disabled models except blip2

app.py

@@ -1,5 +1,7 @@
 import gradio as gr
-from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel
+from transformers import AutoProcessor, BlipForConditionalGeneration
+
+# from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel
 import torch
 import open_clip
 
@@ -18,17 +20,17 @@ torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/as
 # 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")
+# 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)
+# 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")
@@ -42,10 +44,10 @@ blip2_model_8_bit = Blip2ForConditionalGeneration.from_pretrained("Salesforce/bl
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # git_model_base.to(device)
-# blip_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)
+# blip_model_large.to(device)
 # vitgpt_model.to(device)
 # coca_model.to(device)
 # blip2_model.to(device)
@@ -80,9 +82,9 @@ def generate_captions(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_blip_large = generate_caption(blip_processor_large, blip_model_large, image)
 
     # caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer)
 
@@ -90,16 +92,16 @@ def generate_captions(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()
+    # 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
+    return caption_blip_base
 
 
    
 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")] 
+outputs = [gr.outputs.Textbox(label="Caption generated by BLIP-base"),] 
 
 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."