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import torch |
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import re |
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import gradio as gr |
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from PIL import Image |
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from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel |
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import os |
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import tensorflow as tf |
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os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' |
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device='cpu' |
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model_id = "nttdataspain/vit-gpt2-stablediffusion2-lora" |
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model = VisionEncoderDecoderModel.from_pretrained(model_id) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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feature_extractor = ViTFeatureExtractor.from_pretrained(model_id) |
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def predict(image): |
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img = image.convert('RGB') |
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model.eval() |
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pixel_values = feature_extractor(images=[img], return_tensors="pt").pixel_values |
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with torch.no_grad(): |
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output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences |
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preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
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preds = [pred.strip() for pred in preds] |
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return preds[0] |
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input = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True) |
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output = gr.outputs.Textbox(type="text",label="Captions") |
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examples_folder = os.path.join(os.path.dirname(__file__), "examples") |
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examples = [os.path.join(examples_folder, file) for file in os.listdir(examples_folder)] |
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with gr.Blocks() as demo: |
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gr.HTML( |
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""" |
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<div style="text-align: center; max-width: 1200px; margin: 20px auto;"> |
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<h2 style="font-weight: 900; font-size: 3rem; margin: 0rem"> |
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📸 ViT Image-to-Text with LORA 📝 |
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</h2> |
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<h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 2rem; margin-bottom: 1.5rem"> |
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In the field of large language models, the challenge of fine-tuning has long perplexed researchers. Microsoft, however, has unveiled an innovative solution called <b>Low-Rank Adaptation (LoRA)</b>. With the emergence of behemoth models like GPT-3 boasting billions of parameters, the cost of fine-tuning them for specific tasks or domains has become exorbitant. |
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<br> |
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<br> |
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You can find more info here: <u><a href="https://medium.com/@daniel.puenteviejo/fine-tuning-image-to-text-algorithms-with-lora-deb22aa7da27" target="_blank">Medium article</a></u> |
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</h2> |
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</div> |
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""") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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img = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True) |
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button = gr.Button(value="Describe") |
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with gr.Column(scale=1): |
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out = gr.outputs.Textbox(type="text",label="Captions") |
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button.click(predict, inputs=[img], outputs=[out]) |
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gr.Examples( |
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examples=examples, |
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inputs=img, |
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outputs=out, |
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fn=predict, |
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cache_examples=True, |
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) |
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demo.launch(debug=True) |
