diff --git "a/diffsynth/models/svd_unet.py" "b/diffsynth/models/svd_unet.py" new file mode 100644--- /dev/null +++ "b/diffsynth/models/svd_unet.py" @@ -0,0 +1,2003 @@ +import torch, math +from einops import rearrange, repeat +from .sd_unet import Timesteps, PushBlock, PopBlock, Attention, GEGLU, ResnetBlock, AttentionBlock, DownSampler, UpSampler + + +class TemporalResnetBlock(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.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0)) + 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.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0)) + self.nonlinearity = torch.nn.SiLU() + self.conv_shortcut = None + if in_channels != out_channels: + self.conv_shortcut = torch.nn.Conv3d(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 = rearrange(hidden_states, "f c h w -> 1 c f h w") + 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) + emb = repeat(emb, "b c -> b c f 1 1", f=hidden_states.shape[0]) + 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) + x = rearrange(x[0], "c f h w -> f c h w") + hidden_states = hidden_states + x + return hidden_states, time_emb, text_emb, res_stack + + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + flip_sin_to_cos: bool = False, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the + embeddings. :return: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange( + start=0, end=half_dim, dtype=torch.float32, device=timesteps.device + ) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + + # flip sine and cosine embeddings + if flip_sin_to_cos: + emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +class TemporalTimesteps(torch.nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + + def forward(self, timesteps): + t_emb = get_timestep_embedding( + timesteps, + self.num_channels, + flip_sin_to_cos=self.flip_sin_to_cos, + downscale_freq_shift=self.downscale_freq_shift, + ) + return t_emb + + +class TrainableTemporalTimesteps(torch.nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, num_frames: int): + super().__init__() + timesteps = PositionalID()(num_frames) + embeddings = get_timestep_embedding(timesteps, num_channels, flip_sin_to_cos, downscale_freq_shift) + self.embeddings = torch.nn.Parameter(embeddings) + + def forward(self, timesteps): + t_emb = self.embeddings[timesteps] + return t_emb + + +class PositionalID(torch.nn.Module): + def __init__(self, max_id=25, repeat_length=20): + super().__init__() + self.max_id = max_id + self.repeat_length = repeat_length + + def frame_id_to_position_id(self, frame_id): + if frame_id < self.max_id: + position_id = frame_id + else: + position_id = (frame_id - self.max_id) % (self.repeat_length * 2) + if position_id < self.repeat_length: + position_id = self.max_id - 2 - position_id + else: + position_id = self.max_id - 2 * self.repeat_length + position_id + return position_id + + def forward(self, num_frames, pivot_frame_id=0): + position_ids = [self.frame_id_to_position_id(abs(i-pivot_frame_id)) for i in range(num_frames)] + position_ids = torch.IntTensor(position_ids) + return position_ids + + +class TemporalAttentionBlock(torch.nn.Module): + + def __init__(self, num_attention_heads, attention_head_dim, in_channels, cross_attention_dim=None, add_positional_conv=None): + super().__init__() + + self.positional_embedding_proj = torch.nn.Sequential( + torch.nn.Linear(in_channels, in_channels * 4), + torch.nn.SiLU(), + torch.nn.Linear(in_channels * 4, in_channels) + ) + if add_positional_conv is not None: + self.positional_embedding = TrainableTemporalTimesteps(in_channels, True, 0, add_positional_conv) + self.positional_conv = torch.nn.Conv3d(in_channels, in_channels, kernel_size=3, padding=1, padding_mode="reflect") + else: + self.positional_embedding = TemporalTimesteps(in_channels, True, 0) + self.positional_conv = None + + self.norm_in = torch.nn.LayerNorm(in_channels) + self.act_fn_in = GEGLU(in_channels, in_channels * 4) + self.ff_in = torch.nn.Linear(in_channels * 4, in_channels) + + self.norm1 = torch.nn.LayerNorm(in_channels) + self.attn1 = Attention( + q_dim=in_channels, + num_heads=num_attention_heads, + head_dim=attention_head_dim, + bias_out=True + ) + + self.norm2 = torch.nn.LayerNorm(in_channels) + self.attn2 = Attention( + q_dim=in_channels, + kv_dim=cross_attention_dim, + num_heads=num_attention_heads, + head_dim=attention_head_dim, + bias_out=True + ) + + self.norm_out = torch.nn.LayerNorm(in_channels) + self.act_fn_out = GEGLU(in_channels, in_channels * 4) + self.ff_out = torch.nn.Linear(in_channels * 4, in_channels) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + + batch, inner_dim, height, width = hidden_states.shape + pos_emb = torch.arange(batch) + pos_emb = self.positional_embedding(pos_emb).to(dtype=hidden_states.dtype, device=hidden_states.device) + pos_emb = self.positional_embedding_proj(pos_emb) + + hidden_states = rearrange(hidden_states, "T C H W -> 1 C T H W") + rearrange(pos_emb, "T C -> 1 C T 1 1") + if self.positional_conv is not None: + hidden_states = self.positional_conv(hidden_states) + hidden_states = rearrange(hidden_states[0], "C T H W -> (H W) T C") + + residual = hidden_states + hidden_states = self.norm_in(hidden_states) + hidden_states = self.act_fn_in(hidden_states) + hidden_states = self.ff_in(hidden_states) + hidden_states = hidden_states + residual + + norm_hidden_states = self.norm1(hidden_states) + attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) + hidden_states = attn_output + hidden_states + + norm_hidden_states = self.norm2(hidden_states) + attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=text_emb.repeat(height * width, 1)) + hidden_states = attn_output + hidden_states + + residual = hidden_states + hidden_states = self.norm_out(hidden_states) + hidden_states = self.act_fn_out(hidden_states) + hidden_states = self.ff_out(hidden_states) + hidden_states = hidden_states + residual + + hidden_states = hidden_states.reshape(height, width, batch, inner_dim).permute(2, 3, 0, 1) + + return hidden_states, time_emb, text_emb, res_stack + + +class PopMixBlock(torch.nn.Module): + def __init__(self, in_channels=None): + super().__init__() + self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5])) + self.need_proj = in_channels is not None + if self.need_proj: + self.proj = torch.nn.Linear(in_channels, in_channels) + + def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): + res_hidden_states = res_stack.pop() + alpha = torch.sigmoid(self.mix_factor) + hidden_states = alpha * res_hidden_states + (1 - alpha) * hidden_states + if self.need_proj: + hidden_states = hidden_states.permute(0, 2, 3, 1) + hidden_states = self.proj(hidden_states) + hidden_states = hidden_states.permute(0, 3, 1, 2) + res_hidden_states = res_stack.pop() + hidden_states = hidden_states + res_hidden_states + return hidden_states, time_emb, text_emb, res_stack + + +class SVDUNet(torch.nn.Module): + def __init__(self, add_positional_conv=None): + 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.add_time_proj = Timesteps(256) + self.add_time_embedding = torch.nn.Sequential( + torch.nn.Linear(768, 1280), + torch.nn.SiLU(), + torch.nn.Linear(1280, 1280) + ) + self.conv_in = torch.nn.Conv2d(8, 320, kernel_size=3, padding=1) + + self.blocks = torch.nn.ModuleList([ + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PushBlock(), + ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), PushBlock(), + DownSampler(320), PushBlock(), + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(320, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PushBlock(), + ResnetBlock(640, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), PushBlock(), + DownSampler(640), PushBlock(), + # CrossAttnDownBlockSpatioTemporal + ResnetBlock(640, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(), + ResnetBlock(1280, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(), + DownSampler(1280), PushBlock(), + # DownBlockSpatioTemporal + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + # UNetMidBlockSpatioTemporal + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + # UpBlockSpatioTemporal + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), + UpSampler(1280), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + PopBlock(), ResnetBlock(1920, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), + UpSampler(1280), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(1920, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + PopBlock(), ResnetBlock(1280, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + PopBlock(), ResnetBlock(960, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv), PopMixBlock(640), + UpSampler(640), + # CrossAttnUpBlockSpatioTemporal + PopBlock(), ResnetBlock(960, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(), + AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv), PopMixBlock(320), + ]) + + self.conv_norm_out = torch.nn.GroupNorm(32, 320, eps=1e-05, affine=True) + self.conv_act = torch.nn.SiLU() + self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + + + def build_mask(self, data, is_bound): + T, C, H, W = data.shape + t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) + h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) + w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) + border_width = (T + H + W) // 6 + pad = torch.ones_like(t) * border_width + mask = torch.stack([ + pad if is_bound[0] else t + 1, + pad if is_bound[1] else T - t, + pad if is_bound[2] else h + 1, + pad if is_bound[3] else H - h, + pad if is_bound[4] else w + 1, + pad if is_bound[5] else W - w + ]).min(dim=0).values + mask = mask.clip(1, border_width) + mask = (mask / border_width).to(dtype=data.dtype, device=data.device) + mask = rearrange(mask, "T H W -> T 1 H W") + return mask + + + def tiled_forward( + self, sample, timestep, encoder_hidden_states, add_time_id, + batch_time=25, batch_height=128, batch_width=128, + stride_time=5, stride_height=64, stride_width=64, + progress_bar=lambda x:x + ): + data_device = sample.device + computation_device = self.conv_in.weight.device + torch_dtype = sample.dtype + T, C, H, W = sample.shape + + weight = torch.zeros((T, 1, H, W), dtype=torch_dtype, device=data_device) + values = torch.zeros((T, 4, H, W), dtype=torch_dtype, device=data_device) + + # Split tasks + tasks = [] + for t in range(0, T, stride_time): + for h in range(0, H, stride_height): + for w in range(0, W, stride_width): + if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\ + or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\ + or (w-stride_width >= 0 and w-stride_width+batch_width >= W): + continue + tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width)) + + # Run + for tl, tr, hl, hr, wl, wr in progress_bar(tasks): + sample_batch = sample[tl:tr, :, hl:hr, wl:wr].to(computation_device) + sample_batch = self.forward(sample_batch, timestep, encoder_hidden_states, add_time_id).to(data_device) + mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W)) + values[tl:tr, :, hl:hr, wl:wr] += sample_batch * mask + weight[tl:tr, :, hl:hr, wl:wr] += mask + values /= weight + return values + + + def forward(self, sample, timestep, encoder_hidden_states, add_time_id, use_gradient_checkpointing=False, **kwargs): + # 1. time + timestep = torch.tensor((timestep,)).to(sample.device) + t_emb = self.time_proj(timestep).to(sample.dtype) + t_emb = self.time_embedding(t_emb) + + add_embeds = self.add_time_proj(add_time_id.flatten()).to(sample.dtype) + add_embeds = add_embeds.reshape((-1, 768)) + add_embeds = self.add_time_embedding(add_embeds) + + time_emb = t_emb + add_embeds + + # 2. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = self.conv_in(sample) + text_emb = encoder_hidden_states + res_stack = [hidden_states] + + # 3. blocks + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + for i, block in enumerate(self.blocks): + if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock) or isinstance(block, PopMixBlock)): + hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, time_emb, text_emb, res_stack, + use_reentrant=False, + ) + else: + 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 SVDUNetStateDictConverter() + + + +class SVDUNetStateDictConverter: + def __init__(self): + pass + + def get_block_name(self, names): + if names[0] in ["down_blocks", "mid_block", "up_blocks"]: + if names[4] in ["norm", "proj_in"]: + return ".".join(names[:4] + ["transformer_blocks"]) + elif names[4] in ["time_pos_embed"]: + return ".".join(names[:4] + ["temporal_transformer_blocks"]) + elif names[4] in ["proj_out"]: + return ".".join(names[:4] + ["time_mixer"]) + else: + return ".".join(names[:5]) + return "" + + def from_diffusers(self, state_dict): + rename_dict = { + "time_embedding.linear_1": "time_embedding.0", + "time_embedding.linear_2": "time_embedding.2", + "add_embedding.linear_1": "add_time_embedding.0", + "add_embedding.linear_2": "add_time_embedding.2", + "conv_in": "conv_in", + "conv_norm_out": "conv_norm_out", + "conv_out": "conv_out", + } + blocks_rename_dict = [ + "down_blocks.0.resnets.0.spatial_res_block", None, "down_blocks.0.resnets.0.temporal_res_block", "down_blocks.0.resnets.0.time_mixer", None, + "down_blocks.0.attentions.0.transformer_blocks", None, "down_blocks.0.attentions.0.temporal_transformer_blocks", "down_blocks.0.attentions.0.time_mixer", None, + "down_blocks.0.resnets.1.spatial_res_block", None, "down_blocks.0.resnets.1.temporal_res_block", "down_blocks.0.resnets.1.time_mixer", None, + "down_blocks.0.attentions.1.transformer_blocks", None, "down_blocks.0.attentions.1.temporal_transformer_blocks", "down_blocks.0.attentions.1.time_mixer", None, + "down_blocks.0.downsamplers.0.conv", None, + "down_blocks.1.resnets.0.spatial_res_block", None, "down_blocks.1.resnets.0.temporal_res_block", "down_blocks.1.resnets.0.time_mixer", None, + "down_blocks.1.attentions.0.transformer_blocks", None, "down_blocks.1.attentions.0.temporal_transformer_blocks", "down_blocks.1.attentions.0.time_mixer", None, + "down_blocks.1.resnets.1.spatial_res_block", None, "down_blocks.1.resnets.1.temporal_res_block", "down_blocks.1.resnets.1.time_mixer", None, + "down_blocks.1.attentions.1.transformer_blocks", None, "down_blocks.1.attentions.1.temporal_transformer_blocks", "down_blocks.1.attentions.1.time_mixer", None, + "down_blocks.1.downsamplers.0.conv", None, + "down_blocks.2.resnets.0.spatial_res_block", None, "down_blocks.2.resnets.0.temporal_res_block", "down_blocks.2.resnets.0.time_mixer", None, + "down_blocks.2.attentions.0.transformer_blocks", None, "down_blocks.2.attentions.0.temporal_transformer_blocks", "down_blocks.2.attentions.0.time_mixer", None, + "down_blocks.2.resnets.1.spatial_res_block", None, "down_blocks.2.resnets.1.temporal_res_block", "down_blocks.2.resnets.1.time_mixer", None, + "down_blocks.2.attentions.1.transformer_blocks", None, "down_blocks.2.attentions.1.temporal_transformer_blocks", "down_blocks.2.attentions.1.time_mixer", None, + "down_blocks.2.downsamplers.0.conv", None, + "down_blocks.3.resnets.0.spatial_res_block", None, "down_blocks.3.resnets.0.temporal_res_block", "down_blocks.3.resnets.0.time_mixer", None, + "down_blocks.3.resnets.1.spatial_res_block", None, "down_blocks.3.resnets.1.temporal_res_block", "down_blocks.3.resnets.1.time_mixer", None, + "mid_block.mid_block.resnets.0.spatial_res_block", None, "mid_block.mid_block.resnets.0.temporal_res_block", "mid_block.mid_block.resnets.0.time_mixer", None, + "mid_block.mid_block.attentions.0.transformer_blocks", None, "mid_block.mid_block.attentions.0.temporal_transformer_blocks", "mid_block.mid_block.attentions.0.time_mixer", + "mid_block.mid_block.resnets.1.spatial_res_block", None, "mid_block.mid_block.resnets.1.temporal_res_block", "mid_block.mid_block.resnets.1.time_mixer", + None, "up_blocks.0.resnets.0.spatial_res_block", None, "up_blocks.0.resnets.0.temporal_res_block", "up_blocks.0.resnets.0.time_mixer", + None, "up_blocks.0.resnets.1.spatial_res_block", None, "up_blocks.0.resnets.1.temporal_res_block", "up_blocks.0.resnets.1.time_mixer", + None, "up_blocks.0.resnets.2.spatial_res_block", None, "up_blocks.0.resnets.2.temporal_res_block", "up_blocks.0.resnets.2.time_mixer", + "up_blocks.0.upsamplers.0.conv", + None, "up_blocks.1.resnets.0.spatial_res_block", None, "up_blocks.1.resnets.0.temporal_res_block", "up_blocks.1.resnets.0.time_mixer", None, + "up_blocks.1.attentions.0.transformer_blocks", None, "up_blocks.1.attentions.0.temporal_transformer_blocks", "up_blocks.1.attentions.0.time_mixer", + None, "up_blocks.1.resnets.1.spatial_res_block", None, "up_blocks.1.resnets.1.temporal_res_block", "up_blocks.1.resnets.1.time_mixer", None, + "up_blocks.1.attentions.1.transformer_blocks", None, "up_blocks.1.attentions.1.temporal_transformer_blocks", "up_blocks.1.attentions.1.time_mixer", + None, "up_blocks.1.resnets.2.spatial_res_block", None, "up_blocks.1.resnets.2.temporal_res_block", "up_blocks.1.resnets.2.time_mixer", None, + "up_blocks.1.attentions.2.transformer_blocks", None, "up_blocks.1.attentions.2.temporal_transformer_blocks", "up_blocks.1.attentions.2.time_mixer", + "up_blocks.1.upsamplers.0.conv", + None, "up_blocks.2.resnets.0.spatial_res_block", None, "up_blocks.2.resnets.0.temporal_res_block", "up_blocks.2.resnets.0.time_mixer", None, + "up_blocks.2.attentions.0.transformer_blocks", None, "up_blocks.2.attentions.0.temporal_transformer_blocks", "up_blocks.2.attentions.0.time_mixer", + None, "up_blocks.2.resnets.1.spatial_res_block", None, "up_blocks.2.resnets.1.temporal_res_block", "up_blocks.2.resnets.1.time_mixer", None, + "up_blocks.2.attentions.1.transformer_blocks", None, "up_blocks.2.attentions.1.temporal_transformer_blocks", "up_blocks.2.attentions.1.time_mixer", + None, "up_blocks.2.resnets.2.spatial_res_block", None, "up_blocks.2.resnets.2.temporal_res_block", "up_blocks.2.resnets.2.time_mixer", None, + "up_blocks.2.attentions.2.transformer_blocks", None, "up_blocks.2.attentions.2.temporal_transformer_blocks", "up_blocks.2.attentions.2.time_mixer", + "up_blocks.2.upsamplers.0.conv", + None, "up_blocks.3.resnets.0.spatial_res_block", None, "up_blocks.3.resnets.0.temporal_res_block", "up_blocks.3.resnets.0.time_mixer", None, + "up_blocks.3.attentions.0.transformer_blocks", None, "up_blocks.3.attentions.0.temporal_transformer_blocks", "up_blocks.3.attentions.0.time_mixer", + None, "up_blocks.3.resnets.1.spatial_res_block", None, "up_blocks.3.resnets.1.temporal_res_block", "up_blocks.3.resnets.1.time_mixer", None, + "up_blocks.3.attentions.1.transformer_blocks", None, "up_blocks.3.attentions.1.temporal_transformer_blocks", "up_blocks.3.attentions.1.time_mixer", + None, "up_blocks.3.resnets.2.spatial_res_block", None, "up_blocks.3.resnets.2.temporal_res_block", "up_blocks.3.resnets.2.time_mixer", None, + "up_blocks.3.attentions.2.transformer_blocks", None, "up_blocks.3.attentions.2.temporal_transformer_blocks", "up_blocks.3.attentions.2.time_mixer", + ] + blocks_rename_dict = {i:j for j,i in enumerate(blocks_rename_dict) if i is not None} + state_dict_ = {} + for name, param in sorted(state_dict.items()): + names = name.split(".") + if names[0] == "mid_block": + names = ["mid_block"] + names + if names[-1] in ["weight", "bias"]: + name_prefix = ".".join(names[:-1]) + if name_prefix in rename_dict: + state_dict_[rename_dict[name_prefix] + "." + names[-1]] = param + else: + block_name = self.get_block_name(names) + if "resnets" in block_name and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[5:]) + state_dict_[rename] = param + elif ("downsamplers" in block_name or "upsamplers" in block_name) and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[-2:]) + state_dict_[rename] = param + elif "attentions" in block_name and block_name in blocks_rename_dict: + attention_id = names[5] + if "transformer_blocks" in names: + suffix_dict = { + "attn1.to_out.0": "attn1.to_out", + "attn2.to_out.0": "attn2.to_out", + "ff.net.0.proj": "act_fn.proj", + "ff.net.2": "ff", + } + suffix = ".".join(names[6:-1]) + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "transformer_blocks", attention_id, suffix, names[-1]]) + elif "temporal_transformer_blocks" in names: + suffix_dict = { + "attn1.to_out.0": "attn1.to_out", + "attn2.to_out.0": "attn2.to_out", + "ff_in.net.0.proj": "act_fn_in.proj", + "ff_in.net.2": "ff_in", + "ff.net.0.proj": "act_fn_out.proj", + "ff.net.2": "ff_out", + "norm3": "norm_out", + } + suffix = ".".join(names[6:-1]) + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]]) + elif "time_mixer" in block_name: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "proj", names[-1]]) + else: + suffix_dict = { + "linear_1": "positional_embedding_proj.0", + "linear_2": "positional_embedding_proj.2", + } + suffix = names[-2] + suffix = suffix_dict.get(suffix, suffix) + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]]) + state_dict_[rename] = param + else: + print(name) + else: + block_name = self.get_block_name(names) + if len(block_name)>0 and block_name in blocks_rename_dict: + rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), names[-1]]) + state_dict_[rename] = param + return state_dict_ + + + def from_civitai(self, state_dict, add_positional_conv=None): + 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.1.0.time_mixer.mix_factor": "blocks.3.mix_factor", + "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.bias": "blocks.2.time_emb_proj.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.weight": "blocks.2.time_emb_proj.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.bias": "blocks.2.norm1.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.weight": "blocks.2.norm1.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.2.bias": "blocks.2.conv1.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.2.weight": "blocks.2.conv1.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.0.bias": "blocks.2.norm2.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.0.weight": "blocks.2.norm2.weight", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.3.bias": "blocks.2.conv2.bias", + "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.3.weight": "blocks.2.conv2.weight", + "model.diffusion_model.input_blocks.1.1.norm.bias": "blocks.5.norm.bias", + "model.diffusion_model.input_blocks.1.1.norm.weight": "blocks.5.norm.weight", + "model.diffusion_model.input_blocks.1.1.proj_in.bias": "blocks.5.proj_in.bias", + "model.diffusion_model.input_blocks.1.1.proj_in.weight": "blocks.5.proj_in.weight", + "model.diffusion_model.input_blocks.1.1.proj_out.bias": "blocks.8.proj.bias", + "model.diffusion_model.input_blocks.1.1.proj_out.weight": "blocks.8.proj.weight", + "model.diffusion_model.input_blocks.1.1.time_mixer.mix_factor": "blocks.8.mix_factor", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.0.bias": "blocks.7.positional_embedding_proj.0.bias", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.0.weight": "blocks.7.positional_embedding_proj.0.weight", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.2.bias": "blocks.7.positional_embedding_proj.2.bias", + "model.diffusion_model.input_blocks.1.1.time_pos_embed.2.weight": "blocks.7.positional_embedding_proj.2.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_k.weight": "blocks.7.attn1.to_k.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_out.0.bias": "blocks.7.attn1.to_out.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_out.0.weight": "blocks.7.attn1.to_out.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_q.weight": "blocks.7.attn1.to_q.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_v.weight": "blocks.7.attn1.to_v.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_k.weight": "blocks.7.attn2.to_k.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_out.0.bias": "blocks.7.attn2.to_out.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_out.0.weight": "blocks.7.attn2.to_out.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_q.weight": "blocks.7.attn2.to_q.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_v.weight": "blocks.7.attn2.to_v.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.0.proj.bias": "blocks.7.act_fn_out.proj.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.0.proj.weight": "blocks.7.act_fn_out.proj.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.2.bias": "blocks.7.ff_out.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.2.weight": "blocks.7.ff_out.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.0.proj.bias": "blocks.7.act_fn_in.proj.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.0.proj.weight": "blocks.7.act_fn_in.proj.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.2.bias": "blocks.7.ff_in.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.2.weight": "blocks.7.ff_in.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm1.bias": "blocks.7.norm1.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm1.weight": "blocks.7.norm1.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm2.bias": "blocks.7.norm2.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm2.weight": "blocks.7.norm2.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm3.bias": "blocks.7.norm_out.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm3.weight": "blocks.7.norm_out.weight", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm_in.bias": "blocks.7.norm_in.bias", + "model.diffusion_model.input_blocks.1.1.time_stack.0.norm_in.weight": "blocks.7.norm_in.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.5.transformer_blocks.0.attn1.to_k.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.5.transformer_blocks.0.attn1.to_out.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.5.transformer_blocks.0.attn1.to_out.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.5.transformer_blocks.0.attn1.to_q.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.5.transformer_blocks.0.attn1.to_v.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.5.transformer_blocks.0.attn2.to_k.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.5.transformer_blocks.0.attn2.to_out.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.5.transformer_blocks.0.attn2.to_out.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.5.transformer_blocks.0.attn2.to_q.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.5.transformer_blocks.0.attn2.to_v.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.5.transformer_blocks.0.act_fn.proj.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.5.transformer_blocks.0.act_fn.proj.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.5.transformer_blocks.0.ff.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.5.transformer_blocks.0.ff.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.5.transformer_blocks.0.norm1.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.5.transformer_blocks.0.norm1.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.5.transformer_blocks.0.norm2.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.5.transformer_blocks.0.norm2.weight", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.5.transformer_blocks.0.norm3.bias", + "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.5.transformer_blocks.0.norm3.weight", + "model.diffusion_model.input_blocks.10.0.emb_layers.1.bias": "blocks.66.time_emb_proj.bias", + "model.diffusion_model.input_blocks.10.0.emb_layers.1.weight": "blocks.66.time_emb_proj.weight", + "model.diffusion_model.input_blocks.10.0.in_layers.0.bias": "blocks.66.norm1.bias", + "model.diffusion_model.input_blocks.10.0.in_layers.0.weight": "blocks.66.norm1.weight", + "model.diffusion_model.input_blocks.10.0.in_layers.2.bias": "blocks.66.conv1.bias", + 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"blocks.7.positional_conv", "blocks.17.positional_conv", "blocks.29.positional_conv", "blocks.39.positional_conv", + "blocks.51.positional_conv", "blocks.61.positional_conv", "blocks.83.positional_conv", "blocks.113.positional_conv", + "blocks.123.positional_conv", "blocks.133.positional_conv", "blocks.144.positional_conv", "blocks.154.positional_conv", + "blocks.164.positional_conv", "blocks.175.positional_conv", "blocks.185.positional_conv", "blocks.195.positional_conv", + ] + extra_channels = [320, 320, 640, 640, 1280, 1280, 1280, 1280, 1280, 1280, 640, 640, 640, 320, 320, 320] + for name, channels in zip(extra_names, extra_channels): + weight = torch.zeros((channels, channels, 3, 3, 3)) + weight[:,:,1,1,1] = torch.eye(channels, channels) + bias = torch.zeros((channels,)) + state_dict_[name + ".weight"] = weight + state_dict_[name + ".bias"] = bias + return state_dict_