import torch, math from .attention import Attention from .tiler import TileWorker class Timesteps(torch.nn.Module): def __init__(self, num_channels): super().__init__() self.num_channels = num_channels def forward(self, timesteps): half_dim = self.num_channels // 2 exponent = -math.log(10000) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) / half_dim timesteps = timesteps.unsqueeze(-1) emb = timesteps.float() * torch.exp(exponent) emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1) return emb class GEGLU(torch.nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = torch.nn.Linear(dim_in, dim_out * 2) def forward(self, hidden_states): hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1) return hidden_states * torch.nn.functional.gelu(gate) class BasicTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, attention_head_dim, cross_attention_dim): super().__init__() # 1. Self-Attn 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.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True) self.attn2 = Attention(q_dim=dim, kv_dim=cross_attention_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, encoder_hidden_states, ipadapter_kwargs=None): # 1. Self-Attention norm_hidden_states = self.norm1(hidden_states) attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) hidden_states = attn_output + hidden_states # 2. Cross-Attention norm_hidden_states = self.norm2(hidden_states) attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states, ipadapter_kwargs=ipadapter_kwargs) 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 DownSampler(torch.nn.Module): def __init__(self, channels, padding=1, extra_padding=False): super().__init__() self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding) self.extra_padding = extra_padding def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): if self.extra_padding: hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0) hidden_states = self.conv(hidden_states) return hidden_states, time_emb, text_emb, res_stack class UpSampler(torch.nn.Module): def __init__(self, channels): super().__init__() self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest") hidden_states = self.conv(hidden_states) return hidden_states, time_emb, text_emb, res_stack class ResnetBlock(torch.nn.Module): def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5): super().__init__() self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels is not None: self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) self.nonlinearity = torch.nn.SiLU() self.conv_shortcut = None if in_channels != out_channels: self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): x = hidden_states x = self.norm1(x) x = self.nonlinearity(x) x = self.conv1(x) if time_emb is not None: emb = self.nonlinearity(time_emb) emb = self.time_emb_proj(emb)[:, :, None, None] x = x + emb x = self.norm2(x) x = self.nonlinearity(x) x = self.conv2(x) if self.conv_shortcut is not None: hidden_states = self.conv_shortcut(hidden_states) hidden_states = hidden_states + x return hidden_states, time_emb, text_emb, res_stack class AttentionBlock(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, cross_attention_dim=None, norm_num_groups=32, eps=1e-5, need_proj_out=True): 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([ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, cross_attention_dim=cross_attention_dim ) for d in range(num_layers) ]) self.need_proj_out = need_proj_out if need_proj_out: self.proj_out = torch.nn.Linear(inner_dim, in_channels) def forward( self, hidden_states, time_emb, text_emb, res_stack, cross_frame_attention=False, tiled=False, tile_size=64, tile_stride=32, ipadapter_kwargs_list={}, **kwargs ): 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) if cross_frame_attention: hidden_states = hidden_states.reshape(1, batch * height * width, inner_dim) encoder_hidden_states = text_emb.mean(dim=0, keepdim=True) else: encoder_hidden_states = text_emb if encoder_hidden_states.shape[0] != hidden_states.shape[0]: encoder_hidden_states = encoder_hidden_states.repeat(hidden_states.shape[0], 1, 1) if tiled: tile_size = min(tile_size, min(height, width)) hidden_states = hidden_states.permute(0, 2, 1).reshape(batch, inner_dim, height, width) def block_tile_forward(x): b, c, h, w = x.shape x = x.permute(0, 2, 3, 1).reshape(b, h*w, c) x = block(x, encoder_hidden_states) x = x.reshape(b, h, w, c).permute(0, 3, 1, 2) return x for block in self.transformer_blocks: hidden_states = TileWorker().tiled_forward( block_tile_forward, hidden_states, tile_size, tile_stride, tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) else: for block_id, block in enumerate(self.transformer_blocks): hidden_states = block( hidden_states, encoder_hidden_states=encoder_hidden_states, ipadapter_kwargs=ipadapter_kwargs_list.get(block_id, None) ) if cross_frame_attention: hidden_states = hidden_states.reshape(batch, height * width, inner_dim) if self.need_proj_out: 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 else: hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() return hidden_states, time_emb, text_emb, res_stack class PushBlock(torch.nn.Module): def __init__(self): super().__init__() def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): res_stack.append(hidden_states) return hidden_states, time_emb, text_emb, res_stack class PopBlock(torch.nn.Module): def __init__(self): super().__init__() def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): res_hidden_states = res_stack.pop() hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) return hidden_states, time_emb, text_emb, res_stack class SDUNet(torch.nn.Module): def __init__(self): super().__init__() self.time_proj = Timesteps(320) self.time_embedding = torch.nn.Sequential( torch.nn.Linear(320, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) self.blocks = torch.nn.ModuleList([ # CrossAttnDownBlock2D ResnetBlock(320, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), PushBlock(), ResnetBlock(320, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), PushBlock(), DownSampler(320), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(320, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), PushBlock(), ResnetBlock(640, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), PushBlock(), DownSampler(640), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(640, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), PushBlock(), ResnetBlock(1280, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), PushBlock(), DownSampler(1280), PushBlock(), # DownBlock2D ResnetBlock(1280, 1280, 1280), PushBlock(), ResnetBlock(1280, 1280, 1280), PushBlock(), # UNetMidBlock2DCrossAttn ResnetBlock(1280, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), ResnetBlock(1280, 1280, 1280), # UpBlock2D PopBlock(), ResnetBlock(2560, 1280, 1280), PopBlock(), ResnetBlock(2560, 1280, 1280), PopBlock(), ResnetBlock(2560, 1280, 1280), UpSampler(1280), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(2560, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(2560, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(1920, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6), UpSampler(1280), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(1920, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(1280, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(960, 640, 1280), AttentionBlock(8, 80, 640, 1, 768, eps=1e-6), UpSampler(640), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(960, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(640, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), PopBlock(), ResnetBlock(640, 320, 1280), AttentionBlock(8, 40, 320, 1, 768, eps=1e-6), ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) def forward(self, sample, timestep, encoder_hidden_states, **kwargs): # 1. time time_emb = self.time_proj(timestep[None]).to(sample.dtype) time_emb = self.time_embedding(time_emb) # 2. pre-process hidden_states = self.conv_in(sample) text_emb = encoder_hidden_states res_stack = [hidden_states] # 3. blocks for i, block in enumerate(self.blocks): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 4. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states def state_dict_converter(self): return SDUNetStateDictConverter() class SDUNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' ] # Rename each parameter name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: pass elif names[0] in ["time_embedding", "add_embedding"]: if names[0] == "add_embedding": names[0] = "add_time_embedding" names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: if names[0] == "mid_block": names.insert(1, "0") block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] block_type_with_id = ".".join(names[:4]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:4]) names = ["blocks", str(block_id[block_type])] + names[4:] if "ff" in names: ff_index = names.index("ff") component = ".".join(names[ff_index:ff_index+3]) component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] names = names[:ff_index] + [component] + names[ff_index+3:] if "to_out" in names: names.pop(names.index("to_out") + 1) else: raise ValueError(f"Unknown parameters: {name}") rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", "model.diffusion_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias", "model.diffusion_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight", "model.diffusion_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias", "model.diffusion_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight", "model.diffusion_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias", "model.diffusion_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight", "model.diffusion_model.input_blocks.10.0.emb_layers.1.bias": "blocks.24.time_emb_proj.bias", "model.diffusion_model.input_blocks.10.0.emb_layers.1.weight": "blocks.24.time_emb_proj.weight", "model.diffusion_model.input_blocks.10.0.in_layers.0.bias": "blocks.24.norm1.bias", "model.diffusion_model.input_blocks.10.0.in_layers.0.weight": "blocks.24.norm1.weight", "model.diffusion_model.input_blocks.10.0.in_layers.2.bias": "blocks.24.conv1.bias", "model.diffusion_model.input_blocks.10.0.in_layers.2.weight": "blocks.24.conv1.weight", "model.diffusion_model.input_blocks.10.0.out_layers.0.bias": "blocks.24.norm2.bias", "model.diffusion_model.input_blocks.10.0.out_layers.0.weight": "blocks.24.norm2.weight", "model.diffusion_model.input_blocks.10.0.out_layers.3.bias": "blocks.24.conv2.bias", "model.diffusion_model.input_blocks.10.0.out_layers.3.weight": "blocks.24.conv2.weight", "model.diffusion_model.input_blocks.11.0.emb_layers.1.bias": "blocks.26.time_emb_proj.bias", "model.diffusion_model.input_blocks.11.0.emb_layers.1.weight": "blocks.26.time_emb_proj.weight", "model.diffusion_model.input_blocks.11.0.in_layers.0.bias": "blocks.26.norm1.bias", "model.diffusion_model.input_blocks.11.0.in_layers.0.weight": "blocks.26.norm1.weight", "model.diffusion_model.input_blocks.11.0.in_layers.2.bias": "blocks.26.conv1.bias", "model.diffusion_model.input_blocks.11.0.in_layers.2.weight": "blocks.26.conv1.weight", "model.diffusion_model.input_blocks.11.0.out_layers.0.bias": "blocks.26.norm2.bias", "model.diffusion_model.input_blocks.11.0.out_layers.0.weight": "blocks.26.norm2.weight", "model.diffusion_model.input_blocks.11.0.out_layers.3.bias": "blocks.26.conv2.bias", 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