Support SVD img2vid model.

comfy/ldm/modules/diffusionmodules/util.py

@@ -13,11 +13,78 @@ import math
 import torch
 import torch.nn as nn
 import numpy as np
-from einops import repeat
+from einops import repeat, rearrange
 
 from comfy.ldm.util import instantiate_from_config
 import comfy.ops
 
+class AlphaBlender(nn.Module):
+    strategies = ["learned", "fixed", "learned_with_images"]
+
+    def __init__(
+        self,
+        alpha: float,
+        merge_strategy: str = "learned_with_images",
+        rearrange_pattern: str = "b t -> (b t) 1 1",
+    ):
+        super().__init__()
+        self.merge_strategy = merge_strategy
+        self.rearrange_pattern = rearrange_pattern
+
+        assert (
+            merge_strategy in self.strategies
+        ), f"merge_strategy needs to be in {self.strategies}"
+
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif (
+            self.merge_strategy == "learned"
+            or self.merge_strategy == "learned_with_images"
+        ):
+            self.register_parameter(
+                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+            )
+        else:
+            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+    def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor:
+        # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t)
+        if self.merge_strategy == "fixed":
+            # make shape compatible
+            # alpha = repeat(self.mix_factor, '1 -> b () t  () ()', t=t, b=bs)
+            alpha = self.mix_factor
+        elif self.merge_strategy == "learned":
+            alpha = torch.sigmoid(self.mix_factor)
+            # make shape compatible
+            # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
+        elif self.merge_strategy == "learned_with_images":
+            assert image_only_indicator is not None, "need image_only_indicator ..."
+            alpha = torch.where(
+                image_only_indicator.bool(),
+                torch.ones(1, 1, device=image_only_indicator.device),
+                rearrange(torch.sigmoid(self.mix_factor), "... -> ... 1"),
+            )
+            alpha = rearrange(alpha, self.rearrange_pattern)
+            # make shape compatible
+            # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
+        else:
+            raise NotImplementedError()
+        return alpha
+
+    def forward(
+        self,
+        x_spatial,
+        x_temporal,
+        image_only_indicator=None,
+    ) -> torch.Tensor:
+        alpha = self.get_alpha(image_only_indicator)
+        x = (
+            alpha.to(x_spatial.dtype) * x_spatial
+            + (1.0 - alpha).to(x_spatial.dtype) * x_temporal
+        )
+        return x
+
+
 def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
     if schedule == "linear":
         betas = (