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# FastAI-Compatible Colorization Models Guide
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## Current Issue
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The model `Hammad712/GAN-Colorization-Model` contains a PyTorch model (`generator.pt`), not a FastAI model. FastAI models must be `.pkl` files created with FastAI's `export()` function.
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## How to Find FastAI-Compatible Models
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### Option 1: Search Hugging Face
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1. Go to https://huggingface.co/models
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2. Search for: `fastai colorization` or `fastai image colorization`
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3. Look for models that have `.pkl` files in their repository
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4. Check the model's README to confirm it's a FastAI Learner
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### Option 2: Use FastAI's Official Examples
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FastAI course examples often have colorization models. Look for:
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- FastAI course lesson notebooks on image colorization
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- Models exported using `learn.export('model.pkl')`
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### Option 3: Train Your Own
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If you have a FastAI colorization model:
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```python
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from fastai.vision.all import *
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learn = ... # your trained model
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learn.export('model.pkl')
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```
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Then upload `model.pkl` to Hugging Face.
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## Setting a New Model
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### Via Environment Variable (Recommended)
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In your Hugging Face Space settings, add:
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```
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MODEL_ID=your-username/your-fastai-colorization-model
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```
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### Via Code
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Update `app/config.py`:
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```python
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MODEL_ID: str = os.getenv("MODEL_ID", "your-username/your-fastai-colorization-model")
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```
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## Model Requirements
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The model must:
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1. ✅ Be a FastAI Learner exported as `.pkl` file
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2. ✅ Accept PIL Images as input
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3. ✅ Return colorized images (PIL Image or tensor)
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4. ✅ Be uploaded to Hugging Face Hub
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## Testing a Model
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Before switching, you can test locally:
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```python
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from huggingface_hub import from_pretrained_fastai
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from PIL import Image
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learn = from_pretrained_fastai("your-model-id")
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img = Image.open("test.jpg")
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result = learn.predict(img)
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```
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If this works, the model is compatible!
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## Alternative: Switch Back to SDXL+ControlNet
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If you can't find a FastAI model, you can switch back to the SDXL+ControlNet approach which was working before. Update `MODEL_BACKEND` to `"diffusers"` and use a ControlNet colorization model.
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