from .sd_unet import SDUNet, Attention, GEGLU import torch from einops import rearrange, repeat class TemporalTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, attention_head_dim, max_position_embeddings=32): super().__init__() # 1. Self-Attn self.pe1 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim)) self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) # 2. Cross-Attn self.pe2 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim)) self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.attn2 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True) # 3. Feed-forward self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.act_fn = GEGLU(dim, dim * 4) self.ff = torch.nn.Linear(dim * 4, dim) def forward(self, hidden_states, batch_size=1): # 1. Self-Attention norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size) attn_output = self.attn1(norm_hidden_states + self.pe1[:, :norm_hidden_states.shape[1]]) attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size) hidden_states = attn_output + hidden_states # 2. Cross-Attention norm_hidden_states = self.norm2(hidden_states) norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size) attn_output = self.attn2(norm_hidden_states + self.pe2[:, :norm_hidden_states.shape[1]]) attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size) hidden_states = attn_output + hidden_states # 3. Feed-forward norm_hidden_states = self.norm3(hidden_states) ff_output = self.act_fn(norm_hidden_states) ff_output = self.ff(ff_output) hidden_states = ff_output + hidden_states return hidden_states class TemporalBlock(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): super().__init__() inner_dim = num_attention_heads * attention_head_dim self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) self.proj_in = torch.nn.Linear(in_channels, inner_dim) self.transformer_blocks = torch.nn.ModuleList([ TemporalTransformerBlock( inner_dim, num_attention_heads, attention_head_dim ) for d in range(num_layers) ]) self.proj_out = torch.nn.Linear(inner_dim, in_channels) def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_size=1): batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) hidden_states = self.proj_in(hidden_states) for block in self.transformer_blocks: hidden_states = block( hidden_states, batch_size=batch_size ) hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() hidden_states = hidden_states + residual return hidden_states, time_emb, text_emb, res_stack class SDMotionModel(torch.nn.Module): def __init__(self): super().__init__() self.motion_modules = torch.nn.ModuleList([ TemporalBlock(8, 40, 320, eps=1e-6), TemporalBlock(8, 40, 320, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 160, 1280, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 80, 640, eps=1e-6), TemporalBlock(8, 40, 320, eps=1e-6), TemporalBlock(8, 40, 320, eps=1e-6), TemporalBlock(8, 40, 320, eps=1e-6), ]) self.call_block_id = { 1: 0, 4: 1, 9: 2, 12: 3, 17: 4, 20: 5, 24: 6, 26: 7, 29: 8, 32: 9, 34: 10, 36: 11, 40: 12, 43: 13, 46: 14, 50: 15, 53: 16, 56: 17, 60: 18, 63: 19, 66: 20 } def forward(self): pass @staticmethod def state_dict_converter(): return SDMotionModelStateDictConverter() class SDMotionModelStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "norm": "norm", "proj_in": "proj_in", "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q", "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k", "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v", "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out", "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1", "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q", "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k", "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v", "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out", "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2", "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1", "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2", "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj", "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff", "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3", "proj_out": "proj_out", } name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")]) name_list += sorted([i for i in state_dict if i.startswith("mid_block.")]) name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")]) state_dict_ = {} last_prefix, module_id = "", -1 for name in name_list: names = name.split(".") prefix_index = names.index("temporal_transformer") + 1 prefix = ".".join(names[:prefix_index]) if prefix != last_prefix: last_prefix = prefix module_id += 1 middle_name = ".".join(names[prefix_index:-1]) suffix = names[-1] if "pos_encoder" in names: rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]]) else: rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix]) state_dict_[rename] = state_dict[name] return state_dict_ def from_civitai(self, state_dict): return self.from_diffusers(state_dict)