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---
license: mit
language:
- en
pipeline_tag: image-to-text
---
## **Description**
It is a ViT model that has been fine-tuned on a **Stable Diffusion 2.0** image dataset and applied **LORA**.
It produces optimal results in a reasonable time. Moreover, its implementation with Pytorch is straightforward.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/lora-assets/latent-diffusion.png" alt="Image" width="600">
* Reference: *https://huggingface.co/blog/lora*
## **Usage**
```python
# Libraries
from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
# Model
model_id = "nttdataspain/vit-gpt2-coco-lora"
model = VisionEncoderDecoderModel.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
feature_extractor = ViTFeatureExtractor.from_pretrained(model_id)
# Predict function
def predict_prompts(list_images, max_length=16):
model.eval()
pixel_values = feature_extractor(images=list_images, return_tensors="pt").pixel_values
with torch.no_grad():
output_ids = model.generate(pixel_values, max_length=max_length, num_beams=4, return_dict_in_generate=True).sequences
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
# Get an image and predict
img = Image.open(image_path).convert('RGB')
pred_prompts = predict_prompts([img], max_length=16)
``` |