Delete models/dit.py

models/dit.py

@@ -1,617 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# References:
-# GLIDE: https://github.com/openai/glide-text2im
-# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
-# --------------------------------------------------------
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-
-from einops import rearrange, repeat
-from timm.models.vision_transformer import Mlp, PatchEmbed
-
-
-
-import os
-import sys
-# sys.path.append(os.getcwd())
-sys.path.append(os.path.split(sys.path[0])[0])
-# 代码解释
-# sys.path[0]                   : 得到C:\Users\maxu\Desktop\blog_test\pakage2
-# os.path.split(sys.path[0])    : 得到['C:\Users\maxu\Desktop\blog_test',pakage2']
-# mmcls 里面跨包引用是因为安装了mmcls
-    
-
-# for i in sys.path:
-#     print(i)
-
-# the xformers lib allows less memory, faster training and inference
-try:
-    import xformers
-    import xformers.ops
-except:
-    XFORMERS_IS_AVAILBLE = False
-
-# from timm.models.layers.helpers import to_2tuple
-# from timm.models.layers.trace_utils import _assert
-
-def modulate(x, shift, scale):
-    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-
-#################################################################################
-#               Attention Layers from TIMM                                      #
-#################################################################################
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., use_lora=False, attention_mode='math'):
-        super().__init__()
-        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim ** -0.5
-        self.attention_mode = attention_mode
-        self.use_lora = use_lora
-
-        if self.use_lora:
-            self.qkv = lora.MergedLinear(dim, dim * 3, r=500, enable_lora=[True, False, True])
-        else:
-            self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous()
-        q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
-        
-        if self.attention_mode == 'xformers': # cause loss nan while using with amp
-            x = xformers.ops.memory_efficient_attention(q, k, v).reshape(B, N, C)
-
-        elif self.attention_mode == 'flash':
-            # cause loss nan while using with amp
-            # Optionally use the context manager to ensure one of the fused kerenels is run
-            with torch.backends.cuda.sdp_kernel(enable_math=False):
-                x = torch.nn.functional.scaled_dot_product_attention(q, k, v).reshape(B, N, C) # require pytorch 2.0
-
-        elif self.attention_mode == 'math':
-            attn = (q @ k.transpose(-2, -1)) * self.scale
-            attn = attn.softmax(dim=-1)
-            attn = self.attn_drop(attn)
-            x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-
-        else:
-            raise NotImplemented
-
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-#################################################################################
-#               Embedding Layers for Timesteps and Class Labels                 #
-#################################################################################
-
-class TimestepEmbedder(nn.Module):
-    """
-    Embeds scalar timesteps into vector representations.
-    """
-    def __init__(self, hidden_size, frequency_embedding_size=256):
-        super().__init__()
-        self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
-            nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size, bias=True),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-    @staticmethod
-    def timestep_embedding(t, dim, max_period=10000):
-        """
-        Create sinusoidal timestep embeddings.
-        :param t: a 1-D Tensor of N indices, one per batch element.
-                          These may be fractional.
-        :param dim: the dimension of the output.
-        :param max_period: controls the minimum frequency of the embeddings.
-        :return: an (N, D) Tensor of positional embeddings.
-        """
-        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=t.device)
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-        return embedding
-
-    def forward(self, t):
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
-        t_emb = self.mlp(t_freq)
-        return t_emb
-
-
-class LabelEmbedder(nn.Module):
-    """
-    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
-    """
-    def __init__(self, num_classes, hidden_size, dropout_prob):
-        super().__init__()
-        use_cfg_embedding = dropout_prob > 0
-        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
-        self.num_classes = num_classes
-        self.dropout_prob = dropout_prob
-
-    def token_drop(self, labels, force_drop_ids=None):
-        """
-        Drops labels to enable classifier-free guidance.
-        """
-        if force_drop_ids is None:
-            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
-            print(drop_ids)
-        else:
-            drop_ids = force_drop_ids == 1
-        labels = torch.where(drop_ids, self.num_classes, labels)
-        print('******labels******', labels)
-        return labels
-
-    def forward(self, labels, train, force_drop_ids=None):
-        use_dropout = self.dropout_prob > 0
-        if (train and use_dropout) or (force_drop_ids is not None):
-            labels = self.token_drop(labels, force_drop_ids)
-        embeddings = self.embedding_table(labels)
-        return embeddings
-
-
-#################################################################################
-#                                 Core DiT Model                                #
-#################################################################################
-
-class DiTBlock(nn.Module):
-    """
-    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
-    """
-    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
-        super().__init__()
-        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
-        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
-        )
-
-    def forward(self, x, c):
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
-        x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
-        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
-        return x
-
-
-class FinalLayer(nn.Module):
-    """
-    The final layer of DiT.
-    """
-    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)
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
-        )
-
-    def forward(self, x, c):
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
-        x = modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
-        return x
-
-
-class DiT(nn.Module):
-    """
-    Diffusion model with a Transformer backbone.
-    """
-    def __init__(
-        self,
-        input_size=32,
-        patch_size=2,
-        in_channels=4,
-        hidden_size=1152,
-        depth=28,
-        num_heads=16,
-        mlp_ratio=4.0,
-        num_frames=16,
-        class_dropout_prob=0.1,
-        num_classes=1000,
-        learn_sigma=True,
-        class_guided=False,
-        use_lora=False,
-        attention_mode='math',
-    ):
-        super().__init__()
-        self.learn_sigma = learn_sigma
-        self.in_channels = in_channels
-        self.out_channels = in_channels * 2 if learn_sigma else in_channels
-        self.patch_size = patch_size
-        self.num_heads = num_heads
-        self.class_guided = class_guided
-        self.num_frames = num_frames
-
-        self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
-        self.t_embedder = TimestepEmbedder(hidden_size)
-
-        if self.class_guided:
-            self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
-
-        num_patches = self.x_embedder.num_patches
-        # Will use fixed sin-cos embedding:
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
-        self.time_embed = nn.Parameter(torch.zeros(1, num_frames, hidden_size), requires_grad=False)
-
-        if use_lora:
-            self.blocks = nn.ModuleList([
-                DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, attention_mode=attention_mode, use_lora=False if num % 2 ==0 else True) for num in range(depth)
-            ])
-        else:
-            self.blocks = nn.ModuleList([
-                DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, attention_mode=attention_mode) for _ in range(depth)
-            ])
-
-        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
-        self.initialize_weights()
-
-    def initialize_weights(self):
-        # Initialize transformer layers:
-        def _basic_init(module):
-            if isinstance(module, nn.Linear):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-        self.apply(_basic_init)
-
-        # Initialize (and freeze) pos_embed by sin-cos embedding:
-        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
-        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
-
-        time_embed = get_1d_sincos_time_embed(self.time_embed.shape[-1], self.time_embed.shape[-2])
-        self.time_embed.data.copy_(torch.from_numpy(time_embed).float().unsqueeze(0))
-
-        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
-        w = self.x_embedder.proj.weight.data
-        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
-        nn.init.constant_(self.x_embedder.proj.bias, 0)
-
-        if self.class_guided:
-            # Initialize label embedding table:
-            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
-
-        # Initialize timestep embedding MLP:
-        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
-        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
-
-        # Zero-out adaLN modulation layers in DiT blocks:
-        for block in self.blocks:
-            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
-            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
-
-        # Zero-out output layers:
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
-        nn.init.constant_(self.final_layer.linear.weight, 0)
-        nn.init.constant_(self.final_layer.linear.bias, 0)
-
-    def unpatchify(self, x):
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        c = self.out_channels
-        p = self.x_embedder.patch_size[0]
-        h = w = int(x.shape[1] ** 0.5)
-        assert h * w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-        x = torch.einsum('nhwpqc->nchpwq', x)
-        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
-        return imgs
-
-    # @torch.cuda.amp.autocast()
-    # @torch.compile
-    def forward(self, x, t, y=None):
-        """
-        Forward pass of DiT.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N,) tensor of class labels
-        """
-        # print('label: {}'.format(y))
-        batches, frames, channels, high, weight = x.shape # for example, 3, 16, 3, 32, 32
-        # 这里rearrange后每隔f是同一个视频
-        x = rearrange(x, 'b f c h w -> (b f) c h w')
-        x = self.x_embedder(x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(t)                   # (N, D)
-        # timestep_spatial的repeat需要保证每f帧为同一个timesteps
-        timestep_spatial = repeat(t, 'n d -> (n c) d', c=self.time_embed.shape[1]) # 48, 1152; c=num_frames
-        timestep_time = repeat(t, 'n d -> (n c) d', c=self.pos_embed.shape[1]) # 768, 1152; c=num tokens
-
-        if self.class_guided:
-            y = self.y_embedder(y, self.training)
-            y_spatial = repeat(y, 'n d -> (n c) d', c=self.time_embed.shape[1]) # 48, 1152; c=num_frames
-            y_time = repeat(y, 'n d -> (n c) d', c=self.pos_embed.shape[1]) # 768, 1152; c=num tokens
-
-        # if self.class_guided:
-        #     y = self.y_embedder(y, self.training)    # (N, D)
-        #     c = timestep_spatial + y
-        # else:
-        #     c = timestep_spatial
-        
-        # for block in self.blocks:
-        #     x = block(x, c)                      # (N, T, D)
-
-        for i in range(0, len(self.blocks), 2):
-            # print('The {}-th run'.format(i))
-            spatial_block, time_block = self.blocks[i:i+2]
-            # print(spatial_block)
-            # print(time_block)
-            # print(x.shape)
-
-            if self.class_guided:
-                c = timestep_spatial + y_spatial
-            else:
-                c = timestep_spatial
-            x  = spatial_block(x, c)
-            # print(c.shape)
-
-            x = rearrange(x, '(b f) t d -> (b t) f d', b=batches) # t 代表单帧token数; 768, 16, 1152
-            # Add Time Embedding
-            if i == 0:
-                x = x + self.time_embed # 768, 16, 1152
-
-            if self.class_guided:
-                c = timestep_time + y_time
-            else:
-                # timestep_time = repeat(t, 'n d -> (n c) d', c=x.shape[0] // batches) # 768, 1152
-                # print(timestep_time.shape)
-                c = timestep_time
-
-            x = time_block(x, c)
-            # print(x.shape)
-            x = rearrange(x, '(b t) f d -> (b f) t d', b=batches)
-
-        # x = rearrange(x, '(b t) f d -> (b f) t d', b=batches)
-        if self.class_guided:
-            c = timestep_spatial + y_spatial
-        else:
-            c = timestep_spatial
-        x = self.final_layer(x, c)                # (N, T, patch_size ** 2 * out_channels)
-        x = self.unpatchify(x)                   # (N, out_channels, H, W)
-        x = rearrange(x, '(b f) c h w -> b f c h w', b=batches)
-        # print(x.shape)
-        return x
-    
-    def forward_motion(self, motions, t, base_frame, y=None):
-        """
-        Forward pass of DiT.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N,) tensor of class labels
-        """
-        # print('label: {}'.format(y))
-        batches, frames, channels, high, weight = motions.shape # for example, 3, 16, 3, 32, 32
-        # 这里rearrange后每隔f是同一个视频
-        motions = rearrange(motions, 'b f c h w -> (b f) c h w')
-        motions = self.x_embedder(motions) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(t)                   # (N, D)
-        # timestep_spatial的repeat需要保证每f帧为同一个timesteps
-        timestep_spatial = repeat(t, 'n d -> (n c) d', c=self.time_embed.shape[1]) # 48, 1152; c=num_frames
-        timestep_time = repeat(t, 'n d -> (n c) d', c=self.pos_embed.shape[1]) # 768, 1152; c=num tokens
-
-        if self.class_guided:
-            y = self.y_embedder(y, self.training)
-            y_spatial = repeat(y, 'n d -> (n c) d', c=self.time_embed.shape[1]) # 48, 1152; c=num_frames
-            y_time = repeat(y, 'n d -> (n c) d', c=self.pos_embed.shape[1]) # 768, 1152; c=num tokens
-
-        # if self.class_guided:
-        #     y = self.y_embedder(y, self.training)    # (N, D)
-        #     c = timestep_spatial + y
-        # else:
-        #     c = timestep_spatial
-        
-        # for block in self.blocks:
-        #     x = block(x, c)                      # (N, T, D)
-
-        for i in range(0, len(self.blocks), 2):
-            # print('The {}-th run'.format(i))
-            spatial_block, time_block = self.blocks[i:i+2]
-            # print(spatial_block)
-            # print(time_block)
-            # print(x.shape)
-
-            if self.class_guided:
-                c = timestep_spatial + y_spatial
-            else:
-                c = timestep_spatial
-            x  = spatial_block(x, c)
-            # print(c.shape)
-
-            x = rearrange(x, '(b f) t d -> (b t) f d', b=batches) # t 代表单帧token数; 768, 16, 1152
-            # Add Time Embedding
-            if i == 0:
-                x = x + self.time_embed # 768, 16, 1152
-
-            if self.class_guided:
-                c = timestep_time + y_time
-            else:
-                # timestep_time = repeat(t, 'n d -> (n c) d', c=x.shape[0] // batches) # 768, 1152
-                # print(timestep_time.shape)
-                c = timestep_time
-
-            x = time_block(x, c)
-            # print(x.shape)
-            x = rearrange(x, '(b t) f d -> (b f) t d', b=batches)
-
-        # x = rearrange(x, '(b t) f d -> (b f) t d', b=batches)
-        if self.class_guided:
-            c = timestep_spatial + y_spatial
-        else:
-            c = timestep_spatial
-        x = self.final_layer(x, c)                # (N, T, patch_size ** 2 * out_channels)
-        x = self.unpatchify(x)                   # (N, out_channels, H, W)
-        x = rearrange(x, '(b f) c h w -> b f c h w', b=batches)
-        # print(x.shape)
-        return x
-
-
-    def forward_with_cfg(self, x, t, y, cfg_scale):
-        """
-        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
-        """
-        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
-        half = x[: len(x) // 2]
-        combined = torch.cat([half, half], dim=0)
-        model_out = self.forward(combined, t, y)
-        # For exact reproducibility reasons, we apply classifier-free guidance on only
-        # three channels by default. The standard approach to cfg applies it to all channels.
-        # This can be done by uncommenting the following line and commenting-out the line following that.
-        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
-        eps, rest = model_out[:, :3], model_out[:, 3:]
-        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
-        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
-        eps = torch.cat([half_eps, half_eps], dim=0)
-        return torch.cat([eps, rest], dim=1)
-
-
-#################################################################################
-#                   Sine/Cosine Positional Embedding Functions                  #
-#################################################################################
-# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
-
-def get_1d_sincos_time_embed(embed_dim, length):
-    pos = torch.arange(0, length).unsqueeze(1)
-    return get_1d_sincos_pos_embed_from_grid(embed_dim, pos)
-
-def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
-    """
-    grid_size: int of the grid height and width
-    return:
-    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
-    """
-    grid_h = np.arange(grid_size, dtype=np.float32)
-    grid_w = np.arange(grid_size, dtype=np.float32)
-    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
-    grid = np.stack(grid, axis=0)
-
-    grid = grid.reshape([2, 1, grid_size, grid_size])
-    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
-    if cls_token and extra_tokens > 0:
-        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
-    return pos_embed
-
-
-def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
-    assert embed_dim % 2 == 0
-
-    # use half of dimensions to encode grid_h
-    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
-    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
-
-    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
-    return emb
-
-
-def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
-    """
-    embed_dim: output dimension for each position
-    pos: a list of positions to be encoded: size (M,)
-    out: (M, D)
-    """
-    assert embed_dim % 2 == 0
-    omega = np.arange(embed_dim // 2, dtype=np.float64)
-    omega /= embed_dim / 2.
-    omega = 1. / 10000**omega  # (D/2,)
-
-    pos = pos.reshape(-1)  # (M,)
-    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
-
-    emb_sin = np.sin(out) # (M, D/2)
-    emb_cos = np.cos(out) # (M, D/2)
-
-    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
-    return emb
-
-
-#################################################################################
-#                                   DiT Configs                                  #
-#################################################################################
-
-def DiT_XL_2(**kwargs):
-    return DiT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)
-
-def DiT_XL_4(**kwargs):
-    return DiT(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs)
-
-def DiT_XL_8(**kwargs):
-    return DiT(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs)
-
-def DiT_L_2(**kwargs):
-    return DiT(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs)
-
-def DiT_L_4(**kwargs):
-    return DiT(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs)
-
-def DiT_L_8(**kwargs):
-    return DiT(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs)
-
-def DiT_B_2(**kwargs):
-    return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
-
-def DiT_B_4(**kwargs):
-    return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
-
-def DiT_B_8(**kwargs):
-    return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
-
-def DiT_S_2(**kwargs):
-    return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
-
-def DiT_S_4(**kwargs):
-    return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
-
-def DiT_S_8(**kwargs):
-    return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
-
-
-DiT_models = {
-    'DiT-XL/2': DiT_XL_2,  'DiT-XL/4': DiT_XL_4,  'DiT-XL/8': DiT_XL_8,
-    'DiT-L/2':  DiT_L_2,   'DiT-L/4':  DiT_L_4,   'DiT-L/8':  DiT_L_8,
-    'DiT-B/2':  DiT_B_2,   'DiT-B/4':  DiT_B_4,   'DiT-B/8':  DiT_B_8,
-    'DiT-S/2':  DiT_S_2,   'DiT-S/4':  DiT_S_4,   'DiT-S/8':  DiT_S_8,
-}
-
-if __name__ == '__main__':
-
-    import torch
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    img = torch.randn(3, 16, 4, 32, 32).to(device)
-    t = torch.tensor([1, 2, 3]).to(device)
-    y = torch.tensor([1, 2, 3]).to(device)
-    network = DiT_XL_2().to(device)
-    y_embeder = LabelEmbedder(num_classes=100, hidden_size=768, dropout_prob=0.5).to(device)
-    # lora.mark_only_lora_as_trainable(network)
-    out = y_embeder(y, True)
-    # out = network(img, t, y)
-    print(out.shape)