| 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 | |
| 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_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") | |
| 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) | |
| 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_large_coco.to(device) | |
| git_model_large_textcaps.to(device) | |
| blip_model_large.to(device) | |
| coca_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("<end_of_text>")[0].replace("<start_of_text>", "") | |
| def generate_captions(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_large = generate_caption(blip_processor_large, blip_model_large, image) | |
| caption_coca = generate_caption_coca(coca_model, coca_transform, image) | |
| 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 | |
| examples = [["Image1.jpg"], ["Image2.jpg"], ["Image3.jpg"]] | |
| outputs = [gr.outputs.Textbox(label="Caption generated - 1"), gr.outputs.Textbox(label="Caption generated - 2"), gr.outputs.Textbox(label="Caption generated -3"), gr.outputs.Textbox(label="Caption generated - 4"), gr.outputs.Textbox(label="Caption generated - 5")] | |
| title = "Interactive demo: comparing image captioning models" | |
| description = "Image Caption Generator by Sravanth Kurmala" | |
| article = "Assignment for Listed Inc" | |
| 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) |