| import gradio as gr | |
| from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel | |
| import torch | |
| torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') | |
| torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png') | |
| torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg') | |
| git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-coco") | |
| git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco") | |
| git_processor_large = AutoProcessor.from_pretrained("microsoft/git-large-coco") | |
| git_model_large = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco") | |
| blip_processor_base = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| blip_model_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") | |
| vitgpt_processor = AutoImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| vitgpt_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| vitgpt_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| git_model_base.to(device) | |
| blip_model_base.to(device) | |
| git_model_large.to(device) | |
| blip_model_large.to(device) | |
| vitgpt_model.to(device) | |
| def generate_caption(processor, model, image, tokenizer=None): | |
| inputs = processor(images=image, return_tensors="pt").to(device) | |
| generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50) | |
| if tokenizer is not None: | |
| generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| else: | |
| generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return generated_caption | |
| def generate_captions(image): | |
| caption_git_base = generate_caption(git_processor_base, git_model_base, image) | |
| caption_git_large = generate_caption(git_processor_large, git_model_large, image) | |
| caption_blip_base = generate_caption(blip_processor_base, blip_model_base, image) | |
| caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image) | |
| caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer) | |
| return caption_git_base, caption_git_large, caption_blip_base, caption_blip_large, caption_vitgpt | |
| examples = [["cats.jpg"], ["stop_sign.png"], ["astronaut.jpg"]] | |
| outputs = [gr.outputs.Textbox(label="Caption generated by GIT-base"), gr.outputs.Textbox(label="Caption generated by GIT-large"), gr.outputs.Textbox(label="Caption generated by BLIP-base"), gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by ViT+GPT-2")] | |
| title = "Interactive demo: comparing image captioning models" | |
| description = "Gradio Demo to compare GIT, BLIP and ViT+GPT2, 3 state-of-the-art vision+language models. To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://huggingface.co/docs/transformers/main/model_doc/blip' target='_blank'>BLIP docs</a> | <a href='https://huggingface.co/docs/transformers/main/model_doc/git' target='_blank'>GIT docs</a></p>" | |
| interface = gr.Interface(fn=generate_captions, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs=outputs, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| article=article, | |
| enable_queue=True) | |
| interface.launch(debug=True) |