chore: Update .gitignore and add new files for inpainting pipeline

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.gitignore

@@ -6,7 +6,11 @@ variables.tf
 config.env
 /scripts/yolov8s*
 /scripts/*jpg
+<<<<<<< HEAD
 /scripts/outputs
 =======
 scripts/wandb
 >>>>>>> bc53421 (Refactor .gitignore, config.py, and wandb scripts, and update requirements.txt)
+=======
+/scripts/outputs
+>>>>>>> a817fb6 (chore: Update .gitignore and add new files for inpainting pipeline)

configs/inpainting.yaml

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 segmentation_model : 'facebook/sam-vit-base'
 detection_model : 'yolov8s'
 model : 'kandinsky-community/kandinsky-2-2-decoder-inpaint'
@@ -11,4 +12,18 @@ guidance_scale : 7
 num_inference_steps : 150
 output_path : '../outputs'
 
+=======
+segmentation_model : 'facebook/sam-vit-large'
+detection_model : 'yolov8l'
+model : 'kandinsky-community/kandinsky-2-2-decoder-inpaint'
+target_width : 1920
+target_height : 1080
+prompt : 'product is on the kitchen floor , ultrarealistic lighting , commercial, award , winning photography'
+negative_prompt : 'low resolution , bad resolution'
+roi_scale : 0.5
+strength : 0.7
+guidance_scale : 7.5
+num_inference_steps : 600
+output_path : '../outputs'
+>>>>>>> a817fb6 (chore: Update .gitignore and add new files for inpainting pipeline)
  

scripts/api_utils.py

@@ -6,11 +6,15 @@ from PIL import Image, ImageOps
 from config import SEGMENTATION_MODEL_NAME, DETECTION_MODEL_NAME
 from diffusers.utils import load_image
 import gc
+<<<<<<< HEAD:scripts/api_utils.py
 from s3_manager import S3ManagerService
 import io
 from io import BytesIO
 import base64
 import uuid
+=======
+
+>>>>>>> a817fb6 (chore: Update .gitignore and add new files for inpainting pipeline):scripts/utils.py
 
 
 
@@ -161,7 +165,11 @@ def pil_to_s3_json(image: Image.Image, file_name) -> dict:
 
 
 if __name__ == "__main__":
+<<<<<<< HEAD:scripts/api_utils.py
     augmenter = ImageAugmentation(target_width=1024, target_height=1024, roi_scale=0.5)
+=======
+    augmenter = ImageAugmentation(target_width=2560, target_height=1440, roi_scale=0.7)
+>>>>>>> a817fb6 (chore: Update .gitignore and add new files for inpainting pipeline):scripts/utils.py
     image_path = "../sample_data/example3.jpg"
     image = Image.open(image_path)
     extended_image = augmenter.extend_image(image)

scripts/config.py

@@ -1,4 +1,5 @@
 <<<<<<< HEAD
+<<<<<<< HEAD
 MODEL_NAME:str="stabilityai/stable-diffusion-xl-base-1.0"
 ADAPTER_NAME:str = "VikramSingh178/sdxl-lora-finetune-product-caption"
 ADAPTER_NAME_2:str = "VikramSingh178/Products10k-SDXL-Lora"
@@ -13,6 +14,18 @@ ENABLE_COMPILE:bool = True
 INPAINTING_MODEL_NAME:str = 'kandinsky-community/kandinsky-2-2-decoder-inpaint'
 
 
+=======
+MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
+ADAPTER_NAME = "VikramSingh178/sdxl-lora-finetune-product-caption"
+ADAPTER_NAME_2 = "VikramSingh178/Products10k-SDXL-Lora"
+VAE_NAME= "madebyollin/sdxl-vae-fp16-fix"
+DATASET_NAME= "hahminlew/kream-product-blip-captions"
+PROJECT_NAME = "Product Photography"
+PRODUCTS_10k_DATASET = "VikramSingh178/Products-10k-BLIP-captions"
+CAPTIONING_MODEL_NAME = "Salesforce/blip-image-captioning-base"
+SEGMENTATION_MODEL_NAME = "facebook/sam-vit-large"
+DETECTION_MODEL_NAME = "yolov8l"
+>>>>>>> a817fb6 (chore: Update .gitignore and add new files for inpainting pipeline)
 
 
 

scripts/pipeline.py

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 from diffusers import ControlNetModel,StableDiffusionControlNetInpaintPipeline,AutoPipelineForInpainting
 import torch
 
@@ -90,3 +91,82 @@ def fetch_kandinsky_pipeline(controlnet_adapter_model_name, controlnet_base_mode
 
 
 
+=======
+import torch
+from diffusers import AutoPipelineForInpainting
+from diffusers.utils import load_image
+from utils import (accelerator, ImageAugmentation, clear_memory)
+import hydra
+from omegaconf import OmegaConf, DictConfig
+from PIL import Image
+import lightning.pytorch as pl
+pl.seed_everything(42)
+generator = torch.Generator("cuda").manual_seed(92)
+
+class AutoPaintingPipeline:
+    """
+    AutoPaintingPipeline class represents a pipeline for auto painting using an inpainting model from diffusers.
+    
+    Args:
+        model_name (str): The name of the pretrained inpainting model.
+        image (Image): The input image to be processed.
+        mask_image (Image): The mask image indicating the areas to be inpainted.
+    """
+    
+    def __init__(self, model_name: str, image: Image, mask_image: Image):
+        self.model_name = model_name
+        self.device = accelerator()
+        self.pipeline = AutoPipelineForInpainting.from_pretrained(self.model_name, torch_dtype=torch.float16)
+        self.image = load_image(image)
+        self.mask_image = load_image(mask_image)
+        self.pipeline.to(self.device)
+        self.pipeline.unet = torch.compile(self.pipeline.unet, mode="reduce-overhead", fullgraph=True)
+        
+        
+    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 = 1472, width = 2560).images[0]
+        clear_memory()
+        return output
+    
+    
+@hydra.main(version_base=None ,config_path="../configs", config_name="inpainting")
+def inference(cfg: DictConfig):
+    """
+    Load the configuration file for the inpainting pipeline.
+    
+    Args:
+        cfg (DictConfig): The configuration file for the inpainting pipeline.
+    """
+    augmenter = ImageAugmentation(target_width=cfg.target_width, target_height=cfg.target_height, roi_scale=cfg.roi_scale)
+    model_name = cfg.model
+    image_path = "../sample_data/example3.jpg"
+    image = Image.open(image_path)
+    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)
+    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)
+    output = pipeline.run_inference(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, strength=strength, guidance_scale=guidance_scale)
+    output.save(f'{cfg.output_path}/output.jpg')
+    return output
+    
+if __name__ == "__main__":
+    inference()
+
+        
+
+  
+>>>>>>> a817fb6 (chore: Update .gitignore and add new files for inpainting pipeline)

scripts/yolov8l.pt.REMOVED.git-id

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