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pixray /text2image:f6ca4f09
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=pixray/text2image
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet 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";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run pixray/text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"pixray/text2image:f6ca4f09e1cad8c4adca2c86fd1f4c9121f5f2e6c2f00408ab19c4077192fd23",
{
input: {
drawer: "vdiff",
prompts: "beautiful Steampunk lady",
settings: "vdiff_model: cc12m_1\nsize: [320, 320]\nvector_prompts: None\nclip_models: RN101,ViT-B/32,ViT-B/16"
}
}
);
// 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 replicateSet 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 replicateRun pixray/text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"pixray/text2image:f6ca4f09e1cad8c4adca2c86fd1f4c9121f5f2e6c2f00408ab19c4077192fd23",
input={
"drawer": "vdiff",
"prompts": "beautiful Steampunk lady",
"settings": "vdiff_model: cc12m_1\nsize: [320, 320]\nvector_prompts: None\nclip_models: RN101,ViT-B/32,ViT-B/16"
}
)
# The pixray/text2image 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/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 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": "pixray/text2image:f6ca4f09e1cad8c4adca2c86fd1f4c9121f5f2e6c2f00408ab19c4077192fd23",
"input": {
"drawer": "vdiff",
"prompts": "beautiful Steampunk lady",
"settings": "vdiff_model: cc12m_1\\nsize: [320, 320]\\nvector_prompts: None\\nclip_models: RN101,ViT-B/32,ViT-B/16"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
Sign in with GitHub
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terms of service and privacy policy
Output
{
"completed_at": "2022-04-06T07:00:08.275395Z",
"created_at": "2022-04-06T06:53:07.781450Z",
"data_removed": false,
"error": null,
"id": "al42ygxikbhujgqd2yzlz5s4jm",
"input": {
"drawer": "vdiff",
"prompts": "beautiful Steampunk lady",
"settings": "vdiff_model: cc12m_1\nsize: [320, 320]\nvector_prompts: None\nclip_models: RN101,ViT-B/32,ViT-B/16"
},
"logs": "---> BasePixrayPredictor Predict\nUsing seed: 7260706899850638684\nAll CLIP models already loaded: ['RN101', 'ViT-B/32', 'ViT-B/16']\ndrawer <vdiff.VdiffDrawer object at 0x7fc125e77250> needs ViT-B/16\nclip_embed for drawer <vdiff.VdiffDrawer object at 0x7fc125e77250> is torch.Size([1, 512])\nUsing device: cuda:0\n\nOptimising using: Adam\nUsing text prompts: ['beautiful Steampunk lady']\niter: 0, loss: 2.77, losses: 0.766, 1.02, 0.987 (-0=>2.772)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 10, loss: 2.63, losses: 0.741, 0.967, 0.926 (-0=>2.634)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.54, losses: 0.727, 0.924, 0.887 (-3=>2.529)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 30, loss: 2.48, losses: 0.706, 0.889, 0.881 (-2=>2.452)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.29, losses: 0.64, 0.831, 0.82 (-0=>2.291)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 50, loss: 2.16, losses: 0.585, 0.793, 0.779 (-0=>2.157)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.01, losses: 0.537, 0.744, 0.725 (-0=>2.006)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 70, loss: 1.9, losses: 0.5, 0.706, 0.695 (-0=>1.901)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 1.87, losses: 0.489, 0.694, 0.683 (-0=>1.866)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 90, loss: 1.83, losses: 0.476, 0.681, 0.676 (-0=>1.833)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 1.81, losses: 0.465, 0.675, 0.672 (-1=>1.804)\n\n\n0it [00:01, ?it/s]\n0it [00:16, ?it/s]\n\n0it [00:00, ?it/s]\niter: 110, loss: 1.82, losses: 0.474, 0.677, 0.671 (-2=>1.797)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 1.8, losses: 0.467, 0.669, 0.666 (-3=>1.786)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 130, loss: 1.8, losses: 0.467, 0.672, 0.665 (-2=>1.785)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 1.79, losses: 0.461, 0.671, 0.662 (-12=>1.785)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 150, loss: 1.81, losses: 0.466, 0.677, 0.664 (-22=>1.785)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 1.78, losses: 0.456, 0.663, 0.658 (-0=>1.777)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 170, loss: 1.81, losses: 0.469, 0.674, 0.671 (-10=>1.777)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 1.82, losses: 0.472, 0.677, 0.668 (-20=>1.777)\n\n\nDropping learning rate\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 190, loss: 1.81, losses: 0.466, 0.673, 0.668 (-2=>1.804)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 1.82, losses: 0.473, 0.677, 0.671 (-8=>1.791)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\niter: 210, loss: 1.85, losses: 0.483, 0.685, 0.681 (-7=>1.791)\n0it [00:00, ?it/s]\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 1.83, losses: 0.478, 0.682, 0.675 (-17=>1.791)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 230, loss: 1.85, losses: 0.487, 0.685, 0.683 (-27=>1.791)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 1.82, losses: 0.478, 0.671, 0.671 (-37=>1.791)\n\n\n0it [00:01, ?it/s]\n0it [00:15, ?it/s]\n\n0it [00:00, ?it/s]\niter: 250, finished (-47=>1.791)\n\n\n0it [00:00, ?it/s]\n0it [00:00, ?it/s]",
"metrics": {
"predict_time": 420.324331,
"total_time": 420.493945
},
"output": [
"https://replicate.delivery/mgxm/e93fde39-7240-40ba-94be-0c7834fe59ae/tempfile.png",
"https://replicate.delivery/mgxm/2de03213-09cc-4bf1-baea-2e361cbff80b/tempfile.png",
"https://replicate.delivery/mgxm/361488e5-bb01-4e85-a827-8ca609969ebd/tempfile.png",
"https://replicate.delivery/mgxm/7d194191-7470-4e2f-8640-1c82dc647a48/tempfile.png",
"https://replicate.delivery/mgxm/c0d8ab27-bfe9-4034-8433-6937c3e85aaa/tempfile.png",
"https://replicate.delivery/mgxm/a19e9a26-3aba-4ff8-941f-eaf2bbc5dc9f/tempfile.png",
"https://replicate.delivery/mgxm/b707c115-e072-44c3-a9ff-72ecded71f1e/tempfile.png",
"https://replicate.delivery/mgxm/6d562a14-3a1e-4fd3-be99-0a8699df46b5/tempfile.png",
"https://replicate.delivery/mgxm/8307625c-1dee-4970-b18a-c1ade27339e8/tempfile.png",
"https://replicate.delivery/mgxm/5927a52e-712c-499a-8704-2da1a14957e3/tempfile.png",
"https://replicate.delivery/mgxm/b7bd9afb-88c2-47e0-9f67-6dba33ddb045/tempfile.png",
"https://replicate.delivery/mgxm/3238ece4-4c1c-4ae0-91b1-b6da7159bbee/tempfile.png",
"https://replicate.delivery/mgxm/c146f6b3-de88-445f-ab43-6d7a64204639/tempfile.png",
"https://replicate.delivery/mgxm/510b2d42-a7bc-4b62-b729-b52af2524ba4/tempfile.png",
"https://replicate.delivery/mgxm/fd7f1541-2cd2-4bf7-947f-e7da88b0f0aa/tempfile.png",
"https://replicate.delivery/mgxm/97923bf0-b066-4694-8196-d804ac811cc1/tempfile.png",
"https://replicate.delivery/mgxm/7ade4f63-5431-4a53-8f4e-4dc5998d0a5a/tempfile.png",
"https://replicate.delivery/mgxm/19f0b765-2c68-45a4-b254-498cbb8786fb/tempfile.png",
"https://replicate.delivery/mgxm/2c843552-80bd-4d03-8647-7cf7d5a50ed5/tempfile.png",
"https://replicate.delivery/mgxm/0c9ee64e-d2ad-4b0e-9615-dc6f87d88324/tempfile.png",
"https://replicate.delivery/mgxm/791760fc-b49b-49a3-b3df-311290274678/tempfile.png",
"https://replicate.delivery/mgxm/6682241d-d2ef-4077-ba78-c03cdbcc8a3a/tempfile.png",
"https://replicate.delivery/mgxm/4713280f-6602-4e6b-bd07-480fcc603f60/tempfile.png",
"https://replicate.delivery/mgxm/a9ba320d-b627-418f-8b2f-e46a253b9355/tempfile.png",
"https://replicate.delivery/mgxm/172f142d-7082-4d11-829f-d4e50bc52db6/tempfile.png",
"https://replicate.delivery/mgxm/b8270a91-ef27-40ab-9eff-7993f191e17c/tempfile.png"
],
"started_at": "2022-04-06T06:53:07.951064Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/al42ygxikbhujgqd2yzlz5s4jm",
"cancel": "https://api.replicate.com/v1/predictions/al42ygxikbhujgqd2yzlz5s4jm/cancel"
},
"version": "f6ca4f09e1cad8c4adca2c86fd1f4c9121f5f2e6c2f00408ab19c4077192fd23"
}Generated in
---> BasePixrayPredictor Predict
Using seed: 7260706899850638684
All CLIP models already loaded: ['RN101', 'ViT-B/32', 'ViT-B/16']
drawer <vdiff.VdiffDrawer object at 0x7fc125e77250> needs ViT-B/16
clip_embed for drawer <vdiff.VdiffDrawer object at 0x7fc125e77250> is torch.Size([1, 512])
Using device: cuda:0
Optimising using: Adam
Using text prompts: ['beautiful Steampunk lady']
iter: 0, loss: 2.77, losses: 0.766, 1.02, 0.987 (-0=>2.772)
0it [00:01, ?it/s]
0it [00:16, ?it/s]
0it [00:00, ?it/s]
iter: 10, loss: 2.63, losses: 0.741, 0.967, 0.926 (-0=>2.634)
0it [00:01, ?it/s]
0it [00:16, ?it/s]
0it [00:00, ?it/s]
iter: 20, loss: 2.54, losses: 0.727, 0.924, 0.887 (-3=>2.529)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 30, loss: 2.48, losses: 0.706, 0.889, 0.881 (-2=>2.452)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 40, loss: 2.29, losses: 0.64, 0.831, 0.82 (-0=>2.291)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 50, loss: 2.16, losses: 0.585, 0.793, 0.779 (-0=>2.157)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 60, loss: 2.01, losses: 0.537, 0.744, 0.725 (-0=>2.006)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 70, loss: 1.9, losses: 0.5, 0.706, 0.695 (-0=>1.901)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 80, loss: 1.87, losses: 0.489, 0.694, 0.683 (-0=>1.866)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 90, loss: 1.83, losses: 0.476, 0.681, 0.676 (-0=>1.833)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 100, loss: 1.81, losses: 0.465, 0.675, 0.672 (-1=>1.804)
0it [00:01, ?it/s]
0it [00:16, ?it/s]
0it [00:00, ?it/s]
iter: 110, loss: 1.82, losses: 0.474, 0.677, 0.671 (-2=>1.797)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 120, loss: 1.8, losses: 0.467, 0.669, 0.666 (-3=>1.786)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 130, loss: 1.8, losses: 0.467, 0.672, 0.665 (-2=>1.785)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 140, loss: 1.79, losses: 0.461, 0.671, 0.662 (-12=>1.785)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 150, loss: 1.81, losses: 0.466, 0.677, 0.664 (-22=>1.785)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 160, loss: 1.78, losses: 0.456, 0.663, 0.658 (-0=>1.777)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 170, loss: 1.81, losses: 0.469, 0.674, 0.671 (-10=>1.777)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 180, loss: 1.82, losses: 0.472, 0.677, 0.668 (-20=>1.777)
Dropping learning rate
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 190, loss: 1.81, losses: 0.466, 0.673, 0.668 (-2=>1.804)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 200, loss: 1.82, losses: 0.473, 0.677, 0.671 (-8=>1.791)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
iter: 210, loss: 1.85, losses: 0.483, 0.685, 0.681 (-7=>1.791)
0it [00:00, ?it/s]
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 220, loss: 1.83, losses: 0.478, 0.682, 0.675 (-17=>1.791)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 230, loss: 1.85, losses: 0.487, 0.685, 0.683 (-27=>1.791)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 240, loss: 1.82, losses: 0.478, 0.671, 0.671 (-37=>1.791)
0it [00:01, ?it/s]
0it [00:15, ?it/s]
0it [00:00, ?it/s]
iter: 250, finished (-47=>1.791)
0it [00:00, ?it/s]
0it [00:00, ?it/s]