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Flux2: Tensor tuples can cause issues for checkpointing #12777

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Flux2: Tensor tuples can cause issues for checkpointing #12777

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27 changes: 21 additions & 6 deletions src/diffusers/models/transformers/transformer_flux2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -390,6 +390,15 @@ def forward(
return self.processor(self, hidden_states, attention_mask, image_rotary_emb, **kwargs)


def split_mod(mod: torch.Tensor, mod_param_sets: int):
if mod.ndim == 2:
mod = mod.unsqueeze(1)
mod_params = torch.chunk(mod, 3 * mod_param_sets, dim=-1)
# Return tuple of 3-tuples of modulation params shift/scale/gate
return tuple(mod_params[3 * i : 3 * (i + 1)] for i in range(mod_param_sets))



class Flux2SingleTransformerBlock(nn.Module):
def __init__(
self,
Expand Down Expand Up @@ -430,6 +439,8 @@ def forward(
split_hidden_states: bool = False,
text_seq_len: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
temb_mod_params = split_mod(temb_mod_params, 1)[0]

# If encoder_hidden_states is None, hidden_states is assumed to have encoder_hidden_states already
# concatenated
if encoder_hidden_states is not None:
Expand Down Expand Up @@ -504,6 +515,9 @@ def forward(
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
joint_attention_kwargs = joint_attention_kwargs or {}
temb_mod_params_img = split_mod(temb_mod_params_img, 2)
temb_mod_params_txt = split_mod(temb_mod_params_txt, 2)


# Modulation parameters shape: [1, 1, self.dim]
(shift_msa, scale_msa, gate_msa), (shift_mlp, scale_mlp, gate_mlp) = temb_mod_params_img
Expand Down Expand Up @@ -621,11 +635,12 @@ def forward(self, temb: torch.Tensor) -> Tuple[Tuple[torch.Tensor, torch.Tensor,
mod = self.act_fn(temb)
mod = self.linear(mod)

if mod.ndim == 2:
mod = mod.unsqueeze(1)
mod_params = torch.chunk(mod, 3 * self.mod_param_sets, dim=-1)
# Return tuple of 3-tuples of modulation params shift/scale/gate
return tuple(mod_params[3 * i : 3 * (i + 1)] for i in range(self.mod_param_sets))
return mod
# if mod.ndim == 2:
# mod = mod.unsqueeze(1)
# mod_params = torch.chunk(mod, 3 * self.mod_param_sets, dim=-1)
# # Return tuple of 3-tuples of modulation params shift/scale/gate
# return tuple(mod_params[3 * i : 3 * (i + 1)] for i in range(self.mod_param_sets))


class Flux2Transformer2DModel(
Expand Down Expand Up @@ -821,7 +836,7 @@ def forward(

double_stream_mod_img = self.double_stream_modulation_img(temb)
double_stream_mod_txt = self.double_stream_modulation_txt(temb)
single_stream_mod = self.single_stream_modulation(temb)[0]
single_stream_mod = self.single_stream_modulation(temb)

# 2. Input projection for image (hidden_states) and conditioning text (encoder_hidden_states)
hidden_states = self.x_embedder(hidden_states)
Expand Down