| --- |
| library_name: transformers |
| license: bsd-3-clause |
| pipeline_tag: image-to-text |
| --- |
| |
| # BrainBLIP |
| **This model is not ready for production use and is in preliminary stages of training. Use at your own risks** |
|
|
| ### Model Description |
|
|
| BrainBLIP is finetuned to give more natural captions for training text-to-image datasets with an emphasis on natural language while adding a minimal amount of tags for context. |
|
|
| ## How to Get Started with the Model |
|
|
| ```py |
| from transformers import AutoProcessor, BlipForConditionalGeneration |
| from PIL import Image |
| |
| processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") |
| model = BlipForConditionalGeneration.from_pretrained("braintacles/brainblip").to("cuda") |
| |
| image_path_or_url = r"https://imagePath_or_url.jpg" |
| raw_image = Image.open(requests.get(image_path_or_url, stream=True).raw) if image_path_or_url.startswith("http") else Image.open(image_path_or_url) |
| |
| inputs = processor(raw_image, return_tensors="pt").to("cuda") |
| out = model.generate(**inputs, min_length=40, max_new_tokens=75, num_beams=5, repetition_penalty=1.40) |
| caption = processor.decode(out[0], skip_special_tokens=True) |
| print(caption) |
| ``` |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| All captions for this data have been written by myself by hand with some occasional help from GPT4. |
| Very special thanks to the following people who also have contributed a huge amount of time hand captioning some data: |
| - [Temporarium](https://civitai.com/user/Temporarium) |
| - [HailoKnight](https://civitai.com/user/HailoKnight) |
