| import math |
| from dataclasses import dataclass |
| from typing import Optional |
|
|
| import torch |
| from torch import nn |
| import torch.nn.functional as F |
|
|
|
|
| def modulate(x, shift, scale): |
| return x * (1 + scale[:, None, :]) + shift[:, None, :] |
|
|
|
|
| def rotate_half(x): |
| x1, x2 = x.reshape(*x.shape[:-1], 2, -1).unbind(dim=-2) |
| return torch.cat((-x2, x1), dim=-1) |
|
|
|
|
| class RMSNorm(nn.Module): |
| def __init__(self, dim: int, eps: float = 1e-6): |
| super().__init__() |
| self.weight = nn.Parameter(torch.ones(dim)) |
| self.eps = eps |
|
|
| def forward(self, x): |
| y = x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) |
| return y * self.weight |
|
|
|
|
| class TimestepEmbedder(nn.Module): |
| def __init__(self, hidden_size: int, frequency_embedding_size: int = 256): |
| super().__init__() |
| self.frequency_embedding_size = frequency_embedding_size |
| self.mlp = nn.Sequential( |
| nn.Linear(frequency_embedding_size, hidden_size), |
| nn.SiLU(), |
| nn.Linear(hidden_size, hidden_size), |
| ) |
|
|
| def forward(self, t): |
| half = self.frequency_embedding_size // 2 |
| freqs = torch.exp( |
| -math.log(10000.0) |
| * torch.arange(half, device=t.device, dtype=torch.float32) |
| / half |
| ) |
| args = t.float()[:, None] * freqs[None] |
| emb = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) |
| return self.mlp(emb.to(dtype=self.mlp[0].weight.dtype)) |
|
|
|
|
| class BottleneckPatchEmbed(nn.Module): |
| def __init__(self, img_size=512, patch_size=16, in_channels=3, pca_channels=128, hidden_size=1248): |
| super().__init__() |
| self.img_size = img_size |
| self.patch_size = patch_size |
| self.proj1 = nn.Conv2d(in_channels, pca_channels, kernel_size=patch_size, stride=patch_size, bias=False) |
| self.proj2 = nn.Conv2d(pca_channels, hidden_size, kernel_size=1, stride=1, bias=True) |
|
|
| def forward(self, x): |
| x = self.proj2(self.proj1(x)) |
| return x.flatten(2).transpose(1, 2) |
|
|
|
|
| class SwiGLUMlp(nn.Module): |
| def __init__(self, in_features: int, hidden_features: int): |
| super().__init__() |
| hidden_dim = (hidden_features + 7) // 8 * 8 |
| self.w1 = nn.Linear(in_features, hidden_dim, bias=False) |
| self.w3 = nn.Linear(in_features, hidden_dim, bias=False) |
| self.w2 = nn.Linear(hidden_dim, in_features, bias=False) |
|
|
| def forward(self, x): |
| return self.w2(F.silu(self.w1(x)) * self.w3(x)) |
|
|
|
|
| class TextRotaryEmbedding1D(nn.Module): |
| def __init__(self, head_dim: int, theta: float = 10000.0): |
| super().__init__() |
| self.head_dim = head_dim |
| self.theta = theta |
|
|
| def forward(self, x): |
| b, length, h, d = x.shape |
| inv = 1.0 / (self.theta ** (torch.arange(0, d, 2, device=x.device, dtype=torch.float32) / d)) |
| pos = torch.arange(length, device=x.device, dtype=torch.float32) |
| angles = torch.einsum("l,f->lf", pos, inv) |
| angles = torch.cat([angles, angles], dim=-1) |
| cos = angles.cos().to(dtype=x.dtype) |
| sin = angles.sin().to(dtype=x.dtype) |
| return x * cos[None, :, None, :] + rotate_half(x) * sin[None, :, None, :] |
|
|
|
|
| class VisionRotaryEmbeddingFast(nn.Module): |
| def __init__(self, head_dim: int, theta: float = 10000.0): |
| super().__init__() |
| self.dim = head_dim // 2 |
| self.theta = theta |
|
|
| def forward(self, x): |
| length = x.shape[1] |
| side = int(math.sqrt(length)) |
| if side * side != length: |
| raise ValueError(f"image token length must be square, got {length}") |
| freqs = 1.0 / ( |
| self.theta |
| ** (torch.arange(0, self.dim, 2, device=x.device, dtype=torch.float32)[: self.dim // 2] / self.dim) |
| ) |
| t = torch.arange(side, device=x.device, dtype=torch.float32) |
| base = torch.einsum("l,f->lf", t, freqs) |
| f_h, f_w = torch.broadcast_tensors(base[:, None, :], base[None, :, :]) |
| angles = torch.cat([f_h, f_w], dim=-1) |
| angles = torch.cat([angles, angles], dim=-1).reshape(length, -1) |
| cos = angles.cos().to(dtype=x.dtype) |
| sin = angles.sin().to(dtype=x.dtype) |
| return x * cos[None, :, None, :] + rotate_half(x) * sin[None, :, None, :] |
|
|
|
|
| class MultiModalRotaryEmbeddingFast(nn.Module): |
| def __init__(self, head_dim: int): |
| super().__init__() |
| self.text_rope = TextRotaryEmbedding1D(head_dim) |
| self.vision_rope = VisionRotaryEmbeddingFast(head_dim) |
|
|
| def forward(self, x, txt_len: int): |
| txt = self.text_rope(x[:, :txt_len]) |
| img = self.vision_rope(x[:, txt_len:]) |
| return torch.cat([txt, img], dim=1) |
|
|
|
|
| class PlainTextTransformerBlock(nn.Module): |
| def __init__(self, hidden_size=1248, num_heads=24, head_dim=52, mlp_ratio=2.7): |
| super().__init__() |
| self.num_heads = num_heads |
| self.head_dim = head_dim |
| inner_dim = num_heads * head_dim |
| self.norm1 = RMSNorm(hidden_size) |
| self.norm2 = RMSNorm(hidden_size) |
| self.qkv = nn.Linear(hidden_size, inner_dim * 3) |
| self.attn_proj = nn.Linear(inner_dim, hidden_size) |
| self.mlp = SwiGLUMlp(hidden_size, int(hidden_size * mlp_ratio)) |
| self.q_norm = RMSNorm(head_dim) |
| self.k_norm = RMSNorm(head_dim) |
| self.rope = TextRotaryEmbedding1D(head_dim) |
|
|
| def forward(self, txt): |
| b, length, _ = txt.shape |
| qkv = self.qkv(self.norm1(txt)).reshape(b, length, 3, self.num_heads, self.head_dim) |
| q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2] |
| q = self.rope(self.q_norm(q)) |
| k = self.rope(self.k_norm(k)) |
| attn = torch.einsum("bqhd,bkhd->bhqk", q, k) * (self.head_dim ** -0.5) |
| out = torch.einsum("bhqk,bkhd->bqhd", attn.softmax(dim=-1), v).reshape(b, length, -1) |
| txt = txt + self.attn_proj(out) |
| txt = txt + self.mlp(self.norm2(txt)) |
| return txt |
|
|
|
|
| class DoubleStreamDiTBlock(nn.Module): |
| def __init__(self, hidden_size=1248, txt_hidden_size=1248, num_heads=24, head_dim=52, mlp_ratio=2.7): |
| super().__init__() |
| self.hidden_size = hidden_size |
| self.txt_hidden_size = txt_hidden_size |
| self.num_heads = num_heads |
| self.head_dim = head_dim |
| inner_dim = num_heads * head_dim |
| self.img_norm1 = RMSNorm(hidden_size) |
| self.img_norm2 = RMSNorm(hidden_size) |
| self.txt_norm1 = RMSNorm(txt_hidden_size) |
| self.txt_norm2 = RMSNorm(txt_hidden_size) |
| self.img_qkv = nn.Linear(hidden_size, inner_dim * 3) |
| self.txt_qkv = nn.Linear(txt_hidden_size, inner_dim * 3) |
| self.q_norm = RMSNorm(head_dim) |
| self.k_norm = RMSNorm(head_dim) |
| self.rope = MultiModalRotaryEmbeddingFast(head_dim) |
| self.img_attn_proj = nn.Linear(inner_dim, hidden_size) |
| self.txt_attn_proj = nn.Linear(inner_dim, txt_hidden_size) |
| self.img_mlp = SwiGLUMlp(hidden_size, int(hidden_size * mlp_ratio)) |
| self.txt_mlp = SwiGLUMlp(txt_hidden_size, int(txt_hidden_size * mlp_ratio)) |
|
|
| def forward(self, x, txt, vec): |
| b, li, _ = x.shape |
| lt = txt.shape[1] |
| x_norm = self.img_norm1(x) |
| txt_norm = self.txt_norm1(txt) |
| qkv_i = self.img_qkv(x_norm).reshape(b, li, 3, self.num_heads, self.head_dim) |
| qkv_t = self.txt_qkv(txt_norm).reshape(b, lt, 3, self.num_heads, self.head_dim) |
| q_i, k_i, v_i = qkv_i[:, :, 0], qkv_i[:, :, 1], qkv_i[:, :, 2] |
| q_t, k_t, v_t = qkv_t[:, :, 0], qkv_t[:, :, 1], qkv_t[:, :, 2] |
| q_i, k_i = self.q_norm(q_i), self.k_norm(k_i) |
| q_t, k_t = self.q_norm(q_t), self.k_norm(k_t) |
| q = self.rope(torch.cat([q_t, q_i], dim=1), txt_len=lt) |
| k = self.rope(torch.cat([k_t, k_i], dim=1), txt_len=lt) |
| v = torch.cat([v_t, v_i], dim=1) |
| attn = torch.einsum("bqhd,bkhd->bhqk", q, k) * (self.head_dim ** -0.5) |
| out = torch.einsum("bhqk,bkhd->bqhd", attn.softmax(dim=-1), v) |
| x = x + self.img_attn_proj(out[:, lt:].reshape(b, li, -1)) |
| txt = txt + self.txt_attn_proj(out[:, :lt].reshape(b, lt, -1)) |
| x = x + self.img_mlp(self.img_norm2(x)) |
| txt = txt + self.txt_mlp(self.txt_norm2(txt)) |
| return x, txt |
|
|
|
|
| class FinalLayer(nn.Module): |
| def __init__(self, hidden_size=1248, patch_size=16, out_channels=3): |
| super().__init__() |
| self.patch_size = patch_size |
| self.out_channels = out_channels |
| self.norm_final = RMSNorm(hidden_size) |
| self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) |
|
|
| def forward(self, x, vec=None): |
| return self.linear(self.norm_final(x)) |
|
|
|
|
| def get_2d_sincos_pos_embed(embed_dim, grid_size, device, dtype): |
| grid_h = torch.arange(grid_size, device=device, dtype=torch.float32) |
| grid_w = torch.arange(grid_size, device=device, dtype=torch.float32) |
| grid = torch.meshgrid(grid_w, grid_h, indexing="xy") |
| grid = torch.stack(grid, dim=0).reshape(2, 1, grid_size, grid_size) |
| emb_h = get_1d_sincos_pos_embed(embed_dim // 2, grid[0]) |
| emb_w = get_1d_sincos_pos_embed(embed_dim // 2, grid[1]) |
| return torch.cat([emb_h, emb_w], dim=1).to(dtype=dtype) |
|
|
|
|
| def get_1d_sincos_pos_embed(embed_dim, pos): |
| omega = torch.arange(embed_dim // 2, device=pos.device, dtype=torch.float32) |
| omega = 1.0 / (10000 ** (omega / (embed_dim / 2.0))) |
| out = torch.einsum("m,d->md", pos.reshape(-1), omega) |
| return torch.cat([out.sin(), out.cos()], dim=1) |
|
|
|
|
| @dataclass |
| class MMJiTConfig: |
| image_size: int = 512 |
| patch_size: int = 16 |
| in_channels: int = 3 |
| txt_input_size: int = 1024 |
| hidden_size: int = 768 |
| txt_hidden_size: int = 768 |
| cond_vec_size: int = 768 |
| depth_double: int = 17 |
| txt_preamble_depth: int = 2 |
| num_heads: int = 12 |
| head_dim: int = 64 |
| mlp_ratio: float = 2.6667 |
| pca_channels: int = 128 |
| prompt_length: int = 256 |
| n_T: int = 100 |
| prediction: str = "x" |
| sampler: str = "euler" |
| cfg_channels: int = 3 |
| cfg_interval: tuple = (0.0, 1.0) |
| llm: str = "google/flan-t5-large" |
|
|
|
|
| class MMJiT(nn.Module): |
| def __init__(self, cfg: MMJiTConfig): |
| super().__init__() |
| self.cfg = cfg |
| self.latent_img_size = cfg.image_size // cfg.patch_size |
| self.img_embedder = BottleneckPatchEmbed( |
| cfg.image_size, cfg.patch_size, cfg.in_channels, cfg.pca_channels, cfg.hidden_size |
| ) |
| self.txt_embedder = nn.Linear(cfg.txt_input_size, cfg.txt_hidden_size, bias=False) |
| self.mask_token = nn.Parameter(torch.zeros(1, 1, cfg.txt_input_size)) |
| self.t_embedder = TimestepEmbedder(cfg.cond_vec_size) |
| self.pooled_embedder = nn.Linear(cfg.txt_input_size, cfg.cond_vec_size, bias=False) |
| self.txt_preamble_blocks = nn.ModuleList( |
| [PlainTextTransformerBlock(cfg.txt_hidden_size, cfg.num_heads, cfg.head_dim, cfg.mlp_ratio) for _ in range(cfg.txt_preamble_depth)] |
| ) |
| self.double_blocks = nn.ModuleList( |
| [ |
| DoubleStreamDiTBlock( |
| cfg.hidden_size, cfg.txt_hidden_size, cfg.num_heads, cfg.head_dim, cfg.mlp_ratio |
| ) |
| for _ in range(cfg.depth_double) |
| ] |
| ) |
| self.final_layer = FinalLayer(cfg.hidden_size, cfg.patch_size, cfg.in_channels) |
|
|
| def unpatchify(self, x): |
| b = x.shape[0] |
| p = self.cfg.patch_size |
| c = self.cfg.in_channels |
| h = w = int(math.sqrt(x.shape[1])) |
| x = x.reshape(b, h, w, p, p, c) |
| x = torch.einsum("nhwpqc->nchpwq", x) |
| return x.reshape(b, c, h * p, w * p) |
|
|
| def forward(self, img, t, context, attn_mask): |
| if img.ndim == 4 and img.shape[1] != self.cfg.in_channels: |
| img = img.permute(0, 3, 1, 2) |
| attn_mask = attn_mask.to(device=context.device) |
| context = torch.where(attn_mask[:, :, None] > 0.5, context, self.mask_token.to(dtype=context.dtype)) |
| x = self.img_embedder(img) |
| pos = get_2d_sincos_pos_embed(self.cfg.hidden_size, self.latent_img_size, x.device, x.dtype) |
| x = x + pos[None] |
| t_vec = self.t_embedder(t) |
| txt = self.txt_embedder(context.to(dtype=self.txt_embedder.weight.dtype)) |
| pooled_text = context.mean(dim=1) |
| vec = t_vec + self.pooled_embedder(pooled_text.to(dtype=self.pooled_embedder.weight.dtype)) |
| for block in self.txt_preamble_blocks: |
| txt = block(txt) |
| for block in self.double_blocks: |
| x, txt = block(x, txt, vec) |
| combined = torch.cat([txt, x], dim=1) |
| out = self.final_layer(combined, vec) |
| img_out = out[:, txt.shape[1] :, :] |
| return self.unpatchify(img_out) |
|
|
|
|
| class DiffusionModel(nn.Module): |
| def __init__(self, cfg: Optional[MMJiTConfig] = None): |
| super().__init__() |
| self.cfg = cfg or MMJiTConfig() |
| self.net = MMJiT(self.cfg) |
|
|
| def real_t_to_embed_t(self, t): |
| return t |
|
|
| def pred_velocity(self, x, t, text, mask): |
| x0 = self.net(x, self.real_t_to_embed_t(t), text, mask) |
| return (x0 - x) / torch.clamp(1 - t[:, None, None, None], min=0.001) |
|
|
| def cfg_velocity(self, x, t, text, mask, cfg_scale: float): |
| b = x.shape[0] |
| xx = torch.cat([x, x], dim=0) |
| tt = torch.cat([t, t], dim=0) |
| yy = torch.cat([text, text], dim=0) |
| mm = torch.cat([mask, torch.zeros_like(mask)], dim=0) |
| out = self.pred_velocity(xx, tt, yy, mm) |
| cond, uncond = out[:b], out[b:] |
| use_cfg = ((t >= self.cfg.cfg_interval[0]) & (t <= self.cfg.cfg_interval[1])).to(out.dtype) |
| scale = torch.where(use_cfg[:, None, None, None] > 0, torch.tensor(cfg_scale, device=x.device, dtype=out.dtype), torch.tensor(1.0, device=x.device, dtype=out.dtype)) |
| return uncond + (cond - uncond) * scale |
|
|
| @torch.no_grad() |
| def sample(self, text, mask, cfg_scale=6.0, generator=None, progress=False): |
| b = text.shape[0] |
| device = text.device |
| dtype = next(self.parameters()).dtype |
| x = torch.randn( |
| b, self.cfg.in_channels, self.cfg.image_size, self.cfg.image_size, |
| generator=generator, device=device, dtype=dtype, |
| ) * 2 |
| timesteps = torch.linspace(0.0, 1.0, self.cfg.n_T + 1, device=device, dtype=dtype) |
| iterator = range(self.cfg.n_T) |
| if progress: |
| from tqdm.auto import tqdm |
| iterator = tqdm(iterator) |
| for i in iterator: |
| t_cur = timesteps[i].expand(b) |
| t_next = timesteps[i + 1].expand(b) |
| v = self.cfg_velocity(x, t_cur, text.to(dtype), mask.to(dtype), cfg_scale) |
| x = x + (t_next - t_cur)[:, None, None, None] * v |
| return x |
|
|
