chore: Update inpainting pipeline configuration and parameters

Former-commit-id: 954ab0d8f2b70310083bc27d75b1e14dbb5e01d0
Former-commit-id: adb9dc9d2d977e7cac01564d404a7a593a2f4858
Former-commit-id: 3fedb691871dfcf23f718911e3e9063284b8d0b9
Former-commit-id: cf3391c6d9e4778de7150a2e4dec077478844983
Former-commit-id: 79c3a3f8305df1902891a2270e77917a616a8013

scripts/inpainting_pipeline.py

@@ -5,11 +5,6 @@ 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)
@@ -27,7 +22,6 @@ 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(
@@ -43,25 +37,6 @@ 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")
@@ -76,7 +51,6 @@ 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(
@@ -97,15 +71,6 @@ 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')