chore: Update inpainting pipeline configuration and parameters

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api/yolov8l.pt.REMOVED.git-id

@@ -0,0 +1 @@
+4b11a02a7599e520f9f14a4703f4991237d6ce50

configs/inpainting.yaml

@@ -6,7 +6,11 @@ target_width : 2560
 target_height : 1472
 prompt : 'Product on the table 4k ultrarealistic'
 negative_prompt : 'low resolution , bad resolution , Deformation , Weird Artifacts, bad quality,blown up image, high brightness , high saturation '
+<<<<<<< HEAD
 roi_scale : 0.9
+=======
+roi_scale : 0.5
+>>>>>>> bb1cf42 (chore: Update inpainting pipeline configuration and parameters)
 strength : 0.6
 guidance_scale : 7
 num_inference_steps : 150

scripts/inpainting_pipeline.py

@@ -1,11 +1,15 @@
 import torch
-from diffusers import AutoPipelineForInpainting
+from diffusers import AutoPipelineForInpainting,DiffusionPipeline
 from diffusers.utils import load_image
 from api_utils import accelerator, ImageAugmentation
 import hydra
 from omegaconf import DictConfig
 from PIL import Image
+<<<<<<< HEAD
 
+=======
+from functools import lru_cache
+>>>>>>> bb1cf42 (chore: Update inpainting pipeline configuration and parameters)
 
 def load_pipeline(model_name: str, device, enable_compile: bool = True):
     pipeline = AutoPipelineForInpainting.from_pretrained(model_name, torch_dtype=torch.float16)
@@ -15,6 +19,7 @@ def load_pipeline(model_name: str, device, enable_compile: bool = True):
     pipeline.to(device)
     return pipeline
 
+@lru_cache(maxsize=1)
 class AutoPaintingPipeline:
     def __init__(self, pipeline, image: Image, mask_image: Image, target_width: int, target_height: int):
         self.pipeline = pipeline
@@ -22,6 +27,7 @@ class AutoPaintingPipeline:
         self.mask_image = mask_image
         self.target_width = target_width
         self.target_height = target_height
+<<<<<<< HEAD
 
     def run_inference(self, prompt: str, negative_prompt: str, num_inference_steps: int, strength: float, guidance_scale: float,num_images):
         output = self.pipeline(
@@ -37,6 +43,25 @@ class AutoPaintingPipeline:
             width=self.target_width
             
         ).images[0]
+=======
+        self.pipeline.to(self.device)
+        self.pipeline.unet = torch.compile(self.pipeline.unet,mode='max-autotune')
+        
+       
+        
+        
+    def run_inference(self, prompt: str, negative_prompt: str, num_inference_steps: int, strength: float, guidance_scale: float):
+        """
+        Runs the inference on the input image using the inpainting pipeline.
+        
+        Returns:
+            Image: The output image after inpainting.
+        """
+       
+        image = load_image(self.image)
+        mask_image = load_image(self.mask_image)
+        output = self.pipeline(prompt=prompt,negative_prompt=negative_prompt,image=image,mask_image=mask_image,num_inference_steps=num_inference_steps,strength=strength,guidance_scale=guidance_scale, height = self.target_height ,width = self.target_width).images[0]
+>>>>>>> bb1cf42 (chore: Update inpainting pipeline configuration and parameters)
         return output
 
 @hydra.main(version_base=None, config_path="../configs", config_name="inpainting")
@@ -51,6 +76,7 @@ def inference(cfg: DictConfig):
     extended_image = augmenter.extend_image(image)
     mask_image = augmenter.generate_mask_from_bbox(extended_image, cfg.segmentation_model, cfg.detection_model)
     mask_image = augmenter.invert_mask(mask_image)
+<<<<<<< HEAD
     
     # Create AutoPaintingPipeline instance with cached pipeline
     painting_pipeline = AutoPaintingPipeline(
@@ -71,6 +97,15 @@ def inference(cfg: DictConfig):
     )
     
     # Save output and mask images
+=======
+    prompt = cfg.prompt
+    negative_prompt = cfg.negative_prompt
+    num_inference_steps = cfg.num_inference_steps
+    strength = cfg.strength
+    guidance_scale = cfg.guidance_scale
+    pipeline = AutoPaintingPipeline(model_name=model_name, image = extended_image, mask_image=mask_image, target_height=cfg.target_height, target_width=cfg.target_width)
+    output = pipeline.run_inference(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, strength=strength, guidance_scale=guidance_scale)
+>>>>>>> bb1cf42 (chore: Update inpainting pipeline configuration and parameters)
     output.save(f'{cfg.output_path}/output.jpg')
     mask_image.save(f'{cfg.output_path}/mask.jpg')