Create model.py

model.py

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+"""
+TinyFlux: A /12 scaled Flux architecture for experimentation.
+
+Architecture:
+  - hidden: 256 (3072/12)
+  - num_heads: 2 (24/12)  
+  - head_dim: 128 (preserved for RoPE compatibility)
+  - in_channels: 16 (Flux VAE output channels)
+  - double_layers: 3
+  - single_layers: 3
+  
+Text Encoders (runtime):
+  - flan-t5-base (768 dim) → txt_in projects to hidden
+  - CLIP-L (768 dim pooled) → vector_in projects to hidden
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+
+@dataclass
+class TinyFluxConfig:
+    """Configuration for TinyFlux model."""
+    # Core dimensions
+    hidden_size: int = 256
+    num_attention_heads: int = 2
+    attention_head_dim: int = 128  # Preserved for RoPE
+    
+    # Input/output (Flux VAE has 16 channels)
+    in_channels: int = 16  # Flux VAE output channels
+    patch_size: int = 1    # No 2x2 patchification, raw latent tokens
+    
+    # Text encoder interfaces (runtime encoding)
+    joint_attention_dim: int = 768   # flan-t5-base output dim
+    pooled_projection_dim: int = 768  # CLIP-L pooled dim
+    
+    # Layers
+    num_double_layers: int = 3
+    num_single_layers: int = 3
+    
+    # MLP
+    mlp_ratio: float = 4.0
+    
+    # RoPE (must sum to head_dim)
+    axes_dims_rope: Tuple[int, int, int] = (16, 56, 56)
+    
+    # Misc
+    guidance_embeds: bool = True
+    
+    def __post_init__(self):
+        assert self.num_attention_heads * self.attention_head_dim == self.hidden_size, \
+            f"heads ({self.num_attention_heads}) * head_dim ({self.attention_head_dim}) != hidden ({self.hidden_size})"
+        assert sum(self.axes_dims_rope) == self.attention_head_dim, \
+            f"RoPE dims {self.axes_dims_rope} must sum to head_dim {self.attention_head_dim}"
+
+
+class RMSNorm(nn.Module):
+    """Root Mean Square Layer Normalization."""
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        norm = x.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
+        return (x * norm).type_as(x) * self.weight
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary Position Embedding for 2D + temporal."""
+    def __init__(self, dim: int, axes_dims: Tuple[int, int, int], theta: float = 10000.0):
+        super().__init__()
+        self.dim = dim
+        self.axes_dims = axes_dims  # (temporal, height, width)
+        self.theta = theta
+        
+    def forward(self, ids: torch.Tensor) -> torch.Tensor:
+        """
+        ids: (B, N, 3) - temporal, height, width indices
+        Returns: (B, N, dim) rotary embeddings
+        """
+        B, N, _ = ids.shape
+        device = ids.device
+        dtype = torch.float32
+        
+        embeddings = []
+        dim_offset = 0
+        
+        for axis_idx, axis_dim in enumerate(self.axes_dims):
+            # Compute frequencies for this axis
+            freqs = 1.0 / (self.theta ** (torch.arange(0, axis_dim, 2, device=device, dtype=dtype) / axis_dim))
+            # Get positions for this axis
+            positions = ids[:, :, axis_idx].float()  # (B, N)
+            # Outer product: (B, N) x (axis_dim/2) -> (B, N, axis_dim/2)
+            angles = positions.unsqueeze(-1) * freqs.unsqueeze(0).unsqueeze(0)
+            # Interleave sin/cos
+            emb = torch.stack([angles.cos(), angles.sin()], dim=-1)  # (B, N, axis_dim/2, 2)
+            emb = emb.flatten(-2)  # (B, N, axis_dim)
+            embeddings.append(emb)
+            dim_offset += axis_dim
+            
+        return torch.cat(embeddings, dim=-1)  # (B, N, dim)
+
+
+def apply_rope(x: torch.Tensor, rope: torch.Tensor) -> torch.Tensor:
+    """Apply rotary embeddings to input tensor."""
+    # x: (B, heads, N, head_dim)
+    # rope: (B, N, head_dim)
+    B, H, N, D = x.shape
+    rope = rope.unsqueeze(1)  # (B, 1, N, D)
+    
+    # Split into pairs
+    x_pairs = x.reshape(B, H, N, D // 2, 2)
+    rope_pairs = rope.reshape(B, 1, N, D // 2, 2)
+    
+    cos = rope_pairs[..., 0]
+    sin = rope_pairs[..., 1]
+    
+    x0 = x_pairs[..., 0]
+    x1 = x_pairs[..., 1]
+    
+    out0 = x0 * cos - x1 * sin
+    out1 = x1 * cos + x0 * sin
+    
+    return torch.stack([out0, out1], dim=-1).flatten(-2)
+
+
+class MLPEmbedder(nn.Module):
+    """MLP for embedding scalars (timestep, guidance)."""
+    def __init__(self, hidden_size: int):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(256, hidden_size),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size),
+        )
+        
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # Sinusoidal embedding first
+        half_dim = 128
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=x.device, dtype=x.dtype) * -emb)
+        emb = x.unsqueeze(-1) * emb.unsqueeze(0)
+        emb = torch.cat([emb.sin(), emb.cos()], dim=-1)  # (B, 256)
+        return self.mlp(emb)
+
+
+class AdaLayerNormZero(nn.Module):
+    """
+    AdaLN-Zero for double-stream blocks.
+    Outputs 6 modulation params: (shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp)
+    """
+    def __init__(self, hidden_size: int):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(hidden_size, 6 * hidden_size, bias=True)
+        self.norm = RMSNorm(hidden_size)
+        
+    def forward(
+        self, x: torch.Tensor, emb: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+            x: hidden states (B, N, D)
+            emb: conditioning embedding (B, D)
+        Returns:
+            (normed_x, gate_msa, shift_mlp, scale_mlp, gate_mlp)
+        """
+        emb_out = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb_out.chunk(6, dim=-1)
+        x = self.norm(x) * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+class AdaLayerNormZeroSingle(nn.Module):
+    """
+    AdaLN-Zero for single-stream blocks.
+    Outputs 3 modulation params: (shift, scale, gate)
+    """
+    def __init__(self, hidden_size: int):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(hidden_size, 3 * hidden_size, bias=True)
+        self.norm = RMSNorm(hidden_size)
+        
+    def forward(
+        self, x: torch.Tensor, emb: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+            x: hidden states (B, N, D)
+            emb: conditioning embedding (B, D)
+        Returns:
+            (normed_x, gate)
+        """
+        emb_out = self.linear(self.silu(emb))
+        shift, scale, gate = emb_out.chunk(3, dim=-1)
+        x = self.norm(x) * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        return x, gate
+
+
+class Attention(nn.Module):
+    """Multi-head attention with optional RoPE."""
+    def __init__(self, hidden_size: int, num_heads: int, head_dim: int):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.scale = head_dim ** -0.5
+        
+        self.qkv = nn.Linear(hidden_size, 3 * num_heads * head_dim, bias=False)
+        self.out_proj = nn.Linear(num_heads * head_dim, hidden_size, bias=False)
+        
+    def forward(
+        self, 
+        x: torch.Tensor, 
+        rope: Optional[torch.Tensor] = None,
+        mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        B, N, _ = x.shape
+        
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)
+        q, k, v = qkv.permute(2, 0, 3, 1, 4)  # 3 x (B, heads, N, head_dim)
+        
+        if rope is not None:
+            q = apply_rope(q, rope)
+            k = apply_rope(k, rope)
+        
+        # Scaled dot-product attention
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        if mask is not None:
+            attn = attn.masked_fill(mask == 0, float('-inf'))
+        attn = attn.softmax(dim=-1)
+        
+        out = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+        return self.out_proj(out)
+
+
+class JointAttention(nn.Module):
+    """Joint attention for double-stream blocks (separate Q,K,V for txt and img)."""
+    def __init__(self, hidden_size: int, num_heads: int, head_dim: int):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.scale = head_dim ** -0.5
+        
+        # Separate projections for text and image
+        self.txt_qkv = nn.Linear(hidden_size, 3 * num_heads * head_dim, bias=False)
+        self.img_qkv = nn.Linear(hidden_size, 3 * num_heads * head_dim, bias=False)
+        
+        self.txt_out = nn.Linear(num_heads * head_dim, hidden_size, bias=False)
+        self.img_out = nn.Linear(num_heads * head_dim, hidden_size, bias=False)
+        
+    def forward(
+        self,
+        txt: torch.Tensor,
+        img: torch.Tensor,
+        rope: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        B, L, _ = txt.shape
+        _, N, _ = img.shape
+        
+        # Compute Q, K, V for both streams
+        txt_qkv = self.txt_qkv(txt).reshape(B, L, 3, self.num_heads, self.head_dim)
+        img_qkv = self.img_qkv(img).reshape(B, N, 3, self.num_heads, self.head_dim)
+        
+        txt_q, txt_k, txt_v = txt_qkv.permute(2, 0, 3, 1, 4)
+        img_q, img_k, img_v = img_qkv.permute(2, 0, 3, 1, 4)
+        
+        # Apply RoPE to image queries/keys only (text doesn't have positions)
+        if rope is not None:
+            img_q = apply_rope(img_q, rope)
+            img_k = apply_rope(img_k, rope)
+        
+        # Concatenate keys and values for joint attention
+        k = torch.cat([txt_k, img_k], dim=2)  # (B, heads, L+N, head_dim)
+        v = torch.cat([txt_v, img_v], dim=2)
+        
+        # Text attends to all
+        txt_attn = (txt_q @ k.transpose(-2, -1)) * self.scale
+        txt_attn = txt_attn.softmax(dim=-1)
+        txt_out = (txt_attn @ v).transpose(1, 2).reshape(B, L, -1)
+        
+        # Image attends to all
+        img_attn = (img_q @ k.transpose(-2, -1)) * self.scale
+        img_attn = img_attn.softmax(dim=-1)
+        img_out = (img_attn @ v).transpose(1, 2).reshape(B, N, -1)
+        
+        return self.txt_out(txt_out), self.img_out(img_out)
+
+
+class MLP(nn.Module):
+    """Feed-forward network."""
+    def __init__(self, hidden_size: int, mlp_ratio: float = 4.0):
+        super().__init__()
+        mlp_hidden = int(hidden_size * mlp_ratio)
+        self.fc1 = nn.Linear(hidden_size, mlp_hidden)
+        self.act = nn.GELU(approximate='tanh')
+        self.fc2 = nn.Linear(mlp_hidden, hidden_size)
+        
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.fc2(self.act(self.fc1(x)))
+
+
+class DoubleStreamBlock(nn.Module):
+    """
+    Double-stream transformer block (MMDiT style).
+    Text and image have separate weights but attend to each other.
+    Uses AdaLN-Zero with 6 modulation params per stream.
+    """
+    def __init__(self, config: TinyFluxConfig):
+        super().__init__()
+        hidden = config.hidden_size
+        heads = config.num_attention_heads
+        head_dim = config.attention_head_dim
+        mlp_hidden = int(hidden * config.mlp_ratio)
+        
+        # AdaLN-Zero for each stream (outputs 6 params each)
+        self.img_norm1 = AdaLayerNormZero(hidden)
+        self.txt_norm1 = AdaLayerNormZero(hidden)
+        
+        # Joint attention (separate QKV projections)
+        self.attn = JointAttention(hidden, heads, head_dim)
+        
+        # Second norm for MLP (not adaptive, uses params from norm1)
+        self.img_norm2 = RMSNorm(hidden)
+        self.txt_norm2 = RMSNorm(hidden)
+        
+        # MLPs
+        self.img_mlp = MLP(hidden, config.mlp_ratio)
+        self.txt_mlp = MLP(hidden, config.mlp_ratio)
+        
+    def forward(
+        self,
+        txt: torch.Tensor,
+        img: torch.Tensor,
+        vec: torch.Tensor,
+        rope: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Image stream: norm + modulation, get MLP params for later
+        img_normed, img_gate_msa, img_shift_mlp, img_scale_mlp, img_gate_mlp = self.img_norm1(img, vec)
+        
+        # Text stream: norm + modulation, get MLP params for later
+        txt_normed, txt_gate_msa, txt_shift_mlp, txt_scale_mlp, txt_gate_mlp = self.txt_norm1(txt, vec)
+        
+        # Joint attention
+        txt_attn_out, img_attn_out = self.attn(txt_normed, img_normed, rope)
+        
+        # Residual with gate
+        txt = txt + txt_gate_msa.unsqueeze(1) * txt_attn_out
+        img = img + img_gate_msa.unsqueeze(1) * img_attn_out
+        
+        # MLP with modulation (using params from norm1)
+        txt_mlp_in = self.txt_norm2(txt) * (1 + txt_scale_mlp.unsqueeze(1)) + txt_shift_mlp.unsqueeze(1)
+        img_mlp_in = self.img_norm2(img) * (1 + img_scale_mlp.unsqueeze(1)) + img_shift_mlp.unsqueeze(1)
+        
+        txt = txt + txt_gate_mlp.unsqueeze(1) * self.txt_mlp(txt_mlp_in)
+        img = img + img_gate_mlp.unsqueeze(1) * self.img_mlp(img_mlp_in)
+        
+        return txt, img
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    Single-stream transformer block.
+    Text and image are concatenated and share weights.
+    Uses AdaLN-Zero with 3 modulation params (no separate MLP modulation).
+    """
+    def __init__(self, config: TinyFluxConfig):
+        super().__init__()
+        hidden = config.hidden_size
+        heads = config.num_attention_heads
+        head_dim = config.attention_head_dim
+        mlp_hidden = int(hidden * config.mlp_ratio)
+        
+        # AdaLN-Zero (outputs 3 params: shift, scale, gate)
+        self.norm = AdaLayerNormZeroSingle(hidden)
+        
+        # Combined QKV + MLP projection (Flux fuses these)
+        # Linear attention: QKV projection
+        self.attn = Attention(hidden, heads, head_dim)
+        
+        # MLP
+        self.mlp = MLP(hidden, config.mlp_ratio)
+        
+        # Pre-MLP norm (not modulated in single-stream)
+        self.norm2 = RMSNorm(hidden)
+        
+    def forward(
+        self,
+        txt: torch.Tensor,
+        img: torch.Tensor,
+        vec: torch.Tensor,
+        txt_rope: Optional[torch.Tensor] = None,
+        img_rope: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        L = txt.shape[1]
+        
+        # Concatenate txt and img
+        x = torch.cat([txt, img], dim=1)
+        
+        # Concatenate RoPE (zeros for text positions)
+        if img_rope is not None:
+            B, N, D = img_rope.shape
+            txt_rope_zeros = torch.zeros(B, L, D, device=img_rope.device, dtype=img_rope.dtype)
+            rope = torch.cat([txt_rope_zeros, img_rope], dim=1)
+        else:
+            rope = None
+        
+        # Norm + modulation (only 3 params for single stream)
+        x_normed, gate = self.norm(x, vec)
+        
+        # Attention with gated residual
+        x = x + gate.unsqueeze(1) * self.attn(x_normed, rope)
+        
+        # MLP (no separate modulation in single-stream Flux)
+        x = x + self.mlp(self.norm2(x))
+        
+        # Split back
+        txt, img = x.split([L, x.shape[1] - L], dim=1)
+        return txt, img
+
+
+class TinyFlux(nn.Module):
+    """
+    TinyFlux: A scaled-down Flux diffusion transformer.
+    
+    Scaling: /12 from original Flux
+      - hidden: 3072 → 256
+      - heads: 24 → 2
+      - head_dim: 128 (preserved)
+      - in_channels: 16 (Flux VAE)
+    """
+    def __init__(self, config: Optional[TinyFluxConfig] = None):
+        super().__init__()
+        self.config = config or TinyFluxConfig()
+        cfg = self.config
+        
+        # Input projections
+        self.img_in = nn.Linear(cfg.in_channels, cfg.hidden_size)
+        self.txt_in = nn.Linear(cfg.joint_attention_dim, cfg.hidden_size)
+        
+        # Conditioning projections
+        self.time_in = MLPEmbedder(cfg.hidden_size)
+        self.vector_in = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(cfg.pooled_projection_dim, cfg.hidden_size)
+        )
+        if cfg.guidance_embeds:
+            self.guidance_in = MLPEmbedder(cfg.hidden_size)
+        
+        # RoPE
+        self.rope = RotaryEmbedding(cfg.attention_head_dim, cfg.axes_dims_rope)
+        
+        # Transformer blocks
+        self.double_blocks = nn.ModuleList([
+            DoubleStreamBlock(cfg) for _ in range(cfg.num_double_layers)
+        ])
+        self.single_blocks = nn.ModuleList([
+            SingleStreamBlock(cfg) for _ in range(cfg.num_single_layers)
+        ])
+        
+        # Output
+        self.final_norm = RMSNorm(cfg.hidden_size)
+        self.final_linear = nn.Linear(cfg.hidden_size, cfg.in_channels)
+        
+        self._init_weights()
+        
+    def _init_weights(self):
+        """Initialize weights."""
+        def _init(module):
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+        self.apply(_init)
+        
+        # Zero-init output projection for residual
+        nn.init.zeros_(self.final_linear.weight)
+        
+    def forward(
+        self,
+        hidden_states: torch.Tensor,       # (B, N, in_channels) - image patches
+        encoder_hidden_states: torch.Tensor,  # (B, L, joint_attention_dim) - T5 tokens
+        pooled_projections: torch.Tensor,  # (B, pooled_projection_dim) - CLIP pooled
+        timestep: torch.Tensor,            # (B,) - diffusion timestep
+        img_ids: torch.Tensor,             # (B, N, 3) - image position ids
+        guidance: Optional[torch.Tensor] = None,  # (B,) - guidance scale
+    ) -> torch.Tensor:
+        """
+        Forward pass.
+        
+        Returns:
+            Predicted noise/velocity of shape (B, N, in_channels)
+        """
+        # Input projections
+        img = self.img_in(hidden_states)  # (B, N, hidden)
+        txt = self.txt_in(encoder_hidden_states)  # (B, L, hidden)
+        
+        # Conditioning vector
+        vec = self.time_in(timestep)
+        vec = vec + self.vector_in(pooled_projections)
+        if self.config.guidance_embeds and guidance is not None:
+            vec = vec + self.guidance_in(guidance)
+        
+        # RoPE for image positions
+        img_rope = self.rope(img_ids)
+        
+        # Double-stream blocks
+        for block in self.double_blocks:
+            txt, img = block(txt, img, vec, img_rope)
+        
+        # Single-stream blocks
+        for block in self.single_blocks:
+            txt, img = block(txt, img, vec, img_rope=img_rope)
+        
+        # Output (image only)
+        img = self.final_norm(img)
+        img = self.final_linear(img)
+        
+        return img
+    
+    @staticmethod
+    def create_img_ids(batch_size: int, height: int, width: int, device: torch.device) -> torch.Tensor:
+        """Create image position IDs for RoPE."""
+        # height, width are in latent space (image_size / 8)
+        img_ids = torch.zeros(batch_size, height * width, 3, device=device)
+        
+        for i in range(height):
+            for j in range(width):
+                idx = i * width + j
+                img_ids[:, idx, 0] = 0  # temporal (always 0 for images)
+                img_ids[:, idx, 1] = i  # height
+                img_ids[:, idx, 2] = j  # width
+                
+        return img_ids
+    
+    def count_parameters(self) -> dict:
+        """Count parameters by component."""
+        counts = {}
+        counts['img_in'] = sum(p.numel() for p in self.img_in.parameters())
+        counts['txt_in'] = sum(p.numel() for p in self.txt_in.parameters())
+        counts['time_in'] = sum(p.numel() for p in self.time_in.parameters())
+        counts['vector_in'] = sum(p.numel() for p in self.vector_in.parameters())
+        if hasattr(self, 'guidance_in'):
+            counts['guidance_in'] = sum(p.numel() for p in self.guidance_in.parameters())
+        counts['double_blocks'] = sum(p.numel() for p in self.double_blocks.parameters())
+        counts['single_blocks'] = sum(p.numel() for p in self.single_blocks.parameters())
+        counts['final'] = sum(p.numel() for p in self.final_norm.parameters()) + \
+                         sum(p.numel() for p in self.final_linear.parameters())
+        counts['total'] = sum(p.numel() for p in self.parameters())
+        return counts
+
+
+def test_tiny_flux():
+    """Quick test of the model."""
+    print("=" * 60)
+    print("TinyFlux Model Test")
+    print("=" * 60)
+    
+    config = TinyFluxConfig()
+    print(f"\nConfig:")
+    print(f"  hidden_size: {config.hidden_size}")
+    print(f"  num_heads: {config.num_attention_heads}")
+    print(f"  head_dim: {config.attention_head_dim}")
+    print(f"  in_channels: {config.in_channels}")
+    print(f"  double_layers: {config.num_double_layers}")
+    print(f"  single_layers: {config.num_single_layers}")
+    print(f"  joint_attention_dim: {config.joint_attention_dim}")
+    print(f"  pooled_projection_dim: {config.pooled_projection_dim}")
+    
+    model = TinyFlux(config)
+    
+    # Count parameters
+    counts = model.count_parameters()
+    print(f"\nParameters:")
+    for name, count in counts.items():
+        print(f"  {name}: {count:,} ({count/1e6:.2f}M)")
+    
+    # Test forward pass
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model = model.to(device)
+    
+    batch_size = 2
+    latent_h, latent_w = 64, 64  # 512x512 image / 8
+    num_patches = latent_h * latent_w
+    text_len = 77
+    
+    # Create dummy inputs
+    hidden_states = torch.randn(batch_size, num_patches, config.in_channels, device=device)
+    encoder_hidden_states = torch.randn(batch_size, text_len, config.joint_attention_dim, device=device)
+    pooled_projections = torch.randn(batch_size, config.pooled_projection_dim, device=device)
+    timestep = torch.rand(batch_size, device=device)
+    img_ids = TinyFlux.create_img_ids(batch_size, latent_h, latent_w, device)
+    guidance = torch.ones(batch_size, device=device) * 3.5
+    
+    print(f"\nInput shapes:")
+    print(f"  hidden_states: {hidden_states.shape}")
+    print(f"  encoder_hidden_states: {encoder_hidden_states.shape}")
+    print(f"  pooled_projections: {pooled_projections.shape}")
+    print(f"  img_ids: {img_ids.shape}")
+    
+    # Forward pass
+    with torch.no_grad():
+        output = model(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            pooled_projections=pooled_projections,
+            timestep=timestep,
+            img_ids=img_ids,
+            guidance=guidance,
+        )
+    
+    print(f"\nOutput shape: {output.shape}")
+    print(f"Output range: [{output.min():.4f}, {output.max():.4f}]")
+    print("\n✓ Forward pass successful!")
+
+
+if __name__ == "__main__":
+    test_tiny_flux()