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| import gradio as gr | |
| from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel | |
| import torch | |
| import open_clip | |
| from huggingface_hub import hf_hub_download | |
| 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_coco = AutoProcessor.from_pretrained("microsoft/git-large-coco") | |
| git_model_large_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco") | |
| git_processor_large_textcaps = AutoProcessor.from_pretrained("microsoft/git-large-r-textcaps") | |
| git_model_large_textcaps = AutoModelForCausalLM.from_pretrained("microsoft/git-large-r-textcaps") | |
| # 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") | |
| blip2_processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
| blip2_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b") | |
| # 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") | |
| coca_model, _, coca_transform = open_clip.create_model_and_transforms( | |
| model_name="coca_ViT-L-14", | |
| pretrained="mscoco_finetuned_laion2B-s13B-b90k" | |
| ) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # git_model_base.to(device) | |
| # blip_model_base.to(device) | |
| git_model_large_coco.to(device) | |
| git_model_large_textcaps.to(device) | |
| blip_model_large.to(device) | |
| # vitgpt_model.to(device) | |
| coca_model.to(device) | |
| blip2_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_caption_coca(model, transform, image): | |
| im = transform(image).unsqueeze(0).to(device) | |
| with torch.no_grad(), torch.cuda.amp.autocast(): | |
| generated = model.generate(im, seq_len=20) | |
| return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "") | |
| def generate_captions(image): | |
| # caption_git_base = generate_caption(git_processor_base, git_model_base, image) | |
| caption_git_large_coco = generate_caption(git_processor_large_coco, git_model_large_coco, image) | |
| caption_git_large_textcaps = generate_caption(git_processor_large_textcaps, git_model_large_textcaps, 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) | |
| caption_coca = generate_caption_coca(coca_model, coca_transform, image) | |
| caption_blip2 = generate_caption(blip2_processor, blip2_model, image) | |
| return caption_git_large_coco, caption_git_large_textcaps, caption_blip_large, caption_coca, caption_blip2 | |
| examples = [["cats.jpg"], ["stop_sign.png"], ["astronaut.jpg"]] | |
| outputs = [gr.outputs.Textbox(label="Caption generated by GIT-large fine-tuned on COCO"), gr.outputs.Textbox(label="Caption generated by GIT-large fine-tuned on TextCaps"), gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by CoCa"), gr.outputs.Textbox(label="Caption generated by BLIP-2 OPT 2.7b")] | |
| title = "Interactive demo: comparing image captioning models" | |
| description = "Gradio Demo to compare GIT, BLIP, CoCa, and BLIP-2, 4 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) |