# implementation of Chroma for Forge, inspired by https://github.com/lodestone-rock/ComfyUI_FluxMod from dataclasses import dataclass import math import torch from torch import nn from einops import rearrange, repeat from backend.attention import attention_function from backend.utils import fp16_fix, tensor2parameter from backend.nn.flux import attention, rope, timestep_embedding, EmbedND, MLPEmbedder, RMSNorm, QKNorm, SelfAttention class Approximator(nn.Module): def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 4): super().__init__() self.in_proj = nn.Linear(in_dim, hidden_dim, bias=True) self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim) for x in range( n_layers)]) self.norms = nn.ModuleList([RMSNorm( hidden_dim) for x in range( n_layers)]) self.out_proj = nn.Linear(hidden_dim, out_dim) def forward(self, x): x = self.in_proj(x) for layer, norms in zip(self.layers, self.norms): x = x + layer(norms(x)) x = self.out_proj(x) return x @dataclass class ModulationOut: shift: torch.Tensor scale: torch.Tensor gate: torch.Tensor class DoubleStreamBlock(nn.Module): def __init__(self, hidden_size, num_heads, mlp_ratio, qkv_bias=False): super().__init__() mlp_hidden_dim = int(hidden_size * mlp_ratio) self.num_heads = num_heads self.hidden_size = hidden_size self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.img_mlp = nn.Sequential( nn.Linear(hidden_size, mlp_hidden_dim, bias=True), nn.GELU(approximate="tanh"), nn.Linear(mlp_hidden_dim, hidden_size, bias=True), ) self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.txt_mlp = nn.Sequential( nn.Linear(hidden_size, mlp_hidden_dim, bias=True), nn.GELU(approximate="tanh"), nn.Linear(mlp_hidden_dim, hidden_size, bias=True), ) def forward(self, img, txt, mod, pe): (img_mod1, img_mod2), (txt_mod1, txt_mod2) = mod img_modulated = self.img_norm1(img) img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift img_qkv = self.img_attn.qkv(img_modulated) B, L, _ = img_qkv.shape H = self.num_heads D = img_qkv.shape[-1] // (3 * H) img_q, img_k, img_v = img_qkv.view(B, L, 3, H, D).permute(2, 0, 3, 1, 4) img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) txt_modulated = self.txt_norm1(txt) txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift txt_qkv = self.txt_attn.qkv(txt_modulated) B, L, _ = txt_qkv.shape txt_q, txt_k, txt_v = txt_qkv.view(B, L, 3, H, D).permute(2, 0, 3, 1, 4) txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) q = torch.cat((txt_q, img_q), dim=2) k = torch.cat((txt_k, img_k), dim=2) v = torch.cat((txt_v, img_v), dim=2) attn = attention(q, k, v, pe=pe) txt_attn, img_attn = attn[:, :txt.shape[1]], attn[:, txt.shape[1]:] img = img + img_mod1.gate * self.img_attn.proj(img_attn) img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) txt = fp16_fix(txt) return img, txt class SingleStreamBlock(nn.Module): def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, qk_scale=None): super().__init__() self.hidden_dim = hidden_size self.num_heads = num_heads head_dim = hidden_size // num_heads self.scale = qk_scale or head_dim ** -0.5 self.mlp_hidden_dim = int(hidden_size * mlp_ratio) self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) self.norm = QKNorm(head_dim) self.hidden_size = hidden_size self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.mlp_act = nn.GELU(approximate="tanh") def forward(self, x, mod, pe): x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) del x_mod # q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) qkv = qkv.view(qkv.size(0), qkv.size(1), 3, self.num_heads, self.hidden_size // self.num_heads) q, k, v = qkv.permute(2, 0, 3, 1, 4) del qkv q, k = self.norm(q, k, v) attn = attention(q, k, v, pe=pe) del q, k, v, pe output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), dim=2)) del attn, mlp x = x + mod.gate * output x = fp16_fix(x) return x class LastLayer(nn.Module): def __init__(self, hidden_size, patch_size, out_channels): super().__init__() self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) def forward(self, x, mod): shift, scale = mod shift = shift.squeeze(1) scale = scale.squeeze(1) x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] x = self.linear(x) return x class IntegratedChromaTransformer2DModel(nn.Module): def __init__(self, in_channels: int, vec_in_dim: int, context_in_dim: int, hidden_size: int, mlp_ratio: float, num_heads: int, depth: int, depth_single_blocks: int, axes_dim: list[int], theta: int, qkv_bias: bool, guidance_out_dim: int, guidance_hidden_dim: int, guidance_n_layers: int): super().__init__() self.in_channels = in_channels * 4 self.out_channels = self.in_channels if hidden_size % num_heads != 0: raise ValueError(f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}") pe_dim = hidden_size // num_heads if sum(axes_dim) != pe_dim: raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}") self.hidden_size = hidden_size self.num_heads = num_heads self.pe_embedder = EmbedND(dim=pe_dim, theta=theta, axes_dim=axes_dim) self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) self.distilled_guidance_layer = Approximator(64, guidance_out_dim, guidance_hidden_dim, guidance_n_layers) self.txt_in = nn.Linear(context_in_dim, self.hidden_size) self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, ) for _ in range(depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio) for _ in range(depth_single_blocks) ] ) self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) @staticmethod def distribute_modulations(tensor, single_block_count: int = 38, double_blocks_count: int = 19): """ Distributes slices of the tensor into the block_dict as ModulationOut objects. Args: tensor (torch.Tensor): Input tensor with shape [batch_size, vectors, dim]. """ batch_size, vectors, dim = tensor.shape block_dict = {} for i in range(single_block_count): key = f"single_blocks.{i}.modulation.lin" block_dict[key] = None for i in range(double_blocks_count): key = f"double_blocks.{i}.img_mod.lin" block_dict[key] = None for i in range(double_blocks_count): key = f"double_blocks.{i}.txt_mod.lin" block_dict[key] = None block_dict["final_layer.adaLN_modulation.1"] = None idx = 0 # Index to keep track of the vector slices for key in block_dict.keys(): if "single_blocks" in key: # Single block: 1 ModulationOut block_dict[key] = ModulationOut( shift=tensor[:, idx:idx+1, :], scale=tensor[:, idx+1:idx+2, :], gate=tensor[:, idx+2:idx+3, :] ) idx += 3 # Advance by 3 vectors elif "img_mod" in key: # Double block: List of 2 ModulationOut double_block = [] for _ in range(2): # Create 2 ModulationOut objects double_block.append( ModulationOut( shift=tensor[:, idx:idx+1, :], scale=tensor[:, idx+1:idx+2, :], gate=tensor[:, idx+2:idx+3, :] ) ) idx += 3 # Advance by 3 vectors per ModulationOut block_dict[key] = double_block elif "txt_mod" in key: # Double block: List of 2 ModulationOut double_block = [] for _ in range(2): # Create 2 ModulationOut objects double_block.append( ModulationOut( shift=tensor[:, idx:idx+1, :], scale=tensor[:, idx+1:idx+2, :], gate=tensor[:, idx+2:idx+3, :] ) ) idx += 3 # Advance by 3 vectors per ModulationOut block_dict[key] = double_block elif "final_layer" in key: # Final layer: 1 ModulationOut block_dict[key] = [ tensor[:, idx:idx+1, :], tensor[:, idx+1:idx+2, :], ] idx += 2 # Advance by 2 vectors return block_dict def inner_forward(self, img, img_ids, txt, txt_ids, timesteps): if img.ndim != 3 or txt.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") img = self.img_in(img) device = img.device dtype = img.dtype # torch.bfloat16 nb_double_block = len(self.double_blocks) nb_single_block = len(self.single_blocks) mod_index_length = nb_double_block*12 + nb_single_block*3 + 2 distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(device=device, dtype=dtype) distil_guidance = timestep_embedding(torch.zeros_like(timesteps), 16).to(device=device, dtype=dtype) modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(device=device, dtype=dtype) modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1) timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1) input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1) mod_vectors = self.distilled_guidance_layer(input_vec) mod_vectors_dict = self.distribute_modulations(mod_vectors, nb_single_block, nb_double_block) txt = self.txt_in(txt) ids = torch.cat((txt_ids, img_ids), dim=1) del txt_ids, img_ids pe = self.pe_embedder(ids) del ids for i, block in enumerate(self.double_blocks): img_mod = mod_vectors_dict[f"double_blocks.{i}.img_mod.lin"] txt_mod = mod_vectors_dict[f"double_blocks.{i}.txt_mod.lin"] double_mod = [img_mod, txt_mod] img, txt = block(img=img, txt=txt, mod=double_mod, pe=pe) img = torch.cat((txt, img), 1) for i, block in enumerate(self.single_blocks): single_mod = mod_vectors_dict[f"single_blocks.{i}.modulation.lin"] img = block(img, mod=single_mod, pe=pe) del pe img = img[:, txt.shape[1]:, ...] final_mod = mod_vectors_dict["final_layer.adaLN_modulation.1"] img = self.final_layer(img, final_mod) return img def forward(self, x, timestep, context, **kwargs): bs, c, h, w = x.shape input_device = x.device input_dtype = x.dtype patch_size = 2 pad_h = (patch_size - x.shape[-2] % patch_size) % patch_size pad_w = (patch_size - x.shape[-1] % patch_size) % patch_size x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode="circular") img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) del x, pad_h, pad_w h_len = ((h + (patch_size // 2)) // patch_size) w_len = ((w + (patch_size // 2)) // patch_size) img_ids = torch.zeros((h_len, w_len, 3), device=input_device, dtype=input_dtype) img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=input_device, dtype=input_dtype)[:, None] img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=input_device, dtype=input_dtype)[None, :] img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) txt_ids = torch.zeros((bs, context.shape[1], 3), device=input_device, dtype=input_dtype) del input_device, input_dtype out = self.inner_forward(img, img_ids, context, txt_ids, timestep) del img, img_ids, txt_ids, timestep, context out = rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:, :, :h, :w] del h_len, w_len, bs return out