# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py from __future__ import annotations from typing import List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F import comfy.ldm.common_dit from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder from comfy.ldm.modules.attention import optimized_attention_masked from comfy.ldm.flux.layers import EmbedND from comfy.ldm.flux.math import apply_rope import comfy.patcher_extension def modulate(x, scale): return x * (1 + scale.unsqueeze(1)) ############################################################################# # Core NextDiT Model # ############################################################################# def clamp_fp16(x): if x.dtype == torch.float16: return torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) return x class JointAttention(nn.Module): """Multi-head attention module.""" def __init__( self, dim: int, n_heads: int, n_kv_heads: Optional[int], qk_norm: bool, out_bias: bool = False, operation_settings={}, ): """ Initialize the Attention module. Args: dim (int): Number of input dimensions. n_heads (int): Number of heads. n_kv_heads (Optional[int]): Number of kv heads, if using GQA. """ super().__init__() self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads self.n_local_heads = n_heads self.n_local_kv_heads = self.n_kv_heads self.n_rep = self.n_local_heads // self.n_local_kv_heads self.head_dim = dim // n_heads self.qkv = operation_settings.get("operations").Linear( dim, (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.out = operation_settings.get("operations").Linear( n_heads * self.head_dim, dim, bias=out_bias, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) if qk_norm: self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) else: self.q_norm = self.k_norm = nn.Identity() def forward( self, x: torch.Tensor, x_mask: torch.Tensor, freqs_cis: torch.Tensor, transformer_options={}, ) -> torch.Tensor: """ Args: x: x_mask: freqs_cis: Returns: """ bsz, seqlen, _ = x.shape xq, xk, xv = torch.split( self.qkv(x), [ self.n_local_heads * self.head_dim, self.n_local_kv_heads * self.head_dim, self.n_local_kv_heads * self.head_dim, ], dim=-1, ) xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xq = self.q_norm(xq) xk = self.k_norm(xk) xq, xk = apply_rope(xq, xk, freqs_cis) n_rep = self.n_local_heads // self.n_local_kv_heads if n_rep >= 1: xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options) return self.out(output) class FeedForward(nn.Module): def __init__( self, dim: int, hidden_dim: int, multiple_of: int, ffn_dim_multiplier: Optional[float], operation_settings={}, ): """ Initialize the FeedForward module. Args: dim (int): Input dimension. hidden_dim (int): Hidden dimension of the feedforward layer. multiple_of (int): Value to ensure hidden dimension is a multiple of this value. ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. """ super().__init__() # custom dim factor multiplier if ffn_dim_multiplier is not None: hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) self.w1 = operation_settings.get("operations").Linear( dim, hidden_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.w2 = operation_settings.get("operations").Linear( hidden_dim, dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.w3 = operation_settings.get("operations").Linear( dim, hidden_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) # @torch.compile def _forward_silu_gating(self, x1, x3): return clamp_fp16(F.silu(x1) * x3) def forward(self, x): return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) class JointTransformerBlock(nn.Module): def __init__( self, layer_id: int, dim: int, n_heads: int, n_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, qk_norm: bool, modulation=True, z_image_modulation=False, attn_out_bias=False, operation_settings={}, ) -> None: """ Initialize a TransformerBlock. Args: layer_id (int): Identifier for the layer. dim (int): Embedding dimension of the input features. n_heads (int): Number of attention heads. n_kv_heads (Optional[int]): Number of attention heads in key and value features (if using GQA), or set to None for the same as query. multiple_of (int): ffn_dim_multiplier (float): norm_eps (float): """ super().__init__() self.dim = dim self.head_dim = dim // n_heads self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, out_bias=attn_out_bias, operation_settings=operation_settings) self.feed_forward = FeedForward( dim=dim, hidden_dim=dim, multiple_of=multiple_of, ffn_dim_multiplier=ffn_dim_multiplier, operation_settings=operation_settings, ) self.layer_id = layer_id self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.modulation = modulation if modulation: if z_image_modulation: self.adaLN_modulation = nn.Sequential( operation_settings.get("operations").Linear( min(dim, 256), 4 * dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) else: self.adaLN_modulation = nn.Sequential( nn.SiLU(), operation_settings.get("operations").Linear( min(dim, 1024), 4 * dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) def forward( self, x: torch.Tensor, x_mask: torch.Tensor, freqs_cis: torch.Tensor, adaln_input: Optional[torch.Tensor]=None, transformer_options={}, ): """ Perform a forward pass through the TransformerBlock. Args: x (torch.Tensor): Input tensor. freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. Returns: torch.Tensor: Output tensor after applying attention and feedforward layers. """ if self.modulation: assert adaln_input is not None scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( clamp_fp16(self.attention( modulate(self.attention_norm1(x), scale_msa), x_mask, freqs_cis, transformer_options=transformer_options, )) ) x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( clamp_fp16(self.feed_forward( modulate(self.ffn_norm1(x), scale_mlp), )) ) else: assert adaln_input is None x = x + self.attention_norm2( clamp_fp16(self.attention( self.attention_norm1(x), x_mask, freqs_cis, transformer_options=transformer_options, )) ) x = x + self.ffn_norm2( self.feed_forward( self.ffn_norm1(x), ) ) return x class FinalLayer(nn.Module): """ The final layer of NextDiT. """ def __init__(self, hidden_size, patch_size, out_channels, z_image_modulation=False, operation_settings={}): super().__init__() self.norm_final = operation_settings.get("operations").LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.linear = operation_settings.get("operations").Linear( hidden_size, patch_size * patch_size * out_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) if z_image_modulation: min_mod = 256 else: min_mod = 1024 self.adaLN_modulation = nn.Sequential( nn.SiLU(), operation_settings.get("operations").Linear( min(hidden_size, min_mod), hidden_size, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) def forward(self, x, c): scale = self.adaLN_modulation(c) x = modulate(self.norm_final(x), scale) x = self.linear(x) return x class NextDiT(nn.Module): """ Diffusion model with a Transformer backbone. """ def __init__( self, patch_size: int = 2, in_channels: int = 4, dim: int = 4096, n_layers: int = 32, n_refiner_layers: int = 2, n_heads: int = 32, n_kv_heads: Optional[int] = None, multiple_of: int = 256, ffn_dim_multiplier: float = 4.0, norm_eps: float = 1e-5, qk_norm: bool = False, cap_feat_dim: int = 5120, axes_dims: List[int] = (16, 56, 56), axes_lens: List[int] = (1, 512, 512), rope_theta=10000.0, z_image_modulation=False, time_scale=1.0, pad_tokens_multiple=None, clip_text_dim=None, image_model=None, device=None, dtype=None, operations=None, ) -> None: super().__init__() self.dtype = dtype operation_settings = {"operations": operations, "device": device, "dtype": dtype} self.in_channels = in_channels self.out_channels = in_channels self.patch_size = patch_size self.time_scale = time_scale self.pad_tokens_multiple = pad_tokens_multiple self.x_embedder = operation_settings.get("operations").Linear( in_features=patch_size * patch_size * in_channels, out_features=dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.noise_refiner = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation=True, z_image_modulation=z_image_modulation, operation_settings=operation_settings, ) for layer_id in range(n_refiner_layers) ] ) self.context_refiner = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation=False, operation_settings=operation_settings, ) for layer_id in range(n_refiner_layers) ] ) self.t_embedder = TimestepEmbedder(min(dim, 1024), output_size=256 if z_image_modulation else None, **operation_settings) self.cap_embedder = nn.Sequential( operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear( cap_feat_dim, dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) self.clip_text_pooled_proj = None if clip_text_dim is not None: self.clip_text_dim = clip_text_dim self.clip_text_pooled_proj = nn.Sequential( operation_settings.get("operations").RMSNorm(clip_text_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear( clip_text_dim, clip_text_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) self.time_text_embed = nn.Sequential( nn.SiLU(), operation_settings.get("operations").Linear( min(dim, 1024) + clip_text_dim, min(dim, 1024), bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) self.layers = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, z_image_modulation=z_image_modulation, attn_out_bias=False, operation_settings=operation_settings, ) for layer_id in range(n_layers) ] ) self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings) if self.pad_tokens_multiple is not None: self.x_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype)) self.cap_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype)) assert (dim // n_heads) == sum(axes_dims) self.axes_dims = axes_dims self.axes_lens = axes_lens self.rope_embedder = EmbedND(dim=dim // n_heads, theta=rope_theta, axes_dim=axes_dims) self.dim = dim self.n_heads = n_heads def unpatchify( self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False ) -> List[torch.Tensor]: """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ pH = pW = self.patch_size imgs = [] for i in range(x.size(0)): H, W = img_size[i] begin = cap_size[i] end = begin + (H // pH) * (W // pW) imgs.append( x[i][begin:end] .view(H // pH, W // pW, pH, pW, self.out_channels) .permute(4, 0, 2, 1, 3) .flatten(3, 4) .flatten(1, 2) ) if return_tensor: imgs = torch.stack(imgs, dim=0) return imgs def patchify_and_embed( self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={} ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]: bsz = len(x) pH = pW = self.patch_size device = x[0].device if self.pad_tokens_multiple is not None: pad_extra = (-cap_feats.shape[1]) % self.pad_tokens_multiple cap_feats = torch.cat((cap_feats, self.cap_pad_token.to(device=cap_feats.device, dtype=cap_feats.dtype, copy=True).unsqueeze(0).repeat(cap_feats.shape[0], pad_extra, 1)), dim=1) cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device) cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0 B, C, H, W = x.shape x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2)) rope_options = transformer_options.get("rope_options", None) h_scale = 1.0 w_scale = 1.0 h_start = 0 w_start = 0 if rope_options is not None: h_scale = rope_options.get("scale_y", 1.0) w_scale = rope_options.get("scale_x", 1.0) h_start = rope_options.get("shift_y", 0.0) w_start = rope_options.get("shift_x", 0.0) H_tokens, W_tokens = H // pH, W // pW x_pos_ids = torch.zeros((bsz, x.shape[1], 3), dtype=torch.float32, device=device) x_pos_ids[:, :, 0] = cap_feats.shape[1] + 1 x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten() x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten() if self.pad_tokens_multiple is not None: pad_extra = (-x.shape[1]) % self.pad_tokens_multiple x = torch.cat((x, self.x_pad_token.to(device=x.device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(x.shape[0], pad_extra, 1)), dim=1) x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra)) freqs_cis = self.rope_embedder(torch.cat((cap_pos_ids, x_pos_ids), dim=1)).movedim(1, 2) # refine context for layer in self.context_refiner: cap_feats = layer(cap_feats, cap_mask, freqs_cis[:, :cap_pos_ids.shape[1]], transformer_options=transformer_options) padded_img_mask = None for layer in self.noise_refiner: x = layer(x, padded_img_mask, freqs_cis[:, cap_pos_ids.shape[1]:], t, transformer_options=transformer_options) padded_full_embed = torch.cat((cap_feats, x), dim=1) mask = None img_sizes = [(H, W)] * bsz l_effective_cap_len = [cap_feats.shape[1]] * bsz return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs) # def forward(self, x, t, cap_feats, cap_mask): def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs): t = 1.0 - timesteps cap_feats = context cap_mask = attention_mask bs, c, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) """ Forward pass of NextDiT. t: (N,) tensor of diffusion timesteps y: (N,) tensor of text tokens/features """ t = self.t_embedder(t * self.time_scale, dtype=x.dtype) # (N, D) adaln_input = t cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute if self.clip_text_pooled_proj is not None: pooled = kwargs.get("clip_text_pooled", None) if pooled is not None: pooled = self.clip_text_pooled_proj(pooled) else: pooled = torch.zeros((1, self.clip_text_dim), device=x.device, dtype=x.dtype) adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1)) patches = transformer_options.get("patches", {}) x_is_tensor = isinstance(x, torch.Tensor) img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options) freqs_cis = freqs_cis.to(img.device) for i, layer in enumerate(self.layers): img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options) if "double_block" in patches: for p in patches["double_block"]: out = p({"img": img[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options}) if "img" in out: img[:, cap_size[0]:] = out["img"] if "txt" in out: img[:, :cap_size[0]] = out["txt"] img = self.final_layer(img, adaln_input) img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w] return -img