Basic Genmo Mochi video model support.

To use:
"Load CLIP" node with t5xxl + type mochi
"Load Diffusion Model" node with the mochi dit file.
"Load VAE" with the mochi vae file.

EmptyMochiLatentVideo node for the latent.
euler + linear_quadratic in the KSampler node.

comfy/ldm/genmo/joint_model/utils.py

@@ -0,0 +1,102 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor:
+    """
+    Pool tokens in x using mask.
+
+    NOTE: We assume x does not require gradients.
+
+    Args:
+        x: (B, L, D) tensor of tokens.
+        mask: (B, L) boolean tensor indicating which tokens are not padding.
+
+    Returns:
+        pooled: (B, D) tensor of pooled tokens.
+    """
+    assert x.size(1) == mask.size(1)  # Expected mask to have same length as tokens.
+    assert x.size(0) == mask.size(0)  # Expected mask to have same batch size as tokens.
+    mask = mask[:, :, None].to(dtype=x.dtype)
+    mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1)
+    pooled = (x * mask).sum(dim=1, keepdim=keepdim)
+    return pooled
+
+
+class AttentionPool(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        output_dim: int = None,
+        device: Optional[torch.device] = None,
+        dtype=None,
+        operations=None,
+    ):
+        """
+        Args:
+            spatial_dim (int): Number of tokens in sequence length.
+            embed_dim (int): Dimensionality of input tokens.
+            num_heads (int): Number of attention heads.
+            output_dim (int): Dimensionality of output tokens. Defaults to embed_dim.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype)
+        self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+        self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype)
+
+    def forward(self, x, mask):
+        """
+        Args:
+            x (torch.Tensor): (B, L, D) tensor of input tokens.
+            mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding.
+
+        NOTE: We assume x does not require gradients.
+
+        Returns:
+            x (torch.Tensor): (B, D) tensor of pooled tokens.
+        """
+        D = x.size(2)
+
+        # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L).
+        attn_mask = mask[:, None, None, :].bool()  # (B, 1, 1, L).
+        attn_mask = F.pad(attn_mask, (1, 0), value=True)  # (B, 1, 1, 1+L).
+
+        # Average non-padding token features. These will be used as the query.
+        x_pool = pool_tokens(x, mask, keepdim=True)  # (B, 1, D)
+
+        # Concat pooled features to input sequence.
+        x = torch.cat([x_pool, x], dim=1)  # (B, L+1, D)
+
+        # Compute queries, keys, values. Only the mean token is used to create a query.
+        kv = self.to_kv(x)  # (B, L+1, 2 * D)
+        q = self.to_q(x[:, 0])  # (B, D)
+
+        # Extract heads.
+        head_dim = D // self.num_heads
+        kv = kv.unflatten(2, (2, self.num_heads, head_dim))  # (B, 1+L, 2, H, head_dim)
+        kv = kv.transpose(1, 3)  # (B, H, 2, 1+L, head_dim)
+        k, v = kv.unbind(2)  # (B, H, 1+L, head_dim)
+        q = q.unflatten(1, (self.num_heads, head_dim))  # (B, H, head_dim)
+        q = q.unsqueeze(2)  # (B, H, 1, head_dim)
+
+        # Compute attention.
+        x = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=attn_mask, dropout_p=0.0
+        )  # (B, H, 1, head_dim)
+
+        # Concatenate heads and run output.
+        x = x.squeeze(2).flatten(1, 2)  # (B, D = H * head_dim)
+        x = self.to_out(x)
+        return x