pixray/text2image-future – Run with an API on Replicate

pixray / text2image-future

pixray text2image (future branch)

Cold

Public
24.9K runs
T4
GitHub
License

Run with an API

Playground API Examples README Versions

Input

prompts

string

Shift + Return to add a new line

evil medical device from a post apocalyptic veterinary clinic #pixelartevil medical device from a post apocalyptic veterinary clinic #pixelart

text prompt

Default: "Cairo skyline at sunset."

drawer

string

render engine

Default: "vqgan"

settings

string

Shift + Return to add a new line

# random number seed can be a word or number
seed: reference
# higher quality than default
quality: better
# smooth out the result a bit
custom_loss: smoothness:0.5
# enable transparency in image
transparent: true
# how much to encourage transparency (can also be negative) 
transparent_weight: 0.1

# random number seed can be a word or number
seed: reference
# higher quality than default
quality: better
# smooth out the result a bit
custom_loss: smoothness:0.5
# enable transparency in image
transparent: true
# how much to encourage transparency (can also be negative) 
transparent_weight: 0.1

extra settings in `name: value` format. reference: https://dazhizhong.gitbook.io/pixray-docs/docs/primary-settings

Default: "\n"

Run this model in Node.js with one line of code:

npx create-replicate --model=pixray/text2image-future

or set up a project from scratch

Install Replicate’s Node.js client library

npm install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client

import Replicate from "replicate";

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

Run pixray/text2image-future using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pixray/text2image-future:42615782823e77be5ef6d36270ae021c7b1883a189a9277ef699190eb24fd93a",
  {
    input: {
      drawer: "pixel",
      prompts: "evil medical device from a post apocalyptic veterinary clinic #pixelart",
      settings: "# random number seed can be a word or number\nseed: reference\n# higher quality than default\nquality: better\n# smooth out the result a bit\ncustom_loss: smoothness:0.5\n# enable transparency in image\ntransparent: true\n# how much to encourage transparency (can also be negative) \ntransparent_weight: 0.1\n\n"
    }
  }
);
console.log(output);

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

Install Replicate’s Python client library

pip install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client

import replicate

Run pixray/text2image-future using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pixray/text2image-future:42615782823e77be5ef6d36270ae021c7b1883a189a9277ef699190eb24fd93a",
    input={
        "drawer": "pixel",
        "prompts": "evil medical device from a post apocalyptic veterinary clinic #pixelart",
        "settings": "# random number seed can be a word or number\nseed: reference\n# higher quality than default\nquality: better\n# smooth out the result a bit\ncustom_loss: smoothness:0.5\n# enable transparency in image\ntransparent: true\n# how much to encourage transparency (can also be negative) \ntransparent_weight: 0.1\n\n"
    }
)

# The pixray/text2image-future model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/pixray/text2image-future/api#output-schema
    print(item)

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

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run pixray/text2image-future 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": "42615782823e77be5ef6d36270ae021c7b1883a189a9277ef699190eb24fd93a",
    "input": {
      "drawer": "pixel",
      "prompts": "evil medical device from a post apocalyptic veterinary clinic #pixelart",
      "settings": "# random number seed can be a word or number\\nseed: reference\\n# higher quality than default\\nquality: better\\n# smooth out the result a bit\\ncustom_loss: smoothness:0.5\\n# enable transparency in image\\ntransparent: true\\n# how much to encourage transparency (can also be negative) \\ntransparent_weight: 0.1\\n\\n"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Install Cog

brew install cog

If you don’t have Homebrew, there are other installation options available.

Pull and run pixray/text2image-future using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/pixray/text2image-future@sha256:42615782823e77be5ef6d36270ae021c7b1883a189a9277ef699190eb24fd93a \
  -i 'drawer="pixel"' \
  -i 'prompts="evil medical device from a post apocalyptic veterinary clinic #pixelart"' \
  -i $'settings="# random number seed can be a word or number\\nseed: reference\\n# higher quality than default\\nquality: better\\n# smooth out the result a bit\\ncustom_loss: smoothness:0.5\\n# enable transparency in image\\ntransparent: true\\n# how much to encourage transparency (can also be negative) \\ntransparent_weight: 0.1\\n\\n"'

To learn more, take a look at the Cog documentation.

Pull and run pixray/text2image-future using Docker (this will download the full model and run it in your local environment):

docker run -d -p 5000:5000 --gpus=all r8.im/pixray/text2image-future@sha256:42615782823e77be5ef6d36270ae021c7b1883a189a9277ef699190eb24fd93a
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "drawer": "pixel",
      "prompts": "evil medical device from a post apocalyptic veterinary clinic #pixelart",
      "settings": "# random number seed can be a word or number\\nseed: reference\\n# higher quality than default\\nquality: better\\n# smooth out the result a bit\\ncustom_loss: smoothness:0.5\\n# enable transparency in image\\ntransparent: true\\n# how much to encourage transparency (can also be negative) \\ntransparent_weight: 0.1\\n\\n"
    }
  }' \
  http://localhost:5000/predictions

Output

{
  "completed_at": "2022-03-10T01:25:39.824286Z",
  "created_at": "2022-03-10T01:16:41.185854Z",
  "data_removed": false,
  "error": null,
  "id": "4porzbc32fbv7ezhe7u7tprg5i",
  "input": {
    "drawer": "pixel",
    "prompts": "evil medical device from a post apocalyptic veterinary clinic #pixelart",
    "settings": "# random number seed can be a word or number\nseed: reference\n# higher quality than default\nquality: better\n# smooth out the result a bit\ncustom_loss: smoothness:0.5\n# enable transparency in image\ntransparent: true\n# how much to encourage transparency (can also be negative) \ntransparent_weight: 0.1\n\n"
  },
  "logs": "---> BasePixrayPredictor Predict\nUsing seed: 3903845079\nRunning pixeldrawer with 80x45 grid\n\n  0%|                                               | 0.00/244M [00:00<?, ?iB/s]\n  0%|▏                                     | 1.19M/244M [00:00<00:21, 11.9MiB/s]\n  5%|█▉                                    | 12.6M/244M [00:00<00:14, 16.3MiB/s]\n 14%|█████▏                                | 33.6M/244M [00:00<00:09, 22.6MiB/s]\n 21%|███████▉                              | 50.6M/244M [00:00<00:06, 30.6MiB/s]\n 27%|██████████▍                           | 67.0M/244M [00:00<00:04, 40.6MiB/s]\n 34%|████████████▊                         | 82.4M/244M [00:00<00:03, 52.4MiB/s]\n 44%|████████████████▉                      | 106M/244M [00:00<00:02, 68.7MiB/s]\n 53%|████████████████████▌                  | 128M/244M [00:00<00:01, 87.0MiB/s]\n 61%|████████████████████████▌               | 149M/244M [00:00<00:00, 106MiB/s]\n 71%|████████████████████████████▍           | 173M/244M [00:01<00:00, 129MiB/s]\n 79%|███████████████████████████████▊        | 194M/244M [00:01<00:00, 146MiB/s]\n 88%|███████████████████████████████████▏    | 214M/244M [00:01<00:00, 161MiB/s]\n 96%|██████████████████████████████████████▍ | 235M/244M [00:01<00:00, 171MiB/s]\n100%|████████████████████████████████████████| 244M/244M [00:01<00:00, 187MiB/s]\nLoaded CLIP RN50: 224x224 and 102.01M params\nLoaded CLIP ViT-B/32: 224x224 and 151.28M params\nLoaded CLIP ViT-B/16: 224x224 and 149.62M params\nUsing device: cuda:0\n\nOptimising using: Adam\nUsing text prompts: ['evil medical device from a post apocalyptic veterinary clinic #pixelart']\nusing custom losses: smoothness:0.5\n0it [00:00, ?it/s]\niter: 0, loss: 3.23, losses: 1.01, 0.0859, 0.928, 0.0617, 0.904, 0.0643, 0.1, 0.0725 (-0=>3.227)\n\n\n0it [00:00, ?it/s]/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n  warnings.warn(\n\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 10, loss: 2.88, losses: 0.916, 0.0816, 0.799, 0.061, 0.798, 0.0613, 0.0905, 0.0707 (-0=>2.878)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.73, losses: 0.874, 0.0823, 0.753, 0.0629, 0.74, 0.0628, 0.0843, 0.0704 (-0=>2.73)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 30, loss: 2.62, losses: 0.848, 0.0824, 0.724, 0.0635, 0.685, 0.0644, 0.0787, 0.0711 (-0=>2.617)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.53, losses: 0.818, 0.0824, 0.695, 0.0662, 0.66, 0.0663, 0.0731, 0.0716 (-3=>2.533)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 50, loss: 2.53, losses: 0.815, 0.0837, 0.7, 0.0643, 0.66, 0.0655, 0.0681, 0.0704 (-2=>2.493)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.46, losses: 0.786, 0.083, 0.676, 0.0673, 0.648, 0.0676, 0.0642, 0.0714 (-1=>2.428)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 70, loss: 2.46, losses: 0.781, 0.0844, 0.682, 0.0658, 0.648, 0.0672, 0.0608, 0.0695 (-1=>2.412)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.46, losses: 0.788, 0.0835, 0.682, 0.0654, 0.645, 0.0668, 0.0579, 0.0716 (-2=>2.376)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 90, loss: 2.41, losses: 0.771, 0.0833, 0.665, 0.067, 0.631, 0.0677, 0.0552, 0.0682 (-3=>2.364)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.35, losses: 0.746, 0.0847, 0.65, 0.067, 0.619, 0.0692, 0.053, 0.0624 (-0=>2.352)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 110, loss: 2.35, losses: 0.748, 0.0844, 0.651, 0.067, 0.616, 0.0691, 0.051, 0.0596 (-1=>2.331)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.36, losses: 0.756, 0.0844, 0.651, 0.0662, 0.618, 0.069, 0.0491, 0.0617 (-11=>2.331)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 130, loss: 2.36, losses: 0.756, 0.0839, 0.654, 0.0666, 0.622, 0.0688, 0.0472, 0.061 (-6=>2.31)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 2.36, losses: 0.753, 0.0854, 0.652, 0.0665, 0.623, 0.0677, 0.0458, 0.0638 (-6=>2.305)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 150, loss: 2.33, losses: 0.741, 0.0847, 0.646, 0.0665, 0.614, 0.0693, 0.0447, 0.0621 (-5=>2.297)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 2.31, losses: 0.734, 0.0842, 0.641, 0.0675, 0.609, 0.0699, 0.0437, 0.061 (-15=>2.297)\n\n\n0it [00:00, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 170, loss: 2.3, losses: 0.736, 0.0851, 0.633, 0.0673, 0.606, 0.0703, 0.0428, 0.0597 (-3=>2.289)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 2.31, losses: 0.741, 0.0849, 0.637, 0.0668, 0.61, 0.0689, 0.0418, 0.0622 (-5=>2.28)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 190, loss: 2.32, losses: 0.745, 0.0844, 0.642, 0.0659, 0.613, 0.0683, 0.0409, 0.0629 (-15=>2.28)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 2.26, losses: 0.716, 0.0857, 0.624, 0.0677, 0.599, 0.0703, 0.0401, 0.0618 (-0=>2.265)\n\n\n0it [00:01, ?it/s]\n0it [00:17, ?it/s]\n\n0it [00:00, ?it/s]\niter: 210, loss: 2.33, losses: 0.746, 0.085, 0.646, 0.0659, 0.616, 0.0683, 0.0392, 0.0641 (-1=>2.255)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 2.27, losses: 0.724, 0.0844, 0.628, 0.0674, 0.601, 0.0704, 0.0386, 0.0578 (-5=>2.252)\n\n\nDropping learning rate\n0it [00:01, ?it/s]\n0it [00:17, ?it/s]\n\n0it [00:00, ?it/s]\niter: 230, loss: 2.3, losses: 0.739, 0.085, 0.634, 0.0671, 0.606, 0.0693, 0.0384, 0.0602 (-1=>2.264)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 2.26, losses: 0.716, 0.0849, 0.627, 0.0678, 0.599, 0.07, 0.0383, 0.0565 (-5=>2.25)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 250, loss: 2.25, losses: 0.718, 0.0857, 0.619, 0.0679, 0.592, 0.0703, 0.0382, 0.056 (-0=>2.247)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 260, loss: 2.27, losses: 0.719, 0.0846, 0.628, 0.067, 0.6, 0.0704, 0.0382, 0.0594 (-5=>2.244)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 270, loss: 2.27, losses: 0.724, 0.0859, 0.63, 0.0669, 0.598, 0.0703, 0.0381, 0.0555 (-1=>2.241)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 280, loss: 2.26, losses: 0.72, 0.0853, 0.625, 0.0674, 0.597, 0.0703, 0.0381, 0.0581 (-11=>2.241)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 290, loss: 2.25, losses: 0.716, 0.0855, 0.621, 0.0681, 0.593, 0.0701, 0.0381, 0.0574 (-21=>2.241)\n\n\n0it [00:00, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 300, finished (-31=>2.241)\n\n\n0it [00:00, ?it/s]\n0it [00:00, ?it/s]",
  "metrics": {
    "predict_time": 538.420743,
    "total_time": 538.638432
  },
  "output": [
    "https://replicate.delivery/mgxm/ba9efa8b-0536-484d-bb61-c22d801e7ee1/tempfile.png",
    "https://replicate.delivery/mgxm/808b18a0-2632-47b3-b74e-72d62602d03a/tempfile.png",
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    "https://replicate.delivery/mgxm/6797d76f-5e30-48cc-898c-454a2759d59b/tempfile.png",
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    "https://replicate.delivery/mgxm/209549da-c442-42c1-b96f-e010ae14f07a/tempfile.png",
    "https://replicate.delivery/mgxm/03247e3f-2265-48ef-9915-4a25b131a727/tempfile.png",
    "https://replicate.delivery/mgxm/55ebb21e-006f-4b53-a489-1f968c7d64e6/tempfile.png"
  ],
  "started_at": "2022-03-10T01:16:41.403543Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4porzbc32fbv7ezhe7u7tprg5i",
    "cancel": "https://api.replicate.com/v1/predictions/4porzbc32fbv7ezhe7u7tprg5i/cancel"
  },
  "version": "0d01ba09e8fa182455bc3ccc7b11b834356645dbcfa2712d6e5a4d5615a7a9f0"
}

Generated in

8 minutes 58 seconds

Tweak itReport

---> BasePixrayPredictor Predict
Using seed: 3903845079
Running pixeldrawer with 80x45 grid

  0%|                                               | 0.00/244M [00:00<?, ?iB/s]
  0%|▏                                     | 1.19M/244M [00:00<00:21, 11.9MiB/s]
  5%|█▉                                    | 12.6M/244M [00:00<00:14, 16.3MiB/s]
 14%|█████▏                                | 33.6M/244M [00:00<00:09, 22.6MiB/s]
 21%|███████▉                              | 50.6M/244M [00:00<00:06, 30.6MiB/s]
 27%|██████████▍                           | 67.0M/244M [00:00<00:04, 40.6MiB/s]
 34%|████████████▊                         | 82.4M/244M [00:00<00:03, 52.4MiB/s]
 44%|████████████████▉                      | 106M/244M [00:00<00:02, 68.7MiB/s]
 53%|████████████████████▌                  | 128M/244M [00:00<00:01, 87.0MiB/s]
 61%|████████████████████████▌               | 149M/244M [00:00<00:00, 106MiB/s]
 71%|████████████████████████████▍           | 173M/244M [00:01<00:00, 129MiB/s]
 79%|███████████████████████████████▊        | 194M/244M [00:01<00:00, 146MiB/s]
 88%|███████████████████████████████████▏    | 214M/244M [00:01<00:00, 161MiB/s]
 96%|██████████████████████████████████████▍ | 235M/244M [00:01<00:00, 171MiB/s]
100%|████████████████████████████████████████| 244M/244M [00:01<00:00, 187MiB/s]
Loaded CLIP RN50: 224x224 and 102.01M params
Loaded CLIP ViT-B/32: 224x224 and 151.28M params
Loaded CLIP ViT-B/16: 224x224 and 149.62M params
Using device: cuda:0

Optimising using: Adam
Using text prompts: ['evil medical device from a post apocalyptic veterinary clinic #pixelart']
using custom losses: smoothness:0.5
0it [00:00, ?it/s]
iter: 0, loss: 3.23, losses: 1.01, 0.0859, 0.928, 0.0617, 0.904, 0.0643, 0.1, 0.0725 (-0=>3.227)


0it [00:00, ?it/s]/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  warnings.warn(

0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 10, loss: 2.88, losses: 0.916, 0.0816, 0.799, 0.061, 0.798, 0.0613, 0.0905, 0.0707 (-0=>2.878)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 20, loss: 2.73, losses: 0.874, 0.0823, 0.753, 0.0629, 0.74, 0.0628, 0.0843, 0.0704 (-0=>2.73)


0it [00:01, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 30, loss: 2.62, losses: 0.848, 0.0824, 0.724, 0.0635, 0.685, 0.0644, 0.0787, 0.0711 (-0=>2.617)


0it [00:01, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 40, loss: 2.53, losses: 0.818, 0.0824, 0.695, 0.0662, 0.66, 0.0663, 0.0731, 0.0716 (-3=>2.533)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 50, loss: 2.53, losses: 0.815, 0.0837, 0.7, 0.0643, 0.66, 0.0655, 0.0681, 0.0704 (-2=>2.493)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 60, loss: 2.46, losses: 0.786, 0.083, 0.676, 0.0673, 0.648, 0.0676, 0.0642, 0.0714 (-1=>2.428)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 70, loss: 2.46, losses: 0.781, 0.0844, 0.682, 0.0658, 0.648, 0.0672, 0.0608, 0.0695 (-1=>2.412)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 80, loss: 2.46, losses: 0.788, 0.0835, 0.682, 0.0654, 0.645, 0.0668, 0.0579, 0.0716 (-2=>2.376)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 90, loss: 2.41, losses: 0.771, 0.0833, 0.665, 0.067, 0.631, 0.0677, 0.0552, 0.0682 (-3=>2.364)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 100, loss: 2.35, losses: 0.746, 0.0847, 0.65, 0.067, 0.619, 0.0692, 0.053, 0.0624 (-0=>2.352)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 110, loss: 2.35, losses: 0.748, 0.0844, 0.651, 0.067, 0.616, 0.0691, 0.051, 0.0596 (-1=>2.331)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 120, loss: 2.36, losses: 0.756, 0.0844, 0.651, 0.0662, 0.618, 0.069, 0.0491, 0.0617 (-11=>2.331)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 130, loss: 2.36, losses: 0.756, 0.0839, 0.654, 0.0666, 0.622, 0.0688, 0.0472, 0.061 (-6=>2.31)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 140, loss: 2.36, losses: 0.753, 0.0854, 0.652, 0.0665, 0.623, 0.0677, 0.0458, 0.0638 (-6=>2.305)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 150, loss: 2.33, losses: 0.741, 0.0847, 0.646, 0.0665, 0.614, 0.0693, 0.0447, 0.0621 (-5=>2.297)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 160, loss: 2.31, losses: 0.734, 0.0842, 0.641, 0.0675, 0.609, 0.0699, 0.0437, 0.061 (-15=>2.297)


0it [00:00, ?it/s]
0it [00:15, ?it/s]

0it [00:00, ?it/s]
iter: 170, loss: 2.3, losses: 0.736, 0.0851, 0.633, 0.0673, 0.606, 0.0703, 0.0428, 0.0597 (-3=>2.289)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 180, loss: 2.31, losses: 0.741, 0.0849, 0.637, 0.0668, 0.61, 0.0689, 0.0418, 0.0622 (-5=>2.28)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 190, loss: 2.32, losses: 0.745, 0.0844, 0.642, 0.0659, 0.613, 0.0683, 0.0409, 0.0629 (-15=>2.28)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 200, loss: 2.26, losses: 0.716, 0.0857, 0.624, 0.0677, 0.599, 0.0703, 0.0401, 0.0618 (-0=>2.265)


0it [00:01, ?it/s]
0it [00:17, ?it/s]

0it [00:00, ?it/s]
iter: 210, loss: 2.33, losses: 0.746, 0.085, 0.646, 0.0659, 0.616, 0.0683, 0.0392, 0.0641 (-1=>2.255)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 220, loss: 2.27, losses: 0.724, 0.0844, 0.628, 0.0674, 0.601, 0.0704, 0.0386, 0.0578 (-5=>2.252)


Dropping learning rate
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0it [00:17, ?it/s]

0it [00:00, ?it/s]
iter: 230, loss: 2.3, losses: 0.739, 0.085, 0.634, 0.0671, 0.606, 0.0693, 0.0384, 0.0602 (-1=>2.264)


0it [00:01, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 240, loss: 2.26, losses: 0.716, 0.0849, 0.627, 0.0678, 0.599, 0.07, 0.0383, 0.0565 (-5=>2.25)


0it [00:01, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 250, loss: 2.25, losses: 0.718, 0.0857, 0.619, 0.0679, 0.592, 0.0703, 0.0382, 0.056 (-0=>2.247)


0it [00:01, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 260, loss: 2.27, losses: 0.719, 0.0846, 0.628, 0.067, 0.6, 0.0704, 0.0382, 0.0594 (-5=>2.244)


0it [00:01, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 270, loss: 2.27, losses: 0.724, 0.0859, 0.63, 0.0669, 0.598, 0.0703, 0.0381, 0.0555 (-1=>2.241)


0it [00:01, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 280, loss: 2.26, losses: 0.72, 0.0853, 0.625, 0.0674, 0.597, 0.0703, 0.0381, 0.0581 (-11=>2.241)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 290, loss: 2.25, losses: 0.716, 0.0855, 0.621, 0.0681, 0.593, 0.0701, 0.0381, 0.0574 (-21=>2.241)


0it [00:00, ?it/s]
0it [00:16, ?it/s]

0it [00:00, ?it/s]
iter: 300, finished (-31=>2.241)


0it [00:00, ?it/s]
0it [00:00, ?it/s]

This example was created by a different version, pixray/text2image-future:0d01ba09.

Examples

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Run time and cost

This model costs approximately $0.14 to run on Replicate, or 7 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 11 minutes. The predict time for this model varies significantly based on the inputs.

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