limited to only git large and blip large

app.py

@@ -11,17 +11,17 @@ torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/0000000397
 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_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")
+# 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_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")
@@ -32,9 +32,9 @@ blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip
 # 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")
+# 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",
@@ -43,12 +43,12 @@ vitgpt_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-capt
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-git_model_base.to(device)
-blip_model_base.to(device)
+# git_model_base.to(device)
+# blip_model_base.to(device)
 git_model_large_coco.to(device)
-git_model_large_textcaps.to(device)
+# git_model_large_textcaps.to(device)
 blip_model_large.to(device)
-vitgpt_model.to(device)
+# vitgpt_model.to(device)
 # coca_model.to(device)
 # blip2_model.to(device)
 
@@ -76,17 +76,17 @@ def generate_caption_coca(model, transform, image):
 
 
 def generate_captions(image):
-    caption_git_base = generate_caption(git_processor_base, git_model_base, 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_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_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_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer)
 
     # caption_coca = generate_caption_coca(coca_model, coca_transform, image)
 
@@ -95,18 +95,19 @@ def generate_captions(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
-    return caption_git_base, caption_git_large_coco, caption_git_large_textcaps, caption_blip_base, caption_blip_large, caption_vitgpt
+    return caption_git_large_coco, caption_blip_large
 
 
    
 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 GIT-base fine-tuned on COCO"), 
+outputs = [
+    # gr.outputs.Textbox(label="Caption generated by GIT-base fine-tuned on COCO"), 
            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-base"),
+           # gr.outputs.Textbox(label="Caption generated by GIT-large fine-tuned on TextCaps"),
+           # 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 vitgpt")
+           # gr.outputs.Textbox(label="Caption generated by vitgpt")
           ] 
 
 title = "Interactive demo: comparing image captioning models"