![Logo](docs/images/main_logo.png)

## Update — March 31, 2025

**Now with Hugging Face pretrained model support for text classification**

You can use **ComfyUI-Pt-Wrapper** to fine-tune Hugging Face's pretrain models for text classification, such as sentiment analysis on a movie review dataset.

You can choose from the following models:
* bert-base-uncased
* roberta-base
* distilbert-base-uncased
* albert-base-v2

Performance was not compromised in adding this feature — you should achieve around 92-93% validation accuracy on the IMDB dataset with the pretrained DistilBERT model.

Check out [Fine-tuning Hugging Face models for classification](docs/fine_tuning_hugging_face_models_for_classification.md).

---

# ComfyUI-Pt-Wrapper

**ComfyUI-Pt-Wrapper** brings PyTorch model building and training into ComfyUI's node graph environment—**no coding required**.

It is built for ComfyUI users who want to explore machine learning without writing Python, and for researchers who want to prototype directly in visual workflows. Every operation, from tensor math to full training pipelines, can be configured through nodes.

Originally a focused spin-off of [ComfyUI-Data-Analysis](https://github.com/HowToSD/ComfyUI-Data-Analysis), this extension supports a wide range of ML workflows in image and text domains.

---

## What It Offers

- **No-code training workflows** for image and text classification
- Use pre-built model nodes for major architecture such as ResNet, LSTM, GRU, Transformer, or create your own model from various model nodes.
- Perform tensor operations like `add`, `gather`, `scatter`, `where`, etc.
- Support for processing text (e.g. tokenization, embedding) to feed to a model
- Featuring **200 nodes** as of March 28, 2025.
---

## Example Workflows

### Dog vs. Cat Classifier — No Code Needed
Train an image classifier on your own dataset—entirely in ComfyUI nodes.

[Step-by-step guide](docs/dog_cat_classification_model_training.md)

### ResNet on CIFAR-10
Train a ResNet achieving **94% validation accuracy**. A flexible baseline for your own image classification tasks.

![Training](docs/images/resnet_train.png)  
![Evaluation](docs/images/resnet_eval.png)

### Transformer for Text Classification
Train a text classification model using a configurable Transformer model—all node-based.

[`embedding_transformer_classification.json`](examples/workflows/embedding_transformer_classification.json)

---

## Getting Started

- **Node Overview**: [Node Reference](docs/reference/node_reference.md)
- **Model Training Guide**: [Model Training Documentation](docs/model_training.md)

---

## Contributing

This project does not accept pull requests. Unsolicited PRs will be closed without review.

To suggest a feature or report an issue, open an Issue. All issues are reviewed and prioritized.

---

## Node Reference

Every supported node is documented in detail. Browse the [Node Reference](docs/reference/node_reference.md) to explore tensor operations, models, tokenization, distributions, loss functions, tensor operations and more.

Links in the reference section point directly to individual node docs for quick lookup.