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Replicate

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Hugging Face

Integrate Replicate and Hugging Face

Connect Replicate and Hugging Face nodes in your workflow. Integrate with any tool or database and ship powerful backend logic and APIs instantly - No code required!

Node stack

Supported Replicate and Hugging Face Nodes

Add any other tools or your preferred database nodes. If an integration is not available generate your own using AI

Answer Image Questions

Answers questions about images using the Replicate model

Background Remover

Remove the background from images using Replicate API

Face Restoration

Performs practical face restoration algorithm for old photos or AI-generated faces using Replicate

Face Swap

Swaps faces using replicate's yan-ops/face_swap model.

Image Super-Resolution and Restoration

Performs a AI Photorealistic Image Super-Resolution and Restoration using Replicate

Llama 2 Chat

Llama 2 for chat completion

Music Generator

Uses Replicate's music-gen model to generate music based on a user prompt.

Replicate Client

A generic Replicate Client to use any Replicate model.

Replicate Llama Text Generator

Uses Replicate Llama model to generate text based on a provided prompt and system prompt while applying various text generation settings.

Stability AI Image Generation

Text to image generation using Stability AI's sdxl model that creates beautiful 1024x1024 images

Stability AI Text Generation

Generate text using the Stability AI 7 billion parameter language model

Upscale Image

Performs Real-ESRGAN with optional face correction and adjustable upscale

Caption Image

Generate caption for the image using Hugging Face's [Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large) model for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone).

Image Classification

Get classification labels for your image using Hugging Face's [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) model which is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.

Text Summarization

Summarize long text using Hugging Face's [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) model which is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.

Text-To-Image

Generate image from text, using Hugging Face's [openskyml/dalle-3-xl](https://huggingface.co/openskyml/dalle-3-xl) test model very similar to Dall•E 3.

Text-To-Music

Generate music from text using Hugging Face's [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) model capable of generating high-quality music samples conditioned on text descriptions or audio prompts.

Quick start

How to integrate Replicate and Hugging Face

Step 1 — Add the nodes to your workflow

Create a new workflow in BuildShip, click “Add node”, and select the Replicate and Hugging Face actions you want to use.

Step 2 — Configure each node

Go to each node to authenticate (or add your API key) and fill in the required parameters.

Step 3 — Connect the nodes

Each node in BuildShip can connect to others by using their output variables. When you reference a variable from one node in another, BuildShip automatically links them in the workflow.

Step 4 — Test your workflow

Define your starting data in the Inputs node and choose what to do with the result in the Flow Output node. Finally, run a test to see your workflow in action.

blog posts & tutorials

Recommended Reads

Below are recommneded blogs that will help in your journey