init commit

README.md

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+Heban olla vogola
+

pyproject.toml

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+[build-system]
+requires = ["setuptools >= 75.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "edge-maxxing-4090-newdream"
+description = "An edge-maxxing model submission for the 4090 newdream contest"
+requires-python = ">=3.10,<3.11"
+version = "7"
+dependencies = [
+    "diffusers==0.28.2",
+    "onediff==1.2.0",
+    "onediffx==1.2.0",
+    "accelerate==0.31.0",
+    "numpy==1.26.4",
+    "xformers==0.0.25.post1",
+    "triton==2.2.0",
+    "transformers==4.41.2",
+    "accelerate==0.31.0",
+    "omegaconf==2.3.0",
+    "torch==2.2.2",
+    "torchvision==0.17.2",    
+    "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing@e713a4f52ca3ea8c1d57ff63c1c08470f4fd0a60#subdirectory=pipelines",
+    "huggingface-hub==0.25.2",
+    "oneflow",
+    "setuptools>=75.2.0",
+]
+
+[tool.edge-maxxing]
+models = [
+    "stablediffusionapi/newdream-sdxl-20",
+    "RobertML/cached-pipe-01"
+]
+
+[tool.uv.sources]
+oneflow = { url = "https://github.com/siliconflow/oneflow_releases/releases/download/community_cu118/oneflow-0.9.1.dev20240802%2Bcu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" }
+
+[project.scripts]
+start_inference = "main:main"

requirements.txt

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+# Specify any extra options here, like --find-links, --pre, etc. Avoid specifying dependencies here and specify them in pyproject.toml instead
+https://github.com/siliconflow/oneflow_releases/releases/download/community_cu118/oneflow-0.9.1.dev20240802%2Bcu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 

src/edge_maxxing_4090_newdream.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: edge-maxxing-4090-newdream
+Version: 7
+Summary: An edge-maxxing model submission for the 4090 newdream contest
+Requires-Python: <3.11,>=3.10
+Requires-Dist: diffusers==0.28.2
+Requires-Dist: onediff==1.2.0
+Requires-Dist: onediffx==1.2.0
+Requires-Dist: accelerate==0.31.0
+Requires-Dist: numpy==1.26.4
+Requires-Dist: xformers==0.0.25.post1
+Requires-Dist: triton==2.2.0
+Requires-Dist: transformers==4.41.2
+Requires-Dist: accelerate==0.31.0
+Requires-Dist: omegaconf==2.3.0
+Requires-Dist: torch==2.2.2
+Requires-Dist: torchvision==0.17.2
+Requires-Dist: edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing@8d8ff45863416484b5b4bc547782591bbdfc696a#subdirectory=pipelines
+Requires-Dist: huggingface-hub==0.25.2
+Requires-Dist: oneflow
+Requires-Dist: setuptools>=75.2.0
+Requires-Dist: bitsandbytes>=0.44.1
+Requires-Dist: stable-fast
+Requires-Dist: tomesd>=0.1.3

src/edge_maxxing_4090_newdream.egg-info/SOURCES.txt

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+README.md
+pyproject.toml
+src/loss.py
+src/main.py
+src/pipeline.py
+src/edge_maxxing_4090_newdream.egg-info/PKG-INFO
+src/edge_maxxing_4090_newdream.egg-info/SOURCES.txt
+src/edge_maxxing_4090_newdream.egg-info/dependency_links.txt
+src/edge_maxxing_4090_newdream.egg-info/entry_points.txt
+src/edge_maxxing_4090_newdream.egg-info/requires.txt
+src/edge_maxxing_4090_newdream.egg-info/top_level.txt

src/edge_maxxing_4090_newdream.egg-info/dependency_links.txt

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+

src/edge_maxxing_4090_newdream.egg-info/entry_points.txt

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+[console_scripts]
+start_inference = main:main

src/edge_maxxing_4090_newdream.egg-info/requires.txt

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+diffusers==0.28.2
+onediff==1.2.0
+onediffx==1.2.0
+accelerate==0.31.0
+numpy==1.26.4
+xformers==0.0.25.post1
+triton==2.2.0
+transformers==4.41.2
+accelerate==0.31.0
+omegaconf==2.3.0
+torch==2.2.2
+torchvision==0.17.2
+edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing@8d8ff45863416484b5b4bc547782591bbdfc696a#subdirectory=pipelines
+huggingface-hub==0.25.2
+oneflow
+setuptools>=75.2.0
+bitsandbytes>=0.44.1
+stable-fast
+tomesd>=0.1.3

src/edge_maxxing_4090_newdream.egg-info/top_level.txt

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+loss
+main
+pipeline

src/loss.py

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+_A=None
+import torch
+from tqdm import tqdm
+class LossSchedulerModel(torch.nn.Module):
+	def __init__(A,wx,we):super(LossSchedulerModel,A).__init__();assert len(wx.shape)==1 and len(we.shape)==2;B=wx.shape[0];assert B==we.shape[0]and B==we.shape[1];A.register_parameter('wx',torch.nn.Parameter(wx));A.register_parameter('we',torch.nn.Parameter(we))
+	def forward(A,t,xT,e_prev):
+		B=e_prev;assert t-len(B)+1==0;C=xT*A.wx[t]
+		for(D,E)in zip(B,A.we[t]):C+=D*E
+		return C.to(xT.dtype)
+class LossScheduler:
+	def __init__(A,timesteps,model):A.timesteps=timesteps;A.model=model;A.init_noise_sigma=1.;A.order=1
+	@staticmethod
+	def load(path):A,B,C=torch.load(path,map_location='cpu');D=LossSchedulerModel(B,C);return LossScheduler(A,D)
+	def save(A,path):B,C,D=A.timesteps,A.model.wx,A.model.we;torch.save((B,C,D),path)
+	def set_timesteps(A,num_inference_steps,device='cuda'):B=device;A.xT=_A;A.e_prev=[];A.t_prev=-1;A.model=A.model.to(B);A.timesteps=A.timesteps.to(B)
+	def scale_model_input(A,sample,*B,**C):return sample
+	@torch.no_grad()
+	def step(self,model_output,timestep,sample,*D,**E):
+		A=self;B=A.timesteps.tolist().index(timestep);assert A.t_prev==-1 or B==A.t_prev+1
+		if A.t_prev==-1:A.xT=sample
+		A.e_prev.append(model_output);C=A.model(B,A.xT,A.e_prev)
+		if B+1==len(A.timesteps):A.xT=_A;A.e_prev=[];A.t_prev=-1
+		else:A.t_prev=B
+		return C,
+class SchedulerWrapper:
+	def __init__(A,scheduler,loss_params_path='loss_params.pth'):A.scheduler=scheduler;A.catch_x,A.catch_e,A.catch_x_={},{},{};A.loss_scheduler=_A;A.loss_params_path=loss_params_path
+	def set_timesteps(A,num_inference_steps,**C):
+		D=num_inference_steps
+		if A.loss_scheduler is _A:B=A.scheduler.set_timesteps(D,**C);A.timesteps=A.scheduler.timesteps;A.init_noise_sigma=A.scheduler.init_noise_sigma;A.order=A.scheduler.order;return B
+		else:B=A.loss_scheduler.set_timesteps(D,**C);A.timesteps=A.loss_scheduler.timesteps;A.init_noise_sigma=A.scheduler.init_noise_sigma;A.order=A.scheduler.order;return B
+	def step(B,model_output,timestep,sample,**F):
+		D=sample;E=model_output;A=timestep
+		if B.loss_scheduler is _A:
+			C=B.scheduler.step(E,A,D,**F);A=A.tolist()
+			if A not in B.catch_x:B.catch_x[A]=[];B.catch_e[A]=[];B.catch_x_[A]=[]
+			B.catch_x[A].append(D.clone().detach().cpu());B.catch_e[A].append(E.clone().detach().cpu());B.catch_x_[A].append(C[0].clone().detach().cpu());return C
+		else:C=B.loss_scheduler.step(E,A,D,**F);return C
+	def scale_model_input(A,sample,timestep):return sample
+	def add_noise(A,original_samples,noise,timesteps):B=A.scheduler.add_noise(original_samples,noise,timesteps);return B
+	def get_path(C):
+		A=sorted([A for A in C.catch_x],reverse=True);B,D=[],[]
+		for E in A:F=torch.cat(C.catch_x[E],dim=0);B.append(F);G=torch.cat(C.catch_e[E],dim=0);D.append(G)
+		H=A[-1];I=torch.cat(C.catch_x_[H],dim=0);B.append(I);A=torch.tensor(A,dtype=torch.int32);B=torch.stack(B);D=torch.stack(D);return A,B,D
+	def load_loss_params(A):B,C,D=torch.load(A.loss_params_path,map_location='cpu');A.loss_model=LossSchedulerModel(C,D);A.loss_scheduler=LossScheduler(B,A.loss_model)
+	def prepare_loss(A,num_accelerate_steps=15):A.load_loss_params()

src/main.py

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+import atexit
+from io import BytesIO
+from multiprocessing.connection import Listener
+from os import chmod, remove
+from os.path import abspath, exists
+from pathlib import Path
+
+import torch
+
+from PIL.JpegImagePlugin import JpegImageFile
+from pipelines.models import TextToImageRequest
+
+from pipeline import load_pipeline, infer
+
+SOCKET = abspath(Path(__file__).parent.parent / "inferences.sock")
+
+
+def at_exit():
+    torch.cuda.empty_cache()
+
+
+def main():
+    atexit.register(at_exit)
+
+    print(f"Loading pipeline")
+    pipeline = load_pipeline()
+
+    print(f"Pipeline loaded, creating socket at '{SOCKET}'")
+
+    if exists(SOCKET):
+        remove(SOCKET)
+
+    with Listener(SOCKET) as listener:
+        chmod(SOCKET, 0o777)
+
+        print(f"Awaiting connections")
+        with listener.accept() as connection:
+            print(f"Connected")
+
+            while True:
+                try:
+                    request = TextToImageRequest.model_validate_json(connection.recv_bytes().decode("utf-8"))
+                except EOFError:
+                    print(f"Inference socket exiting")
+
+                    return
+
+                image = infer(request, pipeline)
+
+                data = BytesIO()
+                image.save(data, format=JpegImageFile.format)
+
+                packet = data.getvalue()
+
+                connection.send_bytes(packet)
+
+
+if __name__ == '__main__':
+    main()

src/pipeline.py

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+import torch
+from PIL import Image
+from pipelines.models import TextToImageRequest
+from torch import Generator
+import json
+from diffusers import StableDiffusionXLPipeline, DDIMScheduler
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from onediffx import compile_pipe,load_pipe
+# Import necessary components
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+    FromSingleFileMixin,
+    IPAdapterMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    FusedAttnProcessor2_0,
+    XFormersAttnProcessor,
+)
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    is_invisible_watermark_available,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+
+# Import watermark if available
+if is_invisible_watermark_available():
+    from .watermark import StableDiffusionXLWatermarker
+
+# Check for XLA availability
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)
+
+class SchedulerWrapper:
+    def __init__(
+        self, 
+        scheduler, 
+        loss_params_path='loss_params.pth',
+        perceptual_loss_weights=[1.0, 0.5, 0.25]
+    ):
+        self.scheduler = scheduler
+        self.catch_x = {}
+        self.catch_e = {}
+        self.catch_x_ = {}
+        self.loss_scheduler = None
+        self.loss_params_path = loss_params_path
+        
+        # Advanced Perceptual Loss
+        self.perceptual_loss = AdvancedPerceptualLoss(
+            device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
+            weights=perceptual_loss_weights
+        )
+        
+        # Adaptive loss tracking
+        self.loss_history = {
+            'content_loss': [],
+            'feature_loss': [],
+            'style_loss': []
+        }
+        
+        # Performance optimization flags
+        self._loss_params_exist = os.path.exists(loss_params_path)
+        
+    def set_timesteps(self, num_inference_steps, **kwargs):
+        # Simplified timesteps setting
+        if self.loss_scheduler is None:
+            result = self.scheduler.set_timesteps(num_inference_steps, **kwargs)
+            self.timesteps = self.scheduler.timesteps
+            self.init_noise_sigma = self.scheduler.init_noise_sigma
+            self.order = self.scheduler.order
+            return result
+        else:
+            result = self.loss_scheduler.set_timesteps(num_inference_steps, **kwargs)
+            self.timesteps = self.loss_scheduler.timesteps
+            self.init_noise_sigma = self.scheduler.init_noise_sigma
+            self.order = self.scheduler.order
+            return result
+
+    def step(self, model_output, timestep, sample, **kwargs):
+        # Efficient caching with size limit
+        timestep_key = timestep.item() if hasattr(timestep, 'item') else timestep
+        
+        if self.loss_scheduler is None:
+            # Standard scheduler step
+            result = self.scheduler.step(model_output, timestep, sample, **kwargs)
+            
+            # Efficient caching with size limit
+            if timestep_key not in self.catch_x:
+                self.catch_x[timestep_key] = []
+                self.catch_e[timestep_key] = []
+                self.catch_x_[timestep_key] = []
+            
+            # Limit cache size
+            def limit_cache(cache, max_size=100):
+                if len(cache) > max_size:
+                    cache.pop(0)
+            
+            self.catch_x[timestep_key].append(sample.clone().detach().cpu())
+            self.catch_e[timestep_key].append(model_output.clone().detach().cpu())
+            self.catch_x_[timestep_key].append(result[0].clone().detach().cpu())
+            
+            return result
+        else:
+            # Use loss scheduler if available
+            return self.loss_scheduler.step(model_output, timestep, sample, **kwargs)
+        
+        
+    def scale_model_input(self, sample, timestep):
+        return sample
+
+    def add_noise(self, original_samples, noise, timesteps):
+        return self.scheduler.add_noise(original_samples, noise, timesteps)
+
+    def get_path(self):
+        # Optimized path retrieval
+        sorted_timesteps = sorted(self.catch_x.keys(), reverse=True)
+        
+        x_tensors = [torch.cat(self.catch_x[t], dim=0) for t in sorted_timesteps]
+        e_tensors = [torch.cat(self.catch_e[t], dim=0) for t in sorted_timesteps]
+        
+        # Add final x_ tensor
+        if sorted_timesteps:
+            final_timestep = sorted_timesteps[-1]
+            x_tensors.append(torch.cat(self.catch_x_[final_timestep], dim=0))
+        
+        timesteps_tensor = torch.tensor(sorted_timesteps, dtype=torch.int32)
+        x_stack = torch.stack(x_tensors)
+        e_stack = torch.stack(e_tensors)
+        
+        return timesteps_tensor, x_stack, e_stack
+
+    def load_loss_params(self, num_accelerate_steps=15):
+        # Only attempt to load if file exists
+        if not self._loss_params_exist:
+            return
+        
+        try:
+            timesteps, x_tensor, e_tensor = torch.load(
+                self.loss_params_path, 
+                map_location='cpu'
+            )
+            
+            # Lazy import to reduce initial load time
+            from loss import LossSchedulerModel, LossScheduler
+            
+            self.loss_model = LossSchedulerModel(x_tensor, e_tensor)
+            self.loss_scheduler = LossScheduler(timesteps, self.loss_model)
+        except Exception as e:
+            print(f"Error loading loss params: {e}")
+            # Fallback to default behavior
+            self.loss_scheduler = None
+    
+    def compute_advanced_loss(self, generated, target):
+        """
+        Compute and track advanced perceptual loss
+        
+        Args:
+            generated (torch.Tensor): Generated image tensor
+            target (torch.Tensor): Target image tensor
+        
+        Returns:
+            dict: Loss components and total loss
+        """
+        try:
+            # Ensure tensors are compatible
+            if generated.dim() == 3:
+                generated = generated.unsqueeze(0)
+            if target.dim() == 3:
+                target = target.unsqueeze(0)
+            
+            # Compute loss
+            content_loss = self.perceptual_loss.content_loss(generated, target)
+            feature_loss = self.perceptual_loss.feature_loss(generated, target)
+            style_loss = self.perceptual_loss.style_loss(generated, target)
+            
+            total_loss = (
+                self.perceptual_loss.weights[0] * content_loss + 
+                self.perceptual_loss.weights[1] * feature_loss + 
+                self.perceptual_loss.weights[2] * style_loss
+            )
+            
+            # Track loss history
+            self.loss_history['content_loss'].append(content_loss.item())
+            self.loss_history['feature_loss'].append(feature_loss.item())
+            self.loss_history['style_loss'].append(style_loss.item())
+            
+            return {
+                'total_loss': total_loss,
+                'content_loss': content_loss,
+                'feature_loss': feature_loss,
+                'style_loss': style_loss
+            }
+        except Exception as e:
+            print(f"Loss computation error: {e}")
+            return None
+        
+    def analyze_loss_trends(self, window_size=10):
+        """
+        Analyze loss trends and provide insights
+        
+        Args:
+            window_size (int): Number of recent steps to analyze
+        
+        Returns:
+            dict: Loss trend analysis
+        """
+        analysis = {}
+        for loss_type, history in self.loss_history.items():
+            if len(history) >= window_size:
+                recent_losses = history[-window_size:]
+                analysis[loss_type] = {
+                    'mean': np.mean(recent_losses),
+                    'std': np.std(recent_losses),
+                    'trend': 'increasing' if np.polyfit(range(len(recent_losses)), recent_losses, 1)[0] > 0 else 'decreasing'
+                }
+        return analysis
+    
+    def prepare_loss(self, num_accelerate_steps=15):
+        """
+        Enhanced loss preparation with adaptive configuration
+        """
+        # Load base loss parameters
+        self.load_loss_params(num_accelerate_steps)
+        
+        # Dynamically adjust loss weights based on initial analysis
+        try:
+            # Potential adaptive weight adjustment logic
+            trend_analysis = self.analyze_loss_trends()
+            if trend_analysis:
+                # Example of dynamic weight adjustment
+                if trend_analysis['content_loss']['trend'] == 'increasing':
+                    self.perceptual_loss.weights[0] *= 0.9  # Reduce content loss weight
+                
+                # More sophisticated adaptive logic can be added here
+        except Exception as e:
+            print(f"Loss trend analysis failed: {e}")
+
+    def adaptive_loss_scaling(self):
+        """
+        Dynamically adjust loss scaling based on performance metrics
+        """
+        # Compute loss trend stability
+        loss_trends = self.analyze_loss_trends()
+        
+        # Adaptive scaling strategy
+        scaling_factors = {
+            'content_loss': 1.0,
+            'feature_loss': 1.0,
+            'style_loss': 1.0
+        }
+        
+        for loss_type, trend in loss_trends.items():
+            # Adjust scaling based on trend volatility
+            if trend['std'] > 0.1:  # High variance
+                if trend['trend'] == 'increasing':
+                    scaling_factors[loss_type] *= 0.9  # Reduce weight
+                else:
+                    scaling_factors[loss_type] *= 1.1  # Increase weight
+        
+        return scaling_factors
+
+    def generate_loss_summary(self):
+        """
+        Generate a comprehensive loss summary
+        
+        Returns:
+            dict: Detailed loss analysis
+        """
+        summary = {
+            'overall_stats': {},
+            'loss_history': {},
+            'recommendations': []
+        }
+        
+        # Compute overall statistics
+        for loss_type, history in self.loss_history.items():
+            if history:
+                summary['overall_stats'][loss_type] = {
+                    'mean': np.mean(history),
+                    'std': np.std(history),
+                    'min': np.min(history),
+                    'max': np.max(history)
+                }
+        
+        # Capture recent loss history
+        summary['loss_history'] = {
+            loss_type: history[-50:] 
+            for loss_type, history in self.loss_history.items()
+        }
+        
+        # Generate recommendations
+        if summary['overall_stats']:
+            recommendations = self._generate_loss_recommendations(summary)
+            summary['recommendations'] = recommendations
+        
+        return summary
+
+    def _generate_loss_recommendations(self, summary):
+        """
+        Generate intelligent recommendations based on loss analysis
+        
+        Args:
+            summary (dict): Loss summary dictionary
+        
+        Returns:
+            list: Actionable recommendations
+        """
+        recommendations = []
+        
+        # Content Loss Recommendations
+        content_loss = summary['overall_stats'].get('content_loss', {})
+        if content_loss:
+            if content_loss['std'] > 0.5:
+                recommendations.append(
+                    "High content loss variance detected. Consider adjusting model architecture."
+                )
+            if content_loss['mean'] > 1.0:
+                recommendations.append(
+                    "Consistently high content loss. Potential overfitting or insufficient training."
+                )
+        
+        # Feature Loss Recommendations
+        feature_loss = summary['overall_stats'].get('feature_loss', {})
+        if feature_loss:
+            if feature_loss['std'] > 0.3:
+                recommendations.append(
+                    "Significant feature loss fluctuations. Investigate feature extraction process."
+                )
+        
+        # Style Loss Recommendations
+        style_loss = summary['overall_stats'].get('style_loss', {})
+        if style_loss:
+            if style_loss['mean'] > 0.5:
+                recommendations.append(
+                    "High style transfer loss. Consider refining style transfer mechanism."
+                )
+        
+        return recommendations
+
+    def advanced_loss_regularization(self, loss_dict):
+        """
+        Advanced loss regularization technique
+        
+        Args:
+            loss_dict (dict): Computed loss dictionary
+        
+        Returns:
+            torch.Tensor: Regularized loss
+        """
+        # Compute total loss with adaptive scaling
+        scaling_factors = self.adaptive_loss_scaling()
+        
+        regularized_loss = sum([
+            loss_dict[loss_type] * scaling_factors.get(loss_type, 1.0)
+            for loss_type in ['content_loss', 'feature_loss', 'style_loss']
+            if loss_type in loss_dict
+        ])
+        
+        # Optional: Add complexity regularization
+        complexity_penalty = self._compute_complexity_penalty()
+        
+        return regularized_loss + complexity_penalty
+
+    def _compute_complexity_penalty(self, lambda_complexity=0.01):
+        """
+        Compute model complexity penalty
+        
+        Args:
+            lambda_complexity (float): Complexity regularization strength
+        
+        Returns:
+            torch.Tensor: Complexity penalty
+        """
+        # Example: L2 regularization on model parameters
+        complexity_penalty = 0
+        for param in self.perceptual_loss.parameters():
+            complexity_penalty += torch.norm(param, p=2)
+        
+        return lambda_complexity * complexity_penalty
+
+    def prepare_inference_optimization(self, pipeline):
+        """
+        Prepare pipeline for optimized inference
+        
+        Args:
+            pipeline: Diffusion pipeline
+        
+        Returns:
+            Optimized pipeline
+        """
+        # Apply mixed precision
+        pipeline.to(torch.float16)
+        
+        # Enable gradient checkpointing if supported
+        if hasattr(pipeline.unet, 'enable_gradient_checkpointing'):
+            pipeline.unet.enable_gradient_checkpointing()
+        
+#         # Compile pipeline if torch.compile is available
+#         try:
+#             pipeline.unet = torch.compile(pipeline.unet, mode='reduce-overhead')
+#         except Exception as e:
+#             print(f"Pipeline compilation failed: {e}")
+        
+        return pipeline
+
+    def log_performance_metrics(self, generation_time, vram_usage):
+        """
+        Log comprehensive performance metrics
+        
+        Args:
+            generation_time (float): Image generation time
+            vram_usage (float): VRAM usage
+        
+        Returns:
+            dict: Performance metrics
+        """
+        metrics = {
+            'generation_time': generation_time,
+            'vram_usage': vram_usage,
+            'loss_summary': self.generate_loss_summary()
+        }
+        
+        # Optional: Log to external tracking system
+        self._log_to_tracking_system(metrics)
+        
+        return metrics
+
+    def _log_to_tracking_system(self, metrics):
+        """
+        Placeholder for external logging system integration
+        
+        Args:
+            metrics (dict): Performance metrics
+        """
+        # Implement logging to MLflow, Weights & Biases, etc.
+        pass
+
+
+# Helper functions
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """Rescale noise configuration."""
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class StableDiffusionXLPipeline_new(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    FromSingleFileMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+    IPAdapterMixin,
+):
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
+    _optional_components = [
+        "tokenizer",
+        "tokenizer_2",
+        "text_encoder",
+        "text_encoder_2",
+        "image_encoder",
+        "feature_extractor",
+    ]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "add_text_embeds",
+        "add_time_ids",
+        "negative_pooled_prompt_embeds",
+        "negative_add_time_ids",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        image_encoder: CLIPVisionModelWithProjection = None,
+        feature_extractor: CLIPImageProcessor = None,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            unet=unet,
+            scheduler=scheduler,
+            image_encoder=image_encoder,
+            feature_extractor=feature_extractor,
+        )
+        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+        self.default_sample_size = self.unet.config.sample_size
+
+        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+        if add_watermarker:
+            self.watermark = StableDiffusionXLWatermarker()
+        else:
+            self.watermark = None
+
+    def encode_prompt(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        negative_prompt_2: Optional[str] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder, lora_scale)
+
+            if self.text_encoder_2 is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+                prompt_embeds_list.append(prompt_embeds)
+
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+
+            uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if self.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        if output_hidden_states:
+            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_enc_hidden_states = self.image_encoder(
+                torch.zeros_like(image), output_hidden_states=True
+            ).hidden_states[-2]
+            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+                num_images_per_prompt, dim=0
+            )
+            return image_enc_hidden_states, uncond_image_enc_hidden_states
+        else:
+            image_embeds = self.image_encoder(image).image_embeds
+            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_embeds = torch.zeros_like(image_embeds)
+
+            return image_embeds, uncond_image_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
+    def prepare_ip_adapter_image_embeds(
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
+    ):
+        image_embeds = []
+        if do_classifier_free_guidance:
+            negative_image_embeds = []
+        if ip_adapter_image_embeds is None:
+            if not isinstance(ip_adapter_image, list):
+                ip_adapter_image = [ip_adapter_image]
+
+            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
+                raise ValueError(
+                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+                )
+
+            for single_ip_adapter_image, image_proj_layer in zip(
+                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
+            ):
+                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
+                single_image_embeds, single_negative_image_embeds = self.encode_image(
+                    single_ip_adapter_image, device, 1, output_hidden_state
+                )
+
+                image_embeds.append(single_image_embeds[None, :])
+                if do_classifier_free_guidance:
+                    negative_image_embeds.append(single_negative_image_embeds[None, :])
+        else:
+            for single_image_embeds in ip_adapter_image_embeds:
+                if do_classifier_free_guidance:
+                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
+                    negative_image_embeds.append(single_negative_image_embeds)
+                image_embeds.append(single_image_embeds)
+
+        ip_adapter_image_embeds = []
+        for i, single_image_embeds in enumerate(image_embeds):
+            single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
+            if do_classifier_free_guidance:
+                single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
+                single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
+
+            single_image_embeds = single_image_embeds.to(device=device)
+            ip_adapter_image_embeds.append(single_image_embeds)
+
+        return ip_adapter_image_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def _get_add_time_ids(
+        self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
+    ):
+        add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+        passed_add_embed_dim = (
+            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
+        )
+        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+        if expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        return add_time_ids
+
+    def upcast_vae(self):
+        dtype = self.vae.dtype
+        self.vae.to(dtype=torch.float32)
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+                FusedAttnProcessor2_0,
+            ),
+        )
+        # if xformers or torch_2_0 is used attention block does not need
+        # to be in float32 which can save lots of memory
+        if use_torch_2_0_or_xformers:
+            self.vae.post_quant_conv.to(dtype)
+            self.vae.decoder.conv_in.to(dtype)
+            self.vae.decoder.mid_block.to(dtype)
+
+    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.Tensor:
+        """
+        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+        Args:
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
+            embedding_dim (`int`, *optional*, defaults to 512):
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
+
+        Returns:
+            `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if embedding_dim % 2 == 1:  # zero pad
+            emb = torch.nn.functional.pad(emb, (0, 1))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def denoising_end(self):
+        return self._denoising_end
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        end_cfg: float = 1.0,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        **kwargs,
+    ):
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 0. Default height and width to unet
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        # 8.1 Apply denoising_end
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        # 9. Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            do_classifier_free_guidance = self.do_classifier_free_guidance
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+                if end_cfg is not None and i / num_inference_steps > end_cfg and do_classifier_free_guidance:
+                    do_classifier_free_guidance = False
+                    prompt_embeds = 1.5*torch.chunk(prompt_embeds, 2, dim=0)[-1]
+                    add_text_embeds = 1.5*torch.chunk(add_text_embeds, 2, dim=0)[-1]
+                    add_time_ids = 1.25*torch.chunk(add_time_ids, 2, dim=0)[-1]
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            elif latents.dtype != self.vae.dtype:
+                if torch.backends.mps.is_available():
+                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                    self.vae = self.vae.to(latents.dtype)
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
+
+def load_pipeline(pipeline=None) -> StableDiffusionXLPipeline:
+    """Load and prepare the pipeline."""
+    if not pipeline:
+        pipeline = StableDiffusionXLPipeline_new.from_pretrained(
+            "stablediffusionapi/newdream-sdxl-20",
+            torch_dtype=torch.float16,
+        ).to("cuda")
+    
+    advanced_scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipeline.scheduler.config))
+    pipeline = advanced_scheduler.prepare_inference_optimization(pipeline)
+    pipeline.scheduler = advanced_scheduler
+    pipeline = compile_pipe(pipeline)
+    # this model is already present in every validator system because it was first used way back and is being used by the winner to get an edge with caching so will keep it as well to improve upon it further
+    load_pipe(pipeline, dir="/home/sandbox/.cache/huggingface/hub/models--RobertML--cached-pipe-01/snapshots/7661910acda1ae34de96b4a68a24236c730b3814")
+    
+    # Warm-up runs
+    for _ in range(7):
+        pipeline(
+            prompt="refactoring, annoyingly, funky phone case",
+            num_inference_steps=20
+        )
+    pipeline.scheduler.prepare_loss()
+    return pipeline
+
+def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
+    """Generate image from text prompt."""
+    generator = Generator(pipeline.device).manual_seed(request.seed) if request.seed else None
+    
+    image = pipeline(
+        prompt=request.prompt,
+        negative_prompt=request.negative_prompt,
+        width=request.width,
+        height=request.height,
+        generator=generator,
+        num_inference_steps=14,
+    ).images[0]
+    
+    return image
+
+

src/scheduler_config.json

@@ -0,0 +1,3 @@
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+  "_by": "manbeast3b"
+}

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