,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "2024-12-27 21:29:14.917 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-27 21:29:14.918 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  91%|█████████ | 277/304 [00:00<00:00, 2764.25it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2690.62it/s]\n2024-12-27 21:29:15.031 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s\nfree=30274145345536\nDownloading weights\n2024-12-27T21:29:15Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp67txdbdi/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\n2024-12-27T21:29:17Z | INFO  | [ Complete ] dest=/tmp/tmp67txdbdi/weights size=\"172 MB\" total_elapsed=2.351s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\nDownloaded weights in 2.37s\n2024-12-27 21:29:17.405 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1\n2024-12-27 21:29:17.473 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2024-12-27 21:29:17.473 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-27 21:29:17.473 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  91%|█████████ | 277/304 [00:00<00:00, 2768.97it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2694.90it/s]\n2024-12-27 21:29:17.586 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.18s\nUsing seed: 60753\n0it [00:00, ?it/s]\n1it [00:00,  8.38it/s]\n2it [00:00,  5.85it/s]\n3it [00:00,  5.34it/s]\n4it [00:00,  5.14it/s]\n5it [00:00,  5.01it/s]\n6it [00:01,  4.91it/s]\n7it [00:01,  4.87it/s]\n8it [00:01,  4.84it/s]\n9it [00:01,  4.83it/s]\n10it [00:01,  4.82it/s]\n11it [00:02,  4.81it/s]\n12it [00:02,  4.80it/s]\n13it [00:02,  4.80it/s]\n14it [00:02,  4.81it/s]\n15it [00:03,  4.80it/s]\n16it [00:03,  4.80it/s]\n17it [00:03,  4.80it/s]\n18it [00:03,  4.80it/s]\n19it [00:03,  4.79it/s]\n20it [00:04,  4.79it/s]\n21it [00:04,  4.78it/s]\n22it [00:04,  4.78it/s]\n23it [00:04,  4.79it/s]\n24it [00:04,  4.79it/s]\n25it [00:05,  4.79it/s]\n26it [00:05,  4.80it/s]\n27it [00:05,  4.79it/s]\n28it [00:05,  4.79it/s]\n28it [00:05,  4.87it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 8.775819615,
    "total_time": 8.800613
  },
  "output": [
    "https://replicate.delivery/xezq/kDbZnZdLE2reZ6ygLDoS108IiWX03OPIiFssfW9e8HrmxZePB/out-0.webp"
  ],
  "started_at": "2024-12-27T21:29:14.918793Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-hkvg4gjig6p77m4dcmsa3dooxii7xk2uty6oqucaykfn4lij5kia",
    "get": "https://api.replicate.com/v1/predictions/v5pr37g7hsrme0cm183ap3wr00",
    "cancel": "https://api.replicate.com/v1/predictions/v5pr37g7hsrme0cm183ap3wr00/cancel"
  },
  "version": "491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1"
}

Generated in

8.8 seconds

lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1

2024-12-27 21:29:14.917 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-27 21:29:14.918 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  91%|█████████ | 277/304 [00:00<00:00, 2764.25it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2690.62it/s]
2024-12-27 21:29:15.031 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s
free=30274145345536
Downloading weights
2024-12-27T21:29:15Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp67txdbdi/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar
2024-12-27T21:29:17Z | INFO  | [ Complete ] dest=/tmp/tmp67txdbdi/weights size="172 MB" total_elapsed=2.351s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar
Downloaded weights in 2.37s
2024-12-27 21:29:17.405 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1
2024-12-27 21:29:17.473 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded
2024-12-27 21:29:17.473 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2024-12-27 21:29:17.473 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  91%|█████████ | 277/304 [00:00<00:00, 2768.97it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2694.90it/s]
2024-12-27 21:29:17.586 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.18s
Using seed: 60753
0it [00:00, ?it/s]
1it [00:00,  8.38it/s]
2it [00:00,  5.85it/s]
3it [00:00,  5.34it/s]
4it [00:00,  5.14it/s]
5it [00:00,  5.01it/s]
6it [00:01,  4.91it/s]
7it [00:01,  4.87it/s]
8it [00:01,  4.84it/s]
9it [00:01,  4.83it/s]
10it [00:01,  4.82it/s]
11it [00:02,  4.81it/s]
12it [00:02,  4.80it/s]
13it [00:02,  4.80it/s]
14it [00:02,  4.81it/s]
15it [00:03,  4.80it/s]
16it [00:03,  4.80it/s]
17it [00:03,  4.80it/s]
18it [00:03,  4.80it/s]
19it [00:03,  4.79it/s]
20it [00:04,  4.79it/s]
21it [00:04,  4.78it/s]
22it [00:04,  4.78it/s]
23it [00:04,  4.79it/s]
24it [00:04,  4.79it/s]
25it [00:05,  4.79it/s]
26it [00:05,  4.80it/s]
27it [00:05,  4.79it/s]
28it [00:05,  4.79it/s]
28it [00:05,  4.87it/s]
Total safe images: 1 out of 1

Prediction

Model

35rbq9wqdsrme0cm184r8rhknc

Status

Succeeded

Source

Web

Hardware

H100

Total duration

6.0s

Created

4 months ago

Input

model: dev
prompt: a DALGONA cookie in a tin, with a baby hippo shape on top
go_fast
lora_scale: 1
megapixels: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "a DALGONA cookie in a tin, with a baby hippo shape on top",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
  {
    input: {
      model: "dev",
      prompt: "a DALGONA cookie in a tin, with a baby hippo shape on top",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

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

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

pip install replicate

Import the client:

import replicate

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    input={
        "model": "dev",
        "prompt": "a DALGONA cookie in a tin, with a baby hippo shape on top",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run lucataco/flux-dalgona 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": "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    "input": {
      "model": "dev",
      "prompt": "a DALGONA cookie in a tin, with a baby hippo shape on top",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-12-27T21:33:14.379971Z",
  "created_at": "2024-12-27T21:33:08.334000Z",
  "data_removed": false,
  "error": null,
  "id": "35rbq9wqdsrme0cm184r8rhknc",
  "input": {
    "model": "dev",
    "prompt": "a DALGONA cookie in a tin, with a baby hippo shape on top",
    "go_fast": false,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Lora https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar already loaded\nUsing seed: 53199\n0it [00:00, ?it/s]\n1it [00:00,  8.35it/s]\n2it [00:00,  5.85it/s]\n3it [00:00,  5.34it/s]\n4it [00:00,  5.12it/s]\n5it [00:00,  5.00it/s]\n6it [00:01,  4.93it/s]\n7it [00:01,  4.89it/s]\n8it [00:01,  4.87it/s]\n9it [00:01,  4.85it/s]\n10it [00:01,  4.84it/s]\n11it [00:02,  4.82it/s]\n12it [00:02,  4.82it/s]\n13it [00:02,  4.82it/s]\n14it [00:02,  4.81it/s]\n15it [00:03,  4.81it/s]\n16it [00:03,  4.81it/s]\n17it [00:03,  4.80it/s]\n18it [00:03,  4.81it/s]\n19it [00:03,  4.81it/s]\n20it [00:04,  4.81it/s]\n21it [00:04,  4.81it/s]\n22it [00:04,  4.80it/s]\n23it [00:04,  4.80it/s]\n24it [00:04,  4.81it/s]\n25it [00:05,  4.81it/s]\n26it [00:05,  4.81it/s]\n27it [00:05,  4.81it/s]\n28it [00:05,  4.81it/s]\n28it [00:05,  4.88it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 6.04014835,
    "total_time": 6.045971
  },
  "output": [
    "https://replicate.delivery/xezq/db1ENqkdlLrOFhAXpsaE6qN65Fc4wGPsy8RNehQswzPNeMfnA/out-0.webp"
  ],
  "started_at": "2024-12-27T21:33:08.339823Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-3cud7twqlrklogh4onn2itkbx2jdtnharofgwfwtabb5hks36nxq",
    "get": "https://api.replicate.com/v1/predictions/35rbq9wqdsrme0cm184r8rhknc",
    "cancel": "https://api.replicate.com/v1/predictions/35rbq9wqdsrme0cm184r8rhknc/cancel"
  },
  "version": "491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1"
}

Generated in

6.0 seconds

lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1

Prediction

Model

ctymh91ef9rmc0cm1ry8kpj53c

Status

Succeeded

Source

Web

Hardware

H100

Total duration

9.8s

Created

4 months ago

Input

model: dev
prompt: a DALGONA cookie in a tin, with a triangle shape on top
go_fast
lora_scale: 1
megapixels: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "a DALGONA cookie in a tin, with a triangle shape on top",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
  {
    input: {
      model: "dev",
      prompt: "a DALGONA cookie in a tin, with a triangle shape on top",
      go_fast: false,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

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

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

pip install replicate

Import the client:

import replicate

Run lucataco/flux-dalgona using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    input={
        "model": "dev",
        "prompt": "a DALGONA cookie in a tin, with a triangle shape on top",
        "go_fast": False,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run lucataco/flux-dalgona 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": "lucataco/flux-dalgona:491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1",
    "input": {
      "model": "dev",
      "prompt": "a DALGONA cookie in a tin, with a triangle shape on top",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-12-28T17:07:02.453331Z",
  "created_at": "2024-12-28T17:06:52.666000Z",
  "data_removed": false,
  "error": null,
  "id": "ctymh91ef9rmc0cm1ry8kpj53c",
  "input": {
    "model": "dev",
    "prompt": "a DALGONA cookie in a tin, with a triangle shape on top",
    "go_fast": false,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "2024-12-28 17:06:52.688 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-28 17:06:52.689 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  90%|█████████ | 275/304 [00:00<00:00, 2735.24it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2670.13it/s]\n2024-12-28 17:06:52.803 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s\nfree=29314422382592\nDownloading weights\n2024-12-28T17:06:52Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp4y77m7b6/weights url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\n2024-12-28T17:06:56Z | INFO  | [ Complete ] dest=/tmp/tmp4y77m7b6/weights size=\"172 MB\" total_elapsed=3.397s url=https://replicate.delivery/xezq/6NXxN7RiEYYaMxuc8jV5pgzr9OhxPgqLUOCLoFdO3s4BLzfJA/trained_model.tar\nDownloaded weights in 3.42s\n2024-12-28 17:06:56.229 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/77045a58eb98c8d1\n2024-12-28 17:06:56.301 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2024-12-28 17:06:56.301 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2024-12-28 17:06:56.301 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  90%|█████████ | 275/304 [00:00<00:00, 2739.30it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2673.55it/s]\n2024-12-28 17:06:56.416 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s\nUsing seed: 56948\n0it [00:00, ?it/s]\n1it [00:00,  8.35it/s]\n2it [00:00,  5.88it/s]\n3it [00:00,  5.37it/s]\n4it [00:00,  5.16it/s]\n5it [00:00,  5.05it/s]\n6it [00:01,  4.98it/s]\n7it [00:01,  4.94it/s]\n8it [00:01,  4.92it/s]\n9it [00:01,  4.90it/s]\n10it [00:01,  4.89it/s]\n11it [00:02,  4.88it/s]\n12it [00:02,  4.88it/s]\n13it [00:02,  4.87it/s]\n14it [00:02,  4.87it/s]\n15it [00:02,  4.87it/s]\n16it [00:03,  4.86it/s]\n17it [00:03,  4.86it/s]\n18it [00:03,  4.86it/s]\n19it [00:03,  4.86it/s]\n20it [00:04,  4.86it/s]\n21it [00:04,  4.86it/s]\n22it [00:04,  4.85it/s]\n23it [00:04,  4.85it/s]\n24it [00:04,  4.85it/s]\n25it [00:05,  4.86it/s]\n26it [00:05,  4.86it/s]\n27it [00:05,  4.85it/s]\n28it [00:05,  4.85it/s]\n28it [00:05,  4.93it/s]\nTotal safe images: 1 out of 1",
  "metrics": {
    "predict_time": 9.763528131,
    "total_time": 9.787331
  },
  "output": [
    "https://replicate.delivery/xezq/lflrebUpHykS5kSe9NVTX70epKLdwUGFBeUtDmfeIPEKbEvfTA/out-0.webp"
  ],
  "started_at": "2024-12-28T17:06:52.689803Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-kxhb5wx6javq7wx5nlvzejxpapmobwl2i3ns2pz5gme6t4znespq",
    "get": "https://api.replicate.com/v1/predictions/ctymh91ef9rmc0cm1ry8kpj53c",
    "cancel": "https://api.replicate.com/v1/predictions/ctymh91ef9rmc0cm1ry8kpj53c/cancel"
  },
  "version": "491e29bb79efa1f3853c1ad493e66a2fc688956d94049a9131d9f6512888f0d1"
}

Generated in

9.8 seconds