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", torch_dtype=torch.float16) blip2_processor_8_bit = AutoProcessor.from_pretrained("Salesforce/blip2-opt-6.7b") blip2_model_8_bit = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-6.7b", device_map="auto", load_in_8bit=True) # 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, use_float_16=False): inputs = processor(images=image, return_tensors="pt").to(device) if use_float_16: inputs = inputs.to(torch.float16) 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("")[0].replace("", "") 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, use_float_16=True).strip() caption_blip2_8_bit = generate_caption(blip2_processor_8_bit, blip2_model_8_bit, image, use_float_16=True).strip() # return caption_git_large_coco, caption_git_large_textcaps, caption_blip_large, caption_coca, caption_blip2_8_bit return caption_blip_large, caption_blip2_8_bit 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 6.7b")] outputs = [gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by BLIP-2 OPT 6.7b")] 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 = "

BLIP docs | GIT docs

" 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)