Activated 4 models

app.py

@@ -1,54 +1,34 @@
 import gradio as gr
-from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel, BitsAndBytesConfig, BlipProcessor
+from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel, BitsAndBytesConfig
 import torch
 import open_clip
 
 from huggingface_hub import hf_hub_download
 
-quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
-# Use when running on a CPU
-device_map = {
-    "transformer.word_embeddings": 0,
-    "transformer.word_embeddings_layernorm": 0,
-    "lm_head": "cpu",
-    "transformer.h": 0,
-    "transformer.ln_f": 0,
-}
-
-# Load the Blip2 model
-# preprocessor_blip2_8_bit = BlipProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
-# model_blip2_8_bit = Blip2ForConditionalGeneration.from_pretrained(
-# 	"Salesforce/blip2-opt-2.7b",
-# 	device_map="auto",
-#     quantization_config=quantization_config,
-# 	load_in_8bit=True
-# )
-
 # Load the Blip base model
 preprocessor_blip_base = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 model_blip_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
 
-# # Load the Blip large model
-# preprocessor_blip_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
-# model_blip_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+# Load the Blip large model
+preprocessor_blip_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model_blip_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
 
-# # Load the GIT coco model
-# preprocessor_git_large_coco = AutoProcessor.from_pretrained("microsoft/git-large-coco")
-# model_git_large_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
+# Load the GIT coco model
+preprocessor_git_large_coco = AutoProcessor.from_pretrained("microsoft/git-large-coco")
+model_git_large_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
 
-# # Load the CLIP model
-# model_oc_coca, _, transform_oc_coca = open_clip.create_model_and_transforms(
-# 	model_name="coca_ViT-L-14",
-# 	pretrained="mscoco_finetuned_laion2B-s13B-b90k"
-# )
+# Load the CLIP model
+model_oc_coca, _, transform_oc_coca = 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"
 # Transfer the models to the device
-# model_blip2_8_bit.to(device)
 model_blip_base.to(device)
-# model_blip_large.to(device)
-# model_git_large_coco.to(device)
-# model_oc_coca.to(device)
+model_blip_large.to(device)
+model_git_large_coco.to(device)
+model_oc_coca.to(device)
 
 
 def generate_caption(
@@ -149,22 +129,19 @@ def generate_captions(
 	str
 		The generated caption.
 	"""
-	# Generate captions for the image using the Blip2 model
-	# caption_blip2_8_bit = generate_caption(preprocessor_blip2_8_bit, model_blip2_8_bit, image, use_float_16=True).strip()
-
 	# Generate captions for the image using the Blip base model
 	caption_blip_base = generate_caption(preprocessor_blip_base, model_blip_base, image).strip()
 
-	# # Generate captions for the image using the Blip large model
-	# caption_blip_large = generate_caption(preprocessor_blip_large, model_blip_large, image).strip()
+	# Generate captions for the image using the Blip large model
+	caption_blip_large = generate_caption(preprocessor_blip_large, model_blip_large, image).strip()
 	
-	# # Generate captions for the image using the GIT coco model
-	# caption_git_large_coco = generate_caption(preprocessor_git_large_coco, model_git_large_coco, image).strip()
+	# Generate captions for the image using the GIT coco model
+	caption_git_large_coco = generate_caption(preprocessor_git_large_coco, model_git_large_coco, image).strip()
 	
-	# # Generate captions for the image using the CLIP model
-	# caption_oc_coca = generate_captions_clip(model_oc_coca, transform_oc_coca, image).strip()
+	# Generate captions for the image using the CLIP model
+	caption_oc_coca = generate_captions_clip(model_oc_coca, transform_oc_coca, image).strip()
 
-	return caption_blip_base
+	return caption_blip_base, caption_blip_large, caption_git_large_coco, caption_oc_coca
 
 
 # Create the interface
@@ -178,11 +155,10 @@ iface = gr.Interface(
 	],
 	# Define the outputs
 	outputs=[
-		# gr.outputs.Textbox(label="Blip2 8-bit"),
 		gr.outputs.Textbox(label="Blip base"),
-		# gr.outputs.Textbox(label="Blip large"),
-		# gr.outputs.Textbox(label="GIT large coco"),
-		# gr.outputs.Textbox(label="CLIP"),
+		gr.outputs.Textbox(label="Blip large"),
+		gr.outputs.Textbox(label="GIT large coco"),
+		gr.outputs.Textbox(label="CLIP"),
 	],
 	title="Image Captioning",
 	description="Generate captions for images using the Blip2 model, the Blip base model, the Blip large model, the GIT large coco model, and the CLIP model.",