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Fix some cosmos fp8 issues.

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comfyanonymous 2025-01-16 17:45:37 -05:00
parent cca96a85ae
commit 0aa2368e46
2 changed files with 8 additions and 8 deletions
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2
comfy/ldm/cosmos/model.py
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@ -293,7 +293,7 @@ class GeneralDIT(nn.Module):
x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)
if self.extra_per_block_abs_pos_emb: if self.extra_per_block_abs_pos_emb:
extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device) extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype)
else: else:
extra_pos_emb = None extra_pos_emb = None

14
comfy/ldm/cosmos/position_embedding.py
View File

@ -41,12 +41,12 @@ def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0)
class VideoPositionEmb(nn.Module): class VideoPositionEmb(nn.Module):
def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None) -> torch.Tensor: def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor:
""" """
It delegates the embedding generation to generate_embeddings function. It delegates the embedding generation to generate_embeddings function.
""" """
B_T_H_W_C = x_B_T_H_W_C.shape B_T_H_W_C = x_B_T_H_W_C.shape
embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device) embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype)
return embeddings return embeddings
@ -104,6 +104,7 @@ class VideoRopePosition3DEmb(VideoPositionEmb):
w_ntk_factor: Optional[float] = None, w_ntk_factor: Optional[float] = None,
t_ntk_factor: Optional[float] = None, t_ntk_factor: Optional[float] = None,
device=None, device=None,
dtype=None,
): ):
""" """
Generate embeddings for the given input size. Generate embeddings for the given input size.
@ -189,13 +190,12 @@ class LearnablePosEmbAxis(VideoPositionEmb):
self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype)) self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype))
self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype)) self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype))
def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor:
def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None) -> torch.Tensor:
B, T, H, W, _ = B_T_H_W_C B, T, H, W, _ = B_T_H_W_C
if self.interpolation == "crop": if self.interpolation == "crop":
emb_h_H = self.pos_emb_h[:H].to(device=device) emb_h_H = self.pos_emb_h[:H].to(device=device, dtype=dtype)
emb_w_W = self.pos_emb_w[:W].to(device=device) emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype)
emb_t_T = self.pos_emb_t[:T].to(device=device) emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype)
emb = ( emb = (
repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W) repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W)
+ repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W) + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W)