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| import gradio as gr | |
| from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel, InstructBlipForConditionalGeneration | |
| import torch | |
| import open_clip | |
| from huggingface_hub import hf_hub_download | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| 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_large_coco = AutoProcessor.from_pretrained("microsoft/git-large-coco") | |
| git_model_large_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco").to(device) | |
| blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device) | |
| blip2_processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-6.7b-coco") | |
| blip2_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-6.7b-coco", device_map="auto", load_in_4bit=True, torch_dtype=torch.float16) | |
| instructblip_processor = AutoProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b") | |
| instructblip_model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b", device_map="auto", load_in_4bit=True, torch_dtype=torch.float16) | |
| 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, num_beams=3, max_length=20, min_length=5) | |
| 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_blip2(processor, model, image, prompt, replace_token=False): | |
| inputs = processor(images=image, text=prompt, return_tensors="pt").to(device=model.device, dtype=torch.float16) | |
| generated_ids = model.generate(**inputs, | |
| num_beams=5, max_length=50, min_length=1, top_p=0.9, | |
| repetition_penalty=1.5, length_penalty=1.0, temperature=1) | |
| if replace_token: | |
| # TODO remove once https://github.com/huggingface/transformers/pull/24492 is merged | |
| generated_ids[generated_ids == 0] = 2 | |
| return processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| def generate_captions(image): | |
| caption_git_large_coco = generate_caption(git_processor_large_coco, git_model_large_coco, image) | |
| caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image) | |
| caption_blip2 = generate_caption_blip2(blip2_processor, blip2_model, image, prompt="A photo of").strip() | |
| caption_instructblip = generate_caption_blip2(instructblip_processor, instructblip_model, image, prompt="Generate a caption for the image:", replace_token=True) | |
| return caption_git_large_coco, caption_blip_large, caption_blip2, caption_instructblip | |
| 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 BLIP-large"), gr.outputs.Textbox(label="Caption generated by BLIP-2 OPT 6.7b"), gr.outputs.Textbox(label="Caption generated by InstructBLIP"), ] | |
| title = "Interactive demo: comparing image captioning models" | |
| description = "Gradio Demo to compare GIT, BLIP, BLIP-2 and InstructBLIP, 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) |