Add new files and modify existing files

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bfg.jar.REMOVED.git-id

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scripts/clear_memory.py

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+import gc
+import torch
+from logger import rich_logger as l
+
+def clear_memory():
+    """
+    Clears the memory by collecting garbage and emptying the CUDA cache.
+
+    This function is useful when dealing with memory-intensive operations in Python, especially when using libraries like PyTorch.
+
+    Note:
+        This function requires the `gc` and `torch` modules to be imported.
+
+    """
+    gc.collect()
+    torch.cuda.empty_cache()
+    l.info("Memory Cleared")
+    

scripts/models.py

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+from logger import rich_logger as l
+from wandb.integration.diffusers import autolog
+from config import Project_Name
+from clear_memory import clear_memory
+from typing import List
+import numpy as np
+import torch
+from PIL import Image
+from mask_generator import convert_to_numpy_array, generate_mask
+from diffusers.utils import load_image
+import cv2
+from config import controlnet_adapter_model_name,controlnet_base_model_name
+from diffusers import ControlNetModel,StableDiffusionControlNetInpaintPipeline
+autolog(init=dict(project=Project_Name))
+
+
+
+
+
+
+ 
+
+
+
+
+def make_inpaint_condition(init_image, mask_image):
+        # Prepare control image
+        init_image = np.array(init_image.convert("RGB")).astype(np.float32) / 255.0
+        mask_image = np.array(mask_image.convert("L")).astype(np.float32) / 255.0
+
+        assert init_image.shape[0:1] == mask_image.shape[0:1], "image and image_mask must have the same image size"
+        init_image[mask_image > 0.5] = -1.0  # set as masked pixel
+        init_image = np.expand_dims(init_image, 0).transpose(0, 3, 1, 2)
+        init_image = torch.from_numpy(init_image)
+        return init_image
+    
+
+
+
+
+def make_image_controlnet(image,
+                          mask_image,
+                          controlnet_conditioning_image,
+                          positive_prompt: str, negative_prompt: str,
+                          seed: int = 2356132) -> List[Image.Image]:
+    """Method to make image using controlnet
+    Args:
+        image (np.ndarray): input image
+        mask_image (np.ndarray): mask image
+        controlnet_conditioning_image (np.ndarray): conditioning image
+        positive_prompt (str): positive prompt string
+        negative_prompt (str): negative prompt string
+        seed (int, optional): seed. Defaults to 2356132.
+    Returns:
+        List[Image.Image]: list of generated images
+    """
+    controlnet = ControlNetModel.from_pretrained(controlnet_adapter_model_name, torch_dtype=torch.float32)
+    pipe =  StableDiffusionControlNetInpaintPipeline.from_pretrained(
+            controlnet_base_model_name, controlnet=controlnet, torch_dtype=torch.float32
+        )
+   
+
+   
+   
+    
+    image = pipe(prompt=positive_prompt,negative_prompt=negative_prompt, image=init_image, mask_image=mask_image, control_image=controlnet_conditioning_image).images[0]
+
+
+    return image
+
+if __name__ == "__main__":
+    init_image = load_image('/home/product_diffusion_api/sample_data/example1.jpg')
+    mask_image = load_image('/home/product_diffusion_api/scripts/mask.jpg')
+    controlnet_conditioning_image = make_inpaint_condition(init_image=init_image,mask_image=mask_image)
+    result = make_image_controlnet(positive_prompt="Product used in kitchen 4k natural photography",negative_prompt="No artifcats",image=init_image,mask_image=mask_image,controlnet_conditioning_image=controlnet_conditioning_image)
+
+

scripts/yolov8s-seg.pt.REMOVED.git-id

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