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",
+ "model.diffusion_model.input_blocks.10.0.in_layers.2.weight": "blocks.66.conv1.weight",
+ "model.diffusion_model.input_blocks.10.0.out_layers.0.bias": "blocks.66.norm2.bias",
+ "model.diffusion_model.input_blocks.10.0.out_layers.0.weight": "blocks.66.norm2.weight",
+ "model.diffusion_model.input_blocks.10.0.out_layers.3.bias": "blocks.66.conv2.bias",
+ "model.diffusion_model.input_blocks.10.0.out_layers.3.weight": "blocks.66.conv2.weight",
+ "model.diffusion_model.input_blocks.10.0.time_mixer.mix_factor": "blocks.69.mix_factor",
+ "model.diffusion_model.input_blocks.10.0.time_stack.emb_layers.1.bias": "blocks.68.time_emb_proj.bias",
+ "model.diffusion_model.input_blocks.10.0.time_stack.emb_layers.1.weight": "blocks.68.time_emb_proj.weight",
+ "model.diffusion_model.input_blocks.10.0.time_stack.in_layers.0.bias": "blocks.68.norm1.bias",
+ "model.diffusion_model.input_blocks.10.0.time_stack.in_layers.0.weight": "blocks.68.norm1.weight",
+ "model.diffusion_model.input_blocks.10.0.time_stack.in_layers.2.bias": "blocks.68.conv1.bias",
+ "model.diffusion_model.input_blocks.10.0.time_stack.in_layers.2.weight": "blocks.68.conv1.weight",
+ "model.diffusion_model.input_blocks.10.0.time_stack.out_layers.0.bias": "blocks.68.norm2.bias",
+ "model.diffusion_model.input_blocks.10.0.time_stack.out_layers.0.weight": "blocks.68.norm2.weight",
+ "model.diffusion_model.input_blocks.10.0.time_stack.out_layers.3.bias": "blocks.68.conv2.bias",
+ "model.diffusion_model.input_blocks.10.0.time_stack.out_layers.3.weight": "blocks.68.conv2.weight",
+ "model.diffusion_model.input_blocks.11.0.emb_layers.1.bias": "blocks.71.time_emb_proj.bias",
+ "model.diffusion_model.input_blocks.11.0.emb_layers.1.weight": "blocks.71.time_emb_proj.weight",
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+ "model.diffusion_model.output_blocks.9.1.time_stack.0.norm2.bias": "blocks.175.norm2.bias",
+ "model.diffusion_model.output_blocks.9.1.time_stack.0.norm2.weight": "blocks.175.norm2.weight",
+ "model.diffusion_model.output_blocks.9.1.time_stack.0.norm3.bias": "blocks.175.norm_out.bias",
+ "model.diffusion_model.output_blocks.9.1.time_stack.0.norm3.weight": "blocks.175.norm_out.weight",
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+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_v.weight": "blocks.173.transformer_blocks.0.attn2.to_v.weight",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.173.transformer_blocks.0.act_fn.proj.bias",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.173.transformer_blocks.0.act_fn.proj.weight",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.bias": "blocks.173.transformer_blocks.0.ff.bias",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.weight": "blocks.173.transformer_blocks.0.ff.weight",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.bias": "blocks.173.transformer_blocks.0.norm1.bias",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.weight": "blocks.173.transformer_blocks.0.norm1.weight",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.bias": "blocks.173.transformer_blocks.0.norm2.bias",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.weight": "blocks.173.transformer_blocks.0.norm2.weight",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.bias": "blocks.173.transformer_blocks.0.norm3.bias",
+ "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight": "blocks.173.transformer_blocks.0.norm3.weight",
+ "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias",
+ "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight",
+ "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias",
+ "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight",
+ }
+ state_dict_ = {}
+ for name in state_dict:
+ if name in rename_dict:
+ param = state_dict[name]
+ if ".proj_in." in name or ".proj_out." in name:
+ param = param.squeeze()
+ state_dict_[rename_dict[name]] = param
+ if add_positional_conv is not None:
+ extra_names = [
+ "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_