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code/llava_arch.py

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+"""
+LLaVA Architecture with Integrated Mask Prediction for Image Editing
+
+This module contains:
+- LlavaMetaModel: Base model with vision tower, diffusion components, and mask prediction
+- LlavaMetaForCausalLM: Mixin for causal LM with multimodal support
+- MaskPredictor: Predicts edit regions from LLM hidden states
+- BF16SafeLayerNorm: Numerically stable LayerNorm for BF16 training
+
+Key Innovation: MaskPredictor enables mask-free inference by learning to predict
+edit regions from LLM understanding, eliminating the need for external segmentation.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Optional, Tuple, List
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers import FlowMatchEulerDiscreteScheduler, DPMSolverMultistepScheduler
+from diffusers.models.normalization import RMSNorm
+
+from .mobile_block import MobileConditioningProjector
+from .multimodal_llava_encoder.builder import build_vision_tower
+from .multimodal_llava_projector.builder import build_vision_projector
+from .multimodal_projector.builder import build_down_projector
+from .multimodal_decoder.builder import build_vae, build_sana
+from blip3o.constants import (
+    DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, 
+    DEFAULT_IMAGE_PATCH_TOKEN, IGNORE_INDEX, IMAGE_TOKEN_INDEX
+)
+
+
+# ============================================================
+# BF16-Safe LayerNorm
+# ============================================================
+
+class BF16SafeLayerNorm(nn.Module):
+    """
+    LayerNorm that's safe for BF16 training.
+    Performs normalization in float32 for numerical stability.
+    """
+    def __init__(self, hidden_size: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.bias = nn.Parameter(torch.zeros(hidden_size))
+        self.eps = eps
+        self.hidden_size = hidden_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        input_dtype = x.dtype
+        x = x.float()
+        mean = x.mean(-1, keepdim=True)
+        variance = (x - mean).pow(2).mean(-1, keepdim=True)
+        x = (x - mean) / torch.sqrt(variance + self.eps)
+        x = self.weight.float() * x + self.bias.float()
+        return x.to(input_dtype)
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+
+
+# ============================================================
+# Mask Predictor - Enables Mask-Free Inference
+# ============================================================
+
+class MaskPredictor(nn.Module):
+    """
+    Predicts edit mask from LLM hidden states.
+    
+    This is the KEY component that enables mask-free inference.
+    During training: Supervised by SAM-generated masks
+    During inference: Predicts mask directly from LLM understanding
+    
+    Architecture:
+    1. Attention pooling to focus on instruction-relevant tokens
+    2. Project to spatial features
+    3. Decode to mask
+    """
+
+    def __init__(self, hidden_size: int, latent_channels: int, latent_size: int = 32):
+        super().__init__()
+        self.latent_size = latent_size
+        self.hidden_size = hidden_size
+
+        # Attention pooling to focus on instruction-relevant tokens
+        self.attention_pool = nn.Sequential(
+            nn.Linear(hidden_size, hidden_size // 4),
+            nn.Tanh(),
+            nn.Linear(hidden_size // 4, 1),
+        )
+
+        # Layer norm for stability
+        self.input_norm = BF16SafeLayerNorm(hidden_size)
+
+        # Project pooled features to spatial representation
+        intermediate_size = hidden_size // 2
+        spatial_dim = latent_size * latent_size * 64
+
+        self.hidden_proj = nn.Sequential(
+            nn.Linear(hidden_size, intermediate_size),
+            nn.LayerNorm(intermediate_size),
+            nn.GELU(),
+            nn.Dropout(0.1),
+            nn.Linear(intermediate_size, intermediate_size),
+            nn.LayerNorm(intermediate_size),
+            nn.GELU(),
+            nn.Dropout(0.1),
+            nn.Linear(intermediate_size, spatial_dim),
+        )
+
+        # Decode to mask with sufficient capacity
+        self.mask_decoder = nn.Sequential(
+            nn.Conv2d(64, 256, 3, padding=1),
+            nn.GroupNorm(32, 256),
+            nn.GELU(),
+            nn.Conv2d(256, 128, 3, padding=1),
+            nn.GroupNorm(16, 128),
+            nn.GELU(),
+            nn.Conv2d(128, 64, 3, padding=1),
+            nn.GroupNorm(8, 64),
+            nn.GELU(),
+            nn.Conv2d(64, 1, 1),
+        )
+
+        self._init_weights()
+
+    def _init_weights(self):
+        """Initialize weights for stable training."""
+        # Initialize attention pooling
+        for module in self.attention_pool:
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight, gain=0.1)
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+
+        # Initialize LayerNorm
+        self.input_norm.reset_parameters()
+
+        # Initialize projection layers
+        for module in self.hidden_proj:
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight, gain=0.1)
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+            elif isinstance(module, nn.LayerNorm):
+                nn.init.ones_(module.weight)
+                nn.init.zeros_(module.bias)
+
+        # Initialize conv layers
+        for module in self.mask_decoder:
+            if isinstance(module, nn.Conv2d):
+                nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+            elif isinstance(module, nn.GroupNorm):
+                nn.init.ones_(module.weight)
+                nn.init.zeros_(module.bias)
+
+        # Initialize final layer with small weights for stable start
+        for module in reversed(list(self.mask_decoder)):
+            if isinstance(module, nn.Conv2d):
+                nn.init.normal_(module.weight, mean=0.0, std=0.01)
+                nn.init.zeros_(module.bias)
+                break
+
+    def forward(self, hidden_states: torch.Tensor, return_logits: bool = False) -> torch.Tensor:
+        """
+        Predict edit mask from LLM hidden states.
+
+        Args:
+            hidden_states: [B, seq_len, hidden_size] from LLM
+            return_logits: If True, return logits instead of probabilities
+
+        Returns:
+            mask: [B, 1, H, W] predicted edit mask
+        """
+        batch_size = hidden_states.shape[0]
+        device = hidden_states.device
+
+        # Check for NaN/Inf in input
+        if torch.isnan(hidden_states).any() or torch.isinf(hidden_states).any():
+            if return_logits:
+                return torch.zeros(batch_size, 1, self.latent_size, self.latent_size,
+                                   device=device, dtype=torch.float32, requires_grad=True)
+            return torch.full((batch_size, 1, self.latent_size, self.latent_size), 0.5,
+                              device=device, dtype=torch.float32, requires_grad=True)
+
+        # Normalize hidden states
+        hidden_states = self.input_norm(hidden_states)
+
+        # Get dtype from first layer
+        target_dtype = self.attention_pool[0].weight.dtype
+        hidden_states = hidden_states.to(target_dtype)
+
+        # Attention pooling: learn which tokens are important
+        attn_weights = self.attention_pool(hidden_states)
+        attn_weights = F.softmax(attn_weights, dim=1)
+
+        # Weighted sum of hidden states
+        pooled = (hidden_states * attn_weights).sum(dim=1)
+
+        # Project to spatial features
+        spatial = self.hidden_proj(pooled)
+        spatial = spatial.view(-1, 64, self.latent_size, self.latent_size)
+
+        # Decode to mask logits
+        mask_logits = self.mask_decoder(spatial)
+
+        if return_logits:
+            return mask_logits.float()
+
+        return torch.sigmoid(mask_logits.float())
+
+
+# ============================================================
+# Diffusion Connector
+# ============================================================
+
+class DiffusionConnector(nn.Module):
+    def __init__(self, input_dim=896, hidden_dim=1024, output_dim=2304, eps=1e-5):
+        super().__init__()
+        self.linear1 = nn.Linear(input_dim, hidden_dim)
+        self.act = nn.GELU(approximate="tanh")
+        self.linear2 = nn.Linear(hidden_dim, output_dim)
+        self.norm = RMSNorm(output_dim, eps=eps, elementwise_affine=True)
+
+        nn.init.xavier_uniform_(self.linear1.weight)
+        nn.init.zeros_(self.linear1.bias)
+        nn.init.xavier_uniform_(self.linear2.weight)
+        nn.init.zeros_(self.linear2.bias)
+        with torch.no_grad():
+            self.norm.weight.fill_(math.sqrt(5.5))
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = self.act(x)
+        x = self.linear2(x)
+        x = self.norm(x)
+        return x
+
+
+# ============================================================
+# Mask Encoder - Encodes masks for diffusion conditioning
+# ============================================================
+
+class MaskEncoder(nn.Module):
+    """Encodes binary mask into latent conditioning for diffusion."""
+    
+    def __init__(self, latent_channels: int = 32):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            nn.Conv2d(1, 64, 3, padding=1),
+            nn.GroupNorm(8, 64),
+            nn.SiLU(),
+            nn.Conv2d(64, 128, 3, padding=1),
+            nn.GroupNorm(16, 128),
+            nn.SiLU(),
+            nn.Conv2d(128, latent_channels, 3, padding=1),
+        )
+        self._init_weights()
+
+    def _init_weights(self):
+        for module in self.encoder:
+            if isinstance(module, nn.Conv2d):
+                nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+            elif isinstance(module, nn.GroupNorm):
+                nn.init.ones_(module.weight)
+                nn.init.zeros_(module.bias)
+        # Last layer: small random weights, NOT zeros!
+        nn.init.normal_(self.encoder[-1].weight, mean=0.0, std=0.01)
+        nn.init.zeros_(self.encoder[-1].bias)
+
+    def forward(self, mask: torch.Tensor) -> torch.Tensor:
+        return self.encoder(mask.to(torch.bfloat16))
+
+
+# ============================================================
+# Spatial Reference Encoder
+# ============================================================
+
+class SpatialRefEncoder(nn.Module):
+    """Encodes reference image latents for spatial conditioning."""
+    
+    def __init__(self, latent_channels: int = 32):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            nn.Conv2d(latent_channels, 64, 3, padding=1),
+            nn.GroupNorm(8, 64),
+            nn.SiLU(),
+            nn.Conv2d(64, 128, 3, padding=1),
+            nn.GroupNorm(16, 128),
+            nn.SiLU(),
+            nn.Conv2d(128, latent_channels, 3, padding=1),
+        )
+        self._init_weights()
+
+    def _init_weights(self):
+        for module in self.encoder:
+            if isinstance(module, nn.Conv2d):
+                nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+            elif isinstance(module, nn.GroupNorm):
+                nn.init.ones_(module.weight)
+                nn.init.zeros_(module.bias)
+        # Last layer: small random weights
+        nn.init.normal_(self.encoder[-1].weight, mean=0.0, std=0.01)
+        nn.init.zeros_(self.encoder[-1].bias)
+
+    def forward(self, latents: torch.Tensor) -> torch.Tensor:
+        return self.encoder(latents)
+
+
+# ============================================================
+# LlavaMetaModel - Base Model with All Components
+# ============================================================
+
+class LlavaMetaModel:
+    """
+    Base model containing:
+    - Vision tower for image understanding
+    - DiT for diffusion generation
+    - VAE for latent encoding/decoding
+    - MaskPredictor for edit region prediction
+    - MaskEncoder for mask conditioning
+    - Conditioning weights (mask_weight, spatial_weight)
+    """
+
+    def __init__(self, config):
+        super(LlavaMetaModel, self).__init__(config)
+
+        # Vision components
+        if hasattr(config, "mm_vision_tower"):
+            self.vision_tower = build_vision_tower(config, delay_load=True)
+            self.mm_projector = build_vision_projector(config)
+
+        # Diffusion components
+        if hasattr(config, "diffusion_name_or_path"):
+            self.dit = build_sana(config)
+            self.vae = build_vae(config)
+            
+            # Diffusion connector
+            self.diffusion_connector = MobileConditioningProjector(
+                input_dim=896, 
+                hidden_dim=512, 
+                output_dim=2304, 
+                num_layers=config.vlm_num_layers
+            )
+
+            # Noise scheduler
+            if getattr(config, 'is_train', False):
+                print("Using FlowMatchEulerDiscreteScheduler for training")
+                self.noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+                    config.diffusion_name_or_path, subfolder="scheduler"
+                )
+            else:
+                print("Using DPMSolverMultistepScheduler for inference")
+                self.noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(
+                    config.diffusion_name_or_path, subfolder="scheduler"
+                )
+
+        # Get latent config
+        latent_channels = getattr(config, 'latent_channels', 32)
+        latent_size = getattr(config, 'latent_size', 32)
+
+        # ============================================================
+        # Mask Prediction Components (for image editing)
+        # ============================================================
+        
+        # Mask predictor: predicts edit region from LLM hidden states
+        if getattr(config, 'use_mask_predictor', True):
+            self.mask_predictor = MaskPredictor(
+                hidden_size=config.hidden_size,
+                latent_channels=latent_channels,
+                latent_size=latent_size
+            )
+        else:
+            self.mask_predictor = None
+
+        # Mask encoder: encodes mask for diffusion conditioning
+        if getattr(config, 'use_mask_conditioning', True):
+            self.mask_encoder = MaskEncoder(latent_channels=latent_channels)
+            # CRITICAL: This is inside self (LlavaMetaModel), so it gets saved!
+            self.mask_weight = nn.Parameter(torch.tensor(1.0))
+        else:
+            self.mask_encoder = None
+            self.mask_weight = None
+
+        # Spatial reference encoder
+        if getattr(config, 'use_spatial_conditioning', False):
+            self.spatial_ref_encoder = SpatialRefEncoder(latent_channels=latent_channels)
+            self.spatial_weight = nn.Parameter(torch.tensor(0.5))
+        else:
+            self.spatial_ref_encoder = None
+            self.spatial_weight = None
+
+        # Operation embedding for edit type
+        if getattr(config, 'use_operation_embedding', False):
+            num_operations = getattr(config, 'num_operation_types', 10)
+            self.operation_embedding = nn.Embedding(num_operations, latent_channels)
+        else:
+            self.operation_embedding = None
+
+    def get_vision_tower(self):
+        vision_tower = getattr(self, 'vision_tower', None)
+        if type(vision_tower) is list:
+            vision_tower = vision_tower[0]
+        return vision_tower
+    
+    def get_sana(self):
+        dit = getattr(self, 'dit', None)
+        if type(dit) is list:
+            dit = dit[0]
+        if dit is not None:
+            dit.to(self.device)
+        return dit
+
+    def get_sana_vae(self):
+        vae = getattr(self, 'vae', None)
+        if type(vae) is list:
+            vae = vae[0]
+        if vae is not None:
+            vae.to(self.device)
+        return vae
+
+    def reinitialize_mask_components(self):
+        """
+        Reinitialize mask-related components.
+        Call after loading pretrained weights if these components weren't in the original model.
+        """
+        print("Reinitializing mask components...")
+        
+        if self.mask_predictor is not None:
+            self.mask_predictor._init_weights()
+            print("  ✓ mask_predictor reinitialized")
+
+        if self.mask_encoder is not None:
+            self.mask_encoder._init_weights()
+            print("  ✓ mask_encoder reinitialized")
+
+        if self.spatial_ref_encoder is not None:
+            self.spatial_ref_encoder._init_weights()
+            print("  ✓ spatial_ref_encoder reinitialized")
+
+        if self.mask_weight is not None:
+            nn.init.ones_(self.mask_weight)
+            print("  ✓ mask_weight set to 1.0")
+
+        if self.spatial_weight is not None:
+            nn.init.constant_(self.spatial_weight, 0.5)
+            print("  ✓ spatial_weight set to 0.5")
+
+        #if self.operation_embedding is not None:
+        #    nn.init.normal_(self.operation_embedding.weight, mean=0.0, std=0.02)
+        #    print("  ✓ operation_embedding reinitialized")
+
+        print("Reinitialization complete!")
+
+    def initialize_vision_modules(self, model_args, fsdp=None):
+        """Initialize vision and diffusion modules."""
+        mm_vision_select_layer = model_args.mm_vision_select_layer
+        mm_vision_select_feature = model_args.mm_vision_select_feature
+        mm_patch_merge_type = model_args.mm_patch_merge_type
+
+        # Initialize DiT
+        if self.get_sana() is None:
+            dit = build_sana(model_args)
+            if hasattr(model_args, "is_train"):
+                if model_args.is_train:
+                    print("FLOW MATCHING !!")
+                    self.noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_args.diffusion_name_or_path, subfolder="scheduler")
+                else:
+                    print("DPM SOLVER !!")
+                    self.noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(model_args.diffusion_name_or_path, subfolder="scheduler")
+
+            if fsdp is not None and len(fsdp) > 0:
+                self.dit = [dit]
+            else:
+                self.dit = dit
+        else:
+            if fsdp is not None and len(fsdp) > 0:
+                dit = self.dit[0]
+            else:
+                dit = self.dit
+        for p in dit.parameters():
+            p.requires_grad = False
+                
+        if self.get_sana_vae() is None:
+            vae = build_vae(model_args)
+
+            if fsdp is not None and len(fsdp) > 0:
+                self.vae = [vae]
+            else:
+                self.vae = vae
+        else:
+            if fsdp is not None and len(fsdp) > 0:
+                vae = self.vae[0]
+            else:
+                vae = self.vae
+        for p in vae.parameters():
+            p.requires_grad = False
+    
+
+        if self.get_vision_tower() is None:
+            print("=" * 20, "Building vision tower", "=" * 20)
+            vision_tower = build_vision_tower(model_args)
+            
+
+            if fsdp is not None and len(fsdp) > 0:
+                self.vision_tower = [vision_tower]
+            else:
+                self.vision_tower = vision_tower
+        else:
+            if fsdp is not None and len(fsdp) > 0:
+                vision_tower = self.vision_tower[0]
+            else:
+                vision_tower = self.vision_tower
+            vision_tower.load_model()
+        
+        
+        if getattr(self, 'diffusion_connector', None) is None:
+            #self.diffusion_connector = DiffusionConnector(input_dim=self.config.hidden_size,hidden_dim=1024,output_dim=2304)
+            self.diffusion_connector = MobileConditioningProjector(input_dim=896, hidden_dim=512, output_dim=2304, num_layers=model_args.vlm_num_layers)
+
+
+            '''
+            norm = RMSNorm(2304, eps=1e-5, elementwise_affine=True)
+            with torch.no_grad():
+                norm.weight.fill_(math.sqrt(5.5))
+            self.diffusion_connector = nn.Sequential(
+                nn.Linear(self.config.hidden_size, 1024),
+                nn.GELU(approximate="tanh"),
+                nn.Linear(1024, 2304),
+                norm,
+            )
+            '''
+        else:
+            for p in self.diffusion_connector.parameters():
+                p.requires_grad = True
+        
+        # freeze all parameters in dit except for caption_projection
+        for name, param in self.dit.named_parameters():
+            if "caption" in name:
+                param.requires_grad = True
+            else:
+                param.requires_grad = False
+        
+
+        for p in dit.parameters():
+            p.requires_grad = True
+        for p in vision_tower.parameters():
+            p.requires_grad = False
+           # vision_tower().eval()
+
+        self.config.use_mm_proj = True
+        self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
+        self.config.mm_vision_select_layer = mm_vision_select_layer
+        self.config.mm_vision_select_feature = mm_vision_select_feature
+        self.config.mm_patch_merge_type = mm_patch_merge_type
+        self.config.diffusion_name_or_path = model_args.diffusion_name_or_path
+        self.config.is_train = False #model_args.is_train
+
+        if getattr(self, 'down_projector', None) is None:
+            self.down_projector = build_down_projector(self.config)
+        else:
+            # In case it is frozen by LoRA
+            for p in self.down_projector.parameters():
+                p.requires_grad = True
+
+        
+
+
+
+
+def unpad_image(tensor, original_size):
+    """
+    Unpads a PyTorch tensor of a padded and resized image.
+
+    Args:
+    tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
+    original_size (tuple): The original size of PIL image (width, height).
+
+    Returns:
+    torch.Tensor: The unpadded image tensor.
+    """
+    original_width, original_height = original_size
+    current_height, current_width = tensor.shape[1:]
+
+    original_aspect_ratio = original_width / original_height
+    current_aspect_ratio = current_width / current_height
+
+    if original_aspect_ratio > current_aspect_ratio:
+        scale_factor = current_width / original_width
+        new_height = int(original_height * scale_factor)
+        padding = (current_height - new_height) // 2
+        unpadded_tensor = tensor[:, padding:current_height - padding, :]
+    else:
+        scale_factor = current_height / original_height
+        new_width = int(original_width * scale_factor)
+        padding = (current_width - new_width) // 2
+        unpadded_tensor = tensor[:, :, padding:current_width - padding]
+
+    return unpadded_tensor
+
+
+class LlavaMetaForCausalLM(ABC):
+
+    @abstractmethod
+    def get_model(self):
+        pass
+
+    def get_vision_tower(self):
+        return self.get_model().get_vision_tower()
+    
+    def visual(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        image_features = self.get_model().get_vision_tower()(pixel_values)
+        image_features = self.get_model().mm_projector(image_features)
+        return image_features
+
+
+    def get_mm_projector(self):
+        return self.get_model().mm_projector
+
+
+    def get_sigmas(self, timesteps, device, n_dim=4, dtype=torch.float32):
+        sigmas = self.get_model().noise_scheduler.sigmas.to(device=device, dtype=dtype)
+        schedule_timesteps = self.get_model().noise_scheduler.timesteps.to(device=device)
+        timesteps = timesteps.to(device)
+        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+
+        sigma = sigmas[step_indices].flatten()
+        while len(sigma.shape) < n_dim:
+            sigma = sigma.unsqueeze(-1)
+        return sigma
+
+    def mask_drop(self, latents, drop_prob=0.1):
+        if drop_prob <= 0:
+            return latents
+        mask = torch.bernoulli(torch.zeros(latents.shape[0], device=latents.device, dtype=latents.dtype) + drop_prob)
+        while len(mask.shape) < len(latents.shape):
+            mask = mask.unsqueeze(-1)
+        mask = 1 - mask  # need to flip 0 <-> 1
+        return latents * mask
+
+    # ============================================================
+    # Convenience Properties for Mask Components
+    # ============================================================
+    
+    @property
+    def mask_predictor(self):
+        return getattr(self.get_model(), 'mask_predictor', None)
+
+    @property
+    def mask_encoder(self):
+        return getattr(self.get_model(), 'mask_encoder', None)
+
+    @property
+    def mask_weight(self):
+        return getattr(self.get_model(), 'mask_weight', None)
+
+    @property
+    def spatial_weight(self):
+        return getattr(self.get_model(), 'spatial_weight', None)
+
+    @property
+    def spatial_ref_encoder(self):
+        return getattr(self.get_model(), 'spatial_ref_encoder', None)
+
+    @property
+    def operation_embedding(self):
+        return getattr(self.get_model(), 'operation_embedding', None)
+
+    # ============================================================
+    # Multimodal Input Preparation
+    # ============================================================
+
+    def prepare_inputs_labels_for_multimodal(
+        self, input_ids, position_ids, attention_mask, past_key_values, labels,
+        gen_images=None, und_images=None
+    ):
+        if (gen_images is None and und_images is None) or input_ids.shape[1] == 1 or self.get_vision_tower() is None:
+            return input_ids, position_ids, attention_mask, past_key_values, None, labels, None, None, None
+        if gen_images is not None:
+            vae = self.get_model().get_sana_vae()
+            vae_device = vae.device
+            prompt_image_embeds = vae.encode(gen_images.to(vae_device)).latent if gen_images is not None else None
+            prompt_image_embeds = prompt_image_embeds * vae.config.scaling_factor if prompt_image_embeds is not None else None
+            target_image_embeds = torch.clone(prompt_image_embeds).detach()
+        else:
+            target_image_embeds = None
+            
+
+        images = und_images
+        if type(images) is list or images.ndim == 5:
+            if type(images) is list:
+                images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
+            concat_images = torch.cat([image for image in images], dim=0)
+            image_features = self.visual(concat_images)
+            split_sizes = [image.shape[0] for image in images]
+            image_features = torch.split(image_features, split_sizes, dim=0)
+            image_features = [x.flatten(0, 1) for x in image_features]
+        else:
+            image_features = self.visual(images) # [B, image_tokens, hidden_size]
+
+
+        # Let's just add dummy tensors if they do not exist,
+        # it is a headache to deal with None all the time.
+        # But it is not ideal, and if you have a better idea,
+        # please open an issue / submit a PR, thanks.
+        _labels = labels
+        _position_ids = position_ids
+        _attention_mask = attention_mask
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
+        else:
+            attention_mask = attention_mask.bool()
+        if position_ids is None:
+            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
+        if labels is None:
+            labels = torch.full_like(input_ids, IGNORE_INDEX)
+
+        # remove the padding using attention_mask -- FIXME
+        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
+        labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
+
+        new_input_embeds = []
+        new_labels = []
+        new_input_ids = []
+        cur_image_idx = 0
+        for batch_idx, cur_input_ids in enumerate(input_ids):
+            num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
+            if num_images == 0:
+                cur_image_features = image_features[cur_image_idx]
+                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
+                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
+                new_input_embeds.append(cur_input_embeds)
+                new_labels.append(labels[batch_idx])
+                cur_image_idx += 1
+                continue
+            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
+            cur_input_ids_noim = []
+            cur_labels = labels[batch_idx]
+            cur_labels_noim = []
+            for i in range(len(image_token_indices) - 1):
+                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
+                cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
+            split_sizes = [x.shape[0] for x in cur_labels_noim]
+            cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
+            cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
+            cur_new_input_embeds = []
+            cur_new_labels = []
+            cur_new_input_ids = []
+
+            for i in range(num_images + 1):
+                cur_new_input_embeds.append(cur_input_embeds_no_im[i])
+                cur_new_labels.append(cur_labels_noim[i])
+                cur_new_input_ids.append(cur_input_ids_noim[i])
+                if i < num_images:
+                    if cur_image_idx < image_features.shape[0]:
+                        cur_image_features = image_features[cur_image_idx]
+                    else:
+                        cur_image_features = image_features[-1]
+                    cur_image_idx += 1
+                    cur_new_input_embeds.append(cur_image_features)
+                    cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
+                    cur_new_input_ids.append(torch.full((cur_image_features.shape[0],), IMAGE_TOKEN_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
+
+            cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
+
+            cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
+            cur_new_labels = torch.cat(cur_new_labels, dim=0)
+            cur_new_input_ids = torch.cat(cur_new_input_ids, dim=0)
+
+            new_input_embeds.append(cur_new_input_embeds)
+            new_labels.append(cur_new_labels)
+            new_input_ids.append(cur_new_input_ids)
+
+        # Combine them
+        max_len = max(x.shape[0] for x in new_input_embeds)
+        batch_size = len(new_input_embeds)
+
+        new_input_embeds_padded = []
+        new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
+        attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
+        position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
+        new_input_ids_padded = torch.full((batch_size, max_len), -300, dtype=new_input_ids[0].dtype, device=new_input_ids[0].device) if len(new_input_ids) > 0 else None
+        
+
+        for i, (cur_new_embed, cur_new_labels, cur_new_input_ids) in enumerate(zip(new_input_embeds, new_labels, new_input_ids)):
+            cur_len = cur_new_embed.shape[0]
+            new_input_embeds_padded.append(torch.cat((
+                cur_new_embed,
+                torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
+            ), dim=0))
+            if cur_len > 0:
+                new_labels_padded[i, :cur_len] = cur_new_labels
+                attention_mask[i, :cur_len] = True
+                position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
+                new_input_ids_padded[i, :cur_len] = cur_new_input_ids
+
+        new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
+
+        if _labels is None:
+            new_labels = None
+        else:
+            new_labels = new_labels_padded
+
+        if _attention_mask is None:
+            attention_mask = None
+        else:
+            attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
+
+        if _position_ids is None:
+            position_ids = None
+
+        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels, target_image_embeds
+
+
+    def initialize_vision_tokenizer(self, model_args, tokenizer):
+        if model_args.mm_use_im_patch_token:
+            tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+
+        if model_args.mm_use_im_start_end:
+            num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+
+            if num_new_tokens > 0:
+                input_embeddings = self.get_input_embeddings().weight.data
+                output_embeddings = self.get_output_embeddings().weight.data
+
+                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
+                    dim=0, keepdim=True)
+                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
+                    dim=0, keepdim=True)
+
+                input_embeddings[-num_new_tokens:] = input_embeddings_avg
+                output_embeddings[-num_new_tokens:] = output_embeddings_avg
+
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = True
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False
+
+            if model_args.pretrain_mm_mlp_adapter:
+                mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
+                embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
+                assert num_new_tokens == 2
+                if input_embeddings.shape == embed_tokens_weight.shape:
+                    input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
+                elif embed_tokens_weight.shape[0] == num_new_tokens:
+                    input_embeddings[-num_new_tokens:] = embed_tokens_weight
+                else:
+                    raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
+        elif model_args.mm_use_im_patch_token:
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = False
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False
+
+
+
+
+
+
+
+
+