Upload main.py

main.py

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+import os
+import requests
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import (
+    VisionEncoderDecoderModel,
+    ViTImageProcessor,
+    AutoTokenizer,
+    BlipProcessor,
+    BlipForConditionalGeneration,
+)
+from diffusers import (
+    DiffusionPipeline,
+    StableDiffusionPipeline,
+    StableDiffusionImageVariationPipeline,
+)
+
+def generate_image_caption(image_path):
+    # Diffusion pipeline
+    device = torch.device("cpu")
+    os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
+
+    sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
+        "lambdalabs/sd-image-variations-diffusers", revision="v2.0"
+    )
+    sd_pipe = sd_pipe.to(device)
+
+    pipeline = DiffusionPipeline.from_pretrained(
+        "lambdalabs/sd-image-variations-diffusers"
+    )
+
+    # Image transformations
+    img_transforms = transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Resize(
+                (224, 224),
+                interpolation=transforms.InterpolationMode.BICUBIC,
+                antialias=False,
+            ),
+            transforms.Normalize(
+                [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
+            ),
+        ]
+    )
+
+    # Image-to-image
+    with Image.open(image_path) as img:
+        img_tensor = img_transforms(img).to(device).unsqueeze(0)
+        out = sd_pipe(img_tensor, guidance_scale=3)
+        out["images"][0].save("img1.jpg")
+
+    # Blip image captioning
+    raw_image = Image.open(image_path).convert("RGB")
+    
+    processor = BlipProcessor.from_pretrained(
+        "Salesforce/blip-image-captioning-large"
+    )
+    model = BlipForConditionalGeneration.from_pretrained(
+        "Salesforce/blip-image-captioning-large"
+    ).to(device)
+    
+    # Conditional image captioning
+    text = "a photography of"
+    inputs = processor(raw_image, text, return_tensors="pt").to(device)
+    out = model.generate(**inputs)
+    caption = processor.decode(out[0], skip_special_tokens=True)
+    
+    # Unconditional image captioning
+    inputs = processor(raw_image, return_tensors="pt").to(device)
+    out = model.generate(**inputs)
+    caption = processor.decode(out[0], skip_special_tokens=True)
+
+    # Stable diffusion pipeline
+    model_id = "prompthero/openjourney"
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id, torch_dtype=torch.float32
+    )
+    pipe = pipe.to(device)
+
+    Room = "Living Room"
+    AI_Intervention = "High"
+    Mode = "Redesign"
+    Design = "Modern"
+    prompt = (
+        f"Give me a realistic and complete image of {caption} "
+        f"which room type: {Room}, AI Intervention: {AI_Intervention}, "
+        f"Mode: {Mode} and Design style: {Design}"
+    )
+    image = pipe(prompt).images[0]
+    image.save("result3.jpg")
+
+
+
+generate_image_caption("C:\Master\First.jpg")
+
+