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@pharmapsychotic ‘s CLIP-Interrogator, but fully-optimized version. You can get more exact prompt from an image with 3x faster speed. To create cool reference-based image, visit https://flamel.app/
@pharmapsychotic 's CLIP-Interrogator, but 3x faster and more accurate. Specialized on SDXL.
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run smoretalk/clip-interrogator-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"smoretalk/clip-interrogator-turbo:36a9275d4215df72f67de0daa59929fa24866351405b74b8bcdc1991441aafec",
{
input: {
mode: "best",
image: "https://replicate.delivery/pbxt/KgRWg4JUfnnszNV78fo1PMRvYxCD9nCgf26Va4RtLxWcuujW/illust-car.png"
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run smoretalk/clip-interrogator-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"smoretalk/clip-interrogator-turbo:36a9275d4215df72f67de0daa59929fa24866351405b74b8bcdc1991441aafec",
input={
"mode": "best",
"image": "https://replicate.delivery/pbxt/KgRWg4JUfnnszNV78fo1PMRvYxCD9nCgf26Va4RtLxWcuujW/illust-car.png"
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run smoretalk/clip-interrogator-turbo 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": "36a9275d4215df72f67de0daa59929fa24866351405b74b8bcdc1991441aafec",
"input": {
"mode": "best",
"image": "https://replicate.delivery/pbxt/KgRWg4JUfnnszNV78fo1PMRvYxCD9nCgf26Va4RtLxWcuujW/illust-car.png"
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
brew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run smoretalk/clip-interrogator-turbo using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/smoretalk/clip-interrogator-turbo@sha256:36a9275d4215df72f67de0daa59929fa24866351405b74b8bcdc1991441aafec \
-i 'mode="best"' \
-i 'image="https://replicate.delivery/pbxt/KgRWg4JUfnnszNV78fo1PMRvYxCD9nCgf26Va4RtLxWcuujW/illust-car.png"'
To learn more, take a look at the Cog documentation.
Pull and run smoretalk/clip-interrogator-turbo 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/smoretalk/clip-interrogator-turbo@sha256:36a9275d4215df72f67de0daa59929fa24866351405b74b8bcdc1991441aafec
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "mode": "best", "image": "https://replicate.delivery/pbxt/KgRWg4JUfnnszNV78fo1PMRvYxCD9nCgf26Va4RtLxWcuujW/illust-car.png" } }' \ http://localhost:5000/predictions
Add a payment method to run this model.
Each run costs approximately $0.0011. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2024-04-03T16:02:55.865414Z",
"created_at": "2024-04-03T15:59:47.985000Z",
"data_removed": false,
"error": null,
"id": "58wq98gs25rgg0cemjrbw3kj1w",
"input": {
"mode": "best",
"image": "https://replicate.delivery/pbxt/KgRWg4JUfnnszNV78fo1PMRvYxCD9nCgf26Va4RtLxWcuujW/illust-car.png",
"style_only": false
},
"logs": "Both `max_new_tokens` (=16) and `max_length`(=212) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/transformers/generation/utils.py:1156: UserWarning: Unfeasible length constraints: `min_length` (37) is larger than the maximum possible length (16). Generation will stop at the defined maximum length. You should decrease the minimum length and/or increase the maximum length.\nwarnings.warn(\nFinish generating a caption in 1.91 seconds.\nUnused or unrecognized kwargs: padding.\nUnused or unrecognized kwargs: padding.\nFinish generating an image embedding in 0.23 seconds.\nFinish generating a text embedding in 0.15 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.15 seconds.\nFinish generating a text embedding in 0.15 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.15 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.14 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.13 seconds.\nFinish generating a text embedding in 0.15 seconds.\nFinish generating a text embedding in 0.13 seconds.\nBest prompt is 'a painting of a yellow car parked in front of a house with trees in the, by Makoto Shinkai, featured on pixiv, makoto shinkai. high detail, by makoto shinkai, in style of makoto shinkai, makoto shinkai art style, makoto shinkai. —h 2160, anime keyframe'.\nFinish generating a prompt in 8.34 seconds.",
"metrics": {
"predict_time": 9.309009,
"total_time": 187.880414
},
"output": "a painting of a yellow car parked in front of a house with trees in the, by Makoto Shinkai, featured on pixiv, makoto shinkai. high detail, by makoto shinkai, in style of makoto shinkai, makoto shinkai art style, makoto shinkai. —h 2160, anime keyframe",
"started_at": "2024-04-03T16:02:46.556405Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/58wq98gs25rgg0cemjrbw3kj1w",
"cancel": "https://api.replicate.com/v1/predictions/58wq98gs25rgg0cemjrbw3kj1w/cancel"
},
"version": "f66767bbd5c60b841e05dfb72a176d11bf36eca0554c6ea5d6d705cf617bafe8"
}
Both `max_new_tokens` (=16) and `max_length`(=212) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)
/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/transformers/generation/utils.py:1156: UserWarning: Unfeasible length constraints: `min_length` (37) is larger than the maximum possible length (16). Generation will stop at the defined maximum length. You should decrease the minimum length and/or increase the maximum length.
warnings.warn(
Finish generating a caption in 1.91 seconds.
Unused or unrecognized kwargs: padding.
Unused or unrecognized kwargs: padding.
Finish generating an image embedding in 0.23 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
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Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.13 seconds.
Best prompt is 'a painting of a yellow car parked in front of a house with trees in the, by Makoto Shinkai, featured on pixiv, makoto shinkai. high detail, by makoto shinkai, in style of makoto shinkai, makoto shinkai art style, makoto shinkai. —h 2160, anime keyframe'.
Finish generating a prompt in 8.34 seconds.
This example was created by a different version, smoretalk/clip-interrogator-turbo:f66767bb.
This model costs approximately $0.0011 to run on Replicate, or 909 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 L40S GPU hardware. Predictions typically complete within 2 seconds.
@pharmapsychotic ‘s CLIP-Interrogator, but fully-optimized version. You can get more exact prompt from an image with 3x faster speed. To create cool reference-based image, visit https://flamel.app/
This model is warm. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
Choose a file from your machine
Hint: you can also drag files onto the input
Both `max_new_tokens` (=16) and `max_length`(=212) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)
/root/.pyenv/versions/3.11.9/lib/python3.11/site-packages/transformers/generation/utils.py:1156: UserWarning: Unfeasible length constraints: `min_length` (37) is larger than the maximum possible length (16). Generation will stop at the defined maximum length. You should decrease the minimum length and/or increase the maximum length.
warnings.warn(
Finish generating a caption in 1.91 seconds.
Unused or unrecognized kwargs: padding.
Unused or unrecognized kwargs: padding.
Finish generating an image embedding in 0.23 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.14 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.13 seconds.
Finish generating a text embedding in 0.15 seconds.
Finish generating a text embedding in 0.13 seconds.
Best prompt is 'a painting of a yellow car parked in front of a house with trees in the, by Makoto Shinkai, featured on pixiv, makoto shinkai. high detail, by makoto shinkai, in style of makoto shinkai, makoto shinkai art style, makoto shinkai. —h 2160, anime keyframe'.
Finish generating a prompt in 8.34 seconds.